Managing Amazon Msk

MSK Maintenance Operations

How MSK Maintenance Works

Standard brokers

During patching and version upgrades, MSK performs rolling broker restarts — one broker at a time. The cluster enters MAINTENANCE state. You can still produce and consume data, but you cannot perform MSK API update operations until the cluster returns to ACTIVE. These operations appear as SECURITY_PATCHING in the DescribeClusterOperation API.

Expected client impact: Transient disconnect errors and brief p99 latency spikes (high milliseconds, up to ~2 seconds) lasting up to 2 minutes per broker restart as clients reconnect to new leaders. With the default RF=3 and proper client configuration (retries, delivery.timeout.ms >= 60000, acks=all), this does NOT cause data loss or prolonged unavailability — retries transparently reconnect to the new leader within seconds. Topics with RF=1 become completely unavailable while their broker restarts — there is no replica to fail over to, so producers receive errors and consumers stall for the full restart duration (5-15 min). See configure-clients.md and the "Consumer Resilience During Maintenance" section below.

Expected metric impact: UnderReplicatedPartitions increases temporarily (partitions on the offline broker stop replicating). After restart, the broker catches up on missed messages — you may see increased volume throughput and CPU usage during catch-up.

Express brokers

Express brokers have no maintenance windows. MSK updates Express broker software on an ongoing basis in a time-distributed manner — occasional singular broker reboots spread across the month. The cluster stays ACTIVE during all maintenance. These operations appear as BROKER_UPDATE in the DescribeClusterOperation API.

Why Express patching is less disruptive:

• No cluster-wide maintenance window to plan around

• Throughput quotas prevent overloading during broker restarts

• Fixed RF=3 guarantees all topics survive a single broker restart (no RF=1 or RF=2 topics)

• Faster catch-up after restart than Standard brokers

• No advance notification needed

Client contract still applies: Clients must still handle leadership failover. Configure producers with retries, delivery.timeout.ms >= 60000, and acks=all; configure consumers with session.timeout.ms = 45000-60000. See configure-clients.md and the "Consumer Resilience During Maintenance" section below.

Can You Reschedule or Opt Out of Patching?

Patching cannot be opted out of on either broker type — it is mandatory for the health and security of the cluster.

• Standard: the maintenance window is configurable, but only by opening an AWS Support case — there is no self-service control (no MSK API, SDK, CLI, or console setting) to change it yourself. Use a Support case to shift the window or reschedule/postpone a specific pending patch. Do not rely on repeatedly postponing: MSK force-applies patching to clusters whose maintenance is continually delayed.

• Express: patching is continuous and time-distributed with no maintenance window, so there is nothing to schedule or reschedule.

What Happens During a Rolling Restart

When a broker restarts during maintenance:

1. Broker goes offline: The broker's metrics disappear from CloudWatch for several minutes.

2. Leadership transfer: Partition leadership moves from the restarting broker to other in-sync replicas. LeaderCount shifts across brokers.

3. UnderReplicatedPartitions (URP) spikes (Standard only — Express does not emit URP): While the broker is down, its partitions are under-replicated. This is expected and temporary.

4. ActiveControllerCount may change: If the controller broker is restarted, a new controller is elected.

5. Consumer group rebalances: If consumers were connected to the restarting broker, the session timeout triggers a rebalance.

6. Broker restarts and catches up: The broker comes back online, loads logs, replicates missed data, and rejoins ISR. URP decreases as replicas catch up.

7. MSK moves to the next broker: MSK waits for the broker to fully catch up before restarting the next one.

Typical timeline per broker: 5-15 minutes depending on data volume and partition count. Log loading progress can be tracked via the JMX metrics remainingLogsToRecover and remainingSegmentsToRecover (available through Prometheus/JMX monitoring, not via CloudWatch).

Speeding up log recovery: By default, Kafka uses a single thread per log directory for log recovery after an unclean shutdown. With thousands of partitions, recovery can take hours. Set num.recovery.threads.per.data.dir to the number of CPU cores to parallelize recovery. This is a broker-side configuration — update via aws kafka update-cluster-configuration.

What NOT To Do During Maintenance

• NEVER restart additional brokers — MSK is already performing a rolling restart. Manual restarts compound the problem.

• NEVER reassign partitions during URP — Reassignment adds replication load on already-stressed brokers.

• NEVER lower min.insync.replicas — This weakens durability guarantees. The NotEnoughReplicasException during maintenance is transient.

• NEVER escalate as a cluster-level issue if URP is decreasing and only one broker has a metrics gap.

Impact of Scaling Operations (Standard)

Scaling operations on Standard clusters trigger rolling restarts or add replication load. Plan these during low-traffic periods and ensure the cluster has headroom.

Broker size updates

Updating the broker size (e.g., kafka.m5.large → kafka.m5.xlarge) triggers a rolling restart — MSK takes brokers offline one at a time and temporarily reassigns partition leadership to other brokers. This is the same process as a maintenance rolling restart. A size update typically takes 10-15 minutes per broker. During this time:

• UnderReplicatedPartitions will spike per broker, same as during patching

• Remaining brokers absorb extra leadership and replication load

• Ensure CPU is under 60% before initiating a size change

Adding brokers and reassigning partitions

After adding brokers to expand a Standard cluster, existing partitions are NOT automatically redistributed. You must manually reassign partitions using kafka-reassign-partitions.sh. This creates replication load as data is copied from existing brokers to new ones.

Constraints:

• Limit to 10 partitions per reassignment call for safe operations on Standard clusters

• Do NOT reassign partitions when CPU utilization is above 70% — replication adds significant CPU and network load that can cascade

• Do NOT reassign partitions while UnderReplicatedPartitions > 0

• Consider using Cruise Control for continuous, automated partition rebalancing

Storage expansion

Expanding EBS storage does NOT trigger a rolling restart — it happens online. However, the volume enters an optimizing state that can take up to 24 hours, and a second expansion cannot be performed for at least 6 hours. See manage-storage.md for details.

Impact of Scaling Operations (Express)

Express scaling is simpler than Standard, but broker size changes still involve rolling restarts.

Broker size updates

Updating the Express broker size also triggers a rolling restart, same as Standard. MSK takes brokers offline one at a time. However, the cluster stays ACTIVE (not MAINTENANCE) throughout. Key differences from Standard:

• Express does not emit UnderReplicatedPartitions — you cannot use URP to track restart progress. Monitor ProduceThrottleTime, FetchThrottleTime, and consumer lag instead.

• Ensure CPU (CpuUser + CpuSystem) is under 60% before initiating a size change, same as Standard.

Adding brokers and partition redistribution

When you add brokers to an Express cluster:

• If Intelligent Rebalancing is enabled (default): Partitions are automatically redistributed to new brokers. No manual action needed. You cannot use kafka-reassign-partitions.sh while Intelligent Rebalancing is active.

• If Intelligent Rebalancing is disabled: You must manually reassign partitions using kafka-reassign-partitions.sh. Limit to 20 partitions per reassignment call (vs 10 for Standard).

Storage

Express storage is fully managed — there is no expansion operation, no cooldown period, and no provisioning required. Storage scales automatically with data retained. However, you should still monitor StorageUsed and per-topic ingress to catch runaway growth that impacts cost. See manage-storage.md for investigation steps.

Consumer Resilience During Maintenance

Configure consumers to survive broker restarts gracefully. See configure-clients.md for full settings.

Key settings for maintenance resilience:

Producer settings for maintenance:

Preparing for Maintenance Windows (Standard)

1. Ensure CPU < 60%: During maintenance, remaining brokers handle extra leadership and replication. If CPU is already near 60%, the added load during maintenance may cause cascading issues.

2. Ensure storage headroom: Brokers that take over leadership temporarily handle more writes.

3. Use 3-AZ clusters with RF=3 and min.insync.replicas=2: This tolerates one broker offline.

4. Distribute connection strings across AZs: Client bootstrap servers SHOULD include at least one broker from each AZ.

5. Test consumer resilience: Simulate broker failure by rebooting a broker via the MSK API: aws kafka reboot-broker --cluster-arn <arn> --broker-ids <id>.

Kafka Version Upgrades

Version upgrades trigger rolling restarts. The process:

1. MSK updates brokers one at a time to the new version.

2. Between each broker restart, MSK waits for the broker to fully catch up.

3. The cluster enters UPDATING state during the upgrade.

Constraints:

• You MUST check that partition counts per broker are within the limits for the target version before upgrading (see size-and-choose-cluster.md).

• Upgrades are forward-only — you cannot downgrade Kafka versions.

• The supported version list and end-of-support dates change over time — check Supported Apache Kafka versions rather than relying on memory, and query a specific cluster's valid upgrade targets with aws kafka get-compatible-kafka-versions --cluster-arn <arn>. Express supports a narrower set than Standard.

• KRaft metadata mode (no ZooKeeper) is available from Kafka 3.7.x — see Metadata management.

Monitoring During Maintenance

Watch these metrics to track maintenance progress: