Principle of Clickhouse

ClickHouse is a fast, scalable columnar database management system (DBMS) designed to handle large-scale data analysis tasks. Here are some main aspects of how ClickHouse works:

1. Columnar storage: ClickHouse adopts columnar storage, and stores the data of each column separately on the disk, instead of traditional row storage. The advantage of this is that only the required columns can be loaded in the data analysis task, reducing the amount of data read and improving the efficiency of query and analysis.

2. Data compression: ClickHouse uses various compression algorithms to compress columnar data to reduce disk usage and improve read speed. It supports a variety of compression algorithms, such as LZ4, ZSTD, Deflate, etc., and can choose a suitable compression algorithm according to the characteristics of the data.

3. Data partitioning and merging: ClickHouse supports data partitioning and merging. Data partitioning is to divide data into multiple data blocks according to time range or other rules, so that only relevant data partitions can be loaded according to query conditions, improving query efficiency. Data merging is to combine multiple data blocks into larger blocks to reduce the number of data blocks that need to be loaded when querying.

4. Data index: ClickHouse uses a two-level index structure to speed up queries. The first-level index (Bloom Filter) is used to quickly determine whether data exists in a data block, thereby reducing disk I/O; the second-level index (Skip List) is used to perform more precise searches within the data block.

5. Distributed architecture: ClickHouse is deployed in a distributed manner and can run on multiple servers to form a cluster. It uses distributed query and data replication technology, so that data can be stored and processed in a distributed manner in the cluster, providing higher throughput and reliability.

6. Parallel processing: ClickHouse utilizes multi-core processors and multi-threads to achieve parallel query and calculation. It can process multiple queries at the same time and use computing resources for parallel computing to speed up the execution of data analysis tasks.

To sum up, the principles of ClickHouse include columnar storage, data compression, data partitioning and merging, data indexing, distributed architecture, and parallel processing. These features make ClickHouse a high-performance data analysis and query engine, suitable for processing large-scale data sets and complex analysis tasks.