Sharding

How sharding works

Sharding divides data across multiple database nodes using a sharding key (also called a partition key). The key determines which shard will store specific records. Common sharding strategies include:

• Range-based: Data divided by value ranges (e.g., timestamps 2023-01 to 2023-06 on Shard A)
• Hash-based: Records distributed using a hash function on the key
• Directory-based: External mapping service tracks data location

Benefits for time-series workloads

Sharding is particularly valuable for time-series databases because:

1. Time-based sharding aligns naturally with temporal data
2. Write operations can be distributed across shards
3. Parallel query execution improves read performance
4. Individual shards can be optimized for specific time ranges

Key considerations

Performance implications

• Reduced contention for system resources
• Improved write throughput through distribution
• Potential for increased query latency across shards
• Network overhead for cross-shard operations

Operational complexity

Managing a sharded architecture requires careful consideration of:

• Shard rebalancing strategies
• Cross-shard query coordination
• Backup and recovery procedures
• Monitoring and maintenance across nodes

Implementation strategies

Shard key selection

The choice of shard key is crucial for:

• Even data distribution
• Query efficiency
• Future scalability
• Minimizing cross-shard operations

For time-series data, common shard keys include:

• Timestamp ranges
• Customer/tenant IDs
• Geographic location
• Combination of multiple fields

Query routing

Query plans must be optimized to:

1. Identify relevant shards
2. Execute parallel operations where possible
3. Aggregate results across shards
4. Handle failures gracefully

Monitoring and maintenance

Effective shard management requires monitoring:

• Shard size and distribution
• Query performance across shards
• Resource utilization
• Rebalancing operations
• Cross-shard operation frequency

Regular maintenance tasks include:

• Rebalancing data across shards
• Adding or removing shards
• Backup and recovery procedures
• Performance optimization

Real-world applications

Sharding is essential for high-volume time-series applications:

• Financial market data storage
• IoT sensor data collection
• Log aggregation systems
• Large-scale monitoring solutions

For example, a financial market data system might shard trade data by:

• Date ranges for historical analysis
• Symbol ranges for real-time queries
• Exchange ID for regulatory reporting

This enables efficient processing of both real-time tick data and historical analysis workloads.