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Reactive Frameworks: ''Robbing Peter to Pay Paul'' and Dependency Contexts

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Reactive Frameworks: ''Robbing Peter to Pay Paul'' and Dependency Contexts

Reactive functional frameworks rob developers (Peter) of mutability to pay for the multi-threading problem in their frameworks (Paul).

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Reactive frameworks, are effectively data streams that are aligned to message-driven/event architectures.

The problem with building message driven systems is that the message provides the context. The nature of message-driven architectures is that the handler of the message is disconnected from the producer of the message. The only information connecting them is the message. Hence, the message needs to provide all the necessary context and data for the handler to process the message.

This becomes complicated when downstream message handlers require more context (such as transactions). How do you pass a transaction via a message? Yes, we can use XA transactions to suspend and resume transactions, but this creates a lot of overhead to the performance and scale that these reactive frameworks are chasing. Furthermore, Spring Data considers transactions that are not a good fit for reactive.

So, how can we create a Reactive framework that enables context for such things as transactions?

Dependency Contexts

Well, the first problem is co-ordination. Firing off lots of events serviced by different functions in different threads creates a lot of multi-threading co-ordination. How does one know when to commit the transaction? Or possibly, how does one decide to rollback on the transaction? Or, what if an asynchronous I/O takes too long and causes the held transaction to time out. We would be required to monitor all messages and co-ordinate their results to then make a decision on commit/rollback (along with handling further non-application events, such as transaction timeout events). This puts a lot of coding decisions on the developer outside the normal application logic. Gone are the days when the developer just threw an exception and the transaction is rolled back.

So, let's bring back order to the event handling chaos. Load the events to a queue and process events in the order they are added to the queue. As requests on the system should be independent, we create a separate queue for each request to keep requests isolated from each other.

The resulting event loop would look as follows:

  public void startRequest(Function function) {
    this.functions.add(function);
  }

  BlockingQueue<Function> functions = new LinkedBlockingQueue<>();

  public void eventLoop() throws InterruptedException {
    for (;;) {

      // Obtain next function to execute
      Function function = this.functions.take();

      // Determine if new request (no context)
      FunctionContext context = (function instanceof ContextualFunction)
          ? ((ContextualFunction) function).context
          : new FunctionContext();

      // Allow for multi-threading in executing
      synchronized (context) {
        function.doFunction(context);
      }

      // Register the next function to execute
      Function nextFunction = context.functions.pollFirst();
      if (nextFunction != null) {
        this.functions.add(nextFunction);
      }
    }
  }

  public interface Function {
    void doFunction(FunctionContext context);
  }

  public class FunctionContext {

    private Deque<Function> functions = new LinkedList<>();

    public void triggerFunction(Function function) {
      this.functions.add(new ContextualFunction(function, this));
    }
  }

  public class ContextualFunction implements Function {

    private final Function delegate;
    private final FunctionContext context;

    public ContextualFunction(Function delegate, FunctionContext context) {
      this.delegate = delegate;
      this.context = context;
    }

    @Override
    public void doFunction(FunctionContext context) {
      this.delegate.doFunction(context);
    }
  }


Ok, that's a lot of code, but it is now doing two things:

  • ordering all functions to be executed sequentially

  • providing a context that travels with the execution chain of functions

But how do we make use of the FunctionContext to begin and manage the transaction?

We incorporate the ManagedFunction of Invervsion of Control and use a ServiceLocator to enable storing the dependencies within the context:

  public interface ServiceLocator {
    // Uses context for transitive dependencies
    Object locateService(String serviceName, FunctionContext context);
  }

  ServiceLocator serviceLocator = ...; // can, for example, wrap Spring BeanFactory

  public class FunctionContext {

    // ... triggerFunction logic omitted for brevity

    private Map<String, Object> contextDependencies = new HashMap<>();

    public Object getDependency(String dependencyName) {

      // Pull dependency from context cache
      Object dependency = this.contextDependencies.get(dependencyName);
      if (dependency == null) {

        // Not cached, so create new and cache in context
        dependency = serviceLocator.locateService(dependencyName, this);
        this.contextDependencies.put(dependencyName, dependency);
      }

      // Return dependency for Function to use
      return dependency;
    }
  }


The functions are now able to use the getDependency("name") method to retrieve the same objects for a request. As the dependency objects are cached within the context, the various Functions involved in servicing the request are able to retrieve the same object.

Therefore, a transaction can be managed across Functions. The first Function retrieves the Connection and starts the transaction. Further Functions execute, pulling in the same Connection (with the transaction established). The final Function in the chain then commits the transaction.

Should there be a failure, the injected Exception handlers of the ManagedFunction can rollback the transaction. Within thread-per-request architectures, exceptions are thrown by the developer's code typically rolling back the transaction. By having the ManagedFunction injected handlers, they also rollback the transaction; this reproduces the ease of thread-per-request transaction management for exceptions.

Furthermore, the exception handlers would clear theFunctionContext queue of Functions. As the transaction has been rolled back, there is no point further executing the remaining Functions. In typical thread-per-request handling, the remaining part of the try block would be skipped. By clearing the FunctionContext queue, this mimics skipping the remaining logic and goes straight to the catch block. In the case of ManagedFunction exception handler, this would be triggering a new function to handle the failure.

But we've just reduced the reactive framework to a single sequence of functions, losing the concurrency it can provide!

Well, beyond making it easier to code, now that there is some order in function execution, we can introduce concurrency by spawning another sequence of functions. As the FunctionContext ties the sequence of functions together, we just create a new FunctionContext to enable concurrency. The following code shows this:

  public class FunctionContext {

    private Deque<Function> functions = new LinkedList<>();

    public void triggerFunction(Function function) {
      this.functions.add(new ContextualFunction(function, this));
    }

    public void spawnNewSequence(Function function) {
      this.functions.add(function); // event loop will create new FunctionContext
    }

    // ... getDependency(...) removed for brevity
  }


In fact, we have just created Threads running on an event loop. The sequence of functions are executed in order just like imperative statements are executed in order by a thread. So, we now have Threads without the overheads of a thread-per-request. The dependencies are bound to the context and subsequently the Thread of execution, making them effectively ThreadLocal. As ThreadLocals are thread safe, we now have safe multi-threading functional code.

As dependencies are effectivelyThreadLocal to the sequence of Functions, they can be mutated for the next Function to pick up the change. Yes, immutability better removes developer errors; however, this should not be any reason to restrict the developer from doing it. This is especially the case if you want to mutate objects via an ORM (e.g. JPA or Spring Repositories) to do updates in the database.

On that note, OfficeFloor implements dependency contexts in it's threading models. OfficeFloor actually makes this easier to develop by introducing the concept of Governance. Governance does the above transaction management by configuration declaration (much like a @Transaction   annotation on a method). However, as OfficeFloor uses graphical configuration, this is done graphically. Rather than annotating the code directly, OfficeFloor graphically marks functions for transaction governance for better code re-use and flexibility in configuring applications. An example of this configuration can be found here.

Summary

So, when Reactive frameworks are tooting their horn, they are actually doing this while restricting you further as a developer. Because the reactive frameworks can't handle the context problem, they push this problem onto you the developer in avoiding context.

Subsequently, reactive frameworks rob developers (Peter) of context to pay for the message passing problem in their frameworks (Paul).

By incorporating dependency contexts into event loops, you can incorporate context that we have grown to love in thread-per-request for managing things such as transactions.

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Topics:
reactive programming ,functional languages ,inversion of control ,java ,reactive ,functions ,concurrency ,dependency contexts ,mutability ,multi-threading

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