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Testing and Debugging Observable Sequences

Testing your Rx application

If you have an observable sequence that publishes values over an extended period of time, testing it in real time can be a stretch. The Reactive Extension library provides the TestScheduler type to assist testing this kind of time-dependent code without actually waiting for time to pass. The TestScheduler inherits VirtualScheduler and allows you to create, publish and subscribe to sequences in emulated time. For example, you can compact a publication which takes 5 days to complete into a 2 minute run, while maintaining the correct scale. You can also take a sequence which actually has happened in the past (e.g., a sequence of stock ticks for a previous year) and compute or subscribe to it as if it is pushing out new values in real time.

The factory method Start executes all scheduled tasks until the queue is empty, or you can specify a time to so that queued-up tasks are only executed to the specified time.

The following example creates a hot observable sequence with specified OnNext notifications. It then starts the test scheduler and specifies when to subscribe to and dispose of the hot observable sequence. The Start method returns an instance of the ITestableObserver, which contains a Messages property that records all notifications in a list.

After the sequence has completed, we use the ReactiveAssert.AreElementEqual method to compare the Messages property, together with a list of expected values to see if both are identical (with the same number of items, and items are equal and in the same order). By doing so, we can confirm that we have indeed received the notifications that we expect. In our example, since we only start subscribing at 150, we will miss out the value abc. However, when we compare the values we have received so far at 400, we notice that we have in fact received all the published values after we subscribed to the sequence. And we also verify that the OnCompleted notification was fired at the right time at 500. In addition, subscription information is also captured by the ITestableObservable type returned by the CreateHotObservable method.

In the same way, you can use ReactiveAssert.AreElementsEqual to confirm that subscriptions indeed happened at expected times.

using System;
using System.Reactive;
using System.Reactive.Linq;
using Microsoft.Reactive.Testing;

class Program : ReactiveTest
    static void Main(string[] args)
        var scheduler = new TestScheduler();

        var input = scheduler.CreateHotObservable(
            OnNext(100, "abc"),
            OnNext(200, "def"),
            OnNext(250, "ghi"),
            OnNext(300, "pqr"),
            OnNext(450, "xyz"),

        var results = scheduler.Start(
            () => input.Buffer(() => input.Throttle(TimeSpan.FromTicks(100), scheduler))
                       .Select(b => string.Join(",", b)),
            created: 50,
            subscribed: 150,
            disposed: 600);

        ReactiveAssert.AreElementsEqual(results.Messages, new Recorded<Notification<string>>[] {
                OnNext(400, "def,ghi,pqr"),
                OnNext(500, "xyz"),

        ReactiveAssert.AreElementsEqual(input.Subscriptions, new Subscription[] {
                Subscribe(150, 500),
                Subscribe(150, 400),
                Subscribe(400, 500)

Debugging your Rx application

You can use the Do operator to debug your Rx application. The Do operator allows you to specify various actions to be taken for each item of observable sequence (e.g., print or log the item, etc.). This is especially helpful when you are chaining many operators and you want to know what values are produced at each level.

In the following example, we are going to reuse the Buffer example which generates integers every second, while putting them into buffers that can hold 5 items each. In our original example in the Querying Observable Sequences using LINQ Operators topic, we subscribe only to the final Observable(IList<>) sequence when the buffer is full (and before it is emptied). In this example, however, we will use the Do operator to print out the values when they are being pushed out by the original sequence (an integer every second). When the buffer is full, we use the Do operator to print the status, before handing over all this as the final sequence for the observer to subscribe.

var seq1 = Observable.Interval(TimeSpan.FromSeconds(1))
           .Do(x => Console.WriteLine(x.ToString()))
           .Do(x => Console.WriteLine("buffer is full"))
           .Subscribe(x => Console.WriteLine("Sum of the buffer is " + x.Sum()));

As you can see from this sample, a subscription is on the recipient end of a series of chained observable sequences. At first, we create an observable sequence of integers separate by a second using the Interval operator. Then, we put 5 items into a buffer using the Buffer operator, and send them out as another sequence only when the buffer is full. Lastly, this is handed over to the Subscribe operator. Data propagate down all these intermediate sequences until they are pushed to the observer. In the same way, subscriptions are propagated in the reverse direction to the source sequence. By inserting the Do operator in the middle of such propagations, you can “spy” on such data flow just like you use Console.WriteLine in .NET or printf() in C to perform debugging.

You can also use the Timestamp operator to verify the time when an item is pushed out by an observable sequence. This can help you troubleshoot time-based operations to ensure accuracy. Recall the following example from the Creating and Subscribing to Simple Observable Sequences topic, in which we chain the Timestamp operator to the query so that each value pushed out by the source sequence will be appended by the time when it is published. By doing so, when we subscribe to this source sequence, we can receive both its value and timestamp.

Console.WriteLine(“Current Time: “ + DateTime.Now);

var source = Observable.Timer(TimeSpan.FromSeconds(5), TimeSpan.FromSeconds(1))
using (source.Subscribe(x => Console.WriteLine("{0}: {1}", x.Value, x.Timestamp)))
           Console.WriteLine("Press any key to unsubscribe");
Console.WriteLine("Press any key to exit");

The output will be similar to this:

Current Time: 5/31/2011 5:35:08 PM
Press any key to unsubscribe
0: 5/31/2011 5:35:13 PM -07:00
1: 5/31/2011 5:35:14 PM -07:00
2: 5/31/2011 5:35:15 PM -07:00

By using the Timestamp operator, we have verified that the first item is indeed pushed out 5 seconds after the sequence, and each item is published 1 second later.

In addition, you can also set breakpoints inside lambda expressions to assist in debugging. Normally, you can only set a breakpoint for the whole query without singling out a particular value to look it. To workaround this limitation, you can insert the Select operator in the middle of the query and set a breakpoint it, and in the Select statement, project the identical value out as its source using a return statement on its own line. You can then set a breakpoint at the return statement line and examine values as they make their way through the query.

var seq = Observable.Interval(TimeSpan.FromSeconds(1))
          .Do(x => Console.WriteLine(x.ToString()))
          .Select(y => { 
                  return y; }) // set a breakpoint at this line
          .Do(x => Console.WriteLine("buffer is full"))
          .Subscribe(x => Console.WriteLine("Sum of the buffer is " + x.Sum()));

In this example, the breakpoint is set at the return y line. When you debug into the program, the y variable shows up in the Locals window and you can examine its total count (5). If you expand y, you can also examine each item in the list including its value and type.

Alternatively, you can convert a lambda expression to a statement lambda expression, format code so that a statement is on its own line, and then set a breakpoint.

You can remove any Do and Select calls after you finish debugging.

See Also

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