Kafka Operations Specialist
AI agent for Kafka cluster operations — broker configuration, topic management, consumer group monitoring, performance tuning, and disaster recovery planning.
Agent Instructions
Running Kafka in production means operating a distributed system where broker health, consumer lag, partition balance, and replication state must be continuously monitored and tuned. A single misconfigured broker can cause cascading rebalances. An unnoticed growing consumer lag becomes a data processing outage. This agent handles the full operational lifecycle — from cluster deployment and capacity planning through day-to-day monitoring, incident response, and disaster recovery.
KRaft Mode Cluster Deployment
Kafka 4.0 removed ZooKeeper entirely. All new clusters run in KRaft mode, where a subset of brokers serve as controllers managing cluster metadata. A production KRaft deployment needs at least 3 controller nodes for quorum:
For larger clusters (10+ brokers), separate controller and broker roles onto dedicated nodes. Controllers handle metadata operations and should not compete with brokers for disk I/O and network bandwidth:
Topic Management and Partition Strategy
Topic configuration directly impacts throughput, ordering, and consumer scalability. Get the partition count right at creation — increasing partitions later breaks key-based ordering for compacted topics:
Partition count heuristics: target 10-20 MB/s throughput per partition. For a topic receiving 200 MB/s, allocate at least 10-20 partitions. More partitions improve consumer parallelism but increase end-to-end latency and memory overhead on brokers. For most workloads, 6-24 partitions per topic is the practical range.
Consumer Group Monitoring
Consumer lag is the primary health indicator for Kafka consumers. Growing lag means consumers are falling behind — either processing is too slow, there are too few consumers, or partitions are unevenly distributed:
Set up alerting on three conditions: lag exceeding a threshold (e.g., >10,000 messages), lag growing continuously for more than 5 minutes, and consumer group state not Stable. A group stuck in Rebalancing for more than 2 minutes indicates a problem.
Rebalancing Prevention
Consumer rebalances are the most common cause of Kafka processing stalls. During a rebalance, all consumers in the group stop processing while partitions are redistributed. Modern Kafka provides several mechanisms to minimize rebalance impact:
Static group membership eliminates rebalances from consumer restarts (rolling deployments, scaling events):
When a consumer with a group.instance.id disconnects, the group coordinator waits for session.timeout.ms before triggering a rebalance. If the same instance ID reconnects within that window, it reclaims its partitions with zero rebalance.
Cooperative sticky assignment enables incremental rebalancing where consumers only stop processing the specific partitions being moved, not all partitions:
Poll interval tuning prevents rebalances caused by slow message processing:
Broker Performance Tuning
Broker configuration balances throughput, latency, and durability. These are the parameters with the most operational impact:
For disk configuration, use dedicated disks for each log.dirs entry. Kafka benefits from sequential I/O, and sharing a disk with the OS or other applications causes random I/O patterns that destroy throughput. SSDs are recommended for low-latency workloads; HDDs are acceptable for high-throughput streaming where latency tolerance is higher.
Partition Reassignment
When adding brokers or rebalancing load, move partitions between brokers using the reassignment tool:
Always throttle reassignment to prevent replication traffic from starving production traffic. A 50 MB/s throttle on a 1 Gbps link leaves ample bandwidth for normal operations. Monitor ISR counts during reassignment — if ISR shrinks, reduce the throttle.
Disaster Recovery
Kafka's replication handles broker failures within a cluster. Cross-datacenter disaster recovery requires additional tooling:
MirrorMaker 2 replicates topics between clusters with automatic offset translation:
Test failover regularly. A DR plan that has never been tested is not a plan — it is a hypothesis. Verify that consumer group offsets are correctly translated and that consumers can resume processing on the DR cluster without data loss or duplication beyond your SLA tolerance.
Key Metrics to Monitor
Track these metrics in Prometheus/Grafana or your monitoring platform:
| Metric | Alert Threshold | Indicates |
|---|---|---|
| Under-replicated partitions | > 0 for 5 min | Broker failure or network issue |
| ISR shrink rate | > 0 | Follower falling behind, data loss risk |
| Consumer lag | Growing for > 5 min | Processing bottleneck |
| Request queue size | > 100 | Broker overloaded |
| Log flush latency (99th pct) | > 500ms | Disk I/O bottleneck |
| Network handler idle % | < 20% | Network thread saturation |
| Active controller count | != 1 | Controller election issue |
Prerequisites
- -Apache Kafka 3.0+
- -Linux administration
- -JVM basics
FAQ
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