{"id":524,"date":"2011-10-12T03:48:00","date_gmt":"2011-10-12T03:48:00","guid":{"rendered":"\/blogs\/joe\/post\/Parallelism-and-Batch-Execution-Mode-with-Columnstore-Indexes.aspx"},"modified":"2013-01-02T20:32:36","modified_gmt":"2013-01-03T04:32:36","slug":"row-and-batch-execution-modes-and-columnstore-indexes","status":"publish","type":"post","link":"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/","title":{"rendered":"Row and batch execution modes and columnstore indexes"},"content":{"rendered":"

\nThis post covers examples from Denali<\/strike>  SQL Server 2012 CTP3, version 11.0.1440.<\/font><\/font><\/em>\n<\/p>\n

\nIn my last post I hinted towards some interesting findings regarding parallelism and columnstore indexes.  <\/span>I’ll talk about what I observed in this post.<\/font><\/font>\n<\/p>\n

\nJust as a quick level-set, columnstore indexing provides two new areas of functionality within SQL Server 2012 that can potentially improve query performance for typical relational data warehouse workloads.  <\/span>The features contributing to this potential performance gain include the Vertipaq columnstore technology (which uses compression and stores the data by column instead of row) and the new method of query processing that processes blocks of column data in batches.  <\/span>You can benefit from from the columnstore storage alone  – but you may also see further query performance improvements if the query runs in "batch" execution mode versus "row".  <\/font><\/font>\n<\/p>\n

\nOne key point to underscore– just because you add a columnstore index to a table doesn’t mean that your query will use “batch” execution mode<\/em>.<\/font>  <\/font><\/span><\/font>\n<\/p>\n

\nLet’s walk through an example of row vs. batch using the AdventureWorksDWDenali database.  <\/span>The test server used in this illustration has four visible schedulers (not including the DAC) and 8GB of RAM.<\/font><\/font>\n<\/p>\n

\nAlso – in order to increase the cost of the queries against this table and make it worth the columnstore index's time, I put together the below query to bump up the row count in FactInternetSales from 60,398 rows to 483,184 rows (SalesOrderNumber was part of the primary key, so I populated it with NEWID() and kept the other column values static from the existing rows and then used "GO 3" to populate it a few times):<\/font><\/font>\n<\/p>\n

\nINSERT <\/font><\/span>dbo.FactInternetSales
\n<\/span>(<\/span>ProductKey<\/span>, <\/span>OrderDateKey<\/span>, <\/span>DueDateKey<\/span>, <\/span>ShipDateKey<\/span>, <\/span>CustomerKey<\/span>, <\/span>PromotionKey<\/span>, <\/span>CurrencyKey<\/span>, <\/span>SalesTerritoryKey<\/span>, <\/span>SalesOrderNumber<\/span>, <\/span>SalesOrderLineNumber<\/span>, <\/span>RevisionNumber<\/span>, <\/span>OrderQuantity<\/span>, <\/span>UnitPrice<\/span>, <\/span>ExtendedAmount<\/span>, <\/span>UnitPriceDiscountPct<\/span>, <\/span>DiscountAmount<\/span>, <\/span>ProductStandardCost<\/span>, <\/span>TotalProductCost<\/span>, <\/span>SalesAmount<\/span>, <\/span>TaxAmt<\/span>, <\/span>Freight<\/span>, <\/span>CarrierTrackingNumber<\/span>, <\/span>CustomerPONumber<\/span>, <\/span>OrderDate<\/span>, <\/span>DueDate<\/span>, <\/span>ShipDate<\/span><\/font><\/font>)<\/p>\n

<\/span>SELECT <\/span>ProductKey<\/span>, <\/span>OrderDateKey<\/span>, <\/span>DueDateKey<\/span>, <\/span>ShipDateKey<\/span>, <\/span>CustomerKey<\/span>, <\/span>PromotionKey<\/span>, <\/span>CurrencyKey<\/span>, <\/span>SalesTerritoryKey<\/span>, <\/span>LEFT<\/span>(<\/span>CAST<\/span>(<\/span>NEWID<\/span>() <\/span>AS NVARCHAR<\/span>(<\/span>36<\/span>)),<\/span>20<\/span>), <\/span>SalesOrderLineNumber<\/span>, <\/span>RevisionNumber<\/span>, <\/span>OrderQuantity<\/span>, <\/span>UnitPrice<\/span>, <\/span>ExtendedAmount<\/span>, <\/span>UnitPriceDiscountPct<\/span>, <\/span>DiscountAmount<\/span>, <\/span>ProductStandardCost<\/span>, <\/span>TotalProductCost<\/span>, <\/span>SalesAmount<\/span><\/font><\/font>,
\n<\/span>TaxAmt<\/span>, <\/span>Freight<\/span>, <\/span>CarrierTrackingNumber<\/span>, <\/span>CustomerPONumber<\/span>, <\/span>OrderDate<\/span>, <\/span>DueDate<\/span>, <\/span><\/font><\/font>ShipDate
\n<\/span>FROM <\/span><\/font><\/font>dbo.FactInternetSales
\nGO 3<\/font><\/span><\/font><\/font>\n<\/p>\n

\n<\/font>
\n <\/span><\/font><\/font>\n<\/p>\n

<\/font><\/font><\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nThe following query is executed against a table with no columnstore index (I’ve also enabled the actual Execution Plan to show afterwards – and also execute the query twice in order to measure results with the data in cache):<\/font><\/font>\n<\/p>\n

\n

\n\tSET STATISTICS <\/span>IO <\/span><\/font><\/font>ON
\n\tSET STATISTICS <\/span>TIME <\/span><\/font><\/font>ON <\/p>\n

\n\tSELECT <\/span>c.CommuteDistance<\/span><\/font><\/font>, 
\n\t<\/span>       d.CalendarYear<\/span><\/font><\/font>,
\n\t<\/span>       SUM<\/span>(<\/span>f.SalesAmount<\/span>) <\/span><\/font><\/font>TotalSalesByCommuteDistance
\n\t<\/span>FROM <\/span><\/font><\/font>dbo.FactInternetSales as f
\n\t<\/span>INNER JOIN <\/span>dbo.DimCustomer as c <\/span><\/font><\/font>ON
\n\t<\/span>           f.CustomerKey <\/span>= <\/span><\/font><\/font>c.CustomerKey
\n\t<\/span>INNER JOIN <\/span>dbo.DimDate d <\/span><\/font><\/font>ON
\n\t<\/span>           d.DateKey <\/span>= <\/span><\/font><\/font>f.OrderDateKey
\n\t<\/span>GROUP BY <\/span>c.CommuteDistance<\/span><\/font><\/font>,
\n\t<\/font><\/span>         d.CalendarYear <\/font>
\n\t<\/span><\/font><\/font>\n\t<\/p>\n<\/blockquote>\n

\n<\/font><\/font>\n<\/p>\n

\nThe results for STATISTICS IO were as follows:<\/font><\/font>\n<\/p>\n

\nTable 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font>\n<\/p>\n

\nTable 'FactInternetSales'. Scan count 1, logical reads 20289<\/font><\/span>, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font>\n<\/p>\n

\nTable 'DimCustomer'. Scan count 1, logical reads 979, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font>\n<\/p>\n

\nTable 'DimDate'. Scan count 1, logical reads 66, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font>\n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nAnd the results of the SET STATISTICS TIME ON were as follows:<\/font><\/font>\n<\/p>\n

\nSQL Server Execution Times:<\/font><\/font>\n<\/p>\n

\n   <\/font><\/span>CPU time = 562 ms,  <\/span>elapsed time = 625 ms.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nAs for the execution plan, if we hover over the Clustered Index Scan for FactInternetSales you’ll see the “Actual Execution Mode”. <\/font><\/font>\n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\n\"clip_image002[4]\"<\/a><\/span>\n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nSo we see a value of “Row” (the execution mode we’re used to) and “Actual Number of Batches” of 0.<\/font><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nNow let’s create a columnstore index:<\/font><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nCREATE NONCLUSTERED <\/span>COLUMNSTORE <\/span>INDEX <\/span>[CSI_FactInternetSales] <\/span>ON <\/span><\/font><\/font>[dbo].[FactInternetSales]
\n<\/font><\/span>(
\n<\/span>[OrderDateKey]<\/span><\/font><\/font>,
\n<\/span>[CustomerKey]<\/span><\/font><\/font>,
\n<\/font><\/span>[SalesAmount]
\n<\/font><\/span>) <\/p>\n

\n<\/font><\/span>GO <\/font><\/p>\n

<\/span><\/font><\/font><\/span>\n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nRe-executing the query after creating the index, I see the following STATISTICS IO output:<\/font><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nTable 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font><\/span>\n<\/p>\n

\nTable 'FactInternetSales'. Scan count 1, logical reads 597<\/font><\/span>, physical reads 0, read-ahead reads 1167, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font><\/span>\n<\/p>\n

\nTable 'DimCustomer'. Scan count 1, logical reads 979, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font><\/span>\n<\/p>\n

\nTable 'DimDate'. Scan count 1, logical reads 66, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nThat’s a reduction in logical reads against FactInternetSales from <\/font><\/span>20289<\/font> down to 597.  <\/span>Very likeable. <\/font><\/span><\/font>\n<\/p>\n

\n<\/span><\/font>\n<\/p>\n

\n<\/span><\/font>\n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nWhat about STATISTICS TIME?<\/font><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\nSQL Server Execution Times:<\/font><\/font><\/span>\n<\/p>\n

\n   <\/font><\/span>CPU time = 453 ms,  <\/span>elapsed time = 485 ms.<\/font><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nOkay so CPU time was 562 ms now it is 453 ms.  <\/span>Elapsed time was 625 ms and now it is 485 ms.  <\/span><\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nWhat about the execution plan, were we using batch execution mode?<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\n<\/font><\/font><\/span>\n<\/p>\n

\"clip_image004[4]\"<\/a><\/span> <\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nNo.  <\/span>Still using “Row” with a columnstore index scan.   <\/span>So things brings me to what I observed a few days ago.<\/font>  <\/font><\/span><\/font>\n<\/p>\n

\n<\/span><\/font>\n<\/p>\n

\n<\/span><\/font>\n<\/p>\n

\n<\/span><\/font>\n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nOne thing I didn’t mention is that in my test environment –  my max degree of parallelism was set to “1”<\/em><\/u>.  <\/span><\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nWhat happens if I uncap the max degree of parallelism for my 4-scheduler server and re-execute the query?<\/font><\/font>\n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\n\"clip_image005[4]\"<\/a><\/span>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nAfter lifting the cap on max degree of parallelism, the query executes using a parallel plan and also switches from “row” to “batch” mode.  <\/span>Notice also the “Actual Number of Batches” – which shows a value of 1,482.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nThis is all nice – but what about the query performance and I\/O.  <\/span>Any further reductions?<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nThe I\/O results are as follows:<\/font><\/font>\n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\n

\n\tTable 'DimCustomer'. Scan count 5, logical reads 1072, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font>\n\t<\/p>\n

\n\tTable 'DimDate'. Scan count 3, logical reads 56, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font>\n\t<\/p>\n

\n\tTable 'FactInternetSales'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font><\/span>\n\t<\/p>\n

\n\tTable 'Worktable'. Scan count 0, logical reads 0, physical reads 0, read-ahead reads 0, lob logical reads 0, lob physical reads 0, lob read-ahead reads 0.<\/font><\/font>\n\t<\/p>\n<\/blockquote>\n

\n<\/font><\/font>\n<\/p>\n

\nWhat’s this? Zero for all stats related to FactInternetSales?  <\/span>I even dropped clean buffers to double-check, and it still displayed all zeros.  (<\/span>More on this for another day.)<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nWhat about the execution statistics?<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nSQL Server Execution Times:<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n <\/font><\/span>              <\/span>  <\/span>CPU time = 78 ms,  <\/span>elapsed time = 114 ms.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nNow we're talking big improvements. CPU time was 562 ms without columnstore, then 453 ms with columnstore + row processing and then 78 ms for columnstore + batch processing.  <\/span>Elapsed time was 625 ms without columnstore and then 485 ms for columnstore + row processing and then 114 ms for columnstore + batch processing.  <\/span>So more non-trivial reductions.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n<\/font><\/font>\n<\/p>\n

\nSo a few key findings in closing:<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n·<\/font><\/font>        <\/font><\/font><\/span><\/span><\/span>Columnstore + row processing can provide a performance boost (getting column based storage and compression)<\/font><\/font>\n<\/p>\n

\n·<\/font><\/font>        <\/font><\/font><\/span><\/span><\/span>Columnstore + batch processing can provide additional performance improvements on top of the columnstore index I\/O benefits<\/font><\/font>\n<\/p>\n

\n·<\/font><\/font>        <\/font><\/font><\/span><\/span><\/span>When I capped parallelism, I didn’t see batch processing – so check your execution plan and be aware that there are other factors that impact whether batch processing vs. row processing as well<\/font><\/font><\/p>\n","protected":false},"excerpt":{"rendered":"

This post covers examples from Denali  SQL Server 2012 CTP3, version 11.0.1440. In my last post I hinted towards some interesting findings regarding parallelism and columnstore indexes.  I’ll talk about what I observed in this post. Just as a quick level-set, columnstore indexing provides two new areas of functionality within SQL Server 2012 that can potentially […]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-524","post","type-post","status-publish","format-standard","hentry","category-columnstore-indexes"],"yoast_head":"\nRow and batch execution modes and columnstore indexes - Joe Sack<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Row and batch execution modes and columnstore indexes - Joe Sack\" \/>\n<meta property=\"og:description\" content=\"This post covers examples from Denali  SQL Server 2012 CTP3, version 11.0.1440. 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He was given the SQL Server MVP award in 2013.\",\"sameAs\":[\"http:\/\/3.209.169.194\/blogs\/joe\",\"https:\/\/twitter.com\/https:\/\/twitter.com\/josephsack\"],\"url\":\"https:\/\/www.sqlskills.com\/blogs\/joe\/author\/joe\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Row and batch execution modes and columnstore indexes - Joe Sack","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/","og_locale":"en_US","og_type":"article","og_title":"Row and batch execution modes and columnstore indexes - Joe Sack","og_description":"This post covers examples from Denali  SQL Server 2012 CTP3, version 11.0.1440. 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Just as a quick level-set, columnstore indexing provides two new areas of functionality within SQL Server 2012 that can potentially […]","og_url":"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/","og_site_name":"Joe Sack","article_published_time":"2011-10-12T03:48:00+00:00","article_modified_time":"2013-01-03T04:32:36+00:00","og_image":[{"url":"https:\/\/www.sqlskills.com\/blogs\/joe\/wp-content\/uploads\/windows-live-writer\/parallelism-and-batch-execution-mode-wit\/1dbfacf7\/clip_image0024_thumb.png"}],"author":"Joseph Sack","twitter_misc":{"Written by":"Joseph Sack","Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/","url":"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/","name":"Row and batch execution modes and columnstore indexes - Joe Sack","isPartOf":{"@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#website"},"datePublished":"2011-10-12T03:48:00+00:00","dateModified":"2013-01-03T04:32:36+00:00","author":{"@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#\/schema\/person\/533eb0113a15fb5a6e8067a49e4ae648"},"breadcrumb":{"@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/row-and-batch-execution-modes-and-columnstore-indexes\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.sqlskills.com\/blogs\/joe\/"},{"@type":"ListItem","position":2,"name":"Columnstore indexes","item":"https:\/\/www.sqlskills.com\/blogs\/joe\/category\/columnstore-indexes\/"},{"@type":"ListItem","position":3,"name":"Row and batch execution modes and columnstore indexes"}]},{"@type":"WebSite","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#website","url":"https:\/\/www.sqlskills.com\/blogs\/joe\/","name":"Joe Sack","description":"SQL Server Performance Tuning, High Availability and Disaster Recovery Blog","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.sqlskills.com\/blogs\/joe\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#\/schema\/person\/533eb0113a15fb5a6e8067a49e4ae648","name":"Joseph Sack","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/a4b39a7719a6bfff1add3ec00527810734579ee114d6d983e8e68f937b77be96?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/a4b39a7719a6bfff1add3ec00527810734579ee114d6d983e8e68f937b77be96?s=96&d=mm&r=g","caption":"Joseph Sack"},"description":"Joe Sack is a Principal Consultant with SQLskills. He has worked as a SQL Server professional since 1997 and has supported and developed for SQL Server environments in financial services, IT consulting, manufacturing, retail and the real estate industry. Prior to joining SQLskills he worked at Microsoft as a Premier Field Engineer supporting very large enterprise customer environments. He was responsible for providing deep SQL Server advisory services, training, troubleshooting and ongoing solutions guidance. His areas of expertise include performance tuning, scalability, T-SQL development and high-availability. In 2006 Joe earned the \u201cMicrosoft Certified Master: SQL Server 2005\u201d certification and in 2008 he earned the \u201cMicrosoft Certified Master: SQL Server 2008\u201d certification. In 2009 he took over responsibility for the entire SQL Server Microsoft Certified Master program and held that post until 2011. 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