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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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  • Java Slick2d - How to translate mouse coordinates to world coordinates

    - by Corey
    I am translating in my main class render. How do I get the mouse position where my mouse actually is after I scroll the screen public void render(GameContainer gc, Graphics g) throws SlickException { float centerX = 800/2; float centerY = 600/2; g.translate(centerX, centerY); g.translate(-player.playerX, -player.playerY); gen.render(g); player.render(g); } playerX = 800 /2 - sprite.getWidth(); playerY = 600 /2 - sprite.getHeight(); Image to help with explanation I tried implementing a camera but it seems no matter what I can't get the mouse position. I was told to do this worldX = mouseX + camX; but it didn't work the mouse was still off. Here is my Camera class if that helps: public class Camera { public float camX; public float camY; Player player; public void init() { player = new Player(); } public void update(GameContainer gc, int delta) { Input input = gc.getInput(); if(input.isKeyDown(Input.KEY_W)) { camY -= player.speed * delta; } if(input.isKeyDown(Input.KEY_S)) { camY += player.speed * delta; } if(input.isKeyDown(Input.KEY_A)) { camX -= player.speed * delta; } if(input.isKeyDown(Input.KEY_D)) { camX += player.speed * delta; } } Code used to convert mouse worldX = (int) (mouseX + cam.camX); worldY = (int) (mouseY + cam.camY);

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  • Top Down RPG Movement w/ Correction?

    - by Corey Ogburn
    I would hope that we have all played Zelda: A Link to the Past, please correct me if I'm wrong, but I want to emulate that kind of 2D, top-down character movement with a touch of correction. It has been done in other games, but I feel this reference would be the easiest to relate to. More specifically the kind of movement and correction I'm talking about is: Floating movement not restricted to tile based movement like Pokemon and other games where one tap of the movement pad moves you one square in that cardinal direction. This floating movement should be able to achieve diagonal motion. If you're walking West and you come to a wall that is diagonal in a North East/South West fashion, you are corrected into a South West movement even if you continue holding left (West) on the controller. This should work for both diagonals correcting in both directions. If you're a few pixels off from walking squarely into a door or hallway, you are corrected into walking through the hall or down the hallway, i.e. bumping into the corner causes you to be pushed into the hall/door. I've hunted for efficient ways to achieve this and have had no luck. To be clear I'm talking about the human character's movement, not an NPC's movement. Are their resources available on this kind of movement? Equations or algorithms explained on a wiki or something? I'm using the XNA Framework, is there anything in it to help with this?

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  • Where can I find video resources of people programming?

    - by Corey
    This might be a strange question. I'm looking for videos of people actively coding something while explaining it. However, I don't want is a beginner video that delves into what variables and objects are. Nick Gravelyn's tile engine tutorial is a great example of what I'm looking for. (He actually used to host the full, unbroken video files in his site's archive, but I guess he took them down...) I tend to learn best by "action" examples; it's difficult for me to learn by reading through documentation and text tutorials, but if I see somebody actively doing a task, I can immediately register it and apply it myself. I'm hard-of-hearing, so I would really prefer that if the video has a lot of talking, it have captioning or subtitling of some sort, or at the very least, a transcript. The tile engine videos did not have captions, but the code he was writing was very self-documenting, so I understood it for the most part. I've gone through most of the relevant GoogleDevelopers and GoogleTechTalks videos on Youtube, so those need not apply. Are there any resources out there, or even websites dedicated to this kind of thing?

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  • CSS practices: negative positioning

    - by Corey
    I'm somewhat of a novice to CSS. Anyway, I noticed that an extremely common method used in CSS is to have negative or off-screen positioning, whether it be to hide text or preload images or what have you. Even on SE sites, like StackOverflow and this website, have #hlogo a { text-indent: -999999em } set in their CSS. So I guess I have a few questions. is this valid CSS? or is it just a "hack"? are there downsides to doing things this way? why is this so common? aren't there better ways to hide content?

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  • How do you differentiate between "box," "machine," "computer" and whatever else?

    - by Corey
    There seems to be a few terms for referring to a computer, especially in the tech world. Different terms seem to be used based on technical expertise. When talking with people with some technical knowledge, I'll refer to it as a machine. When talking to non-technical people (family, friends) I'll call it a computer. On the rare occasion I'm talking about servers, I might call it a box, but even then I'll probably still call it a machine. Is that just me, or do there exist rules already for what to call a computer?

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  • Tile engine Texture not updating when numbers in array change

    - by Corey
    I draw my map from a txt file. I am using java with slick2d library. When I print the array the number changes in the array, but the texture doesn't change. public class Tiles { public Image[] tiles = new Image[5]; public int[][] map = new int[64][64]; public Image grass, dirt, fence, mound; private SpriteSheet tileSheet; public int tileWidth = 32; public int tileHeight = 32; public void init() throws IOException, SlickException { tileSheet = new SpriteSheet("assets/tiles.png", tileWidth, tileHeight); grass = tileSheet.getSprite(0, 0); dirt = tileSheet.getSprite(7, 7); fence = tileSheet.getSprite(2, 0); mound = tileSheet.getSprite(2, 6); tiles[0] = grass; tiles[1] = dirt; tiles[2] = fence; tiles[3] = mound; int x=0, y=0; BufferedReader in = new BufferedReader(new FileReader("assets/map.dat")); String line; while ((line = in.readLine()) != null) { String[] values = line.split(","); x = 0; for (String str : values) { int str_int = Integer.parseInt(str); map[x][y]=str_int; //System.out.print(map[x][y] + " "); x++; } //System.out.println(""); y++; } in.close(); } public void update(GameContainer gc) { } public void render(GameContainer gc) { for(int y = 0; y < map.length; y++) { for(int x = 0; x < map[0].length; x ++) { int textureIndex = map[x][y]; Image texture = tiles[textureIndex]; texture.draw(x*tileWidth,y*tileHeight); } } } } Mouse Picking Where I change the number in the array Input input = gc.getInput(); gc.getInput().setOffset(cameraX-400, cameraY-300); float mouseX = input.getMouseX(); float mouseY = input.getMouseY(); double mousetileX = Math.floor((double)mouseX/tiles.tileWidth); double mousetileY = Math.floor((double)mouseY/tiles.tileHeight); double playertileX = Math.floor(playerX/tiles.tileWidth); double playertileY = Math.floor(playerY/tiles.tileHeight); double lengthX = Math.abs((float)playertileX - mousetileX); double lengthY = Math.abs((float)playertileY - mousetileY); double distance = Math.sqrt((lengthX*lengthX)+(lengthY*lengthY)); if(input.isMousePressed(Input.MOUSE_LEFT_BUTTON) && distance < 4) { System.out.println("Clicked"); if(tiles.map[(int)mousetileX][(int)mousetileY] == 1) { tiles.map[(int)mousetileX][(int)mousetileY] = 0; } } I never ask a question until I have tried to figure it out myself. I have been stuck with this problem for two weeks. It's not like this site is made for asking questions or anything. So if you actually try to help me instead of telling me to use a debugger thank you. You either get told you have too much or too little code. Nothing is never enough for the people on here it's as bad as something like reddit. Idk what is wrong all my textures work when I render them it just doesn't update when the number in the array changes. I am obviously debugging when I say that I was printing the array and the number is changing like it should, so it's not a problem with my mouse picking code. It is a problem with my textures, but I don't know what because they all render correctly. That is why I need help.

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  • Microsoft hosting free Hyper-V training for VMware Pros

    - by Ryan Roussel
    Microsoft will be hosting free training for virtualization professionals focused on Hyper-V, System Center, and virtualization architecture.  Details are below:   Just one week after Microsoft Management Summit 2011 (MMS), Microsoft Learning will be hosting an exclusive three-day Jump Start class specially tailored for VMware and Microsoft virtualization technology pros.  Registration for “Microsoft Virtualization for VMware Professionals” is open now and will be delivered as an online class on March 29-31, 2010 from 10:00am-4:00pm PDT.    The course is COMPLETELY FREE and OPEN TO ANYONE!  Please share with your customers, blog, Tweet, etc. – help us get the word out to strengthen support for Microsoft’s virtualization offerings. What’s the high-level overview? This cutting edge course will feature expert instruction and real-world demonstrations of Hyper-V and brand new releases from System Center Virtual Machine Manager 2012 Beta (many of which will be announced just one week earlier at MMS).  Register Now!   Day 1 will focus on “Platform” (Hyper-V, virtualization architecture, high availability & clustering) 10:00am – 10:30pm PDT:  Virtualization 360 Overview 10:30am – 12:00pm:  Microsoft Hyper-V Deployment Options & Architecture 1:00pm – 2:00pm:  Differentiating Microsoft and VMware (terminology, etc.) 2:00pm – 4:00pm:  High Availability & Clustering Day 2 will focus on “Management” (System Center Suite, SCVMM 2012 Beta, Opalis, Private Cloud solutions) 10:00am – 11:00pm PDT:  System Center Suite Overview w/ focus on DPM 11:00am – 12:00pm:  Virtual Machine Manager 2012 | Part 1 1:00pm –   1:30pm:  Virtual Machine Manager 2012 | Part 2 1:30pm – 2:30pm:  Automation with System Center Opalis & PowerShell 2:30pm – 4:00pm:  Private Cloud Solutions, Architecture & VMM SSP 2.0 Day 3 will focus on “VDI” (VDI Infrastructure/architecture, v-Alliance, application delivery via VDI) 10:00am – 11:00pm PDT:  Virtual Desktop Infrastructure (VDI) Architecture | Part 1 11:00am – 12:00pm:  Virtual Desktop Infrastructure (VDI) Architecture | Part 2 1:00pm – 2:30pm:  v-Alliance Solution Overview 2:30pm – 4:00pm:  Application Delivery for VDI     Every section will be team-taught by two of the most respected authorities on virtualization technologies: Microsoft Technical Evangelist Symon Perriman and leading Hyper-V, VMware, and XEN infrastructure consultant, Corey Hynes Who is the target audience for this training? Suggested prerequisite skills include real-world experience with Windows Server 2008 R2, virtualization and datacenter management. The course is tailored to these types of roles: · IT Professional · IT Decision Maker · Network Administrators & Architects · Storage/Infrastructure Administrators & Architects How do I to register and learn more about this great training opportunity? · Register: Visit the Registration Page and sign up for all three sessions · Blog: Learn more from the Microsoft Learning Blog · Twitter: Here are a few posts you can retweet: o Mar. 29-31 "Microsoft #Virtualization for VMware Pros" @SymonPerriman Corey Hynes http://bit.ly/JS-Hyper-V @MSLearning #Hyper-V o @SysCtrOpalis Mar. 29-31 "Microsoft #Virtualization for VMware Pros" @SymonPerriman Corey Hynes http://bit.ly/JS-Hyper-V #Hyper-V o Learn all the cool new features in Hyper-V & System Center 2012! SCVMM, Self-Service Portal 2.0, http://bit.ly/JS-Hyper-V #Hyper-V #Opalis What is a “Jump Start” course? A “Jump Start” course is “team-taught” by two expert instructors in an engaging radio talk show style format. The idea is to deliver readiness training on strategic and emerging technologies that drive awareness at scale before Microsoft Learning develops mainstream Microsoft Official Courses (MOC) that map to certifications.  All sessions are professionally recorded and distributed through MS Showcase, Channel 9, Zune Marketplace and iTunes for broader reach.

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  • Do licenses matter if there's nobody around to enforce them?

    - by Corey
    Suppose that the original creators can't (or won't) enforce a license on their software/code, but that work is still popular. I guess if you want to visualize it, I'll throw out a convoluted hypothetical: Imagine a very small group of developers that released a code project under an open-source license. The repository was hosted on their servers. However, the everybody on the immediate development team passed away in a tragic accident or something. Their servers shut down after this happened. The project had a fairly large user base, and so others began to host the last revision on their own servers for others to download. (Yes, I have an active imagination) Does abiding by the license simply become a matter of morality by its users, or can there still exist a legal penalty when there is no one user or group to enforce it? Could anything be done if an unscrupulous user decided to branch off the project and use it under a different license? I am not looking for legal advice -- I am simply curious about how software licenses work. I tend to think of strange situations and wonder what would happen in those scenarios.

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  • Accept keyboard input when game is not in focus?

    - by Corey Ogburn
    I want to be able to control the game via keyboard while the game does not have focus... How can I do this in XNA? EDIT: I bought a tablet. I want to write a separate app to overly the screen with controls that will send keyboard input to the game. Although, it's not sending the input DIRECT to the game, it's using the method discussed in this SO question: http://stackoverflow.com/questions/6446085/emulate-held-down-key-on-keyboard To my understanding, my test app is working the way it should be but the game is not responding to this input. I originally thought that Keyboard.GetState() would get the state regardless that the game is not in focus, but that doesn't appear to be the case.

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  • ubuntu 10.04 logs itself out overnight

    - by Corey
    Every night when I leave work, I lock the screen via ubuntu's "power" button in the top right hand panel. When I come to work in the morning, I'm greeted with the log-in screen. This doesn't happen every night, but most. I'm running ubuntu 10.04 on a Dell inspiron. I've included some HW specs, and also dmesg output. Please let me know what other logs may be useful. thanks! Corey ~$ dmesg [20559.696062] type=1503 audit(1285957687.048:16): operation="open" pid=6212 parent=1 profile="/usr/bin/evince" requested_mask="::r" denied_mask="::r" fsuid=1000 ouid=0 name="/usr/local/lib/libltdl.so.7.2.2" [21127.951621] type=1503 audit(1285958255.300:17): operation="open" pid=6390 parent=1 profile="/usr/bin/evince" requested_mask="::r" denied_mask="::r" fsuid=1000 ouid=0 name="/usr/local/lib/libltdl.so.7.2.2" [291038.528014] [drm:i915_hangcheck_elapsed] *ERROR* Hangcheck timer elapsed... GPU hung [291038.528025] render error detected, EIR: 0x00000000 [291038.528042] [drm:i915_do_wait_request] *ERROR* i915_do_wait_request returns -5 (awaiting 22973891 at 22973890) [291038.828014] [drm:i915_hangcheck_elapsed] *ERROR* Hangcheck timer elapsed... GPU hung [291038.828023] render error detected, EIR: 0x00000000 [291038.828042] [drm:i915_do_wait_request] *ERROR* i915_do_wait_request returns -5 (awaiting 22973894 at 22973890) ~$ lspci -vv 00:00.0 Host bridge: Intel Corporation 4 Series Chipset DRAM Controller (rev 03) Subsystem: Dell Device 02e1 Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B+ ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort+ >SERR- <PERR- INTx- Latency: 0 Capabilities: <access denied> Kernel driver in use: agpgart-intel Kernel modules: intel-agp 00:02.0 VGA compatible controller: Intel Corporation 4 Series Chipset Integrated Graphics Controller (rev 03) Subsystem: Dell Device 02e1 Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx+ Status: Cap+ 66MHz- UDF- FastB2B+ ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin A routed to IRQ 27 Region 0: Memory at fe400000 (64-bit, non-prefetchable) [size=4M] Region 2: Memory at d0000000 (64-bit, prefetchable) [size=256M] Region 4: I/O ports at dc00 [size=8] Capabilities: <access denied> Kernel driver in use: i915 Kernel modules: i915 00:1b.0 Audio device: Intel Corporation N10/ICH 7 Family High Definition Audio Controller (rev 01) Subsystem: Dell Device 02e1 Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0, Cache Line Size: 32 bytes Interrupt: pin A routed to IRQ 16 Region 0: Memory at feaf8000 (64-bit, non-prefetchable) [size=16K] Capabilities: <access denied> Kernel driver in use: HDA Intel Kernel modules: snd-hda-intel 00:1c.0 PCI bridge: Intel Corporation N10/ICH 7 Family PCI Express Port 1 (rev 01) Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR+ FastB2B- DisINTx+ Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0, Cache Line Size: 32 bytes Bus: primary=00, secondary=01, subordinate=01, sec-latency=0 I/O behind bridge: 00001000-00001fff Memory behind bridge: 80000000-801fffff Prefetchable memory behind bridge: 0000000080200000-00000000803fffff Secondary status: 66MHz- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- <SERR- <PERR- BridgeCtl: Parity- SERR+ NoISA+ VGA- MAbort- >Reset- FastB2B- PriDiscTmr- SecDiscTmr- DiscTmrStat- DiscTmrSERREn- Capabilities: <access denied> Kernel driver in use: pcieport Kernel modules: shpchp 00:1c.1 PCI bridge: Intel Corporation N10/ICH 7 Family PCI Express Port 2 (rev 01) Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR+ FastB2B- DisINTx+ Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0, Cache Line Size: 32 bytes Bus: primary=00, secondary=02, subordinate=02, sec-latency=0 I/O behind bridge: 0000e000-0000efff Memory behind bridge: feb00000-febfffff Prefetchable memory behind bridge: 00000000fdf00000-00000000fdffffff Secondary status: 66MHz- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- <SERR- <PERR- BridgeCtl: Parity- SERR+ NoISA+ VGA- MAbort- >Reset- FastB2B- PriDiscTmr- SecDiscTmr- DiscTmrStat- DiscTmrSERREn- Capabilities: <access denied> Kernel driver in use: pcieport Kernel modules: shpchp 00:1d.0 USB Controller: Intel Corporation N10/ICH7 Family USB UHCI Controller #1 (rev 01) Subsystem: Dell Device 02e1 Control: I/O+ Mem- BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap- 66MHz- UDF- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin A routed to IRQ 23 Region 4: I/O ports at d880 [size=32] Kernel driver in use: uhci_hcd 00:1d.1 USB Controller: Intel Corporation N10/ICH 7 Family USB UHCI Controller #2 (rev 01) Subsystem: Dell Device 02e1 Control: I/O+ Mem- BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap- 66MHz- UDF- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin B routed to IRQ 19 Region 4: I/O ports at d800 [size=32] Kernel driver in use: uhci_hcd 00:1d.2 USB Controller: Intel Corporation N10/ICH 7 Family USB UHCI Controller #3 (rev 01) Subsystem: Dell Device 02e1 Control: I/O+ Mem- BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap- 66MHz- UDF- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin C routed to IRQ 18 Region 4: I/O ports at d480 [size=32] Kernel driver in use: uhci_hcd 00:1d.3 USB Controller: Intel Corporation N10/ICH 7 Family USB UHCI Controller #4 (rev 01) Subsystem: Dell Device 02e1 Control: I/O+ Mem- BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap- 66MHz- UDF- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin D routed to IRQ 16 Region 4: I/O ports at d400 [size=32] Kernel driver in use: uhci_hcd 00:1d.7 USB Controller: Intel Corporation N10/ICH 7 Family USB2 EHCI Controller (rev 01) (prog-if 20) Subsystem: Dell Device 02e1 Control: I/O- Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin A routed to IRQ 23 Region 0: Memory at feaf7c00 (32-bit, non-prefetchable) [size=1K] Capabilities: <access denied> Kernel driver in use: ehci_hcd 00:1e.0 PCI bridge: Intel Corporation 82801 PCI Bridge (rev e1) (prog-if 01) Control: I/O- Mem- BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR+ FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Bus: primary=00, secondary=03, subordinate=03, sec-latency=32 Secondary status: 66MHz- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort+ <SERR- <PERR- BridgeCtl: Parity- SERR+ NoISA+ VGA- MAbort- >Reset- FastB2B- PriDiscTmr- SecDiscTmr- DiscTmrStat- DiscTmrSERREn- Capabilities: <access denied> 00:1f.0 ISA bridge: Intel Corporation 82801GB/GR (ICH7 Family) LPC Interface Bridge (rev 01) Subsystem: Dell Device 02e1 Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Capabilities: <access denied> Kernel modules: iTCO_wdt, intel-rng 00:1f.2 IDE interface: Intel Corporation N10/ICH7 Family SATA IDE Controller (rev 01) (prog-if 8f [Master SecP SecO PriP PriO]) Subsystem: Dell Device 02e1 Control: I/O+ Mem- BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap+ 66MHz+ UDF- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0 Interrupt: pin B routed to IRQ 19 Region 0: I/O ports at d080 [size=8] Region 1: I/O ports at d000 [size=4] Region 2: I/O ports at cc00 [size=8] Region 3: I/O ports at c880 [size=4] Region 4: I/O ports at c800 [size=16] Capabilities: <access denied> Kernel driver in use: ata_piix 00:1f.3 SMBus: Intel Corporation N10/ICH 7 Family SMBus Controller (rev 01) Subsystem: Dell Device 02e1 Control: I/O+ Mem- BusMaster- SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx- Status: Cap- 66MHz- UDF- FastB2B+ ParErr- DEVSEL=medium >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Interrupt: pin B routed to IRQ 5 Region 4: I/O ports at 0400 [size=32] Kernel modules: i2c-i801 02:00.0 Ethernet controller: Realtek Semiconductor Co., Ltd. RTL8101E/RTL8102E PCI Express Fast Ethernet controller (rev 02) Subsystem: Dell Device 02e1 Control: I/O+ Mem+ BusMaster+ SpecCycle- MemWINV- VGASnoop- ParErr- Stepping- SERR- FastB2B- DisINTx+ Status: Cap+ 66MHz- UDF- FastB2B- ParErr- DEVSEL=fast >TAbort- <TAbort- <MAbort- >SERR- <PERR- INTx- Latency: 0, Cache Line Size: 32 bytes Interrupt: pin A routed to IRQ 26 Region 0: I/O ports at e800 [size=256] Region 2: Memory at fdfff000 (64-bit, prefetchable) [size=4K] Region 4: Memory at fdfe0000 (64-bit, prefetchable) [size=64K] Expansion ROM at febe0000 [disabled] [size=128K] Capabilities: <access denied> Kernel driver in use: r8169 Kernel modules: r8169 log$ tail -n 15 Xorg.0.log.old for help. Please also check the log file at "/var/log/Xorg.0.log" for additional information. (II) Power Button: Close (II) UnloadModule: "evdev" (II) Power Button: Close (II) UnloadModule: "evdev" (II) USB Optical Mouse: Close (II) UnloadModule: "evdev" (II) Dell Dell USB Entry Keyboard: Close (II) UnloadModule: "evdev" (II) Macintosh mouse button emulation: Close (II) UnloadModule: "evdev" (II) AIGLX: Suspending AIGLX clients for VT switch ddxSigGiveUp: Closing log

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  • Are there any guidelines for laying out screen "real estate?"

    - by Corey
    I'm wondering if there is any information about creating a decent page layout so that your website will appeal to users of all resolutions. For example, the optimal width for pages. It seems like on my resolution, most websites have their content centered and covers about 80% of the page, which is easy on the eyes. Or maybe the height of the website's logo/header -- some sites I stumble upon have a huge logo with links or navigation under it, making it so that I need to scroll down to see the actual content, like articles or images (these sites don't keep me for very long). I understand that every user is different and may have browser extensions, page zoom or may be running some ancient system that displays in 640x480. I'm not looking for a "best" solution, but rather, some guidelines about designing to accommodate different resolutions. Basically, how can I make sure that I don't design a page where a paragraph might display in several easy-to-read lines on my resolution, but it turns into a single line on a 1920x1080 resolution and makes it hard for the user to follow?

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  • Java - 2d Array Tile Map Collision

    - by Corey
    How would I go about making certain tiles in my array collide with my player? Like say I want every number 2 in the array to collide. I am reading my array from a txt file if that matters and I am using the slick2d library. Here is my code if needed. public class Tiles { Image[] tiles = new Image[3]; int[][] map = new int[500][500]; Image grass, dirt, mound; SpriteSheet tileSheet; int tileWidth = 32; int tileHeight = 32; public void init() throws IOException, SlickException { tileSheet = new SpriteSheet("assets/tiles.png", tileWidth, tileHeight); grass = tileSheet.getSprite(0, 0); dirt = tileSheet.getSprite(7, 7); mound = tileSheet.getSprite(2, 6); tiles[0] = grass; tiles[1] = dirt; tiles[2] = mound; int x=0, y=0; BufferedReader in = new BufferedReader(new FileReader("assets/map.txt")); String line; while ((line = in.readLine()) != null) { String[] values = line.split(","); for (String str : values) { int str_int = Integer.parseInt(str); map[x][y]=str_int; //System.out.print(map[x][y] + " "); y=y+1; } //System.out.println(""); x=x+1; y = 0; } in.close(); } public void update() { } public void render(GameContainer gc) { for(int x = 0; x < 50; x++) { for(int y = 0; y < 50; y ++) { int textureIndex = map[y][x]; Image texture = tiles[textureIndex]; texture.draw(x*tileWidth,y*tileHeight); } } } } I tried something like this, but I it doesn't ever "collide". X and y are my player position. if (tiles.map[(int)x/32][(int)y/32] == 2) { System.out.println("Collided"); }

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  • Feeling a bit... under-challenged in my university course

    - by Corey
    I'm currently a sophomore at my university, majoring in Computer Science. Obviously, there are some programming courses as part of my curriculum. However, I'm feeling very underwhelmed by its progress. I've self-taught myself a lot and like to code in my spare time as a hobby. I'm currently in Computer Science II. I never took CS 1 because it seemed rather basic -- I asked someone in the department if they would override my CS 1 requirement if I passed their final (which I did with flying colors). Anyway, the class is going by quite slowly. It seems like the rest of the class has a hard time understanding some basic concepts, which the professor needs to keep going over to help them understand. Is this normal? Looking at the class schedule, I seem to know everything except for one or two things near the very end of the semester. Is there a different perspective I can look at this through so it doesn't seem so boring?

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  • Java - Tile engine changing number in array not changing texture

    - by Corey
    I draw my map from a txt file. Would I have to write to the text file to notice the changes I made? Right now it changes the number in the array but the tile texture doesn't change. Do I have to do more than just change the number in the array? public class Tiles { public Image[] tiles = new Image[5]; public int[][] map = new int[64][64]; private Image grass, dirt, fence, mound; private SpriteSheet tileSheet; public int tileWidth = 32; public int tileHeight = 32; Player player = new Player(); public void init() throws IOException, SlickException { tileSheet = new SpriteSheet("assets/tiles.png", tileWidth, tileHeight); grass = tileSheet.getSprite(0, 0); dirt = tileSheet.getSprite(7, 7); fence = tileSheet.getSprite(2, 0); mound = tileSheet.getSprite(2, 6); tiles[0] = grass; tiles[1] = dirt; tiles[2] = fence; tiles[3] = mound; int x=0, y=0; BufferedReader in = new BufferedReader(new FileReader("assets/map.dat")); String line; while ((line = in.readLine()) != null) { String[] values = line.split(","); for (String str : values) { int str_int = Integer.parseInt(str); map[x][y]=str_int; //System.out.print(map[x][y] + " "); y=y+1; } //System.out.println(""); x=x+1; y = 0; } in.close(); } public void update(GameContainer gc) { } public void render(GameContainer gc) { for(int x = 0; x < map.length; x++) { for(int y = 0; y < map.length; y ++) { int textureIndex = map[y][x]; Image texture = tiles[textureIndex]; texture.draw(x*tileWidth,y*tileHeight); } } } Mouse picking public void checkDistance(GameContainer gc) { Input input = gc.getInput(); float mouseX = input.getMouseX(); float mouseY = input.getMouseY(); double mousetileX = Math.floor((double)mouseX/tiles.tileWidth); double mousetileY = Math.floor((double)mouseY/tiles.tileHeight); double playertileX = Math.floor(playerX/tiles.tileWidth); double playertileY = Math.floor(playerY/tiles.tileHeight); double lengthX = Math.abs((float)playertileX - mousetileX); double lengthY = Math.abs((float)playertileY - mousetileY); double distance = Math.sqrt((lengthX*lengthX)+(lengthY*lengthY)); if(input.isMousePressed(Input.MOUSE_LEFT_BUTTON) && distance < 4) { System.out.println("Clicked"); if(tiles.map[(int)mousetileX][(int)mousetileY] == 1) { tiles.map[(int)mousetileX][(int)mousetileY] = 0; } } System.out.println(tiles.map[(int)mousetileX][(int)mousetileY]); }

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  • How do you name your projects?

    - by Corey
    Naming is hard. Really, really hard. Even StackExchange is a prime example of this -- remember the huge domain name controversy that occurred when SE sites first started graduating? Anyway, I've got a project I'm working on but I have no idea what to name it! This simple fact has caused production to cease, because I'm at the point where I want to create repositories and database tables, and I don't want to name everything "Untitled project" and have to change potentially hundreds of lines of code in the future. Also, I would like to collaborate with others but it's difficult to be taken seriously if I refer to this as "some project I'm working on." It makes it seem like a new project in its infancy and doesn't garner a lot of interest. Just the simple fact act of having a name will make a huge impact in how it registers in others' minds. How do you guys name your projects? This particular one is a website, so not only do I need to find a good name, I need to find one with an available domain, which is next to impossible these days. How do you brainstorm? Who do you talk to (or not talk to)? Is there an "eureka!" moment when you stumble across something that works?

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  • How does one specify raster operations in XNA?

    - by Corey Ogburn
    I'm looking for a way to add a sprite using a particular logic operation (like XOR). I can't find anything on Google and I'm not sure where to look in the documentation. I've looked into SpriteBatch.Begin(...) and its Draw method and several options in the GraphicsDevice class, but I'm not recognizing anything capable of this. I'm still pretty new to XNA so I may just not have recognized the terminology to do this.

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  • In what order do people build websites?

    - by Corey
    For a website, you need to have an idea, you need to have a design and you need to have data, events and output, right? Whether it be a blog, web app, Q&A site, search engine... Anyway, that is only slightly related to my question. My question is, when designing a website, providing I know the purpose, what should I start with? Should I start with the CSS, design and look&feel using dummy data first, or should I program in the logic, events and output, and style it later? What is the design process of most websites that are built from the ground up?

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  • How important is the uniqueness of your domain name?

    - by Corey
    I've finally come up with a domain name that I like and is available. The name is nonsensical and doesn't translate into anything meaningful in any language, as far as I know. It's something like "FOOBARite". (Don't steal that!) I'm wondering about a few search issues. Results-wise, searching for it in Google currently returns about 15k results, none of which are relevant (dead Twitter pages, various unpopular online handles, and botched french translations). However, Google starts off with a spelling suggestion, which removes a letter. ("Did you mean: FOOBARit?") That returns about 250k results for several different and unrelated websites/organizations by that name. One is some technology provider, another is a sign-language organization, another is the name of a font... None of them seem particularly popular, there's not that much activity on any of those pages. Anyway, the two are pronounced differently, they're just a letter off. Should I go with my idea or is this one-letter variation going to cause me problems? If my site becomes ranked well enough, will Google's spelling suggestion go away? I don't want users to search for my site name and be told they've spelled it wrong.

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  • Java Slick2d - Mouse picking how to take into account camera

    - by Corey
    When I move it it obviously changes the viewport so my mouse picking is off. My camera is just a float x and y and I use g.translate(-cam.cameraX+400, -cam.cameraY+300); to translate the graphics. I have the numbers hard coded just for testing purposes. How would I take into account the camera so my mouse picking works correctly. double mousetileX = Math.floor((double)mouseX/tiles.tileWidth); double mousetileY = Math.floor((double)mouseY/tiles.tileHeight); double playertileX = Math.floor(playerX/tiles.tileWidth); double playertileY = Math.floor(playerY/tiles.tileHeight); double lengthX = Math.abs((float)playertileX - mousetileX); double lengthY = Math.abs((float)playertileY - mousetileY); double distance = Math.sqrt((lengthX*lengthX)+(lengthY*lengthY)); if(input.isMousePressed(Input.MOUSE_LEFT_BUTTON) && distance < 4) { if(tiles.map[(int)mousetileX][(int)mousetileY] == 1) { tiles.map[(int)mousetileX][(int)mousetileY] = 0; } } That is my mouse picking code

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  • Normal vector of a face loaded from an FBX model during collision?

    - by Corey Ogburn
    I'm loading a simple 6 sided cube from a UV-mapped FBX model and I'm using a BoundingBox to test for collisions. Once I determine there's a collision, I want to use the normal vector of the collided surface to correct the movement of whatever collided with the cube. I suppose this is a two-part question: 1) How can I determine which face of the cube was collided with in a collision? 2) How can I get the normal vector of that surface?

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  • Can't access windows 7 shared files on Ubuntu 11.10

    - by Corey
    I just set up ubuntu 11.10 and Samba. I got it to access shares on a Vista machine, but when I try to access the shares on a windows 7 machine it asks for a Username, Domain, and Password. I have no password set up on the windows 7 machine so I put in the username, and domain try to connect and the password prompt keeps appearing...also tried guest and admin with no luck...I've tried many different fixes(modifying registry entries & advanced securities on the win 7 machine) with no luck. Thanks

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