Clickhouse vs RocksDB: What are the differences?

Introduction

ClickHouse and RocksDB are two popular database management systems that offer different features and functionalities. Understanding the key differences between these systems can help users make informed decisions based on their specific requirements.

1. Storage Engine: The first major difference between ClickHouse and RocksDB lies in their storage engines. ClickHouse utilizes a columnar storage engine, which is optimized for analytical workloads. This allows for efficient data compression and faster query performance on large datasets. On the other hand, RocksDB uses a log-structured merge-tree (LSM tree) storage engine, which is better suited for online transaction processing (OLTP) workloads with higher write throughput.

2. Data Structure: ClickHouse organizes data in a columnar structure, where each column is stored separately. This allows for efficient compression and faster analytical queries that involve aggregations and filtering on specific columns. In contrast, RocksDB uses a key-value pair data structure, where data is stored and retrieved based on unique keys. This is more suitable for random access and retrieval of individual records.

3. Data Persistence: ClickHouse is primarily an in-memory database and heavily relies on system memory for query processing. Although it provides disk-based data storage options for durability, its main focus is on fast in-memory analytical processing. On the other hand, RocksDB is designed for persistent storage and provides built-in mechanisms to persist data to disk, ensuring durability and crash recovery.

4. Data Distribution: ClickHouse is designed to support high-performance distributed query processing across multiple nodes. It provides features like data replication and distributed table engines to scale horizontally and handle large datasets. While RocksDB can also be used in distributed setups, it mainly operates as a single-node storage engine and requires additional frameworks or systems to achieve distributed data processing.

5. Concurrency Control: ClickHouse utilizes optimistic concurrency control (OCC) to handle concurrent data modifications. It allows multiple readers and writers to access the database simultaneously, while ensuring consistency and avoiding lock-based conflicts. In contrast, RocksDB employs a locking mechanism to guarantee the consistency of updates and prevent concurrent modifications that can lead to data corruption or inconsistencies.

6. Query Language: ClickHouse uses a SQL-like query language for expressing analytical queries and provides support for complex analytical functions and operations. It offers features like filtering, aggregations, joins, and subqueries that are commonly used in analytical workloads. On the other hand, RocksDB does not provide a built-in query language and is mainly used as a storage engine for other applications or databases that handle query processing.

In summary, ClickHouse and RocksDB differ in their storage engines, data structures, data persistence mechanisms, data distribution capabilities, concurrency control methods, and query language support. These differences make them suitable for different types of workloads and use cases.