Practice Kafka operations vocabulary: consumer group lag, partition leaders, consumer rebalancing, topic retention, and broker replication configuration.
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An engineer says 'the consumer group lag is 50K messages'. What does consumer group lag indicate?
Consumer group lag is the difference between the latest offset written to a topic partition and the current offset of the consumer group. A lag of 50K means the consumer is 50K messages behind — it's not processing fast enough. High lag indicates the consumer needs scaling or the producer volume has spiked.
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'The partition leader is on broker 2.' What is a partition leader in Kafka?
In Kafka, each partition has one leader broker that handles all reads and writes for that partition. Other brokers may hold replicas (followers). If the leader broker fails, Kafka elects a new leader from the in-sync replicas. Knowing which broker is the partition leader helps diagnose performance bottlenecks.
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'We rebalanced the consumer group after adding a new instance.' What happens during a Kafka consumer group rebalance?
A consumer group rebalance reassigns which partitions are consumed by which consumer instances. When a new consumer joins (or leaves), Kafka rebalances to distribute partitions evenly. During a rebalance all consumers briefly pause — this is called 'stop the world' and can cause temporary processing delays.
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'The topic retention is 7 days.' What does Kafka topic retention control?
Kafka topic retention is time-based (or size-based) and controls how long messages are kept on brokers. A 7-day retention means messages are available for replay or new consumers to read for 7 days. After that, they are deleted. Retention is independent of consumption — even read messages stay until retention expires.
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'The Kafka cluster has 3 brokers with replication factor 3.' What does replication factor 3 mean?
Replication factor 3 means each partition has 3 copies distributed across 3 different brokers. With 3 brokers and replication factor 3, the cluster can tolerate up to 2 broker failures without losing data. This is a common production configuration — it balances fault tolerance with storage cost.
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Alice (Senior Developer) sends you this Slack message: 'Hey, the Kafka producer is throwing a 500 error. I've checked the logs and it seems to be related to topic 'order_events'. We're getting lots of 'Not enough space available to flush records' errors.' What does 'Not enough space available to flush records' likely indicate in this context, relating to Kafka operations?
This error message points directly to storage limitations. 'Not enough space available to flush records' means the brokers are unable to write new data to disk because they're full. It doesn't suggest a problem with producer speed or network connectivity; rather, it highlights insufficient storage capacity for the topic's data.
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You are reviewing a pull request that introduces a new Kafka consumer. The PR description includes this comment: 'We're using a single consumer group to process events from the `user_activity` topic. This allows us to scale our processing capacity by adding more consumers within the group.' What is the primary benefit of using a single consumer group for this scenario?
The core benefit of a single consumer group is simplified management. While chronological order isn't guaranteed (unless explicitly configured), managing one group is far easier than coordinating multiple groups. Scaling requires adding consumers *within* that group.
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Ben (DevOps Engineer) informs you: 'We've increased the replication factor for the `transaction_logs` topic to 3. This means that each message will be replicated three times across different brokers.' What is the primary purpose of increasing the replication factor in Kafka?
Increasing the replication factor is fundamentally about redundancy and fault tolerance. Having three copies of each message ensures data availability if a broker goes down without losing any data. Latency reduction isn't directly achieved this way; that's more about network topology.
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You are investigating performance issues with a Kafka topic. The monitoring dashboard shows the following: 'Topic retention is set to 7 days.' What does this setting control in Kafka?
Topic retention defines the lifecycle management of messages. After 7 days, old messages are automatically deleted from disk to prevent storage exhaustion. This is crucial for managing long-term data storage and related costs.
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Chloe (Data Engineer) asks you: 'What does a Kafka cluster's 'broker configuration' refer to?'
Broker configuration encompasses all the settings governing a single Kafka broker's operation—memory, network, log management and the specific Kafka parameters that control its behavior. This is distinct from the overall cluster size or physical location.
What will I learn from the "Kafka Operations Vocabulary" exercise?
Practice Kafka operations vocabulary: consumer group lag, partition leaders, consumer rebalancing, topic retention, and broker replication configuration.
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This set contains 10 multiple-choice questions, each with a detailed explanation shown after you answer.
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See the full Streaming Data exercises hub for more vocabulary drills on this topic.
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