TODO.md

Database Scaling Patterns - TODO

This document tracks database scaling methods and patterns to implement in this project.

Completed

• Table Partitioning - simulate_database_partitioning/
  • Range partitioning, list partitioning, hash partitioning within a single database
• Database Sharding - simulate_database_sharding/
  • Horizontal sharding across multiple database instances with application-level routing

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To Implement

Replication Patterns

• Read Replicas

  • Primary-replica setup with read scaling
  • Write to primary, read from replicas
  • Replication lag handling strategies

• Multi-Primary Replication

  • Multiple writable nodes
  • Conflict resolution strategies
  • Use cases: geographic distribution, high availability

• Synchronous vs Asynchronous Replication

  • Trade-offs between consistency and performance
  • Quorum-based replication

Caching Patterns

• Database Query Caching

  • Redis/Memcached as cache layer
  • Cache invalidation strategies (TTL, write-through, write-behind)
  • Cache-aside pattern implementation

• Materialized Views

  • Pre-computed query results
  • Refresh strategies (on-demand, periodic, incremental)

Connection Management

• Connection Pooling

  • PgBouncer / pgpool-II setup
  • Pool sizing strategies
  • Transaction vs session pooling modes

• Connection Load Balancing

  • HAProxy / pgpool for distributing connections
  • Health checks and failover

Data Distribution Patterns

• Consistent Hashing

  • Virtual nodes for better distribution
  • Minimal data movement on node changes
  • Comparison with modulo-based sharding

• Directory-Based Sharding

  • Lookup service for shard mapping
  • Dynamic shard assignment
  • Trade-offs vs hash-based routing

• Range-Based Sharding

  • Sharding by date ranges, geographic regions
  • Handling hotspots and rebalancing

Vertical Scaling Techniques

• Vertical Partitioning

  • Splitting tables by columns
  • Separating hot and cold data
  • BLOB/CLOB storage separation

• Database Denormalization

  • Strategic redundancy for read performance
  • Maintaining consistency with triggers/application logic

Distributed Database Patterns

• Federation / Functional Partitioning

  • Separate databases by domain (users DB, orders DB, analytics DB)
  • Cross-database queries and joins

• CQRS (Command Query Responsibility Segregation)

  • Separate read and write models
  • Event sourcing integration
  • Eventual consistency handling

• Event Sourcing

  • Append-only event log
  • State reconstruction from events
  • Snapshots for performance

High Availability Patterns

• Automatic Failover

  • Patroni / repmgr for PostgreSQL HA
  • Leader election mechanisms
  • Split-brain prevention

• Hot Standby

  • Warm standby vs hot standby
  • Streaming replication setup
  • Point-in-time recovery (PITR)

Query Optimization Patterns

• Read-Write Splitting

  • Proxy-based routing (ProxySQL, pgpool)
  • Application-level routing
  • Handling replication lag for reads

• Database Indexing Strategies

  • B-tree, Hash, GIN, GiST indexes
  • Partial indexes, covering indexes
  • Index maintenance and bloat

Data Archival & Tiering

• Data Archival

  • Moving old data to cheaper storage
  • Archive tables with different storage parameters
  • Retrieval strategies

• Hot/Warm/Cold Data Tiering

  • Automatic data movement based on access patterns
  • Tablespace management
  • Integration with object storage (S3)

Multi-Tenancy Patterns

• Shared Database, Shared Schema

  • Tenant ID column approach
  • Row-level security (RLS)

• Shared Database, Separate Schema

  • Schema per tenant
  • Connection routing

• Separate Database per Tenant

  • Full isolation
  • Resource management challenges

Emerging Patterns

• NewSQL Databases

  • CockroachDB, TiDB, YugabyteDB
  • Distributed SQL with ACID guarantees
  • Comparison with traditional sharding

• Polyglot Persistence

  • Using multiple database types
  • PostgreSQL + Redis + Elasticsearch
  • Data synchronization strategies

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Priority Suggestions

High Priority (Common patterns)

1. Read Replicas
2. Connection Pooling
3. Database Query Caching
4. Read-Write Splitting

Medium Priority (Advanced scaling)

5. Consistent Hashing
6. CQRS
7. Automatic Failover
8. Multi-Tenancy (Shared Schema)

Lower Priority (Specialized use cases)

9. Event Sourcing
10. NewSQL exploration
11. Data Tiering

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Notes

• Each implementation should include:
  • Docker Compose setup
  • Go application demonstrating the pattern
  • DOCUMENTATION.md with line-by-line explanation
  • Pros/cons analysis
  • When to use guidance