Consumer Guide

This guide covers consumer usage, including consumer groups, offset management, partition assignment, and error handling.

Overview

The Krafka consumer is an async-native, feature-rich Kafka consumer with:

Consumer group coordination
Automatic offset management
Multiple partition assignment strategies
Manual offset control
Seek operations
Incremental fetch sessions (KIP-227)
Closest-replica fetching (KIP-392)
Static group membership (KIP-345)
KIP-848 consumer group protocol (server-side assignment)
Interceptor hooks
Log compaction awareness with CompactedTable and CompactedTopicConsumer for key→value tables
Per-partition offset lag tracking

Basic Usage

use krafka::consumer::Consumer;
use krafka::error::Result;
use std::time::Duration;

#[tokio::main]
async fn main() -> Result<()> {
    let consumer = Consumer::builder()
        .bootstrap_servers("localhost:9092")
        .group_id("my-group")
        .build()
        .await?;

    consumer.subscribe(&["my-topic"]).await?;

    loop {
        let records = consumer.poll(Duration::from_secs(1)).await?;
        for record in records {
            println!("Received: {:?}", record);
        }
    }
}

Authentication

Connect to secured Kafka clusters using SASL or TLS:

use krafka::consumer::Consumer;

// SASL/SCRAM-SHA-256
let consumer = Consumer::builder()
    .bootstrap_servers("broker:9093")
    .group_id("secure-group")
    .sasl_scram_sha256("username", "password")
    .build()
    .await?;

// AWS MSK IAM
use krafka::auth::AuthConfig;
let auth = AuthConfig::aws_msk_iam("access_key", "secret_key", "us-east-1");
let consumer = Consumer::builder()
    .bootstrap_servers("broker:9094")
    .group_id("msk-group")
    .auth(auth)
    .build()
    .await?;

See the Authentication Guide for all supported mechanisms.

Consumer Configuration

Auto Offset Reset

Control behavior when no committed offset exists:

use krafka::consumer::{Consumer, AutoOffsetReset};

// Start from the earliest available message
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .auto_offset_reset(AutoOffsetReset::Earliest)
    .build()
    .await?;

// Start from the latest message (only new messages)
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .auto_offset_reset(AutoOffsetReset::Latest)
    .build()
    .await?;

// Error if no committed offset exists
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .auto_offset_reset(AutoOffsetReset::None)
    .build()
    .await?;
// poll() will return an error for partitions without committed offsets

Note: After a consumer group rebalance, Krafka automatically fetches previously committed offsets from the group coordinator (OffsetFetch). Partitions without committed offsets use the configured auto_offset_reset policy.

OffsetOutOfRange Recovery: If the broker returns OffsetOutOfRange during a fetch (e.g., because a partition was truncated or the consumer fell behind log retention), Krafka automatically applies the configured auto_offset_reset policy to recover the partition instead of stalling. This works for both group-based and standalone (manually assigned) consumers.

Offset Resolution: When multiple partitions need offset resolution (e.g., after a rebalance or on first poll), Krafka batches ListOffsets requests by leader broker — resolving 50 partitions in 2-3 RPCs instead of 50. Failed offset resolutions use per-partition exponential backoff (100ms base, 30s cap) to prevent retry storms under sustained broker unavailability.

Offset Commit

Control how offsets are committed. When auto-commit is enabled (the default), Krafka automatically commits offsets during each poll() call when the commit interval has elapsed, during close(), and before partition revocations during rebalances (so the new partition owner sees up-to-date committed positions):

Warning — at-least-once caveat: Auto-commit commits the offset of the last record returned by poll(), not the last record processed by the application. If the application crashes after poll() returns but before processing completes, those records may be skipped on restart. For strict at-least-once guarantees, disable auto-commit and call commit() explicitly after processing each batch.

use krafka::consumer::Consumer;
use std::time::Duration;

// Auto-commit (default)
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .enable_auto_commit(true)
    .auto_commit_interval(Duration::from_secs(5))
    .build()
    .await?;

// Manual commit
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .enable_auto_commit(false)
    .build()
    .await?;

Fetch Configuration

Control message fetching behavior:

use krafka::consumer::Consumer;
use std::time::Duration;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .fetch_min_bytes(1)                          // Min bytes before returning
    .fetch_max_bytes(52428800)                   // Max bytes per fetch (50MB)
    .max_partition_fetch_bytes(1048576)          // Max bytes per partition (1MB)
    .max_poll_records(500)                       // Max records per poll
    .max_buffered_records(500)                   // Buffer cap for recv()
    .fetch_max_wait(Duration::from_millis(500))  // Max wait time
    .build()
    .await?;

Buffer Cap

When using recv(), records from poll() that are not immediately returned are buffered internally. The max_buffered_records setting controls the maximum number of records held in this buffer. When the buffer reaches the limit, poll() skips fetching new data until the buffer drains below the threshold. Auto-commit and rebalance handling still run so the consumer remains healthy in the group.

For single-caller recv() usage the buffer is naturally bounded by max_poll_records (one poll() batch minus the record returned to the caller). The cap adds an additional guard for:

Mixed poll() / recv() usage on the same consumer
Multiple tasks calling recv() concurrently

Set to 0 to disable the buffer cap (unlimited). Defaults to 500.

use krafka::consumer::Consumer;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .max_buffered_records(1000) // Allow up to 1000 buffered records
    .build()
    .await?;

Isolation Level

Control visibility of transactional records. When consuming from topics that receive transactional writes, set isolation_level to read_committed to only see committed records:

use krafka::consumer::{Consumer, IsolationLevel};

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .isolation_level(IsolationLevel::ReadCommitted) // Only see committed txn records
    .build()
    .await?;

Level	Description
`ReadUncommitted` (default)	See all records, including uncommitted transactional records
`ReadCommitted`	Only see committed records; uncommitted transactional records are filtered

Note: isolation_level affects both data fetches and offset resolution (ListOffsets). Krafka passes the isolation level to the broker via ListOffsets (v2+, up to v11).

Metadata Topic Cache TTL

During a partial metadata refresh (where only the subscribed topics are re-fetched rather than the entire cluster), Krafka caches each topic’s metadata between refreshes. By default, a topic entry is evicted from this cache after 5 minutes of not being successfully refreshed — matching Java’s metadata.max.idle.ms — to prevent unbounded growth when topics are deleted or subscriptions change.

use krafka::consumer::Consumer;
use std::time::Duration;

// Use a custom TTL (e.g. 10 minutes):
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .metadata_topic_cache_ttl(Duration::from_secs(600))
    .build()
    .await?;

// Opt out of TTL eviction entirely (topics persist until the cache is flushed):
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .disable_metadata_topic_cache_ttl()
    .build()
    .await?;

Note: TTL eviction only affects the partial-refresh cache. A full metadata refresh (triggered by metadata_max_age expiry or an explicit refresh) always replaces the cache unconditionally.

Consumer Groups

How Consumer Groups Work

Consumers with the same group_id form a consumer group
Partitions are distributed among group members
Each partition is consumed by exactly one consumer
When consumers join/leave, partitions are rebalanced

use krafka::consumer::Consumer;

// Multiple consumers in the same group share partitions
let consumer1 = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("processing-group")
    .build()
    .await?;

let consumer2 = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("processing-group")
    .build()
    .await?;

// Both subscribe to the same topic - partitions are split between them
consumer1.subscribe(&["events"]).await?;
consumer2.subscribe(&["events"]).await?;

Partition Assignment Strategies

Krafka supports multiple assignment strategies. Configure the strategy via the builder:

use krafka::consumer::{Consumer, PartitionAssignmentStrategy};

// Range assignor (default)
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .partition_assignment_strategy(PartitionAssignmentStrategy::Range)
    .build()
    .await?;

// Round-robin for balanced distribution
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .partition_assignment_strategy(PartitionAssignmentStrategy::RoundRobin)
    .build()
    .await?;

// Cooperative sticky for minimal partition movement during rebalances
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .partition_assignment_strategy(PartitionAssignmentStrategy::CooperativeSticky)
    .build()
    .await?;

The underlying assignor implementations are also available directly:

use krafka::consumer::{RangeAssignor, RoundRobinAssignor, CooperativeStickyAssignor, PartitionAssignor};

// Range assignor (default)
// Assigns partition ranges to consumers: [0,1,2] [3,4,5]
// Best for: Co-partitioned topics
let range = RangeAssignor;
assert_eq!(range.name(), "range");

// Round-robin assignor
// Distributes partitions evenly across all consumers
// Best for: Balanced load across many consumers
let round_robin = RoundRobinAssignor;
assert_eq!(round_robin.name(), "roundrobin");

// Cooperative sticky assignor
// Minimizes partition movement during rebalances (incremental cooperative)
// Best for: Production workloads needing minimal disruption
let cooperative = CooperativeStickyAssignor::new();
assert_eq!(cooperative.name(), "cooperative-sticky");

Cooperative Sticky Assignor

The CooperativeStickyAssignor implements the incremental cooperative rebalancing protocol (KIP-429), minimizing partition movement and avoiding stop-the-world rebalances when consumers join or leave the group.

Key features:

Incremental two-phase rebalance: Only the partitions being moved are revoked and cleaned up — unaffected partitions retain their state and do not go through a full revoke/reassign cycle.
Stickiness: Partitions stay with their current owner when possible, reducing unnecessary movement.
Balanced distribution: Ensures fair partition allocation across consumers.
Owned-partition metadata (v1): Encodes each member’s current assignment in JoinGroup metadata so the leader can compute minimal revocations.
Proper revocation semantics: on_partitions_revoked is called only for the diff (partitions being moved) during normal rebalances (including topic deletion), while on_partitions_lost is used when ownership may already have been transferred (e.g., session timeout, fencing, or graceful shutdown via close()).

use krafka::consumer::{ConsumerBuilder, PartitionAssignmentStrategy};

let consumer = ConsumerBuilder::default()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .partition_assignment_strategy(PartitionAssignmentStrategy::CooperativeSticky)
    .build()
    .await?;

// During a rebalance, only affected partitions are revoked/released from this consumer.
// Unaffected partitions keep their assignment and continue being consumed.

How it works:

A rebalance is triggered (new member joins, member leaves, etc.).
Phase 1: All members join and receive new target assignments.
Each member computes which partitions to revoke (old − new).
Revoked partitions are released and on_partitions_revoked fires.
Phase 2: Members rejoin with updated owned-partition metadata.
Final assignments are distributed and on_partitions_assigned fires with the full post-rebalance assignment (committed offsets are only fetched for newly acquired partitions).

Rebalance Listener

Get notified when partition assignments change during rebalances. Register a listener via the builder:

use krafka::consumer::{Consumer, ConsumerRebalanceListener, TopicPartition};
use std::sync::Arc;

struct MyRebalanceListener;

impl ConsumerRebalanceListener for MyRebalanceListener {
    fn on_partitions_assigned(&self, partitions: &[TopicPartition]) {
        println!("Assigned: {:?}", partitions);
        // Initialize state for new partitions
        // Load any existing checkpoints from external storage
    }

    fn on_partitions_revoked(&self, partitions: &[TopicPartition]) {
        println!("Revoked: {:?}", partitions);
        // Commit offsets synchronously before losing partitions
        // Save any in-memory state to external storage
    }

    fn on_partitions_lost(&self, partitions: &[TopicPartition]) {
        // Called when partitions are lost unexpectedly (e.g., session timeout)
        // Unlike revoked, offsets may already be committed by another consumer
        println!("Lost: {:?}", partitions);
    }
}

// Wire into the consumer via the builder:
let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .rebalance_listener(Arc::new(MyRebalanceListener))
    .build()
    .await?;

// Use the NoOpRebalanceListener for a no-op implementation (default):
use krafka::consumer::NoOpRebalanceListener;
let _listener = NoOpRebalanceListener;

The listener is automatically invoked during poll() (before/after rebalance) and close() (partitions lost). Callbacks are useful for:

Committing offsets before partition loss
Saving processing state to external storage
Initializing resources when new partitions are assigned
Proper cleanup during consumer group rebalances

Note: After rebalance completes, Krafka automatically issues OffsetFetch to the group coordinator to retrieve committed offsets for all assigned partitions. This ensures seamless resumption from the last committed position.

Offset Management

Manual Commit

For precise control over offset commits:

use krafka::consumer::Consumer;
use std::time::Duration;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .enable_auto_commit(false)
    .build()
    .await?;

consumer.subscribe(&["orders"]).await?;

loop {
    let records = consumer.poll(Duration::from_secs(1)).await?;
    
    for record in &records {
        // Process each record
        process_order(&record).await?;
    }
    
    // Commit after processing
    if !records.is_empty() {
        consumer.commit().await?;
    }
}

Async Commit

For non-blocking commits:

// Commit asynchronously (spawns a background task)
// The commit is tracked and errors are logged if it fails.
// If offset lock contention occurs, the commit cycle is skipped
// and a warning is logged (rather than silently dropping the commit).
consumer.commit_async();

Commit with Metadata

Commit specific offsets with application-specific metadata:

use std::collections::HashMap;
use krafka::consumer::{Consumer, OffsetAndMetadata, TopicPartition};

// Commit specific offsets with metadata
let mut offsets = HashMap::new();
offsets.insert(
    TopicPartition::new("orders", 0),
    OffsetAndMetadata::with_metadata(1500, "checkpoint-abc123"),
);
offsets.insert(
    TopicPartition::new("orders", 1),
    OffsetAndMetadata::new(2000),
);

consumer.commit_with_metadata(offsets).await?;

This is useful for:

Storing application checkpoints
Recording processing state
Debugging offset issues (metadata is visible in Kafka tools)

Position and Seeking

Query and control consumer position:

// Get current position
let offset = consumer.position("topic", 0).await;
println!("Current position: {:?}", offset);

// Seek to a specific offset
consumer.seek("topic", 0, 1000).await?;

// Seek to the beginning (earliest available)
consumer.seek_to_beginning("topic", 0).await?;

// Seek to the end (latest, only receive new messages)
consumer.seek_to_end("topic", 0).await?;

Pause and Resume

Temporarily pause consumption of specific partitions:

// Pause specific partitions
consumer.pause("orders", &[0, 1]).await;

// Check which partitions are paused
let paused = consumer.paused_partitions().await;
println!("Paused partitions: {:?}", paused);

// Resume consumption
consumer.resume("orders", &[0, 1]).await;

Paused partitions are skipped during poll() until resumed. This is useful for:

Back-pressure handling when downstream is slow
Prioritizing certain partitions
Implementing rate limiting

Rebalance caveat: During an eager rebalance (or unsubscribe), all pause state is cleared — even for partitions that are re-assigned to the same consumer. In cooperative rebalance mode, only revoked partitions lose their pause state; retained partitions stay paused. If your application relies on pause for backpressure, re-apply pause() in your on_partitions_assigned callback.

Manual Partition Assignment

For direct partition control (without consumer groups):

Note: Manual assignment and group subscription are mutually exclusive. Calling assign() on a consumer with a group_id configured will return an error.

Standalone Recovery: Standalone consumers have the same OffsetOutOfRange recovery as group consumers — the configured auto_offset_reset policy is applied automatically to recover stalled partitions.

use krafka::consumer::Consumer;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    // Note: no group_id for manual assignment
    .auto_offset_reset(krafka::consumer::AutoOffsetReset::Earliest)
    .build()
    .await?;

// Assign specific partitions
consumer.assign("topic", vec![0, 1, 2]).await?;

Subscription Management

subscribe() replaces the current subscription (it does not append):

// Subscribe to initial topics
consumer.subscribe(&["orders", "payments"]).await?;

// This REPLACES the subscription — only "shipments" is subscribed now
consumer.subscribe(&["shipments"]).await?;

Check Subscriptions and Assignments

// Get subscribed topics
let topics = consumer.subscription().await;
println!("Subscribed to: {:?}", topics);

// Get assigned partitions
let assignments = consumer.assignment().await;
println!("Assigned partitions: {:?}", assignments);

Unsubscribe

Calling unsubscribe() performs a full cleanup: revokes partitions (notifying the rebalance listener), leaves the consumer group, and clears all internal state (offsets, paused partitions, buffered records).

consumer.unsubscribe().await;

Pause and Resume

Temporarily pause consumption of specific partitions without disconnecting:

// Pause partitions 0 and 1 of "orders" topic
consumer.pause("orders", &[0, 1]).await;

// These partitions will be skipped during poll()
let records = consumer.poll(Duration::from_secs(1)).await?;
// Only records from non-paused partitions are returned

// Check which partitions are paused
let paused = consumer.paused_partitions().await;
println!("Paused partitions: {:?}", paused);

// Resume consumption
consumer.resume("orders", &[0, 1]).await;

Use cases for pause/resume:

Backpressure handling: Pause when downstream systems are slow
Priority processing: Pause low-priority partitions during high load
Graceful degradation: Pause non-essential partitions when resources are constrained

Error Handling

Handling Poll Errors

use krafka::consumer::Consumer;
use krafka::error::KrafkaError;
use std::time::Duration;

async fn consume_with_error_handling(consumer: &Consumer) {
    loop {
        match consumer.poll(Duration::from_secs(1)).await {
            Ok(records) => {
                for record in records {
                    process_record(record).await;
                }
            }
            Err(KrafkaError::Timeout(_)) => {
                // Normal - no messages available
                continue;
            }
            Err(e) => {
                eprintln!("Error polling: {}", e);
                tokio::time::sleep(Duration::from_secs(1)).await;
            }
        }
    }
}

Streaming with `recv()`

The recv() method returns individual records as a stream-like API. It internally buffers records fetched by poll() and returns them one by one, ensuring no data loss even when poll() returns multiple records. Errors propagate to the caller rather than being silently swallowed:

use krafka::consumer::Consumer;
use krafka::error::Result;

async fn consume_stream(consumer: &Consumer) -> Result<()> {
    while let Some(record) = consumer.recv().await? {
        println!(
            "topic={}, partition={}, offset={}, timestamp_type={}",
            record.topic, record.partition, record.offset, record.timestamp_type
        );
        // timestamp_type: 0 = CreateTime, 1 = LogAppendTime
    }
    Ok(())
}

Async `Stream` API

The stream() method returns a futures_core::Stream of Result<ConsumerRecord>, enabling use with tokio-stream combinators (.map(), .filter(), .take(), .buffer_unordered(), etc.):

use krafka::consumer::Consumer;
use krafka::error::Result;
use tokio_stream::StreamExt; // requires tokio-stream dependency

async fn consume_with_stream(consumer: &Consumer) -> Result<()> {
    let mut stream = consumer.stream();
    while let Some(result) = stream.next().await {
        let record = result?;
        println!(
            "topic={}, partition={}, offset={}",
            record.topic, record.partition, record.offset
        );
    }
    Ok(())
}

The stream terminates when the consumer is closed. Internally it delegates to recv(), so all features (auto-commit, rebalancing, fetch sessions, buffering) work identically.

Graceful Shutdown

Always close consumers properly:

use tokio::signal;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .build()
    .await?;

consumer.subscribe(&["topic"]).await?;

tokio::select! {
    _ = signal::ctrl_c() => {
        println!("Shutting down...");
    }
    _ = async {
        loop {
            let records = consumer.poll(Duration::from_secs(1)).await?;
            for record in records {
                process_record(record).await;
            }
        }
        #[allow(unreachable_code)]
        Ok::<(), KrafkaError>(())
    } => {}
}

// Commit final offsets and close
consumer.commit().await?;
consumer.close().await;

Poll Architecture

Batch Fetch by Broker

Krafka optimizes the poll() operation by batching fetch requests per broker. Instead of sending one request per partition (O(n) round trips), it groups partitions by their leader broker and sends one request per broker (O(k) round trips, where k = number of unique leaders).

  Consumer.poll()
         │
         ▼
  ┌──────────────────────────────┐
  │ Group partitions by leader   │
  │                              │
  │ Broker 1: [p0, p1, p2]       │
  │ Broker 2: [p3, p4]           │
  │ Broker 3: [p5]               │
  └──────────────────────────────┘
         │
         ▼
  ┌──────────────────────────────┐
  │ One FetchRequest per broker  │
  │                              │
  │ Request 1 → Broker 1         │
  │ Request 2 → Broker 2         │
  │ Request 3 → Broker 3         │
  └──────────────────────────────┘
         │
         ▼
    Merge results

This optimization significantly improves throughput when consuming from topics with many partitions spread across multiple brokers.

Incremental Fetch Sessions (KIP-227)

Krafka implements KIP-227 fetch sessions to minimize fetch request sizes. Instead of sending the full partition list on every poll(), the broker tracks per-session state and the client sends only partition changes.

How it works:

On the first fetch to a broker, Krafka sends a full fetch request (epoch 0) with all partitions
The broker establishes a session and returns a session_id
On subsequent fetches, Krafka computes a diff against the previous session state:
- Changed partitions: Only partitions with new offsets or different max_bytes
- Forgotten topics: Partitions removed since the last fetch (e.g., after rebalance)
The broker applies the diff to its session state and returns data for all tracked partitions

  First poll()              Subsequent poll()
  (full fetch)              (incremental)
  ┌──────────────┐          ┌──────────────┐
  │ session_id: 0│          │ session_id: 42│
  │ epoch: 0     │          │ epoch: 1      │
  │ topics:      │          │ topics:       │
  │   p0, p1, p2 │    →     │   p1 (changed)│
  │   p3, p4     │          │ forgotten:    │
  └──────────────┘          │   p4 (removed)│
                            └──────────────┘

Benefits:

Reduced bandwidth: With 100 partitions, incremental fetches can be 10-100x smaller
Lower broker CPU: Broker parses smaller requests
Automatic fallback: Falls back to Fetch v4 (full requests) for brokers that don’t support v7+

Error recovery:

FetchSessionIdNotFound or InvalidFetchSessionEpoch errors automatically reset the session
The next fetch sends a full request to re-establish the session
All sessions are reset on consumer group rebalance

Fetch sessions are enabled automatically when the broker supports Fetch API v7+. No configuration is needed.

Closest-Replica Fetching (KIP-392)

Krafka implements KIP-392 to allow consumers to fetch from the closest replica rather than always from the partition leader. This is especially useful in multi-datacenter or multi-availability-zone deployments where cross-rack traffic is expensive.

Configuration:

Set client_rack to the rack or availability zone of the consumer:

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .client_rack("us-east-1a")
    .build()
    .await?;

How it works:

The consumer includes its rack_id in Fetch requests (Fetch API v11+)
The broker compares the consumer’s rack with each partition’s replica placement
If a replica exists in the same rack, the broker returns it as preferred_read_replica
On subsequent polls, Krafka routes that partition’s fetch to the preferred replica
The mapping expires after metadata_max_age (default 5 minutes), causing a fresh lookup

Error fallback:

If a non-leader replica returns an error, the preferred replica mapping is cleared
The next poll falls back to the partition leader
On rebalance or unsubscribe, all preferred replica mappings are cleared

Requirements:

Broker must support Fetch API v11 (Kafka 2.4+)
Brokers must be configured with broker.rack
When client_rack is not set, Krafka negotiates up to Fetch v10 (sessions + leader epoch fencing) but does not send a rack ID

Performance Tips

High Throughput

use krafka::consumer::Consumer;
use std::time::Duration;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("high-throughput")
    .fetch_max_bytes(104857600)              // 100MB max fetch
    .max_partition_fetch_bytes(10485760)     // 10MB per partition
    .max_poll_records(10000)                 // Many records per poll
    .max_buffered_records(10000)              // Match poll batch size
    .fetch_max_wait(Duration::from_millis(100))
    .build()
    .await?;

Low Latency

use krafka::consumer::Consumer;
use std::time::Duration;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("low-latency")
    .fetch_min_bytes(1)                      // Return immediately when data available
    .fetch_max_wait(Duration::from_millis(10))
    .max_poll_records(1)                     // Process one at a time
    .build()
    .await?;

Memory Efficiency

use krafka::consumer::Consumer;
use std::time::Duration;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("memory-efficient")
    .fetch_max_bytes(1048576)                // Limit to 1MB
    .max_partition_fetch_bytes(262144)       // 256KB per partition
    .max_poll_records(100)                   // Limit in-memory records
    .max_buffered_records(200)               // Tight buffer cap
    .build()
    .await?;

Static Group Membership (KIP-345)

Static group membership allows consumers to maintain a persistent identity across restarts, avoiding unnecessary rebalances. When a consumer with a group_instance_id disconnects and reconnects (within the session timeout), it automatically gets the same partition assignment without triggering a rebalance for the entire group.

Enabling Static Membership

use krafka::consumer::Consumer;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .group_instance_id("instance-1")  // Stable identity
    .session_timeout(Duration::from_secs(300))  // Longer timeout for restarts
    .build()
    .await?;

consumer.subscribe(&["my-topic"]).await?;

How It Works

Behavior	Dynamic (default)	Static (with `group_instance_id`)
Disconnect	Immediate rebalance	No rebalance until session timeout
Reconnect	New member, rebalance	Same member, no rebalance
Rolling restart	N rebalances	Zero rebalances
Protocol version	JoinGroup v0	JoinGroup v5

When group_instance_id is set, Krafka automatically:

Uses JoinGroup v5 and Heartbeat v3 protocol versions
Includes the instance ID in all group coordinator requests (Join, Sync, Heartbeat, OffsetCommit, Leave)
Uses LeaveGroup v3 with member identity on graceful shutdown

Best Practices

Assign a unique group_instance_id per consumer instance (e.g., hostname, pod name)
Increase session_timeout to cover restart duration (e.g., 5 minutes for rolling deployments)
Use with CooperativeSticky assignor for minimal partition movement

use krafka::consumer::{Consumer, PartitionAssignmentStrategy};
use std::time::Duration;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .group_instance_id("pod-abc-123")
    .partition_assignment_strategy(PartitionAssignmentStrategy::CooperativeSticky)
    .session_timeout(Duration::from_secs(300))
    .build()
    .await?;

KIP-848 Consumer Group Protocol

KIP-848 introduces a new consumer group protocol where the server performs partition assignment instead of the group leader. This eliminates the JoinGroup/SyncGroup round-trip and replaces it with a single ConsumerGroupHeartbeat API (key 68, v0–v1).

Enabling KIP-848

Set GroupProtocol::Consumer on the builder to use the KIP-848 consumer protocol. Requires Kafka 3.7+ (KIP-848 GA in Kafka 4.0).

use krafka::consumer::{Consumer, GroupProtocol};

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .group_protocol(GroupProtocol::Consumer)  // KIP-848
    .build()
    .await?;

consumer.subscribe(&["my-topic"]).await?;

How It Works

Classic Protocol	KIP-848 Consumer Protocol
JoinGroup + SyncGroup + Heartbeat	ConsumerGroupHeartbeat only
Client-side assignment (group leader)	Server-side assignment
Generation ID	Member epoch
`generation_id = -1` (unjoined)	`member_epoch = 0` (join)
LeaveGroup request	`member_epoch = -1` (permanent leave) or `-2` (static member temporary leave)

With the consumer protocol:

A member joins by sending a heartbeat with member_epoch = 0
The coordinator assigns partitions and returns the assignment in the response
Members maintain their session by sending periodic heartbeats
The heartbeat task updates the local assignment and state when the broker returns new assignments
The consumer layer computes an incremental diff to determine revoked vs. newly assigned partitions. on_partitions_revoked is fired for the affected revoked partitions, while on_partitions_assigned receives the full post-rebalance assignment (consistent with the cooperative and eager paths in this crate)
To leave, a dynamic member sends member_epoch = -1 (permanent). A static member (with group_instance_id) sends member_epoch = -2 (temporary leave — the broker retains the assignment for the session-timeout window so the instance can rejoin quickly)

Subscription Changes

If subscribe() is called with a different topic list while the consumer is already active (state Stable), the existing heartbeat task is stopped and the next poll() sends a full heartbeat with all fields (including the new topic list). This mirrors the cooperative-rebalance subscription-change detection.

Topic UUID Resolution

The ConsumerGroupHeartbeat response uses 16-byte topic UUIDs in assignments. Krafka resolves these UUIDs to topic names with a two-level lookup order:

Cluster metadata lookup — first consult ClusterMetadata::topic_name_for_id. In Metadata v10 and later, brokers can return topic UUID → name mappings in metadata responses, and Krafka uses automatic API version negotiation to take advantage of that when supported.
Local topic names cache — if metadata does not contain the mapping, fall back to a local UUID → name cache built from previously resolved assignments. This cache survives metadata cache flushes and mirrors the Java client’s AbstractMembershipManager behavior once a name has been learned.

Successfully resolved names are cached locally. Unresolvable UUIDs still trigger an automatic metadata refresh.

If topic UUIDs remain unresolved after a metadata refresh during the initial heartbeat response handling, the client returns a protocol error rather than silently operating with an empty or partial assignment. Inside the background heartbeat task, unresolved UUIDs produce a warn! log and the assignment is retained for re-resolution on the next tick. The raw target assignment (with UUIDs) is always retained so resolution can be retried after future updates or once a UUID → name mapping becomes available.

The StaleMemberEpoch error (113) is handled as a transient condition: the member epoch is updated from the response and the heartbeat retries on the next tick without triggering a rebalance.

Dynamic Heartbeat Interval

The coordinator may adjust the heartbeat interval over time by returning a different heartbeat_interval_ms in the ConsumerGroupHeartbeat response. The KIP-848 heartbeat task honours these updates: after each successful response, the current interval is compared with the response value and, if changed, the timer is reset to the new duration (with a minimum floor of 1 000 ms).

Version Notes

v0 — Base version; compatible with Kafka 3.7+ (EA) and 4.0+ (GA)
v1 — Adds SubscribedTopicRegex for regex-based topic subscription (KIP-848) and requires consumer-generated member IDs (KIP-1082); available on Kafka 4.0+

Both v0 and v1 are supported (CONSUMER_GROUP_HEARTBEAT_MIN = 0, CONSUMER_GROUP_HEARTBEAT_MAX = 1).

Error Handling

The ConsumerGroupHeartbeat response may return these KIP-848-specific errors:

Error Code	Name	Handling
8	`RebalanceInProgress`	Signal rebalance; consumer processes assignment diff on next poll
14	`CoordinatorLoadInProgress`	Transient — retry on next heartbeat tick
15	`NotCoordinator`	Clear cached coordinator, trigger rediscovery
16	`CoordinatorNotAvailable`	Clear cached coordinator, trigger rediscovery
110	`FencedMemberEpoch`	Fenced — heartbeat task stops, member preserves its `member_id` and rejoins with epoch 0 via a full heartbeat (all top-level fields)
111	`UnreleasedInstanceId`	Static member instance ID held by another member — same fencing recovery as `FencedMemberEpoch`
112	`UnsupportedAssignor`	Server-side assignor not recognized
113	`StaleMemberEpoch`	Update local epoch from response, retry on next heartbeat
128	`InvalidRegularExpression`	Regex subscription (v1+) is malformed

Fencing Recovery

When the heartbeat task receives FencedMemberEpoch, UnknownMemberId, or UnreleasedInstanceId, it:

Signals the consumer layer (member invalidated + rebalance needed)
Stops the heartbeat task (no more skinny heartbeats with stale state)

On the next poll(), the consumer detects the fencing via needs_rejoin():

Resets member_epoch to 0 and clears assignment/target state
Preserves member_id — per KIP-848, a fenced member must “rejoin with the same member id and epoch 0”
Sets state to Unjoined

The handle_group_rebalance() path then calls ensure_active_membership(), which sends a full heartbeat (subscription, rebalance timeout, all top-level fields) and starts a fresh heartbeat task.

Requirements

Requires Kafka 4.0+ (or earlier brokers with group.coordinator.new.enable=true)
The broker must support API key 68 (ConsumerGroupHeartbeat)

Describing KIP-848 Groups

To inspect a KIP-848 consumer group (state, epochs, member assignments), use the AdminClient’s describe_consumer_groups() method which auto-detects the group type and dispatches to the appropriate API. See the Admin Client Guide for details.

Limitations

Full transactional offset support (TxnOffsetCommit) is not yet implemented.

Consumer Interceptors

Interceptors allow you to observe records after they are fetched and monitor offset commits. See the Interceptors Guide for full details.

use krafka::interceptor::{ConsumerInterceptor, InterceptorResult};
use krafka::consumer::{Consumer, ConsumerRecord};
use std::sync::Arc;

#[derive(Debug)]
struct MetricsInterceptor;

impl ConsumerInterceptor for MetricsInterceptor {
    fn on_consume(&self, records: &[ConsumerRecord]) -> InterceptorResult {
        println!("Consumed {} records", records.len());
        Ok(())
    }
}

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .interceptor(Arc::new(MetricsInterceptor))
    .build()
    .await?;

Log Compaction Awareness

Krafka correctly handles log-compacted topics where records may have been deleted within a batch. Record offsets are calculated using each record’s offset_delta rather than sequential indices, ensuring accurate offset tracking even when records within a batch have been removed by compaction.

This means:

consumer.position() always returns the correct offset, even on compacted topics
Offset commits are accurate — no risk of re-processing or skipping records
No special configuration needed; compaction awareness is built-in

Tombstone Detection

Records in compacted topics with a key but no value are tombstones — deletion markers that eventually cause the key to be removed from the log. Use ConsumerRecord::is_tombstone() to detect them:

use std::time::Duration;

// Assuming `consumer` is an already-configured Consumer instance
let records = consumer.poll(Duration::from_secs(1)).await?;
for record in &records {
    if record.is_tombstone() {
        println!("Key {:?} was deleted", record.key);
    } else {
        println!("Key {:?} = {:?}", record.key, record.value);
    }
}

CompactedTable

CompactedTable is a standalone, Kafka-agnostic data structure that maintains an in-memory key→value snapshot from consumer records. It handles tombstones automatically and tracks changes via TableChange. Because it is decoupled from the consumer, it composes with any consumer setup — group-coordinated, standalone, or manually assigned:

use krafka::consumer::{Consumer, CompactedTable};
use std::time::Duration;

let consumer = Consumer::builder()
    .bootstrap_servers("localhost:9092")
    .group_id("my-group")
    .build()
    .await?;
consumer.subscribe(&["user-profiles"]).await?;

let mut table = CompactedTable::new();
loop {
    let records = consumer.poll(Duration::from_secs(1)).await?;
    let changes = table.apply(&records);
    for change in &changes {
        if change.is_delete() {
            println!("Deleted: {:?}", change.key);
        } else if change.is_insert() {
            println!("New: {:?} = {:?}", change.key, change.new_value);
        } else {
            println!("Updated: {:?} = {:?}", change.key, change.new_value);
        }
    }
}

Key behaviors:

Tombstone handling — keys are removed from the table when a null-valued record arrives
Keyless records — silently skipped (compacted topics require keys)
Metrics — records_processed() and tombstones_processed() are available for monitoring
Read access — get(), contains_key(), keys(), values(), iter(), snapshot(), len(), is_empty()
Bulk load — ingest() applies records without building a change list (ideal for initial scans)
Reset — clear() removes all entries and resets counters (useful during rebalances)
Clone — table.clone() produces a full copy including counters; table.snapshot() clones only the entries
Equality — two tables are equal (PartialEq/Eq) when they contain the same entries; processing counters are ignored
IntoIterator — for (key, value) in &table { ... } or for (key, value) in table { ... } (consuming)

TableChange derives PartialEq and Eq, so changes can be compared directly with assert_eq! in tests.

CompactedTopicConsumer

For the common case of scanning an entire compacted topic from the beginning, CompactedTopicConsumer bundles a Consumer and CompactedTable together with built-in caught-up detection:

use krafka::consumer::CompactedTopicConsumer;
use std::time::Duration;

let mut ctc = CompactedTopicConsumer::builder()
    .bootstrap_servers("localhost:9092")
    .topic("user-profiles")
    .build()
    .await?;

// Build the initial snapshot (blocks until caught up)
ctc.scan(Duration::from_secs(1)).await?;
assert!(ctc.is_caught_up());

// Read individual keys via the table
if let Some(value) = ctc.table().get(b"user-123") {
    println!("User profile: {:?}", value);
}

// Get the full snapshot
let snapshot = ctc.table().snapshot();
println!("{} keys in table", snapshot.len());

// Tail for live updates
loop {
    let changes = ctc.poll(Duration::from_secs(1)).await?;
    for change in &changes {
        if change.is_delete() {
            println!("Deleted: {:?}", change.key);
        } else if change.is_insert() {
            println!("New: {:?} = {:?}", change.key, change.new_value);
        } else {
            println!("Updated: {:?} = {:?}", change.key, change.new_value);
        }
    }
}

Key behaviors:

No consumer group — uses standalone assignment of all partitions
Starts from earliest — auto_offset_reset is set to Earliest internally
Caught-up detection — scan() returns when all partitions reach their high watermarks; poll() also updates the flag. Because the high watermark is refreshed on each fetch, scan() may block indefinitely on actively written topics — treat it as a best-effort catch-up rather than a bounded snapshot
Table access — table() and table_mut() give direct access to the underlying CompactedTable
Consumer access — consumer() and consumer_mut() expose the underlying Consumer for seek, pause, commit, or metrics; into_parts() decomposes the wrapper into (Consumer, CompactedTable)

For custom consumer setups (e.g., consumer groups, manual offsets), use CompactedTable directly.

From an Existing Consumer

If you need full control over the consumer configuration (TLS, auth, custom timeouts), build the consumer yourself and pass it in:

let consumer = Consumer::builder()
    .bootstrap_servers("broker:9093")
    .auto_offset_reset(AutoOffsetReset::Earliest)
    .enable_auto_commit(false)
    .auth(AuthConfig::sasl_scram_sha256("user", "password"))
    .build()
    .await?;
consumer.assign("config-topic", vec![0, 1, 2]).await?;

let mut ctc = CompactedTopicConsumer::from_consumer(consumer, "config-topic");
ctc.scan(Duration::from_secs(1)).await?;

Authentication

Pass an AuthConfig to connect to secured clusters:

use krafka::auth::AuthConfig;

let mut ctc = CompactedTopicConsumer::builder()
    .bootstrap_servers("broker:9093")
    .topic("config-topic")
    .auth(AuthConfig::sasl_scram_sha256("user", "password"))
    .build()
    .await?;

Offset Lag Tracking

Krafka tracks consumer lag automatically by caching the high watermark returned in every fetch response. When the broker supports Fetch v5+, the log start offset is also cached. No additional network calls are needed.

Lag values are returned as u64 (always non-negative, clamped at zero when the position is ahead of the watermark) to match the internal metrics representation.

// Per-partition lag (returns None if no fetch has completed for this partition)
if let Some(lag) = consumer.current_lag("my-topic", 0).await {
    println!("Partition 0 lag: {} records", lag);
}

// All partition lags at once
let lags = consumer.lag().await;
for ((topic, partition), lag) in &lags {
    println!("{}-{}: {} records behind", topic, partition, lag);
}

// Cached beginning/end offsets (no network call)
if let Some(start) = consumer.cached_beginning_offset("my-topic", 0).await {
    println!("Earliest available offset: {}", start);
}
if let Some(end) = consumer.cached_end_offset("my-topic", 0).await {
    println!("High watermark: {}", end);
}

Lag is also exposed via metrics (recomputed after every offset or high-watermark mutation — seek, commit, poll, offset reset, revocation):

Metric	Description
`lag`	Total lag across all assigned partitions
`lag_max`	Maximum per-partition lag

High watermarks and log start offsets are automatically cleared when partitions are revoked or the consumer unsubscribes. Lag metrics are recomputed accordingly.

Staleness caveat — High watermarks are only updated when a fetch response is received from the broker. If the consumer is paused, slow, or not polling, the cached watermarks (and therefore current_lag, compute_aggregate_lag, and the lag/lag_max metrics) can become stale and undercount the true lag. Treat lag values as eventually consistent rather than real-time.

Next Steps

Interceptors Guide - Producer and consumer interceptor hooks
Producer Guide - Learn about producing messages
Configuration Reference - All consumer options
Architecture Overview - How the consumer works internally

Consumer Guide

Overview

Basic Usage

Authentication

Consumer Configuration

Auto Offset Reset

Offset Commit

Fetch Configuration

Buffer Cap

Isolation Level

Metadata Topic Cache TTL

Consumer Groups

How Consumer Groups Work

Partition Assignment Strategies

Cooperative Sticky Assignor

Rebalance Listener

Offset Management

Manual Commit

Async Commit

Commit with Metadata

Position and Seeking

Pause and Resume

Manual Partition Assignment

Subscription Management

Subscribe to Multiple Topics

Check Subscriptions and Assignments

Unsubscribe

Pause and Resume

Error Handling

Handling Poll Errors

Streaming with recv()

Async Stream API

Graceful Shutdown

Poll Architecture

Batch Fetch by Broker

Incremental Fetch Sessions (KIP-227)

Closest-Replica Fetching (KIP-392)

Performance Tips

High Throughput

Low Latency

Memory Efficiency

Static Group Membership (KIP-345)

Enabling Static Membership

How It Works

Best Practices

KIP-848 Consumer Group Protocol

Enabling KIP-848

How It Works

Subscription Changes

Topic UUID Resolution

Dynamic Heartbeat Interval

Version Notes

Error Handling

Fencing Recovery

Requirements

Describing KIP-848 Groups

Limitations

Consumer Interceptors

Log Compaction Awareness

Tombstone Detection

CompactedTable

CompactedTopicConsumer

From an Existing Consumer

Authentication

Offset Lag Tracking

Next Steps

Streaming with `recv()`

Async `Stream` API