awswrangler.s3.store_parquet_metadata

awswrangler.s3.store_parquet_metadata(path: str, database: str, table: str, catalog_id: Optional[str] = None, path_suffix: Optional[str] = None, path_ignore_suffix: Optional[str] = None, dtype: Optional[Dict[str, str]] = None, sampling: float = 1.0, dataset: bool = False, use_threads: bool = True, description: Optional[str] = None, parameters: Optional[Dict[str, str]] = None, columns_comments: Optional[Dict[str, str]] = None, compression: Optional[str] = None, mode: str = 'overwrite', catalog_versioning: bool = False, regular_partitions: bool = True, projection_enabled: bool = False, projection_types: Optional[Dict[str, str]] = None, projection_ranges: Optional[Dict[str, str]] = None, projection_values: Optional[Dict[str, str]] = None, projection_intervals: Optional[Dict[str, str]] = None, projection_digits: Optional[Dict[str, str]] = None, s3_additional_kwargs: Optional[Dict[str, str]] = None, boto3_session: Optional[boto3.session.Session] = None) → Tuple[Dict[str, str], Optional[Dict[str, str]], Optional[Dict[str, List[str]]]]

Infer and store parquet metadata on AWS Glue Catalog.

Infer Apache Parquet file(s) metadata from from a received S3 prefix or list of S3 objects paths And then stores it on AWS Glue Catalog including all inferred partitions (No need of ‘MCSK REPAIR TABLE’)

The concept of Dataset goes beyond the simple idea of files and enable more complex features like partitioning and catalog integration (AWS Glue Catalog).

Note

On append mode, the parameters will be upsert on an existing table.

Note

In case of use_threads=True the number of threads that will be spawned will be gotten from os.cpu_count().

Note

This functions has arguments that can has default values configured globally through wr.config or environment variables:

  • catalog_id

  • database

Check out the Global Configurations Tutorial for details.

Parameters
  • path (Union[str, List[str]]) – S3 prefix (e.g. s3://bucket/prefix) or list of S3 objects paths (e.g. [s3://bucket/key0, s3://bucket/key1]). database : str Glue/Athena catalog: Database name.

  • table (str) – Glue/Athena catalog: Table name.

  • database (str) – AWS Glue Catalog database name.

  • catalog_id (str, optional) – The ID of the Data Catalog from which to retrieve Databases. If none is provided, the AWS account ID is used by default.

  • path_suffix (Union[str, List[str], None]) – Suffix or List of suffixes for filtering S3 keys.

  • path_ignore_suffix (Union[str, List[str], None]) – Suffix or List of suffixes for S3 keys to be ignored.

  • dtype (Dict[str, str], optional) – Dictionary of columns names and Athena/Glue types to be casted. Useful when you have columns with undetermined data types as partitions columns. (e.g. {‘col name’: ‘bigint’, ‘col2 name’: ‘int’})

  • sampling (float) – Random sample ratio of files that will have the metadata inspected. Must be 0.0 < sampling <= 1.0. The higher, the more accurate. The lower, the faster.

  • dataset (bool) – If True read a parquet dataset instead of simple file(s) loading all the related partitions as columns.

  • use_threads (bool) – True to enable concurrent requests, False to disable multiple threads. If enabled os.cpu_count() will be used as the max number of threads.

  • description (str, optional) – Glue/Athena catalog: Table description

  • parameters (Dict[str, str], optional) – Glue/Athena catalog: Key/value pairs to tag the table.

  • columns_comments (Dict[str, str], optional) – Glue/Athena catalog: Columns names and the related comments (e.g. {‘col0’: ‘Column 0.’, ‘col1’: ‘Column 1.’, ‘col2’: ‘Partition.’}).

  • compression (str, optional) – Compression style (None, snappy, gzip, etc).

  • mode (str) – ‘overwrite’ to recreate any possible existing table or ‘append’ to keep any possible existing table.

  • catalog_versioning (bool) – If True and mode=”overwrite”, creates an archived version of the table catalog before updating it.

  • regular_partitions (bool) – Create regular partitions (Non projected partitions) on Glue Catalog. Disable when you will work only with Partition Projection. Keep enabled even when working with projections is useful to keep Redshift Spectrum working with the regular partitions.

  • projection_enabled (bool) – Enable Partition Projection on Athena (https://docs.aws.amazon.com/athena/latest/ug/partition-projection.html)

  • projection_types (Optional[Dict[str, str]]) – Dictionary of partitions names and Athena projections types. Valid types: “enum”, “integer”, “date”, “injected” https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {‘col_name’: ‘enum’, ‘col2_name’: ‘integer’})

  • projection_ranges (Optional[Dict[str, str]]) – Dictionary of partitions names and Athena projections ranges. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {‘col_name’: ‘0,10’, ‘col2_name’: ‘-1,8675309’})

  • projection_values (Optional[Dict[str, str]]) – Dictionary of partitions names and Athena projections values. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {‘col_name’: ‘A,B,Unknown’, ‘col2_name’: ‘foo,boo,bar’})

  • projection_intervals (Optional[Dict[str, str]]) – Dictionary of partitions names and Athena projections intervals. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {‘col_name’: ‘1’, ‘col2_name’: ‘5’})

  • projection_digits (Optional[Dict[str, str]]) – Dictionary of partitions names and Athena projections digits. https://docs.aws.amazon.com/athena/latest/ug/partition-projection-supported-types.html (e.g. {‘col_name’: ‘1’, ‘col2_name’: ‘2’})

  • s3_additional_kwargs – Forward to s3fs, useful for server side encryption https://s3fs.readthedocs.io/en/latest/#serverside-encryption

  • boto3_session (boto3.Session(), optional) – Boto3 Session. The default boto3 session will be used if boto3_session receive None.

Returns

The metadata used to create the Glue Table. columns_types: Dictionary with keys as column names and vales as data types (e.g. {‘col0’: ‘bigint’, ‘col1’: ‘double’}). / partitions_types: Dictionary with keys as partition names and values as data types (e.g. {‘col2’: ‘date’}). / partitions_values: Dictionary with keys as S3 path locations and values as a list of partitions values as str (e.g. {‘s3://bucket/prefix/y=2020/m=10/’: [‘2020’, ‘10’]}).

Return type

Tuple[Dict[str, str], Optional[Dict[str, str]], Optional[Dict[str, List[str]]]]

Examples

Reading all Parquet files metadata under a prefix

>>> import awswrangler as wr
>>> columns_types, partitions_types, partitions_values = wr.s3.store_parquet_metadata(
...     path='s3://bucket/prefix/',
...     database='...',
...     table='...',
...     dataset=True
... )