ClickHouse/docs/en/sql-reference/table-functions/file.md

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file

A table engine which provides a table-like interface to SELECT from and INSERT into files, similar to the s3 table function. Use file() when working with local files, and s3() when working with buckets in object storage such as S3, GCS, or MinIO.

The file function can be used in SELECT and INSERT queries to read from or write to files.

Syntax

file([path_to_archive ::] path [,format] [,structure] [,compression])

Parameters

• path — The relative path to the file from user_files_path. Supports in read-only mode the following globs: *, ?, {abc,def} (with 'abc' and 'def' being strings) and {N..M} (with N and M being numbers).
• path_to_archive - The relative path to a zip/tar/7z archive. Supports the same globs as path.
• format — The format of the file.
• structure — Structure of the table. Format: 'column1_name column1_type, column2_name column2_type, ...'.
• compression — The existing compression type when used in a SELECT query, or the desired compression type when used in an INSERT query. Supported compression types are gz, br, xz, zst, lz4, and bz2.

Returned value

A table for reading or writing data in a file.

Examples for Writing to a File

Write to a TSV file

INSERT INTO TABLE FUNCTION
file('test.tsv', 'TSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
VALUES (1, 2, 3), (3, 2, 1), (1, 3, 2)

As a result, the data is written into the file test.tsv:

# cat /var/lib/clickhouse/user_files/test.tsv
1	2	3
3	2	1
1	3	2

Partitioned write to multiple TSV files

If you specify a PARTITION BY expression when inserting data into a table function of type file(), then a separate file is created for each partition. Splitting the data into separate files helps to improve performance of read operations.

INSERT INTO TABLE FUNCTION
file('test_{_partition_id}.tsv', 'TSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
PARTITION BY column3
VALUES (1, 2, 3), (3, 2, 1), (1, 3, 2)

As a result, the data is written into three files: test_1.tsv, test_2.tsv, and test_3.tsv.

# cat /var/lib/clickhouse/user_files/test_1.tsv
3	2	1

# cat /var/lib/clickhouse/user_files/test_2.tsv
1	3	2

# cat /var/lib/clickhouse/user_files/test_3.tsv
1	2	3

Examples for Reading from a File

SELECT from a CSV file

First, set user_files_path in the server configuration and prepare a file test.csv:

$ grep user_files_path /etc/clickhouse-server/config.xml
    <user_files_path>/var/lib/clickhouse/user_files/</user_files_path>

$ cat /var/lib/clickhouse/user_files/test.csv
    1,2,3
    3,2,1
    78,43,45

Then, read data from test.csv into a table and select its first two rows:

SELECT * FROM
file('test.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
LIMIT 2;

┌─column1─┬─column2─┬─column3─┐
│       1 │       2 │       3 │
│       3 │       2 │       1 │
└─────────┴─────────┴─────────┘

Inserting data from a file into a table:

INSERT INTO FUNCTION
file('test.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
VALUES (1, 2, 3), (3, 2, 1);

SELECT * FROM
file('test.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32');

┌─column1─┬─column2─┬─column3─┐
│       1 │       2 │       3 │
│       3 │       2 │       1 │
└─────────┴─────────┴─────────┘

Reading data from table.csv, located in archive1.zip or/and archive2.zip:

SELECT * FROM file('user_files/archives/archive{1..2}.zip :: table.csv');

Globs in path

Paths may use globbing. Files must match the whole path pattern, not only the suffix or prefix.

• * — Represents arbitrarily many characters except / but including the empty string.
• ? — Represents an arbitrary single character.
• {some_string,another_string,yet_another_one} — Substitutes any of strings 'some_string', 'another_string', 'yet_another_one'. The strings can contain the / symbol.
• {N..M} — Represents any number >= N and <= M.
• ** - Represents all files inside a folder recursively.

Constructions with {} are similar to the remote and hdfs table functions.

Example

Suppose there are these files with the following relative paths:

• some_dir/some_file_1
• some_dir/some_file_2
• some_dir/some_file_3
• another_dir/some_file_1
• another_dir/some_file_2
• another_dir/some_file_3

Query the total number of rows in all files:

SELECT count(*) FROM file('{some,another}_dir/some_file_{1..3}', 'TSV', 'name String, value UInt32');

An alternative path expression which achieves the same:

SELECT count(*) FROM file('{some,another}_dir/*', 'TSV', 'name String, value UInt32');

:::note If your listing of files contains number ranges with leading zeros, use the construction with braces for each digit separately or use ?. :::

Example

Query the total number of rows in files named file000, file001, … , file999:

SELECT count(*) FROM file('big_dir/file{0..9}{0..9}{0..9}', 'CSV', 'name String, value UInt32');

Example

Query the total number of rows from all files inside directory big_dir/ recursively:

SELECT count(*) FROM file('big_dir/**', 'CSV', 'name String, value UInt32');

Example

Query the total number of rows from all files file002 inside any folder in directory big_dir/ recursively:

SELECT count(*) FROM file('big_dir/**/file002', 'CSV', 'name String, value UInt32');

Virtual Columns

• _path — Path to the file. Type: LowCardinalty(String).
• _file — Name of the file. Type: LowCardinalty(String).
• _size — Size of the file in bytes. Type: Nullable(UInt64). If the file size is unknown, the value is NULL.

Settings

• engine_file_empty_if_not_exists - allows to select empty data from a file that doesn't exist. Disabled by default.
• engine_file_truncate_on_insert - allows to truncate file before insert into it. Disabled by default.
• engine_file_allow_create_multiple_files - allows to create a new file on each insert if format has suffix. Disabled by default.
• engine_file_skip_empty_files - allows to skip empty files while reading. Disabled by default.
• storage_file_read_method - method of reading data from storage file, one of: read, pread, mmap (only for clickhouse-local). Default value: pread for clickhouse-server, mmap for clickhouse-local.

See Also

• Virtual columns
• Rename files after processing