ClickHouse Data Models (Table Engines, Partitions, Data Blocks, etc.)

ClickHouse is an open-source relational database management system whose data model is based on columnar storage and partitioning, which significantly improves query performance.

Table Engines

ClickHouse uses a table engine called TableIngestionEngine to manage the structure of tables. This engine handles designing when a table is created, including defining fields, data types, and storage locations for each field in the table.

Table Structure Designation

When users create a new table, they need to specify the following information:

  • Table name
  • Table schema (if it exists)
  • Field names of every column within the table
  • Data type of columns
  • Storage location for each column (partitioning key)

Partitioning

ClickHouse uses partitioning technology to improve table performance and reduce storage space. Each table can be partitioned based on time, geographic locations, or other methods.

Time-based Partitioning

Time-based partitioning is the most common type; by using an index on TIMESTAMP columns, data can be quickly separated according to a specific time range.

Geo-partitioning

Geo-partitioning divides tables based on user location and improves query efficiency for regional data.

Data Blocks

ClickHouse uses block technology to store table data. Each block contains partial data of the entire table; through indexing, it is possible to quickly locate blocks at specific positions, thereby achieving efficient queries.

Block Size

The size of a block can be adjusted as needed, such as by specifying the chunkSize variable to set each block's size.

Data Blocks Management

Data block management is an important component that includes operations like creating, assigning, and deleting data blocks. These operations help effectively manage the stored data in tables.

Summary

ClickHouse's data model is based on columnar storage and partitioning design, which significantly improves query performance while providing high reliability. By understanding table engines, partitions, and data blocks, you can better leverage ClickHouse advantages.