Client Configuration Settings for Confluent Cloud

The following sections provide expert recommendations for configuring Apache Kafka® producers and consumers for Java and librdkafka clients. The following best practices are designed to optimize the performance and reliability of your client applications and help you leverage Kafka’s advanced features and capabilities to their fullest potential. Whether you’re a seasoned developer or just getting started with Kafka, the following information provides valuable insights to help you configure robust, resilient, and scalable producers and consumers for streaming applications.

To learn more about producers and consumers see, Kafka Producer for Confluent Cloud and Kafka Consumer for Confluent Cloud.


Consider the following client configuration recommendations:

  • Always use current, supported clients. Current clients contain bug fixes and default settings tuned to allow clients to gracefully handle warnings without disrupting your streaming applications. For more information, see Client versions and support.
  • Rely on the existing retry logic to resolve retriable errors and warnings.
  • Trigger alerts on actual errors, not retriable errors and warnings. The warnings listed in Cluster upgrades and error handling occur regularly as part of normal cluster operations and may not have any impact on your workload.

For additional recommendations on how to architect, monitor, and optimize your Kafka applications on Confluent Cloud, refer to Build Kafka Client Applications on Confluent Cloud.

JVM settings for Java clients

There are two recommended JVM settings for Java clients when interacting with Confluent Cloud:

  • JVM Security configuration“networkaddress.cache.ttl” , “30");“networkaddress.cache.negative.ttl” , “0");
  • Kafka Producer and Consumer configuration


Cluster upgrades and error handling

Confluent Cloud regularly updates clusters to perform upgrades and maintenance. During this process, Confluent performs rolling restarts of all the brokers in a cluster. The Kafka protocol and architecture are designed for this type of highly-available, fault-tolerant operation. To ensure seamless client handling of cluster updates, you must configure your clients using current client libraries.

Confluent recommends you use the strategies for error handling outlined below. During normal cluster operations that use a rolling restart, clients may encounter the following warning exceptions:

UNKNOWN_TOPIC_OR_PARTITION: "This server does not host this topic-partition."
LEADER_NOT_AVAILABLE: "There is no leader for this topic-partition as we are in the middle of a leadership election."
NOT_COORDINATOR: "This is not the correct coordinator."
NOT_ENOUGH_REPLICAS: "Messages are rejected since there are fewer in-sync replicas than required."
NOT_ENOUGH_REPLICAS_AFTER_APPEND: "Messages are written to the log, but to fewer in-sync replicas than required."
NOT_LEADER_OR_FOLLOWER: "This server is not the leader for the given partition."

The following message is what a client would log at WARN level should it attempts to connect to a broker that is just restarted (in the context of a maintenance):

"Connection to node {} ({}) terminated during authentication. This may happen
due to any of the following reasons: (1) Authentication failed due to invalid
credentials with brokers older than 1.0.0, (2) Firewall blocking Kafka TLS
traffic (eg it may only allow HTTPS traffic), (3) Transient network issue."

Configure clients with a sufficient number of retries or retry time to prevent these warning exceptions from getting logged as errors.

  • By default, Kafka producer clients retry for two minutes, print these warnings to logs, and recover without any intervention.
  • By default, Kafka consumer and admin clients retry for one minute.

Timeout exceptions will occur if clients run out of memory buffer space while retrying or if clients run out of time while waiting for memory.

In general, planning for volatility is a basic tenet of building cloud-native client applications. In addition to normal cluster operations, brokers may disappear for a variety of reasons, such as issues with the underlying infrastructure at the cloud-provider layer. For more information, see Cloud-native applications.

Client configuration properties

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).

For additional information about optimizing and tuning clients, see Optimize and Tune Confluent Cloud Clients.