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  • Changes to the LINQ-to-StreamInsight Dialect

    - by Roman Schindlauer
    In previous versions of StreamInsight (1.0 through 2.0), CepStream<> represents temporal streams of many varieties: Streams with ‘open’ inputs (e.g., those defined and composed over CepStream<T>.Create(string streamName) Streams with ‘partially bound’ inputs (e.g., those defined and composed over CepStream<T>.Create(Type adapterFactory, …)) Streams with fully bound inputs (e.g., those defined and composed over To*Stream – sequences or DQC) The stream may be embedded (where Server.Create is used) The stream may be remote (where Server.Connect is used) When adding support for new programming primitives in StreamInsight 2.1, we faced a choice: Add a fourth variety (use CepStream<> to represent streams that are bound the new programming model constructs), or introduce a separate type that represents temporal streams in the new user model. We opted for the latter. Introducing a new type has the effect of reducing the number of (confusing) runtime failures due to inappropriate uses of CepStream<> instances in the incorrect context. The new types are: IStreamable<>, which logically represents a temporal stream. IQStreamable<> : IStreamable<>, which represents a queryable temporal stream. Its relationship to IStreamable<> is analogous to the relationship of IQueryable<> to IEnumerable<>. The developer can compose temporal queries over remote stream sources using this type. The syntax of temporal queries composed over IQStreamable<> is mostly consistent with the syntax of our existing CepStream<>-based LINQ provider. However, we have taken the opportunity to refine certain aspects of the language surface. Differences are outlined below. Because 2.1 introduces new types to represent temporal queries, the changes outlined in this post do no impact existing StreamInsight applications using the existing types! SelectMany StreamInsight does not support the SelectMany operator in its usual form (which is analogous to SQL’s “CROSS APPLY” operator): static IEnumerable<R> SelectMany<T, R>(this IEnumerable<T> source, Func<T, IEnumerable<R>> collectionSelector) It instead uses SelectMany as a convenient syntactic representation of an inner join. The parameter to the selector function is thus unavailable. Because the parameter isn’t supported, its type in StreamInsight 1.0 – 2.0 wasn’t carefully scrutinized. Unfortunately, the type chosen for the parameter is nonsensical to LINQ programmers: static CepStream<R> SelectMany<T, R>(this CepStream<T> source, Expression<Func<CepStream<T>, CepStream<R>>> streamSelector) Using Unit as the type for the parameter accurately reflects the StreamInsight’s capabilities: static IQStreamable<R> SelectMany<T, R>(this IQStreamable<T> source, Expression<Func<Unit, IQStreamable<R>>> streamSelector) For queries that succeed – that is, queries that do not reference the stream selector parameter – there is no difference between the code written for the two overloads: from x in xs from y in ys select f(x, y) Top-K The Take operator used in StreamInsight causes confusion for LINQ programmers because it is applied to the (unbounded) stream rather than the (bounded) window, suggesting that the query as a whole will return k rows: (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) The use of SelectMany is also unfortunate in this context because it implies the availability of the window parameter within the remainder of the comprehension. The following compiles but fails at runtime: (from win in xs.SnapshotWindow() from x in win orderby x.A select win).Take(k) The Take operator in 2.1 is applied to the window rather than the stream: Before After (from win in xs.SnapshotWindow() from x in win orderby x.A select x.B).Take(k) from win in xs.SnapshotWindow() from b in     (from x in win     orderby x.A     select x.B).Take(k) select b Multicast We are introducing an explicit multicast operator in order to preserve expression identity, which is important given the semantics about moving code to and from StreamInsight. This also better matches existing LINQ dialects, such as Reactive. This pattern enables expressing multicasting in two ways: Implicit Explicit var ys = from x in xs          where x.A > 1          select x; var zs = from y1 in ys          from y2 in ys.ShiftEventTime(_ => TimeSpan.FromSeconds(1))          select y1 + y2; var ys = from x in xs          where x.A > 1          select x; var zs = ys.Multicast(ys1 =>     from y1 in ys1     from y2 in ys1.ShiftEventTime(_ => TimeSpan.FromSeconds(1))     select y1 + y2; Notice the product translates an expression using implicit multicast into an expression using the explicit multicast operator. The user does not see this translation. Default window policies Only default window policies are supported in the new surface. Other policies can be simulated by using AlterEventLifetime. Before After xs.SnapshotWindow(     WindowInputPolicy.ClipToWindow,     SnapshotWindowInputPolicy.Clip) xs.SnapshotWindow() xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.PointAlignToWindowEnd) xs.TumblingWindow(     TimeSpan.FromSeconds(1)) xs.TumblingWindow(     TimeSpan.FromSeconds(1),     HoppingWindowOutputPolicy.ClipToWindowEnd) Not supported … LeftAntiJoin Representation of LASJ as a correlated sub-query in the LINQ surface is problematic as the StreamInsight engine does not support correlated sub-queries (see discussion of SelectMany). The current syntax requires the introduction of an otherwise unsupported ‘IsEmpty()’ operator. As a result, the pattern is not discoverable and implies capabilities not present in the server. The direct representation of LASJ is used instead: Before After from x in xs where     (from y in ys     where x.A > y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, (x, y) => x.A > y.B) from x in xs where     (from y in ys     where x.A == y.B     select y).IsEmpty() select x xs.LeftAntiJoin(ys, x => x.A, y => y.B) ApplyWithUnion The ApplyWithUnion methods have been deprecated since their signatures are redundant given the standard SelectMany overloads: Before After xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count()) xs.GroupBy(x => x.A).SelectMany(     gs =>     from win in gs.SnapshotWindow()     select win.Count()) xs.GroupBy(x => x.A).ApplyWithUnion(gs => from win in gs.SnapshotWindow() select win.Count(), r => new { r.Key, Count = r.Payload }) from x in xs group x by x.A into gs from win in gs.SnapshotWindow() select new { gs.Key, Count = win.Count() } Alternate UDO syntax The representation of UDOs in the StreamInsight LINQ dialect confuses cardinalities. Based on the semantics of user-defined operators in StreamInsight, one would expect to construct queries in the following form: from win in xs.SnapshotWindow() from y in MyUdo(win) select y Instead, the UDO proxy method is referenced within a projection, and the (many) results returned by the user code are automatically flattened into a stream: from win in xs.SnapshotWindow() select MyUdo(win) The “many-or-one” confusion is exemplified by the following example that compiles but fails at runtime: from win in xs.SnapshotWindow() select MyUdo(win) + win.Count() The above query must fail because the UDO is in fact returning many values per window while the count aggregate is returning one. Original syntax New alternate syntax from win in xs.SnapshotWindow() select win.UdoProxy(1) from win in xs.SnapshotWindow() from y in win.UserDefinedOperator(() => new Udo(1)) select y -or- from win in xs.SnapshotWindow() from y in win.UdoMacro(1) select y Notice that this formulation also sidesteps the dynamic type pitfalls of the existing “proxy method” approach to UDOs, in which the type of the UDO implementation (TInput, TOuput) and the type of its constructor arguments (TConfig) need to align in a precise and non-obvious way with the argument and return types for the corresponding proxy method. UDSO syntax UDSO currently leverages the DataContractSerializer to clone initial state for logical instances of the user operator. Initial state will instead be described by an expression in the new LINQ surface. Before After xs.Scan(new Udso()) xs.Scan(() => new Udso()) Name changes ShiftEventTime => AlterEventStartTime: The alter event lifetime overload taking a new start time value has been renamed. CountByStartTimeWindow => CountWindow

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  • StreamInsight/SSIS Integration White Paper

    - by Roman Schindlauer
    This has been tweeted all over the place, but we still want to give it proper attention here in our blog: SSIS (SQL Server Integration Service) is widely used by today’s customers to transform data from different sources and load into a SQL Server data warehouse or other targets. StreamInsight can process large amount of real-time as well as historical data, making it easy to do temporal and incremental processing.  We have put together a white paper to discuss how to bring StreamInsight and SSIS together and leverage both platforms to get crucial insights faster and easier. From the paper’s abstract: The purpose of this paper is to provide guidance for enriching data integration scenarios by integrating StreamInsight with SQL Server Integration Services. Specifically, we looked at the technical challenges and solutions for such integration, by using a case study based on a customer scenarios in the telecommunications sector. Please take a look at this paper and send us your feedback! Using SQL Server Integration Services and StreamInsight Together Regards, Ping Wang

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  • FIX adapter for StreamInsight

    - by Roman Schindlauer
    Over the last couple of month, Rapid Addition, a leading FIX and FAST solutions provider for the financial services industry, has been working closely with the StreamInsight team to enable StreamInsight Complex Event Processing queries to receive input feeds from Rapid Addition’s FIX engine and to send result events back into FIX. Earlier today, Toby Corballis from Rapid Addition blogged about these capabilities here on HedgeHogs. We are very excited to demonstrate these capabilities at the SIFMA conference in New York. The session will take place tomorrow, Tuesday, 11am – 12noon, at the Hilton Hotel New York, 1335 Avenue of the Americas, East Suite 4th floor. Torsten Grabs from the StreamInsight team will join the RapidAddition and local Microsoft teams for the session.  If you are interested in attending the session please register at http://bit.ly/c0bbLL. We are looking forward to meeting you tomorrow at SIFMA! Best regards,The StreamInsight Team

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  • StreamInsight 1.0 Released

    - by Roman Schindlauer
    One piece in the set of products offered in SQL Server 2008 R2 that has generated a lot of buzz and interest during its CTP phase is StreamInsight, Microsoft’s platform for Complex Event Processing. Microsoft’s information platform vision provides enterprises with a “complete approach” to managing information assets, enabling all businesses to gain strategic value from information from the desktop to the datacenter to the cloud. And StreamInsight V1 is one essential piece in this spectrum. After more than a year of blood, sweat, tears, and insane amounts of coffee we are proud to release the first version of our Complex Event Processing Framework.   Those of you who have been following our Community Technology Previews (CTPs) throughout last year have already had the possibility to familiarize themselves with the product. Early feedback was not only incredibly positive, but also very constructive and strongly influenced the final feature set. Four notable increments over our last public CTP are: Count windows Non-occurrence detection (Anti-Join) Dynamic query composition at runtime Synchronize time across input streams Additionally, many smaller issues and bugs were addressed. A few APIs slightly changed with respect to the November CTP, but porting your application to RTM should not require a lot of effort.   Here are the (english) bits - choosing the evaluation license during setup lets you already play with this version. Before you install, make sure to uninstall any previous CTP version:   StreamInsight X86StreamInsight X64   Within a few days, we will update our product page and add download links and instructions there as well.   The StreamInsight documentation is provided through a help file as part of the installation as well as through Books Online on MSDN. We also invite you to visit the StreamInsight Blog and the StreamInsight Forum, which is a great place to discuss questions and issues with the community and the development team.   Regards,Roman Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Linqpad and StreamInsight

    Slightly before the announcement of StreamInsight being available for Linqpad I downloaded it from here. I had seen Roman Schindlauer demonstrate it at Teched and it looked a really good tool to do some StreamInsight dev. You will need .Net 4.0 and StreamInsight installed. NEW! SQL Monitor 2.0Monitor SQL Server Central's servers withRed Gate's new SQL Monitor.No installation required. Find out more.

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  • StreamInsight V2.0 Released!

    - by Roman Schindlauer
    The StreamInsight Team is proud to announce the release of StreamInsight V2.0! This is the version that ships with SQL 2012, and as such it has been available through Connect to SQL CTP customers already since December. As part of the SQL 2012 launch activities, we are now making V2.0 available to everyone, following our tradition of providing a separate download page. StreamInsight V2.0 includes a number of stability and performance fixes over its predecessor V1.2. Moreover it introduces a dependency on the .NET Framework 4.0, as well as on SQL 2012 license keys. For these reasons, we decided to bump the major version number, even though V2.0 does not add new features or API surface. It can be regarded a stepping stone to the upcoming release 2.1 which will contain significantly new APIs (that will depend on .NET 4.0). Head over here to download StreamInsight V2.0. The updated Books Online can be found here. Update: For instructions on how to make your existing application work against the new bits without recompilation, see here. Regards, The StreamInsight Team

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  • StreamInsight V2.0 Released!

    - by Roman Schindlauer
    The StreamInsight Team is proud to announce the release of StreamInsight V2.0! This is the version that ships with SQL 2012, and as such it has been available through Connect to SQL CTP customers already since December. As part of the SQL 2012 launch activities, we are now making V2.0 available to everyone, following our tradition of providing a separate download page. StreamInsight V2.0 includes a number of stability and performance fixes over its predecessor V1.2. Moreover it introduces a dependency on the .NET Framework 4.0, as well as on SQL 2012 license keys. For these reasons, we decided to bump the major version number, even though V2.0 does not add new features or API surface. It can be regarded a stepping stone to the upcoming release 2.1 which will contain significantly new APIs (that will depend on .NET 4.0). Head over here to download StreamInsight V2.0. The updated Books Online can be found here. Update: For instructions on how to make your existing application work against the new bits without recompilation, see here. Regards, The StreamInsight Team

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  • StreamInsight complex query with filter on HoppingWindow

    - by hh354
    I'm trying out StreamInsight and I came across a problem with a query I need. I'm trying to throw a warning if there are several changes in my measured values (of up to 20% change) in the last 30 minutes. This is the query I came up with for now but it isn't working and it's not even correct I think. Apparently I can't filter on a window...? var deviationQuery = from s in wcfStream group s by s.SensorId into grouped from window in grouped.HoppingWindow(TimeSpan.FromMinutes(30),TimeSpan.FromMinutes(1)) where window.StdDev(e => e.Value) > measurableValue * 1.2 select new OutputEvent { Error = "Deviation" }; Thanks in advance!

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  • Building the Internet of Things – with Microsoft StreamInsight and the Microsoft .Net Micro Framework

    - by Roman Schindlauer
    Fresh from the press – The March 2012 issue of MSDN Magazine features an article about the Internet of Things. It discusses in depth how you can use StreamInsight to process all the data that is continuously produced in typical Internet of Things scenarios. It also gives you an end-to-end perspective on developing Internet of Things solutions in the .NET world, ranging from the .NET Micro Framework application running on the device, the communication between the devices and the server-side all the way to powerful cross-device streaming analytics implemented in StreamInsight LINQ. You can find an online version of the article here. Happy reading! Regards, The StreamInsight Team

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  • Hopping/Tumbling Windows Could Introduce Latency.

    This is a pre-article to one I am going to be writing on adjusting an event’s time and duration to satisfy business process requirements but it is one that I think is really useful when understanding the way that Hopping/Tumbling windows work within StreamInsight.  A Tumbling window is just a special shortcut version of  a Hopping window where the width of the window is equal to the size of the hop Here is the simplest and often used definition for a Hopping Window.  You can find them all here public static CepWindowStream<CepWindow<TPayload>> HoppingWindow<TPayload>(     this CepStream<TPayload> source,     TimeSpan windowSize,     TimeSpan hopSize,     WindowInputPolicy inputPolicy,     HoppingWindowOutputPolicy outputPolicy )   And here is the definition for a Tumbling Window public static CepWindowStream<CepWindow<TPayload>> TumblingWindow<TPayload>(     this CepStream<TPayload> source,     TimeSpan windowSize,     WindowInputPolicy inputPolicy,     HoppingWindowOutputPolicy outputPolicy )   These methods allow you to group events into windows of a temporal size.  It is a really useful and simple feature in StreamInsight.  One of the downsides though is that the windows cannot be flushed until an event in a following window occurs.  This means that you will potentially never see some events or see them with a delay.  Let me explain. Remember that a stream is a potentially unbounded sequence of events. Events in StreamInsight are given a StartTime.  It is this StartTime that is used to calculate into which temporal window an event falls.  It is best practice to assign a timestamp from the source system and not one from the system clock on the processing server.  StreamInsight cannot know when a window is over.  It cannot tell whether you have received all events in the window or whether some events have been delayed which means that StreamInsight cannot flush the stream for you.   Imagine you have events with the following Timestamps 12:10:10 PM 12:10:20 PM 12:10:35 PM 12:10:45 PM 11:59:59 PM And imagine that you have defined a 1 minute Tumbling Window over this stream using the following syntax var HoppingStream = from shift in inputStream.TumblingWindow(TimeSpan.FromMinutes(1),HoppingWindowOutputPolicy.ClipToWindowEnd) select new WindowCountPayload { CountInWindow = (Int32)shift.Count() };   The events between 12:10:10 PM and 12:10:45 PM will not be seen until the event at 11:59:59 PM arrives.  This could be a real problem if you need to react to windows promptly This can always be worked around by using a different design pattern but a lot of the examples I see assume there is a constant, very frequent stream of events resulting in windows always being flushed. Further examples of using windowing in StreamInsight can be found here

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  • Parameterized StreamInsight Queries

    - by Roman Schindlauer
    The changes in our APIs enable a set of scenarios that were either not possible before or could only be achieved through workarounds. One such use case that people ask about frequently is the ability to parameterize a query and instantiate it with different values instead of re-deploying the entire statement. I’ll demonstrate how to do this in StreamInsight 2.1 and combine it with a method of using subjects for dynamic query composition in a mini-series of (at least) two blog articles. Let’s start with something really simple: I want to deploy a windowed aggregate to a StreamInsight server, and later use it with different window sizes. The LINQ statement for such an aggregate is very straightforward and familiar: var result = from win in stream.TumblingWindow(TimeSpan.FromSeconds(5))               select win.Avg(e => e.Value); Obviously, we had to use an existing input stream object as well as a concrete TimeSpan value. If we want to be able to re-use this construct, we can define it as a IQStreamable: var avg = myApp     .DefineStreamable((IQStreamable<SourcePayload> s, TimeSpan w) =>         from win in s.TumblingWindow(w)         select win.Avg(e => e.Value)); The DefineStreamable API lets us define a function, in our case from a IQStreamable (the input stream) and a TimeSpan (the window length) to an IQStreamable (the result). We can then use it like a function, with the input stream and the window length as parameters: var result = avg(stream, TimeSpan.FromSeconds(5)); Nice, but you might ask: what does this save me, except from writing my own extension method? Well, in addition to defining the IQStreamable function, you can actually deploy it to the server, to make it re-usable by another process! When we deploy an artifact in V2.1, we give it a name: var avg = myApp     .DefineStreamable((IQStreamable<SourcePayload> s, TimeSpan w) =>         from win in s.TumblingWindow(w)         select win.Avg(e => e.Value))     .Deploy("AverageQuery"); When connected to the same server, we can now use that name to retrieve the IQStreamable and use it with our own parameters: var averageQuery = myApp     .GetStreamable<IQStreamable<SourcePayload>, TimeSpan, double>("AverageQuery"); var result = averageQuery(stream, TimeSpan.FromSeconds(5)); Convenient, isn’t it? Keep in mind that, even though the function “AverageQuery” is deployed to the server, its logic will still be instantiated into each process when the process is created. The advantage here is being able to deploy that function, so another client who wants to use it doesn’t need to ask the author for the code or assembly, but just needs to know the name of deployed entity. A few words on the function signature of GetStreamable: the last type parameter (here: double) is the payload type of the result, not the actual result stream’s type itself. The returned object is a function from IQStreamable<SourcePayload> and TimeSpan to IQStreamable<double>. In the next article we will integrate this usage of IQStreamables with Subjects in StreamInsight, so stay tuned! Regards, The StreamInsight Team

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  • Windows in StreamInsight: Hopping vs. Snapshot

    - by Roman Schindlauer
    Three weeks ago, we explained the basic concept of windows in StreamInsight: defining sets of events that serve as arguments for set-based operations, like aggregations. Today, we want to discuss the so-called Hopping Windows and compare them with Snapshot Windows. We will compare these two, because they can serve similar purposes with different behaviors; we will discuss the remaining window type, Count Windows, another time. Hopping (and its syntactic-sugar-sister Tumbling) windows are probably the most straightforward windowing concept in StreamInsight. A hopping window is defined by its length, and the offset from one window to the next. They are aligned with some absolute point on the timeline (which can also be given as a parameter to the window) and create sets of events. The diagram below shows an example of a hopping window with length of 1h and hop size (the offset) of 15 minutes, hence creating overlapping windows:   Two aspects in this diagram are important: Since this window is overlapping, an event can fall into more than one windows. If an (interval) event spans a window boundary, its lifetime will be clipped to the window, before it is passed to the set-based operation. That’s the default and currently only available window input policy. (This should only concern you if you are using a time-sensitive user-defined aggregate or operator.) The set-based operation will be applied to each of these sets, yielding a result. This result is: A single scalar value in case of built-in or user-defined aggregates. A subset of the input payloads, in case of the TopK operator. Arbitrary events, when using a user-defined operator. The timestamps of the result are almost always the ones of the windows. Only the user-defined  operator can create new events with timestamps. (However, even these event lifetimes are subject to the window’s output policy, which is currently always to clip to the window end.) Let’s assume we were calculating the sum over some payload field: var result = from window in source.HoppingWindow( TimeSpan.FromHours(1), TimeSpan.FromMinutes(15), HoppingWindowOutputPolicy.ClipToWindowEnd) select new { avg = window.Avg(e => e.Value) }; Now each window is reflected by one result event:   As you can see, the window definition defines the output frequency. No matter how many or few events we got from the input, this hopping window will produce one result every 15 minutes – except for those windows that do not contain any events at all, because StreamInsight window operations are empty-preserving (more about that another time). The “forced” output for every window can become a performance issue if you have a real-time query with many events in a wide group & apply – let me explain: imagine you have a lot of events that you group by and then aggregate within each group – classical streaming pattern. The hopping window produces a result in each group at exactly the same point in time for all groups, since the window boundaries are aligned with the timeline, not with the event timestamps. This means that the query output will become very bursty, delivering the results of all the groups at the same point in time. This becomes especially obvious if the events are long-lasting, spanning multiple windows each, so that the produced result events do not change their value very often. In such a case, a snapshot window can remedy. Snapshot windows are more difficult to explain than hopping windows: they represent those periods in time, when no event changes occur. In other words, if you mark all event start and and times on your timeline, then you are looking at all snapshot window boundaries:   If your events are never overlapping, the snapshot window will not make much sense. It is commonly used together with timestamp modification, which make it a very powerful tool. Or as Allan Mitchell expressed in in a recent tweet: “I used to look at SnapshotWindow() with disdain. Now she is my mistress, the one I turn to in times of trouble and need”. Let’s look at a simple example: I want to compute the average of some value in my events over the last minute. I don’t want this output be produced at fixed intervals, but at soon as it changes (that’s the true event-driven spirit!). The snapshot window will include all currently active event at each point in time, hence we need to extend our original events’ lifetimes into the future: Applying the Snapshot window on these events, it will appear to be “looking back into the past”: If you look at the result produced in this diagram, you can easily prove that, at each point in time, the current event value represents the average of all original input event within the last minute. Here is the LINQ representation of that query, applying the lifetime extension before the snapshot window: var result = from window in source .AlterEventDuration(e => TimeSpan.FromMinutes(1)) .SnapshotWindow(SnapshotWindowOutputPolicy.Clip) select new { avg = window.Avg(e => e.Value) }; With more complex modifications of the event lifetimes you can achieve many more query patterns. For instance “running totals” by keeping the event start times, but snapping their end times to some fixed time, like the end of the day. Each snapshot then “sees” all events that have happened in the respective time period so far. Regards, The StreamInsight Team

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  • StreamInsight 2.1 Released

    - by Roman Schindlauer
    The wait is over—we are pleased to announce the release of StreamInsight 2.1. Since the release of version 1.2, we have heard your feedbacks and suggestions and based on that we have come up with a whole new set of features. Here are some of the highlights: A New Programming Model – A more clear and consistent object model, eliminating the need for complex input and output adapters (though they are still completely supported). This new model allows you to provision, name, and manage data sources and sinks in the StreamInsight server. Tight integration with Reactive Framework (Rx) – You can write reactive queries hosted inside StreamInsight as well as compose temporal queries on reactive objects. High Availability – Check-pointing over temporal streams and multiple processes with shared computation. Here is how simple coding can be with the 2.1 Programming Model: class Program {     static void Main(string[] args)     {         using (Server server = Server.Create("Default"))         {             // Create an app             Application app = server.CreateApplication("app");             // Define a simple observable which generates an integer every second             var source = app.DefineObservable(() =>                 Observable.Interval(TimeSpan.FromSeconds(1)));             // Define a sink.             var sink = app.DefineObserver(() =>                 Observer.Create<long>(x => Console.WriteLine(x)));             // Define a query to filter the events             var query = from e in source                         where e % 2 == 0                         select e;             // Bind the query to the sink and create a runnable process             using (IDisposable proc = query.Bind(sink).Run("MyProcess"))             {                 Console.WriteLine("Press a key to dispose the process...");                 Console.ReadKey();             }         }     } }   That’s how easily you can define a source, sink and compose a query and run it. Note that we did not replace the existing APIs, they co-exist with the new surface. Stay tuned, you will see a series of articles coming out over the next few weeks about the new features and how to use them. Come and grab it from our download center page and let us know what you think! You can find the updated MSDN documentation here, and we would appreciate if you could provide feedback to the docs as well—best via email to [email protected]. Moreover, we updated our samples to demonstrate the new programming surface. Regards, The StreamInsight Team

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  • StreamInsight 2.1 Released

    - by Roman Schindlauer
    The wait is over—we are pleased to announce the release of StreamInsight 2.1. Since the release of version 1.2, we have heard your feedbacks and suggestions and based on that we have come up with a whole new set of features. Here are some of the highlights: A New Programming Model – A more clear and consistent object model, eliminating the need for complex input and output adapters (though they are still completely supported). This new model allows you to provision, name, and manage data sources and sinks in the StreamInsight server. Tight integration with Reactive Framework (Rx) – You can write reactive queries hosted inside StreamInsight as well as compose temporal queries on reactive objects. High Availability – Check-pointing over temporal streams and multiple processes with shared computation. Here is how simple coding can be with the 2.1 Programming Model: class Program {     static void Main(string[] args)     {         using (Server server = Server.Create("Default"))         {             // Create an app             Application app = server.CreateApplication("app");             // Define a simple observable which generates an integer every second             var source = app.DefineObservable(() =>                 Observable.Interval(TimeSpan.FromSeconds(1)));             // Define a sink.             var sink = app.DefineObserver(() =>                 Observer.Create<long>(x => Console.WriteLine(x)));             // Define a query to filter the events             var query = from e in source                         where e % 2 == 0                         select e;             // Bind the query to the sink and create a runnable process             using (IDisposable proc = query.Bind(sink).Run("MyProcess"))             {                 Console.WriteLine("Press a key to dispose the process...");                 Console.ReadKey();             }         }     } }   That’s how easily you can define a source, sink and compose a query and run it. Note that we did not replace the existing APIs, they co-exist with the new surface. Stay tuned, you will see a series of articles coming out over the next few weeks about the new features and how to use them. Come and grab it from our download center page and let us know what you think! You can find the updated MSDN documentation here, and we would appreciate if you could provide feedback to the docs as well—best via email to [email protected]. Moreover, we updated our samples to demonstrate the new programming surface. Regards, The StreamInsight Team

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  • Introduction to LinqPad Driver for StreamInsight 2.1

    - by Roman Schindlauer
    We are announcing the availability of the LinqPad driver for StreamInsight 2.1. The purpose of this blog post is to offer a quick introduction into the new features that we added to the StreamInsight LinqPad driver. We’ll show you how to connect to a remote server, how to inspect the entities present of that server, how to compose on top of them and how to manage their lifetime. Installing the driver Info on how to install the driver can be found in an earlier blog post here. Establishing connections As you click on the “Add Connection” link in the left pane you will notice that now it’s possible to build the data context automatically. The new driver appears as an option in the upper list, and if you pick it you will open a connection dialog that lets you connect to a remote StreamInsight server. The connection dialog lets you specify the address of the remote server. You will notice that it’s possible to pick up the binding information from the configuration file of the LinqPad application (which is normally in the same folder as LinqPad.exe and is called LinqPad.exe.config). In order for the context to be generated you need to pick an application from the server. The control is editable hence you can create a new application if you don’t want to make changes to an existing application. If you choose a new application name you will be prompted for confirmation before this gets created. Once you click OK the connection is created and you can start issuing queries against the remote server. If there’s any connectivity error the connection is marked with a red X and you can see the error message informing you what went wrong (i.e., the remote server could not be reached etc.). The context for remote servers Let’s take a look at what happens after we are connected successfully. Every LinqPad query runs inside a context – think of it as a class that wraps all the code that you’re writing. If you’re connecting to a live server the context will contain the following: The application object itself. All entities present in this application (sources, sinks, subjects and processes). The picture below shows a snapshot of the left pane of LinqPad after a successful connection. Every entity on the server has a different icon which will allow users to figure out its purpose. You will also notice that some entities have a string in parentheses following the name. It should be interpreted as such: the first name is the name of the property of the context class and the second name is the name of the entity as it exists on the server. Not all valid entity names are valid identifier names so in cases where we had to make a transformation you see both. Note also that as you hover over the entities you get IntelliSense with their types – more on that later. Remoting is not supported As you play with the entities exposed by the context you will notice that you can’t read and write directly to/from them. If for instance you’re trying to dump the content of an entity you will get an error message telling you that in the current version remoting is not supported. This is because the entity lives on the remote server and dumping its content means reading the events produced by this entity into the local process. ObservableSource.Dump(); Will yield the following error: Reading from a remote 'System.Reactive.Linq.IQbservable`1[System.Int32]' is not supported. Use the 'Microsoft.ComplexEventProcessing.Linq.RemoteProvider.Bind' method to read from the source using a remote observer. This basically tells you that you can call the Bind() method to direct the output of this source to a sink that has to be defined on the remote machine as well. You can’t bring the results to the LinqPad window unless you write code specifically for that. Compose queries You may ask – what's the purpose of all that? After all the same information is present in the EventFlowDebugger, why bother with showing it in LinqPad? First of all, What gets exposed in LinqPad is not what you see in the debugger. In LinqPad we have a property on the context class for every entity that lives on the server. Because LinqPad offers IntelliSense we in fact have much more information about the entity, and more importantly we can compose with that entity very easily. For example, let’s say that this code creates an entity: using (var server = Server.Connect(...)) {     var a = server.CreateApplication("WhiteFish");     var src = a         .DefineObservable<int>(() => Observable.Range(0, 3))         .Deploy("ObservableSource"); If later we want to compose with the source we have to fetch it and then we can bind something to     a.GetObservable<int>("ObservableSource)").Bind(... This means that we had to know a bunch of things about this: that it’s a source, that it’s an observable, it produces a result with payload Int32 and it’s named “ObservableSource”. Only the second and last bits of information are present in the debugger, by the way. As you type in the query window you see that all the entities are present, you get IntelliSense support for them and it’s much easier to make sense of what’s available. Let’s look at a scenario where composition is plausible. With the new programming model it’s possible to create “cold” sources that are parameterized. There was a way to accomplish that even in the previous version by passing parameters to the adapters, but this time it’s much more elegant because the expression declares what parameters are required. Say that we hover the mouse over the ThrottledSource source – we will see that its type is Func<int, int, IQbservable<int>> - this in effect means that we need to pass two int parameters before we can get a source that produces events, and the type for those events is int – in the particular case of my example I had the source produce a range of integers and the two parameters were the start and end of the range. So we see how a developer can create a source that is not running yet. Then someone else (e.g. an administrator) can pass whatever parameters appropriate and run the process. Proxy Types Here’s an interesting scenario – what if someone created a source on a server but they forgot to tell you what type they used. Worse yet, they might have used an anonymous type and even though they can refer to it by name you can’t figure out how to use that type. Let’s walk through an example that shows how you can compose against types you don’t need to have the definition of. This is how we can create a source that returns an anonymous type: Application.DefineObservable(() => Observable.Range(1, 10).Select(i => new { I = i })).Deploy("O1"); Now if we refresh the connection we can see the new source named O1 appear in the list. But what’s more important is that we now have a type to work with. So we can compose a query that refers to the anonymous type. var threshold = new StreamInsightDynamicDriver.TypeProxies.AnonymousType1_0<int>(5); var filter = from i in O1              where i > threshold              select i; filter.Deploy("O2"); You will notice that the anonymous type defined with this statement: new { I = i } can now be manipulated by a client that does not have access to it because the LinqPad driver has generated another type in its stead, named StreamInsightDynamicDriver.TypeProxies.AnonymousType1_0. This type has all the properties and fields of the type defined on the server, except in this case we can instantiate values and use it to compose more queries. It is worth noting that the same thing works for types that are not anonymous – the test is if the LinqPad driver can resolve the type or not. If it’s not possible then a new type will be generated that approximates the type that exists on the server. Control metadata In addition to composing processes on top of the existing entities we can do other useful things. We can delete them – nothing new here as we simply access the entities through the Entities collection of the application class. Here is where having their real name in parentheses comes handy. There’s another way to find out what’s behind a property – dump its expression. The first line in the output tells us what’s the name of the entity used to build this property in the context. Runtime information So let’s create a process to see what happens. We can bind a source to a sink and run the resulting process. If you right click on the connection you can refresh it and see the process present in the list of entities. Then you can drag the process to the query window and see that you can have access to process object in the Processes collection of the application. You can then manipulate the process (delete it, read its diagnostic view etc.). Regards, The StreamInsight Team

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  • Introduction to LinqPad Driver for StreamInsight 2.1

    - by Roman Schindlauer
    We are announcing the availability of the LinqPad driver for StreamInsight 2.1. The purpose of this blog post is to offer a quick introduction into the new features that we added to the StreamInsight LinqPad driver. We’ll show you how to connect to a remote server, how to inspect the entities present of that server, how to compose on top of them and how to manage their lifetime. Installing the driver Info on how to install the driver can be found in an earlier blog post here. Establishing connections As you click on the “Add Connection” link in the left pane you will notice that now it’s possible to build the data context automatically. The new driver appears as an option in the upper list, and if you pick it you will open a connection dialog that lets you connect to a remote StreamInsight server. The connection dialog lets you specify the address of the remote server. You will notice that it’s possible to pick up the binding information from the configuration file of the LinqPad application (which is normally in the same folder as LinqPad.exe and is called LinqPad.exe.config). In order for the context to be generated you need to pick an application from the server. The control is editable hence you can create a new application if you don’t want to make changes to an existing application. If you choose a new application name you will be prompted for confirmation before this gets created. Once you click OK the connection is created and you can start issuing queries against the remote server. If there’s any connectivity error the connection is marked with a red X and you can see the error message informing you what went wrong (i.e., the remote server could not be reached etc.). The context for remote servers Let’s take a look at what happens after we are connected successfully. Every LinqPad query runs inside a context – think of it as a class that wraps all the code that you’re writing. If you’re connecting to a live server the context will contain the following: The application object itself. All entities present in this application (sources, sinks, subjects and processes). The picture below shows a snapshot of the left pane of LinqPad after a successful connection. Every entity on the server has a different icon which will allow users to figure out its purpose. You will also notice that some entities have a string in parentheses following the name. It should be interpreted as such: the first name is the name of the property of the context class and the second name is the name of the entity as it exists on the server. Not all valid entity names are valid identifier names so in cases where we had to make a transformation you see both. Note also that as you hover over the entities you get IntelliSense with their types – more on that later. Remoting is not supported As you play with the entities exposed by the context you will notice that you can’t read and write directly to/from them. If for instance you’re trying to dump the content of an entity you will get an error message telling you that in the current version remoting is not supported. This is because the entity lives on the remote server and dumping its content means reading the events produced by this entity into the local process. ObservableSource.Dump(); Will yield the following error: Reading from a remote 'System.Reactive.Linq.IQbservable`1[System.Int32]' is not supported. Use the 'Microsoft.ComplexEventProcessing.Linq.RemoteProvider.Bind' method to read from the source using a remote observer. This basically tells you that you can call the Bind() method to direct the output of this source to a sink that has to be defined on the remote machine as well. You can’t bring the results to the LinqPad window unless you write code specifically for that. Compose queries You may ask – what's the purpose of all that? After all the same information is present in the EventFlowDebugger, why bother with showing it in LinqPad? First of all, What gets exposed in LinqPad is not what you see in the debugger. In LinqPad we have a property on the context class for every entity that lives on the server. Because LinqPad offers IntelliSense we in fact have much more information about the entity, and more importantly we can compose with that entity very easily. For example, let’s say that this code creates an entity: using (var server = Server.Connect(...)) {     var a = server.CreateApplication("WhiteFish");     var src = a         .DefineObservable<int>(() => Observable.Range(0, 3))         .Deploy("ObservableSource"); If later we want to compose with the source we have to fetch it and then we can bind something to     a.GetObservable<int>("ObservableSource)").Bind(... This means that we had to know a bunch of things about this: that it’s a source, that it’s an observable, it produces a result with payload Int32 and it’s named “ObservableSource”. Only the second and last bits of information are present in the debugger, by the way. As you type in the query window you see that all the entities are present, you get IntelliSense support for them and it’s much easier to make sense of what’s available. Let’s look at a scenario where composition is plausible. With the new programming model it’s possible to create “cold” sources that are parameterized. There was a way to accomplish that even in the previous version by passing parameters to the adapters, but this time it’s much more elegant because the expression declares what parameters are required. Say that we hover the mouse over the ThrottledSource source – we will see that its type is Func<int, int, IQbservable<int>> - this in effect means that we need to pass two int parameters before we can get a source that produces events, and the type for those events is int – in the particular case of my example I had the source produce a range of integers and the two parameters were the start and end of the range. So we see how a developer can create a source that is not running yet. Then someone else (e.g. an administrator) can pass whatever parameters appropriate and run the process. Proxy Types Here’s an interesting scenario – what if someone created a source on a server but they forgot to tell you what type they used. Worse yet, they might have used an anonymous type and even though they can refer to it by name you can’t figure out how to use that type. Let’s walk through an example that shows how you can compose against types you don’t need to have the definition of. This is how we can create a source that returns an anonymous type: Application.DefineObservable(() => Observable.Range(1, 10).Select(i => new { I = i })).Deploy("O1"); Now if we refresh the connection we can see the new source named O1 appear in the list. But what’s more important is that we now have a type to work with. So we can compose a query that refers to the anonymous type. var threshold = new StreamInsightDynamicDriver.TypeProxies.AnonymousType1_0<int>(5); var filter = from i in O1              where i > threshold              select i; filter.Deploy("O2"); You will notice that the anonymous type defined with this statement: new { I = i } can now be manipulated by a client that does not have access to it because the LinqPad driver has generated another type in its stead, named StreamInsightDynamicDriver.TypeProxies.AnonymousType1_0. This type has all the properties and fields of the type defined on the server, except in this case we can instantiate values and use it to compose more queries. It is worth noting that the same thing works for types that are not anonymous – the test is if the LinqPad driver can resolve the type or not. If it’s not possible then a new type will be generated that approximates the type that exists on the server. Control metadata In addition to composing processes on top of the existing entities we can do other useful things. We can delete them – nothing new here as we simply access the entities through the Entities collection of the application class. Here is where having their real name in parentheses comes handy. There’s another way to find out what’s behind a property – dump its expression. The first line in the output tells us what’s the name of the entity used to build this property in the context. Runtime information So let’s create a process to see what happens. We can bind a source to a sink and run the resulting process. If you right click on the connection you can refresh it and see the process present in the list of entities. Then you can drag the process to the query window and see that you can have access to process object in the Processes collection of the application. You can then manipulate the process (delete it, read its diagnostic view etc.). Regards, The StreamInsight Team

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  • StreamInsight SQL Social Followup

    - by simonsabin
    Thank you for those that attended the SQL Social last night I hope you enjoyed it. When I get presentation details from the team I will pass them on, meanwhile have a look at their blog http://blogs.msdn.com/b/streaminsight/ which has details of most of the things that were discussed last night.   The speakers where Azam Husain Balan Sethu Raman Torsten Grabs Roman Schindlauer   Some interesting videos from these guys are here http://channel9.msdn.com/learn/courses/SQL2008R2TrainingKit...(read more)

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  • SQL Server 2008 R2: StreamInsight changes at RTM: Access to grouping keys via explicit typing

    - by Greg Low
    One of the problems that existed in the CTP3 edition of StreamInsight was an error that occurred if you tried to access the grouping key from within your projection expression. That was a real issue as you always need access to the key. It's a bit like using a GROUP BY in TSQL and then not including the columns you're grouping by in the SELECT clause. You'd see the results but not be able to know which results are which. Look at the following code: var laneSpeeds = from e in vehicleSpeeds group e...(read more)

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  • Getting started with StreamInsight

    A quick-start to SQL Server StreamInsight and Complex Event Processing with a step-by-step example on stock quotes. Too many SQL Servers to keep up with?Download a free trial of SQL Response to monitor your SQL Servers in just one intuitive interface."The monitoringin SQL Response is excellent." Mike Towery.

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  • Using SQL Server Integration Services and StreamInsight Together

    The purpose of this paper is to provide guidance for enriching data integration scenarios by integrating StreamInsight with SQL Server Integration Services. Specifically, we looked at the technical challenges and solutions for such integration, by using a case study based on a customer scenarios in the telecommunications sector. NEW! SQL Monitor 2.0Monitor SQL Server Central's servers withRed Gate's new SQL Monitor.No installation required. Find out more.

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  • Mouse Clicks, Reactive Extensions and StreamInsight Mashup

    I had an hour spare this afternoon so I wanted to have another play with Reactive Extensions in .Net and StreamInsight. I also didn’t want to simply use a console window as a way of gathering events so I decided to use a windows form instead. The task I set myself was this. Whenever I click on my form I want to subscribe to the event and output its location to the console window and also the timestamp of the event.

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  • SQL2K8R2: StreamInsight changes at RTM: Hopping Windows

    - by Greg Low
    We've been working on updating our demos and samples for the RTM changes of StreamInsight. I'll detail these as I come across them. The first is that there is a change to the HoppingWindow. The first two parameters are the same in the constructor but the third parameter is now required. It is the HoppingWindowOutputPolicy. Currently, there is only a single option for this which is ClipToWindowEnd. So you can create a HoppingWindow like this: var queryOutput = from w in input.HoppingWindow ( TimeSpan...(read more)

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  • SQL Server 2008 R2: StreamInsight - User-defined aggregates

    - by Greg Low
    I'd briefly played around with user-defined aggregates in StreamInsight with CTP3 but when I started working with the new Count Windows, I found I had to have one working. I learned a few things along the way that I hope will help someone. The first thing you have to do is define a class: public class IntegerAverage : CepAggregate < int , int > { public override int GenerateOutput( IEnumerable < int > eventData) { if (eventData.Count() == 0) { return 0; } else { return eventData.Sum()...(read more)

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  • SQL Server 2008 R2: StreamInsight changes at RTM: Count Windows

    - by Greg Low
    Another interesting change in the RTM version of StreamInsight is the addition of a new window type. Count Windows aren't time based but are based on counting a number of events. The window type provided in this release is called CountByStartTimeWindow. Based on that name, you'd have to presume we might get other types of count windows in the future. This new window type takes two parameters. The first is the number of events. The second is an output policy, similar to the policies now required for...(read more)

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  • SQL Server 2008 R2: StreamInsight changes at RTM: Event Flow Debugger and Management Interface Secur

    - by Greg Low
    In CTP3, I found setting up the StreamInsight Event Flow Debugger fairly easy. For RTM, a number of security changes were made. First config: To be able to connect to the management interface, your user must be added to the Performance Log Users group. After you make this change, you must log off and log back on as the token is only added to your login token when you log on. I forgot this and spent ages trying to work out why I couldn't connect. Second config: You need to reserve the URL that the...(read more)

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