Npy

Input	Output	Alias
✔	✔

Description

The Npy format is designed to load a NumPy array from a .npy file into ClickHouse. The NumPy file format is a binary format used for efficiently storing arrays of numerical data. During import, ClickHouse treats the top level dimension as an array of rows with a single column. The table below gives the supported Npy data types and their corresponding type in ClickHouse:

Data types matching

Npy data type (`INSERT`)	ClickHouse data type	Npy data type (`SELECT`)
`i1`	Int8	`i1`
`i2`	Int16	`i2`
`i4`	Int32	`i4`
`i8`	Int64	`i8`
`u1`, `b1`	UInt8	`u1`
`u2`	UInt16	`u2`
`u4`	UInt32	`u4`
`u8`	UInt64	`u8`
`f2`, `f4`	Float32	`f4`
`f8`	Float64	`f8`
`S`, `U`	String	`S`
	FixedString	`S`

Example usage

Saving an array in .npy format using Python

import numpy as np
arr = np.array([[[1],[2],[3]],[[4],[5],[6]]])
np.save('example_array.npy', arr)

Reading a NumPy file in ClickHouse

Query

SELECT *
FROM file('example_array.npy', Npy)

Response

┌─array─────────┐
│ [[1],[2],[3]] │
│ [[4],[5],[6]] │
└───────────────┘

Selecting data

You can select data from a ClickHouse table and save it into a file in the Npy format using the following command with clickhouse-client:

$ clickhouse-client --query="SELECT {column} FROM {some_table} FORMAT Npy" > {filename.npy}

SQL Reference

Description

Data types matching

Example usage

Saving an array in .npy format using Python

Reading a NumPy file in ClickHouse

Selecting data

Format settings

​Description

​Data types matching

​Example usage

​Saving an array in .npy format using Python

​Reading a NumPy file in ClickHouse

​Selecting data

​Format settings

Description

Data types matching

Example usage

Saving an array in .npy format using Python

Reading a NumPy file in ClickHouse

Selecting data

Format settings