Creates a table from files in HDFS. This table function is similar to the url and file table functions.
Syntax Arguments Argument DescriptionURI
The relative URI to the file in HDFS. Path to file support following globs in readonly mode: *
, ?
, {abc,def}
and {N..M}
where N
, M
— numbers, 'abc', 'def'
— strings. format
The format of the file. structure
Structure of the table. Format 'column1_name column1_type, column2_name column2_type, ...'
. Returned value
A table with the specified structure for reading or writing data in the specified file.
example
Table from hdfs://hdfs1:9000/test
and selection of the first two rows from it:
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 all files inside a folder recursively.?
— 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
.Constructions with {}
are similar to the remote and file table functions.
Example
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 data from files named file000
, file001
, ... , file999
:
_path
— Path to the file. Type: LowCardinality(String)
._file
— Name of the file. Type: LowCardinality(String)
._size
— Size of the file in bytes. Type: Nullable(UInt64)
. If the size is unknown, the value is NULL
._time
— Last modified time of the file. Type: Nullable(DateTime)
. If the time is unknown, the value is NULL
.When setting use_hive_partitioning
is set to 1, ClickHouse will detect Hive-style partitioning in the path (/name=value/
) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with _
.
Example
Use virtual column, created with Hive-style partitioning
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