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Glossary

Kafka Consumer Groups Explained

Learn how Kafka consumer groups enable scalable, parallel data processing. Understand partition assignment, rebalancing, and common patterns for building reliable streaming applications.

Kafka Consumer Groups Explained

Consumer groups are one of Apache Kafka's most powerful features for building scalable data streaming applications. They enable multiple consumers to work together to process messages from Kafka topics in parallel, while ensuring each message is processed exactly once per group. Understanding consumer groups is essential for designing efficient, fault-tolerant streaming architectures.

This article explains what consumer groups are, how they work, and how to use them effectively in production environments.

What Are Consumer Groups?

A consumer group is a collection of consumers that work together to consume messages from one or more Kafka topics. Each consumer in the group is assigned a subset of the topic's partitions, ensuring that no two consumers in the same group read from the same partition simultaneously.

The key benefit is parallel processing. Instead of a single consumer reading all messages sequentially, multiple consumers can process different partitions concurrently. This approach scales horizontally—add more consumers to handle increased throughput.

Kafka tracks which messages each consumer group has processed by storing offsets in a special internal topic called __consumer_offsets. This allows consumers to resume from where they left off after a restart or failure.

How Consumer Groups Enable Scalability

Partition assignment is at the heart of consumer group scalability. When consumers join a group, Kafka's group coordinator assigns partitions to each consumer using a partition assignment strategy.

┌────────────────────────────────────────────────────────────────┐
│              Consumer Group: analytics-processors              │
├────────────────────────────────────────────────────────────────┤
│                                                                  │
│  Topic: user-events (6 partitions)                             │
│                                                                  │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐      │
│  │Partition │  │Partition │  │Partition │  │Partition │ ...  │
│  │    0     │  │    1     │  │    2     │  │    3-5   │      │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘  └────┬─────┘      │
│       │             │             │             │              │
│       ▼             ▼             ▼             ▼              │
│  ┌─────────────────────────────────────────────────────────┐  │
│  │              Group Coordinator (Broker)                 │  │
│  │  • Manages partition assignments                        │  │
│  │  • Tracks consumer heartbeats                           │  │
│  │  • Triggers rebalancing                                 │  │
│  └──────┬──────────────┬──────────────┬────────────────────┘  │
│         │              │              │                        │
│         ▼              ▼              ▼                        │
│  ┌────────────┐ ┌────────────┐ ┌────────────┐                │
│  │ Consumer 1 │ │ Consumer 2 │ │ Consumer 3 │                │
│  ├────────────┤ ├────────────┤ ├────────────┤                │
│  │ Assigned:  │ │ Assigned:  │ │ Assigned:  │                │
│  │ P0, P1     │ │ P2, P3     │ │ P4, P5     │                │
│  │            │ │            │ │            │                │
│  │ Offset: 42 │ │ Offset: 38 │ │ Offset: 51 │                │
│  └────────────┘ └────────────┘ └────────────┘                │
│                                                                  │
│  ┌──────────────────────────────────────────────────────────┐ │
│  │  If Consumer 2 fails → Rebalance triggers:              │ │
│  │  • Consumer 1: P0, P1, P2                                │ │
│  │  • Consumer 3: P3, P4, P5                                │ │
│  └──────────────────────────────────────────────────────────┘ │
│                                                                  │
└────────────────────────────────────────────────────────────────┘

Consider a topic with six partitions and three consumers in the same group. Each consumer might receive two partitions. If you add a fourth consumer, the partitions are redistributed—perhaps three consumers get two partitions each, and one gets none. If you have more consumers than partitions, the extra consumers remain idle.

Common assignment strategies include:

Range: Assigns partitions in ranges, which can lead to uneven distribution across multiple topics
Round-robin: Distributes partitions evenly in a circular fashion
Sticky: Minimizes partition movement during rebalancing to reduce disruption
Cooperative sticky: Allows consumers to keep processing during rebalancing

The group coordinator, a broker elected for each consumer group, manages membership and triggers rebalancing when the group composition changes.

The Rebalancing Process

Rebalancing occurs when the consumer group membership or topic metadata changes. Common triggers include:

A consumer joins the group
A consumer leaves (gracefully or due to failure)
A consumer is considered dead (missed heartbeat deadline)
Partitions are added to a subscribed topic

During a traditional rebalance, all consumers stop processing, release their partitions, and wait for new assignments. This causes a processing pause that can impact latency-sensitive applications.

Modern rebalancing protocols like cooperative rebalancing reduce this impact. Instead of stopping all consumers, only the partitions being reassigned are revoked. Consumers not affected by the change continue processing without interruption.

Rebalancing is necessary for fault tolerance and elasticity, but frequent rebalances can hurt performance. Setting appropriate session timeout and heartbeat interval configurations helps balance responsiveness with stability.

Consumer Group Patterns

Different applications have different consumption patterns. Understanding common approaches helps you design the right architecture.

Multiple Consumer Groups

The same topic can be consumed by multiple independent consumer groups. Each group maintains its own offsets and processes all messages independently.

For example, imagine a topic containing user activity events. One consumer group might run real-time analytics, calculating metrics as events arrive. A second group might feed an alerting system, detecting anomalies. A third group could replicate data to a data warehouse for historical analysis.

Each group processes the complete event stream at its own pace. This pattern enables building multiple independent applications from the same data source without coupling their implementations.

Single Consumer Per Group

Some use cases require processing all messages in order or maintaining state across the entire topic. Using a single consumer in a group ensures sequential processing but sacrifices parallelism and fault tolerance.

This pattern is rare and typically indicates an architectural concern. Consider whether partitioning your data differently could enable parallel processing while maintaining necessary ordering guarantees within each partition.

Monitoring Consumer Groups

Effective monitoring is critical for operating consumer groups in production. The most important metric is consumer lag—the difference between the latest message offset in a partition and the consumer's committed offset.

High lag indicates consumers cannot keep up with incoming messages. This might signal:

Insufficient consumer capacity (need more consumers or instances)
Slow message processing logic
Network or broker issues
Frequent rebalancing disrupting processing

Tracking rebalancing frequency and duration helps identify stability issues. Frequent rebalances suggest configuration problems or unstable consumers.

Tools like Conduktor provide real-time visibility into consumer group health, displaying lag per partition, rebalancing events, and consumer assignment status. These insights help teams quickly identify and resolve performance bottlenecks before they impact downstream systems.

Common Pitfalls and Best Practices

Rebalancing Storms

Misconfigured session timeouts can cause cascading failures. If processing a single message takes longer than the session timeout, the consumer is kicked out, triggering a rebalance. When it rejoins, the cycle repeats—a rebalancing storm.

Set session.timeout.ms higher than your maximum expected processing time, or process messages asynchronously. Use max.poll.interval.ms to control how long consumers can spend in processing loops.

Partition Skew

Uneven data distribution across partitions can leave some consumers idle while others are overloaded. Use partition keys that distribute load evenly. Monitor per-partition metrics to identify skew.

Offset Management

Kafka provides automatic offset commits, but these can lead to message loss or duplication in failure scenarios. For exactly-once semantics, combine idempotent producers with transactional consumers, or implement manual offset management with careful error handling.

Always commit offsets after successfully processing messages, not before. Processing then committing ensures at-least-once delivery.

Scaling Limits

You cannot have more active consumers than partitions in a consumer group. Plan partition counts based on expected parallelism requirements. Creating topics with too few partitions limits future scaling.

Summary

Consumer groups are fundamental to Kafka's scalability model. They enable parallel processing through partition assignment, provide fault tolerance through rebalancing, and allow multiple applications to consume the same data independently.

Key takeaways:

Consumer groups distribute partitions among consumers for parallel processing
Rebalancing maintains balanced assignments when membership changes
Multiple consumer groups enable independent processing of the same data
Monitoring lag and rebalancing is essential for production operations
Proper configuration prevents common issues like rebalancing storms and offset management errors

Understanding consumer groups deeply allows you to build robust, scalable streaming applications that can grow with your data volumes and processing requirements.