from __future__ import annotations import importlib import importlib.util from typing import TYPE_CHECKING, Literal from polars._utils.unstable import issue_unstable_warning from polars._utils.wrap import wrap_ldf from polars.io.cloud._utils import NoPickleOption from polars.io.iceberg._dataset import ( IcebergCatalogConfig, IcebergCatalogTableDescriptor, IcebergScanResolver, IcebergScanTableSerializer, IcebergTableWrap, ) if TYPE_CHECKING: import pyiceberg.catalog import pyiceberg.table import polars.io.iceberg from polars._typing import StorageOptionsDict from polars.lazyframe.frame import LazyFrame def scan_iceberg( source: str | pyiceberg.table.Table, *, snapshot_id: int | None = None, storage_options: StorageOptionsDict | None = None, catalog: pyiceberg.catalog.Catalog | polars.io.iceberg.IcebergCatalogConfig | None = None, reader_override: Literal["native", "pyiceberg"] | None = None, use_metadata_statistics: bool = True, fast_deletion_count: bool | None = None, use_pyiceberg_filter: bool = True, ) -> LazyFrame: """ Lazily read from an Apache Iceberg table. Parameters ---------- source A PyIceberg table, or a 'namespace.table_name' identifier string, or an absolute path to the metadata. snapshot_id The snapshot ID to scan from. storage_options Extra options for the storage backends supported by `pyiceberg`. For cloud storages, this may include configurations for authentication etc. More info is available `here `__. catalog PyIceberg catalog to load the table from if the provided `target` was a table name. reader_override Overrides the reader used to read the data. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. Note that this parameter should not be necessary outside of testing, as polars will by default automatically select the best reader. Available options: * native: Uses polars native reader. This allows for more optimizations to improve performance. * pyiceberg: Uses PyIceberg, which may support more features. use_metadata_statistics Whether to allow using statistics from Iceberg metadata files. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. When a filter is present, this allows using min/max statistics present in the Iceberg metadata files can be used to allow the reader to skip scanning of metadata from data files that are guaranteed to not match the filter. If a row-count is requested (i.e. `scan_iceberg().select(pl.len())`), this allows returning a count directly from Iceberg metadata. Note however that for datasets containing position delete files, `fast_deletion_count` must also be enabled for this to work. fast_deletion_count Allows returning a row count calculated directly from Iceberg metadata for datasets that contain position delete files. This will give incorrect results if position delete files contain duplicated entries. .. warning:: This functionality is considered **unstable**. It may be changed at any point without it being considered a breaking change. use_pyiceberg_filter Convert and push the filter to PyIceberg where possible. Returns ------- LazyFrame Examples -------- Creates a scan for an Iceberg table from local filesystem, or object store. >>> table_path = "file:/path/to/iceberg-table/metadata.json" >>> pl.scan_iceberg(table_path).collect() # doctest: +SKIP Creates a scan for an Iceberg table from S3. See a list of supported storage options for S3 `here `__. >>> table_path = "s3://bucket/path/to/iceberg-table/metadata.json" >>> storage_options = { ... "s3.region": "eu-central-1", ... "s3.access-key-id": "THE_AWS_ACCESS_KEY_ID", ... "s3.secret-access-key": "THE_AWS_SECRET_ACCESS_KEY", ... } >>> pl.scan_iceberg( ... table_path, storage_options=storage_options ... ).collect() # doctest: +SKIP Creates a scan for an Iceberg table from Azure. Supported options for Azure are available `here `__. Following type of table paths are supported: * az:////metadata.json * adl:////metadata.json * abfs[s]:////metadata.json >>> table_path = "az://container/path/to/iceberg-table/metadata.json" >>> storage_options = { ... "adlfs.account-name": "AZURE_STORAGE_ACCOUNT_NAME", ... "adlfs.account-key": "AZURE_STORAGE_ACCOUNT_KEY", ... } >>> pl.scan_iceberg( ... table_path, storage_options=storage_options ... ).collect() # doctest: +SKIP Creates a scan for an Iceberg table from Google Cloud Storage. Supported options for GCS are available `here `__. >>> table_path = "s3://bucket/path/to/iceberg-table/metadata.json" >>> storage_options = { ... "gcs.project-id": "my-gcp-project", ... "gcs.oauth.token": "ya29.dr.AfM...", ... } >>> pl.scan_iceberg( ... table_path, storage_options=storage_options ... ).collect() # doctest: +SKIP Creates a scan for an Iceberg table with additional options. In the below example, `without_files` option is used which loads the table without file tracking information. >>> table_path = "/path/to/iceberg-table/metadata.json" >>> storage_options = {"py-io-impl": "pyiceberg.io.fsspec.FsspecFileIO"} >>> pl.scan_iceberg( ... table_path, storage_options=storage_options ... ).collect() # doctest: +SKIP Creates a scan for an Iceberg table using a specific snapshot ID. >>> table_path = "/path/to/iceberg-table/metadata.json" >>> snapshot_id = 7051579356916758811 >>> pl.scan_iceberg(table_path, snapshot_id=snapshot_id).collect() # doctest: +SKIP """ from polars._plr import PyLazyFrame if reader_override is not None: msg = "the `reader_override` parameter of `scan_iceberg()` is considered unstable." issue_unstable_warning(msg) if fast_deletion_count is not None: msg = "the `fast_deletion_count` parameter of `scan_iceberg()` is considered unstable." issue_unstable_warning(msg) else: fast_deletion_count = False table: pyiceberg.table.Table | None = None if importlib.util.find_spec("pyiceberg.table") is not None: import pyiceberg.table if isinstance(source, pyiceberg.table.Table): table = source table_descriptor_ = None if table is None: source = str(source) table_descriptor_ = ( source # Inferred as static metadata path if "/" in source or "\\" in source else IcebergCatalogTableDescriptor( table_identifier=source, catalog_config=IcebergCatalogConfig._from_api_parameter_or_environment_default( catalog, fn_name="scan_iceberg", ), ) ) dataset = IcebergScanResolver( table=IcebergTableWrap( table_=NoPickleOption(table), table_descriptor_=table_descriptor_, serializer=IcebergScanTableSerializer(), iceberg_storage_properties=storage_options, ), snapshot_id=snapshot_id, reader_override=reader_override, use_metadata_statistics=use_metadata_statistics, fast_deletion_count=fast_deletion_count, use_pyiceberg_filter=use_pyiceberg_filter, ) return wrap_ldf(PyLazyFrame.new_from_dataset_object(dataset))