High-Performance Storage with RocksDB

The Definitive Guide to Building High-Performance Embedded Storage Systems

Introduction

Chapter 1: What Is RocksDB and Why It Matters

1. The Key-Value Store That Powers Modern Infrastructure
2. From LevelDB to RocksDB: A History of Evolution
3. LSM-Trees: The Core Innovation Behind Performance
4. Where RocksDB Lives Today: Ecosystem and Adoption
5. When to Choose RocksDB (and When Not To)

Chapter 2: Getting Started with RocksDB

1. Building and Installing RocksDB from Source
2. Your First Database: Opening, Writing, Reading, Closing
3. Understanding the Basic C++ API Surface
4. Integrating RocksDB into Modern C++ Projects
5. Quick Start with Alternative Language Bindings

Chapter 3: Core Architecture and Data Flow

1. The Write Path: From Application Call to Disk
2. MemTables and Immutable MemTables
3. SST Files and the Level Hierarchy
4. The Read Path: Lookup, Bloom Filters, and Caching
5. Write-Ahead Log and Crash Recovery

Chapter 4: Data Models and Column Families

1. Keys, Values, and Encoding Strategies
2. Column Families: Isolation, Configuration, and Use Cases
3. Designing Key Spaces for Performance
4. Prefixes, Ranges, and Secondary Index Patterns
5. Versioning and Multi-Tenancy with Column Families

Chapter 5: Advanced Write Operations

1. Write Batches: Atomicity and Performance
2. Merge Operators: Semantics and Implementation
3. Conditional Writes and Compare-and-Swap Patterns
4. Single-Delete vs Delete: Handling Tombstones Correctly
5. Optimizing Write Throughput in Production Workloads

Chapter 6: Reading Data: Iterators, Snapshots, and Transactions

1. Point Lookups and Read Options
2. Iterators: Range Scans, Prefix Seeks, and Performance
3. Snapshots: Consistent Views Without Locking
4. Optimistic Transactions in RocksDB
5. Isolation Levels and Concurrency Semantics

Chapter 7: Compaction: The Heart of RocksDB

1. Why Compaction Exists: Trade-offs Explained
2. Level-Based Compaction (LEVEL)
3. Universal Compaction: FIFO, Size-Tiered, and Windowed
4. FIFO Compaction for Log-Like Workloads
5. Choosing and Tuning Compaction for Your Workload

Chapter 8: Memory Management and Caching

1. The Cache Hierarchy: Block Cache, Table Cache, and More
2. MemTable Memory Budgeting and Auto-Tuning
3. Shared vs Per-Instance Caches
4. Subcompaction and Parallelism Controls
5. Diagnosing and Fixing Memory Pressure Issues

Chapter 9: Compression, Bloom Filters, and SST File Format

1. The SST File Format: Anatomy of a Table
2. Compression Algorithms: Choosing the Right One
3. Bloom Filters: Standard, Partitioned, and Full
4. Prefix Bloom Filters for Range Queries
5. Tuning Block Size and Filter Policies Together

Chapter 10: Configuration and Options Reference

1. DatabaseOptions: Global Configuration
2. ColumnFamilyOptions: Per-Column Tuning
3. Env and IO Control: Managing System Resources
4. Runtime Options vs Build-Time Options
5. A Practical Configuration Checklist for Production

Chapter 11: Performance Tuning and Benchmarking

1. Understanding RocksDB Benchmarks and Metrics
2. Using the Built-in rocksdbtool and ldb Utilities
3. Profiling Hot Paths: CPU, IO, and Lock Contention
4. Tuning for Read-Heavy Workloads
5. Tuning for Write-Heavy and Mixed Workloads

Chapter 12: Monitoring, Debugging, and Operations

1. Runtime Stats, Histograms, and Property API
2. Integrating with Prometheus and Grafana
3. Reading and Interpreting ROCKSDB_LOG_LEVEL Output
4. Debugging Slow Queries and Stalls
5. Handling Corruption: Detection and Recovery

Chapter 13: Backup, Restore, and Checkpoints

1. Full Backups with BackupEngine
2. Incremental Backups and Log Shipping Patterns
3. Checkpoints for Instant Snapshots and Migration
4. Point-in-Time Recovery Strategies
5. Cross-Machine Restore and Version Compatibility

Chapter 14: Concurrency, Replication, and Distributed Patterns

1. Concurrent Access Patterns and Thread Safety
2. Using Multiple Instances Safely on One Machine
3. Replication Patterns Built on Top of RocksDB
4. Integrating with Raft, Paxos, and Distributed Consensus
5. RocksDB in Microservices and Cloud-Native Architectures

Chapter 15: Real-World Production Use Cases

1. Time-Series Data and Metrics Storage
2. Blockchain and Ledger Systems
3. Caching Layers and Edge Databases
4. IoT Device Storage and Embedded Analytics
5. Lessons Learned from Production Deployments

Chapter 16: Comparisons, Alternatives, and Migration

1. RocksDB vs LevelDB: What Changed and Why It Matters
2. RocksDB vs WiredTiger: MongoDB’s Choice
3. RocksDB vs SQLite: Different Problems, Different Tools
4. RocksDB in the LSM Family: Cassandra, ScyllaDB, Badger
5. Migrating to RocksDB: Strategies and Pitfalls

Chapter 17: Security, Limitations, and Future Directions

1. Security Considerations: Encryption, Access Control, Sandboxing
2. Known Limitations and Anti-Patterns to Avoid
3. The RocksDB Roadmap and Upcoming Features
4. When to Look Beyond RocksDB
5. Final Recommendations for Production Systems

Conclusion

References