Kafka Migration: Best Practices and Common Challenges

As organizations plan their next Kafka migration, many are discovering that the jump to Kafka 4.0 isn’t a routine version bump — it’s a full architectural overhaul. With the deprecation and removal of ZooKeeper, Kafka’s new KRaft-based control plane reshapes how clusters are designed, upgraded, and operated. However, getting from a legacy ZooKeeper-backed deployment to a fully KRaft-native environment isn’t always straightforward. The upgrade paths vary widely depending on the Kafka version you’re starting from, the protocols in play, and the operational maturity of your environment. 

In this blog, we'll break down why the migration process to Kafka 4 looks so different from past upgrades, the pitfalls organizations are most likely to encounter, and the practical steps and timelines teams should plan for.

Understanding the Kafka 4.0 Migration Landscape

When the Apache Kafka Project Management Committee introduced Kafka Raft Metadata mode or KRaft, it essentially represented a fundamental shift from a two-system architecture to a single, Kafka-native control plane and presents a major departure from legacy Kafka cluster management. ZooKeeper used to store and coordinate all metadata — brokers, ACLs, topics, partitions, ISR, controller elections — while Kafka itself handled the data plane. That split introduced operational complexity, scaling bottlenecks, and controller failover delays. 

KRaft collapses everything into Kafka’s own Raft-based consensus layer, creating a built-in quorum of controllers that manage metadata directly in the log. Eliminating ZooKeeper entirely provides far faster and more deterministic controller elections, improves horizontal scalability of metadata, reduces operatioinal complexity, paves the way for more elastic clusters, better partition-count scaling, and simpler automation tooling in the long run.

While upgrading within the Kafka 3.x line is basically business as usual (i.e. organizations get a normal rolling upgrade, strong backwards compatibility, and the choice to keep running ZooKeeper or experiment with early KRaft, all without changing the cluster’s architecture) moving to Kafka 4.0 is a very different story. ZooKeeper is gone, KRaft is mandatory, and you have to introduce a dedicated controller quorum and rethink how your cluster is laid out. It’s not a simple version bump — it’s a true migration that touches tooling, configs, deployment automation, and operational practices. 

3.x upgrades are incremental and low-risk, while jumping to 4.0 is a full architectural shift that requires additional planning, testing, and intentional design changes.

For instance, moving to Kafka 4.0 is different enough that many organizations are encouraged to step through 3.9 first. The reason is simple: there’s no direct, supported migration path from older ZooKeeper-based versions into Kafka 4.0’s KRaft-only world. Kafka 3.9 acts as the bridge release where the final migration tools, compatibility layers, and KRaft controller behaviors are fully stabilized. 

In short, organizations that are still running Kafka with ZooKeeper — whether that’s 3.2, 3.4, or even 3.6 — generally need to land on 3.9 to safely migrate their metadata and prepare for the architectural shift. That said, organizations already running 3.3–3.9 in KRaft mode can upgrade straight to 4.0 as they are already using the new control plane. But for anyone still tied to ZooKeeper, 3.9 isn’t just a nice-to-have; it’s the required stepping stone before making the jump to 4.0.

Additionally, organizations running really old Kafka versions may need a few more hops along the way. Since Kafka introduced important wire-protocol changes in 2.4, anything older than that can’t cleanly jump straight to 3.9. Instead, those clusters need an extra interim upgrade — usually moving up to 3.4 first, which is the last release that still works with the older ZooKeeper protocol— before proceeding to 3.9 as the true bridge point.
 

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Common Kafka Migration Challenges and How to Avoid Them

• Mixed protocol versions causing rolling upgrade hiccups: It’s surprisingly easy for brokers or clients to get stuck when the inter-broker protocol (IBP) or message format version isn’t bumped at the right time. This often shows up as brokers refusing connections or topics appearing “frozen” until the cluster is fully aligned.
• ZooKeeper and broker version incompatibilities: Older Kafka versions rely on older ZK protocol behavior, so upgrading brokers without upgrading ZooKeeper (or vice versa) can lead to controller election failures, session timeouts, or clusters that never stabilize. This is especially common for organizations jumping from pre-2.4 releases.
• Unexpected load spikes during partition rebalancing: Adding brokers, changing replication factors, or moving to KRaft can trigger heavy partition movement. If the cluster isn’t sized for the extra network and disk I/O, performance can tank or brokers can fall out of the ISR, making the migration look like a failure even when everything is technically working.

To avoid mixed-protocol headaches during a rolling upgrade, keep things simple: upgrade the broker binaries first while leaving the inter-broker protocol version (IBP) pinned to the old value so all brokers speak the same wire format during the rollout. Once every broker is on the new code and stable, then bump the IBP to the new version and roll again. That’s really the whole trick — don’t mix binary upgrades and protocol upgrades in the same step.

Avoiding this common mistake is simple: Always make sure your ZooKeeper ensemble is upgraded before or alongside your Kafka brokers to a version that’s supported by the Kafka release you’re targeting. Kafka relies on specific ZK protocol behaviors and jumping too far ahead with broker versions while leaving ZK behind can cause a lot of problems. A good rule of thumb is: upgrade ZK first, confirm stability, then upgrade Kafka, and never mix extremely old ZK versions with modern 3.x brokers.

Organizations should be proactive in their Kafka cluster configurations and limit the impact this additional I/O utilization can have their cluster by setting things like leader.replication.throttled.rate and follower.replication.throttled.rate. These two settings allow you to limit the impact Kafka has on your networks and disks for planned events like upgrades. You can also use tools like cruise control to throttle replication and of course, it should go without saying, migrations should be planned during off-peak hours, so you aren’t competing with client utilization.

Lastly, as with any migration or upgrade, the most surefire way to avoid issues and guarantee a smooth rollout is test, test, test, and then test some more. It's essential to do this testing on as close to a matching production environment as possible.

Critical Factors to Monitor During Kafka Migration

One of the biggest indicators of success we see for customers at Perforce OpenLogic for migrations and upgrades comes down to the level of observability an organization has in place before the migration/upgrade. 

During a migration, it helps to keep an eye on the signals that tell you whether the cluster is staying healthy under pressure. Start with broker stability metrics — CPU, heap usage, request errors, and any brokers dropping out of the ISR — as those are your early warning signs. Watch replication lag closely so you know if followers are struggling to keep up and keep an eye on consumer lag to make sure your applications aren’t falling behind. It’s also smart to monitor controller behavior, especially election times and leadership flapping, since instability there can ripple through the whole cluster. And of course, track message throughput and latency to spot any performance degradation as you roll brokers, adjust configs, or trigger rebalances.

Having tools in place like Prometheus and Grafana with mature metrics collections and dashboards exponentially increases an organizations' success rate when it comes to any migration, not just Kafka. Even just simply having access to JMX mBeans and counters is better than nothing. You need to be able to identify trending issues before they become showstoppers that cause a rollback. To that end, having a baseline for your cluster and knowing how it was performing well before the migration/upgrade is critical.

Kafka Migration Timeline: Setting Realistic Expectations

• Planning and Assessment (1-2 weeks): Inventory current environment, identify dependencies, develop migration strategy. This is where you map your current version, ZK/KRaft mode, client landscape, and compatibility gaps. It’s a mix of dependency checks, upgrade path decisions, dry-run planning, and getting your tooling and metrics collections aligned.
• Pre-upgrade Cleanup & Prep (2/3 days – 1 Week): You stabilize the cluster, clear out under-replicated partitions, fix lingering broker issues, and make sure ZooKeeper (if used) is on a compatible version. Validate your metrics and dashboarding. The goal here is to start the upgrade from a clean and known baseline.
• Test Environment Migration (2-4 weeks): Execute migration in non-production environment and validate functionality.  This would include any staged interim version upgrades, and Zookeeper to Kraft migrations. This process will inform your production rollout and help determine if multiple rollout phases are required in your production environment, or if you’re good to go all in one shot. It is an often overlooked part of testing upgrades/migrations, but you should also test your rollback procedures as well.
• Production Preparation (1-2 weeks): Finalize runbooks, schedule maintenance windows, brief stakeholders. You should also take this time to review and finalize your rollback procedures as well. Consider setting go/no-go check points based off of your migration testing over the previous weeks.
• Production Migration (1-3 days): Execute migration with staged rollout approach. Evaluate go/no go checkpoints as you are hitting each stage.
• Post-Migration Validation (1-2 weeks): Monitor performance, address issues, and optimize configurations.

Granted, these timelines will vary quite a bit from one environment to the other.  A small three broker cluster running hundreds of messages per minute upgrade plan will look drastically different from a 40 broker cluster processing millions of records per second. That said, the fundamentals of the processes will still be the same no matter the sizing or complexity. 

Regardless of cluster size, the last thing organizations want to do is rush testing or skip key stages in the upgrade process. Whether processing kilobytes per second or terabytes per minute, the most important thing above all else, is testing. Followed by additional testing, and when you think you’ve done enough testing, double that and you’ll be halfway to having done enough testing….did I mention testing? (All kidding aside, testing is extremely important).

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Final Thoughts

Kafka migrations can be very tricky and moving to Kafka 4.0 is ultimately less about adopting a new feature and more about embracing an entirely new control-plane architecture. That shift brings real benefits — faster controller elections, simplified operations, and a more scalable metadata layer — but it also demands careful planning, strong observability, and a disciplined testing strategy. Whether you’re moving from a modern 3.x deployment or several versions behind, success depends on choosing the right upgrade path, validating each step in a realistic test environment, and monitoring the signals that reveal cluster health under pressure. With the right preparation and a methodical rollout, organizations can navigate the transition smoothly and position their Kafka environments for long-term stability and scalability in a KRaft-native future.

And of course, if you need assistance with your migration, technical support, or training for your team, OpenLogic can help. Explore our Kafka solutions or set up time to talk to an expert today!

Additional Resources

• On- Demand Webinar - Kafka Gone Wrong: How to Avoid Data Disasters
• Guide - Enterprise Kafka Resources
• Videos - How OpenLogic Supports Kafka: Solutions and Case Studies
• Blog - Kafka 4.1 Overview
• Blog - 8 Kafka Security Best Practices
• Blog - How to Develop a Winning Kafka Partition Strategy
• Blog - Keeping Up With the Kafka Lifecycle