ClickHouse MergeTree and HBase are the same thing at their core.
Not literally — but architecturally, they share the same DNA: the LSM Tree (Log-Structured Merge Tree).
Here's how it works:
1. Writes hit memory first → fast, no disk I/O
2. When memory fills, flush to an immutable sorted file on disk
3. Background compaction merges files → removes duplicates, applies deletes
4. Bloom filters + sparse indexes make reads fast without scanning everything
HBase calls these HFiles. ClickHouse calls them Parts. Cassandra calls them SSTables. Same idea.
What ClickHouse adds on top:
★ Columnar layout inside each part (OLAP-optimized)
★ The merge step does useful analytical work — deduplication (ReplacingMergeTree), summation (SummingMergeTree), pre-aggregation (AggregatingMergeTree)
★ Sparse indexing at granule level (8192 rows) rather than row-level
I still teach HBase in my data engineering course — as a NoSQL example and as a core part of the Hadoop ecosystem. And honestly, I started my DE career working with it.
Sometimes I wondered: is this too specific? Should I simplify the curriculum and drop it?
But my teaching philosophy has always been to explain technologies by focusing on what's fundamental and shared across many systems. And this connection — HBase and ClickHouse both rooted in LSM Trees — is exactly why that approach pays off.
The tools change. The patterns underneath them don't.
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1March 16, 2026 447 2
