Consumer throughput optimization in Kafka - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the maximum poll records for higher throughput.

Kafka

props.put("max.poll.records", [1]);

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A500

B1000

C10

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Setting max.poll.records too low limits throughput.

Using a value too high can cause long processing delays.

Setting max.poll.records to 500 allows the consumer to fetch up to 500 records per poll, improving throughput.

2fill in blank

medium

Complete the code to set the fetch size for the consumer to optimize throughput.

Kafka

props.put("fetch.min.bytes", [1]);

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A1

B50000

C1000

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Setting fetch.min.bytes too low causes many small fetches.

Too high a value can increase latency.

Setting fetch.min.bytes to 50000 means the consumer waits to fetch at least 50KB of data, improving throughput by reducing fetch frequency.

3fill in blank

hard

Fix the error in the code to properly commit offsets asynchronously for better throughput.

Kafka

consumer.[1]((offsets, exception) -> {});

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Acommit

BcommitOffset

CcommitAsync

DcommitSync

Attempts:

3 left

💡 Hint

Common Mistakes

Using commitSync causes blocking and reduces throughput.

Using a non-existent commit method causes runtime errors.

Using commitAsync() commits offsets asynchronously, which improves throughput by not blocking the consumer thread.

4fill in blank

hard

Fill both blanks to configure the consumer for better throughput by adjusting session timeout and heartbeat interval.

Kafka

props.put("session.timeout.ms", [1]);
props.put("heartbeat.interval.ms", [2]);

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A30000

B10000

C3000

D1000

Attempts:

3 left

💡 Hint

Common Mistakes

Setting heartbeat interval higher than session timeout causes consumer to be considered dead.

Too low session timeout causes frequent rebalances.

Setting session.timeout.ms to 30000 ms and heartbeat.interval.ms to 3000 ms balances consumer liveness detection and heartbeat frequency for throughput optimization.

5fill in blank

hard

Fill all three blanks to create a consumer poll loop that maximizes throughput by adjusting poll timeout, processing batch size, and commit frequency.

Kafka

while (true) {
    ConsumerRecords<String, String> records = consumer.poll([1]);
    for (ConsumerRecord<String, String> record : records) {
        process(record);
    }
    if (records.count() >= [2]) {
        consumer.[3]((offsets, exception) -> {});
    }
}

Drag options to blanks, or click blank then click option'

A1000

B500

CcommitAsync

DcommitSync

Attempts:

3 left

💡 Hint

Common Mistakes

Using commitSync blocks the consumer and reduces throughput.

Polling with too low timeout fetches fewer records, reducing throughput.

Polling with a 1000 ms timeout allows batch fetching, processing batches of 500 records before committing asynchronously improves throughput by reducing commit overhead.