Hybrid Storage: The Best of Both Worlds
Real System: ClickHouse Hybrid Storage
1M inserts/sec + 100ms analytics on same data
The Challenge
-- OLTP: High-frequency real-time inserts
INSERT INTO Listens (listen_id, user_id, song_id, rating, listen_time)
VALUES (75382914, 12345, 456, 4.5, NOW());
-- Happening 1M times per second
-- OLAP: Complex analytics on the same data
SELECT song_id, COUNT(*) as play_count, AVG(rating)
FROM Listens
WHERE listen_time > NOW() - INTERVAL '1 hour'
GROUP BY song_id
ORDER BY play_count DESC;
-- Must complete in <100ms for dashboards
The Impossible Triangle: Pick All Three?
Row Storage
Columnar Storage
Hybrid System
How It Works: The Three Pillars
Adaptive Layout
Fresh Listens (< 1 hour) stored in row format for fast inserts. Aged listening data automatically converted to columnar.
Smart Partitioning
Time-based partitions by listen_time with automatic merging. Background process converts row parts to columnar when listening activity slows.
Unified Query Engine
Single SQL interface. Query planner automatically reads from both row and columnar parts, merges results transparently.
Key Insight: listen_time is the natural partitioning dimension
Recent listening data needs fast writes, historical listens need fast analytics. Hybrid systems automatically optimize storage layout based on listen_time and access patterns.
Real-World Implementation: ClickHouse MergeTree
ClickHouse's MergeTree engine demonstrates hybrid storage in production at companies like Cloudflare, Uber, and Bloomberg.
The Magic Process:
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Incoming Listens land in small row-oriented "parts"
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Background merging combines parts into larger, columnar-compressed segments
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Query execution reads from both formats simultaneously
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Automatic optimization based on listen_time age and size thresholds
Performance Results:
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Insert throughput: 1M+ Listens/second sustained
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Query latency: Sub-second analytics on billions of listening events
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Storage efficiency: 10:1 compression on real-world listening data
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Operational simplicity: Single system, not separate OLTP + OLAP
🔗 Storage Engine Deep Dive
ClickHouse's "parts" + background merging pattern is a variation of LSM trees - the key innovation being that traditional LSM trees merge row-to-row files, while ClickHouse merges row-to-columnar. We'll explore LSM tree fundamentals in LSM Trees.
Academic Foundation: Adaptive Storage Research
The concept of adaptive storage layouts has deep research roots. Systems that automatically switch between row and columnar formats based on workload patterns represent a key advancement in database architecture.
Key Research Areas:
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Workload-adaptive storage: Systems that learn access patterns and optimize layout
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Online reorganization: Converting between formats without stopping queries
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Hybrid indexing: Combining row-based indexes with columnar compression
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Multi-format query processing: Executing queries across different storage layouts
The paper below explores the theoretical foundations and practical implementations of adaptive storage systems:
Beyond ClickHouse: Hybrid Everywhere
The Hybrid Revolution: Once considered impossible, hybrid storage is now everywhere:
Modern Examples: - Apache Kudu: Columnar storage with delta row updates - CockroachDB: Row storage with columnar indexes for JSON - MongoDB: Document storage with columnar analytics nodes - Parquet: Column chunks organized in row groups - Delta Lake: Parquet files with row-based delta logs
Why Hybrid Storage Wins
Operational Benefits
- Single system: No complex ETL pipelines
- Real-time analytics: Query fresh data immediately
- Cost efficiency: One system instead of two
- Consistency: No sync lag between OLTP/OLAP
Technical Advantages
- Workload adaptivity: Storage optimizes for actual usage
- Automatic tuning: No manual format decisions
- Space efficiency: Best compression for each data age
- Query flexibility: Fast regardless of access pattern
Key Takeaways
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False Choice Resolved: You don't have to pick between fast writes OR fast reads anymore
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Time-Based Optimization: Data age is the natural dimension for storage layout decisions
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Hybrid Is Mainstream: Modern systems increasingly use adaptive storage approaches
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Query Transparency: Applications don't need to know about underlying format switching
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Operational Simplicity: One hybrid system often beats separate OLTP + OLAP systems
The Bottom Line: Hybrid storage systems prove that with clever engineering, you can break traditional database trade-offs and get the best of both worlds.