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  • Optimize Many-to-Many with SUMMARIZE and Other Techniques

    - by Marco Russo (SQLBI)
    We are still in the early days of DAX and even if I have been using it since 2 years ago, there is still a lot to learn on that. One of the topics that historically interests me (and many of the readers here, probably) is the many-to-many relationships between dimensions in a dimensional data model. When I and Alberto wrote the The Many to Many Revolution 2.0 we discovered the SUMMARIZE based pattern very late in the whitepaper writing. It is very important for performance optimization and it should be always used. In the last month, Gerhard Brueckl also presented an approach based on cross table filtering behavior that simplify the syntax involved, even if it’s harder to explain how it works internally. I published a short article titled Optimize Many-to-Many Calculation in DAX with SUMMARIZE and Cross Table Filtering on SQLBI website just to provide a quick reference to the three patterns available. A further study is still required to compare performance between SUMMARIZE and Cross Table Filtering patterns. Up to now, I haven’t observed big differences between them, even if their execution plans might be not identical and this suggest me that depending on other conditions you might favor one over the other.

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  • Point-in-time restore of database backup?

    - by TiborKaraszi
    SQL Server 2005 added the STOPAT option for the RESTORE DATABASE command. This sounds great - we can stop at some point in time during the database backup process was running! Or? No, we can't. Here follows some tech stuff why not, and then what the option is really meant for: A database backup includes all used extents and also all log records that were produced while the backup process was running (possibly older as well, to handle open transactions). When you restore such a backup, SQL Server...(read more)

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  • T-SQL Tuesday #34: Help me, Obi-Wan Kenobi, You're My Only Hope!

    - by AllenMWhite
    This T-SQL Tuesday is about a person that helped you understand SQL Server. It's not a stretch to say that it's people that help you get to where you are in life, and Rob Volk ( @sql_r ) is sponsoring this month's T-SQL Tuesday asking who is that person that helped you get there. Over the years, there've been a number of people who've helped me, but one person stands out above the rest, who was patient, kind and always explained the details in a way that just made sense. I first met Don Vilen at...(read more)

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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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  • The case against INFORMATION_SCHEMA views

    - by AaronBertrand
    In SQL Server 2000, INFORMATION_SCHEMA was the way I derived all of my metadata information - table names, procedure names, column names and data types, relationships... the list goes on and on. I used the system tables like sysindexes from time to time, but I tried to stay away from them when I could. In SQL Server 2005, this all changed with the introduction of catalog views. For one thing, they're a lot easier to type. sys.tables vs. INFORMATION_SCHEMA.TABLES? Come on; no contest there - even...(read more)

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  • Parameterize Charts using Excel Slicers in PowerPivot

    - by Marco Russo (SQLBI)
    One new nice feature of Excel 2010 is the Slicer. Usually, slicers are used to filter data in a PivotTable. But they might be also useful to parameterize an algorithm or a chart! We discussed this technique in our book , but Alberto Ferrari wrote a post that shows how to use this technique to allow the user to select two stocks that should be compared in an Excel Chart – as you might imagine, this will work also when you will publish the workbook on SharePoint! This is the result: Nice to see that...(read more)

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  • MicroTraining: Managing SSIS Connections–10 Apr 2012 at 10:00 AM EDT!

    - by andyleonard
    I am pleased to announce another free Linchpin People MicroTraining Event! On Tuesday, 10 Apr 2012 at 10:00 AM EDT, I will present Managing SSIS Connections . In this presentation, I will show you several means for managing SSIS connectivity using built-in functionality and a custom trick or two I picked up over the past few years. Want to learn more? It’s free (and no phone number required)! Register today. :{>...(read more)

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  • Presenting Loading Data Warehouse Partitions with SSIS 2012 at SQL Saturday DC!

    - by andyleonard
    Join Darryll Petrancuri and me as we present Loading Data Warehouse Partitions with SSIS 2012 Saturday 8 Dec 2012 at SQL Saturday 173 in DC ! SQL Server 2012 table partitions offer powerful Big Data solutions to the Data Warehouse ETL Developer. In this presentation, Darryll Petrancuri and Andy Leonard demonstrate one approach to loading partitioned tables and managing the partitions using SSIS 2012, and reporting partition metrics using SSRS 2012. Objectives A practical solution for loading Big...(read more)

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  • Free Webinar - Using Enterprise Data Integration Dashboards

    - by andyleonard
    Join Kent Bradshaw and me as we present Using Enterprise Data Integration Dashboards Tuesday 11 Dec 2012 at 10:00 AM ET! If data is the life of the modern organization, data integration is the heart of an enterprise. Data circulation is vital. Data integration dashboards provide enterprise ETL (Extract, Transform, and Load) teams near-real-time status supported with historical performance analysis. Join Linchpins Kent Bradshaw and Andy Leonard as they demonstrate and discuss the benefits of data...(read more)

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  • Undocumented Query Plans: The ANY Aggregate

    - by Paul White
    As usual, here’s a sample table: With some sample data: And an index that will be useful shortly: There’s a complete script to create the table and add the data at the end of this post.  There’s nothing special about the table or the data (except that I wanted to have some fun with values and data types). The Task We are asked to return distinct values of col1 and col2 , together with any one value from the thing column (it doesn’t matter which) per group.  One possible result set is shown...(read more)

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  • Meet @marcorus and @ferrarialberto at TechEd Europe 2012 #tee2012

    - by Marco Russo (SQLBI)
    I and Alberto are in Amsterdam this week at TechEd Europe 2012. If you are here at the conference, you can meet us here: Wed, Jun 27 10:15 AM - 11:30 AM – Room G106 DBI319 - BISM: Multidimensional vs. Tabular Wed, Jun 27 02:15 PM – 02:30 PM – Microsoft Press Booth in the TechExpo area PowerPivot for Excel 2010 Book Signing Thu, Jun 28 8:30 AM - 9:45 AM – Room E107 Many-to-Many Relationships in BISM Tabular Fri, Jun 29 1:00 PM - 2:45 PM – Breakthrough Insight at Microsoft SQL Server Booth – TechExpo area Staff and Q&A We’ll try to visit the Microsoft Booth very often and we’ll be in the area Breakthrough Insight of SQL Server zone (see the picture to identify it). And don’t miss the PowerPivot for Excel 2010 book signing event:

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  • Placeholder images for testing reports

    - by Greg Low
    Lorem Ipsum has long been used to provide placeholder text for testing report and document layouts. Programs such as Microsoft Word have also included options for generating sample text. (For example, type =rand() anywhere in a blank area of a Microsoft Word document and hit enter).Matthew Roche and Donald Farmer both sent me a link the other day to an online service that provides placeholder images. This could be quite useful when testing report layouts in SQL Server Reporting Services.You'll find it here: http://lorempixel.com/Nice! As an example, here's a random sports image. Of course I have no idea what you'll see on this page :-)

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  • Meet @marcorus and @ferrarialberto at TechEd Europe 2012 #tee2012

    - by Marco Russo (SQLBI)
    I and Alberto are in Amsterdam this week at TechEd Europe 2012. If you are here at the conference, you can meet us here: Wed, Jun 27 10:15 AM - 11:30 AM – Room G106 DBI319 - BISM: Multidimensional vs. Tabular Wed, Jun 27 02:15 PM – 02:30 PM – Microsoft Press Booth in the TechExpo area PowerPivot for Excel 2010 Book Signing Thu, Jun 28 8:30 AM - 9:45 AM – Room E107 Many-to-Many Relationships in BISM Tabular Fri, Jun 29 1:00 PM - 2:45 PM – Breakthrough Insight at Microsoft SQL Server Booth – TechExpo area Staff and Q&A We’ll try to visit the Microsoft Booth very often and we’ll be in the area Breakthrough Insight of SQL Server zone (see the picture to identify it). And don’t miss the PowerPivot for Excel 2010 book signing event:

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  • Power Query in Modern Corporate BI–Copenhagen, June 3, 2014–#powerquery

    - by Marco Russo (SQLBI)
    I will be in Copenhagen to deliver the SSAS Tabular Workshop on June 2-4, 2014 (few seats still available, but hurry up!). In the same week I will be a speaker in an evening community event, MsBIP møde nr. 21, delivering the Power Query in Modern Corporate BI session that I also presented at TechEd North America 2014 last week. It’s not just a session about Power Query, there is a broader scope related to Corporate BI vs. Self-Service BI, which could be open to many consideration. I think that the two worlds can (and should) collaborate, instead of fighting against each other, especially when there is an existing investment in Corporate BI. I hope to meet many of you there!

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  • Avoiding connection timeouts on first connection to LocalDB edition of SQL Server Express

    - by Greg Low
    When you first make a connection to the new LocalDB edition of SQL Server Express, the system files, etc. that are required for a new version are spun up. (The system files such as the master database files, etc. end up in C:\Users\<username>\AppData\Local\Microsoft\Microsoft SQL Server Local DB\Instances\LocalDBApp1) That can take a while on a slower machine, so this means that the default connection timeout of 30 seconds (in most client libraries) could be exceeded. To avoid this hit on the...(read more)

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  • Investigating on xVelocity (VertiPaq) column size

    - by Marco Russo (SQLBI)
      In January I published an article about how to optimize high cardinality columns in VertiPaq. In the meantime, VertiPaq has been rebranded to xVelocity: the official name is now “xVelocity in-memory analytics engine (VertiPaq)” but using xVelocity and VertiPaq when we talk about Analysis Services has the same meaning. In this post I’ll show how to investigate on columns size of an existing Tabular database so that you can find the most important columns to be optimized. A first approach can be looking in the DataDir of Analysis Services and look for the folder containing the database. Then, look for the biggest files in all subfolders and you will find the name of a file that contains the name of the most expensive column. However, this heuristic process is not very optimized. A better approach is using a DMV that provides the exact information. For example, by using the following query (open SSMS, open an MDX query on the database you are interested to and execute it) you will see all database objects sorted by used size in a descending way. SELECT * FROM $SYSTEM.DISCOVER_STORAGE_TABLE_COLUMN_SEGMENTS ORDER BY used_size DESC You can look at the first rows in order to understand what are the most expensive columns in your tabular model. The interesting data provided are: TABLE_ID: it is the name of the object – it can be also a dictionary or an index COLUMN_ID: it is the column name the object belongs to – you can also see ID_TO_POS and POS_TO_ID in case they refer to internal indexes RECORDS_COUNT: it is the number of rows in the column USED_SIZE: it is the used memory for the object By looking at the ration between USED_SIZE and RECORDS_COUNT you can understand what you can do in order to optimize your tabular model. Your options are: Remove the column. Yes, if it contains data you will never use in a query, simply remove the column from the tabular model Change granularity. If you are tracking time and you included milliseconds but seconds would be enough, round the data source column to the nearest second. If you have a floating point number but two decimals are good enough (i.e. the temperature), round the number to the nearest decimal is relevant to you. Split the column. Create two or more columns that have to be combined together in order to produce the original value. This technique is described in VertiPaq optimization article. Sort the table by that column. When you read the data source, you might consider sorting data by this column, so that the compression will be more efficient. However, this technique works better on columns that don’t have too many distinct values and you will probably move the problem to another column. Sorting data starting from the lower density columns (those with a few number of distinct values) and going to higher density columns (those with high cardinality) is the technique that provides the best compression ratio. After the optimization you should be able to reduce the used size and improve the count/size ration you measured before. If you are interested in a longer discussion about internal storage in VertiPaq and you want understand why this approach can save you space (and time), you can attend my 24 Hours of PASS session “VertiPaq Under the Hood” on March 21 at 08:00 GMT.

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  • Coopertition

    - by andyleonard
    Introduction This post is the thirtieth part of a ramble-rant about the software business. The current posts in this series are: Goodwill, Negative and Positive Visions, Quests, Missions Right, Wrong, and Style Follow Me Balance, Part 1 Balance, Part 2 Definition of a Great Team The 15-Minute Meeting Metaproblems: Drama The Right Question Software is Organic, Part 1 Metaproblem: Terror I Don't Work On My Car A Turning Point Human Doings Everything Changes Getting It Right The First Time One-Time...(read more)

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  • Windows 8 client virtualization

    - by John Paul Cook
    Hyper-V is coming to Windows 8, but you must have a processor that supports SLAT. Virtual machines created with Virtual PC aren’t easily transferred to Windows 2008 Hyper-V and vice-versa. With Windows 8, it will be easy to move vhds from Windows 8 on your laptop or desktop to Windows 8 server and back again. To find out if your processor supports SLAT, run coreinfo –v from a command window running as administrator. Download coreinfo from here . My MacBook Pro supports SLAT as this output shows:...(read more)

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  • SQL Saturday 43 (Redmond, WA) Review

    - by BuckWoody
    Last Saturday (June 12th) we held a “SQL Saturday” (more about those here) event in Redmond, Washington. The event was held at the Microsoft campus, at the Mixer in our new location called the “Commons”. This is a mall-like area that we have on campus, and the Mixer is a large building with lots of meeting rooms, so it made a perfect location for the event. There was a sign to find the parking, and once there they had a sign to show how to get to the building. Since it’s a secure facility, Greg Larsen and crew had a person manning the door so that even late arrivals could get in. We had about 400 sign up for the event, and a little over 300 attend (official numbers later). I think we would have had a lot more, but the sun was out – and you just can’t underestimate the effect of that here in the Pacific Northwest. We joke a lot about not seeing the sun much, but when a day like what we had on Saturday comes around, and on a weekend at that, you’d cancel your wedding to go outside to play in the sun. And your spouse would agree with you for doing it. We had some top-notch speakers, including Clifford Dibble and Kalen Delany. The food was great, we had multiple sponsors (including Confio who seems to be at all of these) and the attendees were from all over the professional spectrum, from developers to BI to DBA’s. Everyone I saw was very engaged, and when I visited room-to-room I saw almost no one in the halls – everyone was in the sessions. I also saw a much larger Microsoft presence this year, especially from Dan Jones’ team. I had a great turnout at my session, and yes, I was wearing an Oracle staff shirt. I did that because I wanted to show that the session I gave on “SQL Server for the Oracle DBA” was non-marketing – I couldn’t exactly bash Oracle wearing their colors! These events are amazing. I can’t emphasize enough how much I appreciate the volunteers and how much work they put into these events, and to you for coming. If you’re reading this and you haven’t attended one yet, definitely find out if there is one in your area – and if not, start one. It’s a lot of work, but it’s totally worth it.       Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • 24 Hours of PASS (September 2014): Summit Preview Edition

    - by Sergio Govoni
    Which sessions you can expect to find at the next PASS Summit 2014 ? Find it out on September 09, 2014 (12:00 GMT) at the free online event: 24 Hours of PASS: Summit Preview Edition.Register now at this link.No matter from what part of the world you will follow the event, the important thing is to know that they will be 24 hours of continuous training on SQL Server and Business Intelligence on your computer!

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  • SSISDB Analysis Script on Gist

    - by Davide Mauri
    I've created two simple, yet very useful, script to extract some useful data to quickly monitor SSIS packages execution in SQL Server 2012 and after.get-ssis-execution-status  get-ssis-data-pumped-rows  I've started to use gist since it comes very handy, for this "quick'n'dirty" scripts and snippets, and you can find the above scripts and others (hopefully the number will increase over time...I plan to use gist to store all the code snippet I used to store in a dedicated folder on my machine) there.Now, back to the aforementioned scripts. The first one ("get-ssis-execution-status") returns a list of all executed and executing packages along with latest successful and running executions (so that on can have an idea of the expected run time)error messageswarning messages related to duplicate rows found in lookupsthe second one ("get-ssis-data-pumped-rows") returns information on DataFlows status. Here there's something interesting, IMHO. Nothing exceptional, let it be clear, but nonetheless useful: the script extract information on destinations and row sent to destinations right from the messages produced by the DataFlow component. This helps to quickly understand how many rows as been sent and where...without having to increase the logging level.Enjoy! PSI haven't tested it with SQL Server 2014, but AFAIK they should work without problems. Of course any feedback on this is welcome. 

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  • SQL Saturday #146 : Nashua, NH

    - by AaronBertrand
    Today was SQL Saturday #146, put on by Mike Walsh, Jack Corbett, and a host of other volunteers and organizers. Scott and I missed the speaker dinner last night, but we headed up from Rhode Island at 6:00 AM and made a good day of it. We had lots of great conversations with both existing friends and potential customers. After lunch I participated in a panel discussion with Joey D'Antoni and Andrew Kelly, led my Mike. We basically talked about various things DBAs are responsible for - and ultimately...(read more)

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  • Financial Transparency is Good for Community

    - by ArnieRowland
    I was recently in a conversation with several people that had previously organized one or more community events. The topic evolved into a discussion of Sponsors, and eventually, fund raising. Being able to adequately raise the funds necessary is critical to producing a successful event. Many vendors will readily provide products for raffles and give-aways (SWAG), but the success of the event hangs on being able to raise cold, hard, cash. Venues and equipment have to be rented, refreshments and lunches...(read more)

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  • Microsoft BI Conference 2011 in Lisbon

    - by AlbertoFerrari
    Anyone interested in BI from Portugal or Spain should not miss the Microsoft BI Conference 2011 in Lisbon : one full day ( March, 25, 2011 ) with three tracks on Business Intelligence: Decision Makers BI pros Intro to BI. I am going to present two sessions on PowerPivot: one is a nice deep dive into DAX for BI pros, the other is about self service BI for decision makers. Titles and the complete agenda will be published in the next days, but I suggest to save the date. The full event is free and it...(read more)

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  • StreamInsight 2.1, meet LINQ

    - by Roman Schindlauer
    Someone recently called LINQ “magic” in my hearing. I leapt to LINQ’s defense immediately. Turns out some people don’t realize “magic” is can be a pejorative term. I thought LINQ needed demystification. Here’s your best demystification resource: http://blogs.msdn.com/b/mattwar/archive/2008/11/18/linq-links.aspx. I won’t repeat much of what Matt Warren says in his excellent series, but will talk about some core ideas and how they affect the 2.1 release of StreamInsight. Let’s tell the story of a LINQ query. Compile time It begins with some code: IQueryable<Product> products = ...; var query = from p in products             where p.Name == "Widget"             select p.ProductID; foreach (int id in query) {     ... When the code is compiled, the C# compiler (among other things) de-sugars the query expression (see C# spec section 7.16): ... var query = products.Where(p => p.Name == "Widget").Select(p => p.ProductID); ... Overload resolution subsequently binds the Queryable.Where<Product> and Queryable.Select<Product, int> extension methods (see C# spec sections 7.5 and 7.6.5). After overload resolution, the compiler knows something interesting about the anonymous functions (lambda syntax) in the de-sugared code: they must be converted to expression trees, i.e.,“an object structure that represents the structure of the anonymous function itself” (see C# spec section 6.5). The conversion is equivalent to the following rewrite: ... var prm1 = Expression.Parameter(typeof(Product), "p"); var prm2 = Expression.Parameter(typeof(Product), "p"); var query = Queryable.Select<Product, int>(     Queryable.Where<Product>(         products,         Expression.Lambda<Func<Product, bool>>(Expression.Property(prm1, "Name"), prm1)),         Expression.Lambda<Func<Product, int>>(Expression.Property(prm2, "ProductID"), prm2)); ... If the “products” expression had type IEnumerable<Product>, the compiler would have chosen the Enumerable.Where and Enumerable.Select extension methods instead, in which case the anonymous functions would have been converted to delegates. At this point, we’ve reduced the LINQ query to familiar code that will compile in C# 2.0. (Note that I’m using C# snippets to illustrate transformations that occur in the compiler, not to suggest a viable compiler design!) Runtime When the above program is executed, the Queryable.Where method is invoked. It takes two arguments. The first is an IQueryable<> instance that exposes an Expression property and a Provider property. The second is an expression tree. The Queryable.Where method implementation looks something like this: public static IQueryable<T> Where<T>(this IQueryable<T> source, Expression<Func<T, bool>> predicate) {     return source.Provider.CreateQuery<T>(     Expression.Call(this method, source.Expression, Expression.Quote(predicate))); } Notice that the method is really just composing a new expression tree that calls itself with arguments derived from the source and predicate arguments. Also notice that the query object returned from the method is associated with the same provider as the source query. By invoking operator methods, we’re constructing an expression tree that describes a query. Interestingly, the compiler and operator methods are colluding to construct a query expression tree. The important takeaway is that expression trees are built in one of two ways: (1) by the compiler when it sees an anonymous function that needs to be converted to an expression tree, and; (2) by a query operator method that constructs a new queryable object with an expression tree rooted in a call to the operator method (self-referential). Next we hit the foreach block. At this point, the power of LINQ queries becomes apparent. The provider is able to determine how the query expression tree is evaluated! The code that began our story was intentionally vague about the definition of the “products” collection. Maybe it is a queryable in-memory collection of products: var products = new[]     { new Product { Name = "Widget", ProductID = 1 } }.AsQueryable(); The in-memory LINQ provider works by rewriting Queryable method calls to Enumerable method calls in the query expression tree. It then compiles the expression tree and evaluates it. It should be mentioned that the provider does not blindly rewrite all Queryable calls. It only rewrites a call when its arguments have been rewritten in a way that introduces a type mismatch, e.g. the first argument to Queryable.Where<Product> being rewritten as an expression of type IEnumerable<Product> from IQueryable<Product>. The type mismatch is triggered initially by a “leaf” expression like the one associated with the AsQueryable query: when the provider recognizes one of its own leaf expressions, it replaces the expression with the original IEnumerable<> constant expression. I like to think of this rewrite process as “type irritation” because the rewritten leaf expression is like a foreign body that triggers an immune response (further rewrites) in the tree. The technique ensures that only those portions of the expression tree constructed by a particular provider are rewritten by that provider: no type irritation, no rewrite. Let’s consider the behavior of an alternative LINQ provider. If “products” is a collection created by a LINQ to SQL provider: var products = new NorthwindDataContext().Products; the provider rewrites the expression tree as a SQL query that is then evaluated by your favorite RDBMS. The predicate may ultimately be evaluated using an index! In this example, the expression associated with the Products property is the “leaf” expression. StreamInsight 2.1 For the in-memory LINQ to Objects provider, a leaf is an in-memory collection. For LINQ to SQL, a leaf is a table or view. When defining a “process” in StreamInsight 2.1, what is a leaf? To StreamInsight a leaf is logic: an adapter, a sequence, or even a query targeting an entirely different LINQ provider! How do we represent the logic? Remember that a standing query may outlive the client that provisioned it. A reference to a sequence object in the client application is therefore not terribly useful. But if we instead represent the code constructing the sequence as an expression, we can host the sequence in the server: using (var server = Server.Connect(...)) {     var app = server.Applications["my application"];     var source = app.DefineObservable(() => Observable.Range(0, 10, Scheduler.NewThread));     var query = from i in source where i % 2 == 0 select i; } Example 1: defining a source and composing a query Let’s look in more detail at what’s happening in example 1. We first connect to the remote server and retrieve an existing app. Next, we define a simple Reactive sequence using the Observable.Range method. Notice that the call to the Range method is in the body of an anonymous function. This is important because it means the source sequence definition is in the form of an expression, rather than simply an opaque reference to an IObservable<int> object. The variation in Example 2 fails. Although it looks similar, the sequence is now a reference to an in-memory observable collection: var local = Observable.Range(0, 10, Scheduler.NewThread); var source = app.DefineObservable(() => local); // can’t serialize ‘local’! Example 2: error referencing unserializable local object The Define* methods support definitions of operator tree leaves that target the StreamInsight server. These methods all have the same basic structure. The definition argument is a lambda expression taking between 0 and 16 arguments and returning a source or sink. The method returns a proxy for the source or sink that can then be used for the usual style of LINQ query composition. The “define” methods exploit the compile-time C# feature that converts anonymous functions into translatable expression trees! Query composition exploits the runtime pattern that allows expression trees to be constructed by operators taking queryable and expression (Expression<>) arguments. The practical upshot: once you’ve Defined a source, you can compose LINQ queries in the familiar way using query expressions and operator combinators. Notably, queries can be composed using pull-sequences (LINQ to Objects IQueryable<> inputs), push sequences (Reactive IQbservable<> inputs), and temporal sequences (StreamInsight IQStreamable<> inputs). You can even construct processes that span these three domains using “bridge” method overloads (ToEnumerable, ToObservable and To*Streamable). Finally, the targeted rewrite via type irritation pattern is used to ensure that StreamInsight computations can leverage other LINQ providers as well. Consider the following example (this example depends on Interactive Extensions): var source = app.DefineEnumerable((int id) =>     EnumerableEx.Using(() =>         new NorthwindDataContext(), context =>             from p in context.Products             where p.ProductID == id             select p.ProductName)); Within the definition, StreamInsight has no reason to suspect that it ‘owns’ the Queryable.Where and Queryable.Select calls, and it can therefore defer to LINQ to SQL! Let’s use this source in the context of a StreamInsight process: var sink = app.DefineObserver(() => Observer.Create<string>(Console.WriteLine)); var query = from name in source(1).ToObservable()             where name == "Widget"             select name; using (query.Bind(sink).Run("process")) {     ... } When we run the binding, the source portion which filters on product ID and projects the product name is evaluated by SQL Server. Outside of the definition, responsibility for evaluation shifts to the StreamInsight server where we create a bridge to the Reactive Framework (using ToObservable) and evaluate an additional predicate. It’s incredibly easy to define computations that span multiple domains using these new features in StreamInsight 2.1! Regards, The StreamInsight Team

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