🚀 Building High-Performance Real-Time Data Pipelines with Go and Apache Druid
I recently worked on implementing a distributed ETL system that processes millions of events per day. This post explores the technical decisions, architectural patterns, and performance optimizations that made it possible.
The challenge: build a system that could ingest high-velocity streaming data from multiple sources, transform it on-the-fly, and make it queryable within seconds. Here's how I approached it.
🎯 System Requirements
The system needed to handle:
- Multiple concurrent WebSocket connections with automatic reconnection
- High-throughput message processing (100K+ messages/second)
- Sub-second end-to-end latency from ingestion to query
- Horizontal scalability to handle traffic spikes
- Data durability with replay capabilities
After evaluating various technologies, I chose Go for its excellent concurrency model and built a custom pipeline optimized for our specific use case.
🏗 Architecture Overview
[Data Sources] --WebSocket--> [Producer Service] --Kafka--> [Consumer Service] ---> [Apache Druid]
| |
(Protobuf) (Transformation)
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[Monitoring API] [Processing Rules]
Technology Choices:
- Go: Excellent concurrency primitives and low memory footprint
- Protocol Buffers: Reduced message size by 75% compared to JSON
- Kafka: Provides buffering, replay capability, and decoupling
- Apache Druid: Real-time OLAP queries on streaming data
- Gin Framework: Lightweight HTTP server for monitoring endpoints
🔧 Key Implementation Patterns
1. Concurrent WebSocket Management
Managing multiple WebSocket connections efficiently required careful goroutine orchestration:
// Connection pooling with automatic recovery
type ConnectionManager struct {
connections sync.Map
limiter *rate.Limiter
metrics *Metrics
}
func (cm *ConnectionManager) handleConnection(ctx context.Context, source string) {
for {
select {
case <-ctx.Done():
return
default:
if err := cm.connect(source); err != nil {
cm.metrics.RecordError(source, err)
time.Sleep(cm.backoff(source))
continue
}
}
}
}
2. Efficient Message Processing
Using goroutine pools and channels for backpressure control:
type ProcessingPipeline struct {
input chan Message
output chan ProcessedMessage
workers int
transform TransformFunc
}
func (p *ProcessingPipeline) Start(ctx context.Context) {
for i := 0; i < p.workers; i++ {
go p.worker(ctx)
}
}
func (p *ProcessingPipeline) worker(ctx context.Context) {
for {
select {
case msg := <-p.input:
processed, err := p.transform(msg)
if err != nil {
// Handle error
continue
}
select {
case p.output <- processed:
case <-ctx.Done():
return
}
case <-ctx.Done():
return
}
}
}
3. Protocol Buffers Optimization
Switching from JSON to Protocol Buffers was a game-changer:
syntax = "proto3";
// Optimized for minimal size and fast parsing
message StreamData {
uint32 source_id = 1; // Using IDs instead of strings
uint32 symbol_id = 2; // Pre-mapped symbols
uint64 price_e8 = 3; // Fixed-point arithmetic
uint64 volume_e8 = 4; // Avoiding floating point
uint64 timestamp_ms = 5; // Millisecond precision
bool is_buy = 6; // Boolean instead of enum
}
Optimization results:
- 75% reduction in message size
- 10x faster serialization/deserialization
- Eliminated floating-point precision issues
4. Batching and Buffering Strategy
Implemented adaptive batching to balance latency and throughput:
type AdaptiveBatcher struct {
maxBatchSize int
maxWaitTime time.Duration
currentBatch []Message
lastFlush time.Time
}
func (b *AdaptiveBatcher) Add(msg Message) {
b.currentBatch = append(b.currentBatch, msg)
if b.shouldFlush() {
b.flush()
}
}
func (b *AdaptiveBatcher) shouldFlush() bool {
return len(b.currentBatch) >= b.maxBatchSize ||
time.Since(b.lastFlush) >= b.maxWaitTime
}
5. Zero-Allocation Processing
Minimized garbage collection pressure with object pooling:
var messagePool = sync.Pool{
New: func() interface{} {
return &ProcessedMessage{
Fields: make(map[string]interface{}, 10),
}
},
}
func processMessage(raw []byte) error {
msg := messagePool.Get().(*ProcessedMessage)
defer func() {
msg.Reset()
messagePool.Put(msg)
}()
// Process without allocating new objects
return msg.Unmarshal(raw)
}
6. Druid Integration with Rollups
Configured Druid for efficient storage and fast queries:
// Pre-aggregation at ingestion time
type DruidIngestionConfig struct {
Granularity string
QueryGranularity string
Rollup bool
Metrics []MetricSpec
}
// Reduced storage by 90% with proper rollups
config := DruidIngestionConfig{
Granularity: "minute",
QueryGranularity: "second",
Rollup: true,
Metrics: []MetricSpec{
{Type: "doubleSum", Name: "volume"},
{Type: "doubleMin", Name: "low"},
{Type: "doubleMax", Name: "high"},
},
}
📊 Performance Achievements
Through iterative optimization:
- Throughput: Scaled from 10K to 100K+ messages/second
- Latency: P99 latency under 50ms end-to-end
- Resource Efficiency: 80% reduction in memory usage
- Reliability: 99.95% uptime with zero data loss
Key Optimizations:
- Memory Management
- Object pooling reduced GC pauses by 90%
- Pre-allocated buffers for hot paths
- CPU Optimization
- SIMD operations for batch processing
- Lock-free data structures where possible
- Network Efficiency
- TCP_NODELAY for WebSocket connections
- Compression for Kafka messages
🛠 Monitoring and Observability
Built comprehensive monitoring using Gin and Prometheus:
// Custom middleware for detailed metrics
func MetricsMiddleware() gin.HandlerFunc {
return func(c *gin.Context) {
start := time.Now()
path := c.FullPath()
c.Next()
// Record metrics
duration := time.Since(start)
status := c.Writer.Status()
httpDuration.WithLabelValues(path, strconv.Itoa(status)).
Observe(duration.Seconds())
httpRequests.WithLabelValues(path, strconv.Itoa(status)).
Inc()
}
}
// Health checks with dependency verification
r.GET("/health/live", livenessProbe)
r.GET("/health/ready", readinessProbe)
r.GET("/metrics", promhttp.Handler())
💡 Technical Insights
1. Concurrency Patterns in Go
- Fan-out/fan-in for parallel processing
- Worker pools with dynamic sizing based on load
- Context propagation for graceful shutdowns
- Select with default to prevent goroutine blocking
2. Performance Engineering
- Profile-guided optimization revealed surprising bottlenecks
- Benchmark-driven development for critical paths
- Memory alignment matters for high-frequency operations
- Batch operations dramatically reduce system calls
3. Distributed Systems Challenges
- Exactly-once semantics required careful Kafka configuration
- Clock synchronization critical for time-series data
- Partition strategies affect both performance and correctness
- Circuit breakers prevent cascade failures
🔗 Key Takeaways
Building this real-time ETL system reinforced several principles:
- Measure, don't guess - Profiling revealed unexpected bottlenecks
- Design for failure - Every component can and will fail
- Optimize the hot path - Focus efforts where they matter most
- Simple > Clever - Maintainable code beats clever hacks
The combination of Go's simplicity, Protocol Buffers' efficiency, and Druid's analytical power proved to be an excellent choice for building high-performance data pipelines.
Technologies Used: Go, Protocol Buffers, WebSockets, Apache Kafka, Apache Druid, Gin Framework, Prometheus
Performance: 100K+ messages/sec, <50ms P99 latency, 99.95% uptime