CCAAK Cheatsheet — Kafka Admin Tables, Configs, and Troubleshooting Pickers

Comprehensive CCAAK quick reference: Kafka cluster architecture, broker/topic configs, replication/ISR durability rules, security (TLS/SASL/ACLs), monitoring signals, and safe operational playbooks.

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Use this for last‑mile review. Pair it with the Syllabus for coverage and Practice to validate instincts.

1) The admin mental models (what the exam is testing)

Partition + replication = your durability envelope

Replication factor: how many copies exist.
ISR (in-sync replicas): replicas caught up enough to be considered safe for acknowledgements.

    flowchart LR
	  P["Partition leader"] --> F1["Follower replica"]
	  P --> F2["Follower replica"]
	  F1 --> ISR["ISR set"]
	  F2 --> ISR

High-yield rule: durability choices are mostly about acks (producer) + min.insync.replicas (topic/broker).

2) Topic and partition quick pickers

You want…	Do this	Why
More consumer parallelism	Increase partitions	One consumer per partition per group
Higher durability	Use higher replication factor	More copies; better fault tolerance
“Changelog” style topic	Enable compaction	Keeps latest value per key
Audit/event log	Use retention	Keep full history for N days/size

Ordering reminder: ordering is per partition, not across partitions.

3) High-yield topic configs (recognize these)

Config	What it controls	Notes
`cleanup.policy`	`delete` vs `compact`	Compaction for latest-by-key streams
`retention.ms` / `retention.bytes`	Delete policy bounds	Applies when `cleanup.policy=delete`
`min.insync.replicas`	Required ISR for `acks=all`	Too high can reduce availability
`unclean.leader.election.enable`	Allow data-loss failover	Usually false for durability
`segment.ms` / `segment.bytes`	Log segment roll	Affects compaction/retention behavior
`max.message.bytes`	Max record size	Protects brokers from huge messages
`compression.type`	Broker-side compression	Usually set by producer; broker may enforce

4) Broker configs: the usual suspects

Listener and networking (most common misconfigs)

Setting	Why it matters	Typical pitfall
`listeners`	Where broker binds	Wrong interface/port
`advertised.listeners`	What clients use	Wrong hostname → clients can’t connect
`listener.security.protocol.map`	TLS/SASL mapping	Mismatch between listeners and protocols
`inter.broker.listener.name`	Broker-to-broker traffic listener	Incorrect security settings break replication

Storage and log placement

Setting	Why it matters	Typical pitfall
`log.dirs`	Where partition logs live	Disk fills → ISR shrink/URP
`num.network.threads` / `num.io.threads`	Throughput	Too low for high traffic
`socket.*.bytes`	Network buffers	Can throttle throughput if too small

5) Security cheat sheet (TLS vs SASL vs ACLs)

Control	What it provides	Examples
TLS	Encryption in transit	`SSL` listeners, certs, truststores
SASL	Authentication	`SASL_PLAINTEXT`, `SASL_SSL` with mechanisms
ACLs	Authorization	topic read/write, group access

Remember: Consumers typically need topic READ + group access permissions to operate.

6) Core admin CLI commands (Apache Kafka)

 1# List topics
 2kafka-topics --bootstrap-server <broker:9092> --list
 3
 4# Describe a topic (partitions, ISR, leaders)
 5kafka-topics --bootstrap-server <broker:9092> --describe --topic <topic>
 6
 7# Describe consumer group lag
 8kafka-consumer-groups --bootstrap-server <broker:9092> --describe --group <group>
 9
10# View or alter topic configs
11kafka-configs --bootstrap-server <broker:9092> --entity-type topics --entity-name <topic> --describe

Mental model: almost every operational question reduces to: what is the cluster state → what is unsafe → what is the least risky next step.

7) Troubleshooting pickers (high-yield)

Under-replicated partitions (URP)

Most common causes:

broker down / unstable
disk pressure / slow I/O
network issues between brokers
follower fetch falling behind due to load

Offline partitions

This is more severe:

no leader available
controller instability, multiple broker failures, or misconfiguration

Consumer lag climbing

Common causes:

insufficient partitions for the required throughput
slow processing / downstream bottleneck
frequent rebalances (timeouts / long processing)

8) Safe operations playbooks (exam-friendly)

Change management: prefer small, reversible changes; validate with metrics/logs before and after.
Rolling restarts: maintain quorum/ISR safety; restart one broker at a time; verify health between steps.
Disk incidents: protect brokers first (free space), then restore replication health, then tune retention/traffic.
Security changes: stage configs, validate with a test client, then roll through brokers.

Mini-glossary

Controller (cluster metadata leader) • ISR (in-sync replicas) • URP (under replicated partitions) • Leader election (choosing partition leader) • Compaction (latest per key) • ACL (authorization rules).

Syllabus

Practice

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