Update Java Client Workflow Options: A How-To Guide

In the realm of workflow orchestration using Cadence, keeping your Java client up-to-date with the latest features is crucial. This article delves into the process of updating your Java client to incorporate new WorkflowOptions, specifically focusing on parameters introduced in the StartWorkflowExecutionRequest. We'll explore the significance of these parameters and provide a step-by-step guide on how to integrate them into your Java client.

Understanding the New WorkflowOptions Parameters

The StartWorkflowExecutionRequest has been enhanced with several new parameters that offer greater control and flexibility over workflow execution. Let's examine each of these parameters in detail:

1. Jitter Start (google.protobuf.Duration jitter_start)

With the jitter_start parameter, you can introduce a random delay before a workflow execution begins. This is particularly useful in scenarios where you want to avoid thundering herd problems or distribute the load on your system. Imagine a situation where numerous workflows are scheduled to start simultaneously. By adding a jitter, you can stagger the start times, preventing a sudden spike in resource consumption.

To implement this, you'll need to specify a duration for the jitter. The actual delay will be a random value between zero and the specified duration. For instance, if you set jitter_start to 60 seconds, the workflow might start anywhere between immediately and 60 seconds after the scheduled start time. This seemingly small addition can have a significant impact on the stability and scalability of your system.

This parameter is especially beneficial when dealing with cron-based workflows or scheduled tasks that might otherwise trigger simultaneously. By introducing a degree of randomness, you can ensure smoother operation and prevent potential bottlenecks.

2. First Run At (google.protobuf.Timestamp first_run_at)

The first_run_at parameter allows you to specify the exact timestamp for the first execution of a workflow. This is essential for scenarios where you need precise control over when a workflow begins, such as scheduling a task for a specific date and time in the future. Unlike cron schedules that define recurring patterns, first_run_at is a one-time instruction for the initial execution.

Consider a situation where you need to activate a workflow on a particular date, perhaps to coincide with a marketing campaign launch or a system maintenance window. Using first_run_at, you can ensure that the workflow starts precisely when needed, without relying on complex cron expressions or external triggers. The timestamp should be in UTC format to avoid any ambiguity.

Implementing first_run_at involves providing a timestamp that represents the desired start time. The workflow will then be scheduled to execute at that specific moment. This parameter offers a simple and direct way to manage the initial execution time of your workflows.

3. Cron Overlap Policy (CronOverlapPolicy cron_overlap_policy)

The cron_overlap_policy parameter defines how Cadence should handle situations where a new cron-scheduled workflow execution is triggered while a previous execution is still running. This policy helps prevent resource contention and ensures that your workflows behave predictably, even under heavy load. There are several overlap policies to choose from, each with its own implications:

• Allowing Concurrent Execution: This policy permits multiple instances of the same cron workflow to run simultaneously. While it provides maximum concurrency, it can also lead to resource conflicts if the workflows access shared resources. Use this policy with caution, especially if your workflows are not designed to be reentrant.
• Skipping New Execution: This policy prevents a new execution from starting if a previous execution is still in progress. It ensures that only one instance of the workflow runs at any given time, preventing overlap and potential resource contention. This is a safe option for workflows that are not designed for concurrency.
• Terminating Existing Execution: This policy terminates the currently running execution before starting a new one. It guarantees that only the latest version of the workflow is active, but it can also lead to data loss if the existing execution is interrupted before completing its tasks. Use this policy with caution and ensure that your workflows can handle abrupt termination.

The choice of overlap policy depends on the specific requirements of your workflow and the potential consequences of concurrent execution. Carefully consider the implications of each policy before making a decision.

4. Active Cluster Selection Policy (ActiveClusterSelectionPolicy active_cluster_selection_policy)

In a multi-cluster Cadence deployment, the active_cluster_selection_policy parameter determines which cluster should execute the workflow. This is crucial for ensuring high availability, disaster recovery, and optimal resource utilization. By strategically selecting the active cluster, you can optimize performance and minimize downtime.

The Active Cluster Selection Policy provides you with a mechanism to specify which cluster within your Cadence deployment should handle the execution of your workflow. This is particularly relevant in scenarios involving multiple clusters distributed across different geographical regions or data centers. The available policies might include options such as:

• Preferring Local Cluster: This policy prioritizes the cluster closest to the client or the region where the workflow was initiated. It minimizes latency and improves performance for local users.
• Using a Designated Cluster: This policy directs the workflow to a specific cluster, regardless of its location. This is useful for scenarios where you want to isolate certain workflows to a particular cluster for security or compliance reasons.
• Implementing Failover Logic: This policy automatically switches to a backup cluster if the primary cluster is unavailable. It ensures high availability and business continuity in the event of a failure.

The choice of active cluster selection policy depends on your specific deployment architecture and business requirements. Consider factors such as latency, availability, and data sovereignty when making your decision.

Updating Your Java Client

Now that we understand the significance of these new parameters, let's explore how to integrate them into your Java client.

1. Identify the Need for Update

First, determine if your current Java client version supports these new WorkflowOptions. If not, you'll need to update to a version that includes these features. Check the Cadence Java client release notes or documentation to confirm compatibility.

2. Update Dependencies

Update your project's dependencies to include the latest version of the Cadence Java client. This usually involves modifying your pom.xml (for Maven projects) or build.gradle (for Gradle projects) file. Ensure that the version you're updating to includes the necessary WorkflowOptions parameters.

For Maven, you would update the <version> tag for the Cadence Java client dependency:

<dependency>
    <groupId>com.uber.cadence</groupId>
    <artifactId>cadence-client</artifactId>
    <version>YOUR_NEW_VERSION</version>
</dependency>

For Gradle, the update would look like this:

dependencies {
    implementation 'com.uber.cadence:cadence-client:YOUR_NEW_VERSION'
}

Replace YOUR_NEW_VERSION with the actual version number you're upgrading to.

3. Modify Workflow Starting Code

Next, modify your Java code where you start workflows to include the new WorkflowOptions parameters. This typically involves updating the WorkflowOptions.Builder to set the desired values for jitterStart, firstRunAt, cronOverlapPolicy, and activeClusterSelectionPolicy.

Here's an example of how you might update your code:

import com.uber.cadence.client.WorkflowClient;
import com.uber.cadence.client.WorkflowOptions;
import com.uber.cadence.serviceclient.ClientOptions;
import com.uber.cadence.worker.Worker;

import java.time.Duration;
import java.time.Instant;

public class YourWorkflowStarter {

    public static void main(String[] args) {
        // Create workflow client
        WorkflowClient workflowClient = WorkflowClient.newInstance(
                ClientOptions.newBuilder().setHost("127.0.0.1").setPort(7933).build());

        // Get workflow stub using the client
        YourWorkflow workflow = workflowClient.newWorkflowStub(
                YourWorkflow.class,
                WorkflowOptions.newBuilder()
                        .setTaskList("your-task-list")
                        //Jitter Start
                        .setJitterStart(Duration.ofSeconds(30))
                        //First Run At
                        .setFirstRunAt(Instant.now().plus(Duration.ofMinutes(5)))
                        //Cron Overlap Policy
                        .setCronOverlapPolicy(CronOverlapPolicy.ALLOW_CONCURRENT)
                        //Active Cluster Selection Policy
                        .setActiveClusterSelectionPolicy(ActiveClusterSelectionPolicy.PREFER_LOCAL)
                        .build());

        // Execute workflow
        String greeting = workflow.yourWorkflowMethod("Your Input");

        System.out.println(greeting);
    }
}

//Cron Overlap Policy enum
enum CronOverlapPolicy {
    ALLOW_CONCURRENT,
    SKIP,
    TERMINATE
}

//Active Cluster Selection Policy enum
enum ActiveClusterSelectionPolicy {
    PREFER_LOCAL,
    DESIGNATED,
    FAILOVER
}

//Your workflow interface
interface YourWorkflow {
    String yourWorkflowMethod(String input);
}

In this example, we're setting jitterStart to 30 seconds, firstRunAt to 5 minutes from now, cronOverlapPolicy to ALLOW_CONCURRENT and the activeClusterSelectionPolicy to PREFER_LOCAL. Adjust these values according to your specific needs.

4. Test Your Changes

After modifying your code, thoroughly test your changes to ensure that the new WorkflowOptions parameters are working as expected. Deploy your updated Java client to a test environment and monitor the workflow executions to verify that the parameters are being applied correctly. Pay close attention to the start times, concurrency behavior, and cluster selection.

5. Deploy to Production

Once you're confident that your changes are working correctly, deploy your updated Java client to your production environment. Monitor the workflow executions closely to ensure that everything is running smoothly.

Conclusion

Updating your Java client with the new WorkflowOptions parameters provides greater control and flexibility over workflow execution. By understanding the significance of these parameters and following the steps outlined in this guide, you can seamlessly integrate them into your Java client and optimize your workflow orchestration. From managing jitter to defining cron overlap policies and selecting active clusters, these new options empower you to build more robust, scalable, and resilient Cadence workflows.

For more information about Cadence workflows and best practices, visit the official Cadence Documentation. This external resource provides comprehensive information about Cadence concepts, APIs, and configuration options.