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Building Kafka from the Hardware - up

  • Higher Message Retention ? - Increase disk size
  • Higher Message Throughput ? - Increase network capacity
  • Higher Producer Performance ? - Increase Disk I/O speed
  • Higher Consumer Performance ? - Increase Memory

Critical Configurations (Consumer)

  • queued.min.messages
  • fetch.wait.max.ms
  • socket.blocking.max.ms
  • fetch.error.backoff.ms

queued.min.messages

Minimum number of messages per topic+partition in the local consumer queue.

fetch.wait.max.ms

Maximum time the broker may wait to fill the response with fetch.min.bytes.

socket.blocking.max.ms

Maximum time a broker socket operation may block.
A lower value improves responsiveness at the expense of slightly higher CPU usage.

fetch.error.backoff.ms

How long to postpone the next fetch request for a topic+partition in case of a fetch error.

Critical Configurations (Producer)

  • batch.num.messages
  • queue.buffering.max.ms
  • socket.blocking.max.ms
  • compression.codec
  • request.required.acks

batch.num.messages

Maximum number of messages batched in one MessageSet.

queue.buffering.max.ms

Maximum time, in milliseconds, for buffering data on the producer queue.

compression.codec

Compression codec to use for compressing message sets: none, gzip or snappy.

socket.blocking.max.ms

Maximum time a broker socket operation may block.
A lower value improves responsiveness at the expense of slightly higher CPU usage.

request.required.acks

This field indicates how many acknowledgements the leader broker must receive from ISR (in-sync-replicas) brokers before responding to the request: 0=broker does not send any response, 1=broker will wait until the data is written to local log before sending a response, -1=broker will block until message is committed by all in sync replicas (ISRs) or broker's in.sync.replicas setting before sending response. 1=Only the leader broker will need to ack the message.

Understanding the Kafka Producer

A batch is ready when one of the following is true:

  • batch.num.messages is reached (size based batching)
  • queue.buffering.max.ms is reached (time based batching)
  • Another batch to the same broker is ready (piggyback)
  • flush() or close() is called internally by the client

In general, more batching results in:

  • Better compression ratio => Higher throughput
  • Higher latency (not nice but its reasonable trade-off)

compression.codec

  • Compression is usually dominant part of the producer.send()
  • The speed of different compression types differs A LOT
  • For now it seems like using snappy or lz4 provides the best performance in terms of time to compress the batch.

request.required.acks

  • Defines different durability level for producing messages.
acks Throughput Latency Durability
0 high low No guarantee
1 medium medium only leader
-1 low high ISR
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