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  • Seeking on a Heap, and Two Useful DMVs

    - by Paul White
    So far in this mini-series on seeks and scans, we have seen that a simple ‘seek’ operation can be much more complex than it first appears.  A seek can contain one or more seek predicates – each of which can either identify at most one row in a unique index (a singleton lookup) or a range of values (a range scan).  When looking at a query plan, we will often need to look at the details of the seek operator in the Properties window to see how many operations it is performing, and what type of operation each one is.  As you saw in the first post in this series, the number of hidden seeking operations can have an appreciable impact on performance. Measuring Seeks and Scans I mentioned in my last post that there is no way to tell from a graphical query plan whether you are seeing a singleton lookup or a range scan.  You can work it out – if you happen to know that the index is defined as unique and the seek predicate is an equality comparison, but there’s no separate property that says ‘singleton lookup’ or ‘range scan’.  This is a shame, and if I had my way, the query plan would show different icons for range scans and singleton lookups – perhaps also indicating whether the operation was one or more of those operations underneath the covers. In light of all that, you might be wondering if there is another way to measure how many seeks of either type are occurring in your system, or for a particular query.  As is often the case, the answer is yes – we can use a couple of dynamic management views (DMVs): sys.dm_db_index_usage_stats and sys.dm_db_index_operational_stats. Index Usage Stats The index usage stats DMV contains counts of index operations from the perspective of the Query Executor (QE) – the SQL Server component that is responsible for executing the query plan.  It has three columns that are of particular interest to us: user_seeks – the number of times an Index Seek operator appears in an executed plan user_scans – the number of times a Table Scan or Index Scan operator appears in an executed plan user_lookups – the number of times an RID or Key Lookup operator appears in an executed plan An operator is counted once per execution (generating an estimated plan does not affect the totals), so an Index Seek that executes 10,000 times in a single plan execution adds 1 to the count of user seeks.  Even less intuitively, an operator is also counted once per execution even if it is not executed at all.  I will show you a demonstration of each of these things later in this post. Index Operational Stats The index operational stats DMV contains counts of index and table operations from the perspective of the Storage Engine (SE).  It contains a wealth of interesting information, but the two columns of interest to us right now are: range_scan_count – the number of range scans (including unrestricted full scans) on a heap or index structure singleton_lookup_count – the number of singleton lookups in a heap or index structure This DMV counts each SE operation, so 10,000 singleton lookups will add 10,000 to the singleton lookup count column, and a table scan that is executed 5 times will add 5 to the range scan count. The Test Rig To explore the behaviour of seeks and scans in detail, we will need to create a test environment.  The scripts presented here are best run on SQL Server 2008 Developer Edition, but the majority of the tests will work just fine on SQL Server 2005.  A couple of tests use partitioning, but these will be skipped if you are not running an Enterprise-equivalent SKU.  Ok, first up we need a database: USE master; GO IF DB_ID('ScansAndSeeks') IS NOT NULL DROP DATABASE ScansAndSeeks; GO CREATE DATABASE ScansAndSeeks; GO USE ScansAndSeeks; GO ALTER DATABASE ScansAndSeeks SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE ScansAndSeeks SET AUTO_CLOSE OFF, AUTO_SHRINK OFF, AUTO_CREATE_STATISTICS OFF, AUTO_UPDATE_STATISTICS OFF, PARAMETERIZATION SIMPLE, READ_COMMITTED_SNAPSHOT OFF, RESTRICTED_USER ; Notice that several database options are set in particular ways to ensure we get meaningful and reproducible results from the DMVs.  In particular, the options to auto-create and update statistics are disabled.  There are also three stored procedures, the first of which creates a test table (which may or may not be partitioned).  The table is pretty much the same one we used yesterday: The table has 100 rows, and both the key_col and data columns contain the same values – the integers from 1 to 100 inclusive.  The table is a heap, with a non-clustered primary key on key_col, and a non-clustered non-unique index on the data column.  The only reason I have used a heap here, rather than a clustered table, is so I can demonstrate a seek on a heap later on.  The table has an extra column (not shown because I am too lazy to update the diagram from yesterday) called padding – a CHAR(100) column that just contains 100 spaces in every row.  It’s just there to discourage SQL Server from choosing table scan over an index + RID lookup in one of the tests. The first stored procedure is called ResetTest: CREATE PROCEDURE dbo.ResetTest @Partitioned BIT = 'false' AS BEGIN SET NOCOUNT ON ; IF OBJECT_ID(N'dbo.Example', N'U') IS NOT NULL BEGIN DROP TABLE dbo.Example; END ; -- Test table is a heap -- Non-clustered primary key on 'key_col' CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ; IF @Partitioned = 'true' BEGIN -- Enterprise, Trial, or Developer -- required for partitioning tests IF SERVERPROPERTY('EngineEdition') = 3 BEGIN EXECUTE (' DROP TABLE dbo.Example ; IF EXISTS ( SELECT 1 FROM sys.partition_schemes WHERE name = N''PS'' ) DROP PARTITION SCHEME PS ; IF EXISTS ( SELECT 1 FROM sys.partition_functions WHERE name = N''PF'' ) DROP PARTITION FUNCTION PF ; CREATE PARTITION FUNCTION PF (INTEGER) AS RANGE RIGHT FOR VALUES (20, 40, 60, 80, 100) ; CREATE PARTITION SCHEME PS AS PARTITION PF ALL TO ([PRIMARY]) ; CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ON PS (key_col); '); END ELSE BEGIN RAISERROR('Invalid SKU for partition test', 16, 1); RETURN; END; END ; -- Non-unique non-clustered index on the 'data' column CREATE NONCLUSTERED INDEX [IX dbo.Example data] ON dbo.Example (data) ; -- Add 100 rows INSERT dbo.Example WITH (TABLOCKX) ( key_col, data ) SELECT key_col = V.number, data = V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 100 ; END; GO The second stored procedure, ShowStats, displays information from the Index Usage Stats and Index Operational Stats DMVs: CREATE PROCEDURE dbo.ShowStats @Partitioned BIT = 'false' AS BEGIN -- Index Usage Stats DMV (QE) SELECT index_name = ISNULL(I.name, I.type_desc), scans = IUS.user_scans, seeks = IUS.user_seeks, lookups = IUS.user_lookups FROM sys.dm_db_index_usage_stats AS IUS JOIN sys.indexes AS I ON I.object_id = IUS.object_id AND I.index_id = IUS.index_id WHERE IUS.database_id = DB_ID(N'ScansAndSeeks') AND IUS.object_id = OBJECT_ID(N'dbo.Example', N'U') ORDER BY I.index_id ; -- Index Operational Stats DMV (SE) IF @Partitioned = 'true' SELECT index_name = ISNULL(I.name, I.type_desc), partitions = COUNT(IOS.partition_number), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; ELSE SELECT index_name = ISNULL(I.name, I.type_desc), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; END; The final stored procedure, RunTest, executes a query written against the example table: CREATE PROCEDURE dbo.RunTest @SQL VARCHAR(8000), @Partitioned BIT = 'false' AS BEGIN -- No execution plan yet SET STATISTICS XML OFF ; -- Reset the test environment EXECUTE dbo.ResetTest @Partitioned ; -- Previous call will throw an error if a partitioned -- test was requested, but SKU does not support it IF @@ERROR = 0 BEGIN -- IO statistics and plan on SET STATISTICS XML, IO ON ; -- Test statement EXECUTE (@SQL) ; -- Plan and IO statistics off SET STATISTICS XML, IO OFF ; EXECUTE dbo.ShowStats @Partitioned; END; END; The Tests The first test is a simple scan of the heap table: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example'; The top result set comes from the Index Usage Stats DMV, so it is the Query Executor’s (QE) view.  The lower result is from Index Operational Stats, which shows statistics derived from the actions taken by the Storage Engine (SE).  We see that QE performed 1 scan operation on the heap, and SE performed a single range scan.  Let’s try a single-value equality seek on a unique index next: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 32'; This time we see a single seek on the non-clustered primary key from QE, and one singleton lookup on the same index by the SE.  Now for a single-value seek on the non-unique non-clustered index: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32'; QE shows a single seek on the non-clustered non-unique index, but SE shows a single range scan on that index – not the singleton lookup we saw in the previous test.  That makes sense because we know that only a single-value seek into a unique index is a singleton seek.  A single-value seek into a non-unique index might retrieve any number of rows, if you think about it.  The next query is equivalent to the IN list example seen in the first post in this series, but it is written using OR (just for variety, you understand): EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32 OR data = 33'; The plan looks the same, and there’s no difference in the stats recorded by QE, but the SE shows two range scans.  Again, these are range scans because we are looking for two values in the data column, which is covered by a non-unique index.  I’ve added a snippet from the Properties window to show that the query plan does show two seek predicates, not just one.  Now let’s rewrite the query using BETWEEN: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data BETWEEN 32 AND 33'; Notice the seek operator only has one predicate now – it’s just a single range scan from 32 to 33 in the index – as the SE output shows.  For the next test, we will look up four values in the key_col column: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col IN (2,4,6,8)'; Just a single seek on the PK from the Query Executor, but four singleton lookups reported by the Storage Engine – and four seek predicates in the Properties window.  On to a more complex example: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WITH (INDEX([PK dbo.Example key_col])) WHERE key_col BETWEEN 1 AND 8'; This time we are forcing use of the non-clustered primary key to return eight rows.  The index is not covering for this query, so the query plan includes an RID lookup into the heap to fetch the data and padding columns.  The QE reports a seek on the PK and a lookup on the heap.  The SE reports a single range scan on the PK (to find key_col values between 1 and 8), and eight singleton lookups on the heap.  Remember that a bookmark lookup (RID or Key) is a seek to a single value in a ‘unique index’ – it finds a row in the heap or cluster from a unique RID or clustering key – so that’s why lookups are always singleton lookups, not range scans. Our next example shows what happens when a query plan operator is not executed at all: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 8 AND @@TRANCOUNT < 0'; The Filter has a start-up predicate which is always false (if your @@TRANCOUNT is less than zero, call CSS immediately).  The index seek is never executed, but QE still records a single seek against the PK because the operator appears once in an executed plan.  The SE output shows no activity at all.  This next example is 2008 and above only, I’m afraid: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WHERE key_col BETWEEN 1 AND 30', @Partitioned = 'true'; This is the first example to use a partitioned table.  QE reports a single seek on the heap (yes – a seek on a heap), and the SE reports two range scans on the heap.  SQL Server knows (from the partitioning definition) that it only needs to look at partitions 1 and 2 to find all the rows where key_col is between 1 and 30 – the engine seeks to find the two partitions, and performs a range scan seek on each partition. The final example for today is another seek on a heap – try to work out the output of the query before running it! EXECUTE dbo.RunTest @SQL = 'SELECT TOP (2) WITH TIES * FROM Example WHERE key_col BETWEEN 1 AND 50 ORDER BY $PARTITION.PF(key_col) DESC', @Partitioned = 'true'; Notice the lack of an explicit Sort operator in the query plan to enforce the ORDER BY clause, and the backward range scan. © 2011 Paul White email: [email protected] twitter: @SQL_Kiwi

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  • setAttribute, onClick and cross browser compatability...

    - by Nicholas Kreidberg
    I have read a number of posts about this but none with any solid answer. Here is my code: // button creation onew = document.createElement('input'); onew.setAttribute("type", "button"); onew.setAttribute("value", "hosts"); onew.onclick = function(){fnDisplay_Computers("'" + alines[i] + "'"); }; // ie onew.setAttribute("onclick", "fnDisplay_Computers('" + alines[i] + "')"); // mozilla odiv.appendChild(onew); Now, the setAttribute() method (with the mozilla comment) works fine in mozilla but only if it comes AFTER the line above it. So in other words it seems to just default to whichever gets set last. The .onclick method (with the ie comment) does not work in either case, am I using it incorrectly? Either way I can't find a way to make this work at all in IE, let alone in both. I did change the function call when using the .onclick method and it worked fine using just a simple call to an alert function which is why I believe my syntax is incorrect. Long story short, I can't get the onclick parameter to work consistently between IE/Mozilla. -- Nicholas

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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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  • Eclipse Check for Updates issue

    - by Nicholas Ryan Bowers
    I install Eclipse from the Software Center so it links up and will be updated with the rest of my software. Because I am developing for Android, however, I have to install the ADT Plugin within Eclipse by going to Help Install new software (or something to that effect). Now, I do understand that I can update Eclipse through the actual Ubuntu software center/system, but in order to update plugins and extensions within Eclipse, I have to go to Help Check for Updates (which then scans all plugins for updates). The only issue, is that when I installed through the software center, the owner became root, and whenever I run it without root, I'm not able to update - I get the error message "Insufficient access privileges to apply this update." When I run it as root, all of my plugins disappear, because I guess I installed them as myself, not as root. I tried to install the plugins as root, but the Install New Software choice would not work. Ubuntu 12.04 and Eclipse 3.7.2-1

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  • Installing missing package that provides Xm/Xm.h

    - by Nicholas Kinar
    I'm compiling a software package that requires a header file. The header file is missing from my Ubuntu 11.10 (64-bit) installation. During the compilation using make, gcc and gfortran, I receive the following error message. XMstr.c:7:19: fatal error: Xm/Xm.h: No such file or directory Googling for an answer leads me to believe that a MESA library needs to be installed on my system, but I can't find an exact match for the package name. What might be the name of the package that I need to install? Does the package have the same name on more recent Ubuntu distros?

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  • Pygame Import Error, Python 3.2

    - by Treb Nicholas
    I'm having an issue with the Pygame module. I run Python 3.2 and installed the respective Pygame file, but now when I try to import it in the IDLE, it gives me this error: import pygame Traceback (most recent call last): File "", line 1, in import pygame File "C:\Python32\lib\site-packages\pygame__init__.py", line 95, in from pygame.base import * ImportError: DLL load failed: %1 is not a valid Win32 application. Any help will be appreciated.

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  • Pidgin doesn't show buddy list?

    - by Nicholas Flynt
    I like to use pidgin instead of Empathy because I'm used to it, and because I have to venture into windows for more development than I would prefer. In Ubuntu 11.10, I can't find any way to get to the buddy list. It opens, and just sits there in the messenger panel tray thing. From its previous behavior, I believe Pidgin usually starts up minimized-- you have to click on its system tray to show the buddy list. The messenger applet does not seem to support this functionality. Is this a bug, or am I just missing something obvious?

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  • Author has inserted copyright into code with gnu public license notice - implications?

    - by Nicholas Pickering
    I've found a project on Github that I'm interested in contributing to which claims to be open source and has a GPL license included with it. But the original author has added a copyright notification to each source file. I'm not sure why but I don't feel right contributing to a project that's always going to have someone else's name on it. It really breaks the community-created feel, and makes me uneasy about what the author might choose to do with the project next. What are the implications of copyrighting open source GPL code as so? What power does this give the original author over a contributor? # Copyright (C) 2012, 2013 __AUTHORNAME__ # This file is part of __PROJECTNAME__. # # __PROJECTNAME__ is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # __PROJECTNAME__ is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License

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  • Designing binary operations(AND, OR, NOT) in graphs DB's like neo4j

    - by Nicholas
    I'm trying to create a recipe website using a graph database, specifically neo4j using spring-data-neo4j, to try and see what can be done in Graph Databases. My model so far is: (Chef)-[HAS_INGREDIENT]->(Ingredient) (Chef)-[HAS_VALUE]->(Value) (Ingredient)-[HAS_INGREDIENT_VALUE]->(Value) (Recipe)-[REQUIRES_INGREDIENT]->(Ingredient) (Recipe)-[REQUIRES_VALUE]->(Value) I have this set up so I can do things like have the "chef" enter ingredients they have on hand, and suggest recipes, as well as suggest recipes that are close matches, but missing one ingredient. Some recipes can get complex, utilizing AND, OR, and NOT type logic, something like (Milk AND (Butter OR spread OR (vegetable oil OR olive oil))) and I'm wondering if it would be sane to model this in a graph using a tree type representation? An example of what I was thinking is to create three "node" types of AND, OR, and NOT and have each of them connect to the nodes value underneath. How else might this be represented in a Graph Database or is my example above a decent representation?

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  • 12.10 x64 - RTL8188CE - Intermittent/Slow Internet Connection

    - by Nicholas Ryan Bowers
    I have to reset my internet connection often to get it to work. When it does work, it's definitely slower than when I'm on my Windows installation. My computer uses an RTL8188CE wireless card from Realtek. I'm running the 64-bit version of Ubuntu 12.10. Power management is not on. I've read on other sites that some people fixed the issue pertaining to this wireless card by downloading and installing the drivers from the Realtek site, but it seems as if that only helped people with 32-bit installations. Anyone have any luck with this specific wireless card?

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  • I Installed Ubuntu 12.04 on a dell Inspiron 1501 along side windows vista using the windows installer but it wont boot into Ubuntu

    - by Nicholas
    I Installed Ubuntu 12.04 on a dell Inspiron 1501 with an AMD 64 along side windows vista using the windows installer but it wont boot into Ubuntu. It shows that Ubuntu is on the system when my computer boots up but when I select it to load it goes into a black screen and displays some error messages and tells me that the is no operating system installed. this is the error that i get: Try (hdo, 0):FAT16:no WUBILDR try (hdo, 1)NTFS: error: "Prefix" is not set. symbol not found:'grub_file_get_device_name' Aborted. Broadcom UNDI PXE-2.1 V2-1.0 copyright (c) 2000-2006 Broadcom corporation copyright (c) 1997-2000 Intel corporation All rights reserved PXE-EC8:PXE structure was not found in UNDI driver code segment. PXE-M0F Broadcom PXE Rom Operating system not found How can I fix this? I have tryed re-installing it but i get the same error.

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  • Smooth Camera Rotation around 90 degrees

    - by Nicholas
    I'm developing a third person 3D platformer in XNA. My problem is when I try to rotate the camera around the player. I would like to rotate (and animate) the camera 90 degrees around the player. So the camera should rotate until it has reached 90 degrees from the starting position. I cannot figure out how to keep track of the rotation, and when the rotation has made the full 90 degrees. Currently my cameras update: public void Update(Vector3 playerPosition) { if (rotateCamera) { position = Vector3.Transform(position - playerPosition, Matrix.CreateRotationY(0.1f)) + playerPosition; } this.viewMatrix = Matrix.CreateLookAt(position, playerPosition, Vector3.Up); } The initial position of the camera is set in the constructor. The "rotateCamera" bool is set on keypress. Thanks for the help in advance. Cheers.

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  • Functional/nonfunctional requirements VS design ideas

    - by Nicholas Chow
    Problem domain Functional requirements defines what a system does. Non-Functional requirements defines quality attributes of what the system does as a whole.(performance, security, reliability, volume, useability, etc.) Constraints limits the design space, they restrict designers to certain types of solutions. Solution domain Design ideas , defines how the system does it. For example a stakeholder need might be we want to increase our sales, therefore we must improve the usability of our webshop so more customers will purchase, a requirement can be written for this. (problem domain) Design takes this further into the solution domain by saying "therefore we want to offer credit card payments in addition to the current prepayment option". My problem is that the transition phase from requirement to design seems really vague, therefore when writing requirements I am often confused whether or not I incorporated design ideas in my requirements, that would make my requirement wrong. Another problem is that I often write functional requirements as what a system does, and then I also specify in what timeframe it must be done. But is this correct? Is it then a still a functional requirement or a non functional one? Is it better to seperate it into two distinct requirements? Here are a few requirements I wrote: FR1 Registration of Organizer FR1 describes the registration of an Organizer on CrowdFundum FR1.1 The system shall display a registration form on the website. FR1.2 The system shall require a Name, Username, Document number passport/ID card, Address, Zip code, City, Email address, Telephone number, Bank account, Captcha code on the registration form when a user registers. FR1.4 The system shall display an error message containing: “Registration could not be completed” to the subscriber within 1 seconds after the system check of the registration form was unsuccessful. FR1.5 The system shall send a verification email containing a verification link to the subscriber within 30 seconds after the system check of the registration form was successful. FR1.6 The system shall add the newly registered Organizer to the user base within 5 seconds after the verification link was accessed. FR2 Organizer submits a Project FR2 describes the submission of a Project by an Organizer on CrowdFundum - FR2 The system shall display a submit Project form to the Organizer accounts on the website.< - FR2.3 The system shall check for completeness the Name of the Project, 1-3 Photo’s, Keywords of the Project, Punch line, Minimum and maximum amount of people, Funding threshold, One or more reward tiers, Schedule of when what will be organized, Budget plan, 300-800 Words of additional information about the Project, Contact details within 1 secondin after an Organizer submits the submit Project form. - FR2.8 The system shall add to the homepage in the new Projects category the Project link within 30 seconds after the system made a Project webpage - FR2.9 The system shall include in the Project link for the homepage : Name of the Project, 1 Photo, Punch line within 30 seconds after the system made a Project webpage. Questions: FR 1.1 : Have I incorporated a design idea here, would " the system shall have a registration form" be a better functional requirement? F1.2 ,2.3 : Is this not singular? Would the conditions be better written for each its own separate requirement FR 1.4: Is this a design idea? Is this a correct functional requirement or have I incorporated non functional(performance) in it? Would it be better if I written it like this: FR1 The system shall display an error message when check is unsuccessful. NFR: The system will respond to unsuccesful registration form checks within 1 seconds. Same question with FR 2.8 and 2.9. FR2.3: The system shall check for "completeness", is completeness here used ambigiously? Should I rephrase it? FR1.2: I added that the system shall require a "Captcha code" is this a functional requirement or does it belong to the "security aspect" of a non functional requirement. I am eagerly waiting for your response. Thanks!

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  • Software requirements specification, please help!

    - by Nicholas Chow
    For a school project, I had to create a SRS for a "fictional" application. However they did not show us what it exactly entails, and were very vague with explanations. The SRS asked of us has to have at least 5 functional requirements, 5 non functional requirements and 1 constraint. Now I have tried my best to make one however I think there are still a lot of mistakes in it. Could you all please look at it and provide me with some feedback on which parts I can improve or just tell me which parts are plain out wrong and how to make it better? (The project has a maximum of 12 pages so it is a bit long, I will post it below. FR1 Registration of Organizer FR1 describes the registration of an Organizer on CrowdFundum FR1.1 The system shall display a registration form on the website. FR1.2 The system shall require a Name, Username, Document number passport/ID card, Address, Zip code, City, Email address, Telephone number, Bank account, Captcha code on the registration form when a user registers.

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  • Why are you doing this? [closed]

    - by NIcholas Lawson
    I am working on a story that I am going to be querying to several magazines in my hometown about this work that is being done by the AXR group. This is a group of people who have networked online and are working on developing a higher level syntax structure than CSS and HTML currently offer. I am covering this is as a story because I see potential in this as a human interest story in cosmopolitan society. I have been asked by the group to pose this question to you and would appreciate any and all comments you would have on the following ... To AXR: So when does the internet become finished? At what point does a computer scientist say to himself ... my job here is finished ... the internet is complete? When is the internet ready to be more about the display of content than the uploading of new websites or computer tech? You are embarking on upon a sixty year project every day you work with this internet, what drives you? Why are you spending your hard earned hours working on the code to this computer? I spend thirty hours a week online because I love the writing and I know what would make the internet better ... ease of use ... i know it is difficult to program but I see some very elegant solutions online ... in this early inception phase of your programming development for this HSS prototype ... I would like to know why I do not see you programmers asking questions such as ... What would make the end user's life the easiest when using this code? I know you can solve the problem but an evolution forward would be simple, not simple to a computer scientist but simple to use for a career janitor ... if you could solve the problem of alleviating the stress at using a the computer you could get better content out of the computer ... right now the main problem is that the best content is in the hands of the people least likely to use the computer and the more simple you make the computer to use ... the better the content collection will be in the long run ... That is not what I want to talk about though ... why are you writing code when you could be writing stories? I know the computer is worthless without content so I build content, I know the book is worthless without the combinations of words in them, i know the television is worthless without the television news anchor or the actor, what I want to know from you folks in a very journalistic sense is why are you even bothering to bother to write code for a machine that has only made our lives i would dare say less interesting. why are you feeding the beast your time when you could be writing stories or being an actor or musician or auto mechanic ... why code? why this machine? what do you love about it? what do you hate about it? what do you wonder about it? I want to know so that starting out I know how to further shape my questions with axr ... i want the full story ... i want the real answers ... and i want to know why you are doing this, it would make for great writing if you could elucidate on this point.

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  • How can I use WebGL to create a tile-based multi-layer scrolling platform game?

    - by Nicholas Hill
    I've found WebGL (based on OpenGL) to be a fiendish and unforgiving framework for those learning to write HTML5-based games. Despite the presence of many examples on how to get started, I'm really struggling to understand how I could simply load a bunch of images and render them to a canvas quickly using WebGL. My specific scenario involves trying to render a map using a bespoke but simple multi-layered tile engine, where each value in a three dimensional array points to the image to use for that location in the rendered image. Think "Sonic the Hedgehog" via tilesets, tiles, maps, layers, sprites etc. Can anyone enlighten me: 1) How can I load an image that I can use as a texture in WebGL? 2) How can I dynamically select an image at run time and draw it at any co-ordinate, that I also select at run time?

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  • Tunnels in pseudo 3D racing game

    - by Nicholas
    How would one go about doing tunnels in a pseudo 3D racing game ? The main problem I have at the moment is perspective - I cant think of a way, beyond having to Z sort the sprites and tunnel coordinates, so that vehicles are displayed in front of the tunnel entrance and somehow block the display when out of site. I would like my tunnels to be used on both flat, curved and hills and slopes. The tunnel enterance/exit is made up of 3 separate graphics, (left, right and top), whilst inside the tunnel it is just one line graphic along the top (the idea being its supposed to be a set distance above the current vertical road position). As you can see from the picture, the vehicles are still being rendered whilst in the tunnel. I've converted the Code Incomplete road system to GLBasic.

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  • Can WebGL be used to create a tile-based multi-layer scrolling platform game?

    - by Nicholas Hill
    I've found WebGL (based on OpenGL) to be a fiendish and unforgiving framework for those learning to write HTML5-based games. Despite the presence of many examples on how to get started, I'm really struggling to understand how I could simply load a bunch of images and render them to a canvas quickly using WebGL. My specific scenario involves trying to render a map using a bespoke but simple multi-layered tile engine, where each value in a three dimensional array points to the image to use for that location in the rendered image. Think "Sonic the Hedgehog" via tilesets, tiles, maps, layers, sprites etc. Can anyone enlighten me: 1) How can I load an image that I can use as a texture in WebGL? 2) How can I dynamically select an image at run time and draw it at any co-ordinate, that I also select at run time?

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  • Functional vs. Non-Functional Requirements vs Design ideas in an SRS

    - by Nicholas Chow
    For a school project, I had to create a SRS for a "fictional" application. However they did not show us what it exactly entails, and were very vague with explanations. The SRS asked of us has to have at least 5 functional requirements, 5 non functional requirements and 1 constraint. Now I have tried my best to make one however I there are still some uncertainties left, I hope you experts can tell me whether or not I am thinking in the right direction. I will keep on updating this posts as I have questions regarding requirements that are vague to me, thank you all in advance for making SRS more clear for me FR1 Registration of Organizer FR1 describes the registration of an Organizer on CrowdFundum FR1.1 The system shall display a registration form on the website. FR1.2 The system shall require a Name, Username, Document number passport/ID card, Address, Zip code, City, Email address, Telephone number, Bank account, Captcha code on the registration form when a user registers. FR1.3 The system shall check whether the Name, Username, Document number passport/ID card, Address, Zip code, City, Email address, Telephone number, Bank account, Captcha code are filled out correctly within 1 seconds after a user submits the registration form. FR1.4 The system shall display an error message containing: “Registration could not be completed” to the subscriber within 1 seconds after the system check of the registration form was unsuccessful. FR1.5 The system shall send a verification email containing a verification link to the subscriber within 30 seconds after the system check of the registration form was successful. FR1.6 The system shall add the newly registered Organizer to the user base within 5 seconds after the verification link was accessed. Questions: FR1.1 Is this a functional requirement, or have I incorporated design idea in it by using "shall display on website". If so what would be a better way to write it? FR1.2 Is this better written in one requirement, or should I write each condition as a seperate requirement? FR1.3-1.5 Are these functional requirements or did I mix some non functional elements in it? How is it better phrased? FR1.3-1.6 Are these all correct functional requirements? As in free of ambiguity, complete, implementation free etc.

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  • Given two sets of DNA, what does it take to computationally "grow" that person from a fertilised egg and see what they become? [closed]

    - by Nicholas Hill
    My question is essentially entirely in the title, but let me add some points to prevent some "why on earth would you want to do that" sort of answers: This is more of a mind experiment than an attempt to implement real software. For fun. Don't worry about computational speed or the number of available memory bytes. Computers get faster and better all of the time. Imagine we have two data files: Mother.dna and Father.dna. What else would be required? (Bonus point for someone who tells me approx how many GB each file will be, and if the size of the files are exactly the same number of bytes for everyone alive on Earth!) There would ideally need to be a way to see what the egg becomes as it becomes a human adult. If you fancy, feel free to outline the design. I am initially thinking that there'd need to be some sort of volumetric voxel-based 3D environment for simulation purposes.

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  • System beeps on shutdown

    - by Nicholas Smith
    Very recently a couple of our systems have started making beeps on shutdown, we've not been able to isolate the exact time that they've been updated and it's occuring, but I've narrowed it down to being not pcspkr or snd_pcsp as both are blacklisted under modprobe, and rmmod'ed just to be sure. Now I'm a bit puzzled as I was under the impression those were the only two modules that could make the beep, is there a third lurking that I need to blacklist? Edit: I've copied the results of lsmod over on Pastebin.

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