ClickHouse JOINs Are Slow: Here's Why (And What To Do About It)

You're not imagining it. Your ClickHouse JOINs really are slow.

If you've landed here after watching a 3-way JOIN grind your cluster to a halt — or after discovering that "even joins with small right tables are slow" — you're in good company. JOIN performance is one of the most common complaints in the ClickHouse community, and it's not a bug. It's a fundamental tradeoff in how columnar OLAP databases work.

This post explains why ClickHouse struggles with JOINs, what workarounds actually help, and when it might be time to reach for a different tool entirely.

Why ClickHouse JOINs Are Slow

ClickHouse is a columnar database optimized for scanning massive amounts of data very fast. It excels at queries like "sum revenue by region for the last 90 days" — full table scans over denormalized fact tables. That's its sweet spot.

JOINs are a different beast. They require:

• Row-level matching between tables (not column scans)
• Hash table construction in memory for the right-side table
• Random access patterns that break columnar compression benefits
• Memory pressure that can spill to disk and tank performance

ClickHouse's own documentation is blunt about this: "JOINs are inherently more expensive than querying from a single, denormalized table." Their official advice is to minimize JOINs, limit yourself to 3-4 per query, and denormalize whenever possible.

That's fine advice if your data model allows it. But if you're doing dimensional modeling, need many-to-many relationships, or can't afford the data duplication of full denormalization — you're stuck.

Common Workarounds (And Their Tradeoffs)

The ClickHouse ecosystem has developed several workarounds for JOIN pain. Each has real costs:

Denormalization is the most common advice, and it works — if you can tolerate the tradeoffs. You're essentially trading storage and write-time complexity for read performance. But if your dimension data changes frequently (customer tier updates, product catalog changes), you're now maintaining complex ETL to keep fact tables in sync.

Dictionary tables are clever but limited. They're essentially in-memory key-value stores that you query with `dictGet()` instead of JOIN. Fast, but dictionaries "silently discard duplicate keys, keeping only the last value." If you have one-to-many or many-to-many relationships, dictionaries quietly give you wrong answers.

Materialized views can help for specific JOIN patterns, but users report that "adding a single simple materialized view makes the DB become unresponsive, and you can't remove it afterwards, locks the database engine." They're also limited to a single JOIN, so complex dimensional models are out.

Upstream processing means admitting ClickHouse isn't the right place for JOINs and doing them elsewhere. That's honest, but now you're maintaining two systems and dealing with data freshness gaps between them.

When ClickHouse JOINs Are Fine

To be fair, ClickHouse JOINs work reasonably well in specific scenarios:

• Small dimension tables that fit comfortably in memory (under a few GB)
• Pre-sorted data where both tables are ordered by the join key
• Simple star schemas with 1-2 small dimension lookups
• Infrequent queries where you can tolerate multi-second response times

If your workload fits these constraints, the workarounds above may be enough. Tune your JOIN algorithms, denormalize where you can, and accept some queries will be slow.

When ClickHouse Is the Wrong Tool

But some workloads fundamentally don't fit the ClickHouse model:

• Complex dimensional models with many-to-many relationships
• Real-time enrichment where dimension data changes frequently
• Mixed workloads that need both fast analytical scans AND fast JOINs
• AI/ML feature serving that requires low-latency lookups across multiple tables
• Transactional consistency where you can't tolerate stale or duplicate data

In these cases, no amount of workarounds will fix the fundamental mismatch. You're fighting the architecture.

The Reddit user who said ClickHouse "is great for fast inserts, and sucks utterly for queries" on high-cardinality JOINed data wasn't wrong — they were just using a tool outside its design envelope.

Alternatives to Consider

If your workload is JOIN-heavy, you have a few options:

PostgreSQL handles JOINs natively and well, but struggles at true analytical scale (billions of rows, sub-second aggregations). Good for mixed workloads under ~100M rows.

Snowflake/BigQuery handle JOINs better than ClickHouse due to different architectures, but they're cloud-only, expensive at scale, and not designed for low-latency operational queries.

Hybrid architectures (ClickHouse + PostgreSQL + cache layer) give you the best of each system, but now you're managing three databases and dealing with consistency across them.

How Tacnode Handles JOINs Differently

Tacnode uses a hybrid storage engine that combines row-oriented and columnar storage in a single system. This means:

• JOINs execute on row-oriented indexes optimized for point lookups and range scans
• Analytical scans use columnar storage for fast aggregations
• The query planner chooses the optimal access path per query automatically
• No denormalization required — model your data naturally with proper relationships

You get PostgreSQL-compatible SQL (so your team already knows how to use it), sub-second analytical queries at scale, AND efficient JOINs. No workarounds, no architecture hacks, no maintaining multiple systems.

If you're tired of fighting ClickHouse's JOIN limitations, book a demo and see how a hybrid approach handles your actual workload.

The Bottom Line

ClickHouse JOINs are slow because columnar databases aren't designed for them. That's not a flaw — it's a tradeoff. ClickHouse chose to optimize for full-table analytical scans, and it's exceptional at that.

If your workload is primarily aggregations over denormalized fact tables, ClickHouse is a great choice. Use the workarounds, denormalize aggressively, and enjoy the speed.

But if you're building dimensional models, need real-time enrichment, or find yourself constantly fighting JOIN performance — it might be time to question whether you're using the right tool. Sometimes the answer isn't a better workaround. It's a different architecture.