Migrating Kafka clusters can be a daunting task, especially when minimizing downtime is a priority. However, with careful planning and the right tools, it’s possible to execute a seamless migration. This guide will walk you through the process step by step, ensuring a smooth transition to your new Kafka environment.

Understand the Reasons for Migration

Before diving into the migration process, it’s essential to understand why you’re migrating. Common reasons include:

• Upgrading to a newer Kafka version or a managed service (e.g., Confluent Cloud, AWS MSK).
• Rearchitecting the cluster to improve performance, scalability, or fault tolerance.
• Moving to a new environment, such as transitioning from on-premises to the cloud.

Organizations might have a specific need for migrating other than these mentioned reasons. Knowing the motivation behind your migration will guide your planning and help you make informed decisions throughout the process.

Plan Your Migration Strategy

A successful migration starts with a solid plan. Key factors to consider include:

• Cluster Configuration: Document the current Kafka cluster’s configuration, including topics, partitions, replication factors, and consumer groups. Decide if you’ll replicate the existing setup or make improvements.
• Data Migration: Determine how you’ll move data from the old cluster to the new one. Consider using tools like Apache Kafka’s MirrorMaker 2 or Confluent Replicator, which can synchronize data between clusters.
• Consumer Offset Management: Plan how to manage consumer offsets to ensure that your consumers resume processing at the correct point in the new cluster.
• Downtime Tolerance: Assess your application’s tolerance for downtime. The goal is to minimize it, but understanding the acceptable limits will help in planning.

Define Migration Scope

Assess Your Existing Cluster

A thorough understanding of your existing Kafka cluster deployment is essential for ensuring a smooth transition to the new Kafka environment. This assessment should include:

• Cluster Configurations: Review the current configurations of your Kafka cluster to understand how they might need to change in the new environment.
• Topic Topology and Data Model: Analyze the structure of your topics, partitions, and data flow to ensure compatibility and performance in the new cluster.
• Kafka Client Configurations and Performance: Evaluate how your Kafka clients are configured and their performance metrics. Understanding these will help in replicating or improving them in the new setup.
• Benchmarking: Identify key benchmarks that can be used to validate the performance and functionality of workloads once they are migrated to the new cluster. This includes assessing latency requirements and understanding how latency is measured, so you can effectively evaluate it during benchmarking in the new environment.
• Dependency Planning: Consider all applications that connect to your existing Kafka cluster and understand how they depend on each other. This ensures that all relevant applications are targeted during the migration, and necessary changes are made.

Determine Data Replication Requirements

Kafka migrations can generally be categorized into two scenarios: migrating without data and migrating with data. Each scenario requires different approaches and considerations.

Migrating Without Data Migration

In cases where you are not required to move existing data, the migration process focuses on shifting producers and consumers to a new Kafka cluster without interrupting the data flow.

Steps for Migration Without Data:

• Prepare the New Cluster:
  • Set up the new Kafka cluster with configurations that match or exceed the capabilities of the existing cluster.
  • Ensure the new cluster is fully tested in a non-production environment to avoid unexpected issues during the actual migration.
• Dual Writing (Shadowing):
  • Implement dual writing, where producers send data to both the old and new clusters simultaneously. This ensures that the new cluster starts receiving data without disrupting the current flow.
  • Gradually increase the load on the new cluster while monitoring its performance.
• Consumer Migration:
  • Start migrating consumers to the new cluster in phases. Begin with less critical consumers and verify that they are processing data correctly.
  • Ensure that consumers can handle data from the new cluster without issues such as reprocessing or data loss. More information on this will follow.
• Cutover:
  • Once the stability of the new Confluent cluster is confirmed and the consumers have already been migrated, the next step is to stop dual writes from the producer. At this point, the producer should only write to the new cluster. This ensures a smooth transition while maintaining data consistency and minimizing any potential risks associated with the migration. Make sure a rollback plan is in place to handle any unforeseen issues during this process in case the cutover is unsuccessful. This plan should allow you to revert to the original cluster quickly to minimize downtime and data loss, ensuring business continuity while troubleshooting any issues with the migration.
  • Monitor both clusters during and after the cutover to ensure everything is functioning as expected.
• Decommission the Old Cluster:
  • After a period of stable operation on the new cluster, decommission the old cluster. Ensure all relevant data and metadata have been successfully transitioned before doing so.

Key Considerations:

• Synchronization: Keep producers writing to both clusters until you are certain the new cluster can handle the full load.
• Monitoring: Use tools like Prometheus and Grafana to monitor the performance of both clusters during the transition.

Migrating With Data Migration

Data migration adds complexity, as it involves transferring not only active producers and consumers but also the historical data stored in the Kafka topics.

Options for Data Migration:

Cluster Linking (Confluent): Cluster Linking is a feature that enables real-time replication between Kafka clusters, allowing data to flow from the source cluster to the target cluster.

• Advantages:
  • Low-latency data replication: Enables near real-time data synchronization between clusters.
  • No offset translation required: Consumers on the target cluster can continue from the same offsets as the source.
  • Minimal data loss: Ideal for scenarios where high data consistency is critical.
• Shortcomings:
  • Schema compatibility: You’ll need to ensure schema consistency if using schema registries across clusters.
  • Limited transformation capabilities: Focuses on pure data replication without offering complex filtering or transformation.

When to use: Cluster Linking is ideal for scenarios where you need seamless, low-latency data replication with minimal data transformation. It’s the go-to choice for Confluent users looking for operational simplicity without requiring offset translation.

MirrorMaker 2 (MM2): MM2 is an open-source Kafka tool for replicating data between clusters, offering more flexibility for non-Confluent environments.

• Advantages:
  • Flexibility in deployment: Works well with both open-source Kafka and Confluent deployments.
  • Supports offset translation: It provides offset translation for consumer groups across clusters.
• Shortcomings:
  • Higher complexity: Offset translation may introduce complexities during migration.
  • Potential replication lag: MirrorMaker 2 may experience higher replication lag compared to Cluster Linking, depending on configurations.
  • Limited schema translation: If using schema registries, it lacks advanced schema translation features.

When to use: MM2 is suited for environments where you require flexibility with Kafka distributions or need offset translation between clusters.

Confluent Replicator: A robust tool that enables data replication with advanced features like filtering, transformation, and schema management.

• Advantages:
  • Filtering and transformation: Allows data filtering and transformation during replication, making it more adaptable for complex use cases.
  • Schema management: Supports schema translation, making it ideal for enterprises where schema consistency across clusters is critical.
• Shortcomings:
  • Offset translation issues: Offset translation is not automatic, requiring careful configuration.
  • More resource-intensive: Replicator requires more resources and careful tuning compared to simpler replication methods like Cluster Linking.

When to use: Choose Confluent Replicator for enterprise-grade migrations that require granular control over data replication, including filtering and schema translation.

Common Steps for Migration with Data Replication

1. Start Replication: Set up replication between the source and target clusters using one of the tools mentioned (Cluster Linking, MirrorMaker 2, or Confluent Replicator).
2. Migrate Consumers: After verifying that data replication is working correctly, migrate consumers to the target cluster. Ensure that consumers are configured to process from the appropriate offsets (translated if necessary) based on the replication tool used.
3. Cutover Producers: Once the consumers are stable on the new cluster, stop dual writing and switch the producer to only write to the new cluster. 4 Monitor and Validate: Continuously monitor data flow, replication consistency, and consumer performance to ensure a smooth migration.

Key Considerations:

• Data Integrity: Ensure that data is accurately replicated without loss or duplication. Use checksums or hashing to verify data consistency.
• Replication Lag: Monitor and minimize replication lag to prevent data inconsistencies between clusters.
• Cutover Strategy: Plan the cutover carefully to avoid service disruptions. It’s often best to perform the final switch during a maintenance window.

Evaluate Workload Optimization

A migration is a great time to tackle some of the technical debt that may have built up in your existing deployment, such as optimizing and improving the clients, workload, data model, data quality, and cluster topology. Each option will vary in complexity. Additionally, migration provides a chance to consolidate Kafka topics by renaming or removing unused ones, which helps clean up the environment and improve data flow efficiency. This is also a good opportunity to update your security strategy. Consider enabling Role-Based Access Control (RBAC) for better user management or changing the authentication type to a more secure method. These steps will fortify the system’s security while ensuring compliance with best practices.

Defining Acceptable Downtime

When migrating Kafka clusters, it’s crucial to determine the amount of downtime that is acceptable for your clients. During any Kafka migration, clients need to be restarted to connect to the new bootstrap servers and apply any new security configurations specific to the destination cluster. Therefore, you must either plan for the downtime incurred when clients restart or implement a blue/green deployment strategy to achieve zero downtime. Depending on the client architecture and migration strategy, downtime can be very brief but is almost always greater than zero when the same set of clients is assigned to the new Kafka cluster. It is recommended to schedule the migration for production workloads during a maintenance window or a period of low traffic, such as nighttime or weekends.

Client downtime can be categorized into two types:

1. Downtime for writes
2. Downtime for reads

Typically, downtime for reads is more tolerable than downtime for writes, since consumers can often restart from where they left off after migrating to the new Kafka cluster and quickly catch up.

The amount of client downtime will depend on the following factors:

1. Speed of Updating Client Configurations: The time it takes to update the client bootstrap servers and security configurations. Automating this process can significantly reduce downtime. To automate the updating of client configurations and minimize downtime, consider the following strategies and resources:

   Use Ansible Playbooks: Automate configuration updates using Ansible, which allows for both rolling and parallel deployment strategies. Rolling deployments are safer as they update one node at a time, ensuring high availability. Refer Update a Running Confluent Platform Configuration using Ansible Playbooks

   Controlled Shutdown: Enable controlled shutdown (controlled.shutdown.enable=true) to gracefully stop brokers, syncing logs and migrating partitions before shutdown. This minimizes downtime during updates. Refer Best Practices for Kafka Production Deployments in Confluent Platform

   Client Quotas: Use client quotas to manage resource consumption effectively. This can help prevent any single client from overwhelming the system during configuration updates. Refer Multi-tenancy and Client Quotas on Confluent Cloud

   By implementing these strategies, you can effectively automate client configuration updates while minimizing downtime.

2. Client Startup Time: Clients have varying startup times depending on their implementation. Additionally, specific client dependencies may dictate the order in which clients can start, potentially extending the time taken to fully restart the system.

3. Execution Speed of Migration-Specific Steps: Any other migration-specific steps that need to be completed before restarting the clients with the new Kafka cluster can also contribute to downtime.

Define Processing Requirements

It is essential to clearly define the data processing requirements for each client before initiating a Kafka migration. Understanding whether clients can tolerate duplicates or missed messages will guide you in selecting the appropriate data replication tool and client migration strategy.

1. Can Tolerate Duplicates but Not Missed Messages:
   • Data is Migrated: Start by cutting over the consumer first, ensuring that auto.offset.reset is set to earliest. This setting ensures that the consumer processes all messages from the new Kafka cluster starting from the earliest offset, potentially causing duplicates but preventing any missed messages. Once the consumer has been transitioned, cut over the producer to begin writing to the new cluster.
   • Data is Not Migrated: Start by enabling a dual-write setup for the producer, meaning the producer writes to both the old and new Kafka clusters simultaneously. After ensuring the dual write is functioning, cut over the consumer with auto.offset.reset set to earliest, allowing it to consume from the new cluster without missing any messages. Finally, stop producing to the old Kafka cluster once the transition is stable.
2. Can Tolerate Missed Messages but Not Duplicates:
   • Data is Migrated: Start by cutting over the consumer first, with auto.offset.reset set to latest. This ensures that the consumer will only process new messages from the latest offset on the new Kafka cluster, avoiding duplicates. After this, transition the producer to the new cluster to continue data production.
   • Data is Not Migrated: Cut over the producer first to begin sending data to the new Kafka cluster. Then, transition the consumer with auto.offset.reset set to latest to process only the most recent messages on the new cluster, ensuring no duplicates. Alternatively, both the producer and the consumer can be cut over simultaneously, minimizing potential downtime.
3. Cannot Process Duplicates or Missed Messages:
   • Synchronizing or translating consumer group offsets between clusters can simplify the migration process and reduce client downtime. If this is not feasible, the client migration sequence must be carefully orchestrated to meet the data processing requirements.

Best Practices for Kafka Migration

Regardless of whether you are migrating with or without data, the following best practices will help ensure a successful Kafka cluster migration:

Do:

• Plan Thoroughly: Understand your current setup and define a detailed migration plan, including timelines, stakeholders, and rollback procedures.
• Test Extensively: Before starting the migration, test the new Kafka cluster in a staging environment that mirrors your production setup as closely as possible.
• Monitor in Real-Time: Use monitoring tools like Prometheus, Grafana, or Confluent Control Center to track the health and performance of both clusters during the migration.
• Gradual Rollout: Start with non-critical workloads to test the waters before fully migrating high-priority data streams.

Don’t:

• Neglect Consumer Offsets: Ensure consumer offsets are correctly managed during the migration to prevent data reprocessing or loss.
• Ignore Data Retention Policies: Align data retention policies on both clusters to avoid unexpected data loss or storage issues.
• Skip Backup Plans: Always have a rollback plan and data backups in place. In case of a failure, you need to be able to revert quickly without impacting operations.

Conclusion

Kafka cluster migration without downtime is challenging but achievable with the right strategy and tools. Whether you’re migrating with or without data, thorough planning, testing, and execution are key to a successful transition. By following the steps and best practices outlined in this blog, you can minimize risks and ensure that your Kafka migration is smooth, efficient, and disruption-free, regardless of the Kafka provider or infrastructure you are using.