Data Compression in ClickHouse vs Cassandra

Table of Contents

Introduction

ClickHouse and Cassandra: two different databases optimized for different use cases. ClickHouse is a columnar database designed for analytical queries, while Cassandra is a NoSQL database designed for write-heavy workloads and scalability.

Because of these fundamental differences in design and use cases, it’s crucial to understand the context in which one might appear superior to the other. Let’s explore some reasons why ClickHouse’s data compression could be considered superior in some situations:

1. Columnar Storage:

  • ClickHouse is a columnar store, which means it stores data by columns instead of rows. This makes compression more efficient because data in columns tends to be more homogeneous (i.e., similar data types and often similar values) than row-wise storage.
  • Cassandra, being a row store, will typically store data by rows, which can lead to mixed data types and values, potentially reducing the compression ratio.

2. Compression Algorithms:

  • ClickHouse has support for multiple compression codecs including LZ4, ZSTD, and Delta (used for compressing numbers). ClickHouse also employs techniques like delta-encoding and dictionary encoding to enhance compression, especially for time-series or repetitive data.
  • Cassandra primarily uses LZ4 and Snappy for compression. While these algorithms are efficient for general-purpose compression, they might not be as effective as ClickHouse’s specialized codecs for some analytical workloads.

3. Granularity of Compression:

  • In ClickHouse, each block of data is compressed independently, allowing for more effective utilization of compression algorithms based on the type and nature of the data.
  • Cassandra compresses data at the SSTable level, which might be less granular than ClickHouse’s block-level compression.

4. Data Structure and Layout:

  • ClickHouse’s Mergetree storage engine is designed for high compression ratios, especially with its columnar layout, making aggregation and analytical queries faster.
  • Cassandra’s storage layout is designed for write amplification and read efficiency in a distributed environment. Its primary goal isn’t data compression but ensuring data availability and partition tolerance.

5. Use Cases:

  • ClickHouse shines in scenarios that involve analytical processing where datasets can be massive, and compression can lead to considerable storage and IO savings.
  • Cassandra is designed for high-availability, distributed architectures, and may prioritize other factors like replication, consistency, and partition tolerance over raw compression efficiency.

Conclusion

In conclusion, while ClickHouse’s data compression might be superior in the context of analytical workloads, it doesn’t mean Cassandra’s compression is inadequate. The best choice depends on the specific requirements and use case. For OLAP and analytical processing, ClickHouse is generally more efficient, but for distributed OLTP workloads, Cassandra’s strengths in scalability and fault tolerance come to the fore.

To know more about Clickhouse v/s Cassandra, do read the following articles:

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About Shiv Iyer 215 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.