At Uber, we manage billions of workflows with lifetimes ranging from seconds to years. Over the course of their lifetime, workflow code logic often requires changes. To prevent non-deterministic errors that changes may cause, Cadence offers a Versioning feature. However, the feature's usage is limited because changes are only backward-compatible, but not forward-compatible. This makes potential rollbacks or workflow execution rescheduling unsafe.

To address these issues, we have made recent enhancements to the Versioning API, enabling the safe deployment of versioned workflows by separating code changes from the activation of new logic.

What is a Versioned Workflow?​

Cadence reconstructs a workflow's execution history by replaying past events against your workflow code, expecting the exact same outcome every time. If your workflow code changes in an incompatible way, this replaying process can lead to non-deterministic errors.

A versioned workflow uses a Versioning feature to help you avoid errors. This allows developers to safely update their workflow code without breaking existing executions. The key is the workflow.GetVersion function (available in Go and Java). By using workflow.GetVersion, you can mark points in your code where changes occur, ensuring that future calls will return a specific version number.

Before the rollout, only instances of workflow code v0.1 existed:

v := workflow.GetVersion(ctx, "change-id", workflow.DefaultVersion, 1)
if v == workflow.DefaultVersion {
    err = workflow.ExecuteActivity(ctx, ActivityA, data).Get(ctx, &result1)
} else {
    err = workflow.ExecuteActivity(ctx, ActivityC, data).Get(ctx, &result1)
}

Deployment flow​

Let’s consider an example deployment of a change from workflow code v0.1, where only FooActivity is supported.

// Git tag: v0.1 
func MyWorkflow(ctx workflow.Context) error {
    return workflow.ExecuteActivity(ctx, FooActivity).Get(ctx, nil)
}

to workflow code v0.2, which introduces a new BarActivity and utilizes the Versioning feature:

// Git tag: v0.2 
func MyWorkflow(ctx workflow.Context) error {
	version := workflow.GetVersion(ctx, "MyChange", workflow.DefaultVersion, 1)
	if version == workflow.DefaultVersion {
		return workflow.ExecuteActivity(ctx, FooActivity).Get(ctx, nil) 
	}
return workflow.ExecuteActivity(ctx, BarActivity).Get(ctx, nil)
}

Before the rollout, only instances of workflow code v0.1 existed:

Rollouts are typically performed gradually, with new workers replacing previous worker instances one at a time. This means that multiple workers with workflow code v0.1 and v0.2 can exist simultaneously. When a worker is replaced, a running workflow execution is rescheduled to another worker. Thanks to the Versioning feature, a worker with workflow code v0.2 can support a workflow execution started by a worker with workflow code v0.1.

During rollouts, the service should continue to serve production traffic, allowing new workflows to be initiated. If a new worker processes a "Start Workflow Execution" request, it will execute a workflow based on the new version. However, if an old worker handles the request, it will start a workflow based on the old version.

If a rollout is completed successfully, both the new and old workflows will continue to execute simultaneously.

Versioned Workflow Rescheduling Problem​

Workflows typically execute on the same worker on which they started. However, various factors can necessitate rescheduling with a different worker.:

• Worker Shutdown: Occurs when a worker is shut down due to reasons such as rollouts, rollbacks, restarts, or instance crashes.
• Worker Unavailability: Occurs when a worker is running but loses connection to the server, becoming unavailable.
• High Traffic Load: Occurs when a worker's sticky cache is fully utilized, preventing further workflow execution and causing the server to reschedule the workflow to another worker.

During a rollout or rollback, workflow rescheduling for workflow executions with new versions becomes unsafe, especially during rollbacks:

• If an old workflow is rescheduled to either an old or a new worker, it generally processes correctly.
• If a new workflow is rescheduled to an old worker, it will be blocked or even fail (depending on NonDeterministicWorkflowPolicy).

Why did it happen?​

The old worker doesn't support the new version and cannot replay its history correctly, which leads to a non-deterministic error. The Versioning API allowed customers to make only backward-compatible changes to workflow code definitions; however, these changes were not forward-compatible.

At the same time, there were no workarounds allowing customers to make these changes forward-compatible, so they couldn't separate code changes from the activation of the new version.

What impact did we have at Uber?​

Depending on the workflow code, code changes, and impact, to eliminate the negative impact of a rollback, a Cadence customer needed to identify all problematic workflows, terminate them if they did not fail automatically, and restart them. These steps resulted in a significant on-call burden, leading to possible SLO violations and incidents.

Based on customer impact, we introduced changes in the Versioning API, enabling customers to separate code changes from the activation of the new version.

ExecuteWithVersion and ExecuteWithMinVersion​

The recent release of the Go SDK (Java soon) has extended the GetVersion function and introduced two new options:

// When it's executed for the first time, it returns 2, instead of 10 
version := workflow.GetVersion(ctx, "changeId", 1, 10, workflow.ExecuteWithVersion(2))

// When it's executed for the first time, it returns 1, instead of 10 
version := workflow.GetVersion(ctx, "changeId", 1, 10, workflow.ExecuteWithMinVersion())

These two new options enable customers to choose which version should be returned when GetVersion is executed for the first time, instead of the maximum supported version.

• ExecuteWithVersion returns a specified value.
• ExecuteWithMinVersion returns a minimal supported version.

Let’s extend the example above and consider the deployment of versioned workflows with new functions:

Deployment of Versioned workflows​

Step 0​

The initial version remains v0.1

// Git tag: v0.1 
// MyWorkflow supports: workflow.DefaultVersion 
func MyWorkflow(ctx workflow.Context) error {
return workflow.ExecuteActivity(ctx, FooActivity).Get(ctx, nil)
}

When a StartWorkflowExecution request is processed, a new workflow execution will have a DefaultVersion of the upcoming change ID.

Step 1​

GetVersion is still used; however, workflow.ExecuteWithVersion has also been added.

// Git tag: v0.2   
// MyWorkflow supports: workflow.DefaultVersion and 1
func MyWorkflow(ctx workflow.Context) error {    
    // When GetVersion is executed for the first time, workflow.DefaultVersion will be returned
    version := workflow.GetVersion(ctx, "MyChange", workflow.DefaultVersion, 1, workflow.ExecuteWithVersion(workflow.DefaultVersion))
    
    if version == workflow.DefaultVersion {
       return workflow.ExecuteActivity(ctx, FooActivity).Get(ctx, nil)
    }
    return workflow.ExecuteActivity(ctx, BarActivity).Get(ctx, nil)
}

Worker v0.2 contains the new workflow code definition that supports the new logic. However, when a StartWorkflowExecution request is processed, a new workflow execution will still have the default version of the “MyChange” change ID.

This change enables customers to easily roll back to worker v0.1 without encountering any non-deterministic errors.

Step 2​

Once all v0.2 workers are replaced with v0.1 workers, we can deploy a new worker that begins workflow executions with the new version.

// Git tag: v0.3   
// MyWorkflow supports: workflow.DefaultVersion and 1
func MyWorkflow(ctx workflow.Context) error {    
    // When GetVersion is executed for the first time, Version #1 will be returned 
    version := workflow.GetVersion(ctx, "MyChange", workflow.DefaultVersion, 1)
    
    if version == workflow.DefaultVersion {
       return workflow.ExecuteActivity(ctx, FooActivity).Get(ctx, nil)
    }
    return workflow.ExecuteActivity(ctx, BarActivity).Get(ctx, nil)
}

Worker v0.3 contains the new workflow code definition that supports the new logic while still supporting the previous logic. Therefore, when a StartWorkflowExecution request is processed, a new workflow execution will have Version #1 of the “MyChange” change ID.

This change enables customers to easily roll back to worker v0.2 without any non-deterministic errors, as both worker versions support "DefaultVersion" and "Version #1" of the “MyChange” change ID.

Step 3​

Once all workers v0.3 replace the old worker v0.2 and all workflows with the DefaultVersion of “MyChange” are finished, we can deploy a new worker that starts workflow executions with the new version and doesn’t support the previous logic.

// Git tag: v0.4     
// MyWorkflow supports: 1
func MyWorkflow(ctx workflow.Context) error {    
    // When GetVersion is executed for the first time, Version #1 will be returned 
    _ := workflow.GetVersion(ctx, "MyChange", 1, 1)
    return workflow.ExecuteActivity(ctx, BarActivity).Get(ctx, nil)
  }

Worker v0.4 contains the new workflow code definition that supports the new logic but does not support the previous logic. Therefore, when a StartWorkflowExecution request is processed, a new workflow execution will have Version #1 of the “MyChange” change ID.

This change finalizes the safe rollout of the new versioned workflow. At each step, both versions of workers are fully compatible with one another, making rollouts and rollbacks safe.

Differences with the previous deployment flow​

The previous deployment flow for versioned workflows included only Steps 0, 2, and 3. Therefore, a direct upgrade from Step 0 to Step 2 (skipping Step 1) was not safe due to the inability to perform a safe rollback. The new functions enabled customers to have Step 1, thereby making the deployment process safe.

Conclusion​

The new options introduced into GetVersion address gaps in the Versioning logic that previously led to failed workflow executions. This enhancement improves the safety of deploying versioned workflows, allowing for the separation of code changes from the activation of new logic, making the deployment process more predictable. This extension of GetVersion is a significant improvement that opens the way for future optimizations.

At Uber, we previously relied on a dynamic configuration service to manually control the number of partitions for scalable tasklists. This configuration approach introduced several operational challenges:

• Error-prone: Manual updates and deployments were required.
• Unresponsive: Adjustments were typically reactive, often triggered by customer reports or observed backlogs.
• Irreversible: Once increased, the number of partitions was rarely decreased due to the complexity of the two-phase process, especially when anticipating future traffic spikes.

To address these issues, we introduced a new component in the Cadence Matching service: Adaptive Tasklist Scaler. This component dynamically monitors tasklist traffic and adjusts partition counts automatically. Since its rollout, we've seen a significant reduction in incidents and operational overhead caused by misconfigured tasklists.

What is a Scalable Tasklist?​

A scalable tasklist is one that supports multiple partitions. Since Cadence’s Matching service is sharded by tasklist, all requests to a specific tasklist are routed to a single Matching host. To avoid bottlenecks and enhance scalability, tasklists can be partitioned so that multiple Matching hosts handle traffic concurrently.

These partitions are transparent to clients. When a request arrives at the Cadence server for a scalable tasklist, the server selects an appropriate partition. More details can be found in this document.

How Is the Number of Partitions Manually Configured?​

The number of partitions for a tasklist is controlled by two dynamic configuration properties:

1. matching.numTasklistReadPartitions: Specifies the number of read partitions.
2. matching.numTasklistWritePartitions: Specifies the number of write partitions.

To prevent misconfiguration, a guardrail is in place to ensure that the number of read partitions is never less than the number of write partitions.

When increasing the number of partitions, both properties are typically updated simultaneously. However, due to the guardrail, the order of updates doesn't matter—read and write partitions can be increased in any sequence.

In contrast, decreasing the number of partitions is more complex and requires a two-phase process:

1. First, reduce the number of write partitions.
2. Then, wait for any backlog in the decommissioned partitions to drain completely.
3. Finally, reduce the number of read partitions.

Because this process is tedious, error-prone, and backlog-sensitive, it is rarely performed in production environments.

How Does Adaptive Tasklist Scaler Work?​

The architecture of the adaptive tasklist scaler is shown below:

1. Migrating Configuration to the Database​

The first key change was migrating partition count configuration from dynamic config to the Cadence cluster’s database. This allows the configuration to be updated programmatically.

• The adaptive tasklist scaler runs in the root partition only.
• It reads and updates the partition count.
• Updates propagate to non-root partitions via a push model, and to pollers and producers via a pull model.
• A version number is associated with each config. The version only increments through scaler updates, ensuring monotonicity and consistency across components.

2. Monitoring Tasklist Traffic​

The scaler periodically monitors the write QPS of each tasklist.

• If QPS exceeds an upscale threshold for a sustained period, the number of read and write partitions is increased proportionally.
• If QPS falls below a downscale threshold, only the write partitions are reduced initially. The system then waits for drained partitions to clear before reducing the number of read partitions, ensuring backlog-free downscaling.

Enabling Adaptive Tasklist Scaler​

Prerequisites​

To use this feature, upgrade Cadence to v1.3.0 or later.

Also, migrate tasklist partition configurations to the database using this guide.

Configuration​

The scaler is governed by the following dynamic configuration parameters:

• matching.enableAdaptiveScaler: Enables the scaler at the tasklist level.
• matching.partitionUpscaleSustainedDuration: Duration that QPS must stay above threshold before triggering upscale.
• matching.partitionDownscaleSustainedDuration: Duration below threshold required before triggering downscale.
• matching.adaptiveScalerUpdateInterval: Frequency at which the scaler evaluates and updates partition counts.
• matching.partitionUpscaleRPS: QPS threshold per partition that triggers upscale.
• matching.partitionDownscaleFactor: Factor applied to introduce hysteresis, lowering the QPS threshold for downscaling to avoid oscillations.

Monitoring and Metrics​

Several metrics have been introduced to help monitor the scaler’s behavior:

QPS and Thresholds​

• estimated_add_task_qps_per_tl: Estimated QPS of task additions per tasklist.
• tasklist_partition_upscale_threshold: Upscale threshold for task additions.
• tasklist_partition_downscale_threshold: Downscale threshold for task additions.

The estimated_add_task_qps_per_tl value should remain between the upscale and downscale thresholds. If not, the scaler may not be functioning properly.

Partition Configurations​

• task_list_partition_config_num_read: Number of current read partitions.
• task_list_partition_config_num_write: Number of current write partitions.
• task_list_partition_config_version: Version of the current partition configuration.

These metrics are emitted by various components: root and non-root partitions, pollers, and producers. Their values should align under normal conditions, except immediately after updates.

Status at Uber​

We enabled adaptive tasklist scaler across all Uber clusters in March 2025. Since its deployment:

• Zero incidents have been reported due to misconfigured tasklists.
• Operational workload related to manual scaling has been eliminated.
• Scalability and resilience of Matching service have improved significantly.

We are excited to announce the release of cadence-web v4.0.0—a complete rewrite of the Cadence web app. Cadence has always been about empowering developers to manage complex workflows, and with this release, we not only modernize the web interface by embracing today’s cutting-edge technologies but also strengthen the open source community by aligning our tools with the broader trends seen across the industry.

What's new in cadence-web v4.0.0​

• Revamped UI & Experience – A fresh, modern interface designed for better usability and efficiency.
• Multi-Cluster Support – The UI can now connect to multiple Cadence clusters.
• Performance Improvements – Faster load times, optimised API calls, and a smoother experience.

We’re excited to announce that all Cadence GitHub repositories have been consolidated under the cadence-workflow organization! 🎉

Previously, Cadence repositories were distributed across multiple organizations at Uber: uber, uber-go, uber-common. To improve developer cohesiveness and simplify access, the Cadence Core team has migrated all open-source repositories to the cadence-workflow organization.

For example, our main repository has moved from:

👉 uber/cadence

To its new home:

👉 cadence-workflow/cadence

You can find the full list of Cadence repositories here 👉 orgs/cadence-workflow/repositories

At Uber, we want to achieve regional resilience such that losing a zone within a region can be tolerated without requiring a cross-region failover. We also want to make sure that losing a zone only affects a subset of workload, at most, rather than everything. However, in Cadence-based systems, the workload in a region is distributed randomly across all workers in the region at a “task-level granularity”, which means a workflow may be worked on by any worker in the region where the domain is active. To achieve this goal, we introduced Zonal Isolation for Cadence Workflows - a feature designed to pin workflows to the zone they are started in, so that zonal isolation can be achieved at a workflow-level.

What is Zonal Isolation for Cadence Workflows?​

At high-level, Zonal Isolation for Cadence Workflows can be thought in 2 levels:

1. Task-level isolation: All decision tasks and activity tasks of a workflow are only processed by workers from the zone where the workflow was started
2. Infrastructure-level isolation: Within a regional Cadence cluster, workflows are handled by server instances in the same zone where they were started, and the corresponding data is stored in that zone as well.

Infrastructure-level isolation is quite challenging to implement as it requires significant changes to the core design of the Cadence server. Due to the complexity involved, support for this feature is not planned for the foreseeable future.

As a result, the focus remains on achieving task-level zonal isolation outside the Cadence server, which offers a more practical and immediate way to improve system resilience. It provides the capability of ensuring that an unhealthy zone (i.e. bad deployment of workers) only affect a subset of workflows (started from a certain zone) rather than every workflow in a Cadence domain.

We’ve heard your feedback: deploying Cadence has been a challenge, especially with limited documentation on operational aspects. So far, we’ve only provided a few docker compose files to help you get started on a development machine. However, deploying and managing Cadence at scale requires a deep understanding of underlying services, configurations and their dependencies.

To address these challenges, we’re launching several initiatives to make it easier to deploy and operate Cadence clusters. These include deployment specs for common scenarios, monitoring dashboards, alerts, runbooks, and more comprehensive documentation.

At Uber, we run several big multitenant Cadence clusters with hundreds of domains in each. The clusters being multi-tenant means potential noisy neighbor effects between domains.

An essential aspect of avoiding this is managing how workflows interact with our infrastructure to prevent any single workflow from causing instability for the whole cluster. To this end, we are excited to introduce Workflow ID-based rate limits — a new feature designed to protect our clusters from problematic workflows and ensure stability across the board.

Why Workflow ID-based Rate Limits?​

We already have rate limits for how many requests can be sent to a domain. However, since Cadence is sharded on the workflow ID, a user-provided input, an overused workflow with a particular id might overwhelm a shard by making too many requests. There are two main ways this happens:

1. A user starts, or signals the same workflow ID too aggressively,
2. A workflow starts too many activities over a short period of time (e.g. thousands of activities in seconds).

Introduction​

If you haven’t heard about Cadence, this section is for you. In a short description, Cadence is a code-driven workflow orchestration engine. The definition itself may not tell enough, so it would help splitting it into three parts:

• What’s a workflow? (everyone has a different definition)
• Why does it matter to be code-driven?
• Benefits of Cadence

What is a Workflow?​

In the simplest definition, it is “a multi-step execution”. Step here represents individual operations that are a little heavier than small in-process function calls. Although they are not limited to those: it could be a separate service call, processing a large dataset, map-reduce, thread sleep, scheduling next run, waiting for an external input, starting a sub workflow etc. It’s anything a user thinks as a single unit of logic in their code. Those steps often have dependencies among themselves. Some steps, including the very first step, might require external triggers (e.g. button click) or schedules. In the more broader meaning, any multi-step function or service is a workflow in principle.

If I change code logic inside an Cadence activity (for example, my activity is calling database A but now I want it to call database B), will it trigger an non-deterministic error?​

NO. This change will not trigger non-deterministic error.

An Activity is the smallest unit of execution for Cadence and what happens inside activities are not recorded as historical events and therefore will not be replayed. In short, this change is deterministic and it is fine to modify logic inside activities.

Does changing the workflow definition trigger non-determinstic errors?​

YES. This is a very typical non-deterministic error.

When a new workflow code change is deployed, Cadence will find if it is compatible with Cadence history. Changes to workflow definition will fail the replay process of Cadence as it finds the new workflow definition imcompatible with previous historical events.

Here is a list of common workflow definition changes.

• Changing workflow parameter counts
• Changing workflow parameter types
• Changing workflow return types

The following changes are not categorized as definition changes and therefore will not trigger non-deterministic errors.

• Changes of workflow return values
• Changing workflow parameter names as they are just positional

Welcome to the latest of our regular monthly Community Spotlight updates that gives you news from in and around the Cadence community!

It's been a couple of months since our last update so we have a lot of updates to share with you.

Please see below for a roundup of the highlights: