Guidance for production environments
You can use one of the sample configurations provided by Event Streams to set up a production deployment. For information about the provided samples, see the sample deployments section.
If needed, you can modify the selected sample configuration when you deploy a new instance, or make changes at a later time.
Tuning Event Streams Kafka performance
When preparing for your Event Streams installation, review your workload requirements and consider the configuration options available for performance tuning your Event Streams installation.
Kafka offers a number of configuration settings that can be adjusted as necessary for an Event Streams deployment. These can be used to address any bottlenecks in the system as well as perform fine tuning of Kafka performance.
Kafka provides a wide range of configuration properties to set, but consider the following when reviewing performance requirements:
- The
num.replica.fetchersproperty sets the number of threads available on each broker to replicate messages from topic leaders. Increasing this setting increases I/O parallelism in the follower broker, and can help reduce bottlenecks and message latency. You can start by setting this value to match the number of brokers deployed in the system.
Note: Increasing this value results in brokers using more CPU resources and network bandwidth. - The
num.io.threadsproperty sets the number of threads available to a broker for processing requests. As the load on each broker increases, handling requests can become a bottleneck. Increasing this property value can help mitigate this issue. The value to set depends on the overall system load and the processing power of the worker nodes, which varies for each deployment. There is a correlation between this setting and thenum.network.threadssetting. - The
num.network.threadsproperty sets the number of threads available to the broker for receiving and sending requests and responses to the network. The value to set depends on the overall network load, which varies for each deployment. There is a correlation between this setting and thenum.io.threadssetting. - The
replica.fetch.min.bytes,replica.fetch.max.bytes, andreplica.fetch.response.max.bytesproperties control the minimum and maximum sizes for message payloads when performing inter-broker replication. Set these values to be greater than themessage.max.bytesproperty to ensure that all messages sent by a producer can be replicated between brokers. The value to set depends on message throughput and average size, which varies for each deployment.
These properties are configured in the EventStreams custom resource for an instance when it is first created and can be modified at any time.
Disk space for persistent volumes
You need to ensure you have sufficient disk space in the persistent storage for the Kafka brokers to meet your expected throughput and retention requirements. In Kafka, unlike other messaging systems, the messages on a topic are not immediately removed after they are consumed. Instead, the configuration of each topic determines how much space the topic is permitted and how it is managed.
Each partition of a topic consists of a sequence of files called log segments. The size of the log segments is determined by the cluster-level configuration property log.segment.bytes (default is 1 GB). This can be overridden by using the topic-level configuration segment.bytes.
For each log segment, there are two index files called the time index and the offset index. The size of the index is determined by the cluster-level configuration property log.index.size.max.bytes (default is 10 MB). This can be overridden by using the topic-level configuration property segment.index.bytes.
Log segments can be deleted or compacted, or both, to manage their size. The topic-level configuration property cleanup.policy determines the way the log segments for the topic are managed.
For more information about these settings, see the Kafka documentation.
The cluster-level settings are configured in the EventStreams custom resource for an instance when it is first created and can be modified at any time.
You can specify the cluster and topic-level configurations by using the Event Streams CLI. You can also set topic-level configuration when setting up the topic in the Event Streams UI (click Create a topic, and set Show all available options to On).
Note: When using ephemeral storage, ensure you set retention limits for Kafka topics so that you do not run out of disk space. If message retention is set to long periods and the message volume is high, the storage requirements for the topics could impact the OpenShift nodes that host the Kafka pods, and cause the nodes to run out of allocated disk space, which could impact normal operation.
Log cleanup by deletion
If the topic-level configuration property cleanup.policy is set to delete (the default value), old log segments are discarded when the retention time or size limit is reached, as set by the following properties:
- Retention time is set by
retention.ms, and is the maximum time in milliseconds that a log segment is retained before being discarded to free up space. - Size limit is set by
retention.bytes, and is the maximum size that a partition can grow to before old log segments are discarded.
By default, there is no size limit, only a time limit. The default time limit is 7 days (604,800,000 ms).
You also need to have sufficient disk space for the log segment deletion mechanism to operate. The cluster-level configuration property log.retention.check.interval.ms (default is 5 minutes) controls how often the broker checks to see whether log segments should be deleted. The cluster-level configuration property log.segment.delete.delay.ms (default is 1 minute) controls how long the broker waits before deleting the log segments. This means that by default you also need to ensure you have enough disk space to store log segments for an additional 6 minutes for each partition.
Worked example 1
Consider a cluster that has 3 brokers, and 1 topic with 1 partition with a replication factor of 3. The expected throughput is 3,000 bytes per second. The retention time period is 7 days (604,800 seconds).
Each broker hosts 1 replica of the topic’s single partition.
The log capacity required for the 7 days retention period can be determined as follows: 3,000 * (604,800 + 6 * 60) = 1,815,480,000 bytes.
So, each broker requires approximately 2GB of disk space allocated in its persistent volume, plus approximately 20 MB of space for index files. In addition, allow at least 1 log segment of extra space to make room for the actual cleanup process. Altogether, you need a total of just over 3 GB disk space for persistent volumes.
Worked example 2
Consider a cluster that has 3 brokers, and 1 topic with 1 partition with a replication factor of 3. The expected throughput is 3,000 bytes per second. The retention size configuration is set to 2.5 GB.
Each broker hosts 1 replica of the topic’s single partition.
The number of log segments for 2.5 GB is 3, but you should also allow 1 extra log segment after cleanup.
So, each broker needs approximately 4 GB of disk space allocated in its persistent volume, plus approximately 40 MB of space for index files.
The retention period achieved at this rate is approximately 2,684,354,560 / 3,000 = 894,784 seconds, or 10.36 days.
Log cleanup by compaction
If the topic-level configuration property cleanup.policy is set to compact, the log for the topic is compacted periodically in the background by the log cleaner. In a compacted topic, each message has a key. The log only needs to contain the most recent message for each key, while earlier messages can be discarded. The log cleaner calculates the offset of the most recent message for each key, and then copies the log from start to finish, discarding all but the last of each duplicate key in the log. As each copied segment is created, they are swapped into the log right away to keep the amount of additional space required to a minimum.
Estimating the amount of space that a compacted topic will require is complex, and depends on factors such as the number of unique keys in the messages, the frequency with which each key appears in the uncompacted log, and the size of the messages.
Log cleanup by using both
You can specify both delete and compact values for the cleanup.policy configuration property at the same time. In this case, the log is compacted, but the cleanup process also follows the retention time or size limit settings.
When both methods are enabled, capacity planning is simpler than when you only have compaction set for a topic. However, some use cases for log compaction depend on messages not being deleted by log cleanup, so consider whether using both is right for your scenario.