Client Configuration Properties for Confluent Platform

Client configuration properties for an Apache Kafka® Producer or Consumer determine how the client interacts with a Kafka cluster. You can tweak several default configuration property settings to achieve better performance based on the workload. This document will help you understand how to configure your Kafka Producer and Consumer clients to optimize client performance based on your workload.

Why tuning client configurations is important

Kafka client configurations provide flexibility and control over various aspects of the client’s behavior, performance, security, and reliability. Properly tuning these configurations helps optimize the client’s interactions with the Kafka cluster and ensures efficient message processing. The following are two specific areas where ensuring correct settings positively impacts the workload:

  • Performance: Client configurations can be adjusted to optimize performance. Adjusting properties that control batching, compression, linger, and prefetch can significantly impact client throughput, latency, and resource utilization.
  • Error handling: Kafka clients need to handle errors with retries, or fail gracefully until a solution can be implemented to resolve the error. Ensuring the configuration is correct can enhance workload resilience and ensure reliability for mission-critical applications.

Configuration categories

Client configuration properties are grouped into the following configuration categories:

  • Connection and network properties: A Kafka client must establish a connection with Confluent clusters to produce and consume messages. This category includes settings for bootstrap servers, connection timeout, and network buffer sizes. Optimizing these settings can ensure reliable and efficient communication between the client and the Kafka cluster.
  • Security and authentication properties: Kafka supports various security mechanisms, such as SSL/TLS encryption, SASL authentication, and authorization using Access Control Lists (ACLs). This category includes security-related settings, such as SSL certificates, authentication protocols, and user credentials. Properly configuring security settings ensures the confidentiality, integrity, and authenticity of the communication between clients and the Kafka cluster.
  • Message processing properties: Kafka clients can process messages in various ways, such as consuming messages from specific topics, committing message offsets, or specifying how to handle message errors. This category includes max.poll.records,, acks, and several others. Fine-tuning these property settings may improve client throughput, fault tolerance, and processing guarantees.

Configuration properties

The following tables provide several important configuration properties for Java and librdkafka clients. For a complete listing of configuration properties, see the following documentation:

Before you modify properties

Before you start modifying client configuration properties to find out if you can tweak client performance, be sure to complete the following steps.

  1. Verify your client is using default configuration properties. Someone may have changed configuration properties from their default settings.
  2. Update your client to the latest supported version available. Default configuration property settings are optimized in later clients. For more information, see Client versions and support.


When modifying configuration properties, monitor the impact on your system and ensure it behaves as expected. Always test any changes in a staging or pre-production environment before rolling them out to production.

Common properties

The following table provides several common configuration properties for Producers and Consumers that you can review for potential modification.

Configuration property Java default librdkafka default Notes empty string rdkafka You should set the to something meaningful in your application, especially if you are running multiple clients or want to easily trace logs or activities to specific client instances. 540000 ms (9 min) See librdkafka You can change this when an intermediate load balancer disconnects idle connections after inactivity. For example: AWS 350 seconds, Azure 4 minutes, Google Cloud 10 minutes. null kafka Changing the default service name will cause issues for those who don’t have it configured. 30000 ms (30 sec) not available librdkafka doesn’t have exponential backoff for this timeout. 10000 ms (10 sec) 30000 ms (30 sec) librdkafka doesn’t have exponential backoff for this timeout. 300000 ms (5 min) 900000 ms (15 min) librdkafka has the property that defaults to 300000 milliseconds (5 minutes). 1000 ms (1 sec) 10000 ms (10 sec) 50 ms 100 ms 5 1000000 librdkafka produces to a single partition per batch, setting it to 5 limits producing to 5 partitions per broker.

Producer properties

The following table provides a few configuration properties for Producers that you can review for potential modification.

Configuration property Java default librdkafka default Notes
batch.size 16384 1000000 120000 ms (2 min) 300000 ms (5 min) 0 ms 5 ms librdkafka reduces the number of in-flight Produce requests and increases batching (see
enable.idempotence true false Enabling idempotence sets to 5 (see
partitioner murmur2_random (default Kafka partitioner) consistent_random Changing the default partitioner causes the client to send keyed messages to different partitions. If both a librdkafka-based and a Java client are producing to the same topic, change this property to murmur2_random for the librdkafka client so that messages with the same key are sent to the same partition.

Consumer properties

The following table provides a few configuration properties for Consumers that you can review for potential modification.

Configuration property Java default librdkafka default Notes true false  
isolation.level read_uncommitted read_committed  
partition.assignment.strategy RangeAssignor, CooperativeStickyAssignor range, roundrobin Online upgrade from eager to cooperative assignor is not supported in librdkafka.
check.crcs true false Record checksum validation comes at slightly increased CPU usage. Checksum is also present at the IPv4 and TCP layers. Other types of checks could be available at disk sector (ECC) or file system level (not in ext4 by default).