Multi-Stream Data Merging
# Multi-Stream Data Merging Case Study
# Business Scenario
In enterprise-level applications, data often comes from multiple independent data sources, such as user behavior data from APPs, transaction data from e-commerce platforms, and inventory data from ERP systems. We need to merge these data streams in real-time to provide comprehensive business insights.
# Typical Scenarios
- E-commerce Platform: Merge user browsing data, order data, and inventory data
- Financial Services: Merge transaction data, user data, and risk control data
- Smart City: Merge traffic data, weather data, and population flow data
- Industrial Internet: Merge production data, quality data, and equipment status data
# Data Model
# Input Data Format
User Behavior Stream:
{
"user_id": "user_001",
"event_type": "page_view",
"page_url": "/product/123",
"timestamp": "2024-01-15T10:30:00Z"
}
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Order Stream:
{
"order_id": "order_123",
"user_id": "user_001",
"product_id": "prod_456",
"amount": 99.99,
"status": "paid",
"timestamp": "2024-01-15T10:30:05Z"
}
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Inventory Stream:
{
"product_id": "prod_456",
"stock_quantity": 100,
"warehouse_id": "wh_001",
"timestamp": "2024-01-15T10:30:10Z"
}
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# Expected Output Format
{
"user_id": "user_001",
"order_id": "order_123",
"product_id": "prod_456",
"amount": 99.99,
"page_view_count": 3,
"stock_quantity": 100,
"merge_timestamp": "2024-01-15T10:30:15Z"
}
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# Solutions
# Solution 1: Stream JOIN-Based Data Merging
Solution Description: Use StreamSQL's JOIN operation to merge data from multiple streams based on key fields (such as user_id, product_id). Support INNER JOIN, LEFT JOIN, and other JOIN types.
Applicable Scenarios:
- Data from multiple sources has clear association relationships
- Data volume is moderate, and real-time requirements are high
- Business logic is relatively simple, mainly key-value association
Data Input:
// User Stream
[
{"user_id": "user_001", "username": "Alice", "timestamp": "2024-01-15T10:30:00Z"},
{"user_id": "user_002", "username": "Bob", "timestamp": "2024-01-15T10:30:01Z"}
]
// Order Stream
[
{"order_id": "order_123", "user_id": "user_001", "amount": 99.99, "timestamp": "2024-01-15T10:30:05Z"},
{"order_id": "order_124", "user_id": "user_001", "amount": 199.99, "timestamp": "2024-01-15T10:30:10Z"}
]
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Expected Output:
{
"user_id": "user_001",
"username": "Alice",
"order_id": "order_123",
"amount": 99.99,
"merge_timestamp": "2024-01-15T10:30:05Z"
}
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# Solution 2: Message Queue-Based Data Merging
Solution Description: Use message queues (such as Kafka) as an intermediate layer to buffer and coordinate data from multiple streams. Merge data through window operations and state management.
Applicable Scenarios:
- Data volume is large, and real-time requirements are moderate
- Data arrival time differences are large
- Need to handle out-of-order data
- Business logic is complex, requiring complex state management
Data Input:
// Stream 1: User Behavior
{
"user_id": "user_001",
"behavior": "login",
"timestamp": "2024-01-15T10:30:00Z"
}
// Stream 2: Order Information
{
"order_id": "order_123",
"user_id": "user_001",
"amount": 99.99,
"timestamp": "2024-01-15T10:30:05Z"
}
// Stream 3: Product Information
{
"product_id": "prod_456",
"price": 99.99,
"category": "electronics",
"timestamp": "2024-01-15T10:30:10Z"
}
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Expected Output:
{
"user_id": "user_001",
"latest_behavior": "login",
"orders": [
{"order_id": "order_123", "amount": 99.99}
],
"products": [
{"product_id": "prod_456", "price": 99.99, "category": "electronics"}
],
"merge_timestamp": "2024-01-15T10:30:15Z"
}
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# Advanced Features
# 1. Watermark-Based Event Time Processing
Handle out-of-order data through watermark mechanisms, ensuring the correctness of data merging.
Configuration:
watermark:
strategy: bounded-out-of-orderness
max-out-of-orderness: 30s
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# 2. State Backend Configuration
Optimize state storage and recovery mechanisms to improve system reliability.
Configuration:
state-backend:
type: rocksdb
checkpoint-interval: 60s
min-pause-between-checkpoints: 30s
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# 3. Parallelism Tuning
Improve processing performance by adjusting parallelism.
Configuration:
parallelism:
source: 4
sink: 2
operator: 8
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# Performance Optimization
# 1. Join Strategy Optimization
Broadcast Join:
- Suitable for small table joins
- Reduce network transmission
- Improve join performance
Shuffle Join:
- Suitable for large table joins
- Ensure data distribution balance
- Avoid data skew
# 2. State Management Optimization
State TTL Configuration:
SET 'state.ttl' = '24h';
State Cleanup Strategy:
- Regular cleanup of expired state
- Use incremental cleanup
- Avoid full state scans
# 3. Memory Optimization
Memory Configuration:
memory:
network-buffer: 32mb
taskmanager.memory.process.size: 2gb
taskmanager.memory.managed.fraction: 0.4
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# Best Practices
# 1. Key Selection
- Choose high-cardinality fields as join keys
- Avoid using timestamp fields as join keys
- Ensure key uniqueness
# 2. Window Design
- Reasonably set window size based on business requirements
- Consider data arrival delay
- Balance real-time and accuracy
# 3. Error Handling
- Handle null values and data format errors
- Set appropriate retry mechanisms
- Record and monitor error data
# Running Results
# Solution 1 Output Example
{
"merged_data": {
"user_id": "user_001",
"username": "Alice",
"orders": [
{"order_id": "order_123", "amount": 99.99},
{"order_id": "order_124", "amount": 199.99}
],
"total_amount": 299.98
},
"merge_timestamp": "2024-01-15T10:30:15Z"
}
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# Solution 2 Output Example
{
"user_activity_summary": {
"user_id": "user_001",
"login_count": 3,
"order_count": 2,
"total_spent": 299.98,
"avg_order_amount": 149.99,
"last_activity": "2024-01-15T10:30:10Z"
}
}
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# Summary
Multi-stream data merging is one of the core functions of stream processing. StreamSQL provides flexible and powerful data merging capabilities:
- Stream JOIN: Suitable for scenarios with clear association relationships and moderate data volume
- Message Queue: Suitable for large data volume and complex business scenarios
- State Management: Ensure data consistency and reliability
- Performance Optimization: Improve system throughput through reasonable configuration
When choosing the appropriate solution, consider:
- Data volume and real-time requirements
- Business logic complexity
- System resource constraints
- Data quality and consistency requirements
Through reasonable design and optimization, efficient and stable multi-stream data merging can be achieved, providing strong support for real-time business decisions.