Secondary Index Partitioning

Overview

Secondary indexes enable efficient queries on non-partition key attributes in distributed systems. The choice between global and local secondary indexes involves trade-offs between query flexibility, consistency, and maintenance overhead.

Types of Secondary Indexes

Global Secondary Indexes (GSI)

• Separate partitioning: Index partitioned differently from base table
• Independent distribution: Index data spread across different nodes
• Query flexibility: Support queries on any indexed attribute
• Cross-partition queries: May require querying multiple partitions

Local Secondary Indexes (LSI)

• Same partition key: Index shares partition key with base table
• Co-located data: Index data stored with corresponding base data
• Query limitations: Can only query within same partition
• Strong consistency: Consistent with base table within partition

Hybrid Approaches

• Multi-level indexes: Combine global and local strategies
• Composite indexes: Index on multiple attributes
• Covering indexes: Include additional columns in index
• Partial indexes: Index subset of data based on conditions

Global Secondary Index Design

Partitioning Strategies

• Attribute-based: Partition by indexed attribute value
• Hash partitioning: Distribute index entries evenly
• Range partitioning: Group similar values together
• Composite partitioning: Use multiple attributes for partitioning

Query Patterns

• Point queries: Look up specific index values
• Range queries: Scan ranges of index values
• Prefix queries: Search by attribute prefixes
• Multi-attribute queries: Combine multiple indexed attributes

Consistency Models

• Eventually consistent: Index updates lag behind base table
• Strongly consistent: Index always reflects current state
• Read-your-writes: See own writes immediately
• Monotonic reads: Reads never go backwards in time

Local Secondary Index Design

Co-location Benefits

• Single-node queries: Query data and index on same node
• Strong consistency: Index always consistent with base data
• Lower latency: No cross-partition communication needed
• Simpler transactions: ACID properties within partition

Limitations

• Partition key requirement: Must include partition key in queries
• Limited query patterns: Cannot query across partitions
• Hot partitions: Popular index values create hot spots
• Storage overhead: Index data increases partition size

Use Cases

• Partition-scoped queries: Queries within known partitions
• Strong consistency needs: Require immediate consistency
• Single-tenant applications: Data naturally partitioned by tenant
• Time-series data: Queries within time windows

Implementation Strategies

Asynchronous Index Updates

• Event-driven: Update indexes in response to base table changes
• Message queues: Use queues to decouple index updates
• Batch processing: Update indexes in batches
• Retry mechanisms: Handle failed index updates

Synchronous Index Updates

• Transactional: Update indexes in same transaction
• Two-phase commit: Coordinate across multiple systems
• Performance impact: Slower writes due to coordination
• Consistency guarantee: Strong consistency between table and indexes

Index Maintenance

• Compaction: Merge index segments for efficiency
• Rebuilding: Recreate indexes from base data
• Cleanup: Remove obsolete index entries
• Optimization: Adjust index structure for query patterns

Query Processing

Query Planning

• Index selection: Choose best index for query
• Execution plan: Determine optimal query strategy
• Cost estimation: Estimate query execution cost
• Parallel execution: Coordinate across multiple partitions

Scatter-Gather Queries

• Fan-out: Send queries to multiple partitions
• Result aggregation: Combine results from partitions
• Ordering: Merge sorted results from partitions
• Pagination: Handle large result sets efficiently

Index Intersection

• Multiple indexes: Use multiple indexes for single query
• Bitmap operations: Combine index results efficiently
• Selectivity: Choose most selective indexes first
• Optimization: Minimize data scanned

Performance Considerations

Write Performance

• Index overhead: Additional writes for index maintenance
• Synchronization cost: Coordinate updates across partitions
• Batch efficiency: Amortize costs across multiple operations
• Hotspot avoidance: Distribute writes across partitions

Read Performance

• Index selectivity: How much data index eliminates
• Seek vs scan: Balance between index seeks and scans
• Cache efficiency: Keep frequently used index data in memory
• Parallelization: Execute queries across partitions concurrently

Storage Overhead

• Index size: Additional storage for index data
• Replication: Replicate indexes for availability
• Compression: Reduce index storage requirements
• Cleanup: Remove unused or obsolete indexes

System Examples

Amazon DynamoDB

• GSI: Independently partitioned global indexes
• LSI: Local indexes sharing partition key
• Eventually consistent: GSI updates lag base table
• Query flexibility: GSI supports different access patterns

Apache Cassandra

• Secondary indexes: Local to each node
• Global queries: Scatter-gather across all nodes
• Performance limitations: Not recommended for high-cardinality attributes
• Materialized views: Alternative to secondary indexes

Elasticsearch

• Inverted indexes: Optimized for text search
• Distributed indexes: Sharded across multiple nodes
• Real-time: Near real-time index updates
• Flexible queries: Support complex query combinations

MongoDB

• Compound indexes: Multiple attributes in single index
• Shard key indexes: Required indexes on shard keys
• Background building: Build indexes without blocking operations
• Index intersection: Combine multiple single-field indexes

Best Practices

Index Design

1. Understand query patterns: Design indexes for actual queries
2. Choose appropriate type: GSI for flexibility, LSI for consistency
3. Consider cardinality: High-cardinality attributes for GSI
4. Plan for growth: Design indexes to scale with data
5. Monitor performance: Track index usage and performance

Query Optimization

1. Use selective indexes: Choose indexes that filter most data
2. Combine with filters: Add non-indexed filters to reduce results
3. Paginate results: Handle large result sets efficiently
4. Cache frequently accessed data: Reduce index lookup costs
5. Monitor query patterns: Identify optimization opportunities

Operational Management

1. Monitor index health: Track index size and performance
2. Plan index maintenance: Schedule rebuilding and optimization
3. Handle index failures: Implement fallback strategies
4. Document index usage: Maintain index documentation
5. Regular review: Evaluate and optimize index strategies

Secondary index partitioning is crucial for enabling flexible queries in distributed systems while managing the trade-offs between performance, consistency, and operational complexity.