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  • Day 4 - Game Sprites In Action

    - by dapostolov
    Yesterday I drew an image on the screen. Most exciting, but ... I spent more time blogging about it then actual coding. So this next little while I'm going to streamline my game and research and simply post key notes. Quick notes on the last session: The most important thing I wanted to point out were the following methods:           spriteBatch.Begin(SpriteBlendMode.AlphaBlend);           spriteBatch.Draw(sprite, position, Color.White);           spriteBatch.End(); The spriteBatch object is used to draw Textures and a 2D texture is called a Sprite A texture is generally an image, which is called an Asset in XNA The Draw Method in the Game1.cs is looped (until exit) and utilises the spriteBatch object to draw a Scene To begin drawing a Scene you call the Begin Method. To end a Scene you call the End Method. And to place an image on the Scene you call the Draw method. The most simple implementation of the draw method is:           spriteBatch.Draw(sprite, position, Color.White); 1) sprite - the 2D texture you loaded to draw 2) position - the 2d vector, a set of x & y coordinates 3) Color.White - the tint to apply to the texture, in this case, white light = nothing, nada, no tint. Game Sprites In Action! Today, I played around with Draw methods to get comfortable with their "quirks". The following is an example of the above draw method, but with more parameters available for us to use. Let's investigate!             spriteBatch.Draw(sprite, position2, null, Color.White, MathHelper.ToRadians(45.0f), new Vector2(sprite.Width / 2, sprite.Height / 2), 1.0F, SpriteEffects.None, 0.0F); The parameters (in order): 1) sprite  the texture to display 2) position2 the position on the screen / scene this can also be a rectangle 3) null the portion of the image to display within an image null = display full image this is generally used for animation strips / grids (more on this below) 4) Color.White Texture tinting White = no tint 5) MathHelper.ToRadians(45.0f) rotation of the object, in this case 45 degrees rotates from the set plotting point. 6) new Vector(0,0) the plotting point in this case the top left corner the image will rotate from the top left of the texture in the code above, the point is set to the middle of the image. 7) 1.0f Image scaling (1x) 8) SpriteEffects.None you can flip the image horizontally or vertically 9) 0.0f The z index of the image. 0 = closer, 1 behind? And playing around with different combinations I was able to come up with the following whacky display:   Checking off Yesterdays Intention List: learn game development terminology (in progress) - We learned sprite, scene, texture, and asset. how to place and position (rotate) a static image on the screen (completed) - The thing to note was, it's was in radians and I found a cool helper method to convert degrees into radians. Also, the image rotates from it's specified point. how to layer static images on the screen (completed) - I couldn't seem to get the zIndex working, but one things for sure, the order you draw the image in also determines how it is rendered on the screen. understand image scaling (in progress) - I'm not sure I have this fully covered, but for the most part plug a number in the scaling field and the image grows / shrinks accordingly. can we reuse images? (completed) - yes, I loaded one image and plotted the bugger all over the screen. understand how framerate is handled in XNA (in progress) - I hacked together some code to display the framerate each second. A framerate of 60 appears to be the standard. Interesting to note, the GameTime object does provide you with some cool timing capabilities, such as...is the game running slow? Need to investigate this down the road. how to display text , basic shapes, and colors on the screen (in progress) - i got text rendered on the screen, and i understand containing rectangles. However, I didn't display "shapes" & "colors" how to interact with an image (collision of user input?) (todo) how to animate an image and understand basic animation techniques (in progress) - I was able to create a stripe animation of numbers ranging from 1 - 4, each block was 40 x 40 pixles for a total stripe size of 160 x 40. Using the portion (source Rectangle) parameter, i limited this display to each section at varying intervals. It was interesting to note my first implementation animated at rocket speed. I then tried to create a smoother animation by limiting the redraw capacity, which seemed to work. I guess a little more research will have to be put into this for animating characters / scenes. how to detect colliding images or screen edges (todo) - but the rectangle object can detect collisions I believe. how to manipulate the image, lets say colors, stretching (in progress) - I haven't figured out how to modify a specific color to be another color, but the tinting parameter definately could be used. As for stretching, use the rectangle object as the positioning and the image will stretch to fit! how to focus on a segment of an image...like only displaying a frame on a film reel (completed) - as per basic animation techniques what's the best way to manage images (compression, storage, location, prevent artwork theft, etc.) (todo) Tomorrows Intention Tomorrow I am going to take a stab at rendering a game menu and from there I'm going to investigate how I can improve upon the code and techniques. Intention List: Render a menu, fancy or not Show the mouse cursor Hook up click event A basic animation of somesort Investigate image / menu techniques D.

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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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  • External HDD USB 3.0 failure

    - by Philip
    [ 2560.376113] usb 9-1: new high-speed USB device number 2 using xhci_hcd [ 2560.376186] usb 9-1: Device not responding to set address. [ 2560.580136] usb 9-1: Device not responding to set address. [ 2560.784104] usb 9-1: device not accepting address 2, error -71 [ 2560.840127] hub 9-0:1.0: unable to enumerate USB device on port 1 [ 2561.080182] usb 10-1: new SuperSpeed USB device number 5 using xhci_hcd [ 2566.096163] usb 10-1: device descriptor read/8, error -110 [ 2566.200096] usb 10-1: new SuperSpeed USB device number 5 using xhci_hcd [ 2571.216175] usb 10-1: device descriptor read/8, error -110 [ 2571.376138] hub 10-0:1.0: unable to enumerate USB device on port 1 [ 2571.744174] usb 10-1: new SuperSpeed USB device number 7 using xhci_hcd [ 2576.760116] usb 10-1: device descriptor read/8, error -110 [ 2576.864074] usb 10-1: new SuperSpeed USB device number 7 using xhci_hcd [ 2581.880153] usb 10-1: device descriptor read/8, error -110 [ 2582.040123] hub 10-0:1.0: unable to enumerate USB device on port 1 [ 2582.224139] hub 9-0:1.0: unable to enumerate USB device on port 1 [ 2582.464177] usb 10-1: new SuperSpeed USB device number 9 using xhci_hcd [ 2587.480122] usb 10-1: device descriptor read/8, error -110 [ 2587.584079] usb 10-1: new SuperSpeed USB device number 9 using xhci_hcd [ 2592.600150] usb 10-1: device descriptor read/8, error -110 [ 2592.760134] hub 10-0:1.0: unable to enumerate USB device on port 1 [ 2593.128175] usb 10-1: new SuperSpeed USB device number 11 using xhci_hcd [ 2598.144183] usb 10-1: device descriptor read/8, error -110 [ 2598.248109] usb 10-1: new SuperSpeed USB device number 11 using xhci_hcd [ 2603.264171] usb 10-1: device descriptor read/8, error -110 [ 2603.480157] usb 10-1: new SuperSpeed USB device number 12 using xhci_hcd [ 2608.496162] usb 10-1: device descriptor read/8, error -110 [ 2608.600091] usb 10-1: new SuperSpeed USB device number 12 using xhci_hcd [ 2613.616166] usb 10-1: device descriptor read/8, error -110 [ 2613.832170] usb 10-1: new SuperSpeed USB device number 13 using xhci_hcd [ 2618.848135] usb 10-1: device descriptor read/8, error -110 [ 2618.952079] usb 10-1: new SuperSpeed USB device number 13 using xhci_hcd [ 2623.968155] usb 10-1: device descriptor read/8, error -110 [ 2624.184176] usb 10-1: new SuperSpeed USB device number 14 using xhci_hcd [ 2629.200124] usb 10-1: device descriptor read/8, error -110 [ 2629.304075] usb 10-1: new SuperSpeed USB device number 14 using xhci_hcd [ 2634.320172] usb 10-1: device descriptor read/8, error -110 [ 2634.424135] hub 10-0:1.0: unable to enumerate USB device on port 1 [ 2634.776186] usb 10-1: new SuperSpeed USB device number 15 using xhci_hcd [ 2639.792105] usb 10-1: device descriptor read/8, error -110 [ 2639.896090] usb 10-1: new SuperSpeed USB device number 15 using xhci_hcd [ 2644.912172] usb 10-1: device descriptor read/8, error -110 [ 2645.128174] usb 10-1: new SuperSpeed USB device number 16 using xhci_hcd [ 2650.144160] usb 10-1: device descriptor read/8, error -110 [ 2650.248062] usb 10-1: new SuperSpeed USB device number 16 using xhci_hcd [ 2655.264120] usb 10-1: device descriptor read/8, error -110 [ 2655.480182] usb 10-1: new SuperSpeed USB device number 17 using xhci_hcd [ 2660.496121] usb 10-1: device descriptor read/8, error -110 [ 2660.600086] usb 10-1: new SuperSpeed USB device number 17 using xhci_hcd [ 2665.616167] usb 10-1: device descriptor read/8, error -110 [ 2665.832177] usb 10-1: new SuperSpeed USB device number 18 using xhci_hcd [ 2670.848110] usb 10-1: device descriptor read/8, error -110 [ 2670.952066] usb 10-1: new SuperSpeed USB device number 18 using xhci_hcd [ 2675.968081] usb 10-1: device descriptor read/8, error -110 [ 2676.072124] hub 10-0:1.0: unable to enumerate USB device on port 1 [ 2786.104531] xhci_hcd 0000:02:00.0: remove, state 4 [ 2786.104546] usb usb10: USB disconnect, device number 1 [ 2786.104686] xHCI xhci_drop_endpoint called for root hub [ 2786.104692] xHCI xhci_check_bandwidth called for root hub [ 2786.104942] xhci_hcd 0000:02:00.0: USB bus 10 deregistered [ 2786.105054] xhci_hcd 0000:02:00.0: remove, state 4 [ 2786.105065] usb usb9: USB disconnect, device number 1 [ 2786.105176] xHCI xhci_drop_endpoint called for root hub [ 2786.105181] xHCI xhci_check_bandwidth called for root hub [ 2786.109787] xhci_hcd 0000:02:00.0: USB bus 9 deregistered [ 2786.110134] xhci_hcd 0000:02:00.0: PCI INT A disabled [ 2794.268445] pci 0000:02:00.0: [1b73:1000] type 0 class 0x000c03 [ 2794.268483] pci 0000:02:00.0: reg 10: [mem 0x00000000-0x0000ffff] [ 2794.268689] pci 0000:02:00.0: PME# supported from D0 D3hot [ 2794.268700] pci 0000:02:00.0: PME# disabled [ 2794.276383] pci 0000:02:00.0: BAR 0: assigned [mem 0xd7800000-0xd780ffff] [ 2794.276398] pci 0000:02:00.0: BAR 0: set to [mem 0xd7800000-0xd780ffff] (PCI address [0xd7800000-0xd780ffff]) [ 2794.276419] pci 0000:02:00.0: no hotplug settings from platform [ 2794.276658] xhci_hcd 0000:02:00.0: enabling device (0000 -> 0002) [ 2794.276675] xhci_hcd 0000:02:00.0: PCI INT A -> GSI 16 (level, low) -> IRQ 16 [ 2794.276762] xhci_hcd 0000:02:00.0: setting latency timer to 64 [ 2794.276771] xhci_hcd 0000:02:00.0: xHCI Host Controller [ 2794.276913] xhci_hcd 0000:02:00.0: new USB bus registered, assigned bus number 9 [ 2794.395760] xhci_hcd 0000:02:00.0: irq 16, io mem 0xd7800000 [ 2794.396141] xHCI xhci_add_endpoint called for root hub [ 2794.396144] xHCI xhci_check_bandwidth called for root hub [ 2794.396195] hub 9-0:1.0: USB hub found [ 2794.396203] hub 9-0:1.0: 1 port detected [ 2794.396305] xhci_hcd 0000:02:00.0: xHCI Host Controller [ 2794.396371] xhci_hcd 0000:02:00.0: new USB bus registered, assigned bus number 10 [ 2794.396496] xHCI xhci_add_endpoint called for root hub [ 2794.396499] xHCI xhci_check_bandwidth called for root hub [ 2794.396547] hub 10-0:1.0: USB hub found [ 2794.396553] hub 10-0:1.0: 1 port detected [ 2798.004084] usb 1-3: new high-speed USB device number 8 using ehci_hcd [ 2798.140824] scsi21 : usb-storage 1-3:1.0 [ 2820.176116] usb 1-3: reset high-speed USB device number 8 using ehci_hcd [ 2824.000526] scsi 21:0:0:0: Direct-Access BUFFALO HD-PZU3 0001 PQ: 0 ANSI: 6 [ 2824.002263] sd 21:0:0:0: Attached scsi generic sg2 type 0 [ 2824.003617] sd 21:0:0:0: [sdb] 1953463728 512-byte logical blocks: (1.00 TB/931 GiB) [ 2824.005139] sd 21:0:0:0: [sdb] Write Protect is off [ 2824.005149] sd 21:0:0:0: [sdb] Mode Sense: 1f 00 00 08 [ 2824.009084] sd 21:0:0:0: [sdb] No Caching mode page present [ 2824.009094] sd 21:0:0:0: [sdb] Assuming drive cache: write through [ 2824.011944] sd 21:0:0:0: [sdb] No Caching mode page present [ 2824.011952] sd 21:0:0:0: [sdb] Assuming drive cache: write through [ 2824.049153] sdb: sdb1 [ 2824.051814] sd 21:0:0:0: [sdb] No Caching mode page present [ 2824.051821] sd 21:0:0:0: [sdb] Assuming drive cache: write through [ 2824.051825] sd 21:0:0:0: [sdb] Attached SCSI disk [ 2839.536624] usb 1-3: USB disconnect, device number 8 [ 2844.620178] usb 10-1: new SuperSpeed USB device number 2 using xhci_hcd [ 2844.640281] scsi22 : usb-storage 10-1:1.0 [ 2850.326545] scsi 22:0:0:0: Direct-Access BUFFALO HD-PZU3 0001 PQ: 0 ANSI: 6 [ 2850.327560] sd 22:0:0:0: Attached scsi generic sg2 type 0 [ 2850.329561] sd 22:0:0:0: [sdb] 1953463728 512-byte logical blocks: (1.00 TB/931 GiB) [ 2850.329889] sd 22:0:0:0: [sdb] Write Protect is off [ 2850.329897] sd 22:0:0:0: [sdb] Mode Sense: 1f 00 00 08 [ 2850.330223] sd 22:0:0:0: [sdb] No Caching mode page present [ 2850.330231] sd 22:0:0:0: [sdb] Assuming drive cache: write through [ 2850.331414] sd 22:0:0:0: [sdb] No Caching mode page present [ 2850.331423] sd 22:0:0:0: [sdb] Assuming drive cache: write through [ 2850.384116] usb 10-1: USB disconnect, device number 2 [ 2850.392050] sd 22:0:0:0: [sdb] Unhandled error code [ 2850.392056] sd 22:0:0:0: [sdb] Result: hostbyte=DID_NO_CONNECT driverbyte=DRIVER_OK [ 2850.392061] sd 22:0:0:0: [sdb] CDB: Read(10): 28 00 00 00 00 00 00 00 08 00 [ 2850.392074] end_request: I/O error, dev sdb, sector 0 [ 2850.392079] quiet_error: 70 callbacks suppressed [ 2850.392082] Buffer I/O error on device sdb, logical block 0 [ 2850.392194] ldm_validate_partition_table(): Disk read failed. [ 2850.392271] Dev sdb: unable to read RDB block 0 [ 2850.392377] sdb: unable to read partition table [ 2850.392581] sd 22:0:0:0: [sdb] READ CAPACITY failed [ 2850.392584] sd 22:0:0:0: [sdb] Result: hostbyte=DID_NO_CONNECT driverbyte=DRIVER_OK [ 2850.392588] sd 22:0:0:0: [sdb] Sense not available. [ 2850.392613] sd 22:0:0:0: [sdb] Asking for cache data failed [ 2850.392617] sd 22:0:0:0: [sdb] Assuming drive cache: write through [ 2850.392621] sd 22:0:0:0: [sdb] Attached SCSI disk [ 2850.732182] usb 10-1: new SuperSpeed USB device number 3 using xhci_hcd [ 2850.752228] scsi23 : usb-storage 10-1:1.0 [ 2851.752709] scsi 23:0:0:0: Direct-Access BUFFALO HD-PZU3 0001 PQ: 0 ANSI: 6 [ 2851.754481] sd 23:0:0:0: Attached scsi generic sg2 type 0 [ 2851.756576] sd 23:0:0:0: [sdb] 1953463728 512-byte logical blocks: (1.00 TB/931 GiB) [ 2851.758426] sd 23:0:0:0: [sdb] Write Protect is off [ 2851.758436] sd 23:0:0:0: [sdb] Mode Sense: 1f 00 00 08 [ 2851.758779] sd 23:0:0:0: [sdb] No Caching mode page present [ 2851.758787] sd 23:0:0:0: [sdb] Assuming drive cache: write through [ 2851.759968] sd 23:0:0:0: [sdb] No Caching mode page present [ 2851.759977] sd 23:0:0:0: [sdb] Assuming drive cache: write through [ 2851.817710] sdb: sdb1 [ 2851.820562] sd 23:0:0:0: [sdb] No Caching mode page present [ 2851.820568] sd 23:0:0:0: [sdb] Assuming drive cache: write through [ 2851.820572] sd 23:0:0:0: [sdb] Attached SCSI disk [ 2852.060352] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2852.076533] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2852.076538] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2852.196329] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2852.212593] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2852.212599] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2852.456290] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2852.472402] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2852.472408] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2852.624304] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2852.640531] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2852.640536] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2852.772296] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2852.788536] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2852.788541] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2852.920349] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2852.936536] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2852.936540] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2853.072287] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2853.088565] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2853.088570] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2884.176339] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2884.192561] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2884.192567] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2884.320349] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2884.336526] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2884.336531] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2884.468344] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2884.484551] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2884.484556] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2884.612349] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2884.628540] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2884.628545] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2884.756350] usb 10-1: reset SuperSpeed USB device number 3 using xhci_hcd [ 2884.772528] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b19060 [ 2884.772533] xhci_hcd 0000:02:00.0: xHCI xhci_drop_endpoint called with disabled ep f6b1908c [ 2884.848116] usb 10-1: USB disconnect, device number 3 [ 2884.851493] scsi 23:0:0:0: [sdb] killing request [ 2884.851501] scsi 23:0:0:0: [sdb] killing request [ 2884.851699] scsi 23:0:0:0: [sdb] Unhandled error code [ 2884.851702] scsi 23:0:0:0: [sdb] Result: hostbyte=DID_NO_CONNECT driverbyte=DRIVER_OK [ 2884.851708] scsi 23:0:0:0: [sdb] CDB: Read(10): 28 00 00 5f 2b ee 00 00 3e 00 [ 2884.851721] end_request: I/O error, dev sdb, sector 6237166 [ 2884.851726] Buffer I/O error on device sdb1, logical block 6237102 [ 2884.851730] Buffer I/O error on device sdb1, logical block 6237103 [ 2884.851738] Buffer I/O error on device sdb1, logical block 6237104 [ 2884.851741] Buffer I/O error on device sdb1, logical block 6237105 [ 2884.851744] Buffer I/O error on device sdb1, logical block 6237106 [ 2884.851747] Buffer I/O error on device sdb1, logical block 6237107 [ 2884.851750] Buffer I/O error on device sdb1, logical block 6237108 [ 2884.851753] Buffer I/O error on device sdb1, logical block 6237109 [ 2884.851757] Buffer I/O error on device sdb1, logical block 6237110 [ 2884.851807] scsi 23:0:0:0: [sdb] Unhandled error code [ 2884.851810] scsi 23:0:0:0: [sdb] Result: hostbyte=DID_NO_CONNECT driverbyte=DRIVER_OK [ 2884.851813] scsi 23:0:0:0: [sdb] CDB: Read(10): 28 00 00 5f 2c 2c 00 00 3e 00 [ 2884.851824] end_request: I/O error, dev sdb, sector 6237228 [ 2885.168190] usb 10-1: new SuperSpeed USB device number 4 using xhci_hcd [ 2885.188268] scsi24 : usb-storage 10-1:1.0 Please help me with my problem. I got this after running dmesg.

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  • What are the pros and cons of Coffeescript?

    - by Philip
    Of course one big pro is the amount of syntactic sugar leading to shorter code in a lot of cases. On http://jashkenas.github.com/coffee-script/ there are impressive examples. On the other hand I have doubts that these examples represent code of complex real world applications. In my code for instance I never add functions to bare objects but rather to their prototypes. Moreover the prototype feature is hidden from the user, suggesting classical OOP rather than idiomatic Javascript. The array comprehension example would look in my code probably like this: cubes = $.map(list, math.cube); // which is 8 characters less using jQuery...

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  • What is the difference between CSDA and CSDP?

    - by Philip C
    This question is in reference to the IEEE's CSDA and CSDP certifications. I've already taken the CSDA course, which covered all of the knowledge areas in the Software Engineering Body of Knowledge (SWEBOK). I'm thinking about doing the CSDP certification as well, but I can't find anywhere that tells me how it differs from the CSDA. I know that it's aimed more at professionals rather than recent graduates, and I'd imagine that it covers the same areas as the CSDA, but in greater detail. Unfortunately I can't find much evidence to back that up. This is making it difficult to justify to my boss that he should spend several hundred pounds of the group's training budget on it. So the question is: What does the CSDP offer that the CSDA doesn't?

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  • Guidance on building an au pair-to-family networking site.

    - by Philip Kidd
    I'm building a website for an au pair agency business that will connect au pairs to families around Europe. I know nothing about website building, HTML etc. so I'm using a wysiwyg editer (weebly). How I would like the site to function: Families upload their information into profiles Au pairs do the same families can view a limited part of an au pairs' profile until they pay a deposit After deposit is payed, all au pairs' profile information becomes open to families Families can order au pairs and confirm their order with another payment payment must be made before 'order' is confirmed By 'order' I mean full communications become open between the family and the au pair they have 'ordered' as well as travel information being sent to another agency the site needs to be linked with a bank account (e.g paypal) and another agency, who will look after the flight bookings etc. A website already exists for this business however it just contains information on the business and application forms - if the site becomes fully automated it will relieve a lot of strain on administration in the office (dealing with applications, travel information etc.)

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  • Looking for tips on managing complexity with SCM repositories

    - by Philip Regan
    I am a solo developer in my department and I have a lot of individual projects, all created and managed by me. I started using SVN at ProjectLocker via Versions on the Mac a couple years ago when the variety of projects started getting unwieldy. Scenario 1: Now I have a process that is of reasonable complexity it can be broken up into multiple smaller applications and they all share files. In one phase, there is a single shared file—a constants file—that is shared between a Cocoa app and an iPhone app framework. In the second phase, the iPhone app framework will be used to create individual apps of the same ilk—controller classes and what not will all be the same—but with different content in each. The problem that I am running across is that the file in the first phase is in one repository with the application that started it, and the app framework is in a second, separate repository. Scenario 2: I have another application framework that partially relies on code from an open source project. This is all internal, non-commerical work, but again, the application framework is going to be used to create a variety of unique products and processes. So, now I have an internally managed repository and an externally managed one out of my control. I make little changes to the open source code to meet the needs of my framework when there is an update I download, but I never commit back into the external repository (though, now that I think about it, I don't think I'm committing it to mine either. Oops). The Problem I have all of this set up on my production Mac quite nicely, but duplicating and subsequently maintaining that environment on my laptop has been challenging. For Scenario 1, I've thought of merging these two projects together into the same repository because they are, for all intents and purposes inextricably linked. But, Scenario 2, I think I'm stuck just managing files as best I can. The Question I'm wondering if anyone has any tips on how to manage either of these situations, as well as other complex SCM scenarios when it comes to linking various files from various repositories together. My familiarity with SVN only comes from my work with Versions. It's been great, but I'm a little out of my depth here.

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  • Is there an open source license for this?

    - by Philip
    I have written code at home, on my own time and using my own knowledge and equipment, while under no contract or NDA. I want to make this code open source so that I can use it in software I write for an employer, without denying myself the right to use it at home or elsewhere later. I'm not sure if saying it is in the "public domain" would fit this purpose, or if I need to find an open source license. I want anyone to be able to use the code in closed source proprietary software with zero requirements for including a license with the source or binary. And I want to minimize the risk of anyone being sued for using it. (I'm aware that one can never be 100% safe from being sued.) Is there an open source license that fits this purpose? To what extent is what I want to do even possible? I wouldn't mind putting the license in comments in the code files themselves, but that obviously doesn't go with the binary.

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  • 45 minutes to talk about C# [closed]

    - by Philip
    I have the opportunity to give a 45 minute talk on C# in the theory of programming languages class I'm taking. The college teaches Java almost exclusively, so that's what all the students are most familiar with. (There's a little C, assembly, Prolog and LISP as well.) I decide what to talk about. It seems to me the best approach is to focus on a few of the big, obvious differences between C# and Java. I don't intend it to be a recommendation to use C# -- there are reasons to use each, mostly because of their ecosystems. So I want to focus on C# as a language. I don't want to go too fast and end up listing a whole bunch of features without showing their usefulness. My current plan is this: Functions as first class objects. This is, in my opinion, one of the biggest differences between C# and Java. The professor briefly mentioned this notion and showed a LISP example, but many of the students have probably never used it. I can show real world examples where it's made my code more readable. Lambda expressions as concise syntax for anonymous functions. Obviously with examples to show how this is useful. The real hit-home examples will be at the end when it's combined with the rest. I don't see an advantage to first showing the old delegate syntax and then replacing it with lambdas -- most of us won't have ever seen delegates anyway so it would just be confusing. The yield keyword and how it's different from returning an array. I have the impression that a lot of C# developers aren't familiar with how to use this. It will likely be very foreign to Java developers. I have some examples from my own work where it was really useful, such as iterating over a tree traversal, or iterating over neighbors in a graph where the neighbors aren't stored in memory. In both cases, doing it in Java would likely mean returning a complete list -- with yield I can stop iterating if I find what I want early on, without using memory for superfluous lists or arrays. Extension methods as a way to write implementation on interfaces. We'll all be familiar with how interfaces don't allow method implementation, and how this leads to code duplication. I'll show a specific example of this and how the extension method can solve the problem. Demonstrate how the above can be combined by implementing some simple Linq methods and using them. Where, Select, First, maybe more depending on how much time is left. Ideas on which ones might 'hit home' the best? There are other things I could talk about such as generics, value types, properties and more. I haven't yet though of good ways to incorporate these. In the case of generics and value types, the advantages might not be obvious or as relevant. Properties are obviously useful, particularly since we're taught strict JavaBeans here, but I don't know if I could integrate it with the "path to Linq" discussion above without it feeling tacked on. So I'm looking for thoughts on how to talk about C#, and what to talk about. Even minor details. I'm sure there are more experienced C# developers than me here who have good insight about what's really important in the language, and what would miss the point.

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  • TotalPhase Aardvark driver's GPL license

    - by Philip
    I'm using an SPI host adapter for a project. The Aardvark from TotalPhase. And I did something crazy, I read that EULA license that everyone just clicks through. The driver installation license includes these bits: This driver installer package also includes a WIN32 driver that is entirely based on the libusb-win32 project (release 0.1.10.1). ... LICENSE: The software in this package is distributed under the following licenses: Driver: GNU General Public License (GPL) Library, Test Files: GNU Lesser General Public License (LGPL) Now, my understanding of of the GPL is that it's sticky and viral. If you include software then the whole project has to be released under the GPL (if you distribute it, you can do whatever you want with in-house projects). If the driver was like the library, and was licensed under the LGPL, it could be used by my closed source proprietary project, as long as it's source and license was passed along with it. But it's not, it's pure GPL. If I include this driver in my project and distribute it, am I required to release my project under the GPL?

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  • How do you manage the testing of your Android software on physical devices?

    - by Philip Regan
    I'm in charge of managing mobile application development at my company, and I am currently building a mobile device "library" for testing. Essentially, we want to have a representative device in-house for each of the OSes we are developing for, currently iOS (iPhone-only), Blackberry, and Android. Simulators only go so far, but I'm placing into the process a step to test software on the devices themselves. The problem we're finding is with Android. I don't think any of us here ever really understood just how fragmented the whole platform is until we started looking at devices to acquire. We are going to wait until v2.3 of Android is released, but which products to choose? Do we go by the most popular by market share? Do we get a small range of products by specs from least to most powerful overall? We're trying to avoid having to manage a dozen different devices to test each app, if not because of cost if only for the repeated time sink. How do you manage the testing of your Android software on physical devices?

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  • Game Clock Precision

    - by Philip
    I'm reading a fantastic article about game timer precision and here is a quote about 2/3 of the way into the article: If you start your game clock at about 4 billion (more precisely 2^32, or any large power of two) then your exponent, and hence your precision, will remain constant for the next ~4 billion seconds, or ~136 years. He doesn't give a concrete example of this though. Does this mean I would want to add 2^32 to the game clock value that I store at the beginning of each frame? Or is there a way to actually set the clock in Windows so that the numbers start at 2^32?

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  • How do I fix broken packages in 12.04? [closed]

    - by Philip Gray
    Possible Duplicate: Fixing Broken Packages I am trying to install the nautilus-actions-extra package via synaptic. When I do synaptic advises me that I have broken packages. I have followed How do I locate and remove Broken Packages that I have installed? but when I select the Status category, I do not have a 'Broken Dependencies' option. When I click on the 'Broken' item in the Filter category nothing is displayed. I am using Ubuntu 12.04LTS. What can I do to resolve this? These are my terminal responses: $ sudo apt-get install nautilus-actions-extra Reading package lists... Done Building dependency tree Reading state information... Done Some packages could not be installed. This may mean that you have requested an impossible situation or if you are using the unstable distribution that some required packages have not yet been created or been moved out of Incoming. The following information may help to resolve the situation: The following packages have unmet dependencies. nautilus-actions-extra : Depends: nautilus-gksu but it is not installable E: Unable to correct problems, you have held broken packages. $ sudo apt-get check Reading package lists... Done Building dependency tree Reading state information... Done

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  • Dark or light theme for Android apps?

    - by Philip Sheard
    My app allows the user to choose between a dark and a light theme, but which should it use as the default? It is a field sales app, a kind of glorified invoicing app for enterprise users. It is much larger than most apps in Google Play, and targets a vertical market. Originally I developed the app with a dark font, which was fairly standard at the time. That is still my personal preference, but most modern apps seem to have a light font.

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  • How can I get a list of installed programs and corresponding size of each in Ubuntu?

    - by Philip Baker
    I would like to have a list of the installed software on my machine, with the disk space consumed by them. A previous answer here says "you can do this via GUI in Synaptic". This doesn't mean anything to me. I don't know what GUI is, and when I click on Synaptic, I do not get anything like the display shown in the answer, i.e. with "Settings ? Preferences" and "Columns and Fonts". In Windows, you just select 'Programs and Applications' in the Control Panel, and the list comes up immediately, with sizes. Is there something similar and simple with Ubuntu? Could the size of each program be included on the list of installed software? This would be the most obvious place to put it.

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  • Wifi is connected but internet not working?

    - by Philip
    I have a lenovo x230 running ubuntu 12.04lts. I've been using it just fine for a while, but all of a sudden the internet's stopped working. It's connected to the router just fine (I can see the antenna bars saying it's connected) but when I try to go to a webpage, it's just loading forever. I've tried the fix listed here (http://ubuntuforums.org/showthread.php?t=1985079) but it hasn't done anything (the instructions are a little confusing though). I'm booting windows 7 right now and the internet works just fine when I'm on windows, so I know it must be a problem with ubuntu. I haven't touched any network settings before this problem happened, and I've been using it just fine for months, so I don't know why I'm getting this problem all of a sudden. Oh and I haven't updated anything in a few weeks (you know when the update manager pops up every so often listing all the things there's an update for) so I know it's not some update that broke it.

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  • What are the pros and cons of Coffeescript?

    - by Philip
    Of course one big pro is the amount of syntactic sugar leading to shorter code in a lot of cases. On http://jashkenas.github.com/coffee-script/ there are impressive examples. On the other hand I have doubts that these examples represent code of complex real world applications. In my code for instance I never add functions to bare objects but rather to their prototypes. Moreover the prototype feature is hidden from the user, suggesting classical OOP rather than idiomatic Javascript. The array comprehension example would look in my code probably like this: cubes = $.map(list, math.cube); // which is 8 characters less using jQuery...

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  • Supporting early versions of Android

    - by Philip Sheard
    What policy do developers have when it comes to supporting earlier versions of Android? I still support Android 2.1 and above, but this means that I am unable to use features such as the action bar. Over 40% of my users are still running versions below 3.0, so I feel somewhat constrained about this. The problem is that 3.x was not very successful, so 2.3.x will be with us for some time. But all new devices will now be shipping with 4.x. I am wondering whether 4.x users are more likely to pay for an app, while most 2.3.x users are just looking.

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  • What does the Sys_PageIn() function do in Quake?

    - by Philip
    I've noticed in the initialization process of the original Quake the following function is called. volatile int sys_checksum; // **lots of code** void Sys_PageIn(void *ptr, int size) { byte *x; int j,m,n; //touch all memory to make sure its there. The 16-page skip is to //keep Win 95 from thinking we're trying to page ourselves in (we are //doing that, of course, but there's no reason we shouldn't) x = (byte *)ptr; for (n=0 ; n<4 ; n++) { for (m=0; m<(size - 16 * 0x1000) ; m += 4) { sys_checksum += *(int *)&x[m]; sys_checksum += *(int *)&x[m + 16 * 0x10000]; } } } I think I'm just not familiar enough with paging to understand this function. the void* ptr passed to the function is a recently malloc()'d piece of memory that is size bytes big. This is the whole function - j is an unreferenced variable. My best guess is that the volatile int sys_checksum is forcing the system to physically read all of the space that was just malloc()'d, perhaps to ensure that these spaces exist in virtual memory? Is this right? And why would someone do this? Is it for some antiquated Win95 reason?

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  • Losing 'post' requests sent to Pylons paster server

    - by Philip McDermott
    I'm sending post requests to a Pylons server (served by paster serve), and if I send them with any frequency many don't arrive at the server. One at a time is ok, but if I fire off a few (or more) within seconds, only a small number get dealt with. If I send with no post data, or with get, it works fine, but putting just one character of data in the post fields causes massive losses. For example, sending 200, 2 will come back. Sending 100 more slowly, 10 will come back. I'm making the requests form inside a Qt application. Tis will work ok (no data): QString postFields = "" QNetworkRequest request(QUrl("http://server.com/endpoint")); QNetworkReply *reply = networkAccessManager-post(request, postFields.toAscii()); And this will result in only a fraction of the requests being dealt with: QString postFields = "" QNetworkRequest request(QUrl("http://server.com/endpoint")); QNetworkReply *reply = networkAccessManager-post(request, postFields.toAscii()); I've played around with turning on use_threadpool, and other options (threadpool_workers, threadpool_max_requests = 300), of which some combinations can alter the results slightly (best case 10 responses in 200). If I send similar requests to other (non paster) servers, the replies come back ok, so I'm almost certain its'a paster serve config issue. Any help or advice greatly appreciated. Thanks Philip

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  • PHP | SQL syntax error when inserting array

    - by Philip
    Hi guys, I am having some trouble inserting an array into the sql database. my error is as follows: Unable to add : You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near '06:45:23,i want to leave a comment)' at line 1 My query var_dump is: string(136) "INSERT INTO news_comments (news_id,comment_by,comment_date,comment) VALUES (17263,Philip,2010-05-11 06:45:23,i want to leave a comment)" My question is how can i add an empty value to id as it is the primary key and not news_id my insert function looks like this: function insertQuery($tbl, &$data) { global $mysqli; $_SESSION['errors'] = array(); require_once '../config/mysqli.php'; $query = "INSERT INTO $tbl (".implode(',',array_keys($data)).") VALUES (".implode(',',array_values($data)).")"; var_dump($query); if($result = mysqli_query($mysqli, $query)) { //$id = mysqli_insert_id($mysqli); print 'Very well done sir!'; } else { array_push($_SESSION['errors'], 'Unable to add : ' . mysqli_error($mysqli)); } } Note: arrays are not my strong point so i may be using them in-correctly!

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  • Ubuntu 10.04 on virtualbox gives error: Target filesystem doesn't have /sbin/init \ No init found. Try passing init= bootarg

    - by Philip
    I'm a linux newbie and the only reason I have it installed is so I can stop having Windows incompatibility issues with Ruby on Rails. Having said that, it sure has been nice, and much faster, and I don't think I'll be doing any Winrails stuff anytime soon. So I created a virtualmachine using virtualbox and have had ubuntu on it for the last 3 weeks. Recently ubuntu asked if it could update a few things, I clicked 'ok'. Now it won't boot and I get this error: *mount: mounting /dev on /root/dev failed: No such file or directory mount: mounting /sys on /root/sys failed: No such file or directory ... Target filesystem doesn't have /sbin/init. No init found. Try passing init= bootarg BusyBox v1.13.3... (initramfs) _ * So I cruised the forums and there are a variety of solutions, but they all have to do with booting from the live cd. (which I assume is the ISO image I used to install ubuntu in the first place). But when I boot from that CD, it just hangs on the ubuntu screen, and the little dots keep cycling white to red, but it hung there for an hour so I think it was stuck. Not sure what I can do; can I do anything from the busybox shell (or whatever that is) to fix things? The thing is, it took about 10 hours to get everything the way I needed with all the gems and whatnot. And I didn't really write down what I tweaked, and I'm middle aged, so all that information has leaked out by now and I don't want to do it again. I'd really like to repair my existing install. One question you might have is, is there something wrong with the ISO? I don't think so, because I made a new virtual machine and used that same iso file to install a fresh ubuntu. Any help much appreciated. Phil

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