Consumer Offsets

In Redpanda, all messages are organized by topic and distributed across multiple partitions, based on a partition strategy. For example, when using the round robin strategy, a producer writing to a topic with five partitions would distribute approximately 20% of the messages to each partition.

Within a partition, each message (once accepted and acknowledged by the partition leader) is permanently assigned a unique sequence number called an offset. Offsets enable consumers to resume processing from a specific point, such as after an application outage. If an outage prevents your application from receiving events, you can use the consumer offset to retrieve only the events that occurred during the downtime. By default, the first message in a partition is assigned offset 0, the next is offset 1, and so on. You can manually specify a specific start value for offsets if needed. Once assigned, offsets are immutable, ensuring that the order of messages within a partition is preserved.

How consumers use offsets

As a consumer reads messages from Redpanda, it can save its progress by “committing the offset” (known as an offset commit), an action initiated by the consumer, not Redpanda. Kafka client libraries provide an API for committing offsets, which communicates with Redpanda using the consumer group API. Each committed offset is stored as a message in the __consumer_offsets topic, which is a private Redpanda topic that stores committed offsets from each Kafka consumer attached to Redpanda, allowing the consumer to resume processing from the last committed point. Redpanda exposes the __consumer_offsets key to enable the many tools in the Kafka ecosystem that rely on this value for their operation, providing greater ecosystem interoperability with environments and applications.

When a consumer group works together to consume data from topics, the partitions are divided among the consumers in the group. For example, if a topic has 12 partitions, and there are two consumers, each consumer would be assigned six partitions to consume. If a new consumer starts later and joins this consumer group, a rebalance occurs, such that each consumer ends up with four partitions to consume. You specify a consumer group by setting the group.id property to a unique name for the group.

Kafka tracks the maximum offset it has consumed in each partition and can commit offsets to ensure it can resume processing from the same point in the event of a restart. Kafka allows offsets for a consumer group to be stored on a designated broker, known as the group coordinator. All consumers in the group send their offset commits and fetch requests to this group coordinator.

More advanced consumers can read data from Redpanda without using a consumer group by requesting to read a specific topic, partition, and offset range. This pattern is often used by stream processing systems such as Apache Spark and Apache Flink, which have their own mechanisms for assigning work to consumers.

When the group coordinator receives an OffsetCommitRequest, it appends the request to the compacted Kafka topic __consumer_offsets. The broker sends a successful offset commit response to the consumer only after all the replicas of the offsets topic receive the offsets. If the offsets fail to replicate within a configurable timeout, the offset commit fails and the consumer may retry the commit after backing off. The brokers periodically compact the __consumer_offsets topic, because it only needs to maintain the most recent offset commit for each partition. The coordinator also caches the offsets in an in-memory table to serve offset fetches quickly.

Commit strategies

There are several strategies for managing offset commits:

Automatic offset commit

Auto commit is the default commit strategy, where the client automatically commits offsets at regular intervals. This is set with the enable.auto.commit property. The client then commits offsets every auto.commit.interval.ms milliseconds.

The primary advantage of the auto commit approach is its simplicity. After it is configured, the consumer requires no additional effort. Commits are managed in the background. However, the consumer is unaware of what was committed or when. As a result, after an application restart, some messages may be reprocessed (since consumption resumes from the last committed offset, which may include already-processed messages). The strategy guarantees at-least-once delivery.

If your consume configuration is set up to consume and write to another data store, and the write to that datastore fails, the consumer might not recover when it is auto-committed. It may not only duplicate messages, but could also drop messages intended to be in another datastore. Make sure you understand the trade-off possibilities associated with this default behavior.

Manual offset commit

The manual offset commit strategy gives consumers greater control over when commits occur. This approach is typically used when a consumer needs to align commits with an external system, such as database transactions in an RDBMS. The main advantage of manual commits is that they allow you to decide exactly when a record is considered consumed. You can use two API calls for this: commitSync and commitAsync, which differ in their blocking behavior.

Synchronous commit

The advantage of synchronous commits is that consumers can take appropriate action before continuing to consume messages, albeit at the expense of increased latency (while waiting for the commit to return). The commit (commitSync) will also retry automatically, until it either succeeds or receives an unrecoverable error. The following example shows a synchronous commit:

consumer.subscribe(Arrays.asList("foo", "bar"));
while (true) {
    ConsumerRecords<String, String> records = consumer.poll(100);
    for (ConsumerRecord<String, String> record : records) {
        // process records here ...

        // ... and at the appropriate point, call commit (not after every message)
        consumer.commitSync();
    }
}

Asynchronous commit

The advantage of asynchronous commits is lower latency, because the consumer does not pause to wait for the commit response. However, there is no automatic retry of the commit (commitAsync) if it fails. There is also increased coding complexity (due to the asynchronous callbacks). The following example shows an asynchronous commit in which the consumer will not block. Instead, the commit call registers a callback, which is executed once the commit returns:

void callback() {
    // executed when the commit returns
}

consumer.subscribe(Arrays.asList("foo", "bar"));
while (true) {
    ConsumerRecords<String, String> records = consumer.poll(100);
    for (ConsumerRecord<String, String> record : records) {
        // process records here ...

        // ... and at the appropriate point, call commit
        consumer.commitAsync(callback);
    }
}

External offset management

The external offset management strategy allows consumers to manage offsets independently of Redpanda. In this approach:

  • Consumers bypass the consumer group API and directly assign partitions instead of subscribing to a topic.

  • Offsets are not committed to Redpanda, but are instead stored in an external storage system.

To implement an external offset management strategy:

  1. Set enable.auto.commit to false.

  2. Use assign(Collection<TopicPartition>) to assign partitions.

  3. Use the offset provided with each ConsumerRecord to save your position.

  4. Upon restart, use seek(TopicPartition, long) to restore the position of the consumer.

Hybrid offset management

The hybrid offset management strategy allows consumers to handle their own consumer rebalancing while still leveraging Redpanda’s offset commit functionality. In this approach:

  • Consumers bypass the consumer group API and directly assign partitions instead of subscribing to a topic.

  • Offsets are committed to Redpanda.

Offset commit best practices

Follow these best practices to optimize offset commits.

Avoid over-committing

The purpose of a commit is to save consumer progress. More frequent commits reduce the amount of data to re-read after an application restart, as the commit interval directly affects the recovery point objective (RPO). Because a lower RPO is desirable, application designers may believe that committing frequently is a good design choice.

However, committing too frequently can result in adverse consequences. While individually small, each commit still results in a message being written to the __consumer_offsets topic, because the position of the consumer against every partition must be recorded. At high commit rates, this workload can become a bottleneck for both the client and the server. Additionally, many Kafka client implementations do not coalesce offset commits, meaning redundant commits in a backlog still need to be processed.

In many Kafka client implementations, offset commits aren’t coalesced at the client; so if a backlog of commits forms (when using the asynchronous commit API), the earlier commits still need to be processed, even though they are effectively redundant.

Best practice: Monitor commit latency to ensure commits are timely. If you notice performance issues, commit less frequently.

Use unique consumer groups

Like many topics, the consumer group topic has multiple partitions to help with performance. When writing commit messages, Redpanda groups all of the commits for a consumer group into a specific partition to maintain ordering. Reusing a consumer group across multiple applications, even for different topics, forces all commits to use a single partition, negating the benefits of partitioning.

Best practice: Assign a unique consumer group to each application to distribute the commit load across all partitions.

Tune the consumer group

In highly parallel applications, frequent consumer group heartbeats can create unnecessary overhead. For example, 3,200 consumers checking every 500 milliseconds generate 6,400 heartbeats per second. You can optimize this behavior by increasing the heartbeat.interval.ms (along with session.timeout.ms).

Best practice: Adjust heartbeat and session timeout settings to reduce unnecessary overhead in large-scale applications.

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