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TinyLog

The engine belongs to the log engine family. See Log Engine Family for common properties of log engines and their differences.

This table engine is typically used with the write-once method: write data one time, then read it as many times as necessary. For example, you can use TinyLog-type tables for intermediary data that is processed in small batches. Note that storing data in a large number of small tables is inefficient.

Queries are executed in a single stream. In other words, this engine is intended for relatively small tables (up to about 1,000,000 rows). It makes sense to use this table engine if you have many small tables, since it's simpler than the Log engine (fewer files need to be opened).

Characteristics​

  • Simpler Structure: Unlike the Log engine, TinyLog does not use mark files. This reduces complexity but also limits performance optimizations for large datasets.
  • Single Stream Queries: Queries on TinyLog tables are executed in a single stream, making it suitable for relatively small tables, typically up to 1,000,000 rows.
  • Efficient for Small Table: The simplicity of the TinyLog engine makes it advantageous when managing many small tables, as it requires fewer file operations compared to the Log engine.

Unlike the Log engine, TinyLog does not use mark files. This reduces complexity but also limits performance optimizations for larger datasets.

Creating a Table​

See the detailed description of the CREATE TABLE query.

Writing the Data​

The TinyLog engine stores all the columns in one file. For each INSERT query, ClickHouse appends the data block to the end of a table file, writing columns one by one.

For each table ClickHouse writes the files:

  • <column>.bin: A data file for each column, containing the serialized and compressed data.

The TinyLog engine does not support the ALTER UPDATE and ALTER DELETE operations.

Example of Use​

Creating a table:

Inserting data:

We used two INSERT queries to create two data blocks inside the <column>.bin files.

ClickHouse uses a single stream selecting data. As a result, the order of blocks of rows in the output matches the order of the same blocks in the input. For example: