Apache Pulsar vs Kafka vs RedPanda: Modern Streaming

• 15/4/2025
• 5-minute read

The Evolution of Streaming Platforms

Modern applications demand real-time data processing capabilities that traditional message queues simply cannot provide. While Apache Kafka revolutionized streaming data, newer platforms like Apache Pulsar and RedPanda have emerged to address Kafka’s operational complexity and performance limitations. Each platform takes a fundamentally different approach to distributed streaming, making the choice critical for high-throughput applications.

The streaming landscape has evolved beyond simple publish-subscribe patterns. Today’s platforms must handle massive scale, provide strong durability guarantees, and integrate seamlessly with cloud-native architectures while maintaining sub-millisecond latencies.

Architecture and Design Philosophy

Understanding the core architectural differences helps explain each platform’s strengths:

Kafka’s Proven Foundation

Kafka’s partition-based architecture has proven its scalability across thousands of deployments:

# Kafka topic creation with high throughput configuration
kafka-topics.sh --create --topic user-events \
  --bootstrap-server localhost:9092 \
  --partitions 12 \
  --replication-factor 3 \
  --config min.insync.replicas=2 \
  --config unclean.leader.election.enable=false

Pulsar’s Layered Approach

Pulsar separates serving and storage layers, enabling independent scaling:

# Pulsar topic with namespace and tenant isolation
pulsar-admin topics create \
  persistent://finance/trading/order-events \
  --partitions 8

RedPanda’s Simplified Design

RedPanda eliminates external dependencies with its self-contained architecture:

# RedPanda cluster bootstrap (no ZooKeeper needed)
rpk cluster config bootstrap \
  --id 1 --self 192.168.1.10:9092 \
  --ips 192.168.1.10,192.168.1.11,192.168.1.12

Performance Benchmarks and Analysis

Recent performance evaluations reveal significant differences across platforms:

Throughput Comparison

¹ Apache Kafka and Pulsar community benchmarks, 2024
² RedPanda performance documentation and vendor benchmarks

Latency Characteristics

End-to-end latency measurements under various loads³:

• Kafka: P99 latency 15-25ms (optimized configuration)
• Pulsar: P99 latency 20-35ms (includes BookKeeper overhead)
• RedPanda: P99 latency 8-15ms (C++ implementation advantage)

³ Independent performance testing on c5.2xlarge instances

Resource Efficiency

Operational Complexity and Management

Kafka Operations

Kafka requires careful JVM tuning and ZooKeeper management:

# Kafka broker configuration for high performance
server.properties: |
  num.network.threads=8
  num.io.threads=16
  socket.send.buffer.bytes=102400
  socket.receive.buffer.bytes=102400
  log.segment.bytes=1073741824
  log.retention.hours=168
  compression.type=lz4

Pulsar Management

Pulsar’s multi-component architecture offers flexibility but increases complexity:

# Pulsar cluster configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: pulsar-config
data:
  pulsar.conf: |
    managedLedgerDefaultEnsembleSize=3
    managedLedgerDefaultWriteQuorum=2
    managedLedgerDefaultAckQuorum=2
    managedLedgerCacheSizeMB=1024

RedPanda Simplicity

RedPanda eliminates operational overhead with automatic configuration:

# RedPanda minimal configuration
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: redpanda
spec:
  template:
    spec:
      containers:
      - name: redpanda
        image: vectorized/redpanda:latest
        command:
        - /usr/bin/rpk
        - redpanda
        - start
        - --smp=2
        - --memory=2G

Cloud-Native Integration

Kubernetes Deployment

Multi-Cloud Capabilities

Kafka provides extensive cloud integrations but requires careful tuning. Pulsar offers built-in geo-replication and multi-tenancy. RedPanda simplifies cloud deployments with intelligent defaults and lower resource requirements.

Use Case Suitability

High-Frequency Trading Systems

# RedPanda excels in latency-sensitive scenarios
from kafka import KafkaProducer
import time

producer = KafkaProducer(
    bootstrap_servers=['redpanda-cluster:9092'],
    batch_size=1,  # Immediate send
    linger_ms=0,   # No batching delay
    compression_type=None
)

start_time = time.time_ns()
producer.send('market-data', b'price_update')
producer.flush()
latency_ns = time.time_ns() - start_time

Multi-Tenant Analytics

// Pulsar's built-in multi-tenancy
PulsarClient client = PulsarClient.builder()
    .serviceUrl("pulsar://cluster:6650")
    .build();

Producer<byte[]> producer = client.newProducer()
    .topic("persistent://tenant-a/analytics/user-events")
    .enableBatching(true)
    .batchingMaxMessages(1000)
    .create();

Event Sourcing Architecture

# Kafka's mature ecosystem for event sourcing
kafka-console-producer.sh --topic events \
  --bootstrap-server localhost:9092 \
  --property "key.separator=:" \
  --property "parse.key=true"

Migration and Compatibility

Kafka to RedPanda Migration

RedPanda’s Kafka API compatibility enables seamless migration:

# Existing Kafka clients work unchanged
export KAFKA_BROKERS="redpanda-cluster:9092"
kafka-console-consumer.sh --topic existing-topic \
  --bootstrap-server $KAFKA_BROKERS

Pulsar Migration Strategies

Pulsar requires application changes but offers advanced features:

# Pulsar client with schema registry
import pulsar

client = pulsar.Client('pulsar://localhost:6650')
producer = client.create_producer(
    'my-topic',
    schema=pulsar.schema.AvroSchema(UserActivitySchema)
)

Cost and Licensing Considerations

Making the Decision

Choose Kafka when:

• You need the most mature ecosystem
• Extensive integrations are required
• Your team has Kafka expertise
• Long-term stability is prioritized

Choose Pulsar when:

• Multi-tenancy is essential
• You need built-in geo-replication
• Schema evolution is important
• Flexible messaging patterns are required

Choose RedPanda when:

• Performance is the top priority
• Operational simplicity is valued
• Resource efficiency matters
• You’re starting a new project

The streaming platform landscape continues evolving, with each solution optimizing for different trade-offs. RedPanda’s performance advantages make it compelling for new deployments, while Kafka’s ecosystem maturity ensures continued dominance in enterprise environments.

Code Samples and Performance Disclaimer

Important Note: All code examples, configurations, deployment scripts, and performance benchmarks provided in this article are for educational and demonstration purposes only. These samples are simplified for clarity and should not be used directly in production environments without proper review, security assessment, and adaptation to your specific requirements. Performance metrics are based on specific test conditions and may vary significantly in real-world deployments. Always conduct thorough testing, follow security best practices, and consult official documentation before implementing any streaming platform in production systems.

Further Reading

• Apache Kafka Documentation
• Apache Pulsar Documentation
• RedPanda Documentation
• Performance Benchmark Comparison
• Confluent’s Kafka vs Pulsar Analysis