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• Columnstore Case Study #1: MSIT SONAR Aggregations

  - by aspiringgeek

  Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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• Columnstore Case Study #1: MSIT SONAR Aggregations

  - by aspiringgeek

  Preamble This is the first in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in this deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. Why Columnstore? If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. App: MSIT SONAR Aggregations At MSIT, performance & configuration data is captured by SCOM. We archive much of the data in a partitioned data warehouse table in SQL Server 2012 for reporting via an application called SONAR.  By definition, this is a primary use case for columnstore—report queries requiring aggregation over large numbers of rows.  New data is refreshed each night by an automated table partitioning mechanism—a best practices scenario for columnstore. The Win Compared to performance using classic indexing which resulted in the expected query plan selection including partition elimination vs. SQL Server 2012 nonclustered columnstore, query performance increased significantly.  Logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Other than creating the columnstore index, no special modifications or tweaks to the app or databases schema were necessary to achieve the performance improvements.  Existing nonclustered indexes were rendered superfluous & were deleted, thus mitigating maintenance challenges such as defragging as well as conserving disk capacity. Details The table provides the raw data & summarizes the performance deltas. Logical Reads (8K pages) CPU (ms) Durn (ms) Columnstore 160,323 20,360 9,786 Conventional Table & Indexes 9,053,423 549,608 193,903 ? x56 x27 x20 The charts provide additional perspective of this data.  "Conventional vs. Columnstore Metrics" document the raw data.  Note on this linear display the magnitude of the conventional index performance vs. columnstore.  The “Metrics (?)” chart expresses these values as a ratio. Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the first in a series of reports on columnstore implementations, results from an initial implementation at MSIT in which logical reads were reduced by over a factor of 50; both CPU & duration improved by factors of 20 or more.  Subsequent features in this series document performance enhancements that are even more significant. 

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• T-SQL Tuesday: Aggregations in SSIS

  - by andyleonard

  Introduction Jes Borland ( Blog | @grrl_geek ) is hosting this month's T-SQL Tuesday - started by SQLBlog's own Adam Machanic ( Blog | @AdamMachanic ) - and it is about aggregation. I thought I'd show a couple ways to do aggregation using SSIS. The Aggregate Transformation in SSIS The Aggregate transform in SSIS is fast . I built an SSIS package (AggregateScripts.dtsx) with two Data Flow Tasks (Using the Aggregate Transform and Using a Script Component). Using the Aggregate Transform looks like this:...(read more)

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• Connect Digest : 2011-06-27

  - by AaronBertrand

  Sorry I have fallen off the Connect Digest wagon for the past few weeks; been a little swamped since returning from SQLCruise Alaska. Not sure I'll be able to assemble a digest every week, but I'll certainly try to keep a steady pace. This week I wanted to highlight a few suggestions around indexed views. With the coming of SQL Server code-named "Denali" we will be pushed toward the new columnstore index as an alternative to indexed views. But this won't be for all cases, and it likely won't be available...(read more)

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• Question between aggregations, compositions, references (arrow) and simple link (nothing) relations within UML2 ?

  - by Rushino

  Hello, I have some questions regarding relations between aggregations, compositions, references (arrow) and simple link (nothing) relations within UML2 ? If A - B - C each have composition relationship. Are the root responsable of creation and destruction of the object B and C ? What aggregations have that the simple link or references doesn't ? What make them different ? Thanks. Edit: The question was a bit unclear and not objective. I edited it.

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• How to improve performance of non-scalar aggregations on denormalized tables

  - by The Lazy DBA

  Suppose we have a denormalized table with about 80 columns, and grows at the rate of ~10 million rows (about 5GB) per month. We currently have 3 1/2 years of data (~400M rows, ~200GB). We create a clustered index to best suit retrieving data from the table on the following columns that serve as our primary key... [FileDate] ASC, [Region] ASC, [KeyValue1] ASC, [KeyValue2] ASC ... because when we query the table, we always have the entire primary key. So these queries always result in clustered index seeks and are therefore very fast, and fragmentation is kept to a minimum. However, we do have a situation where we want to get the most recent FileDate for every Region, typically for reports, i.e. SELECT [Region] , MAX([FileDate]) AS [FileDate] FROM HugeTable GROUP BY [Region] The "best" solution I can come up to this is to create a non-clustered index on Region. Although it means an additional insert on the table during loads, the hit isn't minimal (we load 4 times per day, so fewer than 100,000 additional index inserts per load). Since the table is also partitioned by FileDate, results to our query come back quickly enough (200ms or so), and that result set is cached until the next load. However I'm guessing that someone with more data warehousing experience might have a solution that's more optimal, as this, for some reason, doesn't "feel right".

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• Getting multiple aggregations in Single statement

  - by Harikrishnan R

  The table has data of city and its branchs/atms CITY TYPE NAME ---------------------------------- agra atm X agra branch X1 delhi atm X2 agra atm X3 agra atm X4 delhi branch X5 chennai branch X6 The result set expecting is CITY ATM BRANCH ------------------------------------ agra 3 1 delhi 1 1 chennai 0 1 Whether we can do this in one select statement.

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• SolidQ Journal - free SQL goodness for February

  - by Greg Low

  The SolidQ Journal for February just made it out by the end of February 28th. But again, it's great to see the content appearing. I've included the second part of the article on controlling the execution context of stored procedures. The first part was in December. Also this month, along with Fernando Guerrero's editorial, Analysis Services guru Craig Utley has written about aggregations, Herbert Albert and Gianluca Holz have continued their double-act and described how to automate database migrations,...(read more)

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• Window Functions in SQL Server

  When SQL Server introduced Window Functions in SQL Server 2005, it was done in a rather tentative way, with only a handful of functions being introduced. This was frustrating, as they remove the last excuse for cursor-based operations by providing aggregations over a partition of the result set, and imposing an ordered sequence over a partition. Now, with SQL Server 2012, we are soon to enjoy a full range of Window Functions. They are going to make for some much simpler SQL queries.

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• Analysis Services (SSAS) - Unexpected Internal Error when processing (ProcessUpdate). Workaround/Resolution

  - by James Rogers

  Many implementations require the use of ProcessUpdate to support Type 1 slowly changing dimensions. ProcessUpdate drops all of the affected indexes and aggregations in partitions affected by data that changes in the Dimension on which the ProcessUpdate is being performed. Twice now I have had situations where the processing fails with "Internal error: An unexpected exception occurred." Any subsequent ProcessUpdate processing will also fail with the same error. In talking with Microsoft the issue is corrupt indexes for the Dimension(s) being processed in the partitions of the affected measure group. I cannot guarantee that the following will correct your problem but it did in my case and saved us quite a bit of down time.   Workaround: ProcessIndexes on the entire cube that is being processed and throwing the error. This corrected the problem on both 2008 and 2008 R2.   Pros:  Does not require a complete rebuild of the data (ProcessFull) for either the Dimension or Cube. User access can continue while this ProcessIndexes in underway.   Cons: Can take a long time, especially on large cubes with many partitions, dimensions and/or aggregations. Query Performance is usually severely impacted due to the memory and CPU requirements for Aggregation and Index building   <Batch http://schemas.microsoft.com/analysisservices/2003/engine"http://schemas.microsoft.com/analysisservices/2003/engine">  <Parallel>     <Process xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ddl2="http://schemas.microsoft.com/analysisservices/2003/engine/2" xmlns:ddl2_2="http://schemas.microsoft.com/analysisservices/2003/engine/2/2" xmlns:ddl100_100="http://schemas.microsoft.com/analysisservices/2008/engine/100/100" xmlns:ddl200="http://schemas.microsoft.com/analysisservices/2010/engine/200" xmlns:ddl200_200="http://schemas.microsoft.com/analysisservices/2010/engine/200/200">       <Object>         <DatabaseID>MyDatabase</DatabaseID>         <CubeID>MyCube</CubeID>       </Object>       <Type>ProcessIndexes</Type>       <WriteBackTableCreation>UseExisting</WriteBackTableCreation>     </Process>  </Parallel> </Batch>   The cube where the corruption exists can be found by having Profiler running while the ProcessUpdate is executing. The first partition that displays the "The Job has ended in failure." message in the TextData column will be part of the cube/measuregroup that has the corruption. You can try to run ProcessIndexes on just that measure group. This may correct the problem and save additional time if you have other large measure groups in the cube that are not affected by the corruption.   Remember to execute your normal ProcessUpdate batch after the successful completion of the ProcessIndexes. The ProcessIndexes does not pick up data changes.   Things that did not work: ProcessClearIndexes - why this doesn't work and ProcessIndexes does is unclear at this point. ProcessFull on the partition in question. In my latest case, this would clear up the problem for that partition. However, the next partition the ProcessUpdate touched that had data in it would generate and error. This leads me to believe the corruption problem will exist in all partitions in the affected measure group that have data in them.   NOTE: I experience this problem in both a SQL 2008 and SQL 2008 R2 Analysis Services environment, on separate built from the same relational database. This leads me to believe that some data condition in the tables used for the Dimension processing caused the corruption since the two environments were on physically separate hardware. I am waiting on Microsoft to analyze the dumps to give us more insight into what actually caused the corruption and will update this post accordingly.

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• Columnstore Case Study #2: Columnstore faster than SSAS Cube at DevCon Security

  - by aspiringgeek

  Preamble This is the second in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in my big deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. See also Columnstore Case Study #1: MSIT SONAR Aggregations Why Columnstore? As stated previously, If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. The Customer DevCon Security provides home & business security services & has been in business for 135 years. I met DevCon personnel while speaking to the Utah County SQL User Group on 20 February 2012. (Thanks to TJ Belt (b|@tjaybelt) & Ben Miller (b|@DBADuck) for the invitation which serendipitously coincided with the height of ski season.) The App: DevCon Security Reporting: Optimized & Ad Hoc Queries DevCon users interrogate a SQL Server 2012 Analysis Services cube via SSRS. In addition, the SQL Server 2012 relational back end is the target of ad hoc queries; this DW back end is refreshed nightly during a brief maintenance window via conventional table partition switching. SSRS, SSAS, & MDX Conventional relational structures were unable to provide adequate performance for user interaction for the SSRS reports. An SSAS solution was implemented requiring personnel to ramp up technically, including learning enough MDX to satisfy requirements. Ad Hoc Queries Even though the fact table is relatively small—only 22 million rows & 33GB—the table was a typical DW table in terms of its width: 137 columns, any of which could be the target of ad hoc interrogation. As is common in DW reporting scenarios such as this, it is often nearly to optimize for such queries using conventional indexing. DevCon DBAs & developers attended PASS 2012 & were introduced to the marvels of columnstore in a session presented by Klaus Aschenbrenner (b|@Aschenbrenner) The Details Classic vs. columnstore before-&-after metrics are impressive. Scenario Conventional Structures Columnstore ? SSRS via SSAS 10 - 12 seconds 1 second >10x Ad Hoc 5-7 minutes (300 - 420 seconds) 1 - 2 seconds >100x Here are two charts characterizing this data graphically.  The first is a linear representation of Report Duration (in seconds) for Conventional Structures vs. Columnstore Indexes.  As is so often the case when we chart such significant deltas, the linear scale doesn’t expose some the dramatically improved values corresponding to the columnstore metrics.  Just to make it fair here’s the same data represented logarithmically; yet even here the values corresponding to 1 –2 seconds aren’t visible.  The Wins Performance: Even prior to columnstore implementation, at 10 - 12 seconds canned report performance against the SSAS cube was tolerable. Yet the 1 second performance afterward is clearly better. As significant as that is, imagine the user experience re: ad hoc interrogation. The difference between several minutes vs. one or two seconds is a game changer, literally changing the way users interact with their data—no mental context switching, no wondering when the results will appear, no preoccupation with the spinning mind-numbing hurry-up-&-wait indicators.  As we’ve commonly found elsewhere, columnstore indexes here provided performance improvements of one, two, or more orders of magnitude. Simplified Infrastructure: Because in this case a nonclustered columnstore index on a conventional DW table was faster than an Analysis Services cube, the entire SSAS infrastructure was rendered superfluous & was retired. PASS Rocks: Once again, the value of attending PASS is proven out. The trip to Charlotte combined with eager & enquiring minds let directly to this success story. Find out more about the next PASS Summit here, hosted this year in Seattle on November 4 - 7, 2014. DevCon BI Team Lead Nathan Allan provided this unsolicited feedback: “What we found was pretty awesome. It has been a game changer for us in terms of the flexibility we can offer people that would like to get to the data in different ways.” Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the second in a series of reports on columnstore implementations, results from DevCon Security, a live customer production app for which performance increased by factors of from 10x to 100x for all report queries, including canned queries as well as reducing time for results for ad hoc queries from 5 - 7 minutes to 1 - 2 seconds. As a result of columnstore performance, the customer retired their SSAS infrastructure. I invite you to consider leveraging columnstore in your own environment. Let me know if you have any questions.

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• What is the best tool to sync browser passwords and bookmarks?

  - by jgbelacqua

  Sadly, everything I've tried so far has been painful to manage between two computers, (even between different browsers on the same computer). So, right now I have different aggregations of bookmarks passwords in xmarks, delicious, google bookmarks, firefox sync, text files, and in figaro password manager (fpm2). I've also tried to use bindwood in the past. What I would like to do is merge all bookmarks and passwords into some solution that actually works either with tools available under Ubuntu, or with a browser-based tool (addon/plugin/extension) which works between between google-chrome/chromium, and firefox. It would be ideal if there was an ability to send and store passwords encrypted (if not on my own server). Whatever the method, I need the ability to have import from existing sources. (It doesn't have to be pretty, just repeatable.) It's possible that some things I've ruled out are now workable (e.g., xmarks broke for me at one point because I hit their bookmark limit for the server/account, and bindwood, firefox sync were firefox only).

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• How should I define my Java Objects?

  - by HonorGod

  I have a data grid where I sort of show the following information - All Guests Total Adults = 22 Total Children = 27 Confirmed Total Adults = 9 Total Children = 13 Country = Germany Total Adults = 5 Total Childres = 6 Friends Adults = 2 Children = 2 Relatives Adults = 3 Children = 4 Country = USA Total Adults = 4 Total Childres = 7 Friends Adults = 2 Children = 5 Relatives Adults = 2 Children = 2 Tentative Total Adults = 13 Total Children - 14 Country = Australia Total Adults = 7 Total Childres = 8 Friends Adults = 2 Children = 3 Relatives Adults = 5 Children = 5 Country = China Total Adults = 6 Total Childres = 6 Friends Adults = 2 Children = 4 Relatives Adults = 4 Children = 2 And in the database what I have is data at the lowest level which is Friends / Relatives and the corresponding countries set as a look-up value which in indirectly connected to another look-up that can tell me if they fall under confirmed or tentative. I guess my question is how do I layout my Java Object and perform the aggregations and give it back to the client. I am not sure if I am clear with my question, but feel free to comment so I can update the question accordingly.

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• yui compressor maven plugin doesnt compress the js files

  - by hanumant

  I am using yui compressor to compress the js files in my web app. I have configured the plugin as indicated on yui maven plugin site yui compressor maven plugin. This is the pom plugin conf <plugin> <groupId>net.sf.alchim</groupId> <artifactId>yuicompressor-maven-plugin</artifactId> <version>0.7.1</version> <executions> <execution> <phase>compile</phase> <goals> <goal>jslint</goal> <goal>compress</goal> </goals> </execution> </executions> <configuration> <failOnWarning>true</failOnWarning> <nosuffix>true</nosuffix> <force>true</force> <aggregations> <aggregation> <!-- remove files after aggregation (default: false) --> <removeIncluded>false</removeIncluded> <!-- insert new line after each concatenation (default: false) --> <insertNewLine>false</insertNewLine> <output>${project.basedir}/${webcontent.dir}/js/compressedAll.js</output> <!-- files to include, path relative to output's directory or absolute path--> <!--inputDir>base directory for non absolute includes, default to parent dir of output</inputDir--> <includes> <include>**/autocomplete.js</include> <include>**/calendar.js</include> <include>**/dialogs.js</include> <include>**/download.js</include> <include>**/folding.js</include> <include>**/jquery-1.4.2.min.js</include> <include>**/jquery.bgiframe.min.js</include> <include>**/jquery.loadmask.js</include> <include>**/jquery.printelement-1.1.js</include> <include>**/jquery.tablesorter.mod.js</include> <include>**/jquery.tablesorter.pager.js</include> <include>**/jquery.dialogs.plugin.js</include> <include>**/jquery.ui.autocomplete.js</include> <include>**/jquery.validate.js</include> <include>**/jquery-ui-1.8.custom.min.js</include> <include>**/languageDropdown.js</include> <include>**/messages.js</include> <include>**/print.js</include> <include>**/tables.js</include> <include>**/tabs.js</include> <include>**/uwTooltip.js</include> </includes> <!-- files to exclude, path relative to output's directory--> </aggregation> </aggregations> </configuration> <dependencies> <dependency> <groupId>rhino</groupId> <artifactId>js</artifactId> <scope>compile</scope> <version>1.6R5</version> </dependency> <dependency> <groupId>org.apache.maven</groupId> <artifactId>maven-plugin-api</artifactId> <version>2.0.7</version> <scope>provided</scope> </dependency> <dependency> <groupId>org.apache.maven</groupId> <artifactId>maven-project</artifactId> <version>2.0.7</version> <scope>provided</scope> </dependency><dependency> <groupId>net.sf.retrotranslator</groupId> <artifactId>retrotranslator-runtime</artifactId> <version>1.2.9</version> <scope>runtime</scope> </dependency> </dependencies> </plugin> And here is the log at compress time These will use the artifact files already in the core ClassRealm instead, to allow them to be included in PluginDescriptor.getArtifacts(). [DEBUG] Configuring mojo 'net.sf.alchim:yuicompressor-maven-plugin:0.7.1:jslint' [DEBUG] (f) failOnWarning = true [DEBUG] (f) jswarn = true [DEBUG] (f) outputDirectory = C:\test\target\classes [DEBUG] (f) project = MavenProject: com.test.test1:test2:19-SNAPSHOT @ C:\test\pom.xml [DEBUG] (f) resources = [Resource {targetPath: null, filtering: false, FileSet {directory: C:\test\src, PatternSet [includes: {}, excludes: {**/*.class, **/*.java, site/*}]}}] [DEBUG] (f) sourceDirectory = C:\test\src\..\js [DEBUG] (f) warSourceDirectory = C:\test\src\main\webapp [DEBUG] (f) webappDirectory = C:\test\target\test2-19-SNAPSHOT [DEBUG] -- end configuration -- [INFO] [yuicompressor:jslint {execution: default}] [INFO] nb warnings: 0, nb errors: 0 [DEBUG] Configuring mojo 'net.sf.alchim:yuicompressor-maven-plugin:0.7.1:compress' -- [DEBUG] (f) removeIncluded = false [DEBUG] (f) insertNewLine = false [DEBUG] (f) output = C:\test\WebContent\js\compressedAll.js [DEBUG] (f) includes = [**/autocomplete.js, **/calendar.js, **/dialogs.js, **/download.js, **/folding.js, **/jquery-1.4.2.min.js, **/jquery.bgifram e.min.js, **/jquery.loadmask.js, **/jquery.printelement-1.1.js, **/jquery.tablesorter.mod.js, **/jquery.tablesorter.pager.js, **/jquery.dialogs.p lugin.js, **/jquery.ui.autocomplete.js, **/jquery.validate.js, **/jquery-ui-1.8.custom.min.js, **/languageDropdown.js, **/messages.js, **/print.js, * */tables.js, **/tabs.js, **/uwTooltip.js] [DEBUG] (f) aggregations = [net.sf.alchim.mojo.yuicompressor.Aggregation@65646564] [DEBUG] (f) disableOptimizations = false [DEBUG] (f) encoding = Cp1252 [DEBUG] (f) failOnWarning = true [DEBUG] (f) force = true [DEBUG] (f) gzip = false [DEBUG] (f) jswarn = true [DEBUG] (f) linebreakpos = 0 [DEBUG] (f) nomunge = false [DEBUG] (f) nosuffix = true [DEBUG] (f) outputDirectory = C:\test\target\classes [DEBUG] (f) preserveAllSemiColons = false [DEBUG] (f) project = MavenProject: com.test.test1:test2:19-SNAPSHOT @ C:\test\pom.xml [DEBUG] (f) resources = [Resource {targetPath: null, filtering: false, FileSet {directory: C:\test\src, PatternSet [includes: {}, excludes: {**/*.class, **/*.java, site/*}]}}] [DEBUG] (f) sourceDirectory = C:\test\src\..\js [DEBUG] (f) statistics = true [DEBUG] (f) suffix = -min [DEBUG] (f) warSourceDirectory = C:\test\src\main\webapp [DEBUG] (f) webappDirectory = C:\test\target\test2-19-SNAPSHOT [DEBUG] -- end configuration -- [INFO] [yuicompressor:compress {execution: default}] [INFO] generate aggregation : C:\test\WebContent\js\compressedAll.js [INFO] compressedAll.js (407505b) [INFO] nb warnings: 0, nb errors: 0 [DEBUG] Configuring mojo 'org.apache.maven.plugins:maven-resources-plugin:2.2:testResources' -- [DEBUG] (f) filters = [] [DEBUG] (f) outputDirectory = C:\test\target\test-classes [DEBUG] (f) project = MavenProject: com.test.test1:test2:19-SNAPSHOT @ C:\test\pom.xml [DEBUG] (f) resources = [Resource {targetPath: null, filtering: false, FileSet {directory: C:\test\test , PatternSet [includes: {}, excludes: {**/*.class, **/*.java}]}}] [DEBUG] -- end configuration -- The problem is the files are getting aggregated into one file but without compressing. The link above uses version 1.1 and the plugin version I am using is 0.7.1. Is this going to make any diff. Can someone tell what is wrong here. PS: I have obfuscated some text in log to follow the compliance in my firm. So you may find it mismatching somewhere.

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• SQL Server: Difference between PARTITION BY and GROUP BY

  - by Mike Mooney

  I've been using GROUP BY for all types of aggregate queries over the years. Recently, I've been reverse-engineering some code that uses PARTITION BY to perform aggregations. In reading through all the documentation I can find about PARTITION BY, it sounds a lot like GROUP BY, maybe with a little extra functionality added in? Are they two versions of the same general functionality, or are they something different entirely?

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• Generic Aggregation of C++ Objects by Attribute When Attribute Name is Unknown at Runtime

  - by stretch

  I'm currently implementing a system with a number of class's representing objects such as client, business, product etc. Standard business logic. As one might expect each class has a number of standard attributes. I have a long list of essentially identical requirements such as: the ability to retrieve all business' whose industry is manufacturing. the ability to retrieve all clients based in London Class business has attribute sector and client has attribute location. Clearly this a relational problem and in pseudo SQL would look something like: SELECT ALL business in business' WHERE sector == manufacturing Unfortunately plugging into a DB is not an option. What I want to do is have a single generic aggregation function whose signature would take the form: vector<generic> genericAggregation(class, attribute, value); Where class is the class of object I want to aggregate, attribute and value being the class attribute and value of interest. In my example I've put vector as return type, but this wouldn't work. Probably better to declare a vector of relevant class type and pass it as an argument. But this isn't the main problem. How can I accept arguments in string form for class, attribute and value and then map these in a generic object aggregation function? Since it's rude not to post code, below is a dummy program which creates a bunch of objects of imaginatively named classes. Included is a specific aggregation function which returns a vector of B objects whose A object is equal to an id specified at the command line e.g. .. $ ./aggregations 5 which returns all B's whose A objects 'i' attribute is equal to 5. See below: #include <iostream> #include <cstring> #include <sstream> #include <vector> using namespace std; //First imaginativly names dummy class class A { private: int i; double d; string s; public: A(){} A(int i, double d, string s) { this->i = i; this->d = d; this->s = s; } ~A(){} int getInt() {return i;} double getDouble() {return d;} string getString() {return s;} }; //second imaginativly named dummy class class B { private: int i; double d; string s; A *a; public: B(int i, double d, string s, A *a) { this->i = i; this->d = d; this->s = s; this->a = a; } ~B(){} int getInt() {return i;} double getDouble() {return d;} string getString() {return s;} A* getA() {return a;} }; //Containers for dummy class objects vector<A> a_vec (10); vector<B> b_vec;//100 //Util function, not important.. string int2string(int number) { stringstream ss; ss << number; return ss.str(); } //Example function that returns a new vector containing on B objects //whose A object i attribute is equal to 'id' vector<B> getBbyA(int id) { vector<B> result; for(int i = 0; i < b_vec.size(); i++) { if(b_vec.at(i).getA()->getInt() == id) { result.push_back(b_vec.at(i)); } } return result; } int main(int argc, char** argv) { //Create some A's and B's, each B has an A... //Each of the 10 A's are associated with 10 B's. for(int i = 0; i < 10; ++i) { A a(i, (double)i, int2string(i)); a_vec.at(i) = a; for(int j = 0; j < 10; j++) { B b((i * 10) + j, (double)j, int2string(i), &a_vec.at(i)); b_vec.push_back(b); } } //Got some objects so lets do some aggregation //Call example aggregation function to return all B objects //whose A object has i attribute equal to argv[1] vector<B> result = getBbyA(atoi(argv[1])); //If some B's were found print them, else don't... if(result.size() != 0) { for(int i = 0; i < result.size(); i++) { cout << result.at(i).getInt() << " " << result.at(i).getA()->getInt() << endl; } } else { cout << "No B's had A's with attribute i equal to " << argv[1] << endl; } return 0; } Compile with: g++ -o aggregations aggregations.cpp If you wish :) Instead of implementing a separate aggregation function (i.e. getBbyA() in the example) I'd like to have a single generic aggregation function which accounts for all possible class attribute pairs such that all aggregation requirements are met.. and in the event additional attributes are added later, or additional aggregation requirements, these will automatically be accounted for. So there's a few issues here but the main one I'm seeking insight into is how to map a runtime argument to a class attribute. I hope I've provided enough detail to adequately describe what I'm trying to do...

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• how to make Sliding window model for data stream mining?

  - by zeedotcom

  we have a situation that a stream (data from sensor or click stream data at server) is coming with sliding window algorithm we have to store the last (say) 500 samples of data in memory. These samples are then used to create histograms, aggregations & capture information about anomalies in the input data stream. please tell me how to make such sliding window.

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• How do I get Enterprise Achitect to use correct class relationships when importing java source code?

  - by Sam

  I've just imported some java source into 'Enterprise Architect', e.g: class Truck { BumperSticker bumperSticker; Vector<Wheel> wheels; } It's added the first member as an association, and it hasn't added the second as a relationship at all. How do I get it to instead, add them both as aggregations, the second with a multiplicity of '*'?

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• General monitoring for SQL Server Analysis Services using Performance Monitor

  - by Testas

  A recent customer engagement required a setup of a monitoring solution for SSAS, due to the time restrictions placed upon this, native Windows Performance Monitor (Perfmon) and SQL Server Profiler Monitoring Tools was used as using a third party tool would have meant the customer providing an additional monitoring server that was not available.I wanted to outline the performance monitoring counters that was used to monitor the system on which SSAS was running. Due to the slow query performance that was occurring during certain scenarios, perfmon was used to establish if any pressure was being placed on the Disk, CPU or Memory subsystem when concurrent connections access the same query, and Profiler to pinpoint how the query was being managed within SSAS, profiler I will leave for another blogThis guide is not designed to provide a definitive list of what should be used when monitoring SSAS, different situations may require the addition or removal of counters as presented by the situation. However I hope that it serves as a good basis for starting your monitoring of SSAS. I would also like to acknowledge Chris Webb’s awesome chapters from “Expert Cube Development” that also helped shape my monitoring strategy:http://cwebbbi.spaces.live.com/blog/cns!7B84B0F2C239489A!6657.entrySimulating ConnectionsTo simulate the additional connections to the SSAS server whilst monitoring, I used ascmd to simulate multiple connections to the typical and worse performing queries that were identified by the customer. A similar sript can be downloaded from codeplex at http://www.codeplex.com/SQLSrvAnalysisSrvcs.     File name: ASCMD_StressTestingScripts.zip. Performance MonitorWithin performance monitor,  a counter log was created that contained the list of counters below. The important point to note when running the counter log is that the RUN AS property within the counter log properties should be changed to an account that has rights to the SSAS instance when monitoring MSAS counters. Failure to do so means that the counter log runs under the system account, no errors or warning are given while running the counter log, and it is not until you need to view the MSAS counters that they will not be displayed if run under the default account that has no right to SSAS. If your connection simulation takes hours, this could prove quite frustrating if not done beforehand JThe counters used……  Object Counter Instance Justification System Processor Queue legnth N/A Indicates how many threads are waiting for execution against the processor. If this counter is consistently higher than around 5 when processor utilization approaches 100%, then this is a good indication that there is more work (active threads) available (ready for execution) than the machine's processors are able to handle. System Context Switches/sec N/A Measures how frequently the processor has to switch from user- to kernel-mode to handle a request from a thread running in user mode. The heavier the workload running on your machine, the higher this counter will generally be, but over long term the value of this counter should remain fairly constant. If this counter suddenly starts increasing however, it may be an indicating of a malfunctioning device, especially if the Processor\Interrupts/sec\(_Total) counter on your machine shows a similar unexplained increase Process % Processor Time sqlservr Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process % Processor Time msmdsrv Definately should be used if Processor\% Processor Time\(_Total) is maxing at 100% to assess the effect of the SQL Server process on the processor Process Working Set sqlservr If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Process Working Set msmdsrv If the Memory\Available bytes counter is decreaing this counter can be run to indicate if the process is consuming larger and larger amounts of RAM. Process(instance)\Working Set measures the size of the working set for each process, which indicates the number of allocated pages the process can address without generating a page fault. Processor % Processor Time _Total and individual cores measures the total utilization of your processor by all running processes. If multi-proc then be mindful only an average is provided Processor % Privileged Time _Total To see how the OS is handling basic IO requests. If kernel mode utilization is high, your machine is likely underpowered as it's too busy handling basic OS housekeeping functions to be able to effectively run other applications. Processor % User Time _Total To see how the applications is interacting from a processor perspective, a high percentage utilisation determine that the server is dealing with too many apps and may require increasing thje hardware or scaling out Processor Interrupts/sec _Total  The average rate, in incidents per second, at which the processor received and serviced hardware interrupts. Shoulr be consistant over time but a sudden unexplained increase could indicate a device malfunction which can be confirmed using the System\Context Switches/sec counter Memory Pages/sec N/A Indicates the rate at which pages are read from or written to disk to resolve hard page faults. This counter is a primary indicator of the kinds of faults that cause system-wide delays, this is the primary counter to watch for indication of possible insufficient RAM to meet your server's needs. A good idea here is to configure a perfmon alert that triggers when the number of pages per second exceeds 50 per paging disk on your system. May also want to see the configuration of the page file on the Server Memory Available Mbytes N/A is the amount of physical memory, in bytes, available to processes running on the computer. if this counter is greater than 10% of the actual RAM in your machine then you probably have more than enough RAM. monitor it regularly to see if any downward trend develops, and set an alert to trigger if it drops below 2% of the installed RAM. Physical Disk Disk Transfers/sec for each physical disk If it goes above 10 disk I/Os per second then you've got poor response time for your disk. Physical Disk Idle Time _total If Disk Transfers/sec is above  25 disk I/Os per second use this counter. which measures the percent time that your hard disk is idle during the measurement interval, and if you see this counter fall below 20% then you've likely got read/write requests queuing up for your disk which is unable to service these requests in a timely fashion. Physical Disk Disk queue legnth For the OLAP and SQL physical disk A value that is consistently less than 2 means that the disk system is handling the IO requests against the physical disk Network Interface Bytes Total/sec For the NIC Should be monitored over a period of time to see if there is anb increase/decrease in network utilisation Network Interface Current Bandwidth For the NIC is an estimate of the current bandwidth of the network interface in bits per second (BPS). MSAS 2005: Memory Memory Limit High KB N/A Shows (as a percentage) the high memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Limit Low KB N/A Shows (as a percentage) the low memory limit configured for SSAS in C:\Program Files\Microsoft SQL Server\MSAS10.MSSQLSERVER\OLAP\Config\msmdsrv.ini MSAS 2005: Memory Memory Usage KB N/A Displays the memory usage of the server process. MSAS 2005: Memory File Store KB N/A Displays the amount of memory that is reserved for the Cache. Note if total memory limit in the msmdsrv.ini is set to 0, no memory is reserved for the cache MSAS 2005: Storage Engine Query Queries from Cache Direct / sec N/A Displays the rate of queries answered from the cache directly MSAS 2005: Storage Engine Query Queries from Cache Filtered / Sec N/A Displays the Rate of queries answered by filtering existing cache entry. MSAS 2005: Storage Engine Query Queries from File / Sec N/A Displays the Rate of queries answered from files. MSAS 2005: Storage Engine Query Average time /query N/A Displays the average time of a query MSAS 2005: Connection Current connections N/A Displays the number of connections against the SSAS instance MSAS 2005: Connection Requests / sec N/A Displays the rate of query requests per second MSAS 2005: Locks Current Lock Waits N/A Displays thhe number of connections waiting on a lock MSAS 2005: Threads Query Pool job queue Length N/A The number of queries in the job queue MSAS 2005:Proc Aggregations Temp file bytes written/sec N/A Shows the number of bytes of data processed in a temporary file MSAS 2005:Proc Aggregations Temp file rows written/sec N/A Shows the number of bytes of data processed in a temporary file 

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• Columnstore Case Study #2: Columnstore faster than SSAS Cube at DevCon Security

  - by aspiringgeek

  Preamble This is the second in a series of posts documenting big wins encountered using columnstore indexes in SQL Server 2012 & 2014.  Many of these can be found in my big deck along with details such as internals, best practices, caveats, etc.  The purpose of sharing the case studies in this context is to provide an easy-to-consume quick-reference alternative. See also Columnstore Case Study #1: MSIT SONAR Aggregations Why Columnstore? As stated previously, If we’re looking for a subset of columns from one or a few rows, given the right indexes, SQL Server can do a superlative job of providing an answer. If we’re asking a question which by design needs to hit lots of rows—DW, reporting, aggregations, grouping, scans, etc., SQL Server has never had a good mechanism—until columnstore. Columnstore indexes were introduced in SQL Server 2012. However, they're still largely unknown. Some adoption blockers existed; yet columnstore was nonetheless a game changer for many apps.  In SQL Server 2014, potential blockers have been largely removed & they're going to profoundly change the way we interact with our data.  The purpose of this series is to share the performance benefits of columnstore & documenting columnstore is a compelling reason to upgrade to SQL Server 2014. The Customer DevCon Security provides home & business security services & has been in business for 135 years. I met DevCon personnel while speaking to the Utah County SQL User Group on 20 February 2012. (Thanks to TJ Belt (b|@tjaybelt) & Ben Miller (b|@DBADuck) for the invitation which serendipitously coincided with the height of ski season.) The App: DevCon Security Reporting: Optimized & Ad Hoc Queries DevCon users interrogate a SQL Server 2012 Analysis Services cube via SSRS. In addition, the SQL Server 2012 relational back end is the target of ad hoc queries; this DW back end is refreshed nightly during a brief maintenance window via conventional table partition switching. SSRS, SSAS, & MDX Conventional relational structures were unable to provide adequate performance for user interaction for the SSRS reports. An SSAS solution was implemented requiring personnel to ramp up technically, including learning enough MDX to satisfy requirements. Ad Hoc Queries Even though the fact table is relatively small—only 22 million rows & 33GB—the table was a typical DW table in terms of its width: 137 columns, any of which could be the target of ad hoc interrogation. As is common in DW reporting scenarios such as this, it is often nearly to optimize for such queries using conventional indexing. DevCon DBAs & developers attended PASS 2012 & were introduced to the marvels of columnstore in a session presented by Klaus Aschenbrenner (b|@Aschenbrenner) The Details Classic vs. columnstore before-&-after metrics are impressive. Scenario   Conventional Structures   Columnstore   Δ SSRS via SSAS 10 - 12 seconds 1 second >10x Ad Hoc 5-7 minutes (300 - 420 seconds) 1 - 2 seconds >100x Here are two charts characterizing this data graphically.  The first is a linear representation of Report Duration (in seconds) for Conventional Structures vs. Columnstore Indexes.  As is so often the case when we chart such significant deltas, the linear scale doesn’t expose some the dramatically improved values corresponding to the columnstore metrics.  Just to make it fair here’s the same data represented logarithmically; yet even here the values corresponding to 1 –2 seconds aren’t visible.  The Wins Performance: Even prior to columnstore implementation, at 10 - 12 seconds canned report performance against the SSAS cube was tolerable. Yet the 1 second performance afterward is clearly better. As significant as that is, imagine the user experience re: ad hoc interrogation. The difference between several minutes vs. one or two seconds is a game changer, literally changing the way users interact with their data—no mental context switching, no wondering when the results will appear, no preoccupation with the spinning mind-numbing hurry-up-&-wait indicators.  As we’ve commonly found elsewhere, columnstore indexes here provided performance improvements of one, two, or more orders of magnitude. Simplified Infrastructure: Because in this case a nonclustered columnstore index on a conventional DW table was faster than an Analysis Services cube, the entire SSAS infrastructure was rendered superfluous & was retired. PASS Rocks: Once again, the value of attending PASS is proven out. The trip to Charlotte combined with eager & enquiring minds let directly to this success story. Find out more about the next PASS Summit here, hosted this year in Seattle on November 4 - 7, 2014. DevCon BI Team Lead Nathan Allan provided this unsolicited feedback: “What we found was pretty awesome. It has been a game changer for us in terms of the flexibility we can offer people that would like to get to the data in different ways.” Summary For DW, reports, & other BI workloads, columnstore often provides significant performance enhancements relative to conventional indexing.  I have documented here, the second in a series of reports on columnstore implementations, results from DevCon Security, a live customer production app for which performance increased by factors of from 10x to 100x for all report queries, including canned queries as well as reducing time for results for ad hoc queries from 5 - 7 minutes to 1 - 2 seconds. As a result of columnstore performance, the customer retired their SSAS infrastructure. I invite you to consider leveraging columnstore in your own environment. Let me know if you have any questions.

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• "Oracle Coherence 3.5" Book - My Humble Review

  - by [email protected]

    After reviewing the book in more detail I say again that it is a great guide for sure. Lots of important concepts that sometimes can be somewhat confusing are deeply reviewed, including all types of caching schemes and backing maps, and the cache topologies with their corresponding performances and very useful "When to use it?" sections. Some functionalities that are very desirable or used a lot are reviewed with examples and best practices of implementation, including: Data affinity Querying Pagination Indexes Aggregations Event processing, listening and triggering Data persistence Security Regarding the networking and architecture topics, Coherence*Extend is exhaustively reviewed, including C++ and .NET clients, with very good tips and examples, even including source codes. Personally, I am also glad to see that the address providers (<address-provider> tag), new feature in Coherence 3.5 which is a way to programmatically provide well-known addresses in order to connect to the cluster, is mentioned on the book, because it provides new functionalities to satisfy some special configuration requirements for example: Provide a way to switch extend nodes in cases of failure Implement custom load balancing algorithms and/or dynamic discovery of TCP/IP connection acceptors Dynamically assign TCP address and port settings when binding to a server socket Another very interesting and useful section is the "Coherent Bank Sample Application", which is a great tutorial, useful to understand how Coherence interacts with third party products establishing a clear integration with them, including the use of non-Oracle products like MS Visual Studio.  

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• SSAS: distribution of measures over percentage

  - by Alex

  Hi there, I am running a SSAS cube that stores facts of HTTP requests. The is a column "Time Taken" that stores the milliseconds a particular HTTP request took. Like... RequestID Time Taken -------------------------- 1 0 2 10 3 20 4 20 5 2000 I want to provide a report through Excel that shows the distribution of those timings by percentage of requests. A statement like "90% of all requests took less than 20millisecond". Analysis: 100% <2000 80% <20 60% <20 40% <10 20% <=0 I am pretty much lost what would be the right approach to design aggregations, calculations etc. to offer this analysis through Excel. Any ideas? Thanks, Alex

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• Eager-loading association count with Arel (Rails 3)

  - by Matchu

  Simple task: given that an article has many comments, be able to display in a long list of articles how many comments each article has. I'm trying to work out how to preload this data with Arel. The "Complex Aggregations" section of the README file seems to discuss that type of situation, but it doesn't exactly offer sample code, nor does it offer a way to do it in two queries instead of one joined query, which is worse for performance. Given the following: class Article has_many :comments end class Comment belongs_to :article end How can I preload for an article set how many comments each has?

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• MVC without ORM is not real MVC?

  - by ajsie

  at the moment im integrating ORM (doctrine) into a MVC framework (codeigniter). then it hit me that this was the obvious way of setting up a MVC: the controller calls the models that are representing database tables. look at this picture: MVC + ORM then i wondered, how can a MVC without ORM be real MVC? cause then the models are not real objects, rather aggregations of different functions that perform CRUD, then returning the result to the controller. and there is no need for a state (object properties) i guess so the functions would be all static? correct me if im wrong on this one. i guess a lot of people are using models without ORM. please share your thoughts. how do your models look like?

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• Stored procs breaking overnight

  - by Chad

  We are running MS SQL 2005 and we have been experiencing a very peculiar problem the past few days. I have two procs, one that creates an hourly report of data. And another that calls it, puts its results in a temp table, and does some aggregations, and returns a summary. They work fine...until the next morning. The next morning, suddenly the calling report, complains about an invalid column name. The fix, is simply a recompile of the calling proc, and all works well again. How can this happen? It's happened three nights in a row since moving these procs into production.

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