Join me if you can: ClickHouse vs. Databricks vs. Snowflake - Part 1

We took a public benchmark that tests JOIN-heavy SQL queries on Databricks and Snowflake and ran the exact same queries on ClickHouse Cloud. ClickHouse was faster and cheaper at every scale, from 721 million to 7.2 billion rows.

ClickHouse

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AWSSQL

Overview

This article benchmarks join-heavy SQL queries across ClickHouse, Databricks, and Snowflake, demonstrating that ClickHouse outperforms both competitors in speed and cost across various data scales. It provides insights into the benchmarking process and highlights ClickHouse's capabilities in handling joins effectively.

What You'll Learn

1

How to run a benchmark comparing SQL query performance across different platforms

2

Why ClickHouse is a cost-effective solution for join-heavy queries

3

How to utilize ClickHouse Cloud for efficient data processing

Key Questions Answered

How does ClickHouse perform in join-heavy SQL queries compared to Databricks and Snowflake?

ClickHouse consistently outperforms both Databricks and Snowflake in join-heavy SQL queries, being faster and cheaper across all tested scales, from 721 million to 7.2 billion rows. The benchmarks show ClickHouse completing most queries in under 1 second at the 500 million scale, while other platforms take significantly longer.

What methodology was used for benchmarking SQL query performance?

The benchmarking involved running 17 SQL queries, primarily focusing on joins, across three dataset sizes: 721 million, 1.4 billion, and 7.2 billion rows. The queries were executed without any tuning or rewrites, ensuring a fair comparison of raw performance.

What are the key results of the benchmark at different data scales?

At the 500 million scale, ClickHouse completed queries in under 1 second, being 3-5 times faster than its competitors. At the 1 billion scale, it processed 1.7 billion rows in just half a second, while other systems took 5 to 13 seconds. Even at the 5 billion scale, ClickHouse maintained superior performance.

Key Statistics & Figures

Total rows in fact table (Sales)

721 million

This is the dataset size used for the initial benchmark scale.

Performance at 1 billion scale

1.7 billion rows processed in half a second

ClickHouse's performance at this scale shows a significant speed advantage over competitors.

Cost-effectiveness

3-5 times cheaper than alternatives

At the 500 million scale, ClickHouse consistently demonstrated lower costs compared to Databricks and Snowflake.

Technologies & Tools

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Database

Clickhouse

Used for running join-heavy SQL queries in the benchmark.

Data Platform

Databricks

One of the competitors in the benchmark for SQL query performance.

Data Platform

Snowflake

Another competitor in the benchmark for SQL query performance.

Key Actionable Insights

1
Consider using ClickHouse for applications that require fast processing of join-heavy queries.
Given its performance advantages, ClickHouse can significantly reduce query execution time and costs, making it an ideal choice for data-intensive applications.

2
Utilize the ClickHouse Cloud's automated benchmarking features to evaluate performance.
The automated setup allows users to quickly spin up services and run benchmarks, providing valuable insights into performance without extensive configuration.

3
Leverage ClickHouse's Parallel Replicas feature for enhanced query performance.
By processing queries in parallel across multiple compute nodes, users can achieve faster results, especially for large datasets.

Common Pitfalls

1

Assuming ClickHouse cannot handle joins effectively.

This misconception can lead to missed opportunities for performance improvements. The article demonstrates that ClickHouse can handle join-heavy queries efficiently without any special tuning.