Real-Time Streaming Platform: Building Enterprise-Grade Data Infrastructure with Pulsar, Flink & ClickHouse
An overview of a comprehensive event streaming platform designed for high-throughput, real-time data processing across multiple domains
Introduction
Modern businesses generate massive amounts of data every second. From user interactions on e-commerce platforms to sensor readings in IoT networks, the ability to process and analyze this data in real-time has become a competitive necessity.
I've built a comprehensive real-time streaming platform that tackles the fundamental challenges of scalable data ingestion, real-time processing, and high-performance analytics. This platform is designed to handle workloads ranging from development environments to enterprise-scale deployments processing over 1 million messages per second.
🏗️ Platform Architecture
The platform leverages three battle-tested open-source technologies, each serving a specific role in the data pipeline:
📊 Data Sources → 🚀 Apache Pulsar → ⚡ Apache Flink → 🏛️ ClickHouse → 📈 Real-time Analytics
Core Components
Apache Pulsar - The messaging backbone
- Distributed pub-sub messaging with multi-tenancy
- Built-in schema registry for AVRO serialization
- Geo-replication and tiered storage capabilities
Apache Flink - The processing engine
- Stateful stream processing with exactly-once guarantees
- Complex event processing and windowed aggregations
- Fault-tolerant checkpointing and recovery
ClickHouse - The analytical powerhouse
- Columnar database optimized for analytical queries
- Real-time ingestion with sub-second query performance
- Horizontal scaling across distributed clusters
Why This Combination?
This architecture solves a critical integration challenge: ClickHouse lacks native Flink connector support (unlike databases such as MySQL or PostgreSQL). Our platform bridges this gap with a custom integration that maintains performance while ensuring data consistency.
🚀 Platform Scalability
The platform is designed with three distinct deployment tiers to accommodate different organizational needs:
| Configuration | Throughput | Infrastructure | Target Audience |
|---|---|---|---|
| Local Development | ~1K msg/sec | Docker + Kind | Developers & Testing |
| Production Ready | 50K msg/sec | AWS t3 instances | SMBs & Growing Companies |
| Enterprise Scale | 1M+ msg/sec | AWS c5 + NVMe | Large Enterprises |
Each configuration maintains the same architectural principles while scaling the underlying infrastructure to match performance requirements and budget constraints.
🔧 Platform Capabilities
High-Volume Event Generation
The platform includes sophisticated event producers that simulate real-world data patterns:
- Multi-domain events: E-commerce, finance, IoT, gaming, logistics, social media
- AVRO serialization: Schema evolution and type safety
- Configurable throughput: From thousands to millions of events per second
- Realistic data patterns: User sessions, device interactions, transaction flows
Distributed Message Streaming
Apache Pulsar provides the messaging infrastructure with enterprise features:
- Multi-tenancy: Isolated namespaces for different applications
- Schema registry: Centralized schema management and evolution
- Geo-replication: Cross-region data distribution
- Tiered storage: Cost-effective long-term data retention
Real-Time Stream Processing
Apache Flink handles complex stream processing scenarios:
- Windowed aggregations: Time-based data summarization
- Stateful processing: Maintain context across events
- Exactly-once semantics: Data consistency guarantees
- Fault tolerance: Automatic recovery from failures
High-Performance Analytics
ClickHouse delivers sub-second analytical query performance:
- Columnar storage: Optimized for analytical workloads
- Real-time ingestion: Process streaming data as it arrives
- Distributed queries: Scale across multiple nodes
- Materialized views: Pre-computed aggregations for instant results
🎯 Use Cases Across Industries
E-commerce
- Real-time Inventory: Track product availability across warehouses
- Recommendation Engines: Process user interactions for personalized suggestions
- Fraud Detection: Analyze payment patterns for suspicious activity
Finance
- Trading Analytics: Process market data for algorithmic trading
- Risk Assessment: Real-time calculation of portfolio risk metrics
- Compliance Monitoring: Track transactions for regulatory compliance
IoT & Manufacturing
- Predictive Maintenance: Analyze sensor data to predict equipment failures
- Quality Control: Monitor production metrics in real-time
- Energy Optimization: Track and optimize energy consumption patterns
Gaming
- Player Analytics: Real-time analysis of player behavior and engagement
- Live Leaderboards: Update rankings and achievements instantly
- Churn Prediction: Identify players at risk of leaving
🔍 Technical Innovations
Solving the Flink-ClickHouse Integration Challenge
One of the most significant technical hurdles was integrating Apache Flink with ClickHouse. Unlike popular databases such as MySQL, PostgreSQL, or Elasticsearch that have official Flink connectors, ClickHouse lacks native integration support.
The Challenge:
- Architectural mismatch: Flink's continuous streaming vs ClickHouse's batch-oriented operations
- Transaction limitations: ClickHouse lacks full ACID support for Flink's exactly-once guarantees
- Performance optimization: Balancing throughput with data consistency
Our Solution:
- Custom JDBC sink implementation with idempotent writes
- Batch coordination aligned with Flink checkpoints
- Adaptive batching based on ClickHouse cluster performance
- Circuit breaker patterns for fault tolerance
Multi-Domain Event Schema Design
The platform supports diverse event types across industries through a flexible AVRO schema approach. Here's an example of the IoT sensor data schema used in the platform:
{
"type": "record",
"name": "SensorData",
"namespace": "org.apache.pulsar.testclient.avro",
"doc": "IoT Sensor Data for Pulsar Performance Testing - Optimized Integer Schema",
"fields": [
{
"name": "sensorId",
"type": "int",
"doc": "Unique sensor identifier (integer for efficiency)"
},
{
"name": "sensorType",
"type": "int",
"doc": "Type of sensor (1=temperature, 2=humidity, 3=pressure, 4=motion, 5=light, 6=co2, 7=noise, 8=multisensor)"
},
{
"name": "temperature",
"type": "double",
"doc": "Temperature reading in Celsius"
},
{
"name": "humidity",
"type": "double",
"doc": "Humidity reading as percentage"
},
{
"name": "pressure",
"type": "double",
"doc": "Pressure reading in hPa"
},
{
"name": "batteryLevel",
"type": "double",
"doc": "Battery level as percentage"
},
{
"name": "status",
"type": "int",
"doc": "Sensor status (1=online, 2=offline, 3=maintenance, 4=error)"
},
{
"name": "timestamp",
"type": "long",
"logicalType": "timestamp-millis",
"doc": "Timestamp in milliseconds since epoch"
}
]
}
Schema Design Highlights:
- Performance optimized: Uses integers for enums and identifiers to minimize serialization overhead
- Multi-sensor support: Single schema accommodates 8 different sensor types
- Comprehensive telemetry: Captures environmental data, device health, and operational status
- Temporal precision: Millisecond-level timestamps for accurate event ordering
This design enables:
- Schema evolution: Backward and forward compatibility through AVRO
- Type safety: Compile-time validation across the pipeline
- Cross-domain analytics: Unified event processing across business units
- High throughput: Optimized data types for maximum serialization performance
📊 Performance & Scale
Benchmark Results
The platform has been tested across different scales with impressive results:
Enterprise Configuration (1M+ msg/sec):
- Throughput: 1,000,000+ messages per second sustained
- End-to-end latency: P99 < 100ms
- Query performance: Sub-second analytical queries on billions of records
- Availability: 99.9% uptime with automatic failover
Technology Stack
Infrastructure & Orchestration:
- Kubernetes (EKS/Kind) for container orchestration
- Terraform for infrastructure as code
- Docker for containerization
- Helm for application deployment
Monitoring & Observability:
- Grafana dashboards for real-time metrics
- Prometheus for metrics collection
Following will be done in future:
- Custom alerting for system health
- Distributed tracing for performance debugging
🛠️ Exploring the Platform
The complete platform is available as an open-source project on GitHub, featuring:
- Comprehensive documentation for each deployment configuration
- Infrastructure templates using Terraform and Kubernetes
- Monitoring setup with pre-configured Grafana dashboards
- Sample applications demonstrating multi-domain event processing
- Performance tuning guides for production optimization
Repository: RealtimeDataPlatform
Whether you're building a proof of concept or deploying at enterprise scale, the platform provides the foundation for modern real-time analytics infrastructure.
📈 Observability & Operations
Comprehensive Monitoring:
The platform includes production-ready observability through Grafana dashboards tracking:
- Message flow metrics: Throughput, latency, and backlog across all components
- System health: Resource utilization, error rates, and availability
- Business metrics: Event processing rates by domain and event type
- Performance insights: Query execution times and optimization opportunities
Operational Features:
- Checkpoint-based recovery for zero data loss
- Horizontal scaling based on workload patterns
- Cost tracking and optimization recommendations
🔮 Platform Evolution
The platform continues to evolve with planned enhancements:
- Multi-cloud deployment across AWS, GCP, and Azure
- Stream SQL interface for simplified data transformations
- ML pipeline integration for real-time inference
- Enhanced security with end-to-end encryption
- Intelligent auto-scaling based on workload patterns
💡 Key Insights
Building this real-time streaming platform highlighted several critical design principles:
1. Component Synergy: The combination of Pulsar's messaging reliability, Flink's processing power, and ClickHouse's analytical performance creates a platform greater than the sum of its parts.
2. Integration Complexity: Solving the Flink-ClickHouse integration challenge required custom solutions, but the performance benefits justify the engineering investment.
3. Scalable Architecture: Designing for multiple deployment tiers from day one enables organizations to start small and scale without architectural rewrites.
4. Operational Excellence: Production-ready monitoring and automation are essential for managing distributed streaming systems at scale.
5. Cost Optimization: Thoughtful resource allocation and component tuning can achieve enterprise performance at reasonable operational costs.
🤝 Community & Future
This platform represents a comprehensive approach to real-time data infrastructure that balances performance, cost, and operational simplicity. By open-sourcing the complete solution, the goal is to accelerate adoption of modern streaming architectures across the industry.
Interested in real-time streaming platforms?
- ⭐ Star the repository
- 💬 Share your streaming architecture experiences in the comments
- 🔗 Connect for discussions about real-time data engineering challenges
What's your experience with real-time streaming platforms? Have you tackled similar integration challenges? I'd love to hear about your approach and lessons learned!
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