ClickHouse JOIN: Understanding the Internal Mechanics of JOIN operations

Introduction

ClickHouse JOIN operations are executed differently compared to traditional SQL databases, primarily due to its columnar storage architecture and distributed data processing capabilities. Understanding how JOINs work internally in ClickHouse involves looking at the types of JOINs it supports and the mechanisms it employs to perform these operations efficiently.

Types of ClickHouse JOINs

ClickHouse supports several types of JOINs, including:

  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL OUTER JOIN
  • CROSS JOIN

Internal Working of ClickHouse JOINs

1. Algorithm Selection

    • ClickHouse primarily uses two algorithms for JOIN operations: Hash Join and Merge Join.
    • Hash Join: The default and most commonly used. It’s efficient for unequal dataset sizes (one small, one large). The smaller dataset is used to create an in-memory hash table, which is then used to look up rows from the larger table.
    • Merge Join: Used for equi-JOINs on ordered data. It’s less memory-intensive but requires both datasets to be sorted on the JOIN key.

2. Distributed Processing

    • In a distributed setup, ClickHouse performs JOINs in a distributed manner. It collects data from different shards or replicas to the initiator node for processing the JOIN or can process JOINs on each shard separately, depending on the query and table engine.
    • ClickHouse may use data locality optimizations to minimize data transfer across the network.

3. Memory Management

    • For Hash Joins, ClickHouse tries to fit the hash table for the smaller table into available memory. If it doesn’t fit, ClickHouse uses external memory (disk) for overflow, which can impact performance.
    • There are several settings in ClickHouse to control memory usage during JOINs, such as max_memory_usageand join_use_nulls.

4. Columnar Processing

    • ClickHouse processes data in a columnar fashion, which means it fetches only the columns required for the JOIN operation. This enhances performance, especially when dealing with wide tables where only a few columns are involved in the JOIN.

5. Handling of Different Table Engines

    • ClickHouse’s behavior of JOINs can also vary depending on the table engines involved. For example, joining two MergeTree tables can be more efficient than joining a MergeTree table with a Memory table.

Optimizations and Considerations for ClickHouse JOINs

  • Pre-sorting Data: For Merge Joins, having data pre-sorted on the JOIN key can enhance performance.
  • Data Skew: Significant skew in key distributions can lead to inefficiencies in JOIN operations. Balancing the data distribution can help.
  • JOIN Strictness: ClickHouse allows specifying the strictness of the JOIN (ANY or ALL), which impacts how duplicate keys in the JOIN condition are handled.
  • Distributed Aggregations: When performing JOINs in a distributed setup, aggregations may be done both before and after the JOIN to optimize performance.

Conclusion

JOIN operations in ClickHouse are tailored to leverage its columnar storage and distributed processing capabilities. The choice of JOIN algorithm and the careful management of memory and network resources are critical for optimizing JOIN performance. Understanding these internals can help in structuring queries and schemas for efficient JOINs in ClickHouse environments.

To read more about Joins in ClickHouse, do consider reading the following

About Shiv Iyer 206 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.