Hive

Scanning hive partitioned data

Polars supports scanning hive partitioned parquet and IPC datasets, with planned support for other formats in the future.

Hive partition parsing is enabled by default if scan_parquet receives a single directory path, otherwise it is disabled by default. This can be explicitly configured using the hive_partitioning parameter.

Scanning a hive directory

For this example the following directory structure is used:

--8<-- "python/user-guide/io/hive.py:init_paths"

Simply pass the directory to scan_parquet, and all files will be loaded with the hive parts in the path included in the output:

{{code_block('user-guide/io/hive','scan_dir',['scan_parquet'])}}

--8<-- "python/user-guide/io/hive.py:scan_dir"

Handling mixed files

Passing a directory to scan_parquet may not work if there are files with different extensions in the directory.

For this example the following directory structure is used:

--8<-- "python/user-guide/io/hive.py:show_mixed_paths"

{{code_block('user-guide/io/hive','scan_dir_err',['scan_parquet'])}}

The above fails as description.txt is not a valid parquet file:

--8<-- "python/user-guide/io/hive.py:scan_dir_err"

In this situation, a glob pattern can be used to be more specific about which files to load. Note that hive_partitioning must explicitly set to True:

{{code_block('user-guide/io/hive','scan_glob',['scan_parquet'])}}

--8<-- "python/user-guide/io/hive.py:scan_glob"

Scanning file paths with hive parts

hive_partitioning is not enabled by default for file paths:

{{code_block('user-guide/io/hive','scan_file_no_hive',['scan_parquet'])}}

--8<-- "python/user-guide/io/hive.py:scan_file_no_hive"

Pass hive_partitioning=True to enable hive partition parsing:

{{code_block('user-guide/io/hive','scan_file_hive',['scan_parquet'])}}

--8<-- "python/user-guide/io/hive.py:scan_file_hive"

Writing hive partitioned data

Note: The following functionality is considered unstable, and is subject to change.

Polars supports writing hive partitioned parquet datasets, with planned support for other formats.

Example

For this example the following DataFrame is used:

{{code_block('user-guide/io/hive','write_parquet_partitioned_show_data',[])}}

--8<-- "python/user-guide/io/hive.py:write_parquet_partitioned_show_data"

We will write it to a hive-partitioned parquet dataset, partitioned by the columns a and b:

{{code_block('user-guide/io/hive','write_parquet_partitioned',['write_parquet'])}}

--8<-- "python/user-guide/io/hive.py:write_parquet_partitioned"

The output is a hive partitioned parquet dataset with the following paths:

--8<-- "python/user-guide/io/hive.py:write_parquet_partitioned_show_paths"