Cadence

# Implementing workflows

A workflow implementation implements a workflow interface. Each time a new workflow execution is started, a new instance of the workflow implementation object is created. Then, one of the methods (depending on which workflow type has been started) annotated with @WorkflowMethod is invoked. As soon as this method returns, the workflow execution is closed. While workflow execution is open, it can receive calls to signal and query methods. No additional calls to workflow methods are allowed. The workflow object is stateful, so query and signal methods can communicate with the other parts of the workflow through workflow object fields.

# Calling Activities

Workflow.newActivityStub returns a client-side stub that implements an activity interface. It takes activity type and activity options as arguments. Activity options are needed only if some of the required timeouts are not specified through the @ActivityMethod annotation.

Calling a method on this interface invokes an activity that implements this method. An activity invocation synchronously blocks until the activity completes, fails, or times out. Even if activity execution takes a few months, the workflow code still sees it as a single synchronous invocation. It doesn't matter what happens to the processes that host the workflow. The business logic code just sees a single method call.

public class FileProcessingWorkflowImpl implements FileProcessingWorkflow {
    private final FileProcessingActivities activities;
    public FileProcessingWorkflowImpl() {
        this.activities = Workflow.newActivityStub(FileProcessingActivities.class);
    }
    @Override
    public void processFile(Arguments args) {
        String localName = null;
        String processedName = null;
        try {
            localName = activities.download(args.getSourceBucketName(), args.getSourceFilename());
            processedName = activities.processFile(localName);
            activities.upload(args.getTargetBucketName(), args.getTargetFilename(), processedName);
        } finally {
            if (localName != null) { // File was downloaded.
                activities.deleteLocalFile(localName);
            }
            if (processedName != null) { // File was processed.
                activities.deleteLocalFile(processedName);
            }
        }
    }
    ...
}

If different activities need different options, like timeouts or a task list, multiple client-side stubs can be created with different options.

public FileProcessingWorkflowImpl() {
    ActivityOptions options1 = new ActivityOptions.Builder()
             .setTaskList("taskList1")
             .build();
    this.store1 = Workflow.newActivityStub(FileProcessingActivities.class, options1);
    ActivityOptions options2 = new ActivityOptions.Builder()
             .setTaskList("taskList2")
             .build();
    this.store2 = Workflow.newActivityStub(FileProcessingActivities.class, options2);
}

# Calling Activities Asynchronously

Sometimes workflows need to perform certain operations in parallel. The Async class static methods allow you to invoke any activity asynchronously. The calls return a Promise result immediately. Promise is similar to both Java Future and CompletionStage. The Promise get blocks until a result is available. It also exposes the thenApply and handle methods. See the Promise JavaDoc for technical details about differences with Future.

To convert a synchronous call:

String localName = activities.download(sourceBucket, sourceFile);

To asynchronous style, the method reference is passed to Async.function or Async.procedure followed by activity arguments:

Promise<String> localNamePromise = Async.function(activities::download, sourceBucket, sourceFile);

Then to wait synchronously for the result:

String localName = localNamePromise.get();

Here is the above example rewritten to call download and upload in parallel on multiple files:

public void processFile(Arguments args) {
    List<Promise<String>> localNamePromises = new ArrayList<>();
    List<String> processedNames = null;
    try {
        // Download all files in parallel.
        for (String sourceFilename : args.getSourceFilenames()) {
            Promise<String> localName = Async.function(activities::download,
                args.getSourceBucketName(), sourceFilename);
            localNamePromises.add(localName);
        }
        // allOf converts a list of promises to a single promise that contains a list
        // of each promise value.
        Promise<List<String>> localNamesPromise = Promise.allOf(localNamePromises);
        // All code until the next line wasn't blocking.
        // The promise get is a blocking call.
        List<String> localNames = localNamesPromise.get();
        processedNames = activities.processFiles(localNames);
        // Upload all results in parallel.
        List<Promise<Void>> uploadedList = new ArrayList<>();
        for (String processedName : processedNames) {
            Promise<Void> uploaded = Async.procedure(activities::upload,
                args.getTargetBucketName(), args.getTargetFilename(), processedName);
            uploadedList.add(uploaded);
        }
        // Wait for all uploads to complete.
        Promise<?> allUploaded = Promise.allOf(uploadedList);
        allUploaded.get(); // blocks until all promises are ready.
    } finally {
        for (Promise<String> localNamePromise : localNamePromises) {
            // Skip files that haven't completed downloading.
            if (localNamePromise.isCompleted()) {
                activities.deleteLocalFile(localNamePromise.get());
            }
        }
        if (processedNames != null) {
            for (String processedName : processedNames) {
                activities.deleteLocalFile(processedName);
            }
        }
    }
}

# Workflow Implementation Constraints

Cadence uses the Microsoft Azure Event Sourcing pattern (opens new window) to recover the state of a workflow object including its threads and local variable values. In essence, every time a workflow state has to be restored, its code is re-executed from the beginning. When replaying, side effects (such as activity invocations) are ignored because they are already recorded in the workflow event history. When writing workflow logic, the replay is not visible, so the code should be written since it executes only once. This design puts the following constraints on the workflow implementation:

  • Do not use any mutable global variables because multiple instances of workflows are executed in parallel.
  • Do not call any non-deterministic functions like non seeded random or UUID.randomUUID() directly from the workflow code.

Always do the following in workflows:

  • Don’t perform any IO or service calls as they are not usually deterministic. Use activities for this.
  • Only use Workflow.currentTimeMillis() to get the current time inside a workflow.
  • Do not use native Java Thread or any other multi-threaded classes like ThreadPoolExecutor. Use Async.function or Async.procedure to execute code asynchronously.
  • Don't use any synchronization, locks, and other standard Java blocking concurrency-related classes besides those provided by the Workflow class. There is no need in explicit synchronization because multi-threaded code inside a workflow is executed one thread at a time and under a global lock.
    • Call WorkflowThread.sleep instead of Thread.sleep.
    • Use Promise and CompletablePromise instead of Future and CompletableFuture.
    • Use WorkflowQueue instead of BlockingQueue.
  • Use Workflow.getVersion when making any changes to the workflow code. Without this, any deployment of updated workflow code might break already open workflows.
  • Don’t access configuration APIs directly from a workflow because changes in the configuration might affect a workflow execution path. Pass it as an argument to a workflow function or use an activity to load it.

Workflow method arguments and return values are serializable to a byte array using the provided DataConverter (opens new window) interface. The default implementation uses JSON serializer, but you can use any alternative serialization mechanism.

The values passed to workflows through invocation parameters or returned through a result value are recorded in the execution history. The entire execution history is transferred from the Cadence service to workflow workers with every event that the workflow logic needs to process. A large execution history can thus adversely impact the performance of your workflow. Therefore, be mindful of the amount of data that you transfer via activity invocation parameters or return values. Otherwise, no additional limitations exist on activity implementations.

← Starting workflows →