βοΈ ClickHouse vs DuckDB
Two Columnar Engines, Very Different Philosophies β Interactive Deep Dive
ποΈ Architecture at a Glance
ClickHouse is a distributed OLAP server. DuckDB is an embedded analytical engine. Click each card to explore.
ClickHouse
- Model: Client-server (standalone process)
- Storage: MergeTree on disk (LSM-tree inspired)
- Concurrency: Multi-user, 100s of concurrent queries
- Scale: Petabytes, distributed across shards
- Best for: Real-time analytics dashboards, log/event stores
DuckDB
- Model: In-process (like SQLite for analytics)
- Storage: Buffer manager + row groups in single file
- Concurrency: Single-writer, MVCC for reads
- Scale: Single-node, fits-in-memory + spill
- Best for: Local data science, ETL, embedded analytics
πΎ Storage Engine Internals
Both are columnar, but the storage architectures diverge significantly. Toggle between them to compare.
Click a button above to visualize the storage architecture.
β‘ Query Execution: Push vs Pull
ClickHouse uses a push-based pipeline (producers push data). DuckDB uses a pull-based vectorized model (consumers pull batches). Watch them process the same query.