Apache Kylin vs Clickhouse: What are the differences?

Introduction

This Markdown provides a comparison between Apache Kylin and Clickhouse based on key differences.

1. Query Processing: Apache Kylin utilizes pre-built OLAP cubes to accelerate query performance, while Clickhouse processes queries in real-time without the need for pre-aggregation, making it suitable for high-speed data processing.

2. Storage: Apache Kylin requires an additional storage layer (HDFS or HBase) to store pre-aggregated data cubes, whereas Clickhouse stores data in its own highly efficient columnar format, enabling fast data retrieval directly from disk.

3. Scale: Apache Kylin performs better with large amounts of data due to its pre-aggregation models, making it suitable for complex queries and analytics workloads, while Clickhouse is optimized for high-speed data ingestion and indexing, making it ideal for real-time data processing.

4. Data Model: Apache Kylin supports multi-dimensional data models and complex hierarchical aggregations through OLAP cubes, whereas Clickhouse focuses on high-performance analytical queries with a simpler, more flexible data model.

5. Community Support: Apache Kylin has a smaller user base and community support compared to Clickhouse, which has a more active and rapidly growing community, resulting in more frequent updates and improvements.

In Summary, Apache Kylin and Clickhouse differ in their approach to query processing, storage, scale capabilities, data model complexity, and community support.