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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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  • Looking for an open source project in Python

    - by Roman Yankovsky
    I am looking for practical tasks to get experience with Python. Just reading the books and not doing any tasks in the language is not effective. I solved some problems on the Project Euler and TopCoder and it helped me to learn the syntax of the language better. But those tasks are hard algorithmically, but as a rule is quite simple from the point of view of programming. Now I'm looking for an interesting open source project in Python, participation in which will help me to better understand the OO-model of language. Although, this is my first step with Python, in general, I am an experienced programmer and I can be useful for a project. May be someone can suggest something?

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  • SQL Saturday #39 in NYC

    - by roman
    This weekend I will be speaking at the NYC SQL Saturday . The whole event was supposed to be BI focused but now the schedule shows a lot of non BI stuff as well. I will be presenting SQL Server 2008 Reporting Services Programming , one of my favorite topics to present on. It seems that the event if fully booked. I'll be coming down on my bike taking scenic roads through MA and CT so I will not make it to the speaker dinner. But the forecast looks good so I am pretty psyched to finally venture out...(read more)

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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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  • 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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  • Adding my face to my web-site in Google's search result

    - by Roman Matveev
    I'm trying to accomplish the rich snippet to the template of my future web-site. The data format is review and I used the microdata formatting to add all necessary information to the web-page. The Structured Data Testing Tool delivered rating, author information and review date: However there is no my face image and the sections related to authorship are empty: I made all that recommended to link my Google+ profile to the web-site: I did something wrong? Or I will not be able to see my face in the test tools ever and it will be in the real SERP?

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  • Getting Started with StreamInsight 2.1

    - by Roman Schindlauer
    If you're just beginning to get familiar with StreamInsight, you may be looking for a way to get started. What are the basics? How can I get my first StreamInsight application running so I can see how it works? Where is the 'front door' that will get me going? If that describes you, then this blog entry might be just what you need. If you're already a StreamInsight wiz, keep reading anyway - you may find some helpful links here that you weren't aware of. But here's what we'd like from you experienced readers in particular: if you know of other good resources that we missed, please feel free to add them in the comments below. We appreciate you sharing your expertise. The Book The basic documentation for StreamInsight is located in the MSDN Library (Microsoft StreamInsight 2.1). You'll notice that previous versions of StreamInsight are still there (1.2 and 2.0), but if you're just getting started you can stick to the 2.1 section. The documentation has been organized to function as reference material, which is fine after you're familiar with the technology. But if you're trying to learn the basics, you might want to take a different path instead of just starting at the top. The following is one map you can use. What Is StreamInsight? Here is a sequence of topics that should give you a good overview of what StreamInsight is and how it works: Overview answers the question, "what is it?" StreamInsight Server Architecture gives you a quick look at a high-level architectural drawing StreamInsight Concepts lays out an overview of the basic components Deploying StreamInsight Entities to a StreamInsight Server describes the mechanics of how these components work together Getting an Example Running Once you have this background, go ahead and install StreamInsight and get a basic example up and running: Installation download and install the software StreamInsight Examples walk through a set of 3 simple StreamInsight applications that work together to demonstrate what you learned in the topics above; you can copy and paste the code into Visual Studio, compile, and run That's it - you now have a real, functioning StreamInsight system! Now that you have a handle on the basics, you might want to start digging deeper. Digging Deeper Here's a suggested path through the documentation to help you understand the next layer of StreamInsight technologies: Using Event Sources and Event Sinks sources supply data and sinks consume it; this topic gives you an overview of how they work Publishing and Connecting to the StreamInsight Server practical details on how to set up a StreamInsight server A Hitchhiker’s Guide to StreamInsight 2.1 Queries queries are the heart of how StreamInsight performs data analytics, and this whitepaper will help you really understand how they work Using StreamInsight LINQ root through this section for technical details on specific query components Using the StreamInsight Event Flow Debugger in addition to troubleshooting, the debugger is a great way to learn more about what goes on inside a StreamInsight application And Even Deeper Finally, to get a handle on some of the more complex things you can do with StreamInsight, dig into these: Input and Output Adapters adapters can be useful for handling more complex sources and sinks Building Resilient StreamInsight Applications a resilient application is able to recover from system failures Operations this section will help you monitor and troubleshoot a running StreamInsight system The StreamInsight Community As you're designing and developing your StreamInsight solutions, you probably will find it helpful to see working examples or to learn tips and tricks from others. Or maybe you need a place to post a vexing question. Here are some community resources that we have found useful. If you know of others, please add them in the comments below. Code samples and tools Official StreamInsight code samples Introduction to LinqPad Driver for StreamInsight 2.1 - LinqPad is a very useful tool for developing queries The following case studies are based on earlier versions of StreamInsight, but they still are useful examples: Microsoft Media Analytics - real-time monitoring and analytic Edgenet - responding to information from multiple source ICONICS - managing energy usage Blogs Microsoft StreamInsight Ruminations of J.net Richard Seroter's Architecture Musings pluralsight Forums MSDN StreamInsight Forum stackoverflow Training Microsoft StreamInsight Fundamentals (“Introducing StreamInsight” is free) from pluralsight Twitter @streaminsight   You’re a StreamInsight Expert That should get you going. Please add any other resources you have found useful in the comments below.   Regards, The StreamInsight Team

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  • Taming Hopping Windows

    - by Roman Schindlauer
    At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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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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  • Observable Adapter

    - by Roman Schindlauer
    .NET 4.0 introduced a pair of interfaces, IObservable<T> and IObserver<T>, supporting subscriptions to and notifications for push-based sequences. In combination with Reactive Extensions (Rx), these interfaces provide a convenient and uniform way of describing event sources and sinks in .NET. The StreamInsight CTP refresh in November 2009 included an Observable adapter supporting “reactive” event inputs and outputs.   While we continue to believe it enables an important programming model, the Observable adapter was not included in the final (RTM) release of Microsoft StreamInsight 1.0. The release takes a dependency on .NET 3.5 but for timing reasons could not take a dependency on .NET 4.0. Shipping a separate copy of the observable interfaces in StreamInsight – as we did in the CTP refresh – was not a viable option in the RTM release.   Within the next months, we will be shipping another preview of the Observable adapter that targets .NET 4.0. We look forward to gathering your feedback on the new adapter design! We plan to include the Observable adapter implementation into the product in a future release of Microsoft StreamInsight. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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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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  • perl comparing 2 data file as array 2D for finding match one to one [migrated]

    - by roman serpa
    I'm doing a program that uses combinations of variables ( combiData.txt 63 rows x different number of columns) for analysing a data table ( j1j2_1.csv, 1000filas x 19 columns ) , to choose how many times each combination is repeated in data table and which rows come from (for instance, tableData[row][4]). I have tried to compile it , however I get the following message : Use of uninitialized value $val in numeric eq (==) at rowInData.pl line 34. Use of reference "ARRAY(0x1a2eae4)" as array index at rowInData.pl line 56. Use of reference "ARRAY(0x1a1334c)" as array index at rowInData.pl line 56. Use of uninitialized value in subtraction (-) at rowInData.pl line 56. Modification of non-creatable array value attempted, subscript -1 at rowInData.pl line 56. nothing This is my code: #!/usr/bin/perl use strict; use warnings; my $line_match; my $countTrue; open (FILE1, "<combiData.txt") or die "can't open file text1.txt\n"; my @tableCombi; while(<FILE1>) { my @row = split(' ', $_); push(@tableCombi, \@row); } close FILE1 || die $!; open (FILE2, "<j1j2_1.csv") or die "can't open file text1.txt\n"; my @tableData; while(<FILE2>) { my @row2 = split(/\s*,\s*/, $_); push(@tableData, \@row2); } close FILE2 || die $!; #function transform combiData.txt variable (position ) to the real value that i have to find in the data table. sub trueVal($){ my ($val) = $_[0]; if($val == 7){ return ('nonsynonymous_SNV'); } elsif( $val == 14) { return '1'; } elsif( $val == 15) { return '1';} elsif( $val == 16) { return '1'; } elsif( $val == 17) { return '1'; } elsif( $val == 18) { return '1';} elsif( $val == 19) { return '1';} else { print 'nothing'; } } #function IntToStr ( ) , i'm not sure if it is necessary) that transforms $ to strings , to use the function <eq> in the third loop for the array of combinations compared with the data array . sub IntToStr { return "$_[0]"; } for my $combi (@tableCombi) { $line_match = 0; for my $sheetData (@tableData) { $countTrue=0; for my $cell ( @$combi) { #my $temp =\$tableCombi[$combi][$cell] ; #if ( trueVal($tableCombi[$combi][$cell] ) eq $tableData[$sheetData][ $tableCombi[$combi][$cell] - 1 ] ){ #if ( IntToStr(trueVal($$temp )) eq IntToStr( $tableData[$sheetData][ $$temp-1] ) ){ if ( IntToStr(trueVal($tableCombi[$combi][$cell]) ) eq IntToStr($tableData[$sheetData][ $tableCombi[$combi][$cell] -1]) ){ $countTrue++;} if ($countTrue==@$combi){ $line_match++; #if ($line_match < 50){ print $tableData[$sheetData][4]." "; #} } } } print $line_match." \n"; }

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  • Is there any simple game that involves psychological factors?

    - by Roman
    I need to find a simple game in which several people need to interact with each other. The game should be simple for an analysis (it should be simple to describe what happens in the game, what players did). Because of the last reason, the video games are not appropriate for my purposes. I am thinking of a simple, schematic, strategic game where people can make a limited set of simple moves. Moreover, the moves of the game should be conditioned not only by a pure logic (like in chess or go). The behavior in the game should depend on psychological factors, on relations between people. In more details, I think it should be a cooperation game where people make their decisions based on mutual trust. It would be nice if players can express punishment and forgiveness in the game. Does anybody knows a game that is close to what I have described above? ADDED I need to add that I need a game where actions of players are simple and easy to formalize. Because of that I cannot use verbal games (where communication between players is important). By simple actions I understand, for example, moves on the board from one position to another one, or passing chips from one player to another one and so on.

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  • The Madness of March

    - by Kristin Rose
    Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} From “Linsanity” to “LOB City”, there is no doubt that basketball dominates the month of March. As many are aware, March Madness is well underway and continues to be a time when college basketball teams get together to bring their A-game to the court. Here at Oracle we also like to bring our A-game, and that includes some new players and talent from our newly acquired companies. Each new acquisition expands Oracle’s solution portfolio, fills customer requirements, and ultimately brings greater opportunities for partners. OPN follows a consistent approach to delivering key information about these acquisitions to you in a timely manner. We do this so partners can get educated, get trained and gain access to demand gen and sales tools. Through this slam dunk of a process we provide (using Pillar Data Systems as an example): A welcome page where partners can download information and learn how to sell and maximize sales returns. A Discovery section where partners can listen to key Oracle Executives speak about the many benefits this new solution brings, as well review a FAQ sheet. A Prepare section where partners can learn about the product strategies and the different OPN Knowledge Zones that have become available. A Sell and Deliver section that partners can leverage when discussing product positioning and functionality, as well as gain access to relevant deliverables. Just as any competitive team strives to be #1, Oracle also wants to stay best-in-class which is why we have recently joined forces with some ‘baller’ companies such as RightNow, Endeca and Pillar Axiom to secure our place in the industry bracket. By running our 3-2 Oracle play and bringing in our newly acquired products, we are able to deliver a solid, expanded solution to our partners. These and many other MVP companies have helped Oracle broaden its offerings and score big. Watch the half time show below to find out what Judson thinks about Oracle’s current offerings: Mergers and acquisitions are a strategic part of how we currently go to market. If you haven’t done so already, dribble down or post up and visit the Acquisition Catalog to learn more about Oracle’s acquired products and the unique benefits they can bring to your own court. Or click here to learn about the ways of monetizing opportunities through Oracle acquisitions. Until Next Time, It’s Game Time, The OPN Communications Team Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

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  • What Is StreamInsight? A Primer for Non-Programmers

    - by Roman Schindlauer
    Are you trying to figure out whether StreamInsight might be something you could use, but you’re having trouble sifting through all the programming jargon that’s used to describe it? StreamInsight is, ultimately, a set of programming tools, and at some point it takes a programmer to implement a StreamInsight solution. But it really should be possible to get a handle on what StreamInsight is all about even if you’re not a programmer yourself. A new article published in the TechNet Wiki may be able to help: StreamInsight for Non-Programmers. It gives an overview of the technology, but it leaves out the C# references and relates StreamInsight to more familiar SQL databases and queries. Check it out. When you’re done there and are ready to dig a little deeper, take a look at Get Started with StreamInsight 2.1. That article should help you navigate through the StreamInsight official documentation and other resources. And, as always, you can post questions or comments here or on the TechNet Wiki. 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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  • Custom Russian Phonetic Keyboard

    - by roman
    I tried to custom Russian phonetic keyboard through Terminal. To do this I wrote: cd /usr/share/X11/xkb/symbols/ then: sudo gedit ru The document " ru (/usr/share/X11/xkb/symbols) - gedit " appeared on the desktop. I changed all the keys that suited me, saved the file and restarted the computer. However, the changes didn't work out. I checked the document again. The changes are there, but the keys still code for the old layout. I think I am missing some important point. Please help. By the way I get in Terminal this message: ** (gedit:14887): WARNING **: Could not load Gedit repository: Typelib file for namespace 'GtkSource', version '3.0' not found (gedit:14887): IBUS-WARNING **: The owner of /home/roma/.config/ibus/bus is not root! What does this mean?

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  • What Is StreamInsight? A Primer for Non-Programmers

    - by Roman Schindlauer
    Are you trying to figure out whether StreamInsight might be something you could use, but you’re having trouble sifting through all the programming jargon that’s used to describe it? StreamInsight is, ultimately, a set of programming tools, and at some point it takes a programmer to implement a StreamInsight solution. But it really should be possible to get a handle on what StreamInsight is all about even if you’re not a programmer yourself. A new article published in the TechNet Wiki may be able to help: StreamInsight for Non-Programmers. It gives an overview of the technology, but it leaves out the C# references and relates StreamInsight to more familiar SQL databases and queries. Check it out. When you’re done there and are ready to dig a little deeper, take a look at Get Started with StreamInsight 2.1. That article should help you navigate through the StreamInsight official documentation and other resources. And, as always, you can post questions or comments here or on the TechNet Wiki. Regards, The StreamInsight Team

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  • StreamInsight Now Available Through Microsoft Update

    - by Roman Schindlauer
    We are pleased to announce that StreamInsight v1.1 is now available for automatic download and install via Microsoft Update globally. In order to enable agile deployment of StreamInsight solutions, you have asked of us a steady cadence of releases with incremental, but highly impactful features and product improvements. Following our StreamInsight 1.0 launch in Spring 2010, we offered StreamInsight 1.1 in Fall 2010 with implicit compatibility and an upgraded setup to support side by side installs. With this setup, your applications will automatically point to the latest runtime, but you still have the choice to point your application back to a 1.0 runtime if you choose to do so. As the next step, in order to enable timely delivery of our releases to you, we are pleased to announce the support for automatic download and install of StreamInsight 1.1 release via Microsoft Update starting this week. If you have a computer: that is subscribed to Microsoft Update (different from Windows Update) has StreamInsight 1.0 installed, and does not yet have StreamInsight 1.1 installed, Microsoft Update will automatically download and install the corresponding StreamInsight 1.1 update side by side with your existing StreamInsight 1.0 installation – across all supported 32-bit and 64-bit Windows operating systems, across 11 supported languages, and across StreamInsight client and server SKUs. This is also supported in WSUS environments, if all your updates are managed from a corporate server (please talk to the WSUS administrator in your enterprise). As an example, if you have SI Client 1.0 DEU and SI Server 1.0 ENU installed on the same computer, Microsoft Update will selectively download and side-by-side install just the SI Client 1.1 DEU and SI Server 1.1 ENU releases. Going forward, Microsoft Update will be our preferred mode of delivery – in addition to support for our download sites, and media based distribution where appropriate. Regards, The StreamInsight Team

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  • Observable Adapter

    - by Roman Schindlauer
    .NET 4.0 introduced a pair of interfaces, IObservable<T> and IObserver<T>, supporting subscriptions to and notifications for push-based sequences. In combination with Reactive Extensions (Rx), these interfaces provide a convenient and uniform way of describing event sources and sinks in .NET. The StreamInsight CTP refresh in November 2009 included an Observable adapter supporting “reactive” event inputs and outputs.   While we continue to believe it enables an important programming model, the Observable adapter was not included in the final (RTM) release of Microsoft StreamInsight 1.0. The release takes a dependency on .NET 3.5 but for timing reasons could not take a dependency on .NET 4.0. Shipping a separate copy of the observable interfaces in StreamInsight – as we did in the CTP refresh – was not a viable option in the RTM release.   Within the next months, we will be shipping another preview of the Observable adapter that targets .NET 4.0. We look forward to gathering your feedback on the new adapter design! We plan to include the Observable adapter implementation into the product in a future release of Microsoft StreamInsight. Share this post: email it! | bookmark it! | digg it! | reddit! | kick it! | live it!

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  • Why the amount of 'indexed' images can go down?

    - by Roman Matveev
    I have a site with several thousand of images. All those images included into the sitemap submitted to Google Webmaster Tools. The amount of 'submitted' images is OK, but the amount of 'indexed' is significantly lower than the amount of 'submitted' and it is going DOWN! I'd understand if not all of my images got indexed (however it is also not clear and very frustrating for me) but I can not understand how the indexing can go in the negative direction?! All the images stays on their places. And pages containing them stays unchanged. At least they intended to be. Any thoughts?

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  • Ubuntu 12.04 Precise on Dell Inspirion Duo

    - by Roman M. Kos
    I installed 12.04 version of Ubuntu on my Dell Inspiron Duo. After installing with help of program like UNetBootIn or smth like that. ( Besides i have no problem with kernel panic on chargin on/off like in 11.10. ) After that i followed with steps from here, in the first post here: http://ubuntuforums.org/showthread.php?t=1658635 There left one big problem with touchscreen: When i whant to drag with touchscreen like i`m doing it with mouse (for ex. selecting multile files with mouse) the selectable rectangular doesnot shows while im dragging, when dragging was finished (i put my finger off) it shows the rectangular and hides it. This thing disables all my tries to drag a window or smth else.... Also some time after using touchscreen such things are disabeling: - Often - click from a mouse (after keyboard using functinality restores) - less often - mouse movement disables (sometimes restores sometimes not) - lesser than other - keyboards works but no sygnals accepting (keyoards has indicator, so thay react, mouse of course not) The test from eTouchU utility passes perfeclty. Any idia for solving this problem? P.S.: Im from Ukraine, so sorry if my possible grammar mistakes. P.P.S.: Besides how to know the physicall position of my tabled mode? For automaticall rotating. Like on each rotation do some script.

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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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  • System broken after installing Gtk+-3.4.1 with broadway backend enabled

    - by Roman D. Boiko
    I am running Ubuntu 11.10 from VirtualBox. I installed Gtk+ 3.4.1 (latest stable release) from sources with X11 and broadway backends enabled. In order to do that, I also installed latest versions of glib, libffi, libtiff, libjped, gdk-pixbuf, and pango. Each of them was configured with default options. I.e., they were installed to /usr/local (at least, I see respective folders in /usr/local/include). After reboot and login (regardless which user), desktop is grey for about 30 sec, nothing is displayed. Then Nautilus starts, but nothing else (my locale is Ukrainian, but there is nothing important in text): . During boot, I can access command prompt as root, use dpkg, etc. But I don't know what to do. One idea is to reinstall Gtk+ and other libraries with prefix /usr or /usr/shared. I will try that, but it is quite time-consuming, so any ideas would be welcome. Reverting to earlier snapshot is still possible, but it is 6 days old and I would like to try to solve the problem.

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  • Taming Hopping Windows

    - by Roman Schindlauer
    At first glance, hopping windows seem fairly innocuous and obvious. They organize events into windows with a simple periodic definition: the windows have some duration d (e.g. a window covers 5 second time intervals), an interval or period p (e.g. a new window starts every 2 seconds) and an alignment a (e.g. one of those windows starts at 12:00 PM on March 15, 2012 UTC). var wins = xs     .HoppingWindow(TimeSpan.FromSeconds(5),                    TimeSpan.FromSeconds(2),                    new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc)); Logically, there is a window with start time a + np and end time a + np + d for every integer n. That’s a lot of windows. So why doesn’t the following query (always) blow up? var query = wins.Select(win => win.Count()); A few users have asked why StreamInsight doesn’t produce output for empty windows. Primarily it’s because there is an infinite number of empty windows! (Actually, StreamInsight uses DateTimeOffset.MaxValue to approximate “the end of time” and DateTimeOffset.MinValue to approximate “the beginning of time”, so the number of windows is lower in practice.) That was the good news. Now the bad news. Events also have duration. Consider the following simple input: var xs = this.Application                 .DefineEnumerable(() => new[]                     { EdgeEvent.CreateStart(DateTimeOffset.UtcNow, 0) })                 .ToStreamable(AdvanceTimeSettings.IncreasingStartTime); Because the event has no explicit end edge, it lasts until the end of time. So there are lots of non-empty windows if we apply a hopping window to that single event! For this reason, we need to be careful with hopping window queries in StreamInsight. Or we can switch to a custom implementation of hopping windows that doesn’t suffer from this shortcoming. The alternate window implementation produces output only when the input changes. We start by breaking up the timeline into non-overlapping intervals assigned to each window. In figure 1, six hopping windows (“Windows”) are assigned to six intervals (“Assignments”) in the timeline. Next we take input events (“Events”) and alter their lifetimes (“Altered Events”) so that they cover the intervals of the windows they intersect. In figure 1, you can see that the first event e1 intersects windows w1 and w2 so it is adjusted to cover assignments a1 and a2. Finally, we can use snapshot windows (“Snapshots”) to produce output for the hopping windows. Notice however that instead of having six windows generating output, we have only four. The first and second snapshots correspond to the first and second hopping windows. The remaining snapshots however cover two hopping windows each! While in this example we saved only two events, the savings can be more significant when the ratio of event duration to window duration is higher. Figure 1: Timeline The implementation of this strategy is straightforward. We need to set the start times of events to the start time of the interval assigned to the earliest window including the start time. Similarly, we need to modify the end times of events to the end time of the interval assigned to the latest window including the end time. The following snap-to-boundary function that rounds a timestamp value t down to the nearest value t' <= t such that t' is a + np for some integer n will be useful. For convenience, we will represent both DateTime and TimeSpan values using long ticks: static long SnapToBoundary(long t, long a, long p) {     return t - ((t - a) % p) - (t > a ? 0L : p); } How do we find the earliest window including the start time for an event? It’s the window following the last window that does not include the start time assuming that there are no gaps in the windows (i.e. duration < interval), and limitation of this solution. To find the end time of that antecedent window, we need to know the alignment of window ends: long e = a + (d % p); Using the window end alignment, we are finally ready to describe the start time selector: static long AdjustStartTime(long t, long e, long p) {     return SnapToBoundary(t, e, p) + p; } To find the latest window including the end time for an event, we look for the last window start time (non-inclusive): public static long AdjustEndTime(long t, long a, long d, long p) {     return SnapToBoundary(t - 1, a, p) + p + d; } Bringing it together, we can define the translation from events to ‘altered events’ as in Figure 1: public static IQStreamable<T> SnapToWindowIntervals<T>(IQStreamable<T> source, TimeSpan duration, TimeSpan interval, DateTime alignment) {     if (source == null) throw new ArgumentNullException("source");     // reason about DateTime and TimeSpan in ticks     long d = Math.Min(DateTime.MaxValue.Ticks, duration.Ticks);     long p = Math.Min(DateTime.MaxValue.Ticks, Math.Abs(interval.Ticks));     // set alignment to earliest possible window     var a = alignment.ToUniversalTime().Ticks % p;     // verify constraints of this solution     if (d <= 0L) { throw new ArgumentOutOfRangeException("duration"); }     if (p == 0L || p > d) { throw new ArgumentOutOfRangeException("interval"); }     // find the alignment of window ends     long e = a + (d % p);     return source.AlterEventLifetime(         evt => ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p)),         evt => ToDateTime(AdjustEndTime(evt.EndTime.ToUniversalTime().Ticks, a, d, p)) -             ToDateTime(AdjustStartTime(evt.StartTime.ToUniversalTime().Ticks, e, p))); } public static DateTime ToDateTime(long ticks) {     // just snap to min or max value rather than under/overflowing     return ticks < DateTime.MinValue.Ticks         ? new DateTime(DateTime.MinValue.Ticks, DateTimeKind.Utc)         : ticks > DateTime.MaxValue.Ticks         ? new DateTime(DateTime.MaxValue.Ticks, DateTimeKind.Utc)         : new DateTime(ticks, DateTimeKind.Utc); } Finally, we can describe our custom hopping window operator: public static IQWindowedStreamable<T> HoppingWindow2<T>(     IQStreamable<T> source,     TimeSpan duration,     TimeSpan interval,     DateTime alignment) {     if (source == null) { throw new ArgumentNullException("source"); }     return SnapToWindowIntervals(source, duration, interval, alignment).SnapshotWindow(); } By switching from HoppingWindow to HoppingWindow2 in the following example, the query returns quickly rather than gobbling resources and ultimately failing! public void Main() {     var start = new DateTimeOffset(new DateTime(2012, 6, 28), TimeSpan.Zero);     var duration = TimeSpan.FromSeconds(5);     var interval = TimeSpan.FromSeconds(2);     var alignment = new DateTime(2012, 3, 15, 12, 0, 0, DateTimeKind.Utc);     var events = this.Application.DefineEnumerable(() => new[]     {         EdgeEvent.CreateStart(start.AddSeconds(0), "e0"),         EdgeEvent.CreateStart(start.AddSeconds(1), "e1"),         EdgeEvent.CreateEnd(start.AddSeconds(1), start.AddSeconds(2), "e1"),         EdgeEvent.CreateStart(start.AddSeconds(3), "e2"),         EdgeEvent.CreateStart(start.AddSeconds(9), "e3"),         EdgeEvent.CreateEnd(start.AddSeconds(3), start.AddSeconds(10), "e2"),         EdgeEvent.CreateEnd(start.AddSeconds(9), start.AddSeconds(10), "e3"),     }).ToStreamable(AdvanceTimeSettings.IncreasingStartTime);     var adjustedEvents = SnapToWindowIntervals(events, duration, interval, alignment);     var query = from win in HoppingWindow2(events, duration, interval, alignment)                 select win.Count();     DisplayResults(adjustedEvents, "Adjusted Events");     DisplayResults(query, "Query"); } As you can see, instead of producing a massive number of windows for the open start edge e0, a single window is emitted from 12:00:15 AM until the end of time: Adjusted Events StartTime EndTime Payload 6/28/2012 12:00:01 AM 12/31/9999 11:59:59 PM e0 6/28/2012 12:00:03 AM 6/28/2012 12:00:07 AM e1 6/28/2012 12:00:05 AM 6/28/2012 12:00:15 AM e2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM e3 Query StartTime EndTime Payload 6/28/2012 12:00:01 AM 6/28/2012 12:00:03 AM 1 6/28/2012 12:00:03 AM 6/28/2012 12:00:05 AM 2 6/28/2012 12:00:05 AM 6/28/2012 12:00:07 AM 3 6/28/2012 12:00:07 AM 6/28/2012 12:00:11 AM 2 6/28/2012 12:00:11 AM 6/28/2012 12:00:15 AM 3 6/28/2012 12:00:15 AM 12/31/9999 11:59:59 PM 1 Regards, The StreamInsight Team

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