Kafka Producer Batching¶

Kafka producers batch multiple messages together before sending to brokers, significantly improving throughput and efficiency. Understanding batching is essential for optimizing producer performance and balancing latency versus throughput trade-offs.

Batching Overview¶

Why Batching Matters¶

Efficiency Gains¶

Aspect	Without Batching	With Batching
Network requests	1 per message	1 per batch
Header overhead	High (repeated headers)	Amortized
Compression	Per-message	Per-batch (better ratio)
Broker I/O	Many small writes	Fewer large writes

Throughput Impact¶

Record Accumulator¶

The RecordAccumulator is the internal component that manages message batching.

Structure¶

Batch Lifecycle¶

Batch Configuration¶

Core Settings¶

# Maximum batch size in bytes
batch.size=16384  # 16 KB (default)

# Time to wait for batch to fill
linger.ms=0  # No wait (default)

# Total memory for buffering
buffer.memory=33554432  # 32 MB (default)

# Maximum time to block on send() when buffer full
max.block.ms=60000  # 60 seconds (default)

Configuration Trade-offs¶

Setting	Low Value	High Value
`batch.size`	Lower latency, lower throughput	Higher throughput, more memory
`linger.ms`	Immediate send, small batches	Larger batches, added latency
`buffer.memory`	Less memory, more blocking	Higher throughput, more memory

Linger Time¶

How linger.ms Works¶

linger.ms Examples¶

linger.ms	Behavior	Use Case
0	Send immediately (default)	Latency-sensitive
5	Wait up to 5ms	Low-latency with some batching
20	Wait up to 20ms	Balanced throughput/latency
100+	Wait up to 100ms+	Maximum throughput

Batch Triggers¶

A batch is sent when ANY condition is met:

Batch full: batch.size reached
Linger expired: linger.ms elapsed since first record
Buffer pressure: buffer.memory nearly full
Explicit flush: producer.flush() called
Close: producer.close() called

Memory Management¶

Buffer Pool¶

Memory Exhaustion¶

Monitoring Memory¶

// Get producer metrics
Map<MetricName, ? extends Metric> metrics = producer.metrics();

// Key metrics
// - buffer-total-bytes: Total buffer memory
// - buffer-available-bytes: Available buffer memory
// - bufferpool-wait-time: Time blocked waiting for buffer

Per-Partition Batching¶

Partition Isolation¶

Each partition has its own batch queue:

Implications¶

Behavior	Description
Independent batching	Each partition fills independently
Parallel sends	Ready batches sent to different brokers in parallel
Uneven filling	Hot partitions batch faster

Batch Compression¶

Compression at Batch Level¶

Compression + Batching¶

# Enable compression for better efficiency
compression.type=lz4

# Larger batches compress better
batch.size=65536  # 64 KB

# Allow time for batch accumulation
linger.ms=20

Request Building¶

From Batches to Requests¶

Produce Request Structure¶

ProduceRequest {
    transactional_id: string (nullable)
    acks: int16
    timeout_ms: int32
    topic_data: [{
        topic: string
        partition_data: [{
            partition: int32
            records: RecordBatch  // Compressed batch
        }]
    }]
}

Performance Tuning¶

High Throughput Configuration¶

# Large batches
batch.size=131072  # 128 KB

# Wait for batch to fill
linger.ms=50

# Plenty of buffer memory
buffer.memory=134217728  # 128 MB

# Compression for network efficiency
compression.type=lz4

# Multiple in-flight for pipelining
max.in.flight.requests.per.connection=5

Low Latency Configuration¶

# Smaller batches
batch.size=16384  # 16 KB

# Minimal wait
linger.ms=0

# Standard buffer
buffer.memory=33554432  # 32 MB

# No compression (fastest)
compression.type=none

# Still use multiple in-flight
max.in.flight.requests.per.connection=5

Balanced Configuration¶

# Moderate batch size
batch.size=32768  # 32 KB

# Small wait for accumulation
linger.ms=10

# Adequate buffer
buffer.memory=67108864  # 64 MB

# Light compression
compression.type=lz4

max.in.flight.requests.per.connection=5

Batching Metrics¶

Key Metrics¶

Metric	Description	Target
`batch-size-avg`	Average batch size	Close to `batch.size`
`batch-size-max`	Maximum batch size	≤ `batch.size`
`record-queue-time-avg`	Time in accumulator	Close to `linger.ms`
`records-per-request-avg`	Records per request	Higher = better batching
`bufferpool-wait-time`	Time waiting for buffer	Should be 0

Diagnosing Poor Batching¶

Symptom	Likely Cause	Solution
Small `batch-size-avg`	Too many partitions	Consolidate or increase `linger.ms`
Small `batch-size-avg`	Low message rate	Increase `linger.ms`
High `bufferpool-wait-time`	Buffer exhaustion	Increase `buffer.memory`
High `record-queue-time-avg`	Slow broker response	Check broker health

Async vs Sync Behavior¶

Asynchronous (Default)¶

// Non-blocking - returns immediately
Future<RecordMetadata> future = producer.send(record);

// Optional callback
producer.send(record, (metadata, exception) -> {
    if (exception != null) {
        handleError(exception);
    }
});

Synchronous Pattern¶

// Blocking - waits for broker ack
try {
    RecordMetadata metadata = producer.send(record).get();
} catch (ExecutionException e) {
    handleError(e.getCause());
}

Synchronous Impact

Synchronous sends defeat batching benefits. Each send waits for response before next send can proceed. Use async with callbacks for production.

Flush Behavior¶

// Send accumulated batches immediately
producer.flush();  // Blocks until all batches sent

// Flush before close
producer.flush();
producer.close();

Version Compatibility¶

Feature	Minimum Version
Record batching	0.8.0
Compression per batch	0.8.0
Sticky partitioner	2.4.0
Idempotent batching	0.11.0
Transactional batching	0.11.0

Kafka Protocol - Wire protocol and record format
Compression - Batch compression
Producer Guide - Full producer configuration
Performance Internals - Throughput optimization