How to Join Two Large Tables Efficiently in ClickHouse

ClickHouse is famous for lightning-fast analytics on billions of rows, but naïvely joining two very large tables can still bring the cluster to its knees. This article walks through the mechanics of joins in ClickHouse, how the engine executes them under the hood, and—most importantly—what you should do to keep your queries fast, cheap, and reliable.

What Is a Join in ClickHouse?

In SQL, a JOIN combines rows from two tables based on a related column. ClickHouse supports the same ANSI syntax you expect—INNER, LEFT, RIGHT, FULL, and CROSS. Under the covers, however, ClickHouse is a column-oriented, massively parallel engine. That means the efficiency of a join depends on how the data is partitioned across nodes, whether the join keys are sorted, and which physical algorithm (hash, merge, or dictionary) the planner chooses.

Why Does Join Performance Matter?

Joins are ubiquitous in analytics: dimensional look-ups, funnel analyses, and sessionization queries all rely on them. On large data sets—think tens of billions of rows—execution time balloons and memory footprints explode unless you design tables and queries carefully. Slow joins delay dashboards, overload back-pressure-sensitive Kafka ingestion, and raise cloud bills. Getting them right is therefore critical for engineering velocity, stakeholder trust, and financial efficiency.

Join Execution Strategies

1. Hash Join (default)

ClickHouse reads the right side of the join (known as the build table) into memory, constructs a hash map on the join keys, then streams the left side (the probe table). This is extremely fast but can blow up RAM if the build side is large.

2. Merge Join

If both tables are sorted by the join keys and ClickHouse can verify this via ORDER BY expressions (often primary keys), it chooses a merge join. Here, neither side needs to be fully materialized in memory—the engine performs a streaming merge which is much more memory-friendly. You can force it with SET join_algorithm = 'partial_merge'.

3. Distributed Join

When tables are replicated across shards, ClickHouse may broadcast small tables or perform a shuffled hash join in which each node sends only matching partitions to their peers. Ensuring the cluster clause and sharding keys align with the join keys is vital.

Key Design Levers

Choose the Right “Build” Side

Because the build side is loaded into memory during a hash join, always make it the smaller of the two. Swap table order if necessary.

Leverage JOIN ... USING with Primary Keys

When the join keys match the ORDER BY clause, ClickHouse can switch to merge join automatically.

Pre-Aggregate or Filter Early

Use sub-queries to eliminate columns and rows you don’t need before the join executes. ClickHouse’s optimizer is good, but explicit CTEs stop it from scanning or materializing extraneous data.

Set max_memory_usage and max_bytes_before_external_sort

Guardrails prevent a rogue query from evicting cache or crashing the node. When these limits are hit, ClickHouse will spill to disk or abort gracefully.

Dictionary Encoding for Dimension Tables

Small, relatively static lookup tables can live as Dictionary objects. Then you can join with the DICT_GET family of functions—sidestepping full joins entirely.

Step-by-Step Example

Consider two tables:

• events: 50 billion rows, sharded by user_id, ordered by (user_id, event_time)
• users: 200 million rows, ordered by (user_id)

You want session-level analytics enriched with user demographics.

1. Filter & Slice

WITH filtered_events AS (
SELECT user_id,
session_id,
min(event_time) AS session_start,
max(event_time) AS session_end
FROM events
WHERE event_date >= today() - 30
GROUP BY user_id, session_id
)


This CTE reduces the events table to one row per session.

2. Force Merge Join

SET join_algorithm = 'partial_merge';
SET max_bytes_before_external_group_by = 20e9; -- 20 GiB safety valve


3. Execute

SELECT f.user_id,
f.session_id,
u.country,
dateDiff('minute', f.session_start, f.session_end) AS duration_min
FROM filtered_events AS f
JOIN users AS u USING user_id;


Because both tables are ORDER BY user_id, the engine streams blocks rather than allocating a gigantic hash map.

Using Galaxy to Author and Optimize Queries

Galaxy’s desktop SQL editor understands ClickHouse catalogs natively. When you connect, Galaxy’s AI copilot reads table metadata and recommends the cheapest join order, highlights missing ORDER BY alignment, and even proposes dictionary materialization for micro-dimensions—all before you click Run. Query history and “Endorse” features let your team share these tuned joins, preventing expensive copy-paste mistakes.

Best Practices Cheat-Sheet

• Keep the build side small—swap table order when necessary.
• Ensure join keys are part of ORDER BY to enable merge joins.
• Use DISTRIBUTED tables and matching sharding keys.
• Limit columns early; avoid SELECT *.
• Set memory limits and enable disk spilling.
• Profile with EXPLAIN and system tables (system.query_log).
• Consider replacing tiny dimensions with dictionaries.

Common Pitfalls

Missing ORDER BY Alignment

If one table is ordered by user_id but the other by (user_id, event_time), ClickHouse can’t stream optimally and falls back to a hash join.

Swamping Memory

Large build tables plus unlimited max_memory_usage lead to OOM kills. Always set per-query caps.

Mismatched Sharding Keys

In clusters, if tables are sharded by different columns, data must be broadcast—catastrophic at scale.

Monitoring & Troubleshooting

• Check system.part_log for excessive merges.
• Inspect system.metrics: MemoryTracking and DistributedSendBytes.
• Run EXPLAIN PIPELINE to confirm join algorithm.

Conclusion

Efficient joins in ClickHouse are less about mysterious magic and more about data layout, algorithm choice, and sensible guardrails. By applying the techniques above—and using a context-aware IDE like Galaxy—you can keep analytical workloads blazing fast even as data volumes soar.