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• Trouble with copying dictionaries and using deepcopy on an SQLAlchemy ORM object

  - by Az

  Hi there, I'm doing a Simulated Annealing algorithm to optimise a given allocation of students and projects. This is language-agnostic pseudocode from Wikipedia: s ? s0; e ? E(s) // Initial state, energy. sbest ? s; ebest ? e // Initial "best" solution k ? 0 // Energy evaluation count. while k < kmax and e > emax // While time left & not good enough: snew ? neighbour(s) // Pick some neighbour. enew ? E(snew) // Compute its energy. if enew < ebest then // Is this a new best? sbest ? snew; ebest ? enew // Save 'new neighbour' to 'best found'. if P(e, enew, temp(k/kmax)) > random() then // Should we move to it? s ? snew; e ? enew // Yes, change state. k ? k + 1 // One more evaluation done return sbest // Return the best solution found. The following is an adaptation of the technique. My supervisor said the idea is fine in theory. First I pick up some allocation (i.e. an entire dictionary of students and their allocated projects, including the ranks for the projects) from entire set of randomised allocations, copy it and pass it to my function. Let's call this allocation aOld (it is a dictionary). aOld has a weight related to it called wOld. The weighting is described below. The function does the following: Let this allocation, aOld be the best_node From all the students, pick a random number of students and stick in a list Strip (DEALLOCATE) them of their projects ++ reflect the changes for projects (allocated parameter is now False) and lecturers (free up slots if one or more of their projects are no longer allocated) Randomise that list Try assigning (REALLOCATE) everyone in that list projects again Calculate the weight (add up ranks, rank 1 = 1, rank 2 = 2... and no project rank = 101) For this new allocation aNew, if the weight wNew is smaller than the allocation weight wOld I picked up at the beginning, then this is the best_node (as defined by the Simulated Annealing algorithm above). Apply the algorithm to aNew and continue. If wOld < wNew, then apply the algorithm to aOld again and continue. The allocations/data-points are expressed as "nodes" such that a node = (weight, allocation_dict, projects_dict, lecturers_dict) Right now, I can only perform this algorithm once, but I'll need to try for a number N (denoted by kmax in the Wikipedia snippet) and make sure I always have with me, the previous node and the best_node. So that I don't modify my original dictionaries (which I might want to reset to), I've done a shallow copy of the dictionaries. From what I've read in the docs, it seems that it only copies the references and since my dictionaries contain objects, changing the copied dictionary ends up changing the objects anyway. So I tried to use copy.deepcopy().These dictionaries refer to objects that have been mapped with SQLA. Questions: I've been given some solutions to the problems faced but due to my über green-ness with using Python, they all sound rather cryptic to me. Deepcopy isn't playing nicely with SQLA. I've been told thatdeepcopy on ORM objects probably has issues that prevent it from working as you'd expect. Apparently I'd be better off "building copy constructors, i.e. def copy(self): return FooBar(....)." Can someone please explain what that means? I checked and found out that deepcopy has issues because SQLAlchemy places extra information on your objects, i.e. an _sa_instance_state attribute, that I wouldn't want in the copy but is necessary for the object to have. I've been told: "There are ways to manually blow away the old _sa_instance_state and put a new one on the object, but the most straightforward is to make a new object with __init__() and set up the attributes that are significant, instead of doing a full deep copy." What exactly does that mean? Do I create a new, unmapped class similar to the old, mapped one? An alternate solution is that I'd have to "implement __deepcopy__() on your objects and ensure that a new _sa_instance_state is set up, there are functions in sqlalchemy.orm.attributes which can help with that." Once again this is beyond me so could someone kindly explain what it means? A more general question: given the above information are there any suggestions on how I can maintain the information/state for the best_node (which must always persist through my while loop) and the previous_node, if my actual objects (referenced by the dictionaries, therefore the nodes) are changing due to the deallocation/reallocation taking place? That is, without using copy?

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• Improving Partitioned Table Join Performance

  - by Paul White

  The query optimizer does not always choose an optimal strategy when joining partitioned tables. This post looks at an example, showing how a manual rewrite of the query can almost double performance, while reducing the memory grant to almost nothing. Test Data The two tables in this example use a common partitioning partition scheme. The partition function uses 41 equal-size partitions: CREATE PARTITION FUNCTION PFT (integer) AS RANGE RIGHT FOR VALUES ( 125000, 250000, 375000, 500000, 625000, 750000, 875000, 1000000, 1125000, 1250000, 1375000, 1500000, 1625000, 1750000, 1875000, 2000000, 2125000, 2250000, 2375000, 2500000, 2625000, 2750000, 2875000, 3000000, 3125000, 3250000, 3375000, 3500000, 3625000, 3750000, 3875000, 4000000, 4125000, 4250000, 4375000, 4500000, 4625000, 4750000, 4875000, 5000000 ); GO CREATE PARTITION SCHEME PST AS PARTITION PFT ALL TO ([PRIMARY]); There two tables are: CREATE TABLE dbo.T1 ( TID integer NOT NULL IDENTITY(0,1), Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T1 PRIMARY KEY CLUSTERED (TID) ON PST (TID) );   CREATE TABLE dbo.T2 ( TID integer NOT NULL, Column1 integer NOT NULL, Padding binary(100) NOT NULL DEFAULT 0x,   CONSTRAINT PK_T2 PRIMARY KEY CLUSTERED (TID, Column1) ON PST (TID) ); The next script loads 5 million rows into T1 with a pseudo-random value between 1 and 5 for Column1. The table is partitioned on the IDENTITY column TID: INSERT dbo.T1 WITH (TABLOCKX) (Column1) SELECT (ABS(CHECKSUM(NEWID())) % 5) + 1 FROM dbo.Numbers AS N WHERE n BETWEEN 1 AND 5000000; In case you don’t already have an auxiliary table of numbers lying around, here’s a script to create one with 10 million rows: CREATE TABLE dbo.Numbers (n bigint PRIMARY KEY);   WITH L0 AS(SELECT 1 AS c UNION ALL SELECT 1), L1 AS(SELECT 1 AS c FROM L0 AS A CROSS JOIN L0 AS B), L2 AS(SELECT 1 AS c FROM L1 AS A CROSS JOIN L1 AS B), L3 AS(SELECT 1 AS c FROM L2 AS A CROSS JOIN L2 AS B), L4 AS(SELECT 1 AS c FROM L3 AS A CROSS JOIN L3 AS B), L5 AS(SELECT 1 AS c FROM L4 AS A CROSS JOIN L4 AS B), Nums AS(SELECT ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS n FROM L5) INSERT dbo.Numbers WITH (TABLOCKX) SELECT TOP (10000000) n FROM Nums ORDER BY n OPTION (MAXDOP 1); Table T1 contains data like this: Next we load data into table T2. The relationship between the two tables is that table 2 contains ‘n’ rows for each row in table 1, where ‘n’ is determined by the value in Column1 of table T1. There is nothing particularly special about the data or distribution, by the way. INSERT dbo.T2 WITH (TABLOCKX) (TID, Column1) SELECT T.TID, N.n FROM dbo.T1 AS T JOIN dbo.Numbers AS N ON N.n >= 1 AND N.n <= T.Column1; Table T2 ends up containing about 15 million rows: The primary key for table T2 is a combination of TID and Column1. The data is partitioned according to the value in column TID alone. Partition Distribution The following query shows the number of rows in each partition of table T1: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T1 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are 40 partitions containing 125,000 rows (40 * 125k = 5m rows). The rightmost partition remains empty. The next query shows the distribution for table 2: SELECT PartitionID = CA1.P, NumRows = COUNT_BIG(*) FROM dbo.T2 AS T CROSS APPLY (VALUES ($PARTITION.PFT(TID))) AS CA1 (P) GROUP BY CA1.P ORDER BY CA1.P; There are roughly 375,000 rows in each partition (the rightmost partition is also empty): Ok, that’s the test data done. Test Query and Execution Plan The task is to count the rows resulting from joining tables 1 and 2 on the TID column: SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; The optimizer chooses a plan using parallel hash join, and partial aggregation: The Plan Explorer plan tree view shows accurate cardinality estimates and an even distribution of rows across threads (click to enlarge the image): With a warm data cache, the STATISTICS IO output shows that no physical I/O was needed, and all 41 partitions were touched: Running the query without actual execution plan or STATISTICS IO information for maximum performance, the query returns in around 2600ms. Execution Plan Analysis The first step toward improving on the execution plan produced by the query optimizer is to understand how it works, at least in outline. The two parallel Clustered Index Scans use multiple threads to read rows from tables T1 and T2. Parallel scan uses a demand-based scheme where threads are given page(s) to scan from the table as needed. This arrangement has certain important advantages, but does result in an unpredictable distribution of rows amongst threads. The point is that multiple threads cooperate to scan the whole table, but it is impossible to predict which rows end up on which threads. For correct results from the parallel hash join, the execution plan has to ensure that rows from T1 and T2 that might join are processed on the same thread. For example, if a row from T1 with join key value ‘1234’ is placed in thread 5’s hash table, the execution plan must guarantee that any rows from T2 that also have join key value ‘1234’ probe thread 5’s hash table for matches. The way this guarantee is enforced in this parallel hash join plan is by repartitioning rows to threads after each parallel scan. The two repartitioning exchanges route rows to threads using a hash function over the hash join keys. The two repartitioning exchanges use the same hash function so rows from T1 and T2 with the same join key must end up on the same hash join thread. Expensive Exchanges This business of repartitioning rows between threads can be very expensive, especially if a large number of rows is involved. The execution plan selected by the optimizer moves 5 million rows through one repartitioning exchange and around 15 million across the other. As a first step toward removing these exchanges, consider the execution plan selected by the optimizer if we join just one partition from each table, disallowing parallelism: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = 1 AND $PARTITION.PFT(T2.TID) = 1 OPTION (MAXDOP 1); The optimizer has chosen a (one-to-many) merge join instead of a hash join. The single-partition query completes in around 100ms. If everything scaled linearly, we would expect that extending this strategy to all 40 populated partitions would result in an execution time around 4000ms. Using parallelism could reduce that further, perhaps to be competitive with the parallel hash join chosen by the optimizer. This raises a question. If the most efficient way to join one partition from each of the tables is to use a merge join, why does the optimizer not choose a merge join for the full query? Forcing a Merge Join Let’s force the optimizer to use a merge join on the test query using a hint: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN); This is the execution plan selected by the optimizer: This plan results in the same number of logical reads reported previously, but instead of 2600ms the query takes 5000ms. The natural explanation for this drop in performance is that the merge join plan is only using a single thread, whereas the parallel hash join plan could use multiple threads. Parallel Merge Join We can get a parallel merge join plan using the same query hint as before, and adding trace flag 8649: SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (MERGE JOIN, QUERYTRACEON 8649); The execution plan is: This looks promising. It uses a similar strategy to distribute work across threads as seen for the parallel hash join. In practice though, performance is disappointing. On a typical run, the parallel merge plan runs for around 8400ms; slower than the single-threaded merge join plan (5000ms) and much worse than the 2600ms for the parallel hash join. We seem to be going backwards! The logical reads for the parallel merge are still exactly the same as before, with no physical IOs. The cardinality estimates and thread distribution are also still very good (click to enlarge): A big clue to the reason for the poor performance is shown in the wait statistics (captured by Plan Explorer Pro): CXPACKET waits require careful interpretation, and are most often benign, but in this case excessive waiting occurs at the repartitioning exchanges. Unlike the parallel hash join, the repartitioning exchanges in this plan are order-preserving ‘merging’ exchanges (because merge join requires ordered inputs): Parallelism works best when threads can just grab any available unit of work and get on with processing it. Preserving order introduces inter-thread dependencies that can easily lead to significant waits occurring. In extreme cases, these dependencies can result in an intra-query deadlock, though the details of that will have to wait for another time to explore in detail. The potential for waits and deadlocks leads the query optimizer to cost parallel merge join relatively highly, especially as the degree of parallelism (DOP) increases. This high costing resulted in the optimizer choosing a serial merge join rather than parallel in this case. The test results certainly confirm its reasoning. Collocated Joins In SQL Server 2008 and later, the optimizer has another available strategy when joining tables that share a common partition scheme. This strategy is a collocated join, also known as as a per-partition join. It can be applied in both serial and parallel execution plans, though it is limited to 2-way joins in the current optimizer. Whether the optimizer chooses a collocated join or not depends on cost estimation. The primary benefits of a collocated join are that it eliminates an exchange and requires less memory, as we will see next. Costing and Plan Selection The query optimizer did consider a collocated join for our original query, but it was rejected on cost grounds. The parallel hash join with repartitioning exchanges appeared to be a cheaper option. There is no query hint to force a collocated join, so we have to mess with the costing framework to produce one for our test query. Pretending that IOs cost 50 times more than usual is enough to convince the optimizer to use collocated join with our test query: -- Pretend IOs are 50x cost temporarily DBCC SETIOWEIGHT(50);   -- Co-located hash join SELECT COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID OPTION (RECOMPILE);   -- Reset IO costing DBCC SETIOWEIGHT(1); Collocated Join Plan The estimated execution plan for the collocated join is: The Constant Scan contains one row for each partition of the shared partitioning scheme, from 1 to 41. The hash repartitioning exchanges seen previously are replaced by a single Distribute Streams exchange using Demand partitioning. Demand partitioning means that the next partition id is given to the next parallel thread that asks for one. My test machine has eight logical processors, and all are available for SQL Server to use. As a result, there are eight threads in the single parallel branch in this plan, each processing one partition from each table at a time. Once a thread finishes processing a partition, it grabs a new partition number from the Distribute Streams exchange…and so on until all partitions have been processed. It is important to understand that the parallel scans in this plan are different from the parallel hash join plan. Although the scans have the same parallelism icon, tables T1 and T2 are not being co-operatively scanned by multiple threads in the same way. Each thread reads a single partition of T1 and performs a hash match join with the same partition from table T2. The properties of the two Clustered Index Scans show a Seek Predicate (unusual for a scan!) limiting the rows to a single partition: The crucial point is that the join between T1 and T2 is on TID, and TID is the partitioning column for both tables. A thread that processes partition ‘n’ is guaranteed to see all rows that can possibly join on TID for that partition. In addition, no other thread will see rows from that partition, so this removes the need for repartitioning exchanges. CPU and Memory Efficiency Improvements The collocated join has removed two expensive repartitioning exchanges and added a single exchange processing 41 rows (one for each partition id). Remember, the parallel hash join plan exchanges had to process 5 million and 15 million rows. The amount of processor time spent on exchanges will be much lower in the collocated join plan. In addition, the collocated join plan has a maximum of 8 threads processing single partitions at any one time. The 41 partitions will all be processed eventually, but a new partition is not started until a thread asks for it. Threads can reuse hash table memory for the new partition. The parallel hash join plan also had 8 hash tables, but with all 5,000,000 build rows loaded at the same time. The collocated plan needs memory for only 8 * 125,000 = 1,000,000 rows at any one time. Collocated Hash Join Performance The collated join plan has disappointing performance in this case. The query runs for around 25,300ms despite the same IO statistics as usual. This is much the worst result so far, so what went wrong? It turns out that cardinality estimation for the single partition scans of table T1 is slightly low. The properties of the Clustered Index Scan of T1 (graphic immediately above) show the estimation was for 121,951 rows. This is a small shortfall compared with the 125,000 rows actually encountered, but it was enough to cause the hash join to spill to physical tempdb: A level 1 spill doesn’t sound too bad, until you realize that the spill to tempdb probably occurs for each of the 41 partitions. As a side note, the cardinality estimation error is a little surprising because the system tables accurately show there are 125,000 rows in every partition of T1. Unfortunately, the optimizer uses regular column and index statistics to derive cardinality estimates here rather than system table information (e.g. sys.partitions). Collocated Merge Join We will never know how well the collocated parallel hash join plan might have worked without the cardinality estimation error (and the resulting 41 spills to tempdb) but we do know: Merge join does not require a memory grant; and Merge join was the optimizer’s preferred join option for a single partition join Putting this all together, what we would really like to see is the same collocated join strategy, but using merge join instead of hash join. Unfortunately, the current query optimizer cannot produce a collocated merge join; it only knows how to do collocated hash join. So where does this leave us? CROSS APPLY sys.partitions We can try to write our own collocated join query. We can use sys.partitions to find the partition numbers, and CROSS APPLY to get a count per partition, with a final step to sum the partial counts. The following query implements this idea: SELECT row_count = SUM(Subtotals.cnt) FROM ( -- Partition numbers SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1 ) AS P CROSS APPLY ( -- Count per collocated join SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals; The estimated plan is: The cardinality estimates aren’t all that good here, especially the estimate for the scan of the system table underlying the sys.partitions view. Nevertheless, the plan shape is heading toward where we would like to be. Each partition number from the system table results in a per-partition scan of T1 and T2, a one-to-many Merge Join, and a Stream Aggregate to compute the partial counts. The final Stream Aggregate just sums the partial counts. Execution time for this query is around 3,500ms, with the same IO statistics as always. This compares favourably with 5,000ms for the serial plan produced by the optimizer with the OPTION (MERGE JOIN) hint. This is another case of the sum of the parts being less than the whole – summing 41 partial counts from 41 single-partition merge joins is faster than a single merge join and count over all partitions. Even so, this single-threaded collocated merge join is not as quick as the original parallel hash join plan, which executed in 2,600ms. On the positive side, our collocated merge join uses only one logical processor and requires no memory grant. The parallel hash join plan used 16 threads and reserved 569 MB of memory:   Using a Temporary Table Our collocated merge join plan should benefit from parallelism. The reason parallelism is not being used is that the query references a system table. We can work around that by writing the partition numbers to a temporary table (or table variable): SET STATISTICS IO ON; DECLARE @s datetime2 = SYSUTCDATETIME();   CREATE TABLE #P ( partition_number integer PRIMARY KEY);   INSERT #P (partition_number) SELECT p.partition_number FROM sys.partitions AS p WHERE p.[object_id] = OBJECT_ID(N'T1', N'U') AND p.index_id = 1;   SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals;   DROP TABLE #P;   SELECT DATEDIFF(Millisecond, @s, SYSUTCDATETIME()); SET STATISTICS IO OFF; Using the temporary table adds a few logical reads, but the overall execution time is still around 3500ms, indistinguishable from the same query without the temporary table. The problem is that the query optimizer still doesn’t choose a parallel plan for this query, though the removal of the system table reference means that it could if it chose to: In fact the optimizer did enter the parallel plan phase of query optimization (running search 1 for a second time): Unfortunately, the parallel plan found seemed to be more expensive than the serial plan. This is a crazy result, caused by the optimizer’s cost model not reducing operator CPU costs on the inner side of a nested loops join. Don’t get me started on that, we’ll be here all night. In this plan, everything expensive happens on the inner side of a nested loops join. Without a CPU cost reduction to compensate for the added cost of exchange operators, candidate parallel plans always look more expensive to the optimizer than the equivalent serial plan. Parallel Collocated Merge Join We can produce the desired parallel plan using trace flag 8649 again: SELECT row_count = SUM(Subtotals.cnt) FROM #P AS p CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: One difference between this plan and the collocated hash join plan is that a Repartition Streams exchange operator is used instead of Distribute Streams. The effect is similar, though not quite identical. The Repartition uses round-robin partitioning, meaning the next partition id is pushed to the next thread in sequence. The Distribute Streams exchange seen earlier used Demand partitioning, meaning the next partition id is pulled across the exchange by the next thread that is ready for more work. There are subtle performance implications for each partitioning option, but going into that would again take us too far off the main point of this post. Performance The important thing is the performance of this parallel collocated merge join – just 1350ms on a typical run. The list below shows all the alternatives from this post (all timings include creation, population, and deletion of the temporary table where appropriate) from quickest to slowest: Collocated parallel merge join: 1350ms Parallel hash join: 2600ms Collocated serial merge join: 3500ms Serial merge join: 5000ms Parallel merge join: 8400ms Collated parallel hash join: 25,300ms (hash spill per partition) The parallel collocated merge join requires no memory grant (aside from a paltry 1.2MB used for exchange buffers). This plan uses 16 threads at DOP 8; but 8 of those are (rather pointlessly) allocated to the parallel scan of the temporary table. These are minor concerns, but it turns out there is a way to address them if it bothers you. Parallel Collocated Merge Join with Demand Partitioning This final tweak replaces the temporary table with a hard-coded list of partition ids (dynamic SQL could be used to generate this query from sys.partitions): SELECT row_count = SUM(Subtotals.cnt) FROM ( VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10), (11),(12),(13),(14),(15),(16),(17),(18),(19),(20), (21),(22),(23),(24),(25),(26),(27),(28),(29),(30), (31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41) ) AS P (partition_number) CROSS APPLY ( SELECT cnt = COUNT_BIG(*) FROM dbo.T1 AS T1 JOIN dbo.T2 AS T2 ON T2.TID = T1.TID WHERE $PARTITION.PFT(T1.TID) = p.partition_number AND $PARTITION.PFT(T2.TID) = p.partition_number ) AS SubTotals OPTION (QUERYTRACEON 8649); The actual execution plan is: The parallel collocated hash join plan is reproduced below for comparison: The manual rewrite has another advantage that has not been mentioned so far: the partial counts (per partition) can be computed earlier than the partial counts (per thread) in the optimizer’s collocated join plan. The earlier aggregation is performed by the extra Stream Aggregate under the nested loops join. The performance of the parallel collocated merge join is unchanged at around 1350ms. Final Words It is a shame that the current query optimizer does not consider a collocated merge join (Connect item closed as Won’t Fix). The example used in this post showed an improvement in execution time from 2600ms to 1350ms using a modestly-sized data set and limited parallelism. In addition, the memory requirement for the query was almost completely eliminated  – down from 569MB to 1.2MB. The problem with the parallel hash join selected by the optimizer is that it attempts to process the full data set all at once (albeit using eight threads). It requires a large memory grant to hold all 5 million rows from table T1 across the eight hash tables, and does not take advantage of the divide-and-conquer opportunity offered by the common partitioning. The great thing about the collocated join strategies is that each parallel thread works on a single partition from both tables, reading rows, performing the join, and computing a per-partition subtotal, before moving on to a new partition. From a thread’s point of view… If you have trouble visualizing what is happening from just looking at the parallel collocated merge join execution plan, let’s look at it again, but from the point of view of just one thread operating between the two Parallelism (exchange) operators. Our thread picks up a single partition id from the Distribute Streams exchange, and starts a merge join using ordered rows from partition 1 of table T1 and partition 1 of table T2. By definition, this is all happening on a single thread. As rows join, they are added to a (per-partition) count in the Stream Aggregate immediately above the Merge Join. Eventually, either T1 (partition 1) or T2 (partition 1) runs out of rows and the merge join stops. The per-partition count from the aggregate passes on through the Nested Loops join to another Stream Aggregate, which is maintaining a per-thread subtotal. Our same thread now picks up a new partition id from the exchange (say it gets id 9 this time). The count in the per-partition aggregate is reset to zero, and the processing of partition 9 of both tables proceeds just as it did for partition 1, and on the same thread. Each thread picks up a single partition id and processes all the data for that partition, completely independently from other threads working on other partitions. One thread might eventually process partitions (1, 9, 17, 25, 33, 41) while another is concurrently processing partitions (2, 10, 18, 26, 34) and so on for the other six threads at DOP 8. The point is that all 8 threads can execute independently and concurrently, continuing to process new partitions until the wider job (of which the thread has no knowledge!) is done. This divide-and-conquer technique can be much more efficient than simply splitting the entire workload across eight threads all at once. Related Reading Understanding and Using Parallelism in SQL Server Parallel Execution Plans Suck © 2013 Paul White – All Rights Reserved Twitter: @SQL_Kiwi

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• A Taxonomy of Numerical Methods v1

  - by JoshReuben

  Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

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• Node.js Adventure - Host Node.js on Windows Azure Worker Role

  - by Shaun

  In my previous post I demonstrated about how to develop and deploy a Node.js application on Windows Azure Web Site (a.k.a. WAWS). WAWS is a new feature in Windows Azure platform. Since it’s low-cost, and it provides IIS and IISNode components so that we can host our Node.js application though Git, FTP and WebMatrix without any configuration and component installation. But sometimes we need to use the Windows Azure Cloud Service (a.k.a. WACS) and host our Node.js on worker role. Below are some benefits of using worker role. - WAWS leverages IIS and IISNode to host Node.js application, which runs in x86 WOW mode. It reduces the performance comparing with x64 in some cases. - WACS worker role does not need IIS, hence there’s no restriction of IIS, such as 8000 concurrent requests limitation. - WACS provides more flexibility and controls to the developers. For example, we can RDP to the virtual machines of our worker role instances. - WACS provides the service configuration features which can be changed when the role is running. - WACS provides more scaling capability than WAWS. In WAWS we can have at most 3 reserved instances per web site while in WACS we can have up to 20 instances in a subscription. - Since when using WACS worker role we starts the node by ourselves in a process, we can control the input, output and error stream. We can also control the version of Node.js.   Run Node.js in Worker Role Node.js can be started by just having its execution file. This means in Windows Azure, we can have a worker role with the “node.exe” and the Node.js source files, then start it in Run method of the worker role entry class. Let’s create a new windows azure project in Visual Studio and add a new worker role. Since we need our worker role execute the “node.exe” with our application code we need to add the “node.exe” into our project. Right click on the worker role project and add an existing item. By default the Node.js will be installed in the “Program Files\nodejs” folder so we can navigate there and add the “node.exe”. Then we need to create the entry code of Node.js. In WAWS the entry file must be named “server.js”, which is because it’s hosted by IIS and IISNode and IISNode only accept “server.js”. But here as we control everything we can choose any files as the entry code. For example, I created a new JavaScript file named “index.js” in project root. Since we created a C# Windows Azure project we cannot create a JavaScript file from the context menu “Add new item”. We have to create a text file, and then rename it to JavaScript extension. After we added these two files we should set their “Copy to Output Directory” property to “Copy Always”, or “Copy if Newer”. Otherwise they will not be involved in the package when deployed. Let’s paste a very simple Node.js code in the “index.js” as below. As you can see I created a web server listening at port 12345. 1: var http = require("http"); 2: var port = 12345; 3:  4: http.createServer(function (req, res) { 5: res.writeHead(200, { "Content-Type": "text/plain" }); 6: res.end("Hello World\n"); 7: }).listen(port); 8:  9: console.log("Server running at port %d", port); Then we need to start “node.exe” with this file when our worker role was started. This can be done in its Run method. I found the Node.js and entry JavaScript file name, and then create a new process to run it. Our worker role will wait for the process to be exited. If everything is OK once our web server was opened the process will be there listening for incoming requests, and should not be terminated. The code in worker role would be like this. 1: public override void Run() 2: { 3: // This is a sample worker implementation. Replace with your logic. 4: Trace.WriteLine("NodejsHost entry point called", "Information"); 5:  6: // retrieve the node.exe and entry node.js source code file name. 7: var node = Environment.ExpandEnvironmentVariables(@"%RoleRoot%\approot\node.exe"); 8: var js = "index.js"; 9:  10: // prepare the process starting of node.exe 11: var info = new ProcessStartInfo(node, js) 12: { 13: CreateNoWindow = false, 14: ErrorDialog = true, 15: WindowStyle = ProcessWindowStyle.Normal, 16: UseShellExecute = false, 17: WorkingDirectory = Environment.ExpandEnvironmentVariables(@"%RoleRoot%\approot") 18: }; 19: Trace.WriteLine(string.Format("{0} {1}", node, js), "Information"); 20:  21: // start the node.exe with entry code and wait for exit 22: var process = Process.Start(info); 23: process.WaitForExit(); 24: } Then we can run it locally. In the computer emulator UI the worker role started and it executed the Node.js, then Node.js windows appeared. Open the browser to verify the website hosted by our worker role. Next let’s deploy it to azure. But we need some additional steps. First, we need to create an input endpoint. By default there’s no endpoint defined in a worker role. So we will open the role property window in Visual Studio, create a new input TCP endpoint to the port we want our website to use. In this case I will use 80. Even though we created a web server we should add a TCP endpoint of the worker role, since Node.js always listen on TCP instead of HTTP. And then changed the “index.js”, let our web server listen on 80. 1: var http = require("http"); 2: var port = 80; 3:  4: http.createServer(function (req, res) { 5: res.writeHead(200, { "Content-Type": "text/plain" }); 6: res.end("Hello World\n"); 7: }).listen(port); 8:  9: console.log("Server running at port %d", port); Then publish it to Windows Azure. And then in browser we can see our Node.js website was running on WACS worker role. We may encounter an error if we tried to run our Node.js website on 80 port at local emulator. This is because the compute emulator registered 80 and map the 80 endpoint to 81. But our Node.js cannot detect this operation. So when it tried to listen on 80 it will failed since 80 have been used.   Use NPM Modules When we are using WAWS to host Node.js, we can simply install modules we need, and then just publish or upload all files to WAWS. But if we are using WACS worker role, we have to do some extra steps to make the modules work. Assuming that we plan to use “express” in our application. Firstly of all we should download and install this module through NPM command. But after the install finished, they are just in the disk but not included in the worker role project. If we deploy the worker role right now the module will not be packaged and uploaded to azure. Hence we need to add them to the project. On solution explorer window click the “Show all files” button, select the “node_modules” folder and in the context menu select “Include In Project”. But that not enough. We also need to make all files in this module to “Copy always” or “Copy if newer”, so that they can be uploaded to azure with the “node.exe” and “index.js”. This is painful step since there might be many files in a module. So I created a small tool which can update a C# project file, make its all items as “Copy always”. The code is very simple. 1: static void Main(string[] args) 2: { 3: if (args.Length < 1) 4: { 5: Console.WriteLine("Usage: copyallalways [project file]"); 6: return; 7: } 8:  9: var proj = args[0]; 10: File.Copy(proj, string.Format("{0}.bak", proj)); 11:  12: var xml = new XmlDocument(); 13: xml.Load(proj); 14: var nsManager = new XmlNamespaceManager(xml.NameTable); 15: nsManager.AddNamespace("pf", "http://schemas.microsoft.com/developer/msbuild/2003"); 16:  17: // add the output setting to copy always 18: var contentNodes = xml.SelectNodes("//pf:Project/pf:ItemGroup/pf:Content", nsManager); 19: UpdateNodes(contentNodes, xml, nsManager); 20: var noneNodes = xml.SelectNodes("//pf:Project/pf:ItemGroup/pf:None", nsManager); 21: UpdateNodes(noneNodes, xml, nsManager); 22: xml.Save(proj); 23:  24: // remove the namespace attributes 25: var content = xml.InnerXml.Replace("<CopyToOutputDirectory xmlns=\"\">", "<CopyToOutputDirectory>"); 26: xml.LoadXml(content); 27: xml.Save(proj); 28: } 29:  30: static void UpdateNodes(XmlNodeList nodes, XmlDocument xml, XmlNamespaceManager nsManager) 31: { 32: foreach (XmlNode node in nodes) 33: { 34: var copyToOutputDirectoryNode = node.SelectSingleNode("pf:CopyToOutputDirectory", nsManager); 35: if (copyToOutputDirectoryNode == null) 36: { 37: var n = xml.CreateNode(XmlNodeType.Element, "CopyToOutputDirectory", null); 38: n.InnerText = "Always"; 39: node.AppendChild(n); 40: } 41: else 42: { 43: if (string.Compare(copyToOutputDirectoryNode.InnerText, "Always", true) != 0) 44: { 45: copyToOutputDirectoryNode.InnerText = "Always"; 46: } 47: } 48: } 49: } Please be careful when use this tool. I created only for demo so do not use it directly in a production environment. Unload the worker role project, execute this tool with the worker role project file name as the command line argument, it will set all items as “Copy always”. Then reload this worker role project. Now let’s change the “index.js” to use express. 1: var express = require("express"); 2: var app = express(); 3:  4: var port = 80; 5:  6: app.configure(function () { 7: }); 8:  9: app.get("/", function (req, res) { 10: res.send("Hello Node.js!"); 11: }); 12:  13: app.get("/User/:id", function (req, res) { 14: var id = req.params.id; 15: res.json({ 16: "id": id, 17: "name": "user " + id, 18: "company": "IGT" 19: }); 20: }); 21:  22: app.listen(port); Finally let’s publish it and have a look in browser.   Use Windows Azure SQL Database We can use Windows Azure SQL Database (a.k.a. WACD) from Node.js as well on worker role hosting. Since we can control the version of Node.js, here we can use x64 version of “node-sqlserver” now. This is better than if we host Node.js on WAWS since it only support x86. Just install the “node-sqlserver” module from NPM, copy the “sqlserver.node” from “Build\Release” folder to “Lib” folder. Include them in worker role project and run my tool to make them to “Copy always”. Finally update the “index.js” to use WASD. 1: var express = require("express"); 2: var sql = require("node-sqlserver"); 3:  4: var connectionString = "Driver={SQL Server Native Client 10.0};Server=tcp:{SERVER NAME}.database.windows.net,1433;Database={DATABASE NAME};Uid={LOGIN}@{SERVER NAME};Pwd={PASSWORD};Encrypt=yes;Connection Timeout=30;"; 5: var port = 80; 6:  7: var app = express(); 8:  9: app.configure(function () { 10: app.use(express.bodyParser()); 11: }); 12:  13: app.get("/", function (req, res) { 14: sql.open(connectionString, function (err, conn) { 15: if (err) { 16: console.log(err); 17: res.send(500, "Cannot open connection."); 18: } 19: else { 20: conn.queryRaw("SELECT * FROM [Resource]", function (err, results) { 21: if (err) { 22: console.log(err); 23: res.send(500, "Cannot retrieve records."); 24: } 25: else { 26: res.json(results); 27: } 28: }); 29: } 30: }); 31: }); 32:  33: app.get("/text/:key/:culture", function (req, res) { 34: sql.open(connectionString, function (err, conn) { 35: if (err) { 36: console.log(err); 37: res.send(500, "Cannot open connection."); 38: } 39: else { 40: var key = req.params.key; 41: var culture = req.params.culture; 42: var command = "SELECT * FROM [Resource] WHERE [Key] = '" + key + "' AND [Culture] = '" + culture + "'"; 43: conn.queryRaw(command, function (err, results) { 44: if (err) { 45: console.log(err); 46: res.send(500, "Cannot retrieve records."); 47: } 48: else { 49: res.json(results); 50: } 51: }); 52: } 53: }); 54: }); 55:  56: app.get("/sproc/:key/:culture", function (req, res) { 57: sql.open(connectionString, function (err, conn) { 58: if (err) { 59: console.log(err); 60: res.send(500, "Cannot open connection."); 61: } 62: else { 63: var key = req.params.key; 64: var culture = req.params.culture; 65: var command = "EXEC GetItem '" + key + "', '" + culture + "'"; 66: conn.queryRaw(command, function (err, results) { 67: if (err) { 68: console.log(err); 69: res.send(500, "Cannot retrieve records."); 70: } 71: else { 72: res.json(results); 73: } 74: }); 75: } 76: }); 77: }); 78:  79: app.post("/new", function (req, res) { 80: var key = req.body.key; 81: var culture = req.body.culture; 82: var val = req.body.val; 83:  84: sql.open(connectionString, function (err, conn) { 85: if (err) { 86: console.log(err); 87: res.send(500, "Cannot open connection."); 88: } 89: else { 90: var command = "INSERT INTO [Resource] VALUES ('" + key + "', '" + culture + "', N'" + val + "')"; 91: conn.queryRaw(command, function (err, results) { 92: if (err) { 93: console.log(err); 94: res.send(500, "Cannot retrieve records."); 95: } 96: else { 97: res.send(200, "Inserted Successful"); 98: } 99: }); 100: } 101: }); 102: }); 103:  104: app.listen(port); Publish to azure and now we can see our Node.js is working with WASD through x64 version “node-sqlserver”.   Summary In this post I demonstrated how to host our Node.js in Windows Azure Cloud Service worker role. By using worker role we can control the version of Node.js, as well as the entry code. And it’s possible to do some pre jobs before the Node.js application started. It also removed the IIS and IISNode limitation. I personally recommended to use worker role as our Node.js hosting. But there are some problem if you use the approach I mentioned here. The first one is, we need to set all JavaScript files and module files as “Copy always” or “Copy if newer” manually. The second one is, in this way we cannot retrieve the cloud service configuration information. For example, we defined the endpoint in worker role property but we also specified the listening port in Node.js hardcoded. It should be changed that our Node.js can retrieve the endpoint. But I can tell you it won’t be working here. In the next post I will describe another way to execute the “node.exe” and Node.js application, so that we can get the cloud service configuration in Node.js. I will also demonstrate how to use Windows Azure Storage from Node.js by using the Windows Azure Node.js SDK.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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• Advanced TSQL Tuning: Why Internals Knowledge Matters

  - by Paul White

  There is much more to query tuning than reducing logical reads and adding covering nonclustered indexes.  Query tuning is not complete as soon as the query returns results quickly in the development or test environments.  In production, your query will compete for memory, CPU, locks, I/O and other resources on the server.  Today’s entry looks at some tuning considerations that are often overlooked, and shows how deep internals knowledge can help you write better TSQL. As always, we’ll need some example data.  In fact, we are going to use three tables today, each of which is structured like this: Each table has 50,000 rows made up of an INTEGER id column and a padding column containing 3,999 characters in every row.  The only difference between the three tables is in the type of the padding column: the first table uses CHAR(3999), the second uses VARCHAR(MAX), and the third uses the deprecated TEXT type.  A script to create a database with the three tables and load the sample data follows: USE master; GO IF DB_ID('SortTest') IS NOT NULL DROP DATABASE SortTest; GO CREATE DATABASE SortTest COLLATE LATIN1_GENERAL_BIN; GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest', SIZE = 3GB, MAXSIZE = 3GB ); GO ALTER DATABASE SortTest MODIFY FILE ( NAME = 'SortTest_log', SIZE = 256MB, MAXSIZE = 1GB, FILEGROWTH = 128MB ); GO ALTER DATABASE SortTest SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE SortTest SET AUTO_CLOSE OFF ; ALTER DATABASE SortTest SET AUTO_CREATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_SHRINK OFF ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS ON ; ALTER DATABASE SortTest SET AUTO_UPDATE_STATISTICS_ASYNC ON ; ALTER DATABASE SortTest SET PARAMETERIZATION SIMPLE ; ALTER DATABASE SortTest SET READ_COMMITTED_SNAPSHOT OFF ; ALTER DATABASE SortTest SET MULTI_USER ; ALTER DATABASE SortTest SET RECOVERY SIMPLE ; USE SortTest; GO CREATE TABLE dbo.TestCHAR ( id INTEGER IDENTITY (1,1) NOT NULL, padding CHAR(3999) NOT NULL,   CONSTRAINT [PK dbo.TestCHAR (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestMAX ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAX (id)] PRIMARY KEY CLUSTERED (id), ) ; CREATE TABLE dbo.TestTEXT ( id INTEGER IDENTITY (1,1) NOT NULL, padding TEXT NOT NULL,   CONSTRAINT [PK dbo.TestTEXT (id)] PRIMARY KEY CLUSTERED (id), ) ; -- ============= -- Load TestCHAR (about 3s) -- ============= INSERT INTO dbo.TestCHAR WITH (TABLOCKX) ( padding ) SELECT padding = REPLICATE(CHAR(65 + (Data.n % 26)), 3999) FROM ( SELECT TOP (50000) n = ROW_NUMBER() OVER (ORDER BY (SELECT 0)) - 1 FROM master.sys.columns C1, master.sys.columns C2, master.sys.columns C3 ORDER BY n ASC ) AS Data ORDER BY Data.n ASC ; -- ============ -- Load TestMAX (about 3s) -- ============ INSERT INTO dbo.TestMAX WITH (TABLOCKX) ( padding ) SELECT CONVERT(VARCHAR(MAX), padding) FROM dbo.TestCHAR ORDER BY id ; -- ============= -- Load TestTEXT (about 5s) -- ============= INSERT INTO dbo.TestTEXT WITH (TABLOCKX) ( padding ) SELECT CONVERT(TEXT, padding) FROM dbo.TestCHAR ORDER BY id ; -- ========== -- Space used -- ========== -- EXECUTE sys.sp_spaceused @objname = 'dbo.TestCHAR'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAX'; EXECUTE sys.sp_spaceused @objname = 'dbo.TestTEXT'; ; CHECKPOINT ; That takes around 15 seconds to run, and shows the space allocated to each table in its output: To illustrate the points I want to make today, the example task we are going to set ourselves is to return a random set of 150 rows from each table.  The basic shape of the test query is the same for each of the three test tables: SELECT TOP (150) T.id, T.padding FROM dbo.Test AS T ORDER BY NEWID() OPTION (MAXDOP 1) ; Test 1 – CHAR(3999) Running the template query shown above using the TestCHAR table as the target, we find that the query takes around 5 seconds to return its results.  This seems slow, considering that the table only has 50,000 rows.  Working on the assumption that generating a GUID for each row is a CPU-intensive operation, we might try enabling parallelism to see if that speeds up the response time.  Running the query again (but without the MAXDOP 1 hint) on a machine with eight logical processors, the query now takes 10 seconds to execute – twice as long as when run serially. Rather than attempting further guesses at the cause of the slowness, let’s go back to serial execution and add some monitoring.  The script below monitors STATISTICS IO output and the amount of tempdb used by the test query.  We will also run a Profiler trace to capture any warnings generated during query execution. DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TC.id, TC.padding FROM dbo.TestCHAR AS TC ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; Let’s take a closer look at the statistics and query plan generated from this: Following the flow of the data from right to left, we see the expected 50,000 rows emerging from the Clustered Index Scan, with a total estimated size of around 191MB.  The Compute Scalar adds a column containing a random GUID (generated from the NEWID() function call) for each row.  With this extra column in place, the size of the data arriving at the Sort operator is estimated to be 192MB. Sort is a blocking operator – it has to examine all of the rows on its input before it can produce its first row of output (the last row received might sort first).  This characteristic means that Sort requires a memory grant – memory allocated for the query’s use by SQL Server just before execution starts.  In this case, the Sort is the only memory-consuming operator in the plan, so it has access to the full 243MB (248,696KB) of memory reserved by SQL Server for this query execution. Notice that the memory grant is significantly larger than the expected size of the data to be sorted.  SQL Server uses a number of techniques to speed up sorting, some of which sacrifice size for comparison speed.  Sorts typically require a very large number of comparisons, so this is usually a very effective optimization.  One of the drawbacks is that it is not possible to exactly predict the sort space needed, as it depends on the data itself.  SQL Server takes an educated guess based on data types, sizes, and the number of rows expected, but the algorithm is not perfect. In spite of the large memory grant, the Profiler trace shows a Sort Warning event (indicating that the sort ran out of memory), and the tempdb usage monitor shows that 195MB of tempdb space was used – all of that for system use.  The 195MB represents physical write activity on tempdb, because SQL Server strictly enforces memory grants – a query cannot ‘cheat’ and effectively gain extra memory by spilling to tempdb pages that reside in memory.  Anyway, the key point here is that it takes a while to write 195MB to disk, and this is the main reason that the query takes 5 seconds overall. If you are wondering why using parallelism made the problem worse, consider that eight threads of execution result in eight concurrent partial sorts, each receiving one eighth of the memory grant.  The eight sorts all spilled to tempdb, resulting in inefficiencies as the spilled sorts competed for disk resources.  More importantly, there are specific problems at the point where the eight partial results are combined, but I’ll cover that in a future post. CHAR(3999) Performance Summary: 5 seconds elapsed time 243MB memory grant 195MB tempdb usage 192MB estimated sort set 25,043 logical reads Sort Warning Test 2 – VARCHAR(MAX) We’ll now run exactly the same test (with the additional monitoring) on the table using a VARCHAR(MAX) padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TM.id, TM.padding FROM dbo.TestMAX AS TM ORDER BY NEWID() OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query takes around 8 seconds to complete (3 seconds longer than Test 1).  Notice that the estimated row and data sizes are very slightly larger, and the overall memory grant has also increased very slightly to 245MB.  The most marked difference is in the amount of tempdb space used – this query wrote almost 391MB of sort run data to the physical tempdb file.  Don’t draw any general conclusions about VARCHAR(MAX) versus CHAR from this – I chose the length of the data specifically to expose this edge case.  In most cases, VARCHAR(MAX) performs very similarly to CHAR – I just wanted to make test 2 a bit more exciting. MAX Performance Summary: 8 seconds elapsed time 245MB memory grant 391MB tempdb usage 193MB estimated sort set 25,043 logical reads Sort warning Test 3 – TEXT The same test again, but using the deprecated TEXT data type for the padding column: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) TT.id, TT.padding FROM dbo.TestTEXT AS TT ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; This time the query runs in 500ms.  If you look at the metrics we have been checking so far, it’s not hard to understand why: TEXT Performance Summary: 0.5 seconds elapsed time 9MB memory grant 5MB tempdb usage 5MB estimated sort set 207 logical reads 596 LOB logical reads Sort warning SQL Server’s memory grant algorithm still underestimates the memory needed to perform the sorting operation, but the size of the data to sort is so much smaller (5MB versus 193MB previously) that the spilled sort doesn’t matter very much.  Why is the data size so much smaller?  The query still produces the correct results – including the large amount of data held in the padding column – so what magic is being performed here? TEXT versus MAX Storage The answer lies in how columns of the TEXT data type are stored.  By default, TEXT data is stored off-row in separate LOB pages – which explains why this is the first query we have seen that records LOB logical reads in its STATISTICS IO output.  You may recall from my last post that LOB data leaves an in-row pointer to the separate storage structure holding the LOB data. SQL Server can see that the full LOB value is not required by the query plan until results are returned, so instead of passing the full LOB value down the plan from the Clustered Index Scan, it passes the small in-row structure instead.  SQL Server estimates that each row coming from the scan will be 79 bytes long – 11 bytes for row overhead, 4 bytes for the integer id column, and 64 bytes for the LOB pointer (in fact the pointer is rather smaller – usually 16 bytes – but the details of that don’t really matter right now). OK, so this query is much more efficient because it is sorting a very much smaller data set – SQL Server delays retrieving the LOB data itself until after the Sort starts producing its 150 rows.  The question that normally arises at this point is: Why doesn’t SQL Server use the same trick when the padding column is defined as VARCHAR(MAX)? The answer is connected with the fact that if the actual size of the VARCHAR(MAX) data is 8000 bytes or less, it is usually stored in-row in exactly the same way as for a VARCHAR(8000) column – MAX data only moves off-row into LOB storage when it exceeds 8000 bytes.  The default behaviour of the TEXT type is to be stored off-row by default, unless the ‘text in row’ table option is set suitably and there is room on the page.  There is an analogous (but opposite) setting to control the storage of MAX data – the ‘large value types out of row’ table option.  By enabling this option for a table, MAX data will be stored off-row (in a LOB structure) instead of in-row.  SQL Server Books Online has good coverage of both options in the topic In Row Data. The MAXOOR Table The essential difference, then, is that MAX defaults to in-row storage, and TEXT defaults to off-row (LOB) storage.  You might be thinking that we could get the same benefits seen for the TEXT data type by storing the VARCHAR(MAX) values off row – so let’s look at that option now.  This script creates a fourth table, with the VARCHAR(MAX) data stored off-row in LOB pages: CREATE TABLE dbo.TestMAXOOR ( id INTEGER IDENTITY (1,1) NOT NULL, padding VARCHAR(MAX) NOT NULL,   CONSTRAINT [PK dbo.TestMAXOOR (id)] PRIMARY KEY CLUSTERED (id), ) ; EXECUTE sys.sp_tableoption @TableNamePattern = N'dbo.TestMAXOOR', @OptionName = 'large value types out of row', @OptionValue = 'true' ; SELECT large_value_types_out_of_row FROM sys.tables WHERE [schema_id] = SCHEMA_ID(N'dbo') AND name = N'TestMAXOOR' ; INSERT INTO dbo.TestMAXOOR WITH (TABLOCKX) ( padding ) SELECT SPACE(0) FROM dbo.TestCHAR ORDER BY id ; UPDATE TM WITH (TABLOCK) SET padding.WRITE (TC.padding, NULL, NULL) FROM dbo.TestMAXOOR AS TM JOIN dbo.TestCHAR AS TC ON TC.id = TM.id ; EXECUTE sys.sp_spaceused @objname = 'dbo.TestMAXOOR' ; CHECKPOINT ; Test 4 – MAXOOR We can now re-run our test on the MAXOOR (MAX out of row) table: DECLARE @read BIGINT, @write BIGINT ; SELECT @read = SUM(num_of_bytes_read), @write = SUM(num_of_bytes_written) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; SET STATISTICS IO ON ; SELECT TOP (150) MO.id, MO.padding FROM dbo.TestMAXOOR AS MO ORDER BY NEWID() OPTION (MAXDOP 1, RECOMPILE) ; SET STATISTICS IO OFF ; SELECT tempdb_read_MB = (SUM(num_of_bytes_read) - @read) / 1024. / 1024., tempdb_write_MB = (SUM(num_of_bytes_written) - @write) / 1024. / 1024., internal_use_MB = ( SELECT internal_objects_alloc_page_count / 128.0 FROM sys.dm_db_task_space_usage WHERE session_id = @@SPID ) FROM tempdb.sys.database_files AS DBF JOIN sys.dm_io_virtual_file_stats(2, NULL) AS FS ON FS.file_id = DBF.file_id WHERE DBF.type_desc = 'ROWS' ; TEXT Performance Summary: 0.3 seconds elapsed time 245MB memory grant 0MB tempdb usage 193MB estimated sort set 207 logical reads 446 LOB logical reads No sort warning The query runs very quickly – slightly faster than Test 3, and without spilling the sort to tempdb (there is no sort warning in the trace, and the monitoring query shows zero tempdb usage by this query).  SQL Server is passing the in-row pointer structure down the plan and only looking up the LOB value on the output side of the sort. The Hidden Problem There is still a huge problem with this query though – it requires a 245MB memory grant.  No wonder the sort doesn’t spill to tempdb now – 245MB is about 20 times more memory than this query actually requires to sort 50,000 records containing LOB data pointers.  Notice that the estimated row and data sizes in the plan are the same as in test 2 (where the MAX data was stored in-row). The optimizer assumes that MAX data is stored in-row, regardless of the sp_tableoption setting ‘large value types out of row’.  Why?  Because this option is dynamic – changing it does not immediately force all MAX data in the table in-row or off-row, only when data is added or actually changed.  SQL Server does not keep statistics to show how much MAX or TEXT data is currently in-row, and how much is stored in LOB pages.  This is an annoying limitation, and one which I hope will be addressed in a future version of the product. So why should we worry about this?  Excessive memory grants reduce concurrency and may result in queries waiting on the RESOURCE_SEMAPHORE wait type while they wait for memory they do not need.  245MB is an awful lot of memory, especially on 32-bit versions where memory grants cannot use AWE-mapped memory.  Even on a 64-bit server with plenty of memory, do you really want a single query to consume 0.25GB of memory unnecessarily?  That’s 32,000 8KB pages that might be put to much better use. The Solution The answer is not to use the TEXT data type for the padding column.  That solution happens to have better performance characteristics for this specific query, but it still results in a spilled sort, and it is hard to recommend the use of a data type which is scheduled for removal.  I hope it is clear to you that the fundamental problem here is that SQL Server sorts the whole set arriving at a Sort operator.  Clearly, it is not efficient to sort the whole table in memory just to return 150 rows in a random order. The TEXT example was more efficient because it dramatically reduced the size of the set that needed to be sorted.  We can do the same thing by selecting 150 unique keys from the table at random (sorting by NEWID() for example) and only then retrieving the large padding column values for just the 150 rows we need.  The following script implements that idea for all four tables: SET STATISTICS IO ON ; WITH TestTable AS ( SELECT * FROM dbo.TestCHAR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id = ANY (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAX ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestTEXT ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; WITH TestTable AS ( SELECT * FROM dbo.TestMAXOOR ), TopKeys AS ( SELECT TOP (150) id FROM TestTable ORDER BY NEWID() ) SELECT T1.id, T1.padding FROM TestTable AS T1 WHERE T1.id IN (SELECT id FROM TopKeys) OPTION (MAXDOP 1) ; SET STATISTICS IO OFF ; All four queries now return results in much less than a second, with memory grants between 6 and 12MB, and without spilling to tempdb.  The small remaining inefficiency is in reading the id column values from the clustered primary key index.  As a clustered index, it contains all the in-row data at its leaf.  The CHAR and VARCHAR(MAX) tables store the padding column in-row, so id values are separated by a 3999-character column, plus row overhead.  The TEXT and MAXOOR tables store the padding values off-row, so id values in the clustered index leaf are separated by the much-smaller off-row pointer structure.  This difference is reflected in the number of logical page reads performed by the four queries: Table 'TestCHAR' logical reads 25511 lob logical reads 000 Table 'TestMAX'. logical reads 25511 lob logical reads 000 Table 'TestTEXT' logical reads 00412 lob logical reads 597 Table 'TestMAXOOR' logical reads 00413 lob logical reads 446 We can increase the density of the id values by creating a separate nonclustered index on the id column only.  This is the same key as the clustered index, of course, but the nonclustered index will not include the rest of the in-row column data. CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestCHAR (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAX (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestTEXT (id); CREATE UNIQUE NONCLUSTERED INDEX uq1 ON dbo.TestMAXOOR (id); The four queries can now use the very dense nonclustered index to quickly scan the id values, sort them by NEWID(), select the 150 ids we want, and then look up the padding data.  The logical reads with the new indexes in place are: Table 'TestCHAR' logical reads 835 lob logical reads 0 Table 'TestMAX' logical reads 835 lob logical reads 0 Table 'TestTEXT' logical reads 686 lob logical reads 597 Table 'TestMAXOOR' logical reads 686 lob logical reads 448 With the new index, all four queries use the same query plan (click to enlarge): Performance Summary: 0.3 seconds elapsed time 6MB memory grant 0MB tempdb usage 1MB sort set 835 logical reads (CHAR, MAX) 686 logical reads (TEXT, MAXOOR) 597 LOB logical reads (TEXT) 448 LOB logical reads (MAXOOR) No sort warning I’ll leave it as an exercise for the reader to work out why trying to eliminate the Key Lookup by adding the padding column to the new nonclustered indexes would be a daft idea Conclusion This post is not about tuning queries that access columns containing big strings.  It isn’t about the internal differences between TEXT and MAX data types either.  It isn’t even about the cool use of UPDATE .WRITE used in the MAXOOR table load.  No, this post is about something else: Many developers might not have tuned our starting example query at all – 5 seconds isn’t that bad, and the original query plan looks reasonable at first glance.  Perhaps the NEWID() function would have been blamed for ‘just being slow’ – who knows.  5 seconds isn’t awful – unless your users expect sub-second responses – but using 250MB of memory and writing 200MB to tempdb certainly is!  If ten sessions ran that query at the same time in production that’s 2.5GB of memory usage and 2GB hitting tempdb.  Of course, not all queries can be rewritten to avoid large memory grants and sort spills using the key-lookup technique in this post, but that’s not the point either. The point of this post is that a basic understanding of execution plans is not enough.  Tuning for logical reads and adding covering indexes is not enough.  If you want to produce high-quality, scalable TSQL that won’t get you paged as soon as it hits production, you need a deep understanding of execution plans, and as much accurate, deep knowledge about SQL Server as you can lay your hands on.  The advanced database developer has a wide range of tools to use in writing queries that perform well in a range of circumstances. By the way, the examples in this post were written for SQL Server 2008.  They will run on 2005 and demonstrate the same principles, but you won’t get the same figures I did because 2005 had a rather nasty bug in the Top N Sort operator.  Fair warning: if you do decide to run the scripts on a 2005 instance (particularly the parallel query) do it before you head out for lunch… This post is dedicated to the people of Christchurch, New Zealand. © 2011 Paul White email: @[email protected] twitter: @SQL_Kiwi

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• header confusion. Compiler not recognizing datatypes

  - by numerical25

  I am getting confused on why the compiler is not recognizing my classes. So I am just going to show you my code and let you guys decide. My error is this error C2653: 'RenderEngine' : is not a class or namespace name and it's pointing to this line std::vector<RenderEngine::rDefaultVertex> m_verts; Here is the code for rModel, in its entirety. It contains the varible. the class that holds it is further down. #ifndef _MODEL_H #define _MODEL_H #include "stdafx.h" #include <vector> #include <string> //#include "RenderEngine.h" #include "rTri.h" class rModel { public: typedef tri<WORD> sTri; std::vector<sTri> m_tris; std::vector<RenderEngine::rDefaultVertex> m_verts; std::wstring m_name; ID3D10Buffer *m_pVertexBuffer; ID3D10Buffer *m_pIndexBuffer; rModel( const TCHAR *filename ); rModel( const TCHAR *name, int nVerts, int nTris ); ~rModel(); float GenRadius(); void Scale( float amt ); void Draw(); //------------------------------------ Access functions. int NumVerts(){ return m_verts.size(); } int NumTris(){ return m_tris.size(); } const TCHAR *Name(){ return m_name.c_str(); } RenderEngine::cDefaultVertex *VertData(){ return &m_verts[0]; } sTri *TriData(){ return &m_tris[0]; } }; #endif at the very top of the code there is a header file #include "stdafx.h" that includes this // stdafx.h : include file for standard system include files, // or project specific include files that are used frequently, but // are changed infrequently // #include "targetver.h" #define WIN32_LEAN_AND_MEAN // Exclude rarely-used stuff from Windows headers // Windows Header Files: #include <windows.h> // C RunTime Header Files #include <stdlib.h> #include <malloc.h> #include <memory.h> #include <tchar.h> #include "resource.h" #include "d3d10.h" #include "d3dx10.h" #include "dinput.h" #include "RenderEngine.h" #include "rModel.h" // TODO: reference additional headers your program requires here as you can see, RenderEngine.h comes before rModel.h #include "RenderEngine.h" #include "rModel.h" According to my knowledge, it should recognize it. But on the other hand, I am not really that great with organizing headers. Here my my RenderEngine Declaration. #pragma once #include "stdafx.h" #define MAX_LOADSTRING 100 #define MAX_LIGHTS 10 class RenderEngine { public: class rDefaultVertex { public: D3DXVECTOR3 m_vPosition; D3DXVECTOR3 m_vNormal; D3DXCOLOR m_vColor; D3DXVECTOR2 m_TexCoords; }; class rLight { public: rLight() { } D3DXCOLOR m_vColor; D3DXVECTOR3 m_vDirection; }; static HINSTANCE m_hInst; HWND m_hWnd; int m_nCmdShow; TCHAR m_szTitle[MAX_LOADSTRING]; // The title bar text TCHAR m_szWindowClass[MAX_LOADSTRING]; // the main window class name void DrawTextString(int x, int y, D3DXCOLOR color, const TCHAR *strOutput); //static functions static LRESULT CALLBACK WndProc(HWND hWnd, UINT message, WPARAM wParam, LPARAM lParam); static INT_PTR CALLBACK About(HWND hDlg, UINT message, WPARAM wParam, LPARAM lParam); bool InitWindow(); bool InitDirectX(); bool InitInstance(); int Run(); void ShutDown(); void AddLight(D3DCOLOR color, D3DXVECTOR3 pos); RenderEngine() { m_screenRect.right = 800; m_screenRect.bottom = 600; m_iNumLights = 0; } protected: RECT m_screenRect; //direct3d Members ID3D10Device *m_pDevice; // The IDirect3DDevice10 // interface ID3D10Texture2D *m_pBackBuffer; // Pointer to the back buffer ID3D10RenderTargetView *m_pRenderTargetView; // Pointer to render target view IDXGISwapChain *m_pSwapChain; // Pointer to the swap chain RECT m_rcScreenRect; // The dimensions of the screen ID3D10Texture2D *m_pDepthStencilBuffer; ID3D10DepthStencilState *m_pDepthStencilState; ID3D10DepthStencilView *m_pDepthStencilView; //transformation matrixs system D3DXMATRIX m_mtxWorld; D3DXMATRIX m_mtxView; D3DXMATRIX m_mtxProj; //pointers to shaders matrix varibles ID3D10EffectMatrixVariable* m_pmtxWorldVar; ID3D10EffectMatrixVariable* m_pmtxViewVar; ID3D10EffectMatrixVariable* m_pmtxProjVar; //Application Lights rLight m_aLights[MAX_LIGHTS]; // Light array int m_iNumLights; // Number of active lights //light pointers from shader ID3D10EffectVectorVariable* m_pLightDirVar; ID3D10EffectVectorVariable* m_pLightColorVar; ID3D10EffectVectorVariable* m_pNumLightsVar; //Effect members ID3D10Effect *m_pDefaultEffect; ID3D10EffectTechnique *m_pDefaultTechnique; ID3D10InputLayout* m_pDefaultInputLayout; ID3DX10Font *m_pFont; // The font used for rendering text // Sprites used to hold font characters ID3DX10Sprite *m_pFontSprite; ATOM RegisterEngineClass(); void DoFrame(float); bool LoadEffects(); void UpdateMatrices(); void UpdateLights(); }; The classes are defined within the class class rDefaultVertex { public: D3DXVECTOR3 m_vPosition; D3DXVECTOR3 m_vNormal; D3DXCOLOR m_vColor; D3DXVECTOR2 m_TexCoords; }; class rLight { public: rLight() { } D3DXCOLOR m_vColor; D3DXVECTOR3 m_vDirection; }; Not sure if thats good practice, but I am just going by the book. In the end, I just need a good way to organize it so that rModel recognizes RenderEngine. and if possible, the other way around.

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• Run-Time Check Failure #2 - Stack around the variable 'indices' was corrupted.

  - by numerical25

  well I think I know what the problem is. I am just having a hard time debugging it. I am working with the directx api and I am trying to generate a plane along the x and z axis according to a book I have. The problem is when I am creating my indices. I think I am setting values out of the bounds of the indices array. I am just having a hard time figuring out what I did wrong. I am unfamiliar with the this method of generating a plane. so its a little difficult for me. below is my code. Take emphasis on the indices loop. #include "MyGame.h" //#include "CubeVector.h" /* This code sets a projection and shows a turning cube. What has been added is the project, rotation and a rasterizer to change the rasterization of the cube. The issue that was going on was something with the effect file which was causing the vertices not to be rendered correctly.*/ typedef struct { ID3D10Effect* pEffect; ID3D10EffectTechnique* pTechnique; //vertex information ID3D10Buffer* pVertexBuffer; ID3D10Buffer* pIndicesBuffer; ID3D10InputLayout* pVertexLayout; UINT numVertices; UINT numIndices; }ModelObject; ModelObject modelObject; // World Matrix D3DXMATRIX WorldMatrix; // View Matrix D3DXMATRIX ViewMatrix; // Projection Matrix D3DXMATRIX ProjectionMatrix; ID3D10EffectMatrixVariable* pProjectionMatrixVariable = NULL; //grid information #define NUM_COLS 16 #define NUM_ROWS 16 #define CELL_WIDTH 32 #define CELL_HEIGHT 32 #define NUM_VERTSX (NUM_COLS + 1) #define NUM_VERTSY (NUM_ROWS + 1) bool MyGame::InitDirect3D() { if(!DX3dApp::InitDirect3D()) { return false; } D3D10_RASTERIZER_DESC rastDesc; rastDesc.FillMode = D3D10_FILL_WIREFRAME; rastDesc.CullMode = D3D10_CULL_FRONT; rastDesc.FrontCounterClockwise = true; rastDesc.DepthBias = false; rastDesc.DepthBiasClamp = 0; rastDesc.SlopeScaledDepthBias = 0; rastDesc.DepthClipEnable = false; rastDesc.ScissorEnable = false; rastDesc.MultisampleEnable = false; rastDesc.AntialiasedLineEnable = false; ID3D10RasterizerState *g_pRasterizerState; mpD3DDevice->CreateRasterizerState(&rastDesc, &g_pRasterizerState); mpD3DDevice->RSSetState(g_pRasterizerState); // Set up the World Matrix D3DXMatrixIdentity(&WorldMatrix); D3DXMatrixLookAtLH(&ViewMatrix, new D3DXVECTOR3(0.0f, 10.0f, -20.0f), new D3DXVECTOR3(0.0f, 0.0f, 0.0f), new D3DXVECTOR3(0.0f, 1.0f, 0.0f)); // Set up the projection matrix D3DXMatrixPerspectiveFovLH(&ProjectionMatrix, (float)D3DX_PI * 0.5f, (float)mWidth/(float)mHeight, 0.1f, 100.0f); if(!CreateObject()) { return false; } return true; } //These are actions that take place after the clearing of the buffer and before the present void MyGame::GameDraw() { static float rotationAngle = 0.0f; // create the rotation matrix using the rotation angle D3DXMatrixRotationY(&WorldMatrix, rotationAngle); rotationAngle += (float)D3DX_PI * 0.0f; // Set the input layout mpD3DDevice->IASetInputLayout(modelObject.pVertexLayout); // Set vertex buffer UINT stride = sizeof(VertexPos); UINT offset = 0; mpD3DDevice->IASetVertexBuffers(0, 1, &modelObject.pVertexBuffer, &stride, &offset); mpD3DDevice->IASetIndexBuffer(modelObject.pIndicesBuffer, DXGI_FORMAT_R32_UINT, 0); // Set primitive topology mpD3DDevice->IASetPrimitiveTopology(D3D10_PRIMITIVE_TOPOLOGY_TRIANGLELIST); // Combine and send the final matrix to the shader D3DXMATRIX finalMatrix = (WorldMatrix * ViewMatrix * ProjectionMatrix); pProjectionMatrixVariable->SetMatrix((float*)&finalMatrix); // make sure modelObject is valid // Render a model object D3D10_TECHNIQUE_DESC techniqueDescription; modelObject.pTechnique->GetDesc(&techniqueDescription); // Loop through the technique passes for(UINT p=0; p < techniqueDescription.Passes; ++p) { modelObject.pTechnique->GetPassByIndex(p)->Apply(0); // draw the cube using all 36 vertices and 12 triangles mpD3DDevice->DrawIndexed(modelObject.numIndices,0,0); } } //Render actually incapsulates Gamedraw, so you can call data before you actually clear the buffer or after you //present data void MyGame::Render() { DX3dApp::Render(); } bool MyGame::CreateObject() { VertexPos vertices[NUM_VERTSX * NUM_VERTSY]; for(int z=0; z < NUM_VERTSY; ++z) { for(int x = 0; x < NUM_VERTSX; ++x) { vertices[x + z * NUM_VERTSX].pos.x = (float)x * CELL_WIDTH; vertices[x + z * NUM_VERTSX].pos.z = (float)z * CELL_HEIGHT; vertices[x + z * NUM_VERTSX].pos.y = 0.0f; vertices[x + z * NUM_VERTSX].color = D3DXVECTOR4(1.0, 0.0f, 0.0f, 0.0f); } } DWORD indices[NUM_VERTSX * NUM_VERTSY]; int curIndex = 0; for(int z=0; z < NUM_ROWS; ++z) { for(int x = 0; x < NUM_COLS; ++x) { int curVertex = x + (z * NUM_VERTSX); indices[curIndex] = curVertex; indices[curIndex + 1] = curVertex + NUM_VERTSX; indices[curIndex + 2] = curVertex + 1; indices[curIndex + 3] = curVertex + 1; indices[curIndex + 4] = curVertex + NUM_VERTSX; indices[curIndex + 5] = curVertex + NUM_VERTSX + 1; curIndex += 6; } } //Create Layout D3D10_INPUT_ELEMENT_DESC layout[] = { {"POSITION",0,DXGI_FORMAT_R32G32B32_FLOAT, 0 , 0, D3D10_INPUT_PER_VERTEX_DATA, 0}, {"COLOR",0,DXGI_FORMAT_R32G32B32A32_FLOAT, 0 , 12, D3D10_INPUT_PER_VERTEX_DATA, 0} }; UINT numElements = (sizeof(layout)/sizeof(layout[0])); modelObject.numVertices = sizeof(vertices)/sizeof(VertexPos); //Create buffer desc D3D10_BUFFER_DESC bufferDesc; bufferDesc.Usage = D3D10_USAGE_DEFAULT; bufferDesc.ByteWidth = sizeof(VertexPos) * modelObject.numVertices; bufferDesc.BindFlags = D3D10_BIND_VERTEX_BUFFER; bufferDesc.CPUAccessFlags = 0; bufferDesc.MiscFlags = 0; D3D10_SUBRESOURCE_DATA initData; initData.pSysMem = vertices; //Create the buffer HRESULT hr = mpD3DDevice->CreateBuffer(&bufferDesc, &initData, &modelObject.pVertexBuffer); if(FAILED(hr)) return false; modelObject.numIndices = sizeof(indices)/sizeof(DWORD); bufferDesc.ByteWidth = sizeof(DWORD) * modelObject.numIndices; bufferDesc.BindFlags = D3D10_BIND_INDEX_BUFFER; initData.pSysMem = indices; hr = mpD3DDevice->CreateBuffer(&bufferDesc, &initData, &modelObject.pIndicesBuffer); if(FAILED(hr)) return false; ///////////////////////////////////////////////////////////////////////////// //Set up fx files LPCWSTR effectFilename = L"effect.fx"; modelObject.pEffect = NULL; hr = D3DX10CreateEffectFromFile(effectFilename, NULL, NULL, "fx_4_0", D3D10_SHADER_ENABLE_STRICTNESS, 0, mpD3DDevice, NULL, NULL, &modelObject.pEffect, NULL, NULL); if(FAILED(hr)) return false; pProjectionMatrixVariable = modelObject.pEffect->GetVariableByName("Projection")->AsMatrix(); //Dont sweat the technique. Get it! LPCSTR effectTechniqueName = "Render"; modelObject.pTechnique = modelObject.pEffect->GetTechniqueByName(effectTechniqueName); if(modelObject.pTechnique == NULL) return false; //Create Vertex layout D3D10_PASS_DESC passDesc; modelObject.pTechnique->GetPassByIndex(0)->GetDesc(&passDesc); hr = mpD3DDevice->CreateInputLayout(layout, numElements, passDesc.pIAInputSignature, passDesc.IAInputSignatureSize, &modelObject.pVertexLayout); if(FAILED(hr)) return false; return true; }

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• Solving embarassingly parallel problems using Python multiprocessing

  - by gotgenes

  How does one use multiprocessing to tackle embarrassingly parallel problems? Embarassingly parallel problems typically consist of three basic parts: Read input data (from a file, database, tcp connection, etc.). Run calculations on the input data, where each calculation is independent of any other calculation. Write results of calculations (to a file, database, tcp connection, etc.). We can parallelize the program in two dimensions: Part 2 can run on multiple cores, since each calculation is independent; order of processing doesn't matter. Each part can run independently. Part 1 can place data on an input queue, part 2 can pull data off the input queue and put results onto an output queue, and part 3 can pull results off the output queue and write them out. This seems a most basic pattern in concurrent programming, but I am still lost in trying to solve it, so let's write a canonical example to illustrate how this is done using multiprocessing. Here is the example problem: Given a CSV file with rows of integers as input, compute their sums. Separate the problem into three parts, which can all run in parallel: Process the input file into raw data (lists/iterables of integers) Calculate the sums of the data, in parallel Output the sums Below is traditional, single-process bound Python program which solves these three tasks: #!/usr/bin/env python # -*- coding: UTF-8 -*- # basicsums.py """A program that reads integer values from a CSV file and writes out their sums to another CSV file. """ import csv import optparse import sys def make_cli_parser(): """Make the command line interface parser.""" usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV", __doc__, """ ARGUMENTS: INPUT_CSV: an input CSV file with rows of numbers OUTPUT_CSV: an output file that will contain the sums\ """]) cli_parser = optparse.OptionParser(usage) return cli_parser def parse_input_csv(csvfile): """Parses the input CSV and yields tuples with the index of the row as the first element, and the integers of the row as the second element. The index is zero-index based. :Parameters: - `csvfile`: a `csv.reader` instance """ for i, row in enumerate(csvfile): row = [int(entry) for entry in row] yield i, row def sum_rows(rows): """Yields a tuple with the index of each input list of integers as the first element, and the sum of the list of integers as the second element. The index is zero-index based. :Parameters: - `rows`: an iterable of tuples, with the index of the original row as the first element, and a list of integers as the second element """ for i, row in rows: yield i, sum(row) def write_results(csvfile, results): """Writes a series of results to an outfile, where the first column is the index of the original row of data, and the second column is the result of the calculation. The index is zero-index based. :Parameters: - `csvfile`: a `csv.writer` instance to which to write results - `results`: an iterable of tuples, with the index (zero-based) of the original row as the first element, and the calculated result from that row as the second element """ for result_row in results: csvfile.writerow(result_row) def main(argv): cli_parser = make_cli_parser() opts, args = cli_parser.parse_args(argv) if len(args) != 2: cli_parser.error("Please provide an input file and output file.") infile = open(args[0]) in_csvfile = csv.reader(infile) outfile = open(args[1], 'w') out_csvfile = csv.writer(outfile) # gets an iterable of rows that's not yet evaluated input_rows = parse_input_csv(in_csvfile) # sends the rows iterable to sum_rows() for results iterable, but # still not evaluated result_rows = sum_rows(input_rows) # finally evaluation takes place as a chain in write_results() write_results(out_csvfile, result_rows) infile.close() outfile.close() if __name__ == '__main__': main(sys.argv[1:]) Let's take this program and rewrite it to use multiprocessing to parallelize the three parts outlined above. Below is a skeleton of this new, parallelized program, that needs to be fleshed out to address the parts in the comments: #!/usr/bin/env python # -*- coding: UTF-8 -*- # multiproc_sums.py """A program that reads integer values from a CSV file and writes out their sums to another CSV file, using multiple processes if desired. """ import csv import multiprocessing import optparse import sys NUM_PROCS = multiprocessing.cpu_count() def make_cli_parser(): """Make the command line interface parser.""" usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV", __doc__, """ ARGUMENTS: INPUT_CSV: an input CSV file with rows of numbers OUTPUT_CSV: an output file that will contain the sums\ """]) cli_parser = optparse.OptionParser(usage) cli_parser.add_option('-n', '--numprocs', type='int', default=NUM_PROCS, help="Number of processes to launch [DEFAULT: %default]") return cli_parser def main(argv): cli_parser = make_cli_parser() opts, args = cli_parser.parse_args(argv) if len(args) != 2: cli_parser.error("Please provide an input file and output file.") infile = open(args[0]) in_csvfile = csv.reader(infile) outfile = open(args[1], 'w') out_csvfile = csv.writer(outfile) # Parse the input file and add the parsed data to a queue for # processing, possibly chunking to decrease communication between # processes. # Process the parsed data as soon as any (chunks) appear on the # queue, using as many processes as allotted by the user # (opts.numprocs); place results on a queue for output. # # Terminate processes when the parser stops putting data in the # input queue. # Write the results to disk as soon as they appear on the output # queue. # Ensure all child processes have terminated. # Clean up files. infile.close() outfile.close() if __name__ == '__main__': main(sys.argv[1:]) These pieces of code, as well as another piece of code that can generate example CSV files for testing purposes, can be found on github. I would appreciate any insight here as to how you concurrency gurus would approach this problem. Here are some questions I had when thinking about this problem. Bonus points for addressing any/all: Should I have child processes for reading in the data and placing it into the queue, or can the main process do this without blocking until all input is read? Likewise, should I have a child process for writing the results out from the processed queue, or can the main process do this without having to wait for all the results? Should I use a processes pool for the sum operations? If yes, what method do I call on the pool to get it to start processing the results coming into the input queue, without blocking the input and output processes, too? apply_async()? map_async()? imap()? imap_unordered()? Suppose we didn't need to siphon off the input and output queues as data entered them, but could wait until all input was parsed and all results were calculated (e.g., because we know all the input and output will fit in system memory). Should we change the algorithm in any way (e.g., not run any processes concurrently with I/O)?

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• Python regex to parse text file, get the items in list and count the list

  - by Nemo

  I have a text file which contains some data. I m particularly interested in finding the count of the number of items in v_dims v_dims pattern in my text file looks like this : v_dims={ "Sales", "Product Family", "Sales Organization", "Region", "Sales Area", "Sales office", "Sales Division", "Sales Person", "Sales Channel", "Sales Order Type", "Sales Number", "Sales Person", "Sales Quantity", "Sales Amount" } So I m thinking of getting all the elements in v_dims and dumping them out in a Python list. Then compute the len(mylist) to get the count of the items. The challenge is in getting all the elements of v_dims from my text file and putting them in an empty list. I m particularly interested in items in v_dims in my text file. The text file has data in the form of v_dims pattern i showed in my original post. Some data has nested patterns of v_dims. Thanks. Here's what I have tried and failed. Any help is appreciated. TIA. import re fname = "C:\Users\XXXX\Test.mrk" with open(fname, "r") as fo: content_as_string = fo.read() match = re.findall(r'v_dims={\"(.+?)\"}',content_as_string) Though I have a big text file, Here's a snippet of what's the structure of my text file version "1"; // Computer generated object language file object 'MRKR' "Main" { Data_Type=2, HeaderBlock={ Version_String="6.3 (25)" }, Printer_Info={ Orientation=0, Page_Width=8.50000000, Page_Height=11.00000000, Page_Header="", Page_Footer="", Margin_type=0, Top_Margin=0.50000000, Left_Margin=0.50000000, Bottom_Margin=0.50000000, Right_Margin=0.50000000 }, Marker_Options={ Close_All="TRUE", Hide_Console="FALSE", Console_Left="FALSE", Console_Width=217, Main_Style="Maximized", MDI_Rect={ 0, 0, 892, 1063 } }, Dives={ { Dive="A", Windows={ { View_Index=0, Window_Info={ Window_Rect={ 0, -288, 400, 1008 }, Window_Style="Maximized Front", Window_Name="Theater [Previous Qtr Diveplan-Dive A]" }, Dependent_bool="FALSE", Colset={ Dive_Type="Normal", Dimension_Name="Theater", Action_List={ Actions={ { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater" }, Key_Indexes={ { "AMERICAS" } } }, { Action_Type="Focus", Focus_Rows="True" }, { Action_Type="Dimensions", v_dims={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag", "Product Item Family" }, Xtab_Bool="False", Xtab_Flip="False" }, { Action_Type="Select", select_type=5 }, { Action_Type="Select", select_type=0, Key_Names={ "Theater", "Product Family", "Division", "Region", "Install at Country Name", "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "PS Flag", "Avalanche Flag" }, Key_Indexes={ { "AMERICAS", "ATMOS", "Latin America CS Division", "37000 CS Region", "Mexico", "", "", "", "", "DIRECT", "EMC", "N", "0" } } } } }, Num_Palette_cols=0, Num_Palette_rows=0 }, Format={ Window_Type="Tabular", Tabular={ Num_row_labels=8 } } } } } }, Widget_Set={ Widget_Layout="Vertical", Go_Button=1, Picklist_Width=0, Sort_Subset_Dimensions="TRUE", Order={ } }, Views={ { Data_Type=1, dbname="Previous Qtr Diveplan", diveline_dbname="Current Qtr Diveplan", logical_name="Current Qtr Diveplan", cols={ { name="Total TSS installs", column_type="Calc[Total TSS installs]", output_type="Number", format_string="." }, { name="TSS Valid Connectivity Records", column_type="Calc[TSS Valid Connectivity Records]", output_type="Number", format_string="." }, { name="% TSS Connectivity Record", column_type="Calc[% TSS Connectivity Record]", output_type="Number" }, { name="TSS Not Applicable", column_type="Calc[TSS Not Applicable]", output_type="Number", format_string="." }, { name="TSS Customer Refusals", column_type="Calc[TSS Customer Refusals]", output_type="Number", format_string="." }, { name="% TSS Refusals", column_type="Calc[% TSS Refusals]", output_type="Number" }, { name="TSS Eligible for Physical Connectivity", column_type="Calc[TSS Eligible for Physical Connectivity]", output_type="Number", format_string="." }, { name="TSS Boxes with Physical Connectivty", column_type="Calc[TSS Boxes with Physical Connectivty]", output_type="Number", format_string="." }, { name="% TSS Physical Connectivity", column_type="Calc[% TSS Physical Connectivity]", output_type="Number" } }, dim_cols={ { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Model", column_type="Dimension[Model]", output_type="None" }, { name="Connect In Type", column_type="Dimension[Connect In Type]", output_type="None" }, { name="Connect Home Type", column_type="Dimension[Connect Home Type]", output_type="None" }, { name="SymmConnect Enabled", column_type="Dimension[SymmConnect Enabled]", output_type="None" }, { name="Theater", column_type="Dimension[Theater]", output_type="None" }, { name="Division", column_type="Dimension[Division]", output_type="None" }, { name="Region", column_type="Dimension[Region]", output_type="None" }, { name="Sales Order Number", column_type="Dimension[Sales Order Number]", output_type="None" }, { name="Product Item Family", column_type="Dimension[Product Item Family]", output_type="None" }, { name="Item Serial Number", column_type="Dimension[Item Serial Number]", output_type="None" }, { name="Sales Order Deal Number", column_type="Dimension[Sales Order Deal Number]", output_type="None" }, { name="Item Install Date", column_type="Dimension[Item Install Date]", output_type="None" }, { name="SYR Last Dial Home Date", column_type="Dimension[SYR Last Dial Home Date]", output_type="None" }, { name="Maintained By Group", column_type="Dimension[Maintained By Group]", output_type="None" }, { name="PS Flag", column_type="Dimension[PS Flag]", output_type="None" }, { name="Connect Home Refusal Reason", column_type="Dimension[Connect Home Refusal Reason]", output_type="None", col_width=177 }, { name="Cust Name", column_type="Dimension[Cust Name]", output_type="None" }, { name="Sales Order Channel Type", column_type="Dimension[Sales Order Channel Type]", output_type="None" }, { name="Sales Order Type", column_type="Dimension[Sales Order Type]", output_type="None" }, { name="Part Model Key", column_type="Dimension[Part Model Key]", output_type="None" }, { name="Ship Date", column_type="Dimension[Ship Date]", output_type="None" }, { name="Model Number", column_type="Dimension[Model Number]", output_type="None" }, { name="Item Description", column_type="Dimension[Item Description]", output_type="None" }, { name="Customer Classification", column_type="Dimension[Customer Classification]", output_type="None" }, { name="CS Customer Name", column_type="Dimension[CS Customer Name]", output_type="None" }, { name="Install At Customer Number", column_type="Dimension[Install At Customer Number]", output_type="None" }, { name="Install at Country Name", column_type="Dimension[Install at Country Name]", output_type="None" }, { name="TLA Serial Number", column_type="Dimension[TLA Serial Number]", output_type="None" }, { name="Product Version", column_type="Dimension[Product Version]", output_type="None" }, { name="Avalanche Flag", column_type="Dimension[Avalanche Flag]", output_type="None" }, { name="Product Family", column_type="Dimension[Product Family]", output_type="None" }, { name="Project Number", column_type="Dimension[Project Number]", output_type="None" }, { name="PROJECT_STATUS", column_type="Dimension[PROJECT_STATUS]", output_type="None" } }, Available_Columns={ "Total TSS installs", "TSS Valid Connectivity Records", "% TSS Connectivity Record", "TSS Not Applicable", "TSS Customer Refusals", "% TSS Refusals", "TSS Eligible for Physical Connectivity", "TSS Boxes with Physical Connectivty", "% TSS Physical Connectivity", "Total Installs", "All Boxes with Valid Connectivty Record", "% All Connectivity Record", "Overall Refusals", "Overall Refusals %", "All Eligible for Physical Connectivty", "Boxes with Physical Connectivity", "% All with Physical Conectivity" }, Remaining_columns={ { name="Total Installs", column_type="Calc[Total Installs]", output_type="Number", format_string="." }, { name="All Boxes with Valid Connectivty Record", column_type="Calc[All Boxes with Valid Connectivty Record]", output_type="Number", format_string="." }, { name="% All Connectivity Record", column_type="Calc[% All Connectivity Record]", output_type="Number" }, { name="Overall Refusals", column_type="Calc[Overall Refusals]", output_type="Number", format_string="." }, { name="Overall Refusals %", column_type="Calc[Overall Refusals %]", output_type="Number" }, { name="All Eligible for Physical Connectivty", column_type="Calc[All Eligible for Physical Connectivty]", output_type="Number" }, { name="Boxes with Physical Connectivity", column_type="Calc[Boxes with Physical Connectivity]", output_type="Number" }, { name="% All with Physical Conectivity", column_type="Calc[% All with Physical Conectivity]", output_type="Number" } }, calcs={ { name="Total TSS installs", definition="Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Valid Connectivity Records", definition="Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% TSS Connectivity Record", definition="Total[PS Boxes w/ valid connectivity record (1=yes)] /Total[Total TSS installs]", ts_flag="Not TS Calc" }, { name="TSS Not Applicable", definition="Total[Bozes w/ valid connectivity record (1=yes)]-Total[Boxes Eligible (1=yes)]-Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="TSS Customer Refusals", definition="Total[TSS Refusals]", ts_flag="Not TS Calc" }, { name="% TSS Refusals", definition="Total[TSS Refusals]/Total[PS Boxes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="TSS Eligible for Physical Connectivity", definition="Total[TSS Eligible]-Total[Exception]", ts_flag="Not TS Calc" }, { name="TSS Boxes with Physical Connectivty", definition="Total[PS Physical Connectivity] - Total[PS Physical Connectivity, SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% TSS Physical Connectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" }, { name="Total Installs", definition="Total[Total Installs]", ts_flag="Not TS Calc" }, { name="All Boxes with Valid Connectivty Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="% All Connectivity Record", definition="Total[Bozes w/ valid connectivity record (1=yes)]/Total[Total Installs]", ts_flag="Not TS Calc" }, { name="Overall Refusals", definition="Total[Overall Refusals]", ts_flag="Not TS Calc" }, { name="Overall Refusals %", definition="Total[Overall Refusals]/Total[Bozes w/ valid connectivity record (1=yes)]", ts_flag="Not TS Calc" }, { name="All Eligible for Physical Connectivty", definition="Total[Boxes Eligible (1=yes)]-Total[Exception]", ts_flag="Not TS Calc" }, { name="Boxes with Physical Connectivity", definition="Total[Boxes w/ phys conn]-Total[Boxes w/ phys conn,SymmConnect Enabled=\"Capable not enabled\"]", ts_flag="Not TS Calc" }, { name="% All with Physical Conectivity", definition="Total[Boxes w/ phys conn]/Total[Boxes Eligible (1=yes)]", ts_flag="Not TS Calc" } }, merge_type="consolidate", merge_dbs={ { dbname="connectivityallproducts.mdl", diveline_dbname="/DI_PSREPORTING/connectivityallproducts.mdl" } }, skip_constant_columns="FALSE", categories={ { name="Geography", dimensions={ "Theater", "Division", "Region", "Install at Country Name" } }, { name="Mappings and Flags", dimensions={ "Connect Home Type", "Connect In Type", "SymmConnect Enabled", "Connect Home Refusal Reason", "Sales Order Channel Type", "Maintained By Group", "Customer Installable", "PS Flag", "Top Level Flag", "Avalanche Flag" } }, { name="Product Information", dimensions={ "Product Family", "Product Item Family", "Product Version", "Item Description" } }, { name="Sales Order Info", dimensions={ "Sales Order Deal Number", "Sales Order Number", "Sales Order Type" } }, { name="Dates", dimensions={ "Item Install Date", "Ship Date", "SYR Last Dial Home Date" } }, { name="Details", dimensions={ "Item Serial Number", "TLA Serial Number", "Part Model Key", "Model Number" } }, { name="Customer Infor", dimensions={ "CS Customer Name", "Install At Customer Number", "Customer Classification", "Cust Name" } }, { name="Other Dimensions", dimensions={ "Model" } } }, Maintain_Category_Order="FALSE", popup_info="false" } } };

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• how to export bind and keyframe bone poses from blender to use in OpenGL

  - by SaldaVonSchwartz

  EDIT: I decided to reformulate the question in much simpler terms to see if someone can give me a hand with this. Basically, I'm exporting meshes, skeletons and actions from blender into an engine of sorts that I'm working on. But I'm getting the animations wrong. I can tell the basic motion paths are being followed but there's always an axis of translation or rotation which is wrong. I think the problem is most likely not in my engine code (OpenGL-based) but rather in either my misunderstanding of some part of the theory behind skeletal animation / skinning or the way I am exporting the appropriate joint matrices from blender in my exporter script. I'll explain the theory, the engine animation system and my blender export script, hoping someone might catch the error in either or all of these. The theory: (I'm using column-major ordering since that's what I use in the engine cause it's OpenGL-based) Assume I have a mesh made up of a single vertex v, along with a transformation matrix M which takes the vertex v from the mesh's local space to world space. That is, if I was to render the mesh without a skeleton, the final position would be gl_Position = ProjectionMatrix * M * v. Now assume I have a skeleton with a single joint j in bind / rest pose. j is actually another matrix. A transform from j's local space to its parent space which I'll denote Bj. if j was part of a joint hierarchy in the skeleton, Bj would take from j space to j-1 space (that is to its parent space). However, in this example j is the only joint, so Bj takes from j space to world space, like M does for v. Now further assume I have a a set of frames, each with a second transform Cj, which works the same as Bj only that for a different, arbitrary spatial configuration of join j. Cj still takes vertices from j space to world space but j is rotated and/or translated and/or scaled. Given the above, in order to skin vertex v at keyframe n. I need to: take v from world space to joint j space modify j (while v stays fixed in j space and is thus taken along in the transformation) take v back from the modified j space to world space So the mathematical implementation of the above would be: v' = Cj * Bj^-1 * v. Actually, I have one doubt here.. I said the mesh to which v belongs has a transform M which takes from model space to world space. And I've also read in a couple textbooks that it needs to be transformed from model space to joint space. But I also said in 1 that v needs to be transformed from world to joint space. So basically I'm not sure if I need to do v' = Cj * Bj^-1 * v or v' = Cj * Bj^-1 * M * v. Right now my implementation multiples v' by M and not v. But I've tried changing this and it just screws things up in a different way cause there's something else wrong. Finally, If we wanted to skin a vertex to a joint j1 which in turn is a child of a joint j0, Bj1 would be Bj0 * Bj1 and Cj1 would be Cj0 * Cj1. But Since skinning is defined as v' = Cj * Bj^-1 * v , Bj1^-1 would be the reverse concatenation of the inverses making up the original product. That is, v' = Cj0 * Cj1 * Bj1^-1 * Bj0^-1 * v Now on to the implementation (Blender side): Assume the following mesh made up of 1 cube, whose vertices are bound to a single joint in a single-joint skeleton: Assume also there's a 60-frame, 3-keyframe animation at 60 fps. The animation essentially is: keyframe 0: the joint is in bind / rest pose (the way you see it in the image). keyframe 30: the joint translates up (+z in blender) some amount and at the same time rotates pi/4 rad clockwise. keyframe 59: the joint goes back to the same configuration it was in keyframe 0. My first source of confusion on the blender side is its coordinate system (as opposed to OpenGL's default) and the different matrices accessible through the python api. Right now, this is what my export script does about translating blender's coordinate system to OpenGL's standard system: # World transform: Blender -> OpenGL worldTransform = Matrix().Identity(4) worldTransform *= Matrix.Scale(-1, 4, (0,0,1)) worldTransform *= Matrix.Rotation(radians(90), 4, "X") # Mesh (local) transform matrix file.write('Mesh Transform:\n') localTransform = mesh.matrix_local.copy() localTransform = worldTransform * localTransform for col in localTransform.col: file.write('{:9f} {:9f} {:9f} {:9f}\n'.format(col[0], col[1], col[2], col[3])) file.write('\n') So if you will, my "world" matrix is basically the act of changing blenders coordinate system to the default GL one with +y up, +x right and -z into the viewing volume. Then I also premultiply (in the sense that it's done by the time we reach the engine, not in the sense of post or pre in terms of matrix multiplication order) the mesh matrix M so that I don't need to multiply it again once per draw call in the engine. About the possible matrices to extract from Blender joints (bones in Blender parlance), I'm doing the following: For joint bind poses: def DFSJointTraversal(file, skeleton, jointList): for joint in jointList: bindPoseJoint = skeleton.data.bones[joint.name] bindPoseTransform = bindPoseJoint.matrix_local.inverted() file.write('Joint ' + joint.name + ' Transform {\n') translationV = bindPoseTransform.to_translation() rotationQ = bindPoseTransform.to_3x3().to_quaternion() scaleV = bindPoseTransform.to_scale() file.write('T {:9f} {:9f} {:9f}\n'.format(translationV[0], translationV[1], translationV[2])) file.write('Q {:9f} {:9f} {:9f} {:9f}\n'.format(rotationQ[1], rotationQ[2], rotationQ[3], rotationQ[0])) file.write('S {:9f} {:9f} {:9f}\n'.format(scaleV[0], scaleV[1], scaleV[2])) DFSJointTraversal(file, skeleton, joint.children) file.write('}\n') Note that I'm actually grabbing the inverse of what I think is the bind pose transform Bj. This is so I don't need to invert it in the engine. Also note I went for matrix_local, assuming this is Bj. The other option is plain "matrix", which as far as I can tell is the same only that not homogeneous. For joint current / keyframe poses: for kfIndex in keyframes: bpy.context.scene.frame_set(kfIndex) file.write('keyframe: {:d}\n'.format(int(kfIndex))) for i in range(0, len(skeleton.data.bones)): file.write('joint: {:d}\n'.format(i)) currentPoseJoint = skeleton.pose.bones[i] currentPoseTransform = currentPoseJoint.matrix translationV = currentPoseTransform.to_translation() rotationQ = currentPoseTransform.to_3x3().to_quaternion() scaleV = currentPoseTransform.to_scale() file.write('T {:9f} {:9f} {:9f}\n'.format(translationV[0], translationV[1], translationV[2])) file.write('Q {:9f} {:9f} {:9f} {:9f}\n'.format(rotationQ[1], rotationQ[2], rotationQ[3], rotationQ[0])) file.write('S {:9f} {:9f} {:9f}\n'.format(scaleV[0], scaleV[1], scaleV[2])) file.write('\n') Note that here I go for skeleton.pose.bones instead of data.bones and that I have a choice of 3 matrices: matrix, matrix_basis and matrix_channel. From the descriptions in the python API docs I'm not super clear which one I should choose, though I think it's the plain matrix. Also note I do not invert the matrix in this case. The implementation (Engine / OpenGL side): My animation subsystem does the following on each update (I'm omitting parts of the update loop where it's figured out which objects need update and time is hardcoded here for simplicity): static double time = 0; time = fmod((time + elapsedTime),1.); uint16_t LERPKeyframeNumber = 60 * time; uint16_t lkeyframeNumber = 0; uint16_t lkeyframeIndex = 0; uint16_t rkeyframeNumber = 0; uint16_t rkeyframeIndex = 0; for (int i = 0; i < aClip.keyframesCount; i++) { uint16_t keyframeNumber = aClip.keyframes[i].number; if (keyframeNumber <= LERPKeyframeNumber) { lkeyframeIndex = i; lkeyframeNumber = keyframeNumber; } else { rkeyframeIndex = i; rkeyframeNumber = keyframeNumber; break; } } double lTime = lkeyframeNumber / 60.; double rTime = rkeyframeNumber / 60.; double blendFactor = (time - lTime) / (rTime - lTime); GLKMatrix4 bindPosePalette[aSkeleton.jointsCount]; GLKMatrix4 currentPosePalette[aSkeleton.jointsCount]; for (int i = 0; i < aSkeleton.jointsCount; i++) { F3DETQSType& lPose = aClip.keyframes[lkeyframeIndex].skeletonPose.joints[i]; F3DETQSType& rPose = aClip.keyframes[rkeyframeIndex].skeletonPose.joints[i]; GLKVector3 LERPTranslation = GLKVector3Lerp(lPose.t, rPose.t, blendFactor); GLKQuaternion SLERPRotation = GLKQuaternionSlerp(lPose.q, rPose.q, blendFactor); GLKVector3 LERPScaling = GLKVector3Lerp(lPose.s, rPose.s, blendFactor); GLKMatrix4 currentTransform = GLKMatrix4MakeWithQuaternion(SLERPRotation); currentTransform = GLKMatrix4TranslateWithVector3(currentTransform, LERPTranslation); currentTransform = GLKMatrix4ScaleWithVector3(currentTransform, LERPScaling); GLKMatrix4 inverseBindTransform = GLKMatrix4MakeWithQuaternion(aSkeleton.joints[i].inverseBindTransform.q); inverseBindTransform = GLKMatrix4TranslateWithVector3(inverseBindTransform, aSkeleton.joints[i].inverseBindTransform.t); inverseBindTransform = GLKMatrix4ScaleWithVector3(inverseBindTransform, aSkeleton.joints[i].inverseBindTransform.s); if (aSkeleton.joints[i].parentIndex == -1) { bindPosePalette[i] = inverseBindTransform; currentPosePalette[i] = currentTransform; } else { bindPosePalette[i] = GLKMatrix4Multiply(inverseBindTransform, bindPosePalette[aSkeleton.joints[i].parentIndex]); currentPosePalette[i] = GLKMatrix4Multiply(currentPosePalette[aSkeleton.joints[i].parentIndex], currentTransform); } aSkeleton.skinningPalette[i] = GLKMatrix4Multiply(currentPosePalette[i], bindPosePalette[i]); } Finally, this is my vertex shader: #version 100 uniform mat4 modelMatrix; uniform mat3 normalMatrix; uniform mat4 projectionMatrix; uniform mat4 skinningPalette[6]; uniform lowp float skinningEnabled; attribute vec4 position; attribute vec3 normal; attribute vec2 tCoordinates; attribute vec4 jointsWeights; attribute vec4 jointsIndices; varying highp vec2 tCoordinatesVarying; varying highp float lIntensity; void main() { tCoordinatesVarying = tCoordinates; vec4 skinnedVertexPosition = vec4(0.); for (int i = 0; i < 4; i++) { skinnedVertexPosition += jointsWeights[i] * skinningPalette[int(jointsIndices[i])] * position; } vec4 skinnedNormal = vec4(0.); for (int i = 0; i < 4; i++) { skinnedNormal += jointsWeights[i] * skinningPalette[int(jointsIndices[i])] * vec4(normal, 0.); } vec4 finalPosition = mix(position, skinnedVertexPosition, skinningEnabled); vec4 finalNormal = mix(vec4(normal, 0.), skinnedNormal, skinningEnabled); vec3 eyeNormal = normalize(normalMatrix * finalNormal.xyz); vec3 lightPosition = vec3(0., 0., 2.); lIntensity = max(0.0, dot(eyeNormal, normalize(lightPosition))); gl_Position = projectionMatrix * modelMatrix * finalPosition; } The result is that the animation displays wrong in terms of orientation. That is, instead of bobbing up and down it bobs in and out (along what I think is the Z axis according to my transform in the export clip). And the rotation angle is counterclockwise instead of clockwise. If I try with a more than one joint, then it's almost as if the second joint rotates in it's own different coordinate space and does not follow 100% its parent's transform. Which I assume it should from my animation subsystem which I assume in turn follows the theory I explained for the case of more than one joint. Any thoughts?

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• Weblogic 10.0: SAMLSignedObject.verify() failed to validate signature value

  - by joshea

  I've been having this problem for a while and it's driving me nuts. I'm trying to create a client (in C# .NET 2.0) that will use SAML 1.1 to sign on to a WebLogic 10.0 server (i.e., a Single Sign-On scenario, using browser/post profile). The client is on a WinXP machine and the WebLogic server is on a RHEL 5 box. I based my client largely on code in the example here: http://www.codeproject.com/KB/aspnet/DotNetSamlPost.aspx (the source has a section for SAML 1.1). I set up WebLogic based on instructions for SAML Destination Site from here:http://www.oracle.com/technology/pub/articles/dev2arch/2006/12/sso-with-saml4.html I created a certificate using makecert that came with VS 2005. makecert -r -pe -n "CN=whatever" -b 01/01/2010 -e 01/01/2011 -sky exchange whatever.cer -sv whatever.pvk pvk2pfx.exe -pvk whatever.pvk -spc whatever.cer -pfx whatever.pfx Then I installed the .pfx to my personal certificate directory, and installed the .cer into the WebLogic SAML Identity Asserter V2. I read on another site that formatting the response to be readable (ie, adding whitespace) to the response after signing would cause this problem, so I tried various combinations of turning on/off .Indent XMLWriterSettings and turning on/off .PreserveWhiteSpace when loading the XML document, and none of it made any difference. I've printed the SignatureValue both before the message is is encoded/sent and after it arrives/gets decoded, and they are the same. So, to be clear: the Response appears to be formed, encoded, sent, and decoded fine (I see the full Response in the WebLogic logs). WebLogic finds the certificate I want it to use, verifies that a key was supplied, gets the signed info, and then fails to validate the signature. Code: public string createResponse(Dictionary<string, string> attributes){ ResponseType response = new ResponseType(); // Create Response response.ResponseID = "_" + Guid.NewGuid().ToString(); response.MajorVersion = "1"; response.MinorVersion = "1"; response.IssueInstant = System.DateTime.UtcNow; response.Recipient = "http://theWLServer/samlacs/acs"; StatusType status = new StatusType(); status.StatusCode = new StatusCodeType(); status.StatusCode.Value = new XmlQualifiedName("Success", "urn:oasis:names:tc:SAML:1.0:protocol"); response.Status = status; // Create Assertion AssertionType assertionType = CreateSaml11Assertion(attributes); response.Assertion = new AssertionType[] {assertionType}; //Serialize XmlSerializerNamespaces ns = new XmlSerializerNamespaces(); ns.Add("samlp", "urn:oasis:names:tc:SAML:1.0:protocol"); ns.Add("saml", "urn:oasis:names:tc:SAML:1.0:assertion"); XmlSerializer responseSerializer = new XmlSerializer(response.GetType()); StringWriter stringWriter = new StringWriter(); XmlWriterSettings settings = new XmlWriterSettings(); settings.OmitXmlDeclaration = true; settings.Indent = false;//I've tried both ways, for the fun of it settings.Encoding = Encoding.UTF8; XmlWriter responseWriter = XmlTextWriter.Create(stringWriter, settings); responseSerializer.Serialize(responseWriter, response, ns); responseWriter.Close(); string samlString = stringWriter.ToString(); stringWriter.Close(); // Sign the document XmlDocument doc = new XmlDocument(); doc.PreserveWhiteSpace = true; //also tried this both ways to no avail doc.LoadXml(samlString); X509Certificate2 cert = null; X509Store store = new X509Store(StoreName.My, StoreLocation.CurrentUser); store.Open(OpenFlags.ReadOnly); X509Certificate2Collection coll = store.Certificates.Find(X509FindType.FindBySubjectDistinguishedName, "distName", true); if (coll.Count < 1) { throw new ArgumentException("Unable to locate certificate"); } cert = coll[0]; store.Close(); //this special SignDoc just overrides a function in SignedXml so //it knows to look for ResponseID rather than ID XmlElement signature = SamlHelper.SignDoc( doc, cert, "ResponseID", response.ResponseID); doc.DocumentElement.InsertBefore(signature, doc.DocumentElement.ChildNodes[0]); // Base64Encode and URL Encode byte[] base64EncodedBytes = Encoding.UTF8.GetBytes(doc.OuterXml); string returnValue = System.Convert.ToBase64String( base64EncodedBytes); return returnValue; } private AssertionType CreateSaml11Assertion(Dictionary<string, string> attributes){ AssertionType assertion = new AssertionType(); assertion.AssertionID = "_" + Guid.NewGuid().ToString(); assertion.Issuer = "madeUpValue"; assertion.MajorVersion = "1"; assertion.MinorVersion = "1"; assertion.IssueInstant = System.DateTime.UtcNow; //Not before, not after conditions ConditionsType conditions = new ConditionsType(); conditions.NotBefore = DateTime.UtcNow; conditions.NotBeforeSpecified = true; conditions.NotOnOrAfter = DateTime.UtcNow.AddMinutes(10); conditions.NotOnOrAfterSpecified = true; //Name Identifier to be used in Saml Subject NameIdentifierType nameIdentifier = new NameIdentifierType(); nameIdentifier.NameQualifier = domain.Trim(); nameIdentifier.Value = subject.Trim(); SubjectConfirmationType subjectConfirmation = new SubjectConfirmationType(); subjectConfirmation.ConfirmationMethod = new string[] { "urn:oasis:names:tc:SAML:1.0:cm:bearer" }; // // Create some SAML subject. SubjectType samlSubject = new SubjectType(); AttributeStatementType attrStatement = new AttributeStatementType(); AuthenticationStatementType authStatement = new AuthenticationStatementType(); authStatement.AuthenticationMethod = "urn:oasis:names:tc:SAML:1.0:am:password"; authStatement.AuthenticationInstant = System.DateTime.UtcNow; samlSubject.Items = new object[] { nameIdentifier, subjectConfirmation}; attrStatement.Subject = samlSubject; authStatement.Subject = samlSubject; IPHostEntry ipEntry = Dns.GetHostEntry(System.Environment.MachineName); SubjectLocalityType subjectLocality = new SubjectLocalityType(); subjectLocality.IPAddress = ipEntry.AddressList[0].ToString(); authStatement.SubjectLocality = subjectLocality; attrStatement.Attribute = new AttributeType[attributes.Count]; int i=0; // Create SAML attributes. foreach (KeyValuePair<string, string> attribute in attributes) { AttributeType attr = new AttributeType(); attr.AttributeName = attribute.Key; attr.AttributeNamespace= domain; attr.AttributeValue = new object[] {attribute.Value}; attrStatement.Attribute[i] = attr; i++; } assertion.Conditions = conditions; assertion.Items = new StatementAbstractType[] {authStatement, attrStatement}; return assertion; } private static XmlElement SignDoc(XmlDocument doc, X509Certificate2 cert2, string referenceId, string referenceValue) { // Use our own implementation of SignedXml SamlSignedXml sig = new SamlSignedXml(doc, referenceId); // Add the key to the SignedXml xmlDocument. sig.SigningKey = cert2.PrivateKey; // Create a reference to be signed. Reference reference = new Reference(); reference.Uri= String.Empty; reference.Uri = "#" + referenceValue; // Add an enveloped transformation to the reference. XmlDsigEnvelopedSignatureTransform env = new XmlDsigEnvelopedSignatureTransform(); reference.AddTransform(env); // Add the reference to the SignedXml object. sig.AddReference(reference); // Add an RSAKeyValue KeyInfo (optional; helps recipient find key to validate). KeyInfo keyInfo = new KeyInfo(); keyInfo.AddClause(new KeyInfoX509Data(cert2)); sig.KeyInfo = keyInfo; // Compute the signature. sig.ComputeSignature(); // Get the XML representation of the signature and save // it to an XmlElement object. XmlElement xmlDigitalSignature = sig.GetXml(); return xmlDigitalSignature; } To open the page in my client app, string postData = String.Format("SAMLResponse={0}&APID=ap_00001&TARGET={1}", System.Web.HttpUtility.UrlEncode(builder.buildResponse("http://theWLServer/samlacs/acs",attributes)), "http://desiredURL"); webBrowser.Navigate("http://theWLServer/samlacs/acs", "_self", Encoding.UTF8.GetBytes(postData), "Content-Type: application/x-www-form-urlencoded");

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• Non recursive way to position a genogram in 2D points for x axis. Descendant are below

  - by Nassign

  I currently was tasked to make a genogram for a family consisting of siblings, parents with aunts and uncles with grandparents and greatgrandparents for only blood relatives. My current algorithm is using recursion. but I am wondering how to do it in non recursive way to make it more efficient. it is programmed in c# using graphics to draw on a bitmap. Current algorithm for calculating x position, the y position is by getting the generation number. public void StartCalculatePosition() { // Search the start node (The only node with targetFlg set to true) Person start = null; foreach (Person p in PersonDic.Values) { if (start == null) start = p; if (p.Targetflg) { start = p; break; } } CalcPositionRecurse(start); // Normalize the position (shift all values to positive value) // Get the minimum value (must be negative) // Then offset the position of all marriage and person with that to make it start from zero float minPosition = float.MaxValue; foreach (Person p in PersonDic.Values) { if (minPosition > p.Position) { minPosition = p.Position; } } if (minPosition < 0) { foreach (Person p in PersonDic.Values) { p.Position -= minPosition; } foreach (Marriage m in MarriageList) { m.ParentsPosition -= minPosition; m.ChildrenPosition -= minPosition; } } } /// <summary> /// Calculate position of genogram using recursion /// </summary> /// <param name="psn"></param> private void CalcPositionRecurse(Person psn) { // End the recursion if (psn.BirthMarriage == null || psn.BirthMarriage.Parents.Count == 0) { psn.Position = 0.0f; if (psn.BirthMarriage != null) { psn.BirthMarriage.ParentsPosition = 0.0f; psn.BirthMarriage.ChildrenPosition = 0.0f; } CalculateSiblingPosition(psn); return; } // Left recurse if (psn.Father != null) { CalcPositionRecurse(psn.Father); } // Right recurse if (psn.Mother != null) { CalcPositionRecurse(psn.Mother); } // Merge Position if (psn.Father != null && psn.Mother != null) { AdjustConflict(psn.Father, psn.Mother); // Position person in center of parent psn.Position = (psn.Father.Position + psn.Mother.Position) / 2; psn.BirthMarriage.ParentsPosition = psn.Position; psn.BirthMarriage.ChildrenPosition = psn.Position; } else { // Single mom or single dad if (psn.Father != null) { psn.Position = psn.Father.Position; psn.BirthMarriage.ParentsPosition = psn.Position; psn.BirthMarriage.ChildrenPosition = psn.Position; } else if (psn.Mother != null) { psn.Position = psn.Mother.Position; psn.BirthMarriage.ParentsPosition = psn.Position; psn.BirthMarriage.ChildrenPosition = psn.Position; } else { // Should not happen, checking in start of function } } // Arrange the siblings base on my position (left younger, right older) CalculateSiblingPosition(psn); } private float GetRightBoundaryAncestor(Person psn) { float rPos = psn.Position; // Get the rightmost position among siblings foreach (Person sibling in psn.Siblings) { if (sibling.Position > rPos) { rPos = sibling.Position; } } if (psn.Father != null) { float rFatherPos = GetRightBoundaryAncestor(psn.Father); if (rFatherPos > rPos) { rPos = rFatherPos; } } if (psn.Mother != null) { float rMotherPos = GetRightBoundaryAncestor(psn.Mother); if (rMotherPos > rPos) { rPos = rMotherPos; } } return rPos; } private float GetLeftBoundaryAncestor(Person psn) { float rPos = psn.Position; // Get the rightmost position among siblings foreach (Person sibling in psn.Siblings) { if (sibling.Position < rPos) { rPos = sibling.Position; } } if (psn.Father != null) { float rFatherPos = GetLeftBoundaryAncestor(psn.Father); if (rFatherPos < rPos) { rPos = rFatherPos; } } if (psn.Mother != null) { float rMotherPos = GetLeftBoundaryAncestor(psn.Mother); if (rMotherPos < rPos) { rPos = rMotherPos; } } return rPos; } /// <summary> /// Check if two parent group has conflict and compensate on the conflict /// </summary> /// <param name="leftGroup"></param> /// <param name="rightGroup"></param> public void AdjustConflict(Person leftGroup, Person rightGroup) { float leftMax = GetRightBoundaryAncestor(leftGroup); leftMax += 0.5f; float rightMin = GetLeftBoundaryAncestor(rightGroup); rightMin -= 0.5f; float diff = leftMax - rightMin; if (diff > 0.0f) { float moveHalf = Math.Abs(diff) / 2; RecurseMoveAncestor(leftGroup, 0 - moveHalf); RecurseMoveAncestor(rightGroup, moveHalf); } } /// <summary> /// Recursively move a person and all his/her ancestor /// </summary> /// <param name="psn"></param> /// <param name="moveUnit"></param> public void RecurseMoveAncestor(Person psn, float moveUnit) { psn.Position += moveUnit; foreach (Person siblings in psn.Siblings) { if (siblings.Id != psn.Id) { siblings.Position += moveUnit; } } if (psn.BirthMarriage != null) { psn.BirthMarriage.ChildrenPosition += moveUnit; psn.BirthMarriage.ParentsPosition += moveUnit; } if (psn.Father != null) { RecurseMoveAncestor(psn.Father, moveUnit); } if (psn.Mother != null) { RecurseMoveAncestor(psn.Mother, moveUnit); } } /// <summary> /// Calculate the position of the siblings /// </summary> /// <param name="psn"></param> /// <param name="anchor"></param> public void CalculateSiblingPosition(Person psn) { if (psn.Siblings.Count == 0) { return; } List<Person> sibling = psn.Siblings; int argidx; for (argidx = 0; argidx < sibling.Count; argidx++) { if (sibling[argidx].Id == psn.Id) { break; } } // Compute position for each brother that is younger that person int idx; for (idx = argidx - 1; idx >= 0; idx--) { sibling[idx].Position = sibling[idx + 1].Position - 1; } for (idx = argidx + 1; idx < sibling.Count; idx++) { sibling[idx].Position = sibling[idx - 1].Position + 1; } }

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• Criticize my code, please

  - by Micky

  Hey, I was applying for a position, and they asked me to complete a coding problem for them. I did so and submitted it, but I later found out I was rejected from the position. Anyways, I have an eclectic programming background so I'm not sure if my code is grossly wrong or if I just didn't have the best solution out there. I would like to post my code and get some feedback about it. Before I do, here's a description of a problem: You are given a sorted array of integers, say, {1, 2, 4, 4, 5, 8, 9, 9, 9, 9, 9, 9, 10, 10, 10, 11, 13 }. Now you are supposed to write a program (in C or C++, but I chose C) that prompts the user for an element to search for. The program will then search for the element. If it is found, then it should return the first index the entry was found at and the number of instances of that element. If the element is not found, then it should return "not found" or something similar. Here's a simple run of it (with the array I just put up): Enter a number to search for: 4 4 was found at index 2. There are 2 instances for 4 in the array. Enter a number to search for: -4. -4 is not in the array. They made a comment that my code should scale well with large arrays (so I wrote up a binary search). Anyways, my code basically runs as follows: Prompts user for input. Then it checks if it is within bounds (bigger than a[0] in the array and smaller than the largest element of the array). If so, then I perform a binary search. If the element is found, then I wrote two while loops. One while loop will count to the left of the element found, and the second while loop will count to the right of the element found. The loops terminate when the adjacent elements do not match with the desired value. EX: 4, 4, 4, 4, 4 The bold 4 is the value the binary search landed on. One loop will check to the left of it, and another loop will check to the right of it. Their sum will be the total number of instances of the the number four. Anyways, I don't know if there are any advanced techniques that I am missing or if I just don't have the CS background and made a big error. Any constructive critiques would be appreciated! #include <stdio.h> #include <stdlib.h> #include <string.h> #include <stddef.h> /* function prototype */ int get_num_of_ints( const int* arr, size_t r, int N, size_t* first, size_t* count ); int main() { int N; /* input variable */ int arr[]={1,1,2,3,3,4,4,4,4,5,5,7,7,7,7,8,8,8,9,11,12,12}; /* array of sorted integers */ size_t r = sizeof(arr)/sizeof(arr[0]); /* right bound */ size_t first; /* first match index */ size_t count; /* total number of matches */ /* prompts the user to enter input */ printf( "\nPlease input the integer you would like to find.\n" ); scanf( "%d", &N ); int a = get_num_of_ints( arr, r, N, &first, &count ); /* If the function returns -1 then the value is not found. Else it is returned */ if( a == -1) printf( "%d has not been found.\n", N ); else if(a >= 0){ printf( "The first matching index is %d.\n", first ); printf( "The total number of instances is %d.\n", count ); } return 0; } /* function definition */ int get_num_of_ints( const int* arr, size_t r, int N, size_t* first, size_t* count ) { int lo=0; /* lower bound for search */ int m=0; /* middle value obtained */ int hi=r-1; /* upper bound for search */ int w=r-1; /* used as a fixed upper bound to calculate the number of right instances of a particular value. */ /* binary search to find if a value exists */ /* first check if the element is out of bounds */ if( N < arr[0] || arr[hi] < N ){ m = -1; } else{ /* binary search to find a value, if it exists, within given parameters */ while(lo <= hi){ m = (hi + lo)/2; if(arr[m] < N) lo = m+1; else if(arr[m] > N) hi = m-1; else if(arr[m]==N){ m=m; break; } } if (lo > hi) /* if it doesn't we assign it -1 */ m = -1; } /* If the value is found, then we compute the left and right instances of it */ if( m >= 0 ){ int j = m-1; /* starting with the first term to the left */ int L = 0; /* total number of left instances */ /* while loop computes total number of left instances */ while( j >= 0 && arr[j] == arr[m] ){ L++; j--; } /* There are six possible outcomes of this. Depending on the outcome, we must assign the first index variable accordingly */ if( j > 0 && L > 0 ) *first=j+1; else if( j==0 && L==0) *first=m; else if( j > 0 && L==0 ) *first=m; else if(j < 0 && L==0 ) *first=m; else if( j < 0 && L > 0 ) *first=0; else if( j=0 && L > 0 ) *first=j+1; int h = m + 1; /* starting with the first term to the right */ int R = 0; /* total number of right instances */ /* while loop computes total number of right instances */ /* we fixed w earlier so that it's value does not change */ while( arr[h]==arr[m] && h <= w ){ R++; h++; } *count = (R + L + 1); /* total number of instances stored as value of count */ return *first; /* first instance index stored here */ } /* if value does not exist, then we return a negative value */ else if( m==-1) return -1; }

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• How Should I Generate Trade Statistics For CouchDB/Rails3 Application?

  - by James

  My Problem: I am trying to developing a web application for currency traders. The application allows traders to enter or upload information about their trades and I want to calculate a wide variety of statistics based on what the user entered. Now, normally I would use a relational database for this, but I have two requirements that don't fit well with a relational database so I am attempting to use couchdb. Those two problems are: 1) Primarily, I have a companion desktop application that users will be able to work with and replicate to the site using couchdb's awesome replication feature and 2) I would like to allow users to be able to define their own custom things to track about trades and generate results based off of what they enter. The schema less nature of couch seems perfect here, but it may end up being harder than it sounds. (I already know couch requires you to define views in advance and such so I was just planning on sticking all the custom attributes in an array and then emitting the array in the view and further processing from there.) What I Am Doing: Right now I am just emitting each trade in couch keyed by each user's system and querying with the key of the system to get an array of trades per system. Simple. I am not using a reduce function currently to calculate any stats because I couldn't figure out how to get everything I need without getting a reduce overflow error. Here is an example of rows that are getting emitted from couch: {"total_rows":134,"offset":0,"rows":[ {"id":"5b1dcd47221e160d8721feee4ccc64be", "key":["80e40ba2fa43589d57ec3f1d19db41e6","2010/05/14 04:32:37 +0000"], null, "doc":{ "_id":"5b1dcd47221e160d8721feee4ccc64be", "_rev":"1-bc9fe763e2637694df47d6f5efb58e5b", "couchrest-type":"Trade", "system":"80e40ba2fa43589d57ec3f1d19db41e6", "pair":"EUR/USD", "direction":"Buy", "entry":12600, "exit":12700, "stop_loss":12500, "profit_target":12700, "status":"Closed", "slug":"101332132375", "custom_tracking": [{"name":"signal", "value":"Pin Bar"}] "updated_at":"2010/05/14 04:32:37 +0000", "created_at":"2010/05/14 04:32:37 +0000", "result":100}} ]} In my rails 3 controller I am basically just populating an array of trades such as the one above and then extracting out the relevant data into smaller arrays that I can compute my statistics on. Here is my show action for the page that I want to display the stats and all the trades: def show @trades = Trade.by_system(:startkey => [@system.id], :endkey => [@system.id, Time.now ]) @trades.each do |trade| if trade.result > 0 @winning_trades << trade.result elsif trade.result < 0 @losing_trades << trade.result else @breakeven_trades << trade.result end if trade.direction == "Buy" @long_trades << trade.result else @short_trades << trade.result end if trade["custom_tracking"] != nil @custom_tracking << {"result" => trade.result, "variables" => trade["custom_tracking"]} end end end I am omitting some other stuff that is going on, but that is the gist of what I am doing. Then I am calculating stuff in the view layer to produce some results: <% winning_long_trades = @long_trades.reject {|trade| trade <= 0 } %> <% winning_short_trades = @short_trades.reject {|trade| trade <= 0 } %> <ul> <li>Total Trades: <%= @trades.count %></li> <li>Winners: <%= @winning_trades.size %></li> <li>Biggest Winner (Pips): <%= @winning_trades.max %></li> <li>Average Win(Pips): <%= @winning_trades.sum/@winning_trades.size %></li> <li>Losers: <%= @losing_trades.size %></li> <li>Biggest Loser (Pips): <%= @losing_trades.min %></li> <li>Average Loss(Pips): <%= @losing_trades.sum/@losing_trades.size %></li> <li>Breakeven Trades: <%= @breakeven_trades.size %></li> <li>Long Trades: <%= @long_trades.size %></li> <li>Winning Long Trades: <%= winning_long_trades.size %></li> <li>Short Trades: <%= @short_trades.size %></li> <li>Winning Short Trades: <%= winning_short_trades.size %></li> <li>Total Pips: <%= @winning_trades.sum + @losing_trades.sum %></li> <li>Win Rate (%): <%= @winning_trades.size/@trades.count.to_f * 100 %></li> </ul> This produces the following results, which aside from a few things is exactly what I want: Total Trades: 134 Winners: 70 Biggest Winner (Pips): 1488 Average Win(Pips): 440 Losers: 58 Biggest Loser (Pips): -516 Average Loss(Pips): -225 Breakeven Trades: 6 Long Trades: 125 Winning Long Trades: 67 Short Trades: 9 Winning Short Trades: 3 Total Pips: 17819 Win Rate (%): 52.23880597014925 What I Am Wondering- Finally The Actual Questions: I am starting to get really skeptical of how well this method will work when a user has 5,000 trades instead of just 134 like in this example. I anticipate most users will only have somewhere under 200 per year, but some users may have a couple thousand trades per year. Probably no more than 5,000 per year. It seems to work ok now, but the page load times are already getting a tad high for my tastes. (About 800ms to generate the page according to rails logs with about a 250ms of that spent in the view layer.) I will end up caching this page I am sure, but I still need the regenerate the page each time a trade is updated and I can't afford to have this be too slow. Sooo..... Is doing something similar here possible with a straight couchdb reduce function? I am assuming handing this off to couch would possibly help with larger data sets. I couldn't figure out how, but I suppose that doesn't mean it isn't possible. If possible, any hints will be helpful. Could I use a list function if a reduce was not available due to reduce constraints? Are couchdb list functions suitable for this type of calculations? Anyone have any idea of whether or not list functions perform well? Any hints what one would look like for the type of calculations I am trying to achieve? I thought about other options such as running the calculations at the time each trade was saved or nightly if I had to and saving the results to a statistics doc that I could then query so that all the processing was done ahead of time. I would like this to be the last resort because then I can't really filter out trades by time periods dynamically like I would really like to. (I want to have a slider that a user can slide to only show trades from that time period using the startkey and endkey in couchdb if I can.) If I should continue running the calculations inside the rails app at the time of the page view, what can I do to improve my current implementation. I am new to rails, couch and programming in general. I am sure that I could be doing something better here. Do I need to create an array for each stat or is there a better way to do that. I guess I just would really like some advice on how to tackle this problem. I want to keep the page generation time minimal since I anticipate these being some of the highest trafficked pages. My gut is that I will need to offload the statistics calculation to either couch or run the stats in advance of when they are called, but I am not sure. Lastly: Like I mentioned above, one of the primary reasons for using couch is to allow users to define their own things to track per trade. Getting the data into couch is no problem, but how would I be able to take the custom_tracking array and find how many winning trades for each named tracking attribute. If anyone can give me any hints to the possibility of doing this that would be great. Thanks a bunch. Would really appreciate any help. Willing to fork out some $$$ if someone wants to take on the problem for me. (Don't know if that is allowed on stack overflow or not.)

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• Bridge or Factory and How

  - by Chris

  I'm trying to learn patterns and I've got a job that is screaming for a pattern, I just know it but I can't figure it out. I know the filter type is something that can be abstracted and possibly bridged. I'M NOT LOOKING FOR A CODE REWRITE JUST SUGGESTIONS. I'm not looking for someone to do my job. I would like to know how patterns could be applied to this example. using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Data; using System.IO; using System.Xml; using System.Text.RegularExpressions; namespace CopyTool { class CopyJob { public enum FilterType { TextFilter, RegExFilter, NoFilter } public FilterType JobFilterType { get; set; } private string _jobName; public string JobName { get { return _jobName; } set { _jobName = value; } } private int currentIndex; public int CurrentIndex { get { return currentIndex; } } private DataSet ds; public int MaxJobs { get { return ds.Tables["Job"].Rows.Count; } } private string _filter; public string Filter { get { return _filter; } set { _filter = value; } } private string _fromFolder; public string FromFolder { get { return _fromFolder; } set { if (Directory.Exists(value)) { _fromFolder = value; } else { throw new DirectoryNotFoundException(String.Format("Folder not found: {0}", value)); } } } private List<string> _toFolders; public List<string> ToFolders { get { return _toFolders; } } public CopyJob() { Initialize(); } private void Initialize() { if (ds == null) { ds = new DataSet(); } ds.ReadXml(Properties.Settings.Default.ConfigLocation); LoadValues(0); } public void Execute() { ExecuteJob(FromFolder, _toFolders, Filter, JobFilterType); } public void ExecuteAll() { string OrigPath; List<string> DestPaths; string FilterText; FilterType FilterWay; foreach (DataRow rw in ds.Tables["Job"].Rows) { OrigPath = rw["FromFolder"].ToString(); FilterText = rw["FilterText"].ToString(); switch (rw["FilterType"].ToString()) { case "TextFilter": FilterWay = FilterType.TextFilter; break; case "RegExFilter": FilterWay = FilterType.RegExFilter; break; default: FilterWay = FilterType.NoFilter; break; } DestPaths = new List<string>(); foreach (DataRow crw in rw.GetChildRows("Job_ToFolder")) { DestPaths.Add(crw["FolderPath"].ToString()); } ExecuteJob(OrigPath, DestPaths, FilterText, FilterWay); } } private void ExecuteJob(string OrigPath, List<string> DestPaths, string FilterText, FilterType FilterWay) { FileInfo[] files; switch (FilterWay) { case FilterType.RegExFilter: files = GetFilesByRegEx(new Regex(FilterText), OrigPath); break; case FilterType.TextFilter: files = GetFilesByFilter(FilterText, OrigPath); break; default: files = new DirectoryInfo(OrigPath).GetFiles(); break; } foreach (string fld in DestPaths) { CopyFiles(files, fld); } } public void MoveToJob(int RecordNumber) { Save(); LoadValues(RecordNumber - 1); } public void AddToFolder(string folderPath) { if (Directory.Exists(folderPath)) { _toFolders.Add(folderPath); } else { throw new DirectoryNotFoundException(String.Format("Folder not found: {0}", folderPath)); } } public void DeleteToFolder(int index) { _toFolders.RemoveAt(index); } public void Save() { DataRow rw = ds.Tables["Job"].Rows[currentIndex]; rw["JobName"] = _jobName; rw["FromFolder"] = _fromFolder; rw["FilterText"] = _filter; switch (JobFilterType) { case FilterType.RegExFilter: rw["FilterType"] = "RegExFilter"; break; case FilterType.TextFilter: rw["FilterType"] = "TextFilter"; break; default: rw["FilterType"] = "NoFilter"; break; } DataRow[] ToFolderRows = ds.Tables["Job"].Rows[currentIndex].GetChildRows("Job_ToFolder"); for (int i = 0; i <= ToFolderRows.GetUpperBound(0); i++) { ToFolderRows[i].Delete(); } foreach (string fld in _toFolders) { DataRow ToFolderRow = ds.Tables["ToFolder"].NewRow(); ToFolderRow["JobId"] = ds.Tables["Job"].Rows[currentIndex]["JobId"]; ToFolderRow["Job_Id"] = ds.Tables["Job"].Rows[currentIndex]["Job_Id"]; ToFolderRow["FolderPath"] = fld; ds.Tables["ToFolder"].Rows.Add(ToFolderRow); } } public void Delete() { ds.Tables["Job"].Rows.RemoveAt(currentIndex); LoadValues(currentIndex++); } public void MoveNext() { Save(); currentIndex++; LoadValues(currentIndex); } public void MovePrevious() { Save(); currentIndex--; LoadValues(currentIndex); } public void MoveFirst() { Save(); LoadValues(0); } public void MoveLast() { Save(); LoadValues(ds.Tables["Job"].Rows.Count - 1); } public void CreateNew() { Save(); int MaxJobId = 0; Int32.TryParse(ds.Tables["Job"].Compute("Max(JobId)", "").ToString(), out MaxJobId); DataRow rw = ds.Tables["Job"].NewRow(); rw["JobId"] = MaxJobId + 1; ds.Tables["Job"].Rows.Add(rw); LoadValues(ds.Tables["Job"].Rows.IndexOf(rw)); } public void Commit() { Save(); ds.WriteXml(Properties.Settings.Default.ConfigLocation); } private void LoadValues(int index) { if (index > ds.Tables["Job"].Rows.Count - 1) { currentIndex = ds.Tables["Job"].Rows.Count - 1; } else if (index < 0) { currentIndex = 0; } else { currentIndex = index; } DataRow rw = ds.Tables["Job"].Rows[currentIndex]; _jobName = rw["JobName"].ToString(); _fromFolder = rw["FromFolder"].ToString(); _filter = rw["FilterText"].ToString(); switch (rw["FilterType"].ToString()) { case "TextFilter": JobFilterType = FilterType.TextFilter; break; case "RegExFilter": JobFilterType = FilterType.RegExFilter; break; default: JobFilterType = FilterType.NoFilter; break; } if (_toFolders == null) _toFolders = new List<string>(); _toFolders.Clear(); foreach (DataRow crw in rw.GetChildRows("Job_ToFolder")) { AddToFolder(crw["FolderPath"].ToString()); } } private static FileInfo[] GetFilesByRegEx(Regex rgx, string locPath) { DirectoryInfo d = new DirectoryInfo(locPath); FileInfo[] fullFileList = d.GetFiles(); List<FileInfo> filteredList = new List<FileInfo>(); foreach (FileInfo fi in fullFileList) { if (rgx.IsMatch(fi.Name)) { filteredList.Add(fi); } } return filteredList.ToArray(); } private static FileInfo[] GetFilesByFilter(string filter, string locPath) { DirectoryInfo d = new DirectoryInfo(locPath); FileInfo[] fi = d.GetFiles(filter); return fi; } private void CopyFiles(FileInfo[] files, string destPath) { foreach (FileInfo fi in files) { bool success = false; int i = 0; string copyToName = fi.Name; string copyToExt = fi.Extension; string copyToNameWithoutExt = Path.GetFileNameWithoutExtension(fi.FullName); while (!success && i < 100) { i++; try { if (File.Exists(Path.Combine(destPath, copyToName))) throw new CopyFileExistsException(); File.Copy(fi.FullName, Path.Combine(destPath, copyToName)); success = true; } catch (CopyFileExistsException ex) { copyToName = String.Format("{0} ({1}){2}", copyToNameWithoutExt, i, copyToExt); } } } } } public class CopyFileExistsException : Exception { public string Message; } }

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• Access violation writing location, in my loop

  - by numerical25

  The exact error I am getting is First-chance exception at 0x0096234a in chp2.exe: 0xC0000005: Access violation writing location 0x002b0000. Windows has triggered a breakpoint in chp2.exe. And the breakpoint stops here for(DWORD i = 0; i < m; ++i) { //we are start at the top of z float z = halfDepth - i*dx; for(DWORD j = 0; j < n; ++j) { //to the left of us float x = -halfWidth + j*dx; float y = 0.0f; vertices[i*n+j].pos = D3DXVECTOR3(x, y, z); //<----- Right here vertices[i*n+j].color = D3DXVECTOR4(1.0f, 0.0f, 0.0f, 0.0f); } } I am not sure what I am doing wrong. below is the code in its entirety #include "MyGame.h" //#include "CubeVector.h" /* This code sets a projection and shows a turning cube. What has been added is the project, rotation and a rasterizer to change the rasterization of the cube. The issue that was going on was something with the effect file which was causing the vertices not to be rendered correctly.*/ typedef struct { ID3D10Effect* pEffect; ID3D10EffectTechnique* pTechnique; //vertex information ID3D10Buffer* pVertexBuffer; ID3D10Buffer* pIndicesBuffer; ID3D10InputLayout* pVertexLayout; UINT numVertices; UINT numIndices; }ModelObject; ModelObject modelObject; // World Matrix D3DXMATRIX WorldMatrix; // View Matrix D3DXMATRIX ViewMatrix; // Projection Matrix D3DXMATRIX ProjectionMatrix; ID3D10EffectMatrixVariable* pProjectionMatrixVariable = NULL; //grid information #define NUM_COLS 16 #define NUM_ROWS 16 #define CELL_WIDTH 32 #define CELL_HEIGHT 32 #define NUM_VERTSX (NUM_COLS + 1) #define NUM_VERTSY (NUM_ROWS + 1) bool MyGame::InitDirect3D() { if(!DX3dApp::InitDirect3D()) { return false; } D3D10_RASTERIZER_DESC rastDesc; rastDesc.FillMode = D3D10_FILL_WIREFRAME; rastDesc.CullMode = D3D10_CULL_FRONT; rastDesc.FrontCounterClockwise = true; rastDesc.DepthBias = false; rastDesc.DepthBiasClamp = 0; rastDesc.SlopeScaledDepthBias = 0; rastDesc.DepthClipEnable = false; rastDesc.ScissorEnable = false; rastDesc.MultisampleEnable = false; rastDesc.AntialiasedLineEnable = false; ID3D10RasterizerState *g_pRasterizerState; mpD3DDevice->CreateRasterizerState(&rastDesc, &g_pRasterizerState); mpD3DDevice->RSSetState(g_pRasterizerState); // Set up the World Matrix //The first line of code creates your identity matrix. Second line of code //second combines your camera position, target location, and which way is up respectively D3DXMatrixIdentity(&WorldMatrix); D3DXMatrixLookAtLH(&ViewMatrix, new D3DXVECTOR3(200.0f, 60.0f, -20.0f), new D3DXVECTOR3(200.0f, 50.0f, 0.0f), new D3DXVECTOR3(0.0f, 1.0f, 0.0f)); // Set up the projection matrix D3DXMatrixPerspectiveFovLH(&ProjectionMatrix, (float)D3DX_PI * 0.5f, (float)mWidth/(float)mHeight, 0.1f, 100.0f); if(!CreateObject()) { return false; } return true; } //These are actions that take place after the clearing of the buffer and before the present void MyGame::GameDraw() { static float rotationAngle = 0.0f; // create the rotation matrix using the rotation angle D3DXMatrixRotationY(&WorldMatrix, rotationAngle); rotationAngle += (float)D3DX_PI * 0.0f; // Set the input layout mpD3DDevice->IASetInputLayout(modelObject.pVertexLayout); // Set vertex buffer UINT stride = sizeof(VertexPos); UINT offset = 0; mpD3DDevice->IASetVertexBuffers(0, 1, &modelObject.pVertexBuffer, &stride, &offset); mpD3DDevice->IASetIndexBuffer(modelObject.pIndicesBuffer, DXGI_FORMAT_R32_UINT, 0); // Set primitive topology mpD3DDevice->IASetPrimitiveTopology(D3D10_PRIMITIVE_TOPOLOGY_TRIANGLELIST); // Combine and send the final matrix to the shader D3DXMATRIX finalMatrix = (WorldMatrix * ViewMatrix * ProjectionMatrix); pProjectionMatrixVariable->SetMatrix((float*)&finalMatrix); // make sure modelObject is valid // Render a model object D3D10_TECHNIQUE_DESC techniqueDescription; modelObject.pTechnique->GetDesc(&techniqueDescription); // Loop through the technique passes for(UINT p=0; p < techniqueDescription.Passes; ++p) { modelObject.pTechnique->GetPassByIndex(p)->Apply(0); // draw the cube using all 36 vertices and 12 triangles mpD3DDevice->DrawIndexed(modelObject.numIndices,0,0); } } //Render actually incapsulates Gamedraw, so you can call data before you actually clear the buffer or after you //present data void MyGame::Render() { DX3dApp::Render(); } bool MyGame::CreateObject() { //dx will represent the width and the height of the spacing of each vector float dx = 1; //Below are the number of vertices //m is the vertices of each row. n is the columns DWORD m = 30; DWORD n = 30; //This get the width of the entire land //30 - 1 = 29 rows * 1 = 29 * 0.5 = 14.5 float halfWidth = (n-1)*dx*0.5f; float halfDepth = (m-1)*dx*0.5f; float vertexsize = m * n; VertexPos vertices[80]; for(DWORD i = 0; i < m; ++i) { //we are start at the top of z float z = halfDepth - i*dx; for(DWORD j = 0; j < n; ++j) { //to the left of us float x = -halfWidth + j*dx; float y = 0.0f; vertices[i*n+j].pos = D3DXVECTOR3(x, y, z); vertices[i*n+j].color = D3DXVECTOR4(1.0f, 0.0f, 0.0f, 0.0f); } } int k = 0; DWORD indices[540]; for(DWORD i = 0; i < n-1; ++i) { for(DWORD j = 0; j < n-1; ++j) { indices[k] = (i * n) + j; indices[k + 1] = (i * n) + j + 1; indices[k + 2] = (i + 1) * n + j; indices[k + 3] = (i + 1) * n + j; indices[k + 4] = (i * n) + j + 1; indices[k + 5] = (i + 1) * n + j+ 1; k += 6; } } //Create Layout D3D10_INPUT_ELEMENT_DESC layout[] = { {"POSITION",0,DXGI_FORMAT_R32G32B32_FLOAT, 0 , 0, D3D10_INPUT_PER_VERTEX_DATA, 0}, {"COLOR",0,DXGI_FORMAT_R32G32B32A32_FLOAT, 0 , 12, D3D10_INPUT_PER_VERTEX_DATA, 0} }; UINT numElements = (sizeof(layout)/sizeof(layout[0])); modelObject.numVertices = sizeof(vertices)/sizeof(VertexPos); //Create buffer desc D3D10_BUFFER_DESC bufferDesc; bufferDesc.Usage = D3D10_USAGE_DEFAULT; bufferDesc.ByteWidth = sizeof(VertexPos) * modelObject.numVertices; bufferDesc.BindFlags = D3D10_BIND_VERTEX_BUFFER; bufferDesc.CPUAccessFlags = 0; bufferDesc.MiscFlags = 0; D3D10_SUBRESOURCE_DATA initData; initData.pSysMem = vertices; //Create the buffer HRESULT hr = mpD3DDevice->CreateBuffer(&bufferDesc, &initData, &modelObject.pVertexBuffer); if(FAILED(hr)) return false; modelObject.numIndices = sizeof(indices)/sizeof(DWORD); bufferDesc.ByteWidth = sizeof(DWORD) * modelObject.numIndices; bufferDesc.BindFlags = D3D10_BIND_INDEX_BUFFER; initData.pSysMem = indices; hr = mpD3DDevice->CreateBuffer(&bufferDesc, &initData, &modelObject.pIndicesBuffer); if(FAILED(hr)) return false; ///////////////////////////////////////////////////////////////////////////// //Set up fx files LPCWSTR effectFilename = L"effect.fx"; modelObject.pEffect = NULL; hr = D3DX10CreateEffectFromFile(effectFilename, NULL, NULL, "fx_4_0", D3D10_SHADER_ENABLE_STRICTNESS, 0, mpD3DDevice, NULL, NULL, &modelObject.pEffect, NULL, NULL); if(FAILED(hr)) return false; pProjectionMatrixVariable = modelObject.pEffect->GetVariableByName("Projection")->AsMatrix(); //Dont sweat the technique. Get it! LPCSTR effectTechniqueName = "Render"; modelObject.pTechnique = modelObject.pEffect->GetTechniqueByName(effectTechniqueName); if(modelObject.pTechnique == NULL) return false; //Create Vertex layout D3D10_PASS_DESC passDesc; modelObject.pTechnique->GetPassByIndex(0)->GetDesc(&passDesc); hr = mpD3DDevice->CreateInputLayout(layout, numElements, passDesc.pIAInputSignature, passDesc.IAInputSignatureSize, &modelObject.pVertexLayout); if(FAILED(hr)) return false; return true; }

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• Trouble calling a method from an external class

  - by Bradley Hobbs

  Here is my employee database program: import java.util.*; import java.io.*; import java.io.File; import java.io.FileReader; import java.util.ArrayList; public class P { //Instance Variables private static String empName; private static String wage; private static double wages; private static double salary; private static double numHours; private static double increase; // static ArrayList<String> ARempName = new ArrayList<String>(); // static ArrayList<Double> ARwages = new ArrayList<Double>(); // static ArrayList<Double> ARsalary = new ArrayList<Double>(); static ArrayList<Employee> emp = new ArrayList<Employee>(); public static void main(String[] args) throws Exception { clearScreen(); printMenu(); question(); exit(); } public static void printArrayList(ArrayList<Employee> emp) { for (int i = 0; i < emp.size(); i++){ System.out.println(emp.get(i)); } } public static void clearScreen() { System.out.println("\u001b[H\u001b[2J"); } private static void exit() { System.exit(0); } private static void printMenu() { System.out.println("\t------------------------------------"); System.out.println("\t|Commands: n - New employee |"); System.out.println("\t| c - Compute paychecks |"); System.out.println("\t| r - Raise wages |"); System.out.println("\t| p - Print records |"); System.out.println("\t| d - Download data |"); System.out.println("\t| u - Upload data |"); System.out.println("\t| q - Quit |"); System.out.println("\t------------------------------------"); System.out.println(""); } public static void question() { System.out.print("Enter command: "); Scanner q = new Scanner(System.in); String input = q.nextLine(); input.replaceAll("\\s","").toLowerCase(); boolean valid = (input.equals("n") || input.equals("c") || input.equals("r") || input.equals("p") || input.equals("d") || input.equals("u") || input.equals("q")); if (!valid){ System.out.println("Command was not recognized; please try again."); printMenu(); question(); } else if (input.equals("n")){ System.out.print("Enter the name of new employee: "); Scanner stdin = new Scanner(System.in); empName = stdin.nextLine(); System.out.print("Hourly (h) or salaried (s): "); Scanner stdin2 = new Scanner(System.in); wage = stdin2.nextLine(); wage.replaceAll("\\s","").toLowerCase(); if (!(wage.equals("h") || wage.equals("s"))){ System.out.println("Input was not h or s; please try again"); } else if (wage.equals("h")){ System.out.print("Enter hourly wage: "); Scanner stdin4 = new Scanner(System.in); wages = stdin4.nextDouble(); Employee emp1 = new HourlyEmployee(empName, wages); emp.add(emp1); printMenu(); question();} else if (wage.equals("s")){ System.out.print("Enter annual salary: "); Scanner stdin5 = new Scanner(System.in); salary = stdin5.nextDouble(); Employee emp1 = new SalariedEmployee(empName, salary); printMenu(); question();}} else if (input.equals("c")){ for (int i = 0; i < emp.size(); i++){ System.out.println("Enter number of hours worked by " + emp.get(i) + ":"); } Scanner stdin = new Scanner(System.in); numHours = stdin.nextInt(); System.out.println("Pay: " + emp1.computePay(numHours)); System.out.print("Enter number of hours worked by " + empName); Scanner stdin2 = new Scanner(System.in); numHours = stdin2.nextInt(); System.out.println("Pay: " + emp1.computePay(numHours)); printMenu(); question();} else if (input.equals("r")){ System.out.print("Enter percentage increase: "); Scanner stdin = new Scanner(System.in); increase = stdin.nextDouble(); System.out.println("\nNew Wages"); System.out.println("---------"); // System.out.println(Employee.toString()); printMenu(); question(); } else if (input.equals("p")){ printArrayList(emp); printMenu(); question(); } else if (input.equals("q")){ exit(); } } } Here is one of the class files: public abstract class Employee { private String name; private double wage; protected Employee(String name, double wage){ this.name = name; this.wage = wage; } public String getName() { return name; } public double getWage() { return wage; } public void setName(String name) { this.name = name; } public void setWage(double wage) { this.wage = wage; } public void percent(double wage, double percent) { wage *= percent; } } And here are the errors: P.java:108: cannot find symbol symbol : variable emp1 location: class P System.out.println("Pay: " + emp1.computePay(numHours)); ^ P.java:112: cannot find symbol symbol : variable emp1 location: class P System.out.println("Pay: " + emp1.computePay(numHours)); ^ 2 errors I'm trying to the get paycheck to print out but i'm having trouble with how to call the method. It should take the user inputed numHours and calculate it then print on the paycheck for each employee. Thanks!

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• Node.js Adventure - When Node Flying in Wind

  - by Shaun

  In the first post of this series I mentioned some popular modules in the community, such as underscore, async, etc.. I also listed a module named “Wind (zh-CN)”, which is created by one of my friend, Jeff Zhao (zh-CN). Now I would like to use a separated post to introduce this module since I feel it brings a new async programming style in not only Node.js but JavaScript world. If you know or heard about the new feature in C# 5.0 called “async and await”, or you learnt F#, you will find the “Wind” brings the similar async programming experience in JavaScript. By using “Wind”, we can write async code that looks like the sync code. The callbacks, async stats and exceptions will be handled by “Wind” automatically and transparently.   What’s the Problem: Dense “Callback” Phobia Let’s firstly back to my second post in this series. As I mentioned in that post, when we wanted to read some records from SQL Server we need to open the database connection, and then execute the query. In Node.js all IO operation are designed as async callback pattern which means when the operation was done, it will invoke a function which was taken from the last parameter. For example the database connection opening code would be like this. 1: sql.open(connectionString, function(error, conn) { 2: if(error) { 3: // some error handling code 4: } 5: else { 6: // connection opened successfully 7: } 8: }); And then if we need to query the database the code would be like this. It nested in the previous function. 1: sql.open(connectionString, function(error, conn) { 2: if(error) { 3: // some error handling code 4: } 5: else { 6: // connection opened successfully 7: conn.queryRaw(command, function(error, results) { 8: if(error) { 9: // failed to execute this command 10: } 11: else { 12: // records retrieved successfully 13: } 14: }; 15: } 16: }); Assuming if we need to copy some data from this database to another then we need to open another connection and execute the command within the function under the query function. 1: sql.open(connectionString, function(error, conn) { 2: if(error) { 3: // some error handling code 4: } 5: else { 6: // connection opened successfully 7: conn.queryRaw(command, function(error, results) { 8: if(error) { 9: // failed to execute this command 10: } 11: else { 12: // records retrieved successfully 13: target.open(targetConnectionString, function(error, t_conn) { 14: if(error) { 15: // connect failed 16: } 17: else { 18: t_conn.queryRaw(copy_command, function(error, results) { 19: if(error) { 20: // copy failed 21: } 22: else { 23: // and then, what do you want to do now... 24: } 25: }; 26: } 27: }; 28: } 29: }; 30: } 31: }); This is just an example. In the real project the logic would be more complicated. This means our application might be messed up and the business process will be fragged by many callback functions. I would like call this “Dense Callback Phobia”. This might be a challenge how to make code straightforward and easy to read, something like below. 1: try 2: { 3: // open source connection 4: var s_conn = sqlConnect(s_connectionString); 5: // retrieve data 6: var results = sqlExecuteCommand(s_conn, s_command); 7: 8: // open target connection 9: var t_conn = sqlConnect(t_connectionString); 10: // prepare the copy command 11: var t_command = getCopyCommand(results); 12: // execute the copy command 13: sqlExecuteCommand(s_conn, t_command); 14: } 15: catch (ex) 16: { 17: // error handling 18: }   What’s the Problem: Sync-styled Async Programming Similar as the previous problem, the callback-styled async programming model makes the upcoming operation as a part of the current operation, and mixed with the error handling code. So it’s very hard to understand what on earth this code will do. And since Node.js utilizes non-blocking IO mode, we cannot invoke those operations one by one, as they will be executed concurrently. For example, in this post when I tried to copy the records from Windows Azure SQL Database (a.k.a. WASD) to Windows Azure Table Storage, if I just insert the data into table storage one by one and then print the “Finished” message, I will see the message shown before the data had been copied. This is because all operations were executed at the same time. In order to make the copy operation and print operation executed synchronously I introduced a module named “async” and the code was changed as below. 1: async.forEach(results.rows, 2: function (row, callback) { 3: var resource = { 4: "PartitionKey": row[1], 5: "RowKey": row[0], 6: "Value": row[2] 7: }; 8: client.insertEntity(tableName, resource, function (error) { 9: if (error) { 10: callback(error); 11: } 12: else { 13: console.log("entity inserted."); 14: callback(null); 15: } 16: }); 17: }, 18: function (error) { 19: if (error) { 20: error["target"] = "insertEntity"; 21: res.send(500, error); 22: } 23: else { 24: console.log("all done."); 25: res.send(200, "Done!"); 26: } 27: }); It ensured that the “Finished” message will be printed when all table entities had been inserted. But it cannot promise that the records will be inserted in sequence. It might be another challenge to make the code looks like in sync-style? 1: try 2: { 3: forEach(row in rows) { 4: var entity = { /* ... */ }; 5: tableClient.insert(tableName, entity); 6: } 7:  8: console.log("Finished"); 9: } 10: catch (ex) { 11: console.log(ex); 12: }   How “Wind” Helps “Wind” is a JavaScript library which provides the control flow with plain JavaScript for asynchronous programming (and more) without additional pre-compiling steps. It’s available in NPM so that we can install it through “npm install wind”. Now let’s create a very simple Node.js application as the example. This application will take some website URLs from the command arguments and tried to retrieve the body length and print them in console. Then at the end print “Finish”. I’m going to use “request” module to make the HTTP call simple so I also need to install by the command “npm install request”. The code would be like this. 1: var request = require("request"); 2:  3: // get the urls from arguments, the first two arguments are `node.exe` and `fetch.js` 4: var args = process.argv.splice(2); 5:  6: // main function 7: var main = function() { 8: for(var i = 0; i < args.length; i++) { 9: // get the url 10: var url = args[i]; 11: // send the http request and try to get the response and body 12: request(url, function(error, response, body) { 13: if(!error && response.statusCode == 200) { 14: // log the url and the body length 15: console.log( 16: "%s: %d.", 17: response.request.uri.href, 18: body.length); 19: } 20: else { 21: // log error 22: console.log(error); 23: } 24: }); 25: } 26: 27: // finished 28: console.log("Finished"); 29: }; 30:  31: // execute the main function 32: main(); Let’s execute this application. (I made them in multi-lines for better reading.) 1: node fetch.js 2: "http://www.igt.com/us-en.aspx" 3: "http://www.igt.com/us-en/games.aspx" 4: "http://www.igt.com/us-en/cabinets.aspx" 5: "http://www.igt.com/us-en/systems.aspx" 6: "http://www.igt.com/us-en/interactive.aspx" 7: "http://www.igt.com/us-en/social-gaming.aspx" 8: "http://www.igt.com/support.aspx" Below is the output. As you can see the finish message was printed at the beginning, and the pages’ length retrieved in a different order than we specified. This is because in this code the request command, console logging command are executed asynchronously and concurrently. Now let’s introduce “Wind” to make them executed in order, which means it will request the websites one by one, and print the message at the end.   First of all we need to import the “Wind” package and make sure the there’s only one global variant named “Wind”, and ensure it’s “Wind” instead of “wind”. 1: var Wind = require("wind");   Next, we need to tell “Wind” which code will be executed asynchronously so that “Wind” can control the execution process. In this case the “request” operation executed asynchronously so we will create a “Task” by using a build-in helps function in “Wind” named Wind.Async.Task.create. 1: var requestBodyLengthAsync = function(url) { 2: return Wind.Async.Task.create(function(t) { 3: request(url, function(error, response, body) { 4: if(error || response.statusCode != 200) { 5: t.complete("failure", error); 6: } 7: else { 8: var data = 9: { 10: uri: response.request.uri.href, 11: length: body.length 12: }; 13: t.complete("success", data); 14: } 15: }); 16: }); 17: }; The code above created a “Task” from the original request calling code. In “Wind” a “Task” means an operation will be finished in some time in the future. A “Task” can be started by invoke its start() method, but no one knows when it actually will be finished. The Wind.Async.Task.create helped us to create a task. The only parameter is a function where we can put the actual operation in, and then notify the task object it’s finished successfully or failed by using the complete() method. In the code above I invoked the request method. If it retrieved the response successfully I set the status of this task as “success” with the URL and body length. If it failed I set this task as “failure” and pass the error out.   Next, we will change the main() function. In “Wind” if we want a function can be controlled by Wind we need to mark it as “async”. This should be done by using the code below. 1: var main = eval(Wind.compile("async", function() { 2: })); When the application is running, Wind will detect “eval(Wind.compile(“async”, function” and generate an anonymous code from the body of this original function. Then the application will run the anonymous code instead of the original one. In our example the main function will be like this. 1: var main = eval(Wind.compile("async", function() { 2: for(var i = 0; i < args.length; i++) { 3: try 4: { 5: var result = $await(requestBodyLengthAsync(args[i])); 6: console.log( 7: "%s: %d.", 8: result.uri, 9: result.length); 10: } 11: catch (ex) { 12: console.log(ex); 13: } 14: } 15: 16: console.log("Finished"); 17: })); As you can see, when I tried to request the URL I use a new command named “$await”. It tells Wind, the operation next to $await will be executed asynchronously, and the main thread should be paused until it finished (or failed). So in this case, my application will be pause when the first response was received, and then print its body length, then try the next one. At the end, print the finish message.   Finally, execute the main function. The full code would be like this. 1: var request = require("request"); 2: var Wind = require("wind"); 3:  4: var args = process.argv.splice(2); 5:  6: var requestBodyLengthAsync = function(url) { 7: return Wind.Async.Task.create(function(t) { 8: request(url, function(error, response, body) { 9: if(error || response.statusCode != 200) { 10: t.complete("failure", error); 11: } 12: else { 13: var data = 14: { 15: uri: response.request.uri.href, 16: length: body.length 17: }; 18: t.complete("success", data); 19: } 20: }); 21: }); 22: }; 23:  24: var main = eval(Wind.compile("async", function() { 25: for(var i = 0; i < args.length; i++) { 26: try 27: { 28: var result = $await(requestBodyLengthAsync(args[i])); 29: console.log( 30: "%s: %d.", 31: result.uri, 32: result.length); 33: } 34: catch (ex) { 35: console.log(ex); 36: } 37: } 38: 39: console.log("Finished"); 40: })); 41:  42: main().start();   Run our new application. At the beginning we will see the compiled and generated code by Wind. Then we can see the pages were requested one by one, and at the end the finish message was printed. Below is the code Wind generated for us. As you can see the original code, the output code were shown. 1: // Original: 2: function () { 3: for(var i = 0; i < args.length; i++) { 4: try 5: { 6: var result = $await(requestBodyLengthAsync(args[i])); 7: console.log( 8: "%s: %d.", 9: result.uri, 10: result.length); 11: } 12: catch (ex) { 13: console.log(ex); 14: } 15: } 16: 17: console.log("Finished"); 18: } 19:  20: // Compiled: 21: /* async << function () { */ (function () { 22: var _builder_$0 = Wind.builders["async"]; 23: return _builder_$0.Start(this, 24: _builder_$0.Combine( 25: _builder_$0.Delay(function () { 26: /* var i = 0; */ var i = 0; 27: /* for ( */ return _builder_$0.For(function () { 28: /* ; i < args.length */ return i < args.length; 29: }, function () { 30: /* ; i ++) { */ i ++; 31: }, 32: /* try { */ _builder_$0.Try( 33: _builder_$0.Delay(function () { 34: /* var result = $await(requestBodyLengthAsync(args[i])); */ return _builder_$0.Bind(requestBodyLengthAsync(args[i]), function (result) { 35: /* console.log("%s: %d.", result.uri, result.length); */ console.log("%s: %d.", result.uri, result.length); 36: return _builder_$0.Normal(); 37: }); 38: }), 39: /* } catch (ex) { */ function (ex) { 40: /* console.log(ex); */ console.log(ex); 41: return _builder_$0.Normal(); 42: /* } */ }, 43: null 44: ) 45: /* } */ ); 46: }), 47: _builder_$0.Delay(function () { 48: /* console.log("Finished"); */ console.log("Finished"); 49: return _builder_$0.Normal(); 50: }) 51: ) 52: ); 53: /* } */ })   How Wind Works Someone may raise a big concern when you find I utilized “eval” in my code. Someone may assume that Wind utilizes “eval” to execute some code dynamically while “eval” is very low performance. But I would say, Wind does NOT use “eval” to run the code. It only use “eval” as a flag to know which code should be compiled at runtime. When the code was firstly been executed, Wind will check and find “eval(Wind.compile(“async”, function”. So that it knows this function should be compiled. Then it utilized parse-js to analyze the inner JavaScript and generated the anonymous code in memory. Then it rewrite the original code so that when the application was running it will use the anonymous one instead of the original one. Since the code generation was done at the beginning of the application was started, in the future no matter how long our application runs and how many times the async function was invoked, it will use the generated code, no need to generate again. So there’s no significant performance hurt when using Wind.   Wind in My Previous Demo Let’s adopt Wind into one of my previous demonstration and to see how it helps us to make our code simple, straightforward and easy to read and understand. In this post when I implemented the functionality that copied the records from my WASD to table storage, the logic would be like this. 1, Open database connection. 2, Execute a query to select all records from the table. 3, Recreate the table in Windows Azure table storage. 4, Create entities from each of the records retrieved previously, and then insert them into table storage. 5, Finally, show message as the HTTP response. But as the image below, since there are so many callbacks and async operations, it’s very hard to understand my logic from the code. Now let’s use Wind to rewrite our code. First of all, of course, we need the Wind package. Then we need to include the package files into project and mark them as “Copy always”. Add the Wind package into the source code. Pay attention to the variant name, you must use “Wind” instead of “wind”. 1: var express = require("express"); 2: var async = require("async"); 3: var sql = require("node-sqlserver"); 4: var azure = require("azure"); 5: var Wind = require("wind"); Now we need to create some async functions by using Wind. All async functions should be wrapped so that it can be controlled by Wind which are open database, retrieve records, recreate table (delete and create) and insert entity in table. Below are these new functions. All of them are created by using Wind.Async.Task.create. 1: sql.openAsync = function (connectionString) { 2: return Wind.Async.Task.create(function (t) { 3: sql.open(connectionString, function (error, conn) { 4: if (error) { 5: t.complete("failure", error); 6: } 7: else { 8: t.complete("success", conn); 9: } 10: }); 11: }); 12: }; 13:  14: sql.queryAsync = function (conn, query) { 15: return Wind.Async.Task.create(function (t) { 16: conn.queryRaw(query, function (error, results) { 17: if (error) { 18: t.complete("failure", error); 19: } 20: else { 21: t.complete("success", results); 22: } 23: }); 24: }); 25: }; 26:  27: azure.recreateTableAsync = function (tableName) { 28: return Wind.Async.Task.create(function (t) { 29: client.deleteTable(tableName, function (error, successful, response) { 30: console.log("delete table finished"); 31: client.createTableIfNotExists(tableName, function (error, successful, response) { 32: console.log("create table finished"); 33: if (error) { 34: t.complete("failure", error); 35: } 36: else { 37: t.complete("success", null); 38: } 39: }); 40: }); 41: }); 42: }; 43:  44: azure.insertEntityAsync = function (tableName, entity) { 45: return Wind.Async.Task.create(function (t) { 46: client.insertEntity(tableName, entity, function (error, entity, response) { 47: if (error) { 48: t.complete("failure", error); 49: } 50: else { 51: t.complete("success", null); 52: } 53: }); 54: }); 55: }; Then in order to use these functions we will create a new function which contains all steps for data copying. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: } 4: catch (ex) { 5: console.log(ex); 6: res.send(500, "Internal error."); 7: } 8: })); Let’s execute steps one by one with the “$await” keyword introduced by Wind so that it will be invoked in sequence. First is to open the database connection. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: } 7: catch (ex) { 8: console.log(ex); 9: res.send(500, "Internal error."); 10: } 11: })); Then retrieve all records from the database connection. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: } 10: catch (ex) { 11: console.log(ex); 12: res.send(500, "Internal error."); 13: } 14: })); After recreated the table, we need to create the entities and insert them into table storage. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: if (results.rows.length > 0) { 10: // recreate the table 11: $await(azure.recreateTableAsync(tableName)); 12: console.log("table created"); 13: // insert records in table storage one by one 14: for (var i = 0; i < results.rows.length; i++) { 15: var entity = { 16: "PartitionKey": results.rows[i][1], 17: "RowKey": results.rows[i][0], 18: "Value": results.rows[i][2] 19: }; 20: $await(azure.insertEntityAsync(tableName, entity)); 21: console.log("entity inserted"); 22: } 23: } 24: } 25: catch (ex) { 26: console.log(ex); 27: res.send(500, "Internal error."); 28: } 29: })); Finally, send response back to the browser. 1: var copyRecords = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: if (results.rows.length > 0) { 10: // recreate the table 11: $await(azure.recreateTableAsync(tableName)); 12: console.log("table created"); 13: // insert records in table storage one by one 14: for (var i = 0; i < results.rows.length; i++) { 15: var entity = { 16: "PartitionKey": results.rows[i][1], 17: "RowKey": results.rows[i][0], 18: "Value": results.rows[i][2] 19: }; 20: $await(azure.insertEntityAsync(tableName, entity)); 21: console.log("entity inserted"); 22: } 23: // send response 24: console.log("all done"); 25: res.send(200, "All done!"); 26: } 27: } 28: catch (ex) { 29: console.log(ex); 30: res.send(500, "Internal error."); 31: } 32: })); If we compared with the previous code we will find now it became more readable and much easy to understand. It’s very easy to know what this function does even though without any comments. When user go to URL “/was/copyRecords” we will execute the function above. The code would be like this. 1: app.get("/was/copyRecords", function (req, res) { 2: copyRecords(req, res).start(); 3: }); And below is the logs printed in local compute emulator console. As we can see the functions executed one by one and then finally the response back to me browser.   Scaffold Functions in Wind Wind provides not only the async flow control and compile functions, but many scaffold methods as well. We can build our async code more easily by using them. I’m going to introduce some basic scaffold functions here. In the code above I created some functions which wrapped from the original async function such as open database, create table, etc.. All of them are very similar, created a task by using Wind.Async.Task.create, return error or result object through Task.complete function. In fact, Wind provides some functions for us to create task object from the original async functions. If the original async function only has a callback parameter, we can use Wind.Async.Binding.fromCallback method to get the task object directly. For example the code below returned the task object which wrapped the file exist check function. 1: var Wind = require("wind"); 2: var fs = require("fs"); 3:  4: fs.existsAsync = Wind.Async.Binding.fromCallback(fs.exists); In Node.js a very popular async function pattern is that, the first parameter in the callback function represent the error object, and the other parameters is the return values. In this case we can use another build-in function in Wind named Wind.Async.Binding.fromStandard. For example, the open database function can be created from the code below. 1: sql.openAsync = Wind.Async.Binding.fromStandard(sql.open); 2:  3: /* 4: sql.openAsync = function (connectionString) { 5: return Wind.Async.Task.create(function (t) { 6: sql.open(connectionString, function (error, conn) { 7: if (error) { 8: t.complete("failure", error); 9: } 10: else { 11: t.complete("success", conn); 12: } 13: }); 14: }); 15: }; 16: */ When I was testing the scaffold functions under Wind.Async.Binding I found for some functions, such as the Azure SDK insert entity function, cannot be processed correctly. So I personally suggest writing the wrapped method manually.   Another scaffold method in Wind is the parallel tasks coordination. In this example, the steps of open database, retrieve records and recreated table should be invoked one by one, but it can be executed in parallel when copying data from database to table storage. In Wind there’s a scaffold function named Task.whenAll which can be used here. Task.whenAll accepts a list of tasks and creates a new task. It will be returned only when all tasks had been completed, or any errors occurred. For example in the code below I used the Task.whenAll to make all copy operation executed at the same time. 1: var copyRecordsInParallel = eval(Wind.compile("async", function (req, res) { 2: try { 3: // connect to the windows azure sql database 4: var conn = $await(sql.openAsync(connectionString)); 5: console.log("connection opened"); 6: // retrieve all records from database 7: var results = $await(sql.queryAsync(conn, "SELECT * FROM [Resource]")); 8: console.log("records selected. count = %d", results.rows.length); 9: if (results.rows.length > 0) { 10: // recreate the table 11: $await(azure.recreateTableAsync(tableName)); 12: console.log("table created"); 13: // insert records in table storage in parallal 14: var tasks = new Array(results.rows.length); 15: for (var i = 0; i < results.rows.length; i++) { 16: var entity = { 17: "PartitionKey": results.rows[i][1], 18: "RowKey": results.rows[i][0], 19: "Value": results.rows[i][2] 20: }; 21: tasks[i] = azure.insertEntityAsync(tableName, entity); 22: } 23: $await(Wind.Async.Task.whenAll(tasks)); 24: // send response 25: console.log("all done"); 26: res.send(200, "All done!"); 27: } 28: } 29: catch (ex) { 30: console.log(ex); 31: res.send(500, "Internal error."); 32: } 33: })); 34:  35: app.get("/was/copyRecordsInParallel", function (req, res) { 36: copyRecordsInParallel(req, res).start(); 37: });   Besides the task creation and coordination, Wind supports the cancellation solution so that we can send the cancellation signal to the tasks. It also includes exception solution which means any exceptions will be reported to the caller function.   Summary In this post I introduced a Node.js module named Wind, which created by my friend Jeff Zhao. As you can see, different from other async library and framework, adopted the idea from F# and C#, Wind utilizes runtime code generation technology to make it more easily to write async, callback-based functions in a sync-style way. By using Wind there will be almost no callback, and the code will be very easy to understand. Currently Wind is still under developed and improved. There might be some problems but the author, Jeff, should be very happy and enthusiastic to learn your problems, feedback, suggestion and comments. You can contact Jeff by - Email: [email protected] - Group: https://groups.google.com/d/forum/windjs - GitHub: https://github.com/JeffreyZhao/wind/issues   Source code can be download here.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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• Working with Analytic Workflow Manager (AWM) - Part 8 Cube Metadata Analysis

  - by Mohan Ramanuja

  CUBE SIZEselect dbal.owner||'.'||substr(dbal.table_name,4) awname, sum(dbas.bytes)/1024/1024 as mb, dbas.tablespace_name from dba_lobs dbal, dba_segments dbas where dbal.column_name = 'AWLOB' and dbal.segment_name = dbas.segment_name group by dbal.owner, dbal.table_name, dbas.tablespace_name order by dbal.owner, dbal.table_name SESSION RESOURCES select vses.username||':'||vsst.sid username, vstt.name, max(vsst.value) valuefrom v$sesstat vsst, v$statname vstt, v$session vseswhere vstt.statistic# = vsst.statistic# and vsst.sid = vses.sid andVSES.USERNAME LIKE ('ATTRIBDW_OWN') ANDvstt.name in ('session pga memory', 'session pga memory max', 'session uga memory','session uga memory max', 'session cursor cache count', 'session cursor cache hits', 'session stored procedure space', 'opened cursors current', 'opened cursors cumulative') andvses.username is not null group by vsst.sid, vses.username, vstt.name order by vsst.sid, vses.username, vstt.name OLAP PGA USE select 'OLAP Pages Occupying: '|| round((((select sum(nvl(pool_size,1)) from v$aw_calc)) / (select value from v$pgastat where name = 'total PGA inuse')),2)*100||'%' info from dual union select 'Total PGA Inuse Size: '||value/1024||' KB' info from v$pgastat where name = 'total PGA inuse' union select 'Total OLAP Page Size: '|| round(sum(nvl(pool_size,1))/1024,0)||' KB' info from v$aw_calc order by info desc OLAP PGA USAGE PER USER select vs.username, vs.sid, round(pga_used_mem/1024/1024,2)||' MB' pga_used, round(pga_max_mem/1024/1024,2)||' MB' pga_max, round(pool_size/1024/1024,2)||' MB' olap_pp, round(100*(pool_hits-pool_misses)/pool_hits,2) || '%' olap_ratio from v$process vp, v$session vs, v$aw_calc va where session_id=vs.sid and addr = paddr CUBE LOADING SCRIPT REM The 'set define off' statement is needed only if running this script through SQLPlus.REM If you are using another tool to run this script, the line below may be commented out.set define offBEGIN  DBMS_CUBE.BUILD(    'VALIDATE  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/BEGIN  DBMS_CUBE.BUILD(    '  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/ VISUALIZATION OBJECT - AW$ATTRIBDW_OWN  CREATE TABLE "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"        (            "PS#"    NUMBER(10,0),            "GEN#"   NUMBER(10,0),            "EXTNUM" NUMBER(8,0),            "AWLOB" BLOB,            "OBJNAME"  VARCHAR2(256 BYTE),            "PARTNAME" VARCHAR2(256 BYTE)        )        PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE        (            BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "AWLOB"        )        STORE AS SECUREFILE        (            TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        PARTITION BY RANGE        (            "GEN#"        )        SUBPARTITION BY HASH        (            "PS#",            "EXTNUM"        )        SUBPARTITIONS 8        (            PARTITION "PTN1" VALUES LESS THAN (1) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE READS LOGGING NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP661" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP662" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP663" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP664" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP665" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP666" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP667" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP668" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" ) ,            PARTITION "PTNN" VALUES LESS THAN (MAXVALUE) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP669" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP670" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP671" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP672" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP673" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP674" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP675" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP676" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" )        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."ATTRIBDW_OWN_I$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        "PS#", "GEN#", "EXTNUM"    )    PCTFREE 10 INITRANS 4 MAXTRANS 255 COMPUTE STATISTICS STORAGE    (        INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT    )    TABLESPACE "ATTRIBDW_DATA" ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000406980C00004$$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOCAL (PARTITION "SYS_IL_P711" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP695" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP696" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP697" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP698" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP699" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP700" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP701" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP702" TABLESPACE "ATTRIBDW_DATA" ) , PARTITION "SYS_IL_P712" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP703" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP704" TABLESPACE        "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP705" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP706" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP707" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP708" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP709" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP710" TABLESPACE "ATTRIBDW_DATA" ) ) PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE BUILD LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_BUILD_LOG"        (            "BUILD_ID"          NUMBER,            "SLAVE_NUMBER"      NUMBER,            "STATUS"            VARCHAR2(10 BYTE),            "COMMAND"           VARCHAR2(25 BYTE),            "BUILD_OBJECT"      VARCHAR2(30 BYTE),            "BUILD_OBJECT_TYPE" VARCHAR2(10 BYTE),            "OUTPUT" CLOB,            "AW"            VARCHAR2(30 BYTE),            "OWNER"         VARCHAR2(30 BYTE),            "PARTITION"     VARCHAR2(50 BYTE),            "SCHEDULER_JOB" VARCHAR2(100 BYTE),            "TIME" TIMESTAMP (6)WITH TIME ZONE,        "BUILD_SCRIPT" CLOB,        "BUILD_TYPE"            VARCHAR2(22 BYTE),        "COMMAND_DEPTH"         NUMBER(2,0),        "BUILD_SUB_OBJECT"      VARCHAR2(30 BYTE),        "REFRESH_METHOD"        VARCHAR2(1 BYTE),        "SEQ_NUMBER"            NUMBER,        "COMMAND_NUMBER"        NUMBER,        "IN_BRANCH"             NUMBER(1,0),        "COMMAND_STATUS_NUMBER" NUMBER,        "BUILD_NAME"            VARCHAR2(100 BYTE)        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "OUTPUT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        LOB        (            "BUILD_SCRIPT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00013$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00007$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG" ( PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE DIMENSION COMPILE  CREATE TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"        (            "ID"               NUMBER,            "SEQ_NUMBER"       NUMBER,            "ERROR#"           NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE"    VARCHAR2(2000 BYTE),            "DIMENSION"        VARCHAR2(100 BYTE),            "DIMENSION_MEMBER" VARCHAR2(100 BYTE),            "MEMBER_ANCESTOR"  VARCHAR2(100 BYTE),            "HIERARCHY1"       VARCHAR2(100 BYTE),            "HIERARCHY2"       VARCHAR2(100 BYTE),            "ERROR_CONTEXT" CLOB        )        SEGMENT CREATION DEFERRED PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING TABLESPACE "ATTRIBDW_DATA" LOB        (            "ERROR_CONTEXT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION"IS    'Name of dimension being compiled';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION_MEMBER"IS    'Problem dimension member';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."MEMBER_ANCESTOR"IS    'Problem dimension member''s parent';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY1"IS    'First hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY2"IS    'Second hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_CONTEXT"IS    'Extra information for error';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"IS    'Cube dimension compile log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407307C00010$$" ON "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE( INITIAL 1048576 NEXT 1048576 MAXEXTENTS 2147483645) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE OPERATING LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"        (            "INST_ID"    NUMBER NOT NULL ENABLE,            "SID"        NUMBER NOT NULL ENABLE,            "SERIAL#"    NUMBER NOT NULL ENABLE,            "USER#"      NUMBER NOT NULL ENABLE,            "SQL_ID"     VARCHAR2(13 BYTE),            "JOB"        NUMBER,            "ID"         NUMBER,            "PARENT_ID"  NUMBER,            "SEQ_NUMBER" NUMBER,            "TIME" TIMESTAMP (6)WITH TIME ZONE NOT NULL ENABLE,        "LOG_LEVEL"    NUMBER(4,0) NOT NULL ENABLE,        "DEPTH"        NUMBER(4,0),        "OPERATION"    VARCHAR2(15 BYTE) NOT NULL ENABLE,        "SUBOPERATION" VARCHAR2(20 BYTE),        "STATUS"       VARCHAR2(10 BYTE) NOT NULL ENABLE,        "NAME"         VARCHAR2(20 BYTE) NOT NULL ENABLE,        "VALUE"        VARCHAR2(4000 BYTE),        "DETAILS" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "DETAILS"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."INST_ID"IS    'Instance ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SID"IS    'Session ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SERIAL#"IS    'Session serial#';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."USER#"IS    'User ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SQL_ID"IS    'Executing SQL statement ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."JOB"IS    'Identifier of job';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."PARENT_ID"IS    'Parent operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."TIME"IS    'Time of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."LOG_LEVEL"IS    'Verbosity level of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DEPTH"IS    'Nesting depth of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."OPERATION"IS    'Current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SUBOPERATION"IS    'Current suboperation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."STATUS"IS    'Status of current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."NAME"IS    'Name of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."VALUE"IS    'Value of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DETAILS"IS    'Extra information for record';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"IS    'Cube operations log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407301C00018$$" ON "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE REJECTED RECORDS CREATE TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"        (            "ID"            NUMBER,            "SEQ_NUMBER"    NUMBER,            "ERROR#"        NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE" VARCHAR2(2000 BYTE),            "RECORD#"       NUMBER(38,0),            "SOURCE_ROW" ROWID,            "REJECTED_RECORD" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "REJECTED_RECORD"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."RECORD#"IS    'Rejected record number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SOURCE_ROW"IS    'Rejected record''s ROWID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."REJECTED_RECORD"IS    'Rejected record copy';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"IS    'Cube rejected records log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407304C00007$$" ON "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;

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• problem with revalidating a jframe.

  - by John Quesie

  I have this code which should take the radio button input do a little math and display a popup. which it does fine. but then it is supposed to re validate and ask the next question. when i get to the second question, the answer always comes out as the isSelected(true) value no matter which radio button you click on. SO to be clear the first time through it works fin but when the second question comes up, it just takes the default radio button every time. public class EventHandler implements ActionListener { private Main gui; public EventHandler(Main gui){ this.gui = gui; } public void actionPerformed(ActionEvent e){ String answer = ""; double val = 1; //get current answer set String [] anArr = gui.getAnswers(gui.currentStage, gui.currentQuestion); if(e.getSource() == gui.exit){ System.exit(0); } if(e.getSource() == gui.submit){ if(gui.a1.isSelected()){ answer = anArr[0]; val = gui.getScore(1); } if(gui.a2.isSelected()){ answer = anArr[1]; val = gui.getScore(2); } if(gui.a3.isSelected()){ answer = anArr[2]; val = gui.getScore(3); } if(gui.a4.isSelected()){ answer = anArr[3];; val = gui.getScore(4); } JOptionPane.showMessageDialog(null, popupMessage(answer, val), "Your Answer", 1); //compute answer here //figure out what next question is to send gui.moveOn(); gui.setQA(gui.currentStage, gui.currentQuestion); //resets gui gui.goWest(); gui.q.revalidate(); } } public String popupMessage(String ans, double val){ //displays popup after an answer has been choosen gui.computeScore(val); String text = " You Answered " + ans + " Your score is now " + gui.yourScore ; return text; } } public class Main extends JFrame { public JLabel question; public JButton exit; public JButton submit; public JRadioButton a1; public JRadioButton a2; public JRadioButton a3; public JRadioButton a4; public ButtonGroup bg; public double yourScore = 1; public int currentQuestion = 1; public String currentStage = "startup"; JPanel q; public Main(){ setTitle("Ehtics Builder"); setLocation(400,400); setLayout(new BorderLayout(5,5)); setQA("startup", 1); goNorth(); goEast(); goWest(); goSouth(); goCenter(); pack(); setVisible(true); setDefaultCloseOperation(EXIT_ON_CLOSE); } public void goNorth(){ } public void goWest(){ q = new JPanel(); q.setLayout(new GridLayout(0,1)); q.add(question); bg.add(a1); bg.add(a2); bg.add(a3); bg.add(a4); a1.setSelected(true); q.add(a1); q.add(a2); q.add(a3); q.add(a4); add(q, BorderLayout.WEST); System.out.println(); } public void goEast(){ } public void goSouth(){ JPanel p = new JPanel(); p.setLayout(new FlowLayout(FlowLayout.CENTER)); exit = new JButton("Exit"); submit = new JButton("Submit"); p.add(exit); p.add(submit); add(p, BorderLayout.SOUTH); EventHandler myEventHandler = new EventHandler(this); exit.addActionListener(myEventHandler); submit.addActionListener(myEventHandler); } public void goCenter(){ } public static void main(String[] args) { Main open = new Main(); } public String getQuestion(String type, int num){ //reads the questions from a file String question = ""; String filename = ""; String [] ques; num = num - 1; if(type.equals("startup")){ filename = "startup.txt"; }else if(type.equals("small")){ filename = "small.txt"; }else if(type.equals("mid")){ filename = "mid.txt"; }else if(type.equals("large")){ filename = "large.txt"; }else{ question = "error"; return question; } ques = readFile(filename); for(int i = 0;i < ques.length;i++){ if(i == num){ question = ques[i]; } } return question; } public String [] getAnswers(String type, int num){ //reads the answers from a file String filename = ""; String temp = ""; String [] group; String [] ans; num = num - 1; if(type.equals("startup")){ filename = "startupA.txt"; }else if(type.equals("small")){ filename = "smallA.txt"; }else if(type.equals("mid")){ filename = "midA.txt"; }else if(type.equals("large")){ filename = "largeA.txt"; }else{ System.out.println("Error"); } group = readFile(filename); for(int i = 0;i < group.length;i++){ if(i == num){ temp = group[i]; } } ans = temp.split("-"); return ans; } public String [] getValues(String type, int num){ //reads the answers from a file String filename = ""; String temp = ""; String [] group; String [] vals; num = num - 1; if(type.equals("startup")){ filename = "startupV.txt"; }else if(type.equals("small")){ filename = "smallV.txt"; }else if(type.equals("mid")){ filename = "midV.txt"; }else if(type.equals("large")){ filename = "largeV.txt"; }else{ System.out.println("Error"); } group = readFile(filename); for(int i = 0;i < group.length;i++){ if(i == num){ temp = group[i]; } } vals = temp.split("-"); return vals; } public String [] readFile(String filename){ //reads the contentes of a file, for getQuestions and getAnswers String text = ""; int i = -1; FileReader in = null; File f = new File(filename); try{ in = new FileReader(f); }catch(FileNotFoundException e){ System.out.println("file does not exist"); } try{ while((i = in.read()) != -1) text += ((char)i); }catch(IOException e){ System.out.println("Error reading file"); } try{ in.close(); }catch(IOException e){ System.out.println("Error reading file"); } String [] questions = text.split(":"); return questions; } public void computeScore(double val){ //calculates you score times the value of your answer yourScore = val * yourScore; } public double getScore(int aNum){ //gets the score of an answer, stage and q number is already set in the class aNum = aNum - 1; double val = 0; double [] valArr = new double[4]; for(int i = 0;i < getValues(currentStage, currentQuestion).length;i++){ val = Double.parseDouble(getValues(currentStage, currentQuestion)[i]); valArr[i] = val; } if(aNum == 0){ val = valArr[0]; } if(aNum == 1){ val = valArr[1]; } if(aNum == 2){ val = valArr[2]; } if(aNum == 3){ val = valArr[3]; } // use current stage and questiion and trhe aNum to get the value for that answer return val; } public void nextQuestion(int num){ //sets next question to use currentQuestion = num; } public void nextStage(String sta){ // sets next stage to use currentStage = sta; } public void moveOn(){ // uses the score and current question and stage to determine wher to go next and what stage to use next nextQuestion(2); nextStage("startup"); } public void setQA(String level, int num){ String [] arr = getAnswers(level, num); question = new JLabel(getQuestion(level, num)); bg = new ButtonGroup(); a1 = new JRadioButton(arr[0]); a2 = new JRadioButton(arr[1]); a3 = new JRadioButton(arr[2]); a4 = new JRadioButton(arr[3]); } }

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• Why isn't my operator overloading working properly?

  - by Mithrax

  I have the following Polynomial class I'm working on: #include <iostream> using namespace std; class Polynomial { //define private member functions private: int coef[100]; // array of coefficients // coef[0] would hold all coefficients of x^0 // coef[1] would hold all x^1 // coef[n] = x^n ... int deg; // degree of polynomial (0 for the zero polynomial) //define public member functions public: Polynomial::Polynomial() //default constructor { for ( int i = 0; i < 100; i++ ) { coef[i] = 0; } } void set ( int a , int b ) //setter function { //coef = new Polynomial[b+1]; coef[b] = a; deg = degree(); } int degree() { int d = 0; for ( int i = 0; i < 100; i++ ) if ( coef[i] != 0 ) d = i; return d; } void print() { for ( int i = 99; i >= 0; i-- ) { if ( coef[i] != 0 ) { cout << coef[i] << "x^" << i << " "; } } } // use Horner's method to compute and return the polynomial evaluated at x int evaluate ( int x ) { int p = 0; for ( int i = deg; i >= 0; i-- ) p = coef[i] + ( x * p ); return p; } // differentiate this polynomial and return it Polynomial differentiate() { if ( deg == 0 ) { Polynomial t; t.set ( 0, 0 ); return t; } Polynomial deriv;// = new Polynomial ( 0, deg - 1 ); deriv.deg = deg - 1; for ( int i = 0; i < deg; i++ ) deriv.coef[i] = ( i + 1 ) * coef[i + 1]; return deriv; } Polynomial Polynomial::operator + ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) c.coef[i] += a.coef[i]; for ( int i = 0; i <= b.deg; i++ ) c.coef[i] += b.coef[i]; c.deg = c.degree(); return c; } Polynomial Polynomial::operator += ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) c.coef[i] += a.coef[i]; for ( int i = 0; i <= b.deg; i++ ) c.coef[i] += b.coef[i]; c.deg = c.degree(); for ( int i = 0; i < 100; i++) a.coef[i] = c.coef[i]; a.deg = a.degree(); return a; } Polynomial Polynomial::operator -= ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) c.coef[i] += a.coef[i]; for ( int i = 0; i <= b.deg; i++ ) c.coef[i] -= b.coef[i]; c.deg = c.degree(); for ( int i = 0; i < 100; i++) a.coef[i] = c.coef[i]; a.deg = a.degree(); return a; } Polynomial Polynomial::operator *= ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) for ( int j = 0; j <= b.deg; j++ ) c.coef[i+j] += ( a.coef[i] * b.coef[j] ); c.deg = c.degree(); for ( int i = 0; i < 100; i++) a.coef[i] = c.coef[i]; a.deg = a.degree(); return a; } Polynomial Polynomial::operator - ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) c.coef[i] += a.coef[i]; for ( int i = 0; i <= b.deg; i++ ) c.coef[i] -= b.coef[i]; c.deg = c.degree(); return c; } Polynomial Polynomial::operator * ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) for ( int j = 0; j <= b.deg; j++ ) c.coef[i+j] += ( a.coef[i] * b.coef[j] ); c.deg = c.degree(); return c; } }; int main() { Polynomial a, b, c, d; a.set ( 7, 4 ); //7x^4 a.set ( 1, 2 ); //x^2 b.set ( 6, 3 ); //6x^3 b.set ( -3, 2 ); //-3x^2 c = a - b; // (7x^4 + x^2) - (6x^3 - 3x^2) a -= b; c.print(); cout << "\n"; a.print(); cout << "\n"; c = a * b; // (7x^4 + x^2) * (6x^3 - 3x^2) c.print(); cout << "\n"; d = c.differentiate().differentiate(); d.print(); cout << "\n"; cout << c.evaluate ( 2 ); //substitue x with 2 cin.get(); } Now, I have the "-" operator overloaded and it works fine: Polynomial Polynomial::operator - ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) c.coef[i] += a.coef[i]; for ( int i = 0; i <= b.deg; i++ ) c.coef[i] -= b.coef[i]; c.deg = c.degree(); return c; } However, I'm having difficulty with my "-=" operator: Polynomial Polynomial::operator -= ( Polynomial b ) { Polynomial a = *this; //a is the poly on the L.H.S Polynomial c; for ( int i = 0; i <= a.deg; i++ ) c.coef[i] += a.coef[i]; for ( int i = 0; i <= b.deg; i++ ) c.coef[i] -= b.coef[i]; c.deg = c.degree(); // overwrite value of 'a' with the newly computed 'c' before returning 'a' for ( int i = 0; i < 100; i++) a.coef[i] = c.coef[i]; a.deg = a.degree(); return a; } I just slightly modified my "-" operator method to overwrite the value in 'a' and return 'a', and just use the 'c' polynomial as a temp. I've put in some debug print statement and I confirm that at the time of computation, both: c = a - b; and a -= b; are computed to the same value. However, when I go to print them, their results are different: Polynomial a, b; a.set ( 7, 4 ); //7x^4 a.set ( 1, 2 ); //x^2 b.set ( 6, 3 ); //6x^3 b.set ( -3, 2 ); //-3x^2 c = a - b; // (7x^4 + x^2) - (6x^3 - 3x^2) a -= b; c.print(); cout << "\n"; a.print(); cout << "\n"; Result: 7x^4 -6x^3 4x^2 7x^4 1x^2 Why is my c = a - b and a -= b giving me different results when I go to print them?

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• Alright, I'm still stuck on this homework problem. C++

  - by Josh

  Okay, the past few days I have been trying to get some input on my programs. Well I decided to scrap them for the most part and try again. So once again, I'm in need of help. For the first program I'm trying to fix, it needs to show the sum of SEVEN numbers. Well, I'm trying to change is so that I don't need the mem[##] = ####. I just want the user to be able to input the numbers and the program run from there and go through my switch loop. And have some kind of display..saying like the sum is?.. Here's my code so far. #include <iostream> #include <iomanip> #include <ios> using namespace std; int main() { const int READ = 10; const int WRITE = 11; const int LOAD = 20; const int STORE = 21; const int ADD = 30; const int SUBTRACT = 31; const int DIVIDE = 32; const int MULTIPLY = 33; const int BRANCH = 40; const int BRANCHNEG = 41; const int BRANCHZERO = 42; const int HALT = 43; int mem[100] = {0}; //Making it 100, since simpletron contains a 100 word mem. int operation; //taking the rest of these variables straight out of the book seeing as how they were italisized. int operand; int accum = 0; // the special register is starting at 0 int counter; for ( counter=0; counter < 100; counter++) mem[counter] = 0; // This is for part a, it will take in positive variables in //a sent-controlled loop and compute + print their sum. Variables from example in text. mem[0] = 1009; mem[1] = 1109; mem[2] = 2010; mem[3] = 2111; mem[4] = 2011; mem[5] = 3100; mem[6] = 2113; mem[7] = 1113; mem[8] = 4300; counter = 0; //Makes the variable counter start at 0. while(true) { operand = mem[ counter ]%100; // Finds the op codes from the limit on the mem (100) operation = mem[ counter ]/100; //using a switch loop to set up the loops for the cases switch ( operation ){ case READ: //reads a variable into a word from loc. Enter in -1 to exit cout <<"\n Input a positive variable: "; cin >> mem[ operand ]; counter++; break; case WRITE: // takes a word from location cout << "\n\nThe content at location " << operand << " is " << mem[operand]; counter++; break; case LOAD:// loads accum = mem[ operand ];counter++; break; case STORE: //stores mem[ operand ] = accum;counter++; break; case ADD: //adds accum += mem[operand];counter++; break; case SUBTRACT: // subtracts accum-= mem[ operand ];counter++; break; case DIVIDE: //divides accum /=(mem[ operand ]);counter++; break; case MULTIPLY: // multiplies accum*= mem [ operand ];counter++; break; case BRANCH: // Branches to location counter = operand; break; case BRANCHNEG: //branches if acc. is < 0 if (accum < 0) counter = operand; else counter++; break; case BRANCHZERO: //branches if acc = 0 if (accum == 0) counter = operand; else counter++; break; case HALT: // Program ends break; } } return 0; } part B int main() { const int READ = 10; const int WRITE = 11; const int LOAD = 20; const int STORE = 21; const int ADD = 30; const int SUBTRACT = 31; const int DIVIDE = 32; const int MULTIPLY = 33; const int BRANCH = 40; const int BRANCHNEG = 41; const int BRANCHZERO = 41; const int HALT = 43; int mem[100] = {0}; int operation; int operand; int accum = 0; int pos = 0; int j; mem[22] = 7; // loop 7 times mem[25] = 1; // increment by 1 mem[00] = 4306; mem[01] = 2303; mem[02] = 3402; mem[03] = 6410; mem[04] = 3412; mem[05] = 2111; mem[06] = 2002; mem[07] = 2312; mem[08] = 4210; mem[09] = 2109; mem[10] = 4001; mem[11] = 2015; mem[12] = 3212; mem[13] = 2116; mem[14] = 1101; mem[15] = 1116; mem[16] = 4300; j = 0; while ( true ) { operand = memory[ j ]%100; // Finds the op codes from the limit on the memory (100) operation = memory[ j ]/100; //using a switch loop to set up the loops for the cases switch ( operation ){ case 1: //reads a variable into a word from loc. Enter in -1 to exit cout <<"\n enter #: "; cin >> memory[ operand ]; break; case 2: // takes a word from location cout << "\n\nThe content at location " << operand << "is " << memory[operand]; break; case 3:// loads accum = memory[ operand ]; break; case 4: //stores memory[ operand ] = accum; break; case 5: //adds accum += mem[operand];; break; case 6: // subtracts accum-= memory[ operand ]; break; case 7: //divides accum /=(memory[ operand ]); break; case 8: // multiplies accum*= memory [ operand ]; break; case 9: // Branches to location j = operand; break; case 10: //branches if acc. is < 0 break; case 11: //branches if acc = 0 if (accum == 0) j = operand; break; case 12: // Program ends exit(0); break; } j++; } return 0; }

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• C++ cin keeps skipping.....

  - by user69514

  I am having problems with my program. WHen I run it, it asks the user for the album, the title, but then it just exits the loop without asking for the price and the sale tax. Any ideas what's going on? This is a sample run Discounts effective for September 15, 2010 Classical 8% Country 4% International 17% Jazz 0% Rock 16% Show 12% Are there more transactions? Y/N y Enter Artist of CD: Sevendust Enter Title of CD: Self titled Enter Genre of CD: Rock enter price Are there more transactions? Y/N Thank you for shopping with us! Program code: #include <iostream> #include <string> using namespace std; int counter = 0; string discount_tiles[] = {"Classical", "Country", "International", "Jazz", "Rock", "Show"}; int discount_amounts[] = {8, 4, 17, 0, 16, 12, 14}; string date = "September 15, 2010"; // Array Declerations //Artist array char** artist = new char *[100]; //Title array char** title = new char *[100]; //Genres array char** genres = new char *[100]; //Price array double* price[100]; //Discount array double* tax[100]; // sale price array double* sale_price[100]; //sale tax array double* sale_tax[100]; //cash price array double* cash_price[100]; //Begin Prototypes char* getArtist(); char* getTitle(); char* getGenre(); double* getPrice(); double* getTax(); unsigned int* AssignDiscounts(); void ReadTransaction (char ** artist, char ** title, char ** genre, float ** cash, float & taxrate, int albumcount); void computesaleprice(); bool AreThereMore (); //End Prototypes bool areThereMore () { char answer; cout << "Are there more transactions? Y/N" << endl; cin >> answer; if (answer =='y' || answer =='Y') return true; else return false; } char* getArtist() { char * artist= new char [100]; cout << "Enter Artist of CD: " << endl; cin.getline(artist,100); cin.ignore(); return artist; } char* getTitle() { char * title= new char [100]; cout << "Enter Title of CD: " << endl; cin.getline(title,100); cin.ignore(); return title; } char* getGenre() { char * genre= new char [100]; cout << "Enter Genre of CD: " << endl; cin.getline(genre,100); cin.ignore(); return genre; } double* getPrice() { //double* price = new double(); //cout << "Enter Price of CD: " << endl; //cin >> *price; //return price; double p = 0.0; cout<< "enter price" << endl; cin >> p; cin.ignore(); double* pp = &p; return pp; } double* getTax() { double* tax= new double(); cout << "Enter local sales tax: " << endl; cin >> *tax; return tax; } int findDiscount(string str){ if(str.compare(discount_tiles[0]) == 0) return discount_amounts[0]; else if(str.compare(discount_tiles[0]) == 0) return discount_amounts[1]; else if(str.compare(discount_tiles[0]) == 0) return discount_amounts[2]; else if(str.compare(discount_tiles[0]) == 0) return discount_amounts[3]; else if(str.compare(discount_tiles[0]) == 0) return discount_amounts[4]; else if(str.compare(discount_tiles[0]) == 0) return discount_amounts[5]; else{ cout << "Error in findDiscount function" << endl; return 0; } } void computesaleprice() { /** fill in array for all purchases **/ for( int i=0; i<=counter; i++){ double temp = *price[i]; temp -= findDiscount(genres[i]); double* tmpPntr = new double(); tmpPntr = &temp; sale_price[i] = tmpPntr; delete(&temp); delete(tmpPntr); } } void printDailyDiscounts(){ cout << "Discounts effective for " << date << endl; for(int i=0; i < 6; i++){ cout << discount_tiles[i] << "\t" << discount_amounts[i] << "%" << endl; } } //Begin Main int main () { for( int i=0; i<100; i++){ artist[i]=new char [100]; title[i]=new char [100]; genres[i]=new char [100]; price[i] = new double(0.0); tax[i] = new double(0.0); } // End Array Decleration printDailyDiscounts(); bool flag = true; while(flag == true){ if(areThereMore() == true){ artist[counter] = getArtist(); title[counter] = getTitle(); genres[counter] = getGenre(); price[counter] = getPrice(); //tax[counter] = getTax(); //counter++; flag = true; } else { flag = false; } } //compute sale prices //computesaleprice(); cout << "Thank you for shopping with us!" << endl; return 0; } //End Main /** void ReadTransaction (char ** artist, char ** title, char ** genre, float ** cash, float & taxrate, int albumcount) { strcpy(artist[albumcount],getArtist()); strcpy(title[albumcount],getTitle()); strcpy(genre[albumcount],getGenre()); //cash[albumcount][0]=computesaleprice();??????? //taxrate=getTax;?????????????? } * * */ unsigned int * AssignDiscounts() { unsigned int * discount = new unsigned int [7]; cout << "Enter Classical Discount: " << endl; cin >> discount[0]; cout << "Enter Country Discount: " << endl; cin >> discount[1]; cout << "Enter International Discount: " << endl; cin >> discount[2]; cout << "Enter Jazz Discount: " << endl; cin >> discount[3]; cout << "Enter Pop Discount: " << endl; cin >> discount[4]; cout << "Enter Rock Discount: " << endl; cin >> discount[5]; cout << "Enter Show Discount: " << endl; cin >> discount[6]; return discount; } /** char ** AssignGenres () { char ** genres = new char * [7]; for (int x=0;x<7;x++) genres[x] = new char [20]; strcpy(genres [0], "Classical"); strcpy(genres [1], "Country"); strcpy(genres [2], "International"); strcpy(genres [3], "Jazz"); strcpy(genres [4], "Pop"); strcpy(genres [5], "Rock"); strcpy(genres [6], "Show"); return genres; } **/ float getTax(float taxrate) { cout << "Please enter store tax rate: " << endl; cin >> taxrate; return taxrate; }

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• Threading across multiple files

  - by Zach M.

  My program is reading in files and using thread to compute the highest prime number, when I put a print statement into the getNum() function my numbers are printing out. However, it seems to just lag no matter how many threads I input. Each file has 1 million integers in it. Does anyone see something apparently wrong with my code? Basically the code is giving each thread 1000 integers to check before assigning a new thread. I am still a C noobie and am just learning the ropes of threading. My code is a mess right now because I have been switching things around constantly. #include <stdio.h> #include <stdlib.h> #include <time.h> #include <string.h> #include <pthread.h> #include <math.h> #include <semaphore.h> //Global variable declaration char *file1 = "primes1.txt"; char *file2 = "primes2.txt"; char *file3 = "primes3.txt"; char *file4 = "primes4.txt"; char *file5 = "primes5.txt"; char *file6 = "primes6.txt"; char *file7 = "primes7.txt"; char *file8 = "primes8.txt"; char *file9 = "primes9.txt"; char *file10 = "primes10.txt"; char **fn; //file name variable int numberOfThreads; int *highestPrime = NULL; int fileArrayNum = 0; int loop = 0; int currentFile = 0; sem_t semAccess; sem_t semAssign; int prime(int n)//check for prime number, return 1 for prime 0 for nonprime { int i; for(i = 2; i <= sqrt(n); i++) if(n % i == 0) return(0); return(1); } int getNum(FILE* file) { int number; char* tempS = malloc(20 *sizeof(char)); fgets(tempS, 20, file); tempS[strlen(tempS)-1] = '\0'; number = atoi(tempS); free(tempS);//free memory for later call return(number); } void* findPrimality(void *threadnum) //main thread function to find primes { int tNum = (int)threadnum; int checkNum; char *inUseFile = NULL; int x=1; FILE* file; while(currentFile < 10){ if(inUseFile == NULL){//inUseFIle being used to check if a file is still being read sem_wait(&semAccess);//critical section inUseFile = fn[currentFile]; sem_post(&semAssign); file = fopen(inUseFile, "r"); while(!feof(file)){ if(x % 1000 == 0 && tNum !=1){ //go for 1000 integers and then wait sem_wait(&semAssign); } checkNum = getNum(file); /* * * * * I think the issue is here * * * */ if(checkNum > highestPrime[tNum]){ if(prime(checkNum)){ highestPrime[tNum] = checkNum; } } x++; } fclose(file); inUseFile = NULL; } currentFile++; } } int main(int argc, char* argv[]) { if(argc != 2){ //checks for number of arguements being passed printf("To many ARGS\n"); return(-1); } else{//Sets thread cound to user input checking for correct number of threads numberOfThreads = atoi(argv[1]); if(numberOfThreads < 1 || numberOfThreads > 10){ printf("To many threads entered\n"); return(-1); } time_t preTime, postTime; //creating time variables int i; fn = malloc(10 * sizeof(char*)); //create file array and initialize fn[0] = file1; fn[1] = file2; fn[2] = file3; fn[3] = file4; fn[4] = file5; fn[5] = file6; fn[6] = file7; fn[7] = file8; fn[8] = file9; fn[9] = file10; sem_init(&semAccess, 0, 1); //initialize semaphores sem_init(&semAssign, 0, numberOfThreads); highestPrime = malloc(numberOfThreads * sizeof(int)); //create an array to store each threads highest number for(loop = 0; loop < numberOfThreads; loop++){//set initial values to 0 highestPrime[loop] = 0; } pthread_t calculationThread[numberOfThreads]; //thread to do the work preTime = time(NULL); //start the clock for(i = 0; i < numberOfThreads; i++){ pthread_create(&calculationThread[i], NULL, findPrimality, (void *)i); } for(i = 0; i < numberOfThreads; i++){ pthread_join(calculationThread[i], NULL); } for(i = 0; i < numberOfThreads; i++){ printf("this is a prime number: %d \n", highestPrime[i]); } postTime= time(NULL); printf("Wall time: %ld seconds\n", (long)(postTime - preTime)); } } Yes I am trying to find the highest number over all. So I have made some head way the last few hours, rescucturing the program as spudd said, currently I am getting a segmentation fault due to my use of structures, I am trying to save the largest individual primes in the struct while giving them the right indices. This is the revised code. So in short what the first thread is doing is creating all the threads and giving them access points to a very large integer array which they will go through and find prime numbers, I want to implement semaphores around the while loop so that while they are executing every 2000 lines or the end they update a global prime number. #include <stdio.h> #include <stdlib.h> #include <time.h> #include <string.h> #include <pthread.h> #include <math.h> #include <semaphore.h> //Global variable declaration char *file1 = "primes1.txt"; char *file2 = "primes2.txt"; char *file3 = "primes3.txt"; char *file4 = "primes4.txt"; char *file5 = "primes5.txt"; char *file6 = "primes6.txt"; char *file7 = "primes7.txt"; char *file8 = "primes8.txt"; char *file9 = "primes9.txt"; char *file10 = "primes10.txt"; int numberOfThreads; int entries[10000000]; int entryIndex = 0; int fileCount = 0; char** fileName; int largestPrimeNumber = 0; //Register functions int prime(int n); int getNum(FILE* file); void* findPrimality(void *threadNum); void* assign(void *num); typedef struct package{ int largestPrime; int startingIndex; int numberCount; }pack; //Beging main code block int main(int argc, char* argv[]) { if(argc != 2){ //checks for number of arguements being passed printf("To many threads!!\n"); return(-1); } else{ //Sets thread cound to user input checking for correct number of threads numberOfThreads = atoi(argv[1]); if(numberOfThreads < 1 || numberOfThreads > 10){ printf("To many threads entered\n"); return(-1); } int threadPointer[numberOfThreads]; //Pointer array to point to entries time_t preTime, postTime; //creating time variables int i; fileName = malloc(10 * sizeof(char*)); //create file array and initialize fileName[0] = file1; fileName[1] = file2; fileName[2] = file3; fileName[3] = file4; fileName[4] = file5; fileName[5] = file6; fileName[6] = file7; fileName[7] = file8; fileName[8] = file9; fileName[9] = file10; FILE* filereader; int currentNum; for(i = 0; i < 10; i++){ filereader = fopen(fileName[i], "r"); while(!feof(filereader)){ char* tempString = malloc(20 *sizeof(char)); fgets(tempString, 20, filereader); tempString[strlen(tempString)-1] = '\0'; entries[entryIndex] = atoi(tempString); entryIndex++; free(tempString); } } //sem_init(&semAccess, 0, 1); //initialize semaphores //sem_init(&semAssign, 0, numberOfThreads); time_t tPre, tPost; pthread_t coordinate; tPre = time(NULL); pthread_create(&coordinate, NULL, assign, (void**)numberOfThreads); pthread_join(coordinate, NULL); tPost = time(NULL); } } void* findPrime(void* pack_array) { pack* currentPack= pack_array; int lp = currentPack->largestPrime; int si = currentPack->startingIndex; int nc = currentPack->numberCount; int i; int j = 0; for(i = si; i < nc; i++){ while(j < 2000 || i == (nc-1)){ if(prime(entries[i])){ if(entries[i] > lp) lp = entries[i]; } j++; } } return (void*)currentPack; } void* assign(void* num) { int y = (int)num; int i; int count = 10000000/y; int finalCount = count + (10000000%y); int sIndex = 0; pack pack_array[(int)num]; pthread_t workers[numberOfThreads]; //thread to do the workers for(i = 0; i < y; i++){ if(i == (y-1)){ pack_array[i].largestPrime = 0; pack_array[i].startingIndex = sIndex; pack_array[i].numberCount = finalCount; } pack_array[i].largestPrime = 0; pack_array[i].startingIndex = sIndex; pack_array[i].numberCount = count; pthread_create(&workers[i], NULL, findPrime, (void *)&pack_array[i]); sIndex += count; } for(i = 0; i< y; i++) pthread_join(workers[i], NULL); } //Functions int prime(int n)//check for prime number, return 1 for prime 0 for nonprime { int i; for(i = 2; i <= sqrt(n); i++) if(n % i == 0) return(0); return(1); }

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• Java code optimization on matrix windowing computes in more time

  - by rano

  I have a matrix which represents an image and I need to cycle over each pixel and for each one of those I have to compute the sum of all its neighbors, ie the pixels that belong to a window of radius rad centered on the pixel. I came up with three alternatives: The simplest way, the one that recomputes the window for each pixel The more optimized way that uses a queue to store the sums of the window columns and cycling through the columns of the matrix updates this queue by adding a new element and removing the oldes The even more optimized way that does not need to recompute the queue for each row but incrementally adjusts a previously saved one I implemented them in c++ using a queue for the second method and a combination of deques for the third (I need to iterate through their elements without destructing them) and scored their times to see if there was an actual improvement. it appears that the third method is indeed faster. Then I tried to port the code to Java (and I must admit that I'm not very comfortable with it). I used ArrayDeque for the second method and LinkedLists for the third resulting in the third being inefficient in time. Here is the simplest method in C++ (I'm not posting the java version since it is almost identical): void normalWindowing(int mat[][MAX], int cols, int rows, int rad){ int i, j; int h = 0; for (i = 0; i < rows; ++i) { for (j = 0; j < cols; j++) { h = 0; for (int ry =- rad; ry <= rad; ry++) { int y = i + ry; if (y >= 0 && y < rows) { for (int rx =- rad; rx <= rad; rx++) { int x = j + rx; if (x >= 0 && x < cols) { h += mat[y][x]; } } } } } } } Here is the second method (the one optimized through columns) in C++: void opt1Windowing(int mat[][MAX], int cols, int rows, int rad){ int i, j, h, y, col; queue<int>* q = NULL; for (i = 0; i < rows; ++i) { if (q != NULL) delete(q); q = new queue<int>(); h = 0; for (int rx = 0; rx <= rad; rx++) { if (rx < cols) { int mem = 0; for (int ry =- rad; ry <= rad; ry++) { y = i + ry; if (y >= 0 && y < rows) { mem += mat[y][rx]; } } q->push(mem); h += mem; } } for (j = 1; j < cols; j++) { col = j + rad; if (j - rad > 0) { h -= q->front(); q->pop(); } if (j + rad < cols) { int mem = 0; for (int ry =- rad; ry <= rad; ry++) { y = i + ry; if (y >= 0 && y < rows) { mem += mat[y][col]; } } q->push(mem); h += mem; } } } } And here is the Java version: public static void opt1Windowing(int [][] mat, int rad){ int i, j = 0, h, y, col; int cols = mat[0].length; int rows = mat.length; ArrayDeque<Integer> q = null; for (i = 0; i < rows; ++i) { q = new ArrayDeque<Integer>(); h = 0; for (int rx = 0; rx <= rad; rx++) { if (rx < cols) { int mem = 0; for (int ry =- rad; ry <= rad; ry++) { y = i + ry; if (y >= 0 && y < rows) { mem += mat[y][rx]; } } q.addLast(mem); h += mem; } } j = 0; for (j = 1; j < cols; j++) { col = j + rad; if (j - rad > 0) { h -= q.peekFirst(); q.pop(); } if (j + rad < cols) { int mem = 0; for (int ry =- rad; ry <= rad; ry++) { y = i + ry; if (y >= 0 && y < rows) { mem += mat[y][col]; } } q.addLast(mem); h += mem; } } } } I recognize this post will be a wall of text. Here is the third method in C++: void opt2Windowing(int mat[][MAX], int cols, int rows, int rad){ int i = 0; int j = 0; int h = 0; int hh = 0; deque< deque<int> *> * M = new deque< deque<int> *>(); for (int ry = 0; ry <= rad; ry++) { if (ry < rows) { deque<int> * q = new deque<int>(); M->push_back(q); for (int rx = 0; rx <= rad; rx++) { if (rx < cols) { int val = mat[ry][rx]; q->push_back(val); h += val; } } } } deque<int> * C = new deque<int>(M->front()->size()); deque<int> * Q = new deque<int>(M->front()->size()); deque<int> * R = new deque<int>(M->size()); deque< deque<int> *>::iterator mit; deque< deque<int> *>::iterator mstart = M->begin(); deque< deque<int> *>::iterator mend = M->end(); deque<int>::iterator rit; deque<int>::iterator rstart = R->begin(); deque<int>::iterator rend = R->end(); deque<int>::iterator cit; deque<int>::iterator cstart = C->begin(); deque<int>::iterator cend = C->end(); for (mit = mstart, rit = rstart; mit != mend, rit != rend; ++mit, ++rit) { deque<int>::iterator pit; deque<int>::iterator pstart = (* mit)->begin(); deque<int>::iterator pend = (* mit)->end(); for(cit = cstart, pit = pstart; cit != cend && pit != pend; ++cit, ++pit) { (* cit) += (* pit); (* rit) += (* pit); } } for (i = 0; i < rows; ++i) { j = 0; if (i - rad > 0) { deque<int>::iterator cit; deque<int>::iterator cstart = C->begin(); deque<int>::iterator cend = C->end(); deque<int>::iterator pit; deque<int>::iterator pstart = (M->front())->begin(); deque<int>::iterator pend = (M->front())->end(); for(cit = cstart, pit = pstart; cit != cend; ++cit, ++pit) { (* cit) -= (* pit); } deque<int> * k = M->front(); M->pop_front(); delete k; h -= R->front(); R->pop_front(); } int row = i + rad; if (row < rows && i > 0) { deque<int> * newQ = new deque<int>(); M->push_back(newQ); deque<int>::iterator cit; deque<int>::iterator cstart = C->begin(); deque<int>::iterator cend = C->end(); int rx; int tot = 0; for (rx = 0, cit = cstart; rx <= rad; rx++, ++cit) { if (rx < cols) { int val = mat[row][rx]; newQ->push_back(val); (* cit) += val; tot += val; } } R->push_back(tot); h += tot; } hh = h; copy(C->begin(), C->end(), Q->begin()); for (j = 1; j < cols; j++) { int col = j + rad; if (j - rad > 0) { hh -= Q->front(); Q->pop_front(); } if (j + rad < cols) { int val = 0; for (int ry =- rad; ry <= rad; ry++) { int y = i + ry; if (y >= 0 && y < rows) { val += mat[y][col]; } } hh += val; Q->push_back(val); } } } } And finally its Java version: public static void opt2Windowing(int [][] mat, int rad){ int cols = mat[0].length; int rows = mat.length; int i = 0; int j = 0; int h = 0; int hh = 0; LinkedList<LinkedList<Integer>> M = new LinkedList<LinkedList<Integer>>(); for (int ry = 0; ry <= rad; ry++) { if (ry < rows) { LinkedList<Integer> q = new LinkedList<Integer>(); M.addLast(q); for (int rx = 0; rx <= rad; rx++) { if (rx < cols) { int val = mat[ry][rx]; q.addLast(val); h += val; } } } } int firstSize = M.getFirst().size(); int mSize = M.size(); LinkedList<Integer> C = new LinkedList<Integer>(); LinkedList<Integer> Q = null; LinkedList<Integer> R = new LinkedList<Integer>(); for (int k = 0; k < firstSize; k++) { C.add(0); } for (int k = 0; k < mSize; k++) { R.add(0); } ListIterator<LinkedList<Integer>> mit; ListIterator<Integer> rit; ListIterator<Integer> cit; ListIterator<Integer> pit; for (mit = M.listIterator(), rit = R.listIterator(); mit.hasNext();) { Integer r = rit.next(); int rsum = 0; for (cit = C.listIterator(), pit = (mit.next()).listIterator(); cit.hasNext();) { Integer c = cit.next(); Integer p = pit.next(); rsum += p; cit.set(c + p); } rit.set(r + rsum); } for (i = 0; i < rows; ++i) { j = 0; if (i - rad > 0) { for(cit = C.listIterator(), pit = M.getFirst().listIterator(); cit.hasNext();) { Integer c = cit.next(); Integer p = pit.next(); cit.set(c - p); } M.removeFirst(); h -= R.getFirst(); R.removeFirst(); } int row = i + rad; if (row < rows && i > 0) { LinkedList<Integer> newQ = new LinkedList<Integer>(); M.addLast(newQ); int rx; int tot = 0; for (rx = 0, cit = C.listIterator(); rx <= rad; rx++) { if (rx < cols) { Integer c = cit.next(); int val = mat[row][rx]; newQ.addLast(val); cit.set(c + val); tot += val; } } R.addLast(tot); h += tot; } hh = h; Q = new LinkedList<Integer>(); Q.addAll(C); for (j = 1; j < cols; j++) { int col = j + rad; if (j - rad > 0) { hh -= Q.getFirst(); Q.pop(); } if (j + rad < cols) { int val = 0; for (int ry =- rad; ry <= rad; ry++) { int y = i + ry; if (y >= 0 && y < rows) { val += mat[y][col]; } } hh += val; Q.addLast(val); } } } } I guess that most is due to the poor choice of the LinkedList in Java and to the lack of an efficient (not shallow) copy method between two LinkedList. How can I improve the third Java method? Am I doing some conceptual error? As always, any criticisms is welcome. UPDATE Even if it does not solve the issue, using ArrayLists, as being suggested, instead of LinkedList improves the third method. The second one performs still better (but when the number of rows and columns of the matrix is lower than 300 and the window radius is small the first unoptimized method is the fastest in Java)

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