load-balancing

Load Balancing Example

This example demonstrates how multiple consumers work together to process streams in a load-balanced fashion using the Redis Stream Client Go library.

What This Example Shows

• Multiple consumers processing streams concurrently
• Load balancing of stream assignments across consumers
• Producer generating continuous stream of messages
• Statistics and monitoring of consumer performance
• Automatic claiming of streams from failed consumers

Architecture

Producer → LBS (Load Balancer Stream) → Consumer 1
                                     → Consumer 2
                                     → Consumer 3
                                     → ...

The Load Balancer Stream (LBS) distributes incoming stream assignments to available consumers in a round-robin fashion.

Prerequisites

1. Redis server running on localhost:6379
2. Go 1.22 or later
3. Multiple terminal windows for running multiple consumers

Running the Example

Step 1: Start Redis Server

# Using Docker
docker run -d -p 6379:6379 redis:7.2.3

# Or use your local Redis installation
redis-server

Step 2: Start Multiple Consumers

Open multiple terminal windows and run:

Terminal 1 (Consumer 1):

export POD_NAME=consumer-1
cd examples/load-balancing
go run main.go

Terminal 2 (Consumer 2):

export POD_NAME=consumer-2
cd examples/load-balancing
go run main.go

Terminal 3 (Consumer 3):

export POD_NAME=consumer-3
cd examples/load-balancing
go run main.go

Step 3: Start the Producer

Terminal 4 (Producer):

cd examples/load-balancing
go run main.go producer

Expected Output

Consumer Output:

2024/01/15 10:30:00 Starting consumer: consumer-1
2024/01/15 10:30:00 Created client with ID: redis-consumer-consumer-1
2024/01/15 10:30:00 🎉 [consumer-1] New stream assigned
2024/01/15 10:30:00 🔄 [consumer-1] Processing stream: order-stream-0
2024/01/15 10:30:00 📋 [consumer-1] Stream details: map[amount:100 created_at:2024-01-15T10:30:00Z customer_id:customer-0 order_id:order-0 priority:low status:pending]
2024/01/15 10:30:04 ✅ [consumer-1] Completed processing stream: order-stream-0 (took 4s)
2024/01/15 10:30:10 📊 [consumer-1] Statistics: Processed 1 streams

Producer Output:

2024/01/15 10:30:05 🏭 Starting producer...
2024/01/15 10:30:05 📤 Produced message 0: order-stream-0
2024/01/15 10:30:05 📤 Produced message 1: order-stream-1
2024/01/15 10:30:05 📤 Produced message 2: order-stream-2
2024/01/15 10:30:05 📤 Produced message 3: order-stream-3
2024/01/15 10:30:05 📤 Produced message 4: order-stream-4

Key Features Demonstrated

1. Load Balancing

• Streams are distributed among consumers in round-robin fashion
• Each consumer processes different streams
• No single consumer gets overwhelmed

2. Priority Processing

The example includes three priority levels:

• High priority: 1 second processing time
• Normal priority: 2 seconds processing time
• Low priority: 4 seconds processing time

3. Statistics Monitoring

Each consumer reports:

• Total streams processed
• Recent activity (last 10 seconds)
• Processing times and completion status

4. Failure Recovery

If you kill a consumer (Ctrl+C), other consumers will automatically claim its unprocessed streams.

Testing Scenarios

Scenario 1: Basic Load Balancing

1. Start 3 consumers
2. Start the producer
3. Observe how streams are distributed among consumers

Scenario 2: Consumer Failure

1. Start 3 consumers and producer
2. Kill one consumer (Ctrl+C)
3. Observe other consumers claiming the failed consumer's streams

Scenario 3: Dynamic Scaling

1. Start with 1 consumer and producer
2. Add more consumers while producer is running
3. Observe load redistribution

Scenario 4: High Load Testing

1. Start multiple consumers
2. Modify producer to generate more messages
3. Monitor consumer statistics

Code Walkthrough

Producer Logic

// Create messages with different priorities
lbsMessage := notifs.LBSMessage{
    DataStreamName: fmt.Sprintf("order-stream-%d", messageID),
    Info: map[string]interface{}{
        "order_id":    fmt.Sprintf("order-%d", messageID),
        "priority":    []string{"low", "normal", "high"}[messageID%3],
        // ... other fields
    },
}

// Add to LBS
redisClient.XAdd(ctx, &redis.XAddArgs{
    Stream: "load-balance-demo-input",
    Values: map[string]interface{}{
        "lbs-input": string(messageData),
    },
})

Consumer Processing

// Handle new stream assignment
case notifs.StreamAdded:
    go handleStreamAdded(ctx, client, notification.Payload.(string))

// Handle claiming from failed consumer
case notifs.StreamExpired:
    client.Claim(ctx, notification.Payload.(string))
    go handleClaimedStream(ctx, client, notification.Payload.(string))

Statistics Tracking

// Track processed streams
var processedStreams sync.Map
var streamCount int32

// Update counters
processedStreams.Store(streamName, time.Now())
atomic.AddInt32(&streamCount, 1)

Monitoring and Debugging

Redis CLI Commands

# Check LBS stream
redis-cli XINFO STREAM load-balance-demo-input

# Check consumer group
redis-cli XINFO GROUPS load-balance-demo-input

# Check pending messages
redis-cli XPENDING load-balance-demo-input load-balance-demo-group

# Monitor keyspace notifications
redis-cli --csv psubscribe '__keyevent@0__:expired'

Environment Variables

# Set unique consumer IDs
export POD_NAME=consumer-$(hostname)-$(date +%s)

# Enable debug logging
export DEBUG=1

Performance Tuning

Consumer Optimization

• Adjust processing times based on your use case
• Implement proper error handling and retries
• Use connection pooling for database operations

Producer Optimization

• Batch message production
• Use pipelining for better throughput
• Monitor Redis memory usage

Redis Configuration

# Increase memory if needed
redis-cli CONFIG SET maxmemory 1gb

# Optimize for streams
redis-cli CONFIG SET stream-node-max-entries 1000

Troubleshooting

No Load Balancing

• Ensure all consumers use the same service name
• Check that consumers have unique POD_NAME values
• Verify Redis keyspace notifications are enabled

Messages Not Processing

• Check Redis connection and authentication
• Verify LBS stream exists: redis-cli XLEN load-balance-demo-input
• Check for errors in consumer logs

High Memory Usage

• Monitor stream lengths: redis-cli XLEN <stream-name>
• Implement proper message acknowledgment
• Consider stream trimming for old messages

Next Steps

After understanding load balancing, check out:

• Failure Recovery Example - Advanced failure handling
• Basic Usage Example - Fundamental concepts