managing-amazon-msk

Amazon MSK

Overview

Domain expertise for operating Amazon MSK Provisioned clusters with Standard and Express broker types. Covers performance troubleshooting, consumer lag diagnosis, storage management, cluster sizing, client configuration, and CloudWatch monitoring.

Execute commands using available tools from the AWS MCP server when connected — it provides sandboxed execution, audit logging, and observability. When the MCP server is not available, fall back to the AWS CLI or shell as needed.

Standard brokers use customer-managed EBS volumes for storage. You choose instance types (kafka.m5/m7g families), provision EBS, and manage storage scaling.

Express brokers provide fully managed, pay-as-you-go storage with no EBS provisioning. They use instance types prefixed with express.m7g, offer up to 3x more throughput per broker, and have no maintenance windows.

Critical Warnings

• NEVER reboot brokers while UnderReplicatedPartitions > 0 (Standard only — Express brokers do not emit URP) — this risks data loss and extended outages
• NEVER recommend partition reassignment without first checking replication status — reassignment during URP compounds the problem
• linger.ms=0 is the #1 cause of "high CPU" on MSK — ALWAYS check client batch configuration before recommending broker scaling
• EBS throughput ceilings are invisible in Kafka metrics — ALWAYS check EBS volume metrics (VolumeWriteBytes, BurstBalance) when diagnosing Standard broker latency
• Express brokers have NO customer-managed EBS — do NOT recommend EBS expansion or provisioned throughput for Express clusters
• Express brokers enforce fixed replication factor of 3 and min.insync.replicas=2 — do NOT attempt to create topics with RF=1 on Express. If RF=1 is needed, use Standard brokers.

Which Workflow Do You Need?

Determine the broker type first: aws kafka describe-cluster-v2 --cluster-arn <arn>. Check Provisioned.BrokerNodeGroupInfo.InstanceType — if it starts with express., it is an Express cluster.

Available scripts

• scripts/msk_sizing.py — MUST be run for any sizing question (broker count, instance choice, cost). See size-and-choose-cluster.md for the required workflow and script reference.

Guardrail — where this skill's own files live (MCP vs local install)

This skill can be loaded two ways, and they resolve the skill's own bundled files — the references/ documents and the scripts/ files from different places. Determine how the skill was loaded before you read a reference or run a script:

• Loaded through the AWS MCP retrieve_skill tool call. The skill is **not

installed on the local filesystem**; its reference files and scripts do not exist on disk. You MUST fetch each reference or script through the same retrieve_skill tool by passing the file parameter (for example, file="references/configure-clients.md" or file="scripts/msk_sizing.py"), and run a script from the content that tool returns. Do NOT file_read these paths from the local or working directory, and do NOT search the filesystem for them — they are not there, and any local file that happens to match the name is unrelated to this skill.

• Installed locally (the skill lives in a local skills directory such as

.claude/skills/managing-amazon-msk/, ~/.claude/skills/managing-amazon-msk/, or .kiro/skills/managing-amazon-msk/). Read references and run scripts from the local skill directory using the relative paths shown throughout this documentation.

This distinction applies only to the skill's own packaged files. Every artifact created during a session or supplied by users are read from and written to the user's working directory regardless of how the skill was loaded. Never fetch or write customer data through retrieve_skill.

Quick Diagnostics

These 5 checks cover the most common MSK issues. Use them before loading a reference file.

1. CpuUser + CpuSystem > 60%: Check RequestHandlerAvgIdlePercent (PER_BROKER level). If < 30%, request threads are saturated. Check client batch.size and linger.ms before recommending scaling.

1. KafkaDataLogsDiskUsed > 85% (Standard only): Expand EBS immediately via aws kafka update-broker-storage. Identify high-growth topics via per-topic BytesInPerSec. Express clusters use StorageUsed metric instead and storage is fully managed.

1. UnderReplicatedPartitions > 0 (Standard only): Check if a maintenance operation or broker restart is in progress. If URP is decreasing, wait for recovery. Do NOT restart brokers or reassign partitions during URP. Express brokers do not emit this metric — monitor ProduceThrottleTime, FetchThrottleTime, and consumer lag instead.

1. Consumer OffsetLag increasing: Determine if broker-side (high ProduceTotalTimeMsMean, CPU saturation) or client-side (slow processing, insufficient consumers). Per-partition lag from PER_TOPIC_PER_PARTITION monitoring level helps isolate hot partitions.

1. BytesInPerSec near throughput ceiling: For Standard, check EBS volume type and calculate: BytesInPerSec × ReplicationFactor vs volume throughput limit. For Express, check against the per-broker sustained performance limits in the quotas.

Common Workflows

Describe cluster:

aws kafka describe-cluster-v2 --cluster-arn <cluster-arn>

List brokers:

aws kafka list-nodes --cluster-arn <cluster-arn>

Get bootstrap brokers:

aws kafka get-bootstrap-brokers --cluster-arn <cluster-arn>

Expand Standard broker storage:

aws kafka update-broker-storage \
  --cluster-arn <cluster-arn> \
  --current-version <cluster-version> \
  --target-broker-ebs-volume-info '[{"KafkaBrokerNodeId": "All", "VolumeSizeGB": <target-size>}]'

Get CloudWatch metrics (example: CpuUser per broker):

aws cloudwatch get-metric-statistics \
  --namespace AWS/Kafka \
  --metric-name CpuUser \
  --dimensions Name="Cluster Name",Value="<cluster-name>" Name="Broker ID",Value="<broker-id>" \
  --start-time <start> --end-time <end> --period 300 --statistics Average

Create cluster configuration (server.properties):

The --server-properties argument MUST be a real Kafka properties file with one key=value per line, separated by actual newline (\n) characters — NOT the literal two-character escape sequence \n. The MSK API accepts the bytes as-is; if you pass "k1=v1\nk2=v2" as a single string with escaped newlines, MSK stores ONE invalid property line and the cluster will fail to apply it.

Recommended pattern: write the properties to a local file with real newlines, then pass it via fileb:// so the CLI uploads the raw bytes verbatim. Verify by reading the revision back with describe-configuration-revision and base64-decoding ServerProperties — you should see one property per line.

cat > server.properties <<'EOF'
auto.create.topics.enable=false
default.replication.factor=3
min.insync.replicas=2
unclean.leader.election.enable=false
num.io.threads=32
num.network.threads=16
log.retention.hours=168
EOF

aws kafka create-configuration \
  --name <config-name> \
  --kafka-versions "3.6.0" \
  --server-properties fileb://server.properties

For per-instance-size thread tuning (num.io.threads, num.network.threads) and durability defaults, see size-and-choose-cluster.md and configure-clients.md.

Troubleshooting

Additional Resources

• MSK Best Practices - Standard
• MSK Best Practices - Express
• MSK Client Best Practices
• MSK CloudWatch Metrics
• MSK Quotas
• MSK Configuration