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  • Solve Physics exercise by brute force approach..

    - by Nils
    Being unable to reproduce a given result. (either because it's wrong or because I was doing something wrong) I was asking myself if it would be easy to just write a small program which takes all the constants and given number and permutes it with a possible operators (* / - + exp(..)) etc) until the result is found. Permutations of n distinct objects with repetition allowed is n^r. At least as long as r is small I think you should be able to do this. I wonder if anybody did something similar here..

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  • What is this C function supposed to do based on description?

    - by user1261445
    unsigned int hex_c0c0c0c0(): Allowed operators: + - = & | ~ << ! >> Allowed constants: 1 2 4 8 16 Return 0xc0c0c0c0 The above is the description I have been given and I have to write the code for it. Can someone tell me what exactly the function is supposed to do? All the description says is what I have pasted above, so I'm not sure what my goal is. I'm sure it is an easy enough function to program on my own, but it would help if someone could tell me what the function is supposed to do, and maybe provide sample input/output so that I know my code is working correctly once I program this. Thanks.

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  • c++ link temporary allocations in fuction to custom allocator?

    - by user300713
    Hi, I am currently working on some simple custom allocators in c++ which generally works allready. I also overloaded the new/delete operators to allocate memory from my own allocator. Anyways I came across some scenarios where I don't really know where the memory comes from like this: void myFunc(){ myObj testObj(); ....do something with it } In this case testObj would only be valid inside the function, but where would its memory come from? Is there anyway I could link it to my allocator? Would I have to create to object using new and delete or is there another way? Thanks

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  • Sphinx without using an auto_increment id

    - by squeeks
    I am current in planning on creating a big database (2+ million rows) with a variety of data from separate sources. I would like to avoid structuring the database around auto_increment ids to help prevent against sync issues with replication, and also because each item inserted will have a alphanumeric product code that is guaranteed to be unique - it seems to me more sense to use that instead. I am looking at a search engine to index this database with Sphinx looking rather appealing due to its design around indexing relational databases. However, looking at various tutorials and documentation seems to show database designs being dependent on an auto_increment field in one form or another and a rather bold statement in the documentation saying that document ids must be 32/64bit integers only or things break. Is there a way to have a database indexed by Sphinx without auto_increment fields as the id?

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  • Problem with non-copyable classes

    - by DeadMG
    I've got some non-copyable classes. I don't invoke any of the copy operators or constructor, and this code compiles fine. But then I upgraded to Visual Studio 2010 Ultimate instead of Professional. Now the compiler is calling the copy constructor- even when the move constructor should be invoked. For example, in the following snippet: inline D3D9Mesh CreateSphere(D3D9Render& render, float radius, float slices) { D3D9Mesh retval(render); /* ... */ return std::move(retval); } Error: Cannot create copy constructor, because the class is non-copyable. However, I quite explicitly moved it.

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  • EF Linq Product Sum when no records returned

    - by user1622713
    I’ve seen variations of this question all over the place but none of the answers work for me. Most of them are just trying to sum a single column too – nothing more complex such as the sum of a product as below: public double Total { get { return _Context.Sales.Where(t => t.Quantity > 0) .DefaultIfEmpty() .Sum(t => t.Quantity * t.Price); } } If no rows are returned I want to return zero. However if no rows are returned the .Sum() fails. There are various options of trying to insert Convert.ToDouble and using null coalesce operators, but they all still gave me errors. I’m sure I am missing a simple way to do this – any help greatly appreciated after too long banging head against google brick wall!

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  • FastObjects.NET(is an OODB from Versant) performence in Real Scenerios?

    - by Lalit
    FastObjects.NET Saves the whole class object(if marked with attribute Persistent) at once in file system(using serilization or similar technology). They are promissing that it is even faster then normal SQL DB approach. My team also thought it is better and faster to save the whole object once instead of each field one by one. Defination of their website: FastObjects .NET 10.0 fully conforms to the Microsoft.NET 2.0 framework. Tightly integrated with Visual Studio 2005, it offers a developer-friendly, object-oriented alternative to a relational database for .NET persistence. I want to have your experiences of using FastObjects in production scenerio? They are promising for Indexing/Transaction/clustoring/replication.

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  • C: Comparing two long integers (very strange)

    - by Kyle
    Hi, I have following situation (unix) : x is a long and has value 300 y is a long and has value 50000 if (x <= y) printf("Correct."); if (x > y) printf("Ouch."); Now I always get "Ouch". That means the program keeps telling me that 300 is greater than 50000! It only works again when I do if ((int)x <=(int) y) printf("Correct."); if ((int)x > (int)y) printf("Ouch."); So what is wrong with the comparison operators?

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  • A step-up from TiddlyWiki that is still 100% portable?

    - by Smandoli
    TiddlyWiki is a great idea, brilliantly implemented. I'm using it as a portable personal "knowledge manager," and these are the prize virtues: It travels on my USB flash memory stick and runs on any computer, regardless of operating system No software installation is needed on the computer (TiddlyWiki merely uses the Internet browser) No Internet connection is needed In terms of data retrieval functionality, it mimics a relational database (use of tags and internal links) Let's say I've got a million words of prose in 4,000 tiddlers (posts). I'm still testing, but it looks like TiddlyWiki gets very slow. Is there an app like TiddlyWiki that keeps all the virtues I listed above, and allows more storage? NOTE: Separation of content and presentation would be ideal. It's nifty that TiddlyWiki has everything in a single HTML document, but it's unhelpful in many ways. I don't care if a directory of assorted docs is needed (SQLite, XML?), as long as it's functionally self-contained.

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  • Using linked servers, OPENROWSET and OPENQUERY

    - by BuckWoody
    SQL Server has a few mechanisms to reach out to another server (even another server type) and query data from within a Transact-SQL statement. Among them are a set of stored credentials and information (called a Linked Server), a statement that uses a linked server called called OPENQUERY, another called OPENROWSET, and one called OPENDATASOURCE. This post isn’t about those particular functions or statements – hit the links for more if you’re new to those topics. I’m actually more concerned about where I see these used than the particular method. In many cases, a Linked server isn’t another Relational Database Management System (RDMBS) like Oracle or DB2 (which is possible with a linked server), but another SQL Server. My concern is that linked servers are the new Data Transformation Services (DTS) from SQL Server 2000 – something that was designed for one purpose but which is being morphed into something much more. In the case of DTS, most of us turned that feature into a full-fledged job system. What was designed as a simple data import and export system has been pressed into service doing logic, routing and timing. And of course we all know how painful it was to move off of a complex DTS system onto SQL Server Integration Services. In the case of linked servers, what should be used as a method of running a simple query or two on another server where you have occasional connection or need a quick import of a small data set is morphing into a full federation strategy. In some cases I’ve seen a complex web of linked servers, and when credentials, names or anything else changes there are huge problems. Now don’t get me wrong – linked servers and other forms of distributing queries is a fantastic set of tools that we have to move data around. I’m just saying that when you start having lots of workarounds and when things get really complicated, you might want to step back a little and ask if there’s a better way. Are you able to tolerate some latency? Perhaps you’re able to use Service Broker. Would you like to be platform-independent on the data source? Perhaps a middle-tier might make more sense, abstracting the queries there and sending them to the proper server. Designed properly, I’ve seen these systems scale further and be more resilient than loading up on linked servers. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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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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  • Ternary operator in VB.NET

    - by Jalpesh P. Vadgama
    We all know about Ternary operator in C#.NET. I am a big fan of ternary operator and I like to use it instead of using IF..Else. Those who don’t know about ternary operator please go through below link. http://msdn.microsoft.com/en-us/library/ty67wk28(v=vs.80).aspx Here you can see ternary operator returns one of the two values based on the condition. See following example. bool value = false;string output=string.Empty;//using If conditionif (value==true) output ="True";else output="False";//using tenary operatoroutput = value == true ? "True" : "False"; In the above example you can see how we produce same output with the ternary operator without using If..Else statement. Recently in one of the project I was working with VB.NET language and I was eager to know if there is a ternary operator equivalent there or not. After searching on internet I have found two ways to do it. IF operator which works for VB.NET 2008 and higher version and IIF operator which is there since VB 6.0. So let’s check same above example with both of this operators. So let’s create a console application which has following code. Module Module1 Sub Main() Dim value As Boolean = False Dim output As String = String.Empty ''Output using if else statement If value = True Then output = "True" Else output = "False" Console.WriteLine("Output Using If Loop") Console.WriteLine(output) output = If(value = True, "True", "False") Console.WriteLine("Output using If operator") Console.WriteLine(output) output = IIf(value = True, "True", "False") Console.WriteLine("Output using IIF Operator") Console.WriteLine(output) Console.ReadKey() End If End SubEnd Module As you can see in the above code I have written all three-way to condition check using If.Else statement and If operator and IIf operator. You can see that both IIF and If operator has three parameter first parameter is the condition which you need to check and then another parameter is true part of you need to put thing which you need as output when condition is ‘true’. Same way third parameter is for the false part where you need to put things which you need as output when condition as ‘false’. Now let’s run that application and following is the output as expected. That’s it. You can see all three ways are producing same output. Hope you like it. Stay tuned for more..Till then Happy Programming.

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  • SQLAuthority News – Book Review – Beginning T-SQL 2008 by Kathi Kellenberger

    - by pinaldave
    Beginning T-SQL 2008 by Kathi Kellenberger Amazon Link Detail Review: Beginning T-SQL 2008 is one of the best books on the market if you are just beginning to work with Microsoft SQL, or have a little bit of experience and need to learn more quickly. Each chapter of the book introduces a new subject, and builds upon topics covered in previous chapters.  The author of the book, Kathi Kellenberger understands that you need to form a solid foundation of knowledge before moving on to new topics, and sets up each subject nicely.  Because the chapters move in an orderly progression, you continue to use skills you learned earlier. One of the best features of Beginning T-SQL 2008 is that each chapter has multiple examples and exercises.  Many books introduce a topic and then never go back to it.  This book gives enough examples that you will be familiar with the subject when you come across it in real life.  The exercises at the end of the chapter mean that you will be using the skills you learned – and there is no better way to cement a subject in your brain. The book also includes discussions of the common errors that programmers will come across, how to avoid them, and how to fix them if they happen.  Ms. Kellenberger understands that not only do mistakes happen, but they are bound to happen if you aren’t trained properly.  Mistakes are part of the learning process! The book begins by discussions relational theory, so that programmers will understand the way T-SQL works from the ground up.  It also walks readers through writing accurate queries, combining set-based and procedural processing, embedding logic in stored functions, and so much more. Overall, the main goal of Beginning T-SQL 2008 is to introduce novices to SQL programming, and quickly familiarize them with the basics of running the program.  The book is written with the idea that readers will not know any of the technical terms or vocabulary.  However, if you are a little more familiar with SQL and looking to become better, you will still find this book very helpful. Ratting: 4.5+ Stars Summary: I must recommend Beginning T-SQL 2008 highly enough.  If you are going to buy any beginners guide to Transect-SQL, this is the one you should spend your money on.  You can save yourself a lot of time and effort later by using this very affordable manual to learn the basics, which will allow you to become an expert much faster. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority Book Review, SQLAuthority News, T SQL, Technology

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  • Learn about MySQL with the Authentic MySQL for Beginners course

    - by Antoinette O'Sullivan
    Learn about the MySQL Server and other MySQL products by taking the authentic MySQL for Beginners course. This course covers all the basics from MySQL download and installation, to relational database concepts and database design. This course is your first step to becoming a MySQL administrator. You can take this course through one of the following delivery types: Training-on-Demand: Start the class from your desk, at your base and within 24 hrs of registering. Read Ben Krug on Day 3 of his experience taking the MySQL for Beginners course Training-on-Demand option. Live-Virtual Class: Attend this live class from your own office - no travel required. Choose from a selection of events on the schedule to suit different timezones. Delivery languages include English and German. In-Class event: Attend this class in an education center. Events already on the schedule include:  Location  Date  Delivery Language  Mechelen, Belgium  14 January 2013  English  London, England  5 March 2013  English  Hamburg, Germany  25 March 2013  German  Munich, Germany  3 June 2013  German  Budapest, Hungary  5 February 2013  Hungary  Milan, Italy  11 February 2013  Italian  Rome, Italy  4 March 2013  Italian  Riga, Latvia  18 February 2013  Latvian  Amsterdam, Netherlands  21 May 2013  Dutch  Nieuwegein, Netherlands  18 February 2013  Dutch  Warsaw, Poland  18 February 2013  Polish  Lisbon, Portugal  25 March 2013  European Portugese  Porto, Portugal  25 March 2013  European Portugese  Barcelona, Spain  11 February 2013  Spanish  Madrid, Spain  22 April 2013  Spanish  Nairobi, Kenya  14 January 2013  English  Capetown, South Africa  22 July 2013  English  Pretoria, South Africa  22 April 2013  English  Petaling Jaya, Malaysia  28 January 2013  English  Ottawa, Canada  25 March 2013  English  Toronto, Canada  25 March 2013  English  Montreal, Canada 25 March 2013   English Mexico City, Mexico  14 January 2013   Spanish  San Pedro Garza Garcia, Mexico  5 February 2013  Spanish  Sao Paolo, Brazil  29 January 2013  Brazilian Portugese For more information on this or other courses on the authentic MySQL Curriculum, go to http://oracle.com/education/mysql. Note, many organizations deploy both Oracle Database and MySQL side by side to serve different needs, and as a database professional you can find training courses on both topics at Oracle University! Check out the upcoming Oracle Database training courses and MySQL training courses. Even if you're only managing Oracle Databases at this point of time, getting familiar with MySQL will broaden your career path with growing job demand.

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  • A few things I learned regarding Azure billing policies

    - by Vincent Grondin
    An hour of small computing time: 0,12$ per hour A Gig of storage in the cloud: 0,15$ per hour 1 Gig of relational database using Azure SQL: 9,99$  per month A Visual Studio Professional with MSDN Premium account: 2500$ per year Winning an MSDN Professional account that comes preloaded with 750 free hours of Azure per month:  PRICELESS !!!      But was it really free???? Hmmm… Let’s see.....   Here's a few things I learned regarding Azure billing policies when I attended a promotional training at Microsoft last week...   1)  An instance deployed in the cloud really means whatever you upload in there... it doesn't matter if it's in STAGING OR PRODUCTION!!!!   Your MSDN account comes with 750 free hours of small computing time per month which should be enough hours per month for one instance of one application deployed in the cloud...  So we're cool, the application you run in the cloud doesn't cost you a penny....  BUT the one that's in staging is still consuming time!!!   So if you don’t want to end up having to pay 42$ at the end of the month on your credit card like this happened to a friend of mine, DELETE them staging applications once you’ve put them in production! This also applies to the instance count you can modify in the configuration file… So stop and think before you decide you want to spawn 50 of those hello world apps  .     2) If you have an MSDN account, then you have the promotional 750 hours of Azure credits per month and can use the Azure credits to explore the Cloud! But be aware, this promotion ends in 8 months (maybe more like 7 now) and then you will most likely go back to the standard 250 hours of Azure credits. If you do not delete your applications by then, you’ll get billed for the extra hours, believe me…   There is a switch that you can toggle and which will STOP your automatic enrollment after the promotion and prevent you from renewing the Azure Account automatically. Yes the default setting is to automatically renew your account and remember, you entered your credit card information in the registration process so, yes, you WILL be billed…  Go disable that ASAP    Log into your account, go to “Windows Azure Platform” then click the “Subscriptions” tab and on the right side, you’ll see a drop down with different “Actions” into it… Choose “Opt out of auto renew” and, NOW you’re safe…   Still, this is a great offer by Microsoft and I think everyone that has a chance should play a bit with Azure to get to know this technology a bit more...     Happy Cloud Computing All

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  • LLBLGen Pro feature highlights: model views

    - by FransBouma
    (This post is part of a series of posts about features of the LLBLGen Pro system) To be able to work with large(r) models, it's key you can view subsets of these models so you can have a better, more focused look at them. For example because you want to display how a subset of entities relate to one another in a different way than the list of entities. LLBLGen Pro offers this in the form of Model Views. Model Views are views on parts of the entity model of a project, and the subsets are displayed in a graphical way. Additionally, one can add documentation to a Model View. As Model Views are displaying parts of the model in a graphical way, they're easier to explain to people who aren't familiar with entity models, e.g. the stakeholders you're interviewing for your project. The documentation can then be used to communicate specifics of the elements on the model view to the developers who have to write the actual code. Below I've included an example. It's a model view on a subset of the entities of AdventureWorks. It displays several entities, their relationships (both relational and inheritance relationships) and also some specifics gathered from the interview with the stakeholder. As the information is inside the actual project the developer will work with, the information doesn't have to be converted back/from e.g .word documents or other intermediate formats, it's the same project. This makes sure there are less errors / misunderstandings. (of course you can hide the docked documentation pane or dock it to another corner). The Model View can contain entities which are placed in different groups. This makes it ideal to group entities together for close examination even though they're stored in different groups. The Model View is a first-class citizen of the code-generator. This means you can write templates which consume Model Views and generate code accordingly. E.g. you can write a template which generates a service per Model View and exposes the entities in the Model View as a single entity graph, fetched through a method. (This template isn't included in the LLBLGen Pro package, but it's easy to write it up yourself with the built-in template editor). Viewing an entity model in different ways is key to fully understand the entity model and Model Views help with that.

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  • Oracle ADF Core Functionality Now Available for Free - Presenting Oracle ADF Essentials

    - by Shay Shmeltzer
    Normal 0 false false false EN-US X-NONE HE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:Arial;} We are happy to announce the new Oracle ADF Essentials - a free to develop and deploy version of the core technologies at the base of Oracle ADF – Oracle’s strategic development framework that was used, among other things, to build the new generation of the enterprise Oracle Fusion Applications. This release is aligned with the new Oracle JDeveloper 11.1.2.3 version that we released today. Oracle ADF Essentials enables developers to use the following free: Oracle ADF Faces Rich Client components –over 150 JSF 2.0 components that include extensive charting and data visualization components, supports skinning, internalization, accessibility and touch gestures and providing advanced Ajax, windowing, drag and drop and other UI capabilities in a declarative way. Oracle ADF Controller – an extension on top of the JSF controller providing complete process flow definition and enabling advanced reusability of flows inside page’s regions. Oracle ADF Binding – a declarative way to bind various business services to JSF user interfaces eliminating tedious managed-beans coding. Oracle ADF Business Components – a declarative layer for building Java based business services on top of relational databases. The main goal of Oracle ADF Essentials is to bring the benefits of Oracle ADF to a broader community of developers. If you are already using Oracle ADF, a key new functionality for you would be the ability to deploy your application on GlassFish. Several other interesting points: We provide instructions for deployment of Oracle ADF Essentials on GlassFish and officially support this platform for Oracle ADF Essentials deployment. Developers can choose to use the whole Oracle ADF Essentials, or just pieces of the technology. Visual development for Oracle ADF Essentials is provided in Oracle JDeveloper. Eclipse support via Oracle Enterprise for Eclipse (OEPE) is also planned. Want to learn more? Here is a quick overview and development demo of Oracle ADF Essentials For more visit the Oracle ADF Essentials page on OTN

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  • Retrieving only the first record or record at a certain index in LINQ

    - by vik20000in
    While working with data it’s not always required that we fetch all the records. Many a times we only need to fetch the first record, or some records in some index, in the record set. With LINQ we can get the desired record very easily with the help of the provided element operators. Simple get the first record. If you want only the first record in record set we can use the first method [Note that this can also be done easily done with the help of the take method by providing the value as one].     List<Product> products = GetProductList();      Product product12 = (         from prod in products         where prod.ProductID == 12         select prod)         .First();   We can also very easily put some condition on which first record to be fetched.     string[] strings = { "zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine" };     string startsWithO = strings.First(s => s[0] == 'o');  In the above example the result would be “one” because that is the first record starting with “o”.  Also the fact that there will be chances that there are no value returned in the result set. When we know such possibilities we can use the FirstorDefault() method to return the first record or incase there are no records get the default value.        int[] numbers = {};     int firstNumOrDefault = numbers.FirstOrDefault();  In case we do not want the first record but the second or the third or any other later record then we can use the ElementAt() method. In the ElementAt() method we need to pass the index number for which we want the record and we will receive the result for that element.      int[] numbers = { 5, 4, 1, 3, 9, 8, 6, 7, 2, 0 };      int fourthLowNum = (         from num in numbers         where num > 5         select num )         .ElementAt(1); Vikram

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  • When Less is More

    - by aditya.agarkar
    How do you reconcile the fact that while the overall warehouse volume is down you still need more workers in the warehouse to ship all the orders? A WMS customer recently pointed out this seemingly perplexing fact in a customer conference. So what is going on? Didn't we tell you before that for a warehouse the customer is really the "king"? In this case customers are merely responding to a low overall low demand and uncertainty. They do not want to hold down inventory and one of the ways to do that is by decreasing the order size and ordering more frequently. Overall impact to the warehouse? Two words: "More work!!" This is not all. Smaller order sizes also mean challenges from a transportation perspective including a rise in costlier parcel or LTL shipments instead of cheaper TL shipments. Here is a hypothetical scenario where a customer reduces the order size by 10% and increases the order frequency by 10%. As you can see in the following table, the overall volume declines by 1% but the warehouse has to ship roughly 10% more lines. Order Frequency (Line Count)Order Size (Units)Total VolumeChange (%)10010010,000 -110909,900-1% If you want to see how "Less is More" in graphical terms, this is how it appears: Even though the volume is down, there is going to be more work in the warehouse in terms of number of lines shipped. The operators need to pick more discrete orders, pack them into more shipping containers and ship more deliveries. What do you do differently if you are facing this situation?In this case here are some obvious steps to take:Uno: Change your pick methods. If you are used to doing order picks, it needs to go out the door. You need to evaluate batch picking and grouping techniques. Go for cluster picking, go for zone picking, pick and pass...anything that improves your picker productivity. More than anything, cluster picking works like a charm and above all, its simple and very effective. Dos: Are you minimize "touch" points in your pick process? Consider doing one step pick, pack and confirm i.e. pick and pack stuff directly into shipping cartons. Done correctly the container will not require any more "touch" points all the way to the trailer loading. Use cartonization!Tres: Are the being picked from an optimized pick face? Are the items slotted correctly? This needs to be looked into. Consider automated "pull" or "push" replenishment into your pick face and also make sure that high demand items are occupying the golden zones.  Cuatro: Are you tracking labor productivity? If not there needs to be a concerted push for having labor standards in place. Hope you found these ideas useful.

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  • SQL Developer Data Modeler: On Notes, Comments, and Comments in RDBMS

    - by thatjeffsmith
    Ah the beautiful data model. They say a picture is worth a 1,000 words. And then we have our diagrams, how many words are they worth? Our friends from the Human Relations sample schema So our models describe how the data ‘works’ – whether that be at a logical-business level, or a technical-physical level. Developers like to say that their code is self-documenting. These would be very lazy or very bad (or both) developers. Models are the same way, you should document your models with comments and notes! I have 3 basic options: Comments Comments in RDBMS Notes So what’s the difference? Comments You’re describing the entity/table or attribute/column. This information will NOT be published in the database. It will only be available to the model, and hence, folks with access to the model. Table Comments (in the design only!) Comments in RDBMS You’re doing the same thing as above, but your words will be stored IN the data dictionary of the database. Oracle allows you to store comments on the table and column definitions. So your awesome documentation is going to be viewable to anyone with access to the database. RDBMS is an acronym for Relational Database Management System – of which Oracle is one of the first commercial examples If the DDL is produced and ran against a database, these comments WILL be stored in the data dictionary. Notes A place for you to add notes, maybe from a design meeting. Or maybe you’re using this as a to-do or requirements list. Basically it’s for anything that doesn’t literally describe the object at hand – that’s what the comments are for. I totally made these up. Now these are free text fields and you can put whatever you want here. Just make sure you put stuff here that’s worth reading. And it will live on…forever.

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  • Oracle AutoVue Key Highlights from Oracle OpenWorld 2012

    - by Celine Beck
    We closed another successful Oracle Open World for AutoVue. Thanks to everyone who joined us this year. As usual, from customer presentations to evening networking activities, there was enough to keep us busy during the entire event. Here is a summary of some of the key highlights of the conference: Sessions:We had two AutoVue-specific sessions during Oracle Open World this year. The first session was part of the Product Lifecycle Management track and covered how AutoVue can be used to help drive effective decision making and streamline design for manufacturing processes. Attendees had the opportunity to learn from customer speaker GLOBALFOUNDRIES how they have been leveraging Oracle AutoVue within Agile PLM to enable high degree of collaboration during the exceptionally creative phases of their product development processes, securely, without risking valuable intellectual property. If you are interested, you can actually download the presentation by visiting launch.oracle.com/?plmopenworld2012.AutoVue was also featured as part of the Utilities track. This session focused on how visualization solutions play a critical role in effective plant optimization and configuration strategies defined by owners and operators of power generation facilities. Attendees learnt how integrated with document management systems, and enterprise applications like Oracle Primavera and Asset Lifecycle Management, AutoVue improves change management processes; minimizes risks by providing access to accurate engineering drawings which capture and reflect the as-maintained status of assets; and allows customers to drive complex maintenance projects to successful completion.Augmented Business Visualization for Agile PLMDuring Oracle Open World, we also showcased an Augmented Business Visualization-based solution for Oracle Agile PLM. An Augmented Business Visualization (ABV) solution is one where your structured data (from Oracle Agile PLM for instance) and your unstructured data (documents, designs, 3D models, etc) come together to allow you to make better decisions (check out our blog posts on the topic: Augment the Value of Your Data (or Time to replace the “attach” button) and Context is Everything ). As part of the Agile PLM, the idea is to support more effective decision-making by turning 3D assemblies into color-coded reports, and streamlining business processes like Engineering Change Management by enabling the automatic creation of engineering change requests in Agile PLM directly from documents being viewed in AutoVue. More on this coming soon...probably during the Oracle Value Chain Summit to be held in San Francisco, from Feb. 4-6, 2013 in San Francisco! Mark your calendars and stay tuned for more information! And thanks again for joining us at Oracle OpenWorld!

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  • Site Search Engine for 1,000 page website

    - by Ian
    I manage a website with about 1,000 articles that need to be searchable by my members. The site search engines I've tried all had their own problems: Fluid Dynamics Search Engine Since it's written in perl, it was a bit hacky to integrate with my PHP-based CMS. I basically had to file_get_contents the search results page. However, FDSE had the best search results. Google CSE Ugh, the search results SUCK. It can't find documents even using unique strings. I'm so surprised that a Google search product is this bad. Nor can I get any answers on their 'help' forums, and I am a paying user. Boo, Google. Boo. Sphider Again, bad search results. Unable to locate some phrases used in link text. Better results than Google CSE though. Shame on Google that a free PHP script has better search results than their paid application. IndexTank This one looked really promising. I got all set up with their PHP API client. But it would only randomly add articles that I submitted. Out of 700+ articles I pushed to the index through their API, only 8 made it in. Unable to find any help on this subject. Update for IndexTank -- Got the above issue fixed, so this looks most promising so far. The site itself runs on php/mysql and FreeBSD, though this shouldn't matter for a web crawling indexer. I've looked at Lucene, but I don't know anything about Java or installing Java programs on my web server. I also do not have root access on my web server, if this would be required for installation. I really don't need a lot of fancy features. It just needs to be able to crawl my web site and return great (even decent!) search results. I don't need any crazy search operators. It doesn't need to index off my primary domain. It just needs to work! Thanks, Hive Mind!

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  • SQLAuthority News – SQL Server 2012 Upgrade Technical Guide – A Comprehensive Whitepaper – (454 pages – 9 MB)

    - by pinaldave
    Microsoft has just released SQL Server 2012 Upgrade Technical Guide. This guide is very comprehensive and covers the subject of upgrade in-depth. This is indeed a helpful detailed white paper. Even writing a summary of this white paper would take over 100 pages. This further proves that SQL Server 2012 is quite an important release from Microsoft. This white paper discusses how to upgrade from SQL Server 2008/R2 to SQL Server 2012. I love how it starts with the most interesting and basic discussion of upgrade strategies: 1) In-place upgrades, 2) Side by side upgrade, 3) One-server, and 4) Two-server. This whitepaper is not just pure theory but is also an excellent source for some tips and tricks. Here is an example of a good tip from the paper: “If you want to upgrade just one database from a legacy instance of SQL Server and not upgrade the other databases on the server, use the side-by-side upgrade method instead of the in-place method.” There are so many trivia, tips and tricks that make creating the list seems humanly impossible given a short period of time. My friend Vinod Kumar, an SQL Server expert, wrote a very interesting article on SQL Server 2012 Upgrade before. In that article, Vinod addressed the most interesting and practical questions related to upgrades. He started with the fundamentals of how to start backup before upgrade and ended with fail-safe strategies after the upgrade is over. He covered end-to-end concepts in his blog posts in simple words in extremely precise statements. A successful upgrade uses a cycle of: planning, document process, testing, refine process, testing, planning upgrade window, execution, verifying of upgrade and opening for business. If you are at Vinod’s blog post, I suggest you go all the way down and collect the gold mine of most important links. I have bookmarked the blog by blogging about it and I suggest that you bookmark it as well with the way you prefer. Vinod Kumar’s blog post on SQL Server 2012 Upgrade Technical Guide SQL Server 2012 Upgrade Technical Guide is a detailed resource that’s also available online for free. Each chapter was carefully crafted and explained in detail. Here is a quick list of the chapters included in the whitepaper. Before downloading the guide, beware of its size of 9 MB and 454 pages. Here’s the list of chapters: Chapter 1: Upgrade Planning and Deployment Chapter 2: Management Tools Chapter 3: Relational Databases Chapter 4: High Availability Chapter 5: Database Security Chapter 6: Full-Text Search Chapter 7: Service Broker Chapter 8: SQL Server Express Chapter 9: SQL Server Data Tools Chapter 10: Transact-SQL Queries Chapter 11: Spatial Data Chapter 12: XML and XQuery Chapter 13: CLR Chapter 14: SQL Server Management Objects Chapter 15: Business Intelligence Tools Chapter 16: Analysis Services Chapter 17: Integration Services Chapter 18: Reporting Services Chapter 19: Data Mining Chapter 20: Other Microsoft Applications and Platforms Appendix 1: Version and Edition Upgrade Paths Appendix 2: SQL Server 2012: Upgrade Planning Checklist Download SQL Server 2012 Upgrade Technical Guide [454 pages and 9 MB] Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Database, DBA, PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, SQLServer, T SQL, Technology

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  • Now Shipping! NetAdvantage for .NET 2010 Volume 3!

    The new NetAdvantage Ultimate includes all four Line of Business user interface control sets for ASP .NET, Windows Forms, WPF and Silverlight plus two advanced Data Visualization UI control sets for WPF and Silverlight. With six NetAdvantage products in one robust package, Infragistics® gives you hundreds of controls and infinite development possibilities. Unified XAML Product Strategy-Share Code, Get More Controls In the 10.3 release, Infragistics continues to deliver code parity between the XAML platforms, WPF and Silverlight. In the line of business toolsets, Infragistics introduces the new xamSchedule™, full-featured, Outlook® 2010-style schedule controls, and the new xamDataTree™, a data bound tree view that comfortably handles tens of thousands of tree nodes. Mimicking our Silverlight Drag and Drop Framework, the WPF Drag and Drop Framework CTP empowers you to add your own rich touches to your applications. Track Users' Behaviors New to all NetAdvantage Silverlight controls is the Infragistics Analytics Framework (IGAF), which empowers you to track user behavior in RIAs running on Silverlight 4. Building on the Microsoft® Silverlight Analytics Framework, with IGAF you can analyze the user's behaviors to ensure the experience you want to deliver. NetAdvantage for Windows Forms--New Office® 2010 Ribbon and Application Menu 2010 Create new experiences with Windows Forms. Now with Office 2010 styling, NetAdvantage for Windows Forms has new features such as Microsoft® Office 2010 ribbon and enhanced Infragistics.Excel to export the contents of the high performance WinGrid™ into Microsoft Excel® 2010. The new Windows Message Support enables Infragistics standalone editor controls to process numerous Windows® OS messages, allowing them to respond just like native controls to changes in the Windows environment. Create Faster Web 2.0 Experiences with NetAdvantage for ASP .NET Infragistics continues to push the envelope to deliver the fastest ASP .NET WebForms controls available on the market. Our lightning fast ASP .NET grids are now enhanced with XPS/PDF Exporting and Summary Rows. This release also includes support for jQuery Templating (as a CTP) within our WebDataGrid™ and WebDataTree™ controls allowing you to quickly cut down overall page size. Deliver Business Intelligence with Power, Flexibility and the Office 2010 Experience NetAdvantage for WPF Data Visualization and NetAdvantage for Silverlight Data Visualization help you deliver flexible, powerful and usable end user experiences in Business Intelligence applications. Both suites include the Pivot Grid that delivers the full power of online analytical processing (OLAP) to present multi-dimensional data, sliced and diced in cross-tabulated form for end users to drill down into, interact with and easily extract meaning from the data. Mapping Made Easy 10.3 marks the official release of the WPF Data Visualization xamMap™ control to map anything and everything from geographic to geo-spacial mapping data. Map layers allow you to add successive levels of detail, navigational panes for panning in all directions, color swatch panes that facilitate value scales like Choropleth shading, and scale panes allowing users to zoom-in and out. Both toolsets introduce the first of many relationship maps! With the xamOrgChart™ CTP you can map out organizational charts of up to 50K employees, competitive brackets (think World Cup) and any other relational, organizational map your application needs. http://www.infragistics.com span.fullpost {display:none;}

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  • A brief introduction to BRM and architecture

    - by Yani Miguel
    Oracle Communications Billing and Revenue Management (Oracle BRM) is the telcos industry´s leading solution intended for communications service providers. This post encourages to know BRM starting with the basics. History Portal was a billing and revenue managament solution to communications industry created by Portal Software. In 2006 Oracle acquired Portal Software and the solution was renamed BRM. Today Oracle BRM is the first end-to-end packaged enterprise software suite for the communications industry, however BRM is just one more product in the catalog of OSS solutions that Oracle offers. BRM can bill and manage all communications services including wireline, wireless, broadband, cable, voice over IP, IPTV, music, and video. BRM Architecture BRM´s architecture consists of 4 layers or tiers. Through these layers are the data, bussines logic and interfaces to connect graphical client tools.Application tier This layer provides GUI client tools enabling communication to other layers through open APIs. Some BRM client applications are: Customer Center Pricing Center Universal Event Loader Web Server BRM Billing Application Collections Center Permissioning Center Furthermore, this layer is where are provided real-time external events. Bussines Process Tier Although all layers are equally important, I think it deserves more atention because in this tier BRM functionality is implemented. All functions that give life to BRM are in this layer coded in C language called Opcodes (System Processes in the image). Any changes or additional functionality should be made here, so when we try to customize the product, we will most of the time programming in this layer (Business Policies in the image).Bussines Process Tier Features: Implements Portal system functionalityValidates data from the application tierModifies Portal behavior through business policies. Business policies can by customized.Triggers external systems using event notification. Object Tier This layer is responsible for transfer the BRM requests into database language and translate BRM requests into external system requests. Without it, the business logic (data from Bussines Process Tier) could not be understood by the relational database. Data tier Data tier is responsable for the storage of BRM database and other external systems databases. External systems include credit card, tax, and directory servers. Finally, It's important to note that BRM is designed to easily integrate with the following solutions:AIA 2.4 Siebel CRM E-Business Suite - G/L onlyCommunications Services Gatekeeper Oracle BI Publisher. Personally, I think that BRM could improve migrating client-server architecture to a fully web platform that works with Oracle Middleware like any product of the Fusion Middleware family. Hopefully there are already initiatives in this area.

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