Skip to content

Latest commit

 

History

History
110 lines (85 loc) · 6.41 KB

README.md

File metadata and controls

110 lines (85 loc) · 6.41 KB
Raw

Kafka Producer Configurations

Kafka producers send records to topics. The efficiency, reliability, and performance of data production can be significantly influenced by how producers are configured.

Key Points for CCDAK on Producers

  • Efficiency and Throughput: Configurations like batch size and compression type can greatly affect the throughput.
  • Data Integrity: Settings such as acks and retries ensure data integrity and delivery guarantees.
  • Latency: Configurations like linger.ms can be tuned to balance between latency and throughput.
  • Reliability: Features like idempotence and transactions ensure reliable data delivery.
  • Simplicity: Producers are conceptually simpler than consumers since they do not require group coordination.
  • Broker and Partition Awareness: Producers automatically know which broker and partition to write to.
  • Partitioning: A producer partitioner maps each message to a topic partition, and the producer sends a produce request to the leader of that partition.
  • Message Key Guarantee: The partitioners shipped with Kafka ensure that all messages with the same non-empty key are sent to the same partition.
  • Round-Robin Sending: If producers send data without a key, the data is sent round-robin to all available brokers.
  • Acknowledgements: Producers can choose to receive acknowledgements of data writes.

Important Producer Properties

acks

  • Default: 1
  • Description: The number of acknowledgments the producer requires from brokers. Options are 0, 1, or 'all'.
  • Trade-offs: More acks increase reliability but may reduce throughput.

linger.ms

  • Default: 0
  • Description: The delay to aggregate batches of records. Higher values allow larger and more efficient batches.
  • Trade-offs: Increases latency but improves throughput and efficiency.

batch.size

  • Default: 16384 (16KB)
  • Description: Maximum batch size in bytes. Larger batches are more efficient but take more memory and may delay transmission.
  • Trade-offs: Balances memory usage with throughput.

max.inflight.requests.per.connection

  • Default: 5
  • Description: The maximum number of unacknowledged requests the client will send on a single connection before blocking.
  • Trade-offs: Higher values can improve throughput but may compromise ordering guarantees, especially with retries.

enable.idempotence

  • Default: false
  • Description: Ensures exactly-once delivery by preventing duplicate records.
  • Trade-offs: May slightly impact throughput but significantly increases reliability.

compression.type

  • Default: none
  • Description: The compression type for all data generated by the producer. Options are 'gzip', 'snappy', 'lz4', and 'zstd'.
  • Trade-offs: Compression reduces the size of data sent over the network at the cost of CPU usage.

retries

  • Default: 2147483647 (Integer.MAX_VALUE)
  • Description: Setting a high value allows Kafka to retry indefinitely on transient errors.
  • Trade-offs: Ensures delivery at the potential cost of ordering if max.inflight.requests.per.connection is greater than 1.

transaction.timeout.ms

  • Default: 60000 (1 minute)
  • Description: Maximum time a transaction can remain open. Relevant for exactly-once semantics.
  • Trade-offs: Longer transactions can hold resources but ensure completeness of operations.

max.request.size

  • Default: 1048576 (1MB)
  • Description: The maximum size of a request. Limits the size of a message that can be sent.
  • Trade-offs: Larger sizes can improve throughput but risk overwhelming brokers or dropping connections on large messages.

buffer.memory

  • Default: 33554432 (32MB)
  • Description: Total memory available to the producer for buffering.
  • Trade-offs: More memory allows more batching and in-flight messages, improving throughput but increasing memory usage.

transactional.id

  • Default: null
  • Description: Unique identifier for transactional messages. Necessary for exactly-once semantics.
  • Trade-offs: Enables transaction support at the cost of additional overhead for maintaining state.

Handling Errors in Kafka Producers

Kafka classifies errors as retriable or non-retriable, affecting how producers react to them.

Retriable Errors

Retriable errors are temporary and often resolved through retries. Kafka producers automatically retry these errors up to the retries limit. Common retriable errors include:

  • Leader Not Available: Occurs when a new leader is being elected. The producer automatically recovers.
  • Not Leader for Partition: The targeted leader is not the current leader for the partition. The producer automatically recovers.
  • Not Enough Replicas: Not enough replicas are available for the producer to satisfy the acks configuration.

Producers handle these errors by:

  • Retrying with a delay specified by retry.backoff.ms.
  • Continuing retries up to the retries limit.

Non-Retriable Errors

Non-retriable errors indicate issues not resolved by retrying and often require configuration changes or other interventions:

  • Message Too Large: The message size exceeds max.request.size. Requires adjusting the message size or configuration.
  • Invalid Topic Exception: The specified topic is invalid or does not exist.
  • Topic Authorization Failed: The producer lacks permission to publish to the specified topic.
  • Offset Out of Range: The specified offset is not within the range of the topic.
  • Broker Not Available: The broker is not available for connections.
  • Invalid Required Acks: The acks configuration is set to an invalid value.

Non-retriable errors demand an intervention or change in configuration or permissions. They point to conditions that retries alone cannot overcome, often requiring administrative action or code changes.

Handling non-retriable errors requires:

  • Catching and logging the exception.
  • Reviewing the error to understand its cause.
  • Adjusting the configuration or addressing the permission issues as needed.

Best Practices for Error Handling

  • Monitoring: Continuously monitor log files for error patterns to proactively address emerging issues.
  • Smart Retrying: Use the retry.backoff.ms setting wisely to avoid overwhelming the system.
  • Idempotent Producers: Enable enable.idempotence to ensure that messages are not duplicated in the event of retries.