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• AR242x / AR542x wireless card not working

  - by Pipan87

  My wifi worked perfect until I updated to the latest version of Ubuntu. Now I don't find any wireless connections at all. I have tried lots of guides on the internet but I can't get it to work. I did however start to work once after writing something I don't remember in Terminal, but after rebooting it stopped working again. Some info (don't know if you need more to help): 01:00.0 Ethernet controller: Atheros Communications AR8121/AR8113/AR8114 Gigabit or Fast Ethernet (rev b0) Subsystem: Acer Incorporated [ALI] Device 022c Flags: bus master, fast devsel, latency 0, IRQ 44 Memory at 55200000 (64-bit, non-prefetchable) [size=256K] I/O ports at 3000 [size=128] Capabilities: [40] Power Management version 2 Capabilities: [48] MSI: Enable+ Count=1/1 Maskable- 64bit+ Capabilities: [58] Express Endpoint, MSI 00 Capabilities: [100] Advanced Error Reporting Capabilities: [180] Device Serial Number ff-93-2e-de-00-23-8b-ff Kernel driver in use: ATL1E Kernel modules: atl1e 02:00.0 Ethernet controller: Atheros Communications Inc. AR242x / AR542x Wireless Network Adapter (PCI-Express) (rev 01) Subsystem: Foxconn International, Inc. Device e00d Flags: bus master, fast devsel, latency 0, IRQ 18 Memory at 54100000 (64-bit, non-prefetchable) [size=64K] Capabilities: [40] Power Management version 2 Capabilities: [50] MSI: Enable- Count=1/1 Maskable- 64bit- Capabilities: [60] Express Legacy Endpoint, MSI 00 Capabilities: [90] MSI-X: Enable- Count=1 Masked- Capabilities: [100] Advanced Error Reporting Capabilities: [140] Virtual Channel Kernel driver in use: ath5k Kernel modules: ath5k

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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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• What would you do if you just had this code dumped in your lap?

  - by chickeninabiscuit

  Man, I just had this project given to me - expand on this they say. This is an example of ONE function: <?php //500+ lines of pure wonder. function page_content_vc($content) { global $_DBH, $_TPL, $_SET; $_SET['ignoreTimezone'] = true; lu_CheckUpdateLogin(); if($_SESSION['dash']['VC']['switch'] == 'unmanned' || $_SESSION['dash']['VC']['switch'] == 'touchscreen') { if($content['page_name'] != 'vc') { header('Location: /vc/'); die(); } } if($_GET['l']) { unset($_SESSION['dash']['VC']); if($loc_id = lu_GetFieldValue('ID', 'Location', $_GET['l'])) { if(lu_CheckPermissions('vc', $loc_id)) { $timezone = lu_GetFieldValue('Time Zone', 'Location', $loc_id, 'ID'); if(strlen($timezone) > 0) { $_SESSION['time_zone'] = $timezone; } $_SESSION['dash']['VC']['loc_ID'] = $loc_id; header('Location: /vc/'); die(); } } } if($_SESSION['dash']['VC']['loc_ID']) { $timezone = lu_GetFieldValue('Time Zone', 'Location', $_SESSION['dash']['VC']['loc_ID'], 'ID'); if(strlen($timezone) > 0) { $_SESSION['time_zone'] = $timezone; } $loc_id = $_SESSION['dash']['VC']['loc_ID']; $org_id = lu_GetFieldValue('record_ID', 'Location', $loc_id); $_TPL->assign('loc_id', $loc_id); $location_name = lu_GetFieldValue('Location Name', 'Location', $loc_id); $_TPL->assign('LocationName', $location_name); $customer_name = lu_GetFieldValue('Customer Name', 'Organisation', $org_id); $_TPL->assign('CustomerName', $customer_name); $enable_visitor_snap = lu_GetFieldValue('VisitorSnap', 'Location', $loc_id); $_TPL->assign('EnableVisitorSnap', $enable_visitor_snap); $lacps = explode("\n", lu_GetFieldValue('Location Access Control Point', 'Location', $loc_id)); array_walk($lacps, 'trim_value'); if(count($lacps) > 0) { if(count($lacps) == 1) { $_SESSION['dash']['VC']['lacp'] = $lacps[0]; } else { if($_GET['changeLACP'] && in_array($_GET['changeLACP'], $lacps)) { $_SESSION['dash']['VC']['lacp'] = $_GET['changeLACP']; header('Location: /vc/'); die(); } else if(!in_array($_SESSION['dash']['VC']['lacp'], $lacps)) { $_SESSION['dash']['VC']['lacp'] = $lacps[0]; } $_TPL->assign('LACP_array', $lacps); } $_TPL->assign('current_LACP', $_SESSION['dash']['VC']['lacp']); $_TPL->assign('showContractorSearch', true); /* if($contractorStaff = lu_GetTableRow('ContractorStaff', $org_id, 'record_ID', 'record_Inactive != "checked"')) { foreach($contractorStaff['rows'] as $contractor) { $lacp_rights = lu_OrganiseCustomDataFunctionMultiselect($contractor[lu_GetFieldName('Location Access Rights', 'ContractorStaff')]); if(in_array($_SESSION['dash']['VC']['lacp'], $lacp_rights)) { $_TPL->assign('showContractorSearch', true); } } } */ } $selectedOptions = explode(',', lu_GetFieldValue('Included Fields', 'Location', $_SESSION['dash']['VC']['loc_ID'])); $newOptions = array(); foreach($selectedOptions as $selOption) { $so_array = explode('|', $selOption, 2); if(count($so_array) > 1) { $newOptions[$so_array[0]] = $so_array[1]; } else { $newOptions[$so_array[0]] = "Both"; } } if($newOptions[lu_GetFieldName('Expected Length of Visit', 'Visitor')]) { $alert = false; if($visitors = lu_OrganiseVisitors( lu_GetTableRow('Visitor', 'checked', lu_GetFieldName('Checked In', 'Visitor'), lu_GetFieldName('Location for Visit', 'Visitor').'="'.$_SESSION['dash']['VC']['loc_ID'].'" AND '.lu_GetFieldName('Checked Out', 'Visitor').' != "checked"'), false, true, true)) { foreach($visitors['rows'] as $key => $visitor) { if($visitor['expected'] && $visitor['expected'] + (60*30) < time()) { $alert = true; } } } if($alert == true) { $_TPL->assign('showAlert', 'red'); } else { //$_TPL->assign('showAlert', 'green'); } } $_TPL->assign('switch', $_SESSION['dash']['VC']['switch']); if($_SESSION['dash']['VC']['switch'] == 'touchscreen') { $_TPL->assign('VC_unmanned', true); } if($_GET['check'] == 'in') { if($_SESSION['dash']['VC']['switch'] == 'touchscreen') { lu_CheckInTouchScreen(); } else { lu_CheckIn(); } } else if($_GET['check'] == 'out') { if($_SESSION['dash']['VC']['switch'] == 'touchscreen') { lu_CheckOutTouchScreen(); } else { lu_CheckOut(); } } else if($_GET['switch'] == 'unmanned') { $_SESSION['dash']['VC']['switch'] = 'unmanned'; if($_GET['printing'] == true && (lu_GetFieldValue('Printing', 'Location', $_SESSION['dash']['VC']['loc_ID']) != "No" && lu_GetFieldValue('Printing', 'Location', $_SESSION['dash']['VC']['loc_ID']) != "")) { $_SESSION['dash']['VC']['printing'] = true; } else { $_SESSION['dash']['VC']['printing'] = false; } header('Location: /vc/'); die(); } else if($_GET['switch'] == 'touchscreen') { $_SESSION['dash']['VC']['switch'] = 'touchscreen'; if($_GET['printing'] == true && (lu_GetFieldValue('Printing', 'Location', $_SESSION['dash']['VC']['loc_ID']) != "No" && lu_GetFieldValue('Printing', 'Location', $_SESSION['dash']['VC']['loc_ID']) != "")) { $_SESSION['dash']['VC']['printing'] = true; } else { $_SESSION['dash']['VC']['printing'] = false; } header('Location: /vc/'); die(); } else if($_GET['switch'] == 'manned') { if($_POST['password']) { if(md5($_POST['password']) == $_SESSION['dash']['password']) { unset($_SESSION['dash']['VC']['switch']); //setcookie('email', "", time() - 3600); //setcookie('location', "", time() - 3600); header('Location: /vc/'); die(); } else { $_TPL->assign('switchLoginError', 'Incorrect Password'); } } $_TPL->assign('switchLogin', 'true'); } else if($_GET['m'] == 'visitor') { lu_ModifyVisitorVC(); } else if($_GET['m'] == 'enote') { lu_ModifyEnoteVC(); } else if($_GET['m'] == 'medical') { lu_ModifyMedicalVC(); } else if($_GET['print'] == 'label' && $_GET['v']) { lu_PrintLabelVC(); } else { unset($_SESSION['dash']['VC']['checkin']); unset($_SESSION['dash']['VC']['checkout']); $_TPL->assign('icon', 'GroupCheckin'); if($_SESSION['dash']['VC']['switch'] != 'unmanned' && $_SESSION['dash']['VC']['switch'] != 'touchscreen') { $staff_ids = array(); if($staffs = lu_GetTableRow('Staff', $_SESSION['dash']['VC']['loc_ID'], 'record_ID')) { foreach($staffs['rows'] as $staff) { $staff_ids[] = $staff['ID']; } } if($_GET['view'] == "tomorrow") { $dateStart = date('Y-m-d', mktime(0, 0, 0, date("m") , date("d")+1, date("Y"))); $dateEnd = date('Y-m-d', mktime(0, 0, 0, date("m") , date("d")+1, date("Y"))); } else if($_GET['view'] == "month") { $dateStart = date('Y-m-d', mktime(0, 0, 0, date("m"), date("d"), date("Y"))); $dateEnd = date('Y-m-d', mktime(0, 0, 0, date("m"), date("d")+30, date("Y"))); } else if($_GET['view'] == "week") { $dateStart = date('Y-m-d', mktime(0, 0, 0, date("m"), date("d"), date("Y"))); $dateEnd = date('Y-m-d', mktime(0, 0, 0, date("m"), date("d")+7, date("Y"))); } else { $dateStart = date('Y-m-d'); $dateEnd = date('Y-m-d'); } if(lu_GetFieldValue('Enable Survey', 'Location', $_SESSION['dash']['VC']['loc_ID']) == 'checked' && lu_GetFieldValue('Add Survey', 'Location', $_SESSION['dash']['VC']['loc_ID']) == 'checked') { $_TPL->assign('enableSurvey', true); } //lu_GetFieldName('Checked In', 'Visitor') //!= "checked" //date('d/m/Y'), lu_GetFieldName('Date of Visit', 'Visitor') if($visitors = lu_OrganiseVisitors(lu_GetTableRow('Visitor', $_SESSION['dash']['VC']['loc_ID'], lu_GetFieldName('Location for Visit', 'Visitor'), lu_GetFieldName('Checked In', 'Visitor').' != "checked" AND '.lu_GetFieldName('Checked Out', 'Visitor').' != "checked" AND '.lu_GetFieldName('Date of Visit', 'Visitor').' >= "'.$dateStart.'" AND '.lu_GetFieldName('Date of Visit', 'Visitor').' <= "'.$dateEnd.'"'))) { foreach($visitors['days'] as $day => $visitors_day) { foreach($visitors_day['rows'] as $key => $visitor) { $visitors['days'][$day]['rows'][$key]['visiting'] = lu_GetTableRow('Staff', $visitor['record_ID'], 'ID'); $visitors['days'][$day]['rows'][$key]['visiting']['notify'] = $_DBH->getRow('SELECT * FROM lu_notification WHERE ent_ID = "'.$visitor['record_ID'].'"'); } } //array_dump($visitors); $_TPL->assign('visitors', $visitors); } if($_GET['conGroup']) { if($_GET['action'] == 'add') { $_SESSION['dash']['VC']['conGroup'][$_GET['conGroup']] = $_GET['conGroup']; } else { unset($_SESSION['dash']['VC']['conGroup'][$_GET['conGroup']]); } } if(count($_SESSION['dash']['VC']['conGroup']) > 0) { if($conGroupResult = lu_GetTableRow('ContractorStaff', '1', '1', ' ID IN ('.implode(',', $_SESSION['dash']['VC']['conGroup']).')')) { if($_POST['_submit'] == 'Check-In Group >>') { $form = lu_GetForm('VisitorStandard'); $standarddata = array(); foreach($form['items'] as $key=>$item) { $standarddata[$key] = $_POST[lu_GetFieldName($item['name'], 'Visitor')]; } foreach($conGroupResult['rows'] as $conStaff) { $data = $standarddata; foreach($form['items'] as $key=>$item) { if($key != 'ID' && $key != 'record_ID' && $conStaff[lu_GetFieldName(lu_GetNameField($key, 'Visitor'), 'ContractorStaff')]) { $data[$key] = $conStaff[lu_GetFieldName(lu_GetNameField($key, 'Visitor'), 'ContractorStaff')]; } } $data['record_ID'] = $data[lu_GetFieldName('Visiting', 'Visitor')]; $data[lu_GetFieldName('Date of Visit', 'Visitor')] = date('Y-m-d'); $data[lu_GetFieldName('Time of Visit', 'Visitor')] = date('H:i'); $data[lu_GetFieldName('Checked In', 'Visitor')] = 'checked'; $data[lu_GetFieldName('Location for Visit', 'Visitor')] = $_SESSION['dash']['VC']['loc_ID']; $data[lu_GetFieldName('ConStaff ID', 'Visitor')] = $conStaff['ID']; $data[lu_GetFieldName('From', 'Visitor')] = lu_GetFieldValue('Legal Name', 'Contractor', $conStaff[lu_GetFieldName('Contractor', 'ContractorStaff')]); $id = lu_UpdateData($form, $data); lu_VisitorCheckIn($id); //array_dump($data); //array_dump($id); } unset($_SESSION['dash']['VC']['conGroup']); header('Location: /vc/'); die(); } if(count($conGroupResult['rows'])) { foreach($conGroupResult['rows'] as $key => $cstaff) { $conGroupResult['rows'][$key]['contractor'] = lu_GetTableRow('Contractor', $cstaff[lu_GetFieldName('Contractor', 'ContractorStaff')], 'ID'); } $_TPL->assign('conGroupResult', $conGroupResult); } $conGroupForm = lu_GetForm('VisitorConGroup'); $conGroupForm = lu_OrganiseVisitorForm($conGroupForm, $_SESSION['dash']['VC']['loc_ID'], 'Contractor'); $secure_options_array = lu_GetSecureOptions($org_id); if($secure_options_array[$_SESSION['dash']['VC']['loc_ID']]) { $conGroupForm['items'][lu_GetFieldName('Secure Area', 'Visitor')]['options']['values'] = $secure_options_array[$_SESSION['dash']['VC']['loc_ID']]; $conGroupForm['items'][lu_GetFieldName('Secure Area', 'Visitor')]['name'] = 'Secure Area'; } else { unset($conGroupForm['items'][lu_GetFieldName('Secure Area', 'Visitor')]); } if($secure_options_array) { $form['items'][lu_GetFieldName('Secure Area', 'Visitor')]['options']['values'] = $secure_options_array; $form['items'][lu_GetFieldName('Secure Area', 'Visitor')]['name'] = 'Secure Area'; } else { unset($form['items'][lu_GetFieldName('Secure Area', 'Visitor')]); } $_TPL->assign('conGroupForm', $conGroupForm); $_TPL->assign('hideFormCancel', true); } } if($_GET['searchVisitors']) { $_TPL->assign('searchVisitorsQuery', $_GET['searchVisitors']); $where = ''; if($_GET['searchVisitorsIn'] == 'Yes') { $where .= ' AND '.lu_GetFieldName('Checked In', 'Visitor').' = "checked"'; $_TPL->assign('searchVisitorsIn', 'Yes'); } else { $where .= ' AND '.lu_GetFieldName('Checked In', 'Visitor').' != "checked"'; $_TPL->assign('searchVisitorsIn', 'No'); } if($_GET['searchVisitorsOut'] == 'Yes') { $where = ''; $where .= ' AND '.lu_GetFieldName('Checked Out', 'Visitor').' = "checked"'; $_TPL->assign('searchVisitorsOut', 'Yes'); } else { $where .= ' AND '.lu_GetFieldName('Checked Out', 'Visitor').' != "checked"'; $_TPL->assign('searchVisitorsOut', 'No'); } if($searchVisitors = lu_OrganiseVisitors(lu_GetTableRow('Visitor', $_GET['searchVisitors'], '#search#', lu_GetFieldName('Location for Visit', 'Visitor').'="'.$_SESSION['dash']['VC']['loc_ID'].'"'.$where))) { foreach($searchVisitors['rows'] as $key => $visitor) { $searchVisitors['rows'][$key]['visiting'] = lu_GetTableRow('Staff', $visitor['record_ID'], 'ID'); } $_TPL->assign('searchVisitors', $searchVisitors); } else { $_TPL->assign('searchVisitorsNotFound', true); } } else if($_GET['searchStaff']) { if($_POST['staff_id']) { if(lu_CheckPermissions('staff', $_POST['staff_id'])) { $_DBH->query('UPDATE '.lu_GetTableName('Staff').' SET '.lu_GetFieldName('Current Location', 'Staff').' = "'.$_POST['current_location'].'" WHERE ID="'.$_POST['staff_id'].'"'); } } $locations = lu_GetTableRow('Location', $org_id, 'record_ID'); if(count($locations['rows']) > 1) { $_TPL->assign('staffLocations', $locations); } $loc_ids = array(); foreach($locations['rows'] as $location) { $loc_ids[] = $location['ID']; } // array_dump($locations); // array_dump($_POST); $_TPL->assign('searchStaffQuery', $_GET['searchStaff']); $where = ' AND record_Inactive != "checked"'; if($_GET['searchStaffIn'] == 'Yes' && $_GET['searchStaffOut'] != 'Yes') { $where .= ' AND ('.lu_GetFieldName('Staff Status', 'Staff').' = "" OR '.lu_GetFieldName('Staff Status', 'Staff').' = "On-Site")'. $_TPL->assign('searchStaffIn', 'Yes'); $_TPL->assign('searchStaffOut', 'No'); } else if($_GET['searchStaffOut'] == 'Yes' && $_GET['searchStaffIn'] != 'Yes') { $where .= ' AND ('.lu_GetFieldName('Staff Status', 'Staff').' != "" AND '.lu_GetFieldName('Staff Status', 'Staff').' != "On-Site")'. $_TPL->assign('searchStaffOut', 'Yes'); $_TPL->assign('searchStaffIn', 'No'); } else { $_TPL->assign('searchStaffOut', 'Yes'); $_TPL->assign('searchStaffIn', 'Yes'); } if($searchStaffs = lu_GetTableRow('Staff', $_GET['searchStaff'], '#search#', 'record_ID IN ('.implode(',', $loc_ids).')'.$where, lu_GetFieldName('First Name', 'Staff').','.lu_GetFieldName('Surname', 'Staff'))) { $_TPL->assign('searchStaffs', $searchStaffs); } else { $_TPL->assign('searchStaffNotFound', true); } } else if($_GET['searchContractor']) { $_TPL->assign('searchContractorQuery', $_GET['searchContractor']); //$where = ' AND '.lu_GetTableName('ContractorStaff').'.record_Inactive != "checked"'; $where = ' '; if($_GET['searchContractorIn'] == 'Yes' && $_GET['searchContractorOut'] != 'Yes') { $where .= ' AND ('.lu_GetFieldName('Onsite Status', 'ContractorStaff').' = "Onsite")'; $_TPL->assign('searchContractorIn', 'Yes'); $_TPL->assign('searchContractorOut', 'No'); } else if($_GET['searchContractorOut'] == 'Yes' && $_GET['searchContractorIn'] != 'Yes') { $where .= ' AND ('.lu_GetFieldName('Onsite Status', 'ContractorStaff').' != "Onsite")'. $_TPL->assign('searchContractorOut', 'Yes'); $_TPL->assign('searchContractorIn', 'No'); } else { $_TPL->assign('searchContractorOut', 'Yes'); $_TPL->assign('searchContractorIn', 'Yes'); } $join = 'LEFT JOIN '.lu_GetTableName('Contractor').' ON '.lu_GetTableName('Contractor').'.ID = '.lu_GetTableName('ContractorStaff').'.'.lu_GetFieldName('Contractor', 'ContractorStaff'); $extrasearch = array ( lu_GetTableName('Contractor').'.'.lu_GetFieldName('Legal Name', 'Contractor') ); if($searchContractorResult = lu_GetTableRow('ContractorStaff', $_GET['searchContractor'], '#search#', lu_GetTableName('ContractorStaff').'.record_ID = "'.$org_id.'" '.$where, lu_GetFieldName('First Name', 'ContractorStaff').','.lu_GetFieldName('Surname', 'ContractorStaff'), $join, $extrasearch)) { /* foreach($searchContractorResult['rows'] as $key=>$contractor) { $lacp_rights = lu_OrganiseCustomDataFunctionMultiselect($contractor[lu_GetFieldName('Location Access Rights', 'ContractorStaff')]); if(!in_array($_SESSION['dash']['VC']['lacp'], $lacp_rights)) { unset($searchContractorResult['rows'][$key]); } } */ if(count($searchContractorResult['rows'])) { foreach($searchContractorResult['rows'] as $key => $cstaff) { /* if($cstaff[lu_GetFieldName('Onsite_Status', 'Contractor')] == 'Onsite')) { if($visitor['rows'][0][lu_GetFieldName('ConStaff ID', 'Visitor')]) { $_DBH->query('UPDATE '.lu_GetTableName('ContractorStaff').' SET '.lu_GetFieldName('Onsite Status', 'ContractorStaff').' = "" WHERE ID="'.$visitor['rows'][0][lu_GetFieldName('ConStaff ID', 'Visitor')].'"'); } } */ if($cstaff[lu_GetFieldName('SACN Expiry Date', 'ContractorStaff')] != '0000-00-00') { if(strtotime($cstaff[lu_GetFieldName('SACN Expiry Date', 'ContractorStaff')]) < time()) { $searchContractorResult['rows'][$key]['sacn_expiry'] = true; } else { $searchContractorResult['rows'][$key]['sacn_expiry'] = false; } } else { $searchContractorResult['rows'][$key]['sacn_expiry'] = false; } if($cstaff[lu_GetFieldName('Induction Valid Until', 'ContractorStaff')] != '0000-00-00') { if(strtotime($cstaff[lu_GetFieldName('Induction Valid Until', 'ContractorStaff')]) < time()) { $searchContractorResult['rows'][$key]['induction_expiry'] = true; } else { $searchContractorResult['rows'][$key]['induction_expiry'] = false; } } else { $searchContractorResult['rows'][$key]['induction_expiry'] = false; } $searchContractorResult['rows'][$key]['contractor'] = lu_GetTableRow('Contractor', $cstaff[lu_GetFieldName('Contractor', 'ContractorStaff')], 'ID'); } $_TPL->assign('searchContractorResult', $searchContractorResult); } else { $_TPL->assign('searchContractorNotFound', true); } } else { $_TPL->assign('searchContractorNotFound', true); } } $occupancy = array(); $occupancy['staffNumber'] = $_DBH->getOne('SELECT count(*) FROM '.lu_GetTableName('Staff').' WHERE record_ID = "'.$_SESSION['dash']['VC']['loc_ID'].'" AND record_Inactive != "checked" AND '.lu_GetFieldName('Ignore Counts', 'Staff').' != "checked"'); $occupancy['staffNumberOnsite']= $_DBH->getOne( 'SELECT count(*) FROM '.lu_GetTableName('Staff').' WHERE ( (record_ID = "'.$_SESSION['dash']['VC']['loc_ID'].'" AND ('.lu_GetFieldName('Staff Status', 'Staff').' = "" OR '.lu_GetFieldName('Staff Status', 'Staff').' = "On-Site")) OR '.lu_GetFieldName('Current Location', 'Staff').' = "'.$_SESSION['dash']['VC']['loc_ID'].'") AND record_Inactive != "checked" AND '.lu_GetFieldName('Ignore Counts', 'Staff').' != "checked"'); $occupancy['visitorsOnsite'] = $_DBH->getOne('SELECT count(*) FROM '.lu_GetTableName('Visitor').' WHERE '.lu_GetFieldName('Location for Visit', 'Visitor').' = "'.$_SESSION['dash']['VC']['loc_ID'].'" AND '.lu_GetFieldName('Checked In', 'Visitor').' = "checked" AND '.lu_GetFieldName('Checked Out', 'Visitor').' != "checked"'); $_TPL->assign('occupancy', $occupancy); if($enotes = lu_GetTableRow('Enote', $org_id, 'record_ID', lu_GetFieldName('Note Emailed', 'Enote').' = "0000-00-00" AND '.lu_GetFieldName('Note Passed On', 'Enote').' != "Yes"')) { $_TPL->assign('EnoteNotice', true); } if($medical = lu_GetTableRow('MedicalRoom', $_SESSION['dash']['VC']['loc_ID'], 'record_ID', 'record_Inactive != "Yes"')) { $_TPL->assign('MedicalNotice', true); } if(lu_GetFieldValue('Printing', 'Location', $_SESSION['dash']['VC']['loc_ID']) != "No" && lu_GetFieldValue('Printing', 'Location', $_SESSION['dash']['VC']['loc_ID']) != "") { $_TPL->assign('UnmannedPrinting', true); } } else { if($_SESSION['dash']['VC']['printing'] == true) { $_TPL->assign('UnmannedPrinting', true); } } // enable if contractor check-in buttons should be enabled if(lu_GetFieldValue('Enable Contractor Check In', 'Location', $_SESSION['dash']['VC']['loc_ID']) == "checked") { $_TPL->assign('ContractorCheckin', true); } } if($_SESSION['dash']['entity_id'] && $_GET['fixupCon'] == 'true') { $conStaffs = lu_GetTableRow('ContractorStaff', $_SESSION['dash']['ModifyConStaffs']['org_ID'], 'record_ID', '', lu_GetFieldName('First Name', 'ContractorStaff').','.lu_GetFieldName('Surname', 'ContractorStaff')); foreach($conStaffs['rows'] as $key => $cstaff) { if($cstaff[lu_GetFieldName('Site Access Card Number', 'ContractorStaff')] && $cstaff[lu_GetFieldName('Site Access Card Type', 'ContractorStaff')]) { echo $cstaff['ID'].' '; $_DBH->query('UPDATE '.lu_GetTableName('Visitor').' SET '.lu_GetFieldName('Site Access Card Number', 'Visitor').' = "'.$cstaff[lu_GetFieldName('Site Access Card Number', 'ContractorStaff')].'", '.lu_GetFieldName('Site Access Card Type', 'Visitor').' = "'.$cstaff[lu_GetFieldName('Site Access Card Type', 'ContractorStaff')].'" WHERE '.lu_GetFieldName('ConStaff ID', 'Visitor').'="'.$cstaff['ID'].'"'); } } } } else { if($_SESSION['dash']['staffs']) { foreach($_SESSION['dash']['staffs']['rows'] as $staff) { if($staff[lu_GetFieldName('Reception Manager', 'Staff')] == 'checked') { $loc_id = $staff['record_ID']; unset($_SESSION['dash']['VC']); if($loc_id = lu_GetFieldValue('ID', 'Location', $loc_id)) { $_SESSION['dash']['VC']['loc_ID'] = $loc_id; header('Location: /vc/'); die(); } } } } $_TPL->assign('mode', 'public'); } $content['page_content'] = $_TPL->fetch('modules/vc.htm'); return $content; } ?> die();die();die();die();die(); This question will probably be closed - i just need some support from my coding brothers and sisters. *SOB*

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• How to stop UITableView moveRowAtIndexPath from leaving blank rows upon reordering

  - by coneybeare

  I am having an issue where in reordering my UITableViewCells, the tableView is not scrolling with the cell. Only a blank row appears and any subsequent scrolling gets an Array out of bounds error without any of my code in the Stack Trace. Here is a quick video of the problem. Here is the relevant code: - (BOOL)tableView:(UITableView *)tableView canEditRowAtIndexPath:(NSIndexPath *)indexPath { return indexPath.section == 1; } - (BOOL)tableView:(UITableView *)tableView canMoveRowAtIndexPath:(NSIndexPath *)indexPath { BOOL ret = indexPath.section == 1 && indexPath.row < self.count; DebugLog(@"canMoveRowAtIndexPath: %d:%d %@", indexPath.section, indexPath.row, (ret ? @"YES" : @"NO")); return ret; } - (void)delayedUpdateCellBackgroundPositionsForTableView:(UITableView *)tableView { [self performSelectorOnMainThread:@selector(updateCellBackgroundPositionsForTableView:) withObject:tableView waitUntilDone:NO]; } - (void)tableView:(UITableView *)tableView moveRowAtIndexPath:(NSIndexPath *)fromIndexPath toIndexPath:(NSIndexPath *)toIndexPath { if (fromIndexPath.row == toIndexPath.row) return; DebugLog(@"Moved audio from %d:%d to %d:%d", fromIndexPath.section, fromIndexPath.row, toIndexPath.section, toIndexPath.row); NSMutableArray *audio = [self.items objectAtIndex:fromIndexPath.section]; [audio exchangeObjectAtIndex:fromIndexPath.row withObjectAtIndex:toIndexPath.row]; [self performSelector:@selector(delayedUpdateCellBackgroundPositionsForTableView:) withObject:tableView afterDelay:kDefaultAnimationDuration/3]; } And here is the generated Stack Trace of the crash: Exception Type: EXC_BREAKPOINT (SIGTRAP) Exception Codes: 0x0000000000000002, 0x0000000000000000 Crashed Thread: 0 Dispatch queue: com.apple.main-thread Application Specific Information: iPhone Simulator 3.2 (193.3), iPhone OS 3.0 (7A341) *** Terminating app due to uncaught exception 'NSRangeException', reason: '*** -[NSCFArray removeObjectsInRange:]: index (6) beyond bounds (6)' Thread 0 Crashed: Dispatch queue: com.apple.main-thread 0 CoreFoundation 0x302ac924 ___TERMINATING_DUE_TO_UNCAUGHT_EXCEPTION___ + 4 1 libobjc.A.dylib 0x93cb2509 objc_exception_throw + 56 2 CoreFoundation 0x3028e5fb +[NSException raise:format:arguments:] + 155 3 CoreFoundation 0x3028e55a +[NSException raise:format:] + 58 4 Foundation 0x305684e9 _NSArrayRaiseBoundException + 121 5 Foundation 0x30553a6e -[NSCFArray removeObjectsInRange:] + 142 6 UIKit 0x30950105 -[UITableView(_UITableViewPrivate) _updateVisibleCellsNow] + 862 7 UIKit 0x30947715 -[UITableView layoutSubviews] + 250 8 QuartzCore 0x0090bd94 -[CALayer layoutSublayers] + 78 9 QuartzCore 0x0090bb55 CALayerLayoutIfNeeded + 229 10 QuartzCore 0x0090b3ae CA::Context::commit_transaction(CA::Transaction*) + 302 11 QuartzCore 0x0090b022 CA::Transaction::commit() + 292 12 QuartzCore 0x009132e0 CA::Transaction::observer_callback(__CFRunLoopObserver*, unsigned long, void*) + 84 13 CoreFoundation 0x30245c32 __CFRunLoopDoObservers + 594 14 CoreFoundation 0x3024503f CFRunLoopRunSpecific + 2575 15 CoreFoundation 0x30244628 CFRunLoopRunInMode + 88 16 GraphicsServices 0x32044c31 GSEventRunModal + 217 17 GraphicsServices 0x32044cf6 GSEventRun + 115 18 UIKit 0x309021ee UIApplicationMain + 1157 19 XXXXXXXX 0x0000278a main + 104 (main.m:12) 20 XXXXXXXX 0x000026f6 start + 54 NOte that the array out of bounds length is not the length of my elements (I have 9), but always something smaller. I have been trying to solve this for many hours days without avail… any ideas? UPDATE: More code as requested In my delegate: - (UITableViewCellEditingStyle)tableView:(UITableView *)tableView editingStyleForRowAtIndexPath:(NSIndexPath *)indexPath { return UITableViewCellEditingStyleNone; } - (NSIndexPath *)tableView:(UITableView *)tableView targetIndexPathForMoveFromRowAtIndexPath:(NSIndexPath *)sourceIndexPath toProposedIndexPath:(NSIndexPath *)proposedDestinationIndexPath { int count = [(UAPlaylistEditDataSource *)self.dataSource count]; if (proposedDestinationIndexPath.section == 0) { return [NSIndexPath indexPathForRow:0 inSection:sourceIndexPath.section]; }else if (proposedDestinationIndexPath.row >= count) { return [NSIndexPath indexPathForRow:count-1 inSection:sourceIndexPath.section]; } return proposedDestinationIndexPath; } …thats about it. I am using the three20 framework and I have not had any issues with reordering till now. The problem is also not in the updateCellBackgroundPositionsForTableView: method as it still crashes when this is commented out.

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• Fun with Aggregates

  - by Paul White

  There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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• Page expired issue with back button and wicket SortableDataProvider and DataTable

  - by David

  Hi, I've got an issue with SortableDataProvider and DataTable in wicket. I've defined my DataTable as such: IColumn<Column>[] columns = new IColumn[9]; //column values are mapped to the private attributes listed in ColumnImpl.java columns[0] = new PropertyColumn(new Model("#"), "columnPosition", "columnPosition"); columns[1] = new PropertyColumn(new Model("Description"), "description"); columns[2] = new PropertyColumn(new Model("Type"), "dataType", "dataType"); Adding it to the table: DataTable<Column> dataTable = new DataTable<Column>("columnsTable", columns, provider, maxRowsPerPage) { @Override protected Item<Column> newRowItem(String id, int index, IModel<Column> model) { return new OddEvenItem<Column>(id, index, model); } }; My data provider: public class ColumnSortableDataProvider extends SortableDataProvider<Column> { private static final long serialVersionUID = 1L; private List list = null; public ColumnSortableDataProvider(Table table, String sortProperty) { this.list = Arrays.asList(table.getColumns().toArray(new Column[0])); setSort(sortProperty, true); } public ColumnSortableDataProvider(List list, String sortProperty) { this.list = list; setSort(sortProperty, true); } @Override public Iterator iterator(int first, int count) { /* first - first row of data count - minimum number of elements to retrieve So this method returns an iterator capable of iterating over {first, first+count} items */ Iterator iterator = null; try { if(getSort() != null) { Collections.sort(list, new Comparator() { private static final long serialVersionUID = 1L; @Override public int compare(Column c1, Column c2) { int result=1; PropertyModel<Comparable> model1= new PropertyModel<Comparable>(c1, getSort().getProperty()); PropertyModel<Comparable> model2= new PropertyModel<Comparable>(c2, getSort().getProperty()); if(model1.getObject() == null && model2.getObject() == null) result = 0; else if(model1.getObject() == null) result = 1; else if(model2.getObject() == null) result = -1; else result = ((Comparable)model1.getObject()).compareTo(model2.getObject()); result = getSort().isAscending() ? result : -result; return result; } }); } if (list.size() (first+count)) iterator = list.subList(first, first+count).iterator(); else iterator = list.iterator(); } catch (Exception e) { e.printStackTrace(); } return iterator; } The problem is the following: - I click a column header to sort by that column. - I navigate to a different page - I click Back (or Forward if I do the opposite scenario) - Page has expired. It'd be nice to generate the page using PageParameters but I somehow need to intercept the sort event to do so. Any pointers would be greatly appreciated. Thanks a ton!! David

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• Coupling/Cohesion

  - by user559142

  Hi All, Whilst there are many good examples on this forum that contain examples of coupling and cohesion, I am struggling to apply it to my code fully. I can identify parts in my code that may need changing. Would any Java experts be able to take a look at my code and explain to me what aspects are good and bad. I don't mind changing it myself at all. It's just that many people seem to disagree with each other and I'm finding it hard to actually understand what principles to follow... package familytree; /** * * @author David */ public class Main { /** * @param args the command line arguments */ public static void main(String[] args) { // TODO code application logic here KeyboardInput in = new KeyboardInput(); FamilyTree familyTree = new FamilyTree(in, System.out); familyTree.start(); } } package familytree; import java.io.PrintStream; /** * * @author David */ public class FamilyTree { /** * @param args the command line arguments */ private static final int DISPLAY_FAMILY_MEMBERS = 1; private static final int ADD_FAMILY_MEMBER = 2; private static final int REMOVE_FAMILY_MEMBER = 3; private static final int EDIT_FAMILY_MEMBER = 4; private static final int SAVE_FAMILY_TREE = 5; private static final int LOAD_FAMILY_TREE = 6; private static final int DISPLAY_ANCESTORS = 7; private static final int DISPLAY_DESCENDANTS = 8; private static final int QUIT = 9; private KeyboardInput in; private Family family; private PrintStream out; public FamilyTree(KeyboardInput in, PrintStream out) { this.in = in; this.out = out; family = new Family(); } public void start() { out.println("\nWelcome to the Family Tree Builder"); //enterUserDetails(); initialise(); while (true) { displayFamilyTreeMenu(); out.print("\nEnter Choice: "); int option = in.readInteger(); if (option > 0 && option <= 8) { if (quit(option)) { break; } executeOption(option); } else { out.println("Invalid Choice!"); } } } //good private void displayFamilyTreeMenu() { out.println("\nFamily Tree Menu"); out.println(DISPLAY_FAMILY_MEMBERS + ". Display Family Members"); out.println(ADD_FAMILY_MEMBER + ". Add Family Member"); out.println(REMOVE_FAMILY_MEMBER + ". Remove Family Member"); out.println(EDIT_FAMILY_MEMBER + ". Edit Family Member"); out.println(SAVE_FAMILY_TREE + ". Save Family Tree"); out.println(LOAD_FAMILY_TREE + ". Load Family Tree"); out.println(DISPLAY_ANCESTORS + ". Display Ancestors"); out.println(DISPLAY_DESCENDANTS + ". Display Descendants"); out.println(QUIT + ". Quit"); } //good private boolean quit(int opt) { return (opt == QUIT) ? true : false; } //good private void executeOption(int choice) { switch (choice) { case DISPLAY_FAMILY_MEMBERS: displayFamilyMembers(); break; case ADD_FAMILY_MEMBER: addFamilyMember(); break; case REMOVE_FAMILY_MEMBER: break; case EDIT_FAMILY_MEMBER: break; case SAVE_FAMILY_TREE: break; case LOAD_FAMILY_TREE: break; case DISPLAY_ANCESTORS: displayAncestors(); break; case DISPLAY_DESCENDANTS: displayDescendants(); break; default: out.println("Not a valid option! Try again."); break; } } //for selecting family member for editing adding nodes etc private void displayFamilyMembers() { out.println("\nDisplay Family Members"); int count = 0; for (FamilyMember member : family.getFamilyMembers()) { out.println(); if (count + 1 < 10) { out.println((count + 1) + ". " + member.getFirstName() + " " + member.getLastName()); out.println(" " + member.getDob()); out.println(" Generation: " + member.getGeneration()); } else { out.println((count + 1) + ". " + member.getFirstName() + " " + member.getLastName()); out.println(" " + member.getDob()); out.println(" Generation: " + member.getGeneration()); } count++; } } private int selectRelative() { out.println("\nSelect Relative"); out.println("1. Add Parents"); out.println("2. Add Child"); out.println("3. Add Partner"); out.println("4. Add Sibling"); out.print("\nEnter Choice: "); int choice = in.readInteger(); if (choice > 0 && choice < 5) { return choice; } return (-1); } private void addFamilyMember() { int memberIndex = selectMember(); if (memberIndex >= 0) { FamilyMember member = family.getFamilyMember(memberIndex); int relative = selectRelative(); if (relative > 0) { out.println("\nAdd Member"); //if choice is valid switch (relative) { case 1: //adding parents if (member.getFather() == null) { FamilyMember mum, dad; out.println("Enter Mothers Details"); mum = addMember(relative, "Female"); out.println("\nEnter Fathers Details"); dad = addMember(relative, "Male"); member.linkParent(mum); member.linkParent(dad); mum.linkPartner(dad); mum.setGeneration(member.getGeneration() - 1); dad.setGeneration(member.getGeneration() - 1); sortGenerations(); } else { out.println(member.getFirstName() + " " + member.getLastName() + " already has parents."); } break; case 2: //adding child if (member.getPartner() == null) { FamilyMember partner; if (member.getGender().equals("Male")) { out.println("Enter Mothers Details"); partner = addMember(1, "Female"); } else { out.println("Enter Fathers Details"); partner = addMember(1, "Male"); } //create partner member.linkPartner(partner); partner.setGeneration(member.getGeneration()); out.println(); } out.println("Enter Childs Details"); FamilyMember child = addMember(relative, ""); child.linkParent(member); child.linkParent(member.getPartner()); child.setGeneration(member.getGeneration() + 1); sortGenerations(); break; case 3: //adding partner if (member.getPartner() == null) { out.println("Enter Partners Details"); FamilyMember partner = addMember(relative, ""); member.linkPartner(partner); partner.setGeneration(member.getGeneration()); } else { out.println(member.getFirstName() + " " + member.getLastName() + " already has a partner."); } break; case 4: //adding sibling FamilyMember mum, dad; if (member.getFather() == null) { out.println("Enter Mothers Details"); mum = addMember(1, "Female"); out.println("\nEnter Fathers Details"); dad = addMember(1, "Male"); member.linkParent(mum); member.linkParent(dad); mum.linkPartner(dad); mum.setGeneration(member.getGeneration() - 1); dad.setGeneration(member.getGeneration() - 1); sortGenerations(); out.println("\nEnter Siblings Details"); } else { out.println("Enter Siblings Details"); } FamilyMember sibling = addMember(relative, ""); //create mum and dad mum = member.getMother(); dad = member.getFather(); sibling.linkParent(mum); sibling.linkParent(dad); sibling.setGeneration(member.getGeneration()); break; } } else { out.println("Invalid Option!"); } } else { out.println("Invalid Option!"); } } private int selectMember() { displayFamilyMembers(); out.print("\nSelect Member: "); int choice = in.readInteger(); if (choice > 0 && choice <= family.getFamilyMembers().size()) { return (choice - 1); } return -1; } private FamilyMember addMember(int option, String gender) { out.print("Enter First Name: "); String fName = formatString(in.readString().trim()); out.print("Enter Last Name: "); String lName = formatString(in.readString().trim()); if (option != 1) { //if not adding parents out.println("Select Gender"); out.println("1. Male"); out.println("2. Female"); out.print("Enter Choice: "); int gOpt = in.readInteger(); if (gOpt == 1) { gender = "Male"; } else if (gOpt == 2) { gender = "Female"; } else { out.println("Invalid Choice"); return null; } } String dob = enterDateOfBirth(); lName = formatString(lName); FamilyMember f = family.getFamilyMember(family.addMember(fName, lName, gender, dob)); f.setIndex(family.getFamilyMembers().size() - 1); return (f); } private String formatString(String s){ String firstLetter = s.substring(0, 1); String remainingLetters = s.substring(1, s.length()); s = firstLetter.toUpperCase() + remainingLetters.toLowerCase(); return s; } private String enterDateOfBirth(){ out.print("Enter Year Of Birth (0 - 2011): "); String y = in.readString(); out.print("Enter Month Of Birth (1-12): "); String m = in.readString(); if (Integer.parseInt(m) < 10) { m = "0" + m; } m += "-"; out.print("Enter Date of Birth (1-31): "); String d = in.readString(); if (Integer.parseInt(d) < 10) { d = "0" + d; } d += "-"; String dob = d + m + y; while(!DateValidator.isValid(dob)){ out.println("Invalid Date. Try Again:"); dob = enterDateOfBirth(); } return (dob); } private void displayAncestors() { out.print("\nDisplay Ancestors For Which Member: "); int choice = selectMember(); if (choice >= 0) { FamilyMember node = family.getFamilyMember(choice ); FamilyMember ms = findRootNode(node, 0, 2, -1); FamilyMember fs = findRootNode(node, 1, 2, -1); out.println("\nPrint Ancestors"); out.println("\nMothers Side"); printDescendants(ms, node, ms.getGeneration()); out.println("\nFathers Side"); printDescendants(fs, node, fs.getGeneration()); } else { out.println("Invalid Option!"); } } private void displayDescendants() { out.print("\nDisplay Descendants For Which Member: "); int choice = selectMember(); if (choice >= 0) { FamilyMember node = family.getFamilyMember(choice); out.println("\nPrint Descendants"); printDescendants(node, null, 0); } else { out.println("Invalid Option!"); } } private FamilyMember findRootNode(FamilyMember node, int parent, int numGenerations, int count) { FamilyMember root; count++; if (node.hasParents() && count < numGenerations) { if (parent == 0) { node = node.getMother(); root = findRootNode(node, 1, numGenerations, count); } else { node = node.getFather(); root = findRootNode(node, 1, numGenerations, count); } return root; } return node; } private int findHighestLeafGeneration(FamilyMember node) { int gen = node.getGeneration(); for (int i = 0; i < node.getChildren().size(); i++) { int highestChild = findHighestLeafGeneration(node.getChild(i)); if (highestChild > gen) { gen = highestChild; } } return gen; } private void printDescendants(FamilyMember root, FamilyMember node, int gen) { out.print((root.getGeneration() + 1) + " " + root.getFullName()); out.print(" [" + root.getDob() + "] "); if (root.getPartner() != null) { out.print("+Partner: " + root.getPartner().getFullName() + " [" + root.getPartner().getDob() + "] "); } if (root == node) { out.print("*"); } out.println(); if (!root.getChildren().isEmpty() && root != node) { for (int i = 0; i < root.getChildren().size(); i++) { for (int j = 0; j < root.getChild(i).getGeneration() - gen; j++) { out.print(" "); } printDescendants(root.getChild(i), node, gen); } } else { return; } } //retrieve highest generation public int getRootGeneration(){ int min = family.getFamilyMember(0).getGeneration(); for(int i = 0; i < family.getFamilyMembers().size(); i++){ min = Math.min(min, family.getFamilyMember(i).getGeneration()); } return Math.abs(min); } public void sortGenerations(){ int amount = getRootGeneration(); for (FamilyMember member : family.getFamilyMembers()) { member.setGeneration(member.getGeneration() + amount); } } //test method - temporary private void initialise() { family.addMember("Bilbo", "Baggins", "Male", "23-06-1920"); } } package familytree; import java.util.ArrayList; import java.util.Date; /** * * @author David */ public class Family { //family members private ArrayList<FamilyMember> family; //create Family public Family() { family = new ArrayList<FamilyMember>(); } //add member to the family public int addMember(String f, String l, String g, String d) { family.add(new FamilyMember(f, l, g, d)); return family.size()-1; } //remove member from family public void removeMember(int index) { family.remove(index); } public FamilyMember getFamilyMember(int index) { return family.get(index); } //return family public ArrayList <FamilyMember> getFamilyMembers() { return family; } public void changeFirstName(int index, String f) { family.get(index).setFirstName(f);//change to setfirstname and others } public void changeLastName(int index, String l) { family.get(index).setLastName(l); } public void changeAge(int index, int a) { family.get(index).setAge(a); } public void changeDOB() { //implement } } package familytree; import java.util.ArrayList; import java.util.Collections; /** * * @author David */ public class FamilyMember extends Person { private FamilyMember mother; private FamilyMember father; private FamilyMember partner; private ArrayList<FamilyMember> children; private int generation; private int index; //initialise family member public FamilyMember(String f, String l, String g, String d) { super(f, l, g, d); mother = null; father = null; partner = null; children = new ArrayList<FamilyMember>(); generation = 0; index = -1; } public void linkParent(FamilyMember parent) { if (parent.getGender().equals("Female")) { this.setMother(parent); } else { this.setFather(parent); } parent.addChild(this); } public void linkPartner(FamilyMember partner) { partner.setPartner(this); this.setPartner(partner); } public boolean hasParents() { if (this.getMother() == null && this.getFather() == null) { return false; } return true; } public FamilyMember getMother() { return mother; } public FamilyMember getFather() { return father; } public FamilyMember getPartner() { return partner; } public FamilyMember getChild(int index) { return children.get(index); } public int getGeneration() { return generation; } public int getIndex() { return index; } public ArrayList<FamilyMember> getChildren() { return children; } public void setMother(FamilyMember f) { mother = f; } public void setFather(FamilyMember f) { father = f; } public void setPartner(FamilyMember f) { partner = f; } public void addChild(FamilyMember f) { children.add(f); //add child if(children.size() > 1){ //sort in ascending order Collections.sort(children, new DateComparator()); } } public void addChildAt(FamilyMember f, int index) { children.set(index, f); } public void setGeneration(int g) { generation = g; } public void setIndex(int i){ index = i; } } package familytree; /** * * @author David */ public class Person{ private String fName; private String lName; private String gender; private int age; private String dob; public Person(String fName, String lName, String gender, String dob){ this.fName = fName; this.lName = lName; this.gender = gender; this.dob = dob; } public String getFullName(){ return (this.fName + " " + this.lName); } public String getFirstName(){ return (fName); } public String getLastName(){ return (lName); } public String getGender(){ return (gender); } public String getDob(){ return dob; } public int getAge(){ return age; } public void setFirstName(String fName){ this.fName = fName; } public void setLastName(String lName){ this.lName = lName; } public void setGender(String gender){ this.gender = gender; } public void setAge(int age){ this.age = age; } }

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• Heroku Push Problem part 2 - Postgresql - PGError Relations does not exist - Ruby on Rails

  - by bgadoci

  Ok so got through my last problem with the difference between Postgresql and SQLite and seems like Heroku is telling me I have another one. I am new to ruby and rails so a lot of this stuff I can't decipher at first. Looking for a little direction here. The error message and PostsController Index are below. I checked my routes.rb file and all seems well there but I could be missing something. I will post if you need. Processing PostsController#index (for 99.7.50.140 at 2010-04-23 15:19:22) [GET] ActiveRecord::StatementInvalid (PGError: ERROR: relation "tags" does not exist : SELECT a.attname, format_type(a.atttypid, a.atttypmod), d.adsrc, a.attnotnull FROM pg_attribute a LEFT JOIN pg_attrdef d ON a.attrelid = d.adrelid AND a.attnum = d.adnum WHERE a.attrelid = '"tags"'::regclass AND a.attnum > 0 AND NOT a.attisdropped ORDER BY a.attnum ): PostsController#index def index @tag_counts = Tag.count(:group => :tag_name, :order => 'count_all DESC', :limit => 20) conditions, joins = {}, :votes @ugtag_counts = Ugtag.count(:group => :ugctag_name, :order => 'count_all DESC', :limit => 20) conditions, joins = {}, :votes @vote_counts = Vote.count(:group => :post_title, :order => 'count_all DESC', :limit => 20) conditions, joins = {}, :votes unless(params[:tag_name] || "").empty? conditions = ["tags.tag_name = ? ", params[:tag_name]] joins = [:tags, :votes] end @posts=Post.paginate( :select => "posts.*, count(*) as vote_total", :joins => joins, :conditions=> conditions, :group => "votes.post_id, posts.id ", :order => "created_at DESC", :page => params[:page], :per_page => 5) @popular_posts=Post.paginate( :select => "posts.*, count(*) as vote_total", :joins => joins, :conditions=> conditions, :group => "votes.post_id, posts.id", :order => "vote_total DESC", :page => params[:page], :per_page => 3) respond_to do |format| format.html # index.html.erb format.xml { render :xml => @posts } format.json { render :json => @posts } format.atom end end

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• How to write this Linq SQL as a Dynamic Query (using strings)?

  - by Dr. Zim

  Skip to the "specific question" as needed. Some background: The scenario: I have a set of products with a "drill down" filter (Query Object) populated with DDLs. Each progressive DDL selection will further limit the product list as well as what options are left for the DDLs. For example, selecting a hammer out of tools limits the Product Sizes to only show hammer sizes. Current setup: I created a query object, sent it to a repository, and fed each option to a SQL "table valued function" where null values represent "get all products". I consider this a good effort, but far from DDD acceptable. I want to avoid any "programming" in SQL, hopefully doing everything with a repository. Comments on this topic would be appreciated. Specific question: How would I rewrite this query as a Dynamic Query? A link to something like 101 Linq Examples would be fantastic, but with a Dynamic Query scope. I really want to pass to this method the field in quotes "" for which I want a list of options and how many products have that option. from p in db.Products group p by p.ProductSize into g select new Category { PropertyType = g.Key, Count = g.Count() } Each DDL option will have "The selection (21)" where the (21) is the quantity of products that have that attribute. Upon selecting an option, all other remaining DDLs will update with the remaining options and counts. Edit: Additional notes: .OrderBy("it.City") // "it" refers to the entire record .GroupBy("City", "new(City)") // This produces a unique list of City .Select("it.Count()") //This gives a list of counts... getting closer .Select("key") // Selects a list of unique City .Select("new (key, count() as string)") // +1 to me LOL. key is a row of group .GroupBy("new (City, Manufacturer)", "City") // New = list of fields to group by .GroupBy("City", "new (Manufacturer, Size)") // Second parameter is a projection Product .Where("ProductType == @0", "Maps") .GroupBy("new(City)", "new ( null as string)")// Projection not available later? .Select("new (key.City, it.count() as string)")// GroupBy new makes key an object Product .Where("ProductType == @0", "Maps") .GroupBy("new(City)", "new ( null as string)")// Projection not available later? .Select("new (key.City, it as object)")// the it object is the result of GroupBy var a = Product .Where("ProductType == @0", "Maps") .GroupBy("@0", "it", "City") // This fails to group Product at all .Select("new ( Key, it as Product )"); // "it" is property cast though What I have learned so far is LinqPad is fantastic, but still looking for an answer. Eventually, completely random research like this will prevail I guess. LOL. Edit:

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• Fill a list from JSP in Spring

  - by Javi

  Hello, I have something like this in my Spring Application: public class Book{ public Book(){ sheets = new LinkedList<Sheet>(); } protected List<Sheet> sheets; //getter and setter } I add several Sheets to the sheet list and I print a form in a JSP like this: <form:form modelAttribute="book" action="${dest_url}" method="POST"> <c:forEach items="${mybook.sheets}" var="sheet" varStatus="status"> <form:hidden path="sheet[${status.count -1}].header"/> <form:hidden path="sheet[${status.count -1}].footer"/> <form:hidden path="sheet[${status.count -1}].operador"/> <form:hidden path="sheet[${status.count -1}].number"/> <form:hidden path="sheet[${status.count -1}].lines"/> </c:forEach> ... </form:form> I need to get back this list in the controller when the form is submitted. So in my controller I have a method with a parameter like this: public String myMethod (@ModelAttribute("book") Book book, Model model){ ... } The problem is that it doesn't fill the sheets list unless in the constructor of Book I add as much Sheet's as I want to get. The problem is that I don't know in advance the number of Sheets the book is going to have. I think the problem is that in my method it instantiates Book which has a list of sheets with 0 elements. When it tries to access to sheets[0] the list is empty and it doen't add a Sheet. I've tried to create a getter method for the list with an index parameter (so it can create the element if it doesn't exists in the list like in Struts framework) like this one: public Sheet getSheets(int index){ if(sheets.size() <= index){ Sheet sheet = new Sheet(); sheets.add(index, sheet); } Sheet sheetToReturn = sheets.get(index); if(sheetToReturn == null){ sheetToReturn = new Sheet(); sheets.add(index, sheetToReturn); } return sheetToReturn; } but with this method the JSP doesn't work because sheets has an invalid getter. What's the proper way of filling a list when you don't know the number of items in advanced? Thanks

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• how to find maximum frequent item sets from large transactional data file

  - by ANIL MANE

  Hi, I have the input file contains large amount of transactions like Transaction ID Items T1 Bread, milk, coffee, juice T2 Juice, milk, coffee T3 Bread, juice T4 Coffee, milk T5 Bread, Milk T6 Coffee, Bread T7 Coffee, Bread, Juice T8 Bread, Milk, Juice T9 Milk, Bread, Coffee, T10 Bread T11 Milk T12 Milk, Coffee, Bread, Juice i want the occurrence of every unique item like Item Name Count Bread 9 Milk 8 Coffee 7 Juice 6 and from that i want an a fp-tree now by traversing this tree i want the maximal frequent itemsets as follows The basic idea of method is to dispose nodes in each “layer” from bottom to up. The concept of “layer” is different to the common concept of layer in a tree. Nodes in a “layer” mean the nodes correspond to the same item and be in a linked list from the “Head Table”. For nodes in a “layer” NBN method will be used to dispose the nodes from left to right along the linked list. To use NBN method, two extra fields will be added to each node in the ordered FP-Tree. The field tag of node N stores the information of whether N is maximal frequent itemset, and the field count’ stores the support count information in the nodes at left. In Figure, the first node to be disposed is “juice: 2”. If the min_sup is equal to or less than 2 then “bread, milk, coffee, juice” is a maximal frequent itemset. Firstly output juice:2 and set the field tag of “coffee:3” as “false” (the field tag of each node is “true” initially ). Next check whether the right four itemsets juice:1 be the subset of juice:2. If the itemset one node “juice:1” corresponding to is the subset of juice:2 set the field tag of the node “false”. In the following process when the field tag of the disposed node is FALSE we can omit the node after the same tagging. If the min_sup is more than 2 then check whether the right four juice:1 is the subset of juice:2. If the itemset one node “juice:1” corresponding to is the subset of juice:2 then set the field count’ of the node with the sum of the former count’ and 2 After all the nodes “juice” disposed ,begin to dispose the node “coffee:3”. Any suggestions or available source code, welcome. thanks in advance

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• Reporting Services as PDF through WebRequest in C# 3.5 "Not Supported File Type"

  - by Heath Allison

  I've inherited a legacy application that is supposed to grab an on the fly pdf from a reporting services server. Everything works fine up until the point where you try to open the pdf being returned and adobe acrobat tells you: Adobe Reader could not open 'thisStoopidReport'.pdf' because it is either not a supported file type or because the file has been damaged(for example, it was sent as an email attachment and wasn't correctly decoded). I've done some initial troubleshooting on this. If I replace the url in the WebRequest.Create() call with a valid pdf file on my local machine ie: @"C:temp/validpdf.pdf") then I get a valid PDF. The report itself seems to work fine. If I manually type the URL to the reporting services report that should generate the pdf file I am prompted for user authentication. But after supplying it I get a valid pdf file. I've replace the actual url,username,userpass and domain strings in the code below with bogus values for obvious reasons. WebRequest request = WebRequest.Create(@"http://x.x.x.x/reportServer?/reports/reportNam&rs:format=pdf&rs:command=render&rc:parameters=blahblahblah"); int totalSize = 0; request.Credentials = new NetworkCredential("validUser", "validPass", "validDomain"); request.Timeout = 360000; // 6 minutes in milliseconds. request.Method = WebRequestMethods.Http.Post; request.ContentLength = 0; WebResponse response = request.GetResponse(); Response.Clear(); BinaryReader reader = new BinaryReader(response.GetResponseStream()); Byte[] buffer = new byte[2048]; int count = reader.Read(buffer, 0, 2048); while (count > 0) { totalSize += count; Response.OutputStream.Write(buffer, 0, count); count = reader.Read(buffer, 0, 2048); } Response.ContentType = "application/pdf"; Response.Cache.SetCacheability(HttpCacheability.Private); Response.CacheControl = "private"; Response.Expires = 30; Response.AddHeader("Content-Disposition", "attachment; filename=thisStoopidReport.pdf"); Response.AddHeader("Content-Length", totalSize.ToString()); reader.Close(); Response.Flush(); Response.End();

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• My vertex shader doesn't affect texture coords or diffuse info but works for position

  - by tina nyaa

  I am new to 3D and DirectX - in the past I have only used abstractions for 2D drawing. Over the past month I've been studying really hard and I'm trying to modify and adapt some of the shaders as part of my personal 'study project'. Below I have a shader, modified from one of the Microsoft samples. I set diffuse and tex0 vertex shader outputs to zero, but my model still shows the full texture and lighting as if I hadn't changed the values from the vertex buffer. Changing the position of the model works, but nothing else. Why is this? // // Skinned Mesh Effect file // Copyright (c) 2000-2002 Microsoft Corporation. All rights reserved. // float4 lhtDir = {0.0f, 0.0f, -1.0f, 1.0f}; //light Direction float4 lightDiffuse = {0.6f, 0.6f, 0.6f, 1.0f}; // Light Diffuse float4 MaterialAmbient : MATERIALAMBIENT = {0.1f, 0.1f, 0.1f, 1.0f}; float4 MaterialDiffuse : MATERIALDIFFUSE = {0.8f, 0.8f, 0.8f, 1.0f}; // Matrix Pallette static const int MAX_MATRICES = 100; float4x3 mWorldMatrixArray[MAX_MATRICES] : WORLDMATRIXARRAY; float4x4 mViewProj : VIEWPROJECTION; /////////////////////////////////////////////////////// struct VS_INPUT { float4 Pos : POSITION; float4 BlendWeights : BLENDWEIGHT; float4 BlendIndices : BLENDINDICES; float3 Normal : NORMAL; float3 Tex0 : TEXCOORD0; }; struct VS_OUTPUT { float4 Pos : POSITION; float4 Diffuse : COLOR; float2 Tex0 : TEXCOORD0; }; float3 Diffuse(float3 Normal) { float CosTheta; // N.L Clamped CosTheta = max(0.0f, dot(Normal, lhtDir.xyz)); // propogate scalar result to vector return (CosTheta); } VS_OUTPUT VShade(VS_INPUT i, uniform int NumBones) { VS_OUTPUT o; float3 Pos = 0.0f; float3 Normal = 0.0f; float LastWeight = 0.0f; // Compensate for lack of UBYTE4 on Geforce3 int4 IndexVector = D3DCOLORtoUBYTE4(i.BlendIndices); // cast the vectors to arrays for use in the for loop below float BlendWeightsArray[4] = (float[4])i.BlendWeights; int IndexArray[4] = (int[4])IndexVector; // calculate the pos/normal using the "normal" weights // and accumulate the weights to calculate the last weight for (int iBone = 0; iBone < NumBones-1; iBone++) { LastWeight = LastWeight + BlendWeightsArray[iBone]; Pos += mul(i.Pos, mWorldMatrixArray[IndexArray[iBone]]) * BlendWeightsArray[iBone]; Normal += mul(i.Normal, mWorldMatrixArray[IndexArray[iBone]]) * BlendWeightsArray[iBone]; } LastWeight = 1.0f - LastWeight; // Now that we have the calculated weight, add in the final influence Pos += (mul(i.Pos, mWorldMatrixArray[IndexArray[NumBones-1]]) * LastWeight); Normal += (mul(i.Normal, mWorldMatrixArray[IndexArray[NumBones-1]]) * LastWeight); // transform position from world space into view and then projection space //o.Pos = mul(float4(Pos.xyz, 1.0f), mViewProj); o.Pos = mul(float4(Pos.xyz, 1.0f), mViewProj); o.Diffuse.x = 0.0f; o.Diffuse.y = 0.0f; o.Diffuse.z = 0.0f; o.Diffuse.w = 0.0f; o.Tex0 = float2(0,0); return o; } technique t0 { pass p0 { VertexShader = compile vs_3_0 VShade(4); } } I am currently using the SlimDX .NET wrapper around DirectX, but the API is extremely similar: public void Draw() { var device = vertexBuffer.Device; device.Clear(ClearFlags.Target | ClearFlags.ZBuffer, Color.White, 1.0f, 0); device.SetRenderState(RenderState.Lighting, true); device.SetRenderState(RenderState.DitherEnable, true); device.SetRenderState(RenderState.ZEnable, true); device.SetRenderState(RenderState.CullMode, Cull.Counterclockwise); device.SetRenderState(RenderState.NormalizeNormals, true); device.SetSamplerState(0, SamplerState.MagFilter, TextureFilter.Anisotropic); device.SetSamplerState(0, SamplerState.MinFilter, TextureFilter.Anisotropic); device.SetTransform(TransformState.World, Matrix.Identity * Matrix.Translation(0, -50, 0)); device.SetTransform(TransformState.View, Matrix.LookAtLH(new Vector3(-200, 0, 0), Vector3.Zero, Vector3.UnitY)); device.SetTransform(TransformState.Projection, Matrix.PerspectiveFovLH((float)Math.PI / 4, (float)device.Viewport.Width / device.Viewport.Height, 10, 10000000)); var material = new Material(); material.Ambient = material.Diffuse = material.Emissive = material.Specular = new Color4(Color.White); material.Power = 1f; device.SetStreamSource(0, vertexBuffer, 0, vertexSize); device.VertexDeclaration = vertexDeclaration; device.Indices = indexBuffer; device.Material = material; device.SetTexture(0, texture); var param = effect.GetParameter(null, "mWorldMatrixArray"); var boneWorldTransforms = bones.OrderedBones.OrderBy(x => x.Id).Select(x => x.CombinedTransformation).ToArray(); effect.SetValue(param, boneWorldTransforms); effect.SetValue(effect.GetParameter(null, "mViewProj"), Matrix.Identity);// Matrix.PerspectiveFovLH((float)Math.PI / 4, (float)device.Viewport.Width / device.Viewport.Height, 10, 10000000)); effect.SetValue(effect.GetParameter(null, "MaterialDiffuse"), material.Diffuse); effect.SetValue(effect.GetParameter(null, "MaterialAmbient"), material.Ambient); effect.Technique = effect.GetTechnique(0); var passes = effect.Begin(FX.DoNotSaveState); for (var i = 0; i < passes; i++) { effect.BeginPass(i); device.DrawIndexedPrimitives(PrimitiveType.TriangleList, 0, 0, skin.Vertices.Length, 0, skin.Indicies.Length / 3); effect.EndPass(); } effect.End(); } Again, I set diffuse and tex0 vertex shader outputs to zero, but my model still shows the full texture and lighting as if I hadn't changed the values from the vertex buffer. Changing the position of the model works, but nothing else. Why is this? Also, whatever I set in the bone transformation matrices doesn't seem to have an effect on my model. If I set every bone transformation to a zero matrix, the model still shows up as if nothing had happened, but changing the Pos field in shader output makes the model disappear. I don't understand why I'm getting this kind of behaviour. Thank you!

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• Fun with Aggregates

  - by Paul White

  There are interesting things to be learned from even the simplest queries.  For example, imagine you are given the task of writing a query to list AdventureWorks product names where the product has at least one entry in the transaction history table, but fewer than ten. One possible query to meet that specification is: SELECT p.Name FROM Production.Product AS p JOIN Production.TransactionHistory AS th ON p.ProductID = th.ProductID GROUP BY p.ProductID, p.Name HAVING COUNT_BIG(*) < 10; That query correctly returns 23 rows (execution plan and data sample shown below): The execution plan looks a bit different from the written form of the query: the base tables are accessed in reverse order, and the aggregation is performed before the join.  The general idea is to read all rows from the history table, compute the count of rows grouped by ProductID, merge join the results to the Product table on ProductID, and finally filter to only return rows where the count is less than ten. This ‘fully-optimized’ plan has an estimated cost of around 0.33 units.  The reason for the quote marks there is that this plan is not quite as optimal as it could be – surely it would make sense to push the Filter down past the join too?  To answer that, let’s look at some other ways to formulate this query.  This being SQL, there are any number of ways to write logically-equivalent query specifications, so we’ll just look at a couple of interesting ones.  The first query is an attempt to reverse-engineer T-SQL from the optimized query plan shown above.  It joins the result of pre-aggregating the history table to the Product table before filtering: SELECT p.Name FROM ( SELECT th.ProductID, cnt = COUNT_BIG(*) FROM Production.TransactionHistory AS th GROUP BY th.ProductID ) AS q1 JOIN Production.Product AS p ON p.ProductID = q1.ProductID WHERE q1.cnt < 10; Perhaps a little surprisingly, we get a slightly different execution plan: The results are the same (23 rows) but this time the Filter is pushed below the join!  The optimizer chooses nested loops for the join, because the cardinality estimate for rows passing the Filter is a bit low (estimate 1 versus 23 actual), though you can force a merge join with a hint and the Filter still appears below the join.  In yet another variation, the < 10 predicate can be ‘manually pushed’ by specifying it in a HAVING clause in the “q1” sub-query instead of in the WHERE clause as written above. The reason this predicate can be pushed past the join in this query form, but not in the original formulation is simply an optimizer limitation – it does make efforts (primarily during the simplification phase) to encourage logically-equivalent query specifications to produce the same execution plan, but the implementation is not completely comprehensive. Moving on to a second example, the following query specification results from phrasing the requirement as “list the products where there exists fewer than ten correlated rows in the history table”: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) < 10 ); Unfortunately, this query produces an incorrect result (86 rows): The problem is that it lists products with no history rows, though the reasons are interesting.  The COUNT_BIG(*) in the EXISTS clause is a scalar aggregate (meaning there is no GROUP BY clause) and scalar aggregates always produce a value, even when the input is an empty set.  In the case of the COUNT aggregate, the result of aggregating the empty set is zero (the other standard aggregates produce a NULL).  To make the point really clear, let’s look at product 709, which happens to be one for which no history rows exist: -- Scalar aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709;   -- Vector aggregate SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = 709 GROUP BY th.ProductID; The estimated execution plans for these two statements are almost identical: You might expect the Stream Aggregate to have a Group By for the second statement, but this is not the case.  The query includes an equality comparison to a constant value (709), so all qualified rows are guaranteed to have the same value for ProductID and the Group By is optimized away. In fact there are some minor differences between the two plans (the first is auto-parameterized and qualifies for trivial plan, whereas the second is not auto-parameterized and requires cost-based optimization), but there is nothing to indicate that one is a scalar aggregate and the other is a vector aggregate.  This is something I would like to see exposed in show plan so I suggested it on Connect.  Anyway, the results of running the two queries show the difference at runtime: The scalar aggregate (no GROUP BY) returns a result of zero, whereas the vector aggregate (with a GROUP BY clause) returns nothing at all.  Returning to our EXISTS query, we could ‘fix’ it by changing the HAVING clause to reject rows where the scalar aggregate returns zero: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID HAVING COUNT_BIG(*) BETWEEN 1 AND 9 ); The query now returns the correct 23 rows: Unfortunately, the execution plan is less efficient now – it has an estimated cost of 0.78 compared to 0.33 for the earlier plans.  Let’s try adding a redundant GROUP BY instead of changing the HAVING clause: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY th.ProductID HAVING COUNT_BIG(*) < 10 ); Not only do we now get correct results (23 rows), this is the execution plan: I like to compare that plan to quantum physics: if you don’t find it shocking, you haven’t understood it properly :)  The simple addition of a redundant GROUP BY has resulted in the EXISTS form of the query being transformed into exactly the same optimal plan we found earlier.  What’s more, in SQL Server 2008 and later, we can replace the odd-looking GROUP BY with an explicit GROUP BY on the empty set: SELECT p.Name FROM Production.Product AS p WHERE EXISTS ( SELECT * FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ); I offer that as an alternative because some people find it more intuitive (and it perhaps has more geek value too).  Whichever way you prefer, it’s rather satisfying to note that the result of the sub-query does not exist for a particular correlated value where a vector aggregate is used (the scalar COUNT aggregate always returns a value, even if zero, so it always ‘EXISTS’ regardless which ProductID is logically being evaluated). The following query forms also produce the optimal plan and correct results, so long as a vector aggregate is used (you can probably find more equivalent query forms): WHERE Clause SELECT p.Name FROM Production.Product AS p WHERE ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) < 10; APPLY SELECT p.Name FROM Production.Product AS p CROSS APPLY ( SELECT NULL FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () HAVING COUNT_BIG(*) < 10 ) AS ca (dummy); FROM Clause SELECT q1.Name FROM ( SELECT p.Name, cnt = ( SELECT COUNT_BIG(*) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID GROUP BY () ) FROM Production.Product AS p ) AS q1 WHERE q1.cnt < 10; This last example uses SUM(1) instead of COUNT and does not require a vector aggregate…you should be able to work out why :) SELECT q.Name FROM ( SELECT p.Name, cnt = ( SELECT SUM(1) FROM Production.TransactionHistory AS th WHERE th.ProductID = p.ProductID ) FROM Production.Product AS p ) AS q WHERE q.cnt < 10; The semantics of SQL aggregates are rather odd in places.  It definitely pays to get to know the rules, and to be careful to check whether your queries are using scalar or vector aggregates.  As we have seen, query plans do not show in which ‘mode’ an aggregate is running and getting it wrong can cause poor performance, wrong results, or both. © 2012 Paul White Twitter: @SQL_Kiwi email: [email protected]

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• BST insert operation. don't insert a node if a duplicate exists already

  - by jeev

  the following code reads an input array, and constructs a BST from it. if the current arr[i] is a duplicate, of a node in the tree, then arr[i] is discarded. count in the struct node refers to the number of times a number appears in the array. fi refers to the first index of the element found in the array. after the insertion, i am doing a post-order traversal of the tree and printing the data, count and index (in this order). the output i am getting when i run this code is: 0 0 7 0 0 6 thank you for your help. Jeev struct node{ int data; struct node *left; struct node *right; int fi; int count; }; struct node* binSearchTree(int arr[], int size); int setdata(struct node**node, int data, int index); void insert(int data, struct node **root, int index); void sortOnCount(struct node* root); void main(){ int arr[] = {2,5,2,8,5,6,8,8}; int size = sizeof(arr)/sizeof(arr[0]); struct node* temp = binSearchTree(arr, size); sortOnCount(temp); } struct node* binSearchTree(int arr[], int size){ struct node* root = (struct node*)malloc(sizeof(struct node)); if(!setdata(&root, arr[0], 0)) fprintf(stderr, "root couldn't be initialized"); int i = 1; for(;i<size;i++){ insert(arr[i], &root, i); } return root; } int setdata(struct node** nod, int data, int index){ if(*nod!=NULL){ (*nod)->fi = index; (*nod)->left = NULL; (*nod)->right = NULL; return 1; } return 0; } void insert(int data, struct node **root, int index){ struct node* new = (struct node*)malloc(sizeof(struct node)); setdata(&new, data, index); struct node** temp = root; while(1){ if(data<=(*temp)->data){ if((*temp)->left!=NULL) *temp=(*temp)->left; else{ (*temp)->left = new; break; } } else if(data>(*temp)->data){ if((*temp)->right!=NULL) *temp=(*temp)->right; else{ (*temp)->right = new; break; } } else{ (*temp)->count++; free(new); break; } } } void sortOnCount(struct node* root){ if(root!=NULL){ sortOnCount(root->left); sortOnCount(root->right); printf("%d %d %d\n", (root)->data, (root)->count, (root)->fi); } }

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• Is there anything else I can do to optimize this MySQL query?

  - by Legend

  I have two tables, Table A with 700,000 entries and Table B with 600,000 entries. The structure is as follows: Table A: +-----------+---------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-----------+---------------------+------+-----+---------+----------------+ | id | bigint(20) unsigned | NO | PRI | NULL | auto_increment | | number | bigint(20) unsigned | YES | | NULL | | +-----------+---------------------+------+-----+---------+----------------+ Table B: +-------------+---------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-------------+---------------------+------+-----+---------+----------------+ | id | bigint(20) unsigned | NO | PRI | NULL | auto_increment | | number_s | bigint(20) unsigned | YES | MUL | NULL | | | number_e | bigint(20) unsigned | YES | MUL | NULL | | | source | varchar(50) | YES | | NULL | | +-------------+---------------------+------+-----+---------+----------------+ I am trying to find if any of the values in Table A are present in Table B using the following code: $sql = "SELECT number from TableA"; $result = mysql_query($sql) or die(mysql_error()); while($row = mysql_fetch_assoc($result)) { $number = $row['number']; $sql = "SELECT source, count(source) FROM TableB WHERE number_s < $number AND number_e > $number GROUP BY source"; $re = mysql_query($sql) or die(mysql_error); while($ro = mysql_fetch_array($re)) { echo $number."\t".$ro[0]."\t".$ro[1]."\n"; } } I was hoping that the query would go fast but then for some reason, it isn't terrible fast. My explain on the select (with a particular value of "number") gives me the following: mysql> explain SELECT source, count(source) FROM TableB WHERE number_s < 1812194440 AND number_e > 1812194440 GROUP BY source; +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ | 1 | SIMPLE | TableB | ALL | number_s,number_e | NULL | NULL | NULL | 696325 | Using where; Using temporary; Using filesort | +----+-------------+------------+------+-------------------------+------+---------+------+--------+----------------------------------------------+ 1 row in set (0.00 sec) Is there any optimization that I can squeeze out of this? I tried writing a stored procedure for the same task but it doesn't even seem to work in the first place... It doesn't give any syntax errors... I tried running it for a day and it was still running which felt odd. CREATE PROCEDURE Filter() Begin DECLARE number BIGINT UNSIGNED; DECLARE x INT; DECLARE done INT DEFAULT 0; DECLARE cur1 CURSOR FOR SELECT number FROM TableA; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; CREATE TEMPORARY TABLE IF NOT EXISTS Flags(number bigint unsigned, count int(11)); OPEN cur1; hist_loop: LOOP FETCH cur1 INTO number; SELECT count(*) from TableB WHERE number_s < number AND number_e > number INTO x; IF done = 1 THEN LEAVE hist_loop; END IF; IF x IS NOT NULL AND x>0 THEN INSERT INTO Flags(number, count) VALUES(number, x); END IF; END LOOP hist_loop; CLOSE cur1; END

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• whats wrong in this LINQ synatx?

  - by Saurabh Kumar

  Hi, I am trying to convert a SQL query to LINQ. Somehow my count(distinct(x)) logic does not seem to be working correctly. The original SQL is quite efficient(or so i think), but the generated SQL is not even returning the correct result. I am trying to fix this LINQ to do what the original SQL is doing, AND in an efficient way as the original query is doing. Help here would be really apreciated as I am stuck here :( SQL which is working and I need to make a comparable LINQ of: SELECT [t1].[PersonID] AS [personid] FROM [dbo].[Code] AS [t0] INNER JOIN [dbo].[phonenumbers] AS [t1] ON [t1].[PhoneCode] = [t0].[Code] INNER JOIN [dbo].[person] ON [t1].[PersonID]= [dbo].[Person].PersonID WHERE ([t0].[codetype] = 'phone') AND ( ([t0].[CodeDescription] = 'Home') AND ([t1].[PhoneNum] = '111') OR ([t0].[CodeDescription] = 'Work') AND ([t1].[PhoneNum] = '222') ) GROUP BY [t1].[PersonID] HAVING COUNT(DISTINCT([t1].[PhoneNum]))=2 The LINQ which I made is approximately as below: var ids = context.Code.Where(predicate); var rs = from r in ids group r by new { r.phonenumbers.person.PersonID} into g let matchcount=g.Select(p => p.phonenumbers.PhoneNum).Distinct().Count() where matchcount ==2 select new { personid = g.Key }; Unfortunately, the above LINQ is NOT generating the correct result, and is actually internally getting generated to the SQL shown below. By the way, this generated query is also reading ALL the rows(about 19592040) around 2 times due to the COUNTS :( Wich is a big performance issue too. Please help/point me to the right direction. Declare @p0 VarChar(10)='phone' Declare @p1 VarChar(10)='Home' Declare @p2 VarChar(10)='111' Declare @p3 VarChar(10)='Work' Declare @p4 VarChar(10)='222' Declare @p5 VarChar(10)='2' SELECT [t9].[PersonID], ( SELECT COUNT(*) FROM ( SELECT DISTINCT [t13].[PhoneNum] FROM [dbo].[Code] AS [t10] INNER JOIN [dbo].[phonenumbers] AS [t11] ON [t11].[PhoneType] = [t10].[Code] INNER JOIN [dbo].[Person] AS [t12] ON [t12].[PersonID] = [t11].[PersonID] INNER JOIN [dbo].[phonenumbers] AS [t13] ON [t13].[PhoneType] = [t10].[Code] WHERE ([t9].[PersonID] = [t12].[PersonID]) AND ([t10].[codetype] = @p0) AND ((([t10].[codetype] = @p1) AND ([t11].[PhoneNum] = @p2)) OR (([t10].[codetype] = @p3) AND ([t11].[PhoneNum] = @p4))) ) AS [t14] ) AS [cnt] FROM ( SELECT [t3].[PersonID], ( SELECT COUNT(*) FROM ( SELECT DISTINCT [t7].[PhoneNum] FROM [dbo].[Code] AS [t4] INNER JOIN [dbo].[phonenumbers] AS [t5] ON [t5].[PhoneType] = [t4].[Code] INNER JOIN [dbo].[Person] AS [t6] ON [t6].[PersonID] = [t5].[PersonID] INNER JOIN [dbo].[phonenumbers] AS [t7] ON [t7].[PhoneType] = [t4].[Code] WHERE ([t3].[PersonID] = [t6].[PersonID]) AND ([t4].[codetype] = @p0) AND ((([t4].[codetype] = @p1) AND ([t5].[PhoneNum] = @p2)) OR (([t4].[codetype] = @p3) AND ([t5].[PhoneNum] = @p4))) ) AS [t8] ) AS [value] FROM ( SELECT [t2].[PersonID] FROM [dbo].[Code] AS [t0] INNER JOIN [dbo].[phonenumbers] AS [t1] ON [t1].[PhoneType] = [t0].[Code] INNER JOIN [dbo].[Person] AS [t2] ON [t2].[PersonID] = [t1].[PersonID] WHERE ([t0].[codetype] = @p0) AND ((([t0].[codetype] = @p1) AND ([t1].[PhoneNum] = @p2)) OR (([t0].[codetype] = @p3) AND ([t1].[PhoneNum] = @p4))) GROUP BY [t2].[PersonID] ) AS [t3] ) AS [t9] WHERE [t9].[value] = @p5 Thanks!

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• How to launch a Windows service network process to listen to a port on a localhost socket that is vi

  - by rwired

  Here's the code (in a standard TService in Delphi): const ProcessExe = 'MyNetApp.exe'; function RunService: Boolean; var StartInfo : TStartupInfo; ProcInfo : TProcessInformation; CreateOK : Boolean; begin CreateOK := false; FillChar(StartInfo,SizeOf(TStartupInfo),#0); FillChar(ProcInfo,SizeOf(TProcessInformation),#0); StartInfo.cb := SizeOf(TStartupInfo); CreateOK := CreateProcess(nil, PChar(ProcessEXE),nil,nil,False, CREATE_NEW_PROCESS_GROUP+NORMAL_PRIORITY_CLASS, nil, PChar(InstallDir), StartInfo, ProcInfo); CloseHandle(ProcInfo.hProcess); CloseHandle(ProcInfo.hThread); Result := CreateOK; end; procedure TServicel.ServiceExecute(Sender: TService); const IntervalsBetweenRuns = 4; //no of IntTimes between checks IntTime = 250; //ms var Count: SmallInt; begin Count := IntervalsBetweenRuns; //first time run immediately while not Terminated do begin Inc(Count); if Count >= IntervalsBetweenRuns then begin Count := 0; //We check to see if the process is running, //if not we run it. That's all there is to it. //if ProcessEXE crashes, this service host will just rerun it if processExists(ProcessEXE)=0 then RunService; end; Sleep(IntTime); ServiceThread.ProcessRequests(False); end; end; MyNetApp.exe is a SOCKS5 proxy listening on port 9870. Users configure their browser to this proxy which acts as a secure-tunnel/anonymizer. All works perfectly fine on 2000/XP/2003, but on Vista/Win7 with UAC the service runs in Session0 under LocalSystem and port 9870 doesn't show up in netstat for the logged-in user or Administrator. Seems UAC is getting in my way. Is there something I can do with the SECURITY_ATTRIBUTES or CreateProcess, or is there something I can do with CreateProcessAsUser or impersonation to ensure that a network socket on a service is available to logged-in users on the system (note, this app is for mass deployment, I don't have access to user credentials, and require the user elevate their privileges to install a service on Vista/Win7)

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• How to solve Memory leaks in Lib Xml Parser in objective-C where the list is returned?

  - by Madan Mohan

  Hi Guys, I got leaks in Lib Xml Parser while retrieving the data from the net, Here in the below code, I have allocated the list - (void)getCustomersList { // make an operation so we can push it into the queue SEL method = @selector(parseForData); NSInvocationOperation *op = [[NSInvocationOperation alloc] initWithTarget:self selector:method object:nil]; customersTempList = [[NSMutableArray alloc] initWithCapacity:20];// allocated list [self.retrieverQueue addOperation:op]; [op release]; } // return each recode // in parser .m class one of the condition in endElement where it shows a leak. else if(0 == strncmp((const char *)localname, kCustomerElement, kCustomerElementLength)) { [customersTempList addObject:customer]; printf("\n no of objects in temp list:%d", [customersTempList count]); if ([customersTempList count] == 20) { NSMutableArray* argsList = [customersTempList copy];//////////////////////here it is showing leak. printf("\n Calling reload data with %d new objects", [argsList count]); SEL selector = @selector(parser:addCustomerObject:); NSMethodSignature *sig = [(id)self.delegate methodSignatureForSelector:selector]; if(nil != sig && [self.delegate respondsToSelector:selector]) { NSInvocation *invocation = [NSInvocation invocationWithMethodSignature:sig]; [invocation retainArguments]; [invocation setTarget:self.delegate]; [invocation setSelector:selector]; [invocation setArgument:&self atIndex:2]; [invocation setArgument:&argsList atIndex:3]; [invocation performSelectorOnMainThread:@selector(invoke) withObject:NULL waitUntilDone:NO]; } [customersTempList removeAllObjects]; } } // returned the list after all the records are stored in the list else if(0 == strncmp((const char *)localname, kCustomersElement, kCustomersElementLength)) { printf("\n Calling reload data with %d new objects", [customersTempList count]); NSMutableArray* argsList = [customersTempList copy]; printf("\n Calling reload data with %d new objects", [argsList count]); SEL selector = @selector(parser:addCustomerObject:); NSMethodSignature *sig = [(id)self.delegate methodSignatureForSelector:selector]; if(nil != sig && [self.delegate respondsToSelector:selector]) { NSInvocation *invocation = [NSInvocation invocationWithMethodSignature:sig]; [invocation retainArguments]; [invocation setTarget:self.delegate]; [invocation setSelector:selector]; [invocation setArgument:&self atIndex:2]; [invocation setArgument:&argsList atIndex:3]; [invocation performSelectorOnMainThread:@selector(invoke) withObject:NULL waitUntilDone:NO]; } [customersTempList removeAllObjects]; } } please help me out of this, Thanks, Madan.

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• mounting ext4 fs with block size of 65536

  - by seaquest

  I am doing some benchmarking on EXT4 performance on Compact Flash media. I have created an ext4 fs with block size of 65536. however I can not mount it on ubuntu-10.10-netbook-i386. (it is already mounting ext4 fs with 4096 bytes of block sizes) According to my readings on ext4 it should allow such big block sized fs. I want to hear your comments. root@ubuntu:~# mkfs.ext4 -b 65536 /dev/sda3 Warning: blocksize 65536 not usable on most systems. mke2fs 1.41.12 (17-May-2010) mkfs.ext4: 65536-byte blocks too big for system (max 4096) Proceed anyway? (y,n) y Warning: 65536-byte blocks too big for system (max 4096), forced to continue Filesystem label= OS type: Linux Block size=65536 (log=6) Fragment size=65536 (log=6) Stride=0 blocks, Stripe width=0 blocks 19968 inodes, 19830 blocks 991 blocks (5.00%) reserved for the super user First data block=0 1 block group 65528 blocks per group, 65528 fragments per group 19968 inodes per group Writing inode tables: done Creating journal (1024 blocks): done Writing superblocks and filesystem accounting information: done This filesystem will be automatically checked every 37 mounts or 180 days, whichever comes first. Use tune2fs -c or -i to override. root@ubuntu:~# tune2fs -l /dev/sda3 tune2fs 1.41.12 (17-May-2010) Filesystem volume name: <none> Last mounted on: <not available> Filesystem UUID: 4cf3f507-e7b4-463c-be11-5b408097099b Filesystem magic number: 0xEF53 Filesystem revision #: 1 (dynamic) Filesystem features: has_journal ext_attr resize_inode dir_index filetype extent flex_bg sparse_super large_file huge_file uninit_bg dir_nlink extra_isize Filesystem flags: signed_directory_hash Default mount options: (none) Filesystem state: clean Errors behavior: Continue Filesystem OS type: Linux Inode count: 19968 Block count: 19830 Reserved block count: 991 Free blocks: 18720 Free inodes: 19957 First block: 0 Block size: 65536 Fragment size: 65536 Blocks per group: 65528 Fragments per group: 65528 Inodes per group: 19968 Inode blocks per group: 78 Flex block group size: 16 Filesystem created: Sat Feb 5 14:39:55 2011 Last mount time: n/a Last write time: Sat Feb 5 14:40:02 2011 Mount count: 0 Maximum mount count: 37 Last checked: Sat Feb 5 14:39:55 2011 Check interval: 15552000 (6 months) Next check after: Thu Aug 4 14:39:55 2011 Lifetime writes: 70 MB Reserved blocks uid: 0 (user root) Reserved blocks gid: 0 (group root) First inode: 11 Inode size: 256 Required extra isize: 28 Desired extra isize: 28 Journal inode: 8 Default directory hash: half_md4 Directory Hash Seed: afb5b570-9d47-4786-bad2-4aacb3b73516 Journal backup: inode blocks root@ubuntu:~# mount -t ext4 /dev/sda3 /mnt/ mount: wrong fs type, bad option, bad superblock on /dev/sda3, missing codepage or helper program, or other error In some cases useful info is found in syslog - try dmesg | tail or so

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• C# Select clause returns system exception instead of relevant object

  - by Kashif

  I am trying to use the select clause to pick out an object which matches a specified name field from a database query as follows: objectQuery = from obj in objectList where obj.Equals(objectName) select obj; In the results view of my query, I get: base {System.SystemException} = {"Boolean Equals(System.Object)"} Where I should be expecting something like a Car, Make, or Model Would someone please explain what I am doing wrong here? The method in question can be seen here: // this function searches the database's table for a single object that matches the 'Name' property with 'objectName' public static T Read<T>(string objectName) where T : IEquatable<T> { using (ISession session = NHibernateHelper.OpenSession()) { IQueryable<T> objectList = session.Query<T>(); // pull (query) all the objects from the table in the database int count = objectList.Count(); // return the number of objects in the table // alternative: int count = makeList.Count<T>(); IQueryable<T> objectQuery = null; // create a reference for our queryable list of objects T foundObject = default(T); // create an object reference for our found object if (count > 0) { // give me all objects that have a name that matches 'objectName' and store them in 'objectQuery' objectQuery = from obj in objectList where obj.Equals(objectName) select obj; // make sure that 'objectQuery' has only one object in it try { foundObject = (T)objectQuery.Single(); } catch { return default(T); } // output some information to the console (output screen) Console.WriteLine("Read Make: " + foundObject.ToString()); } // pass the reference of the found object on to whoever asked for it return foundObject; } } Note that I am using the interface "IQuatable<T>" in my method descriptor. An example of the classes I am trying to pull from the database is: public class Make: IEquatable<Make> { public virtual int Id { get; set; } public virtual string Name { get; set; } public virtual IList<Model> Models { get; set; } public Make() { // this public no-argument constructor is required for NHibernate } public Make(string makeName) { this.Name = makeName; } public override string ToString() { return Name; } // Implementation of IEquatable<T> interface public virtual bool Equals(Make make) { if (this.Id == make.Id) { return true; } else { return false; } } // Implementation of IEquatable<T> interface public virtual bool Equals(String name) { if (this.Name.Equals(name)) { return true; } else { return false; } } } And the interface is described simply as: public interface IEquatable<T> { bool Equals(T obj); }

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• Is this BlockingQueue susceptible to deadlock?

  - by unforgiven3

  I've been using this code as a queue that blocks on Dequeue() until an element is enqueued. I've used this code for a few years now in several projects, all with no issues... until now. I'm seeing a deadlock in some code I'm writing now, and in investigating the problem, my 'eye of suspicion' has settled on this BlockingQueue<T>. I can't prove it, so I figured I'd ask some people smarter than me to review it for potential issues. Can you guys see anything that might cause a deadlock in this code? public class BlockingQueue<T> { private readonly Queue<T> _queue; private readonly ManualResetEvent _event; /// <summary> /// Constructor /// </summary> public BlockingQueue() { _queue = new Queue<T>(); _event = new ManualResetEvent(false); } /// <summary> /// Read-only property to get the size of the queue /// </summary> public int Size { get { int count; lock (_queue) { count = _queue.Count; } return count; } } /// <summary> /// Enqueues element on the queue /// </summary> /// <param name="element">Element to enqueue</param> public void Enqueue(T element) { lock (_queue) { _queue.Enqueue(element); _event.Set(); } } /// <summary> /// Dequeues an element from the queue /// </summary> /// <returns>Dequeued element</returns> public T Dequeue() { T element; while (true) { if (Size == 0) { _event.Reset(); _event.WaitOne(); } lock (_queue) { if (_queue.Count == 0) continue; element = _queue.Dequeue(); break; } } return element; } /// <summary> /// Clears the queue /// </summary> public void Clear() { lock (_queue) { _queue.Clear(); } } }

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• Help with bugs in a C code

  - by Yanki Twizzy

  This C code is giving me some unpredictable results. The program is meant to collect 6 nos and print out the max, position of the max no and the average. It's supposed to have only 3 functions - input, max_avr_pos and output for doing what the code is supposed to do but I am getting unpredictable results. Please what could be the problem #include <stdio.h> #include <stdlib.h> #include <conio.h> void input_vals(int arrnum[]); void max_ave_val(int arrnum1[],double *average,int *maxval,int *position); void print_output(double *average1,int *maxval1,int *position1); int main(void) { int arrnum[6],maxval2,position2; double average2; input_vals(arrnum); max_ave_val(arrnum,&average2,&maxval2,&position2); print_output(&average2,&maxval2,&position2); _getche(); return 0; } void input_vals(int arrnum[]) { int count; printf("\n Please enter six numbers\n"); for(count=0;count<6;count++) { scanf("%d",&arrnum[count]); } } void max_ave_val(int arrnum1[],double *average,int *maxval,int *position) { int total=0; int cnt,cnt1,cnt2,limit,maxval2,post; limit=6; /* finding the max value*/ for(cnt=0;cnt<limit-1;cnt++) for(cnt1=limit-1;cnt1>cnt;--cnt1) { if(arrnum1[cnt1-1]>arrnum1[cnt1]) { maxval2=arrnum1[cnt-1]; post=(cnt-1)+1; } else { maxval2=arrnum1[cnt1]; post=cnt1+1; } } *maxval=maxval2; *position=post; /* solving for total */ for(cnt2=0;cnt2<limit;cnt2++); { total=total+arrnum1[cnt2]; } *average=total/limit; } void print_output(double *average1,int *maxval1,int *position1) { printf("\n value of the highest of the numbers is %d\n",*maxval1); printf("\n the average of all the numbers is %g\n",*average1); printf("\n the postion of the highest number in the list is %d\n",*position1); }

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• How to download file into string with progress callback?

  - by Kaminari

  I would like to use the WebClient (or there is another better option?) but there is a problem. I understand that opening up the stream takes some time and this can not be avoided. However, reading it takes a strangely much more amount of time compared to read it entirely immediately. Is there a best way to do this? I mean two ways, to string and to file. Progress is my own delegate and it's working good. FIFTH UPDATE: Finally, I managed to do it. In the meantime I checked out some solutions what made me realize that the problem lies elsewhere. I've tested custom WebResponse and WebRequest objects, library libCURL.NET and even Sockets. The difference in time was gzip compression. Compressed stream lenght was simply half the normal stream lenght and thus download time was less than 3 seconds with the browser. I put some code if someone will want to know how i solved this: (some headers are not needed) public static string DownloadString(string URL) { WebClient client = new WebClient(); client.Headers["User-Agent"] = "Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/532.5 (KHTML, like Gecko) Chrome/4.1.249.1045 Safari/532.5"; client.Headers["Accept"] = "application/xml,application/xhtml+xml,text/html;q=0.9,text/plain;q=0.8,image/png,*/*;q=0.5"; client.Headers["Accept-Encoding"] = "gzip,deflate,sdch"; client.Headers["Accept-Charset"] = "ISO-8859-2,utf-8;q=0.7,*;q=0.3"; Stream inputStream = client.OpenRead(new Uri(URL)); MemoryStream memoryStream = new MemoryStream(); const int size = 32 * 4096; byte[] buffer = new byte[size]; if (client.ResponseHeaders["Content-Encoding"] == "gzip") { inputStream = new GZipStream(inputStream, CompressionMode.Decompress); } int count = 0; do { count = inputStream.Read(buffer, 0, size); if (count > 0) { memoryStream.Write(buffer, 0, count); } } while (count > 0); string result = Encoding.Default.GetString(memoryStream.ToArray()); memoryStream.Close(); inputStream.Close(); return result; } I think that asyncro functions will be almost the same. But i will simply use another thread to fire this function. I dont need percise progress indication.

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• Generic class for performing mass-parallel queries. Feedback?

  - by Aaron

  I don't understand why, but there appears to be no mechanism in the client library for performing many queries in parallel for Windows Azure Table Storage. I've created a template class that can be used to save considerable time, and you're welcome to use it however you wish. I would appreciate however, if you could pick it apart, and provide feedback on how to improve this class. public class AsyncDataQuery<T> where T: new() { public AsyncDataQuery(bool preserve_order) { m_preserve_order = preserve_order; this.Queries = new List<CloudTableQuery<T>>(1000); } public void AddQuery(IQueryable<T> query) { var data_query = (DataServiceQuery<T>)query; var uri = data_query.RequestUri; // required this.Queries.Add(new CloudTableQuery<T>(data_query)); } /// <summary> /// Blocking but still optimized. /// </summary> public List<T> Execute() { this.BeginAsync(); return this.EndAsync(); } public void BeginAsync() { if (m_preserve_order == true) { this.Items = new List<T>(Queries.Count); for (var i = 0; i < Queries.Count; i++) { this.Items.Add(new T()); } } else { this.Items = new List<T>(Queries.Count * 2); } m_wait = new ManualResetEvent(false); for (var i = 0; i < Queries.Count; i++) { var query = Queries[i]; query.BeginExecuteSegmented(callback, i); } } public List<T> EndAsync() { m_wait.WaitOne(); return this.Items; } private List<T> Items { get; set; } private List<CloudTableQuery<T>> Queries { get; set; } private bool m_preserve_order; private ManualResetEvent m_wait; private int m_completed = 0; private void callback(IAsyncResult ar) { int i = (int)ar.AsyncState; CloudTableQuery<T> query = Queries[i]; var response = query.EndExecuteSegmented(ar); if (m_preserve_order == true) { // preserve ordering only supports one result per query this.Items[i] = response.Results.First(); } else { // add any number of items this.Items.AddRange(response.Results); } if (response.HasMoreResults == true) { // more data to pull query.BeginExecuteSegmented(response.ContinuationToken, callback, i); return; } m_completed = Interlocked.Increment(ref m_completed); if (m_completed == Queries.Count) { m_wait.Set(); } } }

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