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  • Unexpected behaviour with glFramebufferTexture1D

    - by Roshan
    I am using render to texture concept with glFramebufferTexture1D. I am drawing a cube on non-default FBO with all the vertices as -1,1 (maximum) in X Y Z direction. Now i am setting viewport to X while rendering on non default FBO. My background is blue with white color of cube. For default FBO, i have created 1-D texture and attached this texture to above FBO with color attachment. I am setting width of texture equal to width*height of above FBO view-port. Now, when i render this texture to on another cube, i can see continuous white color on start or end of each face of the cube. That means part of the face is white and rest is blue. I am not sure whether this behavior is correct or not. I expect all the texels should be white as i am using -1 and 1 coordinates for cube rendered on non-default FBO. code: #define WIDTH 3 #define HEIGHT 3 GLfloat vertices8[]={ 1.0f,1.0f,1.0f, -1.0f,1.0f,1.0f, -1.0f,-1.0f,1.0f, 1.0f,-1.0f,1.0f,//face 1 1.0f,-1.0f,-1.0f, -1.0f,-1.0f,-1.0f, -1.0f,1.0f,-1.0f, 1.0f,1.0f,-1.0f,//face 2 1.0f,1.0f,1.0f, 1.0f,-1.0f,1.0f, 1.0f,-1.0f,-1.0f, 1.0f,1.0f,-1.0f,//face 3 -1.0f,1.0f,1.0f, -1.0f,1.0f,-1.0f, -1.0f,-1.0f,-1.0f, -1.0f,-1.0f,1.0f,//face 4 1.0f,1.0f,1.0f, 1.0f,1.0f,-1.0f, -1.0f,1.0f,-1.0f, -1.0f,1.0f,1.0f,//face 5 -1.0f,-1.0f,1.0f, -1.0f,-1.0f,-1.0f, 1.0f,-1.0f,-1.0f, 1.0f,-1.0f,1.0f//face 6 }; GLfloat vertices[]= { 0.5f,0.5f,0.5f, -0.5f,0.5f,0.5f, -0.5f,-0.5f,0.5f, 0.5f,-0.5f,0.5f,//face 1 0.5f,-0.5f,-0.5f, -0.5f,-0.5f,-0.5f, -0.5f,0.5f,-0.5f, 0.5f,0.5f,-0.5f,//face 2 0.5f,0.5f,0.5f, 0.5f,-0.5f,0.5f, 0.5f,-0.5f,-0.5f, 0.5f,0.5f,-0.5f,//face 3 -0.5f,0.5f,0.5f, -0.5f,0.5f,-0.5f, -0.5f,-0.5f,-0.5f, -0.5f,-0.5f,0.5f,//face 4 0.5f,0.5f,0.5f, 0.5f,0.5f,-0.5f, -0.5f,0.5f,-0.5f, -0.5f,0.5f,0.5f,//face 5 -0.5f,-0.5f,0.5f, -0.5f,-0.5f,-0.5f, 0.5f,-0.5f,-0.5f, 0.5f,-0.5f,0.5f//face 6 }; GLuint indices[] = { 0, 2, 1, 0, 3, 2, 4, 5, 6, 4, 6, 7, 8, 9, 10, 8, 10, 11, 12, 15, 14, 12, 14, 13, 16, 17, 18, 16, 18, 19, 20, 23, 22, 20, 22, 21 }; GLfloat texcoord[] = { 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0 }; glGenTextures(1, &id1); glBindTexture(GL_TEXTURE_1D, id1); glGenFramebuffers(1, &Fboid); glTexParameterf(GL_TEXTURE_1D, GL_TEXTURE_MIN_FILTER, GL_NEAREST); glTexParameterf(GL_TEXTURE_1D, GL_TEXTURE_MAG_FILTER, GL_NEAREST); glTexParameterf(GL_TEXTURE_1D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE); glTexImage1D(GL_TEXTURE_1D, 0, GL_RGBA, WIDTH*HEIGHT , 0, GL_RGBA, GL_UNSIGNED_BYTE,0); glBindFramebuffer(GL_FRAMEBUFFER, Fboid); glFramebufferTexture1D(GL_DRAW_FRAMEBUFFER,GL_COLOR_ATTACHMENT0,GL_TEXTURE_1D,id1,0); draw_cube(); glBindFramebuffer(GL_FRAMEBUFFER, 0); draw(); } draw_cube() { glViewport(0, 0, WIDTH, HEIGHT); glClearColor(0.0f, 0.0f, 0.5f, 1.0f); glClear(GL_COLOR_BUFFER_BIT); glEnableVertexAttribArray(glGetAttribLocation(temp.psId,"position")); glVertexAttribPointer(glGetAttribLocation(temp.psId,"position"), 3, GL_FLOAT, GL_FALSE, 0,vertices8); glDrawArrays (GL_TRIANGLE_FAN, 0, 24); } draw() { glClearColor(1.0f, 0.0f, 0.0f, 1.0f); glClearDepth(1.0f); glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT); glEnableVertexAttribArray(glGetAttribLocation(shader_data.psId,"tk_position")); glVertexAttribPointer(glGetAttribLocation(shader_data.psId,"tk_position"), 3, GL_FLOAT, GL_FALSE, 0,vertices); nResult = GL_ERROR_CHECK((GL_NO_ERROR, "glVertexAttribPointer(position, 3, GL_FLOAT, GL_FALSE, 0,vertices);")); glEnableVertexAttribArray(glGetAttribLocation(shader_data.psId,"inputtexcoord")); glVertexAttribPointer(glGetAttribLocation(shader_data.psId,"inputtexcoord"), 2, GL_FLOAT, GL_FALSE, 0,texcoord); glBindTexture(*target11, id1); glDrawElements ( GL_TRIANGLES, 36,GL_UNSIGNED_INT, indices ); when i change WIDTH=HEIGHT=2, and call a glreadpixels with height, width equal to 4 in draw_cube() i can see first 2 pixels with white color, next two with blue(glclearcolor), next two white and then blue and so on.. Now when i change width parameter in glTeximage1D to 16 then ideally i should see alternate patches of white and blue right? But its not the case here. why so?

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  • How to change all selected chars to _ in Vim

    - by Kev
    I try to draw a class diagram using Vim. I fill the editor window with white-spaces. Type :match SpellBad /\s/ to highlight all the white-spaces. Ctrl+Q to select vertical white-spaces. Ctrl+I to insert Bar(|) and then Esc ........................... v+l +... + l to select horizontal white-spaces But I don't know how to change all selected horizontal white-spaces to underscore(_). I have to hit _ serval times. When comes to long horizontal line, it's bad. ___________ ___________ | | | | | BaseClass |/__________| Client | |___________|\ |___________| /_\ | |____________________________________ | | | _____|_____ _____|_____ _____|_____ | | | | | | | SubClass1 | | SubClass2 | | SubClass3 | |___________| |___________| |¦¦¦¦¦¦¦¦¦¦¦| I want a quick method to do this. Select it - Change it - Done! Maybe map F6 to do it. Thanks!

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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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  • Advanced TSQL Tuning: Why Internals Knowledge Matters

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

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  • Will Ubuntu break my RAID 0 array?

    - by Chad
    I am upgrading an older machine today with new Motherboard, RAM, and CPU. Then I am going to do a fresh install of Ubuntu 64bit. Currently the old machine has an 80gb system drive, and a 4TB RAID 0 array. The old Motherboard has no SATA ports, so I used a SATA card. Ubuntu set up the old RAID array, will it still recognize the array on a newer machine? Are there any steps I should take to ensure the array isn't damaged? It's non-crucial data, but I would rather not start over if it can be avoided. Thanks.

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  • Is this a secure solution for RESTful authentication?

    - by Chad Johnson
    I need to quickly implement a RESTful authentication system for my JavaScript application to use. I think I understand how it should work, but I just want to double check. Here's what I'm thinking -- what do you guys think? Database schema users id : integer first_name : varchar(50) last_name : varchar(50) password : varchar(32) (MD5 hashed) etc. user_authentications id : integer user_id : integer auth_token : varchar(32) (AES encrypted, with keys outside database) access_token : varchar(32) (AES encrypted, with keys outside database) active : boolean Steps The following happens over SSL. I'm using Sinatra for the API. JavaScript requests authentication via POST to /users/auth/token. The /users/auth/token API method generates an auth_token hash, creates a record in user_authentications, and returns auth_token. JavaScript hashes the user's password and then salts it with auth_token -- SHA(access_token + MD5(password)) POST the user's username and hashed+salted password to /users/auth/authenticate. The /users/auth/authenticate API method will verify that SHA(AES.decrypt(access_token) + user.password) == what was received via POST. The /users/auth/authenticate will generate, AES encrypt, store, and return an access token if verification is successful; otherwise, it will return 401 Unauthorized. For any future requests against the API, JavaScript will include access_token, and the API will find the user account based on that.

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  • Launch a real install of Ubuntu already on another hard-drive in Windows 7 like a VM

    - by Chad M
    I'm not too familiar with VMs and the like so this may not even be possible. Here is what I have: A real, full install of Windows 7 on hard drive A. A real, full install of Ubuntu 10.04 on hard drive B. Grub allowing me to select what I want to launch when I start up my computer. It would be Amazing if I could do one of two things. Within Windows 7, launch my real install of ubuntu as if it were a VM. That means i would get all the installed software, all of the files, and all of the settings. Launch a VM copy of ubuntu 10.04 but some how make it use all of the installed software and settings from my real copy. Thanks!

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  • How do I redirect www and non but not IP

    - by Chad T Parson
    I am trying to redirect www.domain.com or domain.com to www.domain.com/temp.html I am using the following code: RewriteCond %{HTTP_HOST} ^.*$ RewriteRule ^/?$ "http\:\/\/www\.domain\.com\/temp\.html" [R=301,L] That works however I do not want to redirect IP. So if someone types in the static IP of the domain then I do not want them to be redirected to www.domain.com/temp.html Anyone have the code to take care of this?

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  • Making an interactive 2D map

    - by Chad
    So recently I have been working on a Legend of Zelda: A Link to the Past clone, and I am wondering how I could handle certain map interactions (like cutting grass, lifting rocks, etc). The way I am currently doing the tilemap is with 2 PNGs. The first is the "tilemap" where each pixel represents a 16x16 tile and the (red, green) values are the (x, y) coords for the tile in the second PNG (the "tileset"). I am then using the blue channel to store collision data. Each tile is split into 4 8x8 tiles and represented by a 2 bit value (0 = empty, 1 = Jumpdown point, 2 = unused right now, 3 = blocking). 4 of these 2 bit values make up the full blue channel (1 byte). So collisions work great, and I am moving on to putting interactive units on the level; but I am not sure what a good way is to do it. I have experimented with spawning an entity for each grass and rock, but there are just WAY to many; FPS just dies even if I confine it to the current "zone" the user is in (for those who remember LTTP it had zones you moved between). It does make a difference that this is a browser-based JavaScript game. tl;dr: What is a good way to have an interactive map without using full blown entities for each interactive item?

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  • Shadows shimmer when camera moves

    - by Chad Layton
    I've implemented shadow maps in my simple block engine as an exercise. I'm using one directional light and using the view volume to create the shadow matrices. I'm experiencing some problems with the shadows shimmering when the camera moves and I'd like to know if it's an issue with my implementation or just an issue with basic/naive shadow mapping itself. Here's a video: http://www.youtube.com/watch?v=vyprATt5BBg&feature=youtu.be Here's the code I use to create the shadow matrices. The commented out code is my original attempt to perfectly fit the view frustum. You can also see my attempt to try clamping movement to texels in the shadow map which didn't seem to make any difference. Then I tried using a bounding sphere instead, also to no apparent effect. public void CreateViewProjectionTransformsToFit(Camera camera, out Matrix viewTransform, out Matrix projectionTransform, out Vector3 position) { BoundingSphere cameraViewFrustumBoundingSphere = BoundingSphere.CreateFromFrustum(camera.ViewFrustum); float lightNearPlaneDistance = 1.0f; Vector3 lookAt = cameraViewFrustumBoundingSphere.Center; float distanceFromLookAt = cameraViewFrustumBoundingSphere.Radius + lightNearPlaneDistance; Vector3 directionFromLookAt = -Direction * distanceFromLookAt; position = lookAt + directionFromLookAt; viewTransform = Matrix.CreateLookAt(position, lookAt, Vector3.Up); float lightFarPlaneDistance = distanceFromLookAt + cameraViewFrustumBoundingSphere.Radius; float diameter = cameraViewFrustumBoundingSphere.Radius * 2.0f; Matrix.CreateOrthographic(diameter, diameter, lightNearPlaneDistance, lightFarPlaneDistance, out projectionTransform); //Vector3 cameraViewFrustumCentroid = camera.ViewFrustum.GetCentroid(); //position = cameraViewFrustumCentroid - (Direction * (camera.FarPlaneDistance - camera.NearPlaneDistance)); //viewTransform = Matrix.CreateLookAt(position, cameraViewFrustumCentroid, Up); //Vector3[] cameraViewFrustumCornersWS = camera.ViewFrustum.GetCorners(); //Vector3[] cameraViewFrustumCornersLS = new Vector3[8]; //Vector3.Transform(cameraViewFrustumCornersWS, ref viewTransform, cameraViewFrustumCornersLS); //Vector3 min = cameraViewFrustumCornersLS[0]; //Vector3 max = cameraViewFrustumCornersLS[0]; //for (int i = 1; i < 8; i++) //{ // min = Vector3.Min(min, cameraViewFrustumCornersLS[i]); // max = Vector3.Max(max, cameraViewFrustumCornersLS[i]); //} //// Clamp to nearest texel //float texelSize = 1.0f / Renderer.ShadowMapSize; //min.X -= min.X % texelSize; //min.Y -= min.Y % texelSize; //min.Z -= min.Z % texelSize; //max.X -= max.X % texelSize; //max.Y -= max.Y % texelSize; //max.Z -= max.Z % texelSize; //// We just use an orthographic projection matrix. The sun is so far away that it's rays are essentially parallel. //Matrix.CreateOrthographicOffCenter(min.X, max.X, min.Y, max.Y, -max.Z, -min.Z, out projectionTransform); } And here's the relevant part of the shader: if (CastShadows) { float4 positionLightCS = mul(float4(position, 1.0f), LightViewProj); float2 texCoord = clipSpaceToScreen(positionLightCS) + 0.5f / ShadowMapSize; float shadowMapDepth = tex2D(ShadowMapSampler, texCoord).r; float distanceToLight = length(LightPosition - position); float bias = 0.2f; if (shadowMapDepth < (distanceToLight - bias)) { return float4(0.0f, 0.0f, 0.0f, 0.0f); } } The shimmer is slightly better if I drastically reduce the view volume but I think that's mostly just because the texels become smaller and it's harder to notice them flickering back and forth. I'd appreciate any insight, I'd very much like to understand what's going on before I try other techniques.

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  • How do I change the Creation/Mod Date of all folders on a USB drive using Touch command?

    - by Chad--24216
    I've got a USB drive with mp3 music on it that I play in my car. My car has a USB drive port. Problem is that the car sorts the music by Creation/Modification Date (and not alphabetically). This makes it a pain to find what artist I'm looking for. To solve this problem, I need a way to "update" the Creation/Modification Date for all folders on the USB drive every time I add a new folder of music to the USB drive. Anyone know how I can do this? The Touch command works great, but the format touch <filename> would take forever to do on each folder on the drive. Anyway to select all folders on the drive and then touch <all folders>? On my USB drive I have a folder for each album and the songs within each respective folder, like so: Album-1-folder Album-2-folder I need to apply the Touch command to each of the "album" folders on the drive. I stumbled upon a simple solution to solve this issue:touch /media/USB_Drive/*

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  • Name of web design technique with vertically contiguous but separate screen-sized blocks?

    - by Chad Schultz
    I don't know the proper name for this, which makes it hard to Google it or describe this. On a site like http://p2theme.com/ as you scroll down you see that content is arranged into screen-sized chunks, each with its own chunk of content, each with a different background color, making them visually distinctive. I've seen this used more and more frequently on websites, but don't know what it's called. People have tried telling me "responsive design" or "flat UI" or "infinite scroll" or "parallax" -- but those are completely different things. What do you call this technique?

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  • How can I get H.264 support?

    - by Chad--24216
    Canonical Limited is shown as a licensee of H.264. I am interested in being able to play H.264 video online when using the Chrome web-browser in Ubuntu (and in the future on Firefox when Firefox supports H.264). Is H.264 support enabled on self-installs of Ubuntu? If not, is there some way I can buy H.264 support for my Ubuntu install? Assume a scenario where I self-installed Ubuntu on a computer that came pre-installed with Windows OS. I'd like to know any and all options available to me for getting H.264 to work on Ubuntu.

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  • How to change FAT32 sort order on drive?

    - by Chad--24216
    I use a USB drive to play music in my car. Unfortunately, the car does not sort the music alphabetically and relies on how the music is sorted on the FAT32 drive. This Windows software here solves the problem. Anything comparable available for me on Ubuntu? PS: at first I thought it was a file creation date problem askubuntu question. But although I figured out the answer to that question, it didn't solve the problem like I thought it would.

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  • Unable to download torrents when using a VPN

    - by chad
    I am running Ubuntu 13.04 and using OpenVPN and vpnbook. When I am using a VPN I am unable to download torrents. I have tried it on 3 different torrent clients (qBittorrent, Deluge, and Transmission). Deluge just says "Checking" and never begins downloading. qBittorrent says "stalled" next to the torrent and Transmission does not say anything and just doesn't download. Is there some network setting I am missing or some OpenVPN config I need to do?

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  • why is emacs allowing multiple instances?

    - by Chad
    Around the time I fresh installed Ubuntu 12.04, I noticed that I can start multiple instances of Emacs. I find this annoying because I will think that a buffer should be open, but I'm in the wrong Emacs window. I may have changed something in .emacs, but I really don't think I did. I also reverted all of my customizations that are stored in ~/.emacs.d/custom.el. Emacs previously would give some error about another emacs server being open when I would attempt to start an additional instance of it, but it no longer does this. Any ideas on how to restore this behavior?

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  • ubuntu 12.04 returns to login screen on resume from suspend. Is there a fix?

    - by Chad
    When I resume from a suspend, Ubuntu 12.04 will come back ok for about 10 seconds, and then the screen blanks out. After about another second or so, it returns to the login screen for Unity 2d. It normally runs Unity 3d. How can I fix this? I've lost a lot of work from this problem. I sometimes get a error report window asking me to report a Compiz crash after rebooting. I think it may be Compiz or the Xorg server causing the problem. I'm not really sure. Thanks if you can provide help.

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  • WatiN SelectList Methods - Page not refreshing/actions not being fired after interacting with a sele

    - by Chad M
    Preface: If you don't care about the preface, skip down to the section marked "Question." Hi, Recently my company has upgraded to the latest version of WatiN for its test automation framework. We upgraded to avoid a problem where interacting with a select list would cause an ACCSES DENIED error. This error seems to be a product of the fact that our web application reloads the page it is on (which sits in a frame which sits in a frameset) with new fields after certain select lists options are selected. It could also be that our framework, which wraps around WatiN, often does actions on the same SelectList after the page refresh (I'm still looking into this, I'm new to the framework). The new version of WatiN does solve the ACCESS DENIED error, but also seems to stop select lists from firing the action that causes the page to reload w/ its new options. In fact, if you use WatiN to make the selection, the select list won't work correctly, even if manually interacted with, until the page has been forced to refresh. Question: When selecting an option in a SelectList using the newest WatiN code, the event that causes our web app's page to reload with new fields/values does not execute. What are some possibilities that could cause this? The term i've seen used most often to describe the refreshing that occurs when our select lists are used is "double post-back". Many thanks, Chad

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  • JavaFX 2.0 - How to change legend color of a LineChart dynamically?

    - by marie
    I am trying to style my JavaFX linechart but I have some trouble with the legend. I know how to change the legend color of a line chart in the css file: .default-color0.chart-series-line { -fx-stroke: #FF0000, white; } .default-color1.chart-series-line { -fx-stroke: #00FF00, white; } .default-color2.chart-series-line { -fx-stroke: #0000FF, white; } .default-color0.chart-line-symbol { -fx-background-color: #FF0000, white; } .default-color1.chart-line-symbol { -fx-background-color: #00FF00, white; } .default-color2.chart-line-symbol { -fx-background-color: #0000FF, white; } But this is not enough for my purposes. I have three or more colored toggle buttons and a series of data for every button. The data should be displayed in the same color the button has after I have selected the button. This should be possible with a multiselection of the buttons, so that more than one series of data can be displayed simultaneously. For the chart lines I have managed it by changing the style after I clicked the button: .. dataList.add(series); .. series.getNode().setStyle("-fx-stroke: rgba(" + rgba + ")"); If I deselect the button I remove the data from the list. dataList.remove(series); That is working fine for the strokes, but how can I do the same for the legend? You can see an example below. First I clicked the red button, thus the stroke and the legend is red (default-color0). After that I clicked the blue button. Here you can see the problem. The stroke is blue but the legend is green, because default color1 is used and I do not know how to change the legend color.

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  • How to Implement Overlay blend method using opengles 1.1

    - by Cylon
    Blow is the algorithm of overlay. and i want using it on iphone, but iphone 3g only support opengles 1.1, can not using glsl. can i using blend function or texture combine to implement it. thank you /////////Reference from OpenGL Shading® Language Third Edition /////////// 19.6.12 Overlay OVERLAY first computes the luminance of the base value. If the luminance value is less than 0.5, the blend and base values are multiplied together. If the luminance value is greater than 0.5, a screen operation is performed. The effect is that the base value is mixed with the blend value, rather than being replaced. This allows patterns and colors to overlay the base image, but shadows and highlights in the base image are preserved. A discontinuity occurs where luminance = 0.5. To provide a smooth transition, we actually do a linear blend of the two equations for luminance in the range [0.45,0.55]. float luminance = dot(base, lumCoeff); if (luminance < 0.45) result = 2.0 * blend * base; else if (luminance 0.55) result = white - 2.0 * (white - blend) * (white - base); else { vec4 result1 = 2.0 * blend * base; vec4 result2 = white - 2.0 * (white - blend) * (white - base); result = mix(result1, result2, (luminance - 0.45) * 10.0); }

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  • Keeping up with New Releases

    - by Jeremy Smyth
    You can keep up with the latest developments in MySQL software in a number of ways, including various blogs and other channels. However, for the most correct (if somewhat dry and factual) information, you can go directly to the source.  Major Releases  For every major release, the MySQL docs team creates and maintains a "nutshell" page containing the significant changes in that release. For the current GA release (whatever that is) you'll find it at this location: https://dev.mysql.com/doc/mysql/en/mysql-nutshell.html  At the moment, this redirects to the summary notes for MySQL 5.6. The notes for MySQL 5.7 are also available at that website, at the URL http://dev.mysql.com/doc/refman/5.7/en/mysql-nutshell.html, and when eventually that version goes GA, it will become the currently linked notes from the URL shown above. Incremental Releases  For more detail on each incremental release, you can have a look at the release notes for each revision. For MySQL 5.6, the release notes are stored at the following location: http://dev.mysql.com/doc/relnotes/mysql/5.6/en/ At the time I write this, the topmost entry is a link for MySQL 5.6.15. Each linked page shows the changes in that particular version, so if you are currently running 5.6.11 and are interested in what bugs were fixed in versions since then, you can look at each subsequent release and see all changes in glorious detail. One really clever thing you can do with that site is do an advanced Google search to find exactly when a feature was released, and find out its release notes. By using the preceding link in a "site:" directive in Google, you can search only within those pages for an entry. For example, the following Google search shows pages within the release notes that reference the --slow-start-timeout option:     site:http://dev.mysql.com/doc/relnotes/mysql/ "--slow-start-timeout" By running that search, you can see that the option was added in MySQL 5.6.5 and also rolled into MySQL 5.5.20.   White Papers Also, with each major release you can usually find a white paper describing what's new in that release. In MySQL 5.6 there was a "What's new" whitepaper at this location: http://www.mysql.com/why-mysql/white-papers/whats-new-mysql-5-6/ You'll find other white papers at: http://www.mysql.com/why-mysql/white-papers/ Search the page for "5.6" to see any papers dealing specificallly with that version.

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  • Why is Spritebatch drawing my Textures out of order?

    - by Andrew
    I just started working with XNA Studio after programming 2D games in java. Because of this, I have absolutely no experience with Spritebatch and sprite sorting. In java, I could just layer the images by calling the draw methods in order. For a while, my Spritebatch was working fine in deferred sorting mode, but when I made a change to one of my textures, it suddenly started drawing them out of order. I have searched for a solution to this problem, but nothing seems to work. I have tried adding layer depths to the sprites and changing the sort mode to BackToFront or FrontToBack or even immediate, but nothing seems to work. Here is my drawing code: protected override void Draw(GameTime gameTime) { GraphicsDevice.Clear(Color.Gray); Game1.spriteBatch.Begin(SpriteSortMode.Deferred, BlendState.AlphaBlend, SamplerState.PointClamp, null, null); for (int x = 0; x < 5; x++) { for (int y = 0; y < 5; y++) { region[x, y].draw(((float)w / aw)); // Draws the Tile-Based background } } player.draw(spriteBatch, ((float)w / aw));//draws the character (This method is where the problem occurs) enemy.draw(spriteBatch, (float)w/aw); // draws a basic enemy Game1.spriteBatch.End(); base.Draw(gameTime); } player.draw method: public void draw(SpriteBatch sb, float ratio){ //draws the player base (The character without hair or equipment) sb.Draw(playerbase[0], new Rectangle((int)(pos.X - (24 * ratio)), (int)(pos.Y - (48 * ratio)), (int)(48 * ratio), (int)(48 * ratio)), new Rectangle(orientation * 48, animFrame * 48, 48, 48), Color.White,0,Vector2.Zero,SpriteEffects.None,0); //draws the player's hair sb.Draw(playerbase[3], new Rectangle((int)(pos.X - (24 * ratio)), (int)(pos.Y - (48 * ratio)), (int)(48 * ratio), (int)(48 * ratio)), new Rectangle(orientation * 48, animFrame * 48, 48, 48), Color.White, 0, Vector2.Zero, SpriteEffects.None, 0); //draws the player's shirt sb.Draw(equipment[0], new Rectangle((int)(pos.X - (24 * ratio)), (int)(pos.Y - (48 * ratio)), (int)(48 * ratio), (int)(48 * ratio)), new Rectangle(orientation * 48, animFrame * 48, 48, 48), Color.White, 0, Vector2.Zero, SpriteEffects.None, 0); //draws the player's pants sb.Draw(equipment[1], new Rectangle((int)(pos.X - (24 * ratio)), (int)(pos.Y - (48 * ratio)), (int)(48 * ratio), (int)(48 * ratio)), new Rectangle(orientation * 48, animFrame * 48, 48, 48), Color.White, 0, Vector2.Zero, SpriteEffects.None, 0); //draws the player's shoes sb.Draw(equipment[2], new Rectangle((int)(pos.X - (24 * ratio)), (int)(pos.Y - (48 * ratio)), (int)(48 * ratio), (int)(48 * ratio)), new Rectangle(orientation * 48, animFrame * 48, 48, 48), Color.White, 0, Vector2.Zero, SpriteEffects.None, 0); } the game has a top-down perspective much like the early legend of zelda games. It draws sections of the texture depending on which direction the character is facing and the animation frame. However, instead of drawing the character in the order the draw methods are called, it ends up drawing the character out of order. Please help me with this problem.

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  • Jquery Slidetoggle open 1 div and close another

    - by Stephen
    I'm trying to close one div when clicking on another div . Currently, it opens multiple divs at one time. JQUERY: $(document).ready(function() { $(".dropdown dt a").click(function() { var dropID = $(this).closest("dl").attr("id"); $("#"+dropID+" dd ul").slideToggle(200); return false; }); $(".dropdown dd ul li a").click(function() { var dropID = $(this).closest("dl").attr("id"); var text = $(this).html(); var selVal = $(this).find(".dropdown_value").html(); $("#"+dropID+" dt a").html(text); $("#"+dropID+" dd ul").hide(); return false; }); $("dl[class!=dropdown]").click(function() { $(".dropdown dd ul").hide(); return false; }); $("id!=quotetoolContainer").click(function() { $(".dropdown dd ul").hide(); return false; }); $('body').click(function() { $(".dropdown dd ul").hide(); return false; }); $('.productSelection').children().hover(function() { $(this).siblings().stop().fadeTo(200,0.5); }, function() { $(this).siblings().stop().fadeTo(200,1); }); }); HTML: <div id="quotetoolContainer"> <div class="top"></div> <div id="quotetool"> <h2>Instant Price Calculator</h2> <p>Document Type</p> <dl id="docType" class="dropdown"> <dt><a href="#"><span>Select a Document Type</span></a></dt> <dd> <ul> <li><a href="#" id="1">Datasheets<span class="value">Datasheets</span></a></li> <li><a href="#">Manuals<span class="value">Manuals</span></a></li> <li><a href="#">Brochures<span class="value">Brochures</span></a></li> <li><a href="#">Newsletters<span class="value">Newsletters</span></a></li> <li><a href="#">Booklets<span class="value">Booklets</span></a></li> </ul> </dd> </dl> <p>Flat Size</p> <dl id="flatSize" class="dropdown"> <dt><a href="#">8.5" x 11"<span class="value">8.5" x 11"</span></a></dt> <dd> <ul> <li><a href="#">8.5" x 11"<span class="value">8.5" x 11"</span></a></li> <li><a href="#">11" x 17"<span class="value">11" x 17"</span></a></li> </ul> </dd> </dl> <p>Full Color or Black &amp; White?</p> <dl id="color" class="dropdown"> <dt><a href="#">Full Color<span class="value">Full Color</span></a></dt> <dd> <ul> <li><a href="#">Full Color<span class="value">Full Color</span></a></li> <li><a href="#">Black &amp; White<span class="value">Black &amp; White</span></a></li> </ul> </dd> </dl> <p>Paper</p> <dl id="paper" class="dropdown"> <dt><a href="#">Value White Paper (20 lb.)<span class="value">Value White Paper (20 lb.)</span></a></dt> <dd> <ul> <li><a href="#">Value White Paper (20 lb.)<span class="value">Value White Paper (20 lb.)</span></a></li> <li><a href="#">Premium White Paper (28 lb.)<span class="value">Premium White Paper (28 lb.)</span></a></li> <li><a href="#">Glossy White Text (80 lb.) - Recycled<span class="value">Glossy White Text (80 lb.) - Recycled</span></a></li> <li><a href="#">Glossy White Cover (80 lb.) - Recycled<span class="value">Glossy White Cover (80 lb.) - Recycled</span></a></li> </ul> </dd> </dl> <p>Folding</p> <dl id="folding" class="dropdown"> <dt><a href="#">Fold in Half<span class="value">Fold in Half</span></a></dt> <dd> <ul> <li><a href="#">Fold in Half<span class="value">Fold in Half</span></a></li> <li><a href="#">Tri-Fold<span class="value">Tri-Fold</span></a></li> <li><a href="#">Z-Fold<span class="value">Z-Fold</span></a></li> <li><a href="#">Double-Parallel Fold<span class="value">Double-Parallel Fold</span></a></li> </ul> </dd> </dl> <p>Three-Hole Drill</p> <dl id="drill" class="dropdown"> <dt><a href="#">No<span class="value">No</span></a></dt> <dd> <ul> <li><a href="#">No<span class="value">No</span></a></li> <li><a href="#">Yes<span class="value">Yes</span></a></li> </ul> </dd> </dl> <p>Qty</p> <dl id="quantity" class="dropdown"> <dt><a href="#">50<span class="value">50</span></a></dt> <dd> <ul> <li><a href="#">50<span class="value">50</span></a></li> <li><a href="#">100<span class="value">100</span></a></li> <li><a href="#">150<span class="value">150</span></a></li> <li><a href="#">200<span class="value">200</span></a></li> <li><a href="#">250<span class="value">250</span></a></li> <li><a href="#">500<span class="value">500</span></a></li> <li><a href="#">750<span class="value">750</span></a></li> <li><a href="#">1,000<span class="value">1,000</span></a></li> <li><a href="#">1,500<span class="value">1,500</span></a></li> <li><a href="#">2,000<span class="value">2,000</span></a></li> <li><a href="#">2,500<span class="value">2,500</span></a></li> <li><a href="#">3,000<span class="value">3,000</span></a></li> <li><a href="#">3,500<span class="value">3,500</span></a></li> <li><a href="#">4,000<span class="value">4,000</span></a></li> <li><a href="#">4,500<span class="value">4,500</span></a></li> <li><a href="#">5,000<span class="value">5,000</span></a></li> <li><a href="#">5,500<span class="value">5,500</span></a></li> <li><a href="#">6,000<span class="value">6,000</span></a></li> <li><a href="#">6,500<span class="value">6,500</span></a></li> <li><a href="#">7,000<span class="value">7,000</span></a></li> <li><a href="#">7,500<span class="value">7,500</span></a></li> <li><a href="#">8,000<span class="value">8,000</span></a></li> <li><a href="#">8,500<span class="value">8,500</span></a></li> <li><a href="#">9,000<span class="value">9,000</span></a></li> <li><a href="#">9,500<span class="value">9,500</span></a></li> <li><a href="#">10,000<span class="value">10,000</span></a></li> <li><a href="#">12,500<span class="value">12,500</span></a></li> <li><a href="#">15,000<span class="value">15,000</span></a></li> <li><a href="#">17,500<span class="value">17,500</span></a></li> <li><a href="#">20,000<span class="value">20,000</span></a></li> </ul> </dd> </dl> <div id="priceTotal"> <div class="priceText">Your Price:</div> <div class="price">$29.00</div> <div class="clear"></div> </div> <div id="buttonQuoteStart"><a href="#" title="Start Printing">Start Printing</a></div> </div> <div class="bottom"></div> </div>

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  • Adding Client Validation To DataAnnotations DataType Attribute

    - by srkirkland
    The System.ComponentModel.DataAnnotations namespace contains a validation attribute called DataTypeAttribute, which takes an enum specifying what data type the given property conforms to.  Here are a few quick examples: public class DataTypeEntity { [DataType(DataType.Date)] public DateTime DateTime { get; set; }   [DataType(DataType.EmailAddress)] public string EmailAddress { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This attribute comes in handy when using ASP.NET MVC, because the type you specify will determine what “template” MVC uses.  Thus, for the DateTime property if you create a partial in Views/[loc]/EditorTemplates/Date.ascx (or cshtml for razor), that view will be used to render the property when using any of the Html.EditorFor() methods. One thing that the DataType() validation attribute does not do is any actual validation.  To see this, let’s take a look at the EmailAddress property above.  It turns out that regardless of the value you provide, the entity will be considered valid: //valid new DataTypeEntity {EmailAddress = "Foo"}; .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Hmmm.  Since DataType() doesn’t validate, that leaves us with two options: (1) Create our own attributes for each datatype to validate, like [Date], or (2) add validation into the DataType attribute directly.  In this post, I will show you how to hookup client-side validation to the existing DataType() attribute for a desired type.  From there adding server-side validation would be a breeze and even writing a custom validation attribute would be simple (more on that in future posts). Validation All The Way Down Our goal will be to leave our DataTypeEntity class (from above) untouched, requiring no reference to System.Web.Mvc.  Then we will make an ASP.NET MVC project that allows us to create a new DataTypeEntity and hookup automatic client-side date validation using the suggested “out-of-the-box” jquery.validate bits that are included with ASP.NET MVC 3.  For simplicity I’m going to focus on the only DateTime field, but the concept is generally the same for any other DataType. Building a DataTypeAttribute Adapter To start we will need to build a new validation adapter that we can register using ASP.NET MVC’s DataAnnotationsModelValidatorProvider.RegisterAdapter() method.  This method takes two Type parameters; The first is the attribute we are looking to validate with and the second is an adapter that should subclass System.Web.Mvc.ModelValidator. Since we are extending DataAnnotations we can use the subclass of ModelValidator called DataAnnotationsModelValidator<>.  This takes a generic argument of type DataAnnotations.ValidationAttribute, which lucky for us means the DataTypeAttribute will fit in nicely. So starting from there and implementing the required constructor, we get: public class DataTypeAttributeAdapter : DataAnnotationsModelValidator<DataTypeAttribute> { public DataTypeAttributeAdapter(ModelMetadata metadata, ControllerContext context, DataTypeAttribute attribute) : base(metadata, context, attribute) { } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Now you have a full-fledged validation adapter, although it doesn’t do anything yet.  There are two methods you can override to add functionality, IEnumerable<ModelValidationResult> Validate(object container) and IEnumerable<ModelClientValidationRule> GetClientValidationRules().  Adding logic to the server-side Validate() method is pretty straightforward, and for this post I’m going to focus on GetClientValidationRules(). Adding a Client Validation Rule Adding client validation is now incredibly easy because jquery.validate is very powerful and already comes with a ton of validators (including date and regular expressions for our email example).  Teamed with the new unobtrusive validation javascript support we can make short work of our ModelClientValidationDateRule: public class ModelClientValidationDateRule : ModelClientValidationRule { public ModelClientValidationDateRule(string errorMessage) { ErrorMessage = errorMessage; ValidationType = "date"; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } If your validation has additional parameters you can the ValidationParameters IDictionary<string,object> to include them.  There is a little bit of conventions magic going on here, but the distilled version is that we are defining a “date” validation type, which will be included as html5 data-* attributes (specifically data-val-date).  Then jquery.validate.unobtrusive takes this attribute and basically passes it along to jquery.validate, which knows how to handle date validation. Finishing our DataTypeAttribute Adapter Now that we have a model client validation rule, we can return it in the GetClientValidationRules() method of our DataTypeAttributeAdapter created above.  Basically I want to say if DataType.Date was provided, then return the date rule with a given error message (using ValidationAttribute.FormatErrorMessage()).  The entire adapter is below: public class DataTypeAttributeAdapter : DataAnnotationsModelValidator<DataTypeAttribute> { public DataTypeAttributeAdapter(ModelMetadata metadata, ControllerContext context, DataTypeAttribute attribute) : base(metadata, context, attribute) { }   public override System.Collections.Generic.IEnumerable<ModelClientValidationRule> GetClientValidationRules() { if (Attribute.DataType == DataType.Date) { return new[] { new ModelClientValidationDateRule(Attribute.FormatErrorMessage(Metadata.GetDisplayName())) }; }   return base.GetClientValidationRules(); } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Putting it all together Now that we have an adapter for the DataTypeAttribute, we just need to tell ASP.NET MVC to use it.  The easiest way to do this is to use the built in DataAnnotationsModelValidatorProvider by calling RegisterAdapter() in your global.asax startup method. DataAnnotationsModelValidatorProvider.RegisterAdapter(typeof(DataTypeAttribute), typeof(DataTypeAttributeAdapter)); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Show and Tell Let’s see this in action using a clean ASP.NET MVC 3 project.  First make sure to reference the jquery, jquery.vaidate and jquery.validate.unobtrusive scripts that you will need for client validation. Next, let’s make a model class (note we are using the same built-in DataType() attribute that comes with System.ComponentModel.DataAnnotations). public class DataTypeEntity { [DataType(DataType.Date, ErrorMessage = "Please enter a valid date (ex: 2/14/2011)")] public DateTime DateTime { get; set; } } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Then we make a create page with a strongly-typed DataTypeEntity model, the form section is shown below (notice we are just using EditorForModel): @using (Html.BeginForm()) { @Html.ValidationSummary(true) <fieldset> <legend>Fields</legend>   @Html.EditorForModel()   <p> <input type="submit" value="Create" /> </p> </fieldset> } .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } The final step is to register the adapter in our global.asax file: DataAnnotationsModelValidatorProvider.RegisterAdapter(typeof(DataTypeAttribute), typeof(DataTypeAttributeAdapter)); Now we are ready to run the page: Looking at the datetime field’s html, we see that our adapter added some data-* validation attributes: <input type="text" value="1/1/0001" name="DateTime" id="DateTime" data-val-required="The DateTime field is required." data-val-date="Please enter a valid date (ex: 2/14/2011)" data-val="true" class="text-box single-line valid"> .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Here data-val-required was added automatically because DateTime is non-nullable, and data-val-date was added by our validation adapter.  Now if we try to add an invalid date: Our custom error message is displayed via client-side validation as soon as we tab out of the box.  If we didn’t include a custom validation message, the default DataTypeAttribute “The field {0} is invalid” would have been shown (of course we can change the default as well).  Note we did not specify server-side validation, but in this case we don’t have to because an invalid date will cause a server-side error during model binding. Conclusion I really like how easy it is to register new data annotations model validators, whether they are your own or, as in this post, supplements to existing validation attributes.  I’m still debating about whether adding the validation directly in the DataType attribute is the correct place to put it versus creating a dedicated “Date” validation attribute, but it’s nice to know either option is available and, as we’ve seen, simple to implement. I’m also working through the nascent stages of an open source project that will create validation attribute extensions to the existing data annotations providers using similar techniques as seen above (examples: Email, Url, EqualTo, Min, Max, CreditCard, etc).  Keep an eye on this blog and subscribe to my twitter feed (@srkirkland) if you are interested for announcements.

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  • GIMP: change the exact color on the image - OK, but transparency disappears

    - by Haradzieniec
    There is no problem for me now to change color for pixels of the same color: I click on "Select by Color Tool"(Ctrl+O). The pixels of the same color are selected now. Then I press Ctrl+,. The selected pixels are white now (the white color is taken from a foreground color). The problem is the transparency. The green color I want to change to the white one has transparency. Once I use my method, the transparency disappears so all my-green-color with any transparency becomes white and without transparency. How do I keep the transparency by using my method? My picture is in .png format.

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