Joining Data in ClickHouse

ClickHouse supports multiple join algorithms, each optimized for different use cases and memory constraints. Understanding how these joins work is essential for writing efficient queries, especially when working with large datasets.

Key Considerations Before Joining

Memory usage: Some join types require the entire right-hand table to fit in memory.
Table size: The right-hand table (the one being joined) should ideally be smaller.
Performance trade-offs: Faster joins often require more memory or pre-sorted data.

Join Algorithms Overview

1. Hash Join

The default and most commonly used join type.
The right-hand table is fully loaded into memory and hashed.
The left-hand table is scanned and matched against the hash.
Best used when the right-hand table is small enough to fit in memory. 🧠 Tip: Always place the smaller table on the right side of the join.

2. Parallel Hash Join

Functions like a standard hash join but uses parallel processing to improve performance.
Can significantly reduce join time on large datasets with sufficient compute resources.

3. Grace Hash Join

Designed for low-memory environments.
Splits data into partitions and performs the join in stages.
Slower than hash joins but more memory-efficient.

4. Partial Merge Join

The most memory-efficient but also the slowest join type.
Performs a merge join on partially sorted data.
Useful as a fallback when memory is extremely limited.

5. Full Sorting Merge Join

Requires both tables to be sorted on the join key.
Offers fast performance when this condition is met.
Ideal for joining large, pre-sorted datasets.

6. Direct Join

Used when the right-hand table is fully loaded into memory, such as a dictionary.
Enables fast lookups and is ideal for joining against small, static reference tables.

Example: Forcing a Join Algorithm

You can explicitly specify the join algorithm using the SETTINGS clause:

  
SELECT *
FROM TABLE 
JOIN TABLE
SETTING joinalgorithm = 'hash'

Available options include:

'hash'
'parallel_hash'
'grace_hash'
'partial_merge'
'full_sorting_merge'
'direct'

Best Practices

Always benchmark different join strategies when working with large datasets.
Use EXPLAIN to inspect the query plan and verify which join algorithm is being used.
Consider pre-sorting tables or using projections to optimize merge joins.
For small reference data, consider using dictionaries for direct joins.

Joining data in Clickhouse