Apache Kafka is optimized for small messages. According to benchmarks, the best performance occurs with 1 KB messages. Larger messages (for example, 10 MB to 100 MB) can decrease throughput and significantly impact operations.

For a quick video introduction to load balancing, see tl;dr: Balancing Apache Kafka Clusters.

Brokers allocate a buffer the size of replica.fetch.max.bytes for each partition they replicate. If replica.fetch.max.bytes is set to 1 MiB, and you have 1000 partitions, about 1 GiB of RAM is required. Ensure that the number of partitions multiplied by the size of the largest message does not exceed available memory.

The same consideration applies for the consumer fetch.message.max.bytes setting. Ensure that you have enough memory for the largest message for each partition the consumer replicates. With larger messages, you might need to use fewer partitions or provide more RAM.

Large messages can cause longer garbage collection (GC) pauses as brokers allocate large chunks. Monitor the GC log and the server log. If long GC pauses cause Kafka to abandon the ZooKeeper session, you may need to configure longer timeout values for zookeeper.session.timeout.ms.

Before configuring Kafka to handle large messages, first consider the following options to reduce message size:

• The Kafka producer can compress messages. For example, if the original message is a text-based format (such as XML), in most cases the compressed message will be sufficiently small.

  Use the compression.codec and compressed.topics producer configuration parameters to enable compression. Gzip and Snappy are supported.

• If shared storage (such as NAS, HDFS, or S3) is available, consider placing large files on the shared storage and using Kafka to send a message with the file location. In many cases, this can be much faster than using Kafka to send the large file itself.
• Split large messages into 1 KB segments with the producing client, using partition keys to ensure that all segments are sent to the same Kafka partition in the correct order. The consuming client can then reconstruct the original large message.

If you still need to send large messages with Kafka, modify the following configuration parameters to match your requirements:

For a quick video introduction to tuning Kafka, see tl;dr: Tuning Your Apache Kafka Cluster.

Performance tuning involves two important metrics: Latencymeasures how long it takes to process one event, and throughput measures how many events arrive within a specific amount of time. Most systems are optimized for either latency or throughput. Kafka is balanced for both. A well tuned Kafka system has just enough brokers to handle topic throughput, given the latency required to process information as it is received.

Tuning your producers, brokers, and consumers to send, process, and receive the largest possible batches within a manageable amount of time results in the best balance of latency and throughput for your Kafka cluster.

Kafka uses an asynchronous publish/subscribe model. When your producer calls the send() command, the result returned is a future. The future provides methods to let you check the status of the information in process. When the batch is ready, the producer sends it to the broker. The Kafka broker waits for an event, receives the result, and then responds that the transaction is complete.

If you do not use a future, you could get just one record, wait for the result, and then send a response. Latency is very low, but so is throughput. If each transaction takes 5 ms, throughput is 200 events per second.—slower than the expected 100,000 events per second.

When you use Producer.send(), you fill up buffers on the producer. When a buffer is full, the producer sends the buffer to the Kafka broker and begins to refill the buffer.

Two parameters are particularly important for latency and throughput: batch size and linger time.

batch.size measures batch size in total bytes instead of the number of messages. It controls how many bytes of data to collect before sending messages to the Kafka broker. Set this as high as possible, without exceeding available memory. The default value is 16384.

If you increase the size of your buffer, it might never get full. The Producer sends the information eventually, based on other triggers, such as linger time in milliseconds. Although you can impair memory usage by setting the buffer batch size too high, this does not impact latency.

If your producer is sending all the time, you are probably getting the best throughput possible. If the producer is often idle, you might not be writing enough data to warrant the current allocation of resources.

linger.ms sets the maximum time to buffer data in asynchronous mode. For example, a setting of 100 batches 100ms of messages to send at once. This improves throughput, but the buffering adds message delivery latency.

By default, the producer does not wait. It sends the buffer any time data is available.

Instead of sending immediately, you can set linger.ms to 5 and send more messages in one batch. This would reduce the number of requests sent, but would add up to 5 milliseconds of latency to records sent, even if the load on the system does not warrant the delay.

The farther away the broker is from the producer, the more overhead required to send messages. Increase linger.ms for higher latency and higher throughput in your producer.

Topics are divided into partitions. Each partition has a leader. Most partitions are written into leaders with multiple replicas. When the leaders are not balanced properly, one might be overworked, compared to others. For more information on load balancing, see Partitions and Memory Usage.

Depending on your system and how critical your data is, you want to be sure that you have sufficient replication sets to preserve your data. Cloudera recommends starting with one partition per physical storage disk and one consumer per partition.

Consumers can create throughput issues on the other side of the pipeline. The maximum number of consumers for a topic is equal to the number of partitions. You need enough partitions to handle all the consumers needed to keep up with the producers.

Consumers in the same consumer group split the partitions among them. Adding more consumers to a group can enhance performance. Adding more consumer groups does not affect performance.

How you use the replica.high.watermark.checkpoint.interval.ms property can affect throughput. When reading from a partition, you can mark the last point where you read information. That way, if you have to go back and locate missing data, you have a checkpoint from which to move forward without having to reread prior data. If you set the checkpoint watermark for every event, you will never lose a message, but it significantly impacts performance. If, instead, you set it to check the offset every hundred messages, you have a margin of safety with much less impact on throughput.

Kafka uses two high-numbered ephemeral ports for JMX. These ports are listed when you view netstat -anpinformation for the Kafka Broker process.

You can change the number for the first port by adding a command similar to -Dcom.sun.management.jmxremote.rmi.port=<port number> to the field Additional Broker Java Options (broker_java_opts) in Cloudera Manager. The JMX_PORT configuration maps to com.sun.management.jmxremote.port by default.

The second ephemeral port used for JMX communication is implemented for the JRMP protocol and cannot be changed.

For a quick video introduction to quotas, see tl;dr: Quotas.

In CDK 2.0 and higher Powered By Apache Kafka, Kafka can enforce quotas on produce and fetch requests. Producers and consumers can use very high volumes of data. This can monopolize broker resources, cause network saturation, and generally deny service to other clients and the brokers themselves. Quotas protect against these issues and are important for large, multi-tenant clusters where a small set of clients using high volumes of data can degrade the user experience.

Quotas are byte-rate thresholds, defined per client ID. A client ID logically identifies an application making a request. A single client ID can span multiple producer and consumer instances. The quota is applied for all instances as a single entity: For example, if a client ID has a produce quota of 10 MB/s, that quota is shared across all instances with that same ID.

When running Kafka as a service, quotas can enforce API limits. By default, each unique client ID receives a fixed quota in bytes per second, as configured by the cluster (quota.producer.default, quota.consumer.default). This quota is defined on a per-broker basis. Each client can publish or fetch a maximum of X bytes per second per broker before it gets throttled.

The broker does not return an error when a client exceeds its quota, but instead attempts to slow the client down. The broker computes the amount of delay needed to bring a client under its quota and delays the response for that amount of time. This approach keeps the quota violation transparent to clients (outside of client-side metrics). This also prevents clients from having to implement special backoff and retry behavior.

You can override the default quota for client IDs that need a higher or lower quota. The mechanism is similar to per-topic log configuration overrides. Write your client ID overrides to ZooKeeper under /config/clients. All brokers read the overrides, which are effective immediately. You can change quotas without having to do a rolling restart of the entire cluster.

By default, each client ID receives an unlimited quota. The following configuration sets the default quota per producer and consumer client ID to 10 MB/s.

quota.producer.default=10485760

quota.consumer.default=10485760

To set quotas using Cloudera Manager, open the Kafka Configuration page and search for Quota. Use the fields provided to set the Default Consumer Quota or Default Producer Quota. For more information, see Modifying Configuration Properties Using Cloudera Manager.

ERROR Error in acceptor (kafka.network.Acceptor)

java.io.IOException: Too many open files

Cloudera recommends setting the value to a relatively high starting point, such as 32,768.