ClickHouse vs Cassandra for Real-time Analytics

Shiv Iyer ChistaDATA, ClickHouse, ClickHouse DBA Script, ClickHouse DBA Support, ClickHouse Internals, ClickHouse Performance

Introduction

ClickHouse and Cassandra are both powerful data management systems, but they are designed for different use cases.

1. Data Model

  • ClickHouse is a column-oriented database. This means it stores data by columns, which allows it to perform faster reads on specific columns for analytical queries.
  • Cassandra, on the other hand, is a wide-column store, best suited for write-heavy workloads and high-velocity data ingestion.

2. Query Language

  • ClickHouse uses SQL-like syntax, which is more suited to complex analytical queries.
  • Cassandra uses CQL (Cassandra Query Language) which is similar to SQL, but lacks some features like JOINs which are crucial for analytics.

3. Aggregation

  • ClickHouse is designed for online analytical processing (OLAP), which means it’s optimized for complex, aggregated queries.
  • Cassandra is more of an online transaction processing (OLTP) system, optimized for simple, point queries.

4. Data Compression

  • ClickHouse has superior data compression algorithms, which reduce the amount of I/O operations and speed up query execution:
    • 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.

5. Indices

  • ClickHouse supports various types of indices including primary, secondary, and materialized views. This flexibility allows it to optimize for different query patterns.
  • Cassandra primarily uses partition keys for data distribution and does not provide the same level of flexibility for index optimization.

6. Throughput

  • ClickHouse can process hundreds of thousands to more than a million rows per second per server, making it extremely fast for real-time analytics.

Conclusion

The right choice between ClickHouse and Cassandra depends on the specific use case.

  • If the primary task is real-time analytics, ClickHouse might be a better choice.
  • Cassandra could be more appropriate for high-speed data ingestion and simple read/write operations.

Extra Reading:

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About Shiv Iyer 249 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.
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