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

1M/sec

Fast inserts

Single row access

Analytics: 30 seconds

Columnar Storage

50ms

Fast analytics

Compressed scans

Inserts: 10K/sec

Hybrid System

BOTH

1M inserts/sec

100ms analytics

How?

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:

Incoming Listens land in small row-oriented "parts"
Background merging combines parts into larger, columnar-compressed segments
Query execution reads from both formats simultaneously
Automatic optimization based on listen_time age and size thresholds

Performance Results:

Insert throughput: 1M+ Listens/second sustained
Query latency: Sub-second analytics on billions of listening events
Storage efficiency: 10:1 compression on real-world listening data
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.

Adaptive Storage Research

Key Research Areas:

Workload-adaptive storage: Systems that learn access patterns and optimize layout
Online reorganization: Converting between formats without stopping queries
Hybrid indexing: Combining row-based indexes with columnar compression
Multi-format query processing: Executing queries across different storage layouts

The paper below explores the theoretical foundations and practical implementations of adaptive storage systems: