Clickhouse vs Scylla: What are the differences?

Introduction

ClickHouse and Scylla are both popular database management systems that are widely used in different applications. While they have some similarities, they also have key differences that set them apart from each other. In this markdown code, we will explore and highlight the main differences between ClickHouse and Scylla.

1. Data Model and Query Language: ClickHouse is a columnar database that is designed to handle analytical workloads efficiently. It uses a SQL-like query language that supports complex analytical queries and allows users to perform various transformations and aggregations on large datasets. On the other hand, Scylla is a distributed database that is based on Apache Cassandra. It uses CQL (Cassandra Query Language) for querying data and follows the key-value model. This means that Scylla is optimized for high-throughput transactional workloads rather than complex analytics.

2. Replication and Consistency: ClickHouse supports both synchronous and asynchronous replication methods, allowing users to choose the level of consistency they require for their data. It provides ways to replicate data across different servers and data centers to ensure high availability and fault tolerance. In contrast, Scylla has a built-in distributed architecture that automatically replicates data across multiple nodes. It provides high availability and fault tolerance by replicating data within the same data center or across different data centers, depending on the configuration.

3. Data Storage and Compression: ClickHouse uses a columnar storage format, which means that data is stored in a column-wise manner rather than row-wise. This allows for efficient compression techniques like dictionary and run-length encoding, resulting in reduced storage space and improved query performance for analytical workloads. Scylla, on the other hand, uses a row-based storage format that is optimized for write-heavy workloads. It incorporates compression techniques like LZ4 and Snappy to reduce the storage footprint of data.

4. Data Consistency and Durability: ClickHouse provides eventual consistency for data replication, which means that changes made to the data are eventually propagated to all replicas in the cluster. It also provides durability by storing data on disk and supports configurable storage policies for data retention. Scylla, being based on Apache Cassandra, provides tunable consistency levels for data replication. It ensures durability by writing data to disk and also provides the option of replicating data to multiple data centers for increased fault tolerance.

5. Scalability and Performance: ClickHouse is known for its exceptional performance when it comes to complex analytical queries on large datasets. It can handle high concurrency and provides efficient data compression and caching mechanisms. Scylla, on the other hand, is designed for high-throughput transactional workloads and can handle a massive number of read and write operations in real-time. It provides low-latency responses and supports horizontal scalability by adding more nodes to the cluster.

6. Community and Ecosystem: ClickHouse has a growing community and a rich ecosystem of tools and integrations that have been developed around it. It is widely adopted by companies for data analytics and reporting purposes. Scylla, being based on Cassandra, also has a large community and ecosystem. It benefits from the existing tools and integrations available for Cassandra and provides seamless integration with other Cassandra-compatible systems.

In summary, ClickHouse is a columnar database optimized for analytical workloads with a SQL-like query language, while Scylla is a distributed database based on Cassandra that is designed for high-throughput transactional workloads. ClickHouse excels in complex analytics and has a growing community, while Scylla provides high availability, low-latency, and scalability for real-time transactional workloads.