diff --git a/awswrangler/athena/_statements.py b/awswrangler/athena/_statements.py index be5825d8e..01ddd7134 100644 --- a/awswrangler/athena/_statements.py +++ b/awswrangler/athena/_statements.py @@ -97,7 +97,9 @@ def create_prepared_statement( @apply_configs -def list_prepared_statements(workgroup: str = "primary", boto3_session: boto3.Session | None = None) -> list[str]: +def list_prepared_statements( + workgroup: str = "primary", boto3_session: boto3.Session | None = None +) -> list[dict[str, Any]]: """ List the prepared statements in the specified workgroup. diff --git a/awswrangler/athena/_utils.py b/awswrangler/athena/_utils.py index a1e9be637..1564909bf 100644 --- a/awswrangler/athena/_utils.py +++ b/awswrangler/athena/_utils.py @@ -16,7 +16,6 @@ Dict, Generator, NamedTuple, - Sequence, TypedDict, Union, cast, @@ -36,7 +35,7 @@ from ._cache import _cache_manager, _LocalMetadataCacheManager if TYPE_CHECKING: - from mypy_boto3_glue.type_defs import ColumnTypeDef + from mypy_boto3_glue.type_defs import ColumnOutputTypeDef _QUERY_FINAL_STATES: list[str] = ["FAILED", "SUCCEEDED", "CANCELLED"] _QUERY_WAIT_POLLING_DELAY: float = 1.0 # SECONDS @@ -993,7 +992,7 @@ def generate_create_query( """ - def parse_columns(columns_description: Sequence["ColumnTypeDef"]) -> str: + def parse_columns(columns_description: list["ColumnOutputTypeDef"]) -> str: columns_str: list[str] = [] for column in columns_description: column_str = f" `{column['Name']}` {column['Type']}" diff --git a/awswrangler/catalog/_get.py b/awswrangler/catalog/_get.py index d397950c6..8f469748b 100644 --- a/awswrangler/catalog/_get.py +++ b/awswrangler/catalog/_get.py @@ -5,7 +5,7 @@ import base64 import itertools import logging -from typing import TYPE_CHECKING, Any, Dict, Iterator, Mapping, cast +from typing import TYPE_CHECKING, Any, Dict, Iterator, cast import boto3 import botocore.exceptions @@ -893,7 +893,7 @@ def get_columns_parameters( table: str, catalog_id: str | None = None, boto3_session: boto3.Session | None = None, -) -> dict[str, Mapping[str, str] | None]: +) -> dict[str, dict[str, str] | None]: """Get all columns parameters. Parameters diff --git a/awswrangler/distributed/ray/datasources/arrow_parquet_datasource.py b/awswrangler/distributed/ray/datasources/arrow_parquet_datasource.py index 33cc2351e..5785e849e 100644 --- a/awswrangler/distributed/ray/datasources/arrow_parquet_datasource.py +++ b/awswrangler/distributed/ray/datasources/arrow_parquet_datasource.py @@ -9,6 +9,7 @@ from __future__ import annotations import logging +from dataclasses import dataclass from typing import ( TYPE_CHECKING, Any, @@ -34,10 +35,11 @@ ) from ray.data.datasource.datasource import ReadTask from ray.data.datasource.file_meta_provider import ( - DefaultParquetMetadataProvider, - ParquetMetadataProvider, _handle_read_os_error, ) +from ray.data.datasource.parquet_meta_provider import ( + ParquetMetadataProvider, +) from ray.data.datasource.partitioning import PathPartitionFilter from ray.data.datasource.path_util import ( _has_file_extension, @@ -55,8 +57,6 @@ _logger: logging.Logger = logging.getLogger(__name__) -FRAGMENTS_PER_META_FETCH = 6 -PARALLELIZE_META_FETCH_THRESHOLD = 24 # The number of rows to read per batch. This is sized to generate 10MiB batches # for rows about 1KiB in size. @@ -93,6 +93,12 @@ PARQUET_ENCODING_RATIO_ESTIMATE_NUM_ROWS = 1024 +@dataclass(frozen=True) +class _SampleInfo: + actual_bytes_per_row: int | None + estimated_bytes_per_row: int | None + + # TODO(ekl) this is a workaround for a pyarrow serialization bug, where serializing a # raw pyarrow file fragment causes S3 network calls. class _SerializedFragment: @@ -117,38 +123,6 @@ def _deserialize_fragments( return [p.deserialize() for p in serialized_fragments] -class _ParquetFileFragmentMetaData: - """Class to store metadata of a Parquet file fragment. - - This includes all attributes from `pyarrow.parquet.FileMetaData` except for `schema`, - which is stored in `self.schema_pickled` as a pickled object from - `cloudpickle.loads()`, used in deduplicating schemas across multiple fragments. - """ - - def __init__(self, fragment_metadata: "pyarrow.parquet.FileMetaData"): - self.created_by = fragment_metadata.created_by - self.format_version = fragment_metadata.format_version - self.num_columns = fragment_metadata.num_columns - self.num_row_groups = fragment_metadata.num_row_groups - self.num_rows = fragment_metadata.num_rows - self.serialized_size = fragment_metadata.serialized_size - # This is a pickled schema object, to be set later with - # `self.set_schema_pickled()`. To get the underlying schema, use - # `cloudpickle.loads(self.schema_pickled)`. - self.schema_pickled: bytes | None = None - - # Calculate the total byte size of the file fragment using the original - # object, as it is not possible to access row groups from this class. - self.total_byte_size = 0 - for row_group_idx in range(fragment_metadata.num_row_groups): - row_group_metadata = fragment_metadata.row_group(row_group_idx) - self.total_byte_size += row_group_metadata.total_byte_size - - def set_schema_pickled(self, schema_pickled: bytes) -> None: - """Note: to get the underlying schema, use `cloudpickle.loads(self.schema_pickled)`.""" - self.schema_pickled = schema_pickled - - # This retry helps when the upstream datasource is not able to handle # overloaded read request or failed with some retriable failures. # For example when reading data from HA hdfs service, hdfs might @@ -213,7 +187,7 @@ def __init__( # noqa: PLR0912,PLR0915 arrow_parquet_args: dict[str, Any] | None = None, _block_udf: Callable[[Block], Block] | None = None, filesystem: "pyarrow.fs.FileSystem" | None = None, - meta_provider: ParquetMetadataProvider = DefaultParquetMetadataProvider(), + meta_provider: ParquetMetadataProvider = ParquetMetadataProvider(), partition_filter: PathPartitionFilter | None = None, shuffle: Literal["files"] | None = None, include_paths: bool = False, @@ -299,8 +273,7 @@ def __init__( # noqa: PLR0912,PLR0915 prefetch_remote_args = {} if self._local_scheduling: prefetch_remote_args["scheduling_strategy"] = self._local_scheduling - raw_metadata = meta_provider.prefetch_file_metadata(pq_ds.fragments, **prefetch_remote_args) or [] - self._metadata = self._dedupe_metadata(raw_metadata) + self._metadata = meta_provider.prefetch_file_metadata(pq_ds.fragments, **prefetch_remote_args) or [] except OSError as e: _handle_read_os_error(e, paths) except pa.ArrowInvalid as ex: @@ -319,43 +292,15 @@ def __init__( # noqa: PLR0912,PLR0915 self._columns = columns self._schema = schema self._arrow_parquet_args = arrow_parquet_args - self._encoding_ratio = self._estimate_files_encoding_ratio() self._file_metadata_shuffler = None self._include_paths = include_paths self._path_root = path_root if shuffle == "files": self._file_metadata_shuffler = np.random.default_rng() - def _dedupe_metadata( - self, - raw_metadatas: list["pyarrow.parquet.FileMetaData"], - ) -> list[_ParquetFileFragmentMetaData]: - """Deduplicate schemas to reduce memory usage. - - For datasets with a large number of columns, the FileMetaData - (in particular the schema) can be very large. We can reduce the - memory usage by only keeping unique schema objects across all - file fragments. This method deduplicates the schemas and returns - a list of `_ParquetFileFragmentMetaData` objects. - """ - schema_to_id: dict[int, Any] = {} # schema_id -> serialized_schema - id_to_schema: dict[Any, bytes] = {} # serialized_schema -> schema_id - stripped_metadatas = [] - for fragment_metadata in raw_metadatas: - stripped_md = _ParquetFileFragmentMetaData(fragment_metadata) - - schema_ser = cloudpickle.dumps(fragment_metadata.schema.to_arrow_schema()) # type: ignore[no-untyped-call] - if schema_ser not in schema_to_id: - schema_id: int | None = len(schema_to_id) - schema_to_id[schema_ser] = schema_id - id_to_schema[schema_id] = schema_ser - stripped_md.set_schema_pickled(schema_ser) - else: - schema_id = schema_to_id.get(schema_ser) - existing_schema_ser = id_to_schema[schema_id] - stripped_md.set_schema_pickled(existing_schema_ser) - stripped_metadatas.append(stripped_md) - return stripped_metadatas + sample_infos = self._sample_fragments() + self._encoding_ratio = _estimate_files_encoding_ratio(sample_infos) + self._default_read_batch_size_rows = _estimate_default_read_batch_size_rows(sample_infos) def estimate_inmemory_data_size(self) -> int | None: """Return an estimate of the Parquet files encoding ratio. @@ -414,25 +359,18 @@ def get_read_tasks(self, parallelism: int) -> list[ReadTask]: if meta.size_bytes is not None: meta.size_bytes = int(meta.size_bytes * self._encoding_ratio) - if meta.num_rows and meta.size_bytes: - # Make sure the batches read are small enough to enable yielding of - # output blocks incrementally during the read. - row_size = meta.size_bytes / meta.num_rows - # Make sure the row batch size is small enough that block splitting - # is still effective. - max_parquet_reader_row_batch_size_bytes = DataContext.get_current().target_max_block_size // 10 - default_read_batch_size_rows = max( - 1, - min( - PARQUET_READER_ROW_BATCH_SIZE, - max_parquet_reader_row_batch_size_bytes // row_size, - ), - ) - else: - default_read_batch_size_rows = PARQUET_READER_ROW_BATCH_SIZE - block_udf, arrow_parquet_args, columns, schema, path_root, include_paths = ( + ( + block_udf, + arrow_parquet_args, + default_read_batch_size_rows, + columns, + schema, + path_root, + include_paths, + ) = ( self._block_udf, self._arrow_parquet_args, + self._default_read_batch_size_rows, self._columns, self._schema, self._path_root, @@ -456,14 +394,7 @@ def get_read_tasks(self, parallelism: int) -> list[ReadTask]: return read_tasks - def _estimate_files_encoding_ratio(self) -> float: - """Return an estimate of the Parquet files encoding ratio. - - To avoid OOMs, it is safer to return an over-estimate than an underestimate. - """ - if not DataContext.get_current().decoding_size_estimation: - return PARQUET_ENCODING_RATIO_ESTIMATE_DEFAULT - + def _sample_fragments(self) -> list[_SampleInfo]: # Sample a few rows from Parquet files to estimate the encoding ratio. # Launch tasks to sample multiple files remotely in parallel. # Evenly distributed to sample N rows in i-th row group in i-th file. @@ -495,11 +426,10 @@ def _estimate_files_encoding_ratio(self) -> float: ) ) sample_bar = ProgressBar("Parquet Files Sample", len(futures)) - sample_ratios = sample_bar.fetch_until_complete(futures) + sample_infos = sample_bar.fetch_until_complete(futures) sample_bar.close() # type: ignore[no-untyped-call] - ratio = np.mean(sample_ratios) - _logger.debug(f"Estimated Parquet encoding ratio from sampling is {ratio}.") - return max(ratio, PARQUET_ENCODING_RATIO_ESTIMATE_LOWER_BOUND) # type: ignore[no-any-return] + + return sample_infos def get_name(self) -> str: """Return a human-readable name for this datasource. @@ -577,33 +507,12 @@ def _read_fragments( yield table -def _fetch_metadata_serialization_wrapper( - fragments: list[_SerializedFragment], -) -> list["pyarrow.parquet.FileMetaData"]: - fragments: list["pyarrow._dataset.ParquetFileFragment"] = _deserialize_fragments_with_retry(fragments) # type: ignore[no-redef] - - return _fetch_metadata(fragments) - - -def _fetch_metadata( - fragments: list["pyarrow.dataset.ParquetFileFragment"], -) -> list["pyarrow.parquet.FileMetaData"]: - fragment_metadata = [] - for f in fragments: - try: - fragment_metadata.append(f.metadata) - except AttributeError: - break - return fragment_metadata - - def _sample_fragment( columns: list[str] | None, schema: type | "pyarrow.lib.Schema" | None, file_fragment: _SerializedFragment, -) -> float: +) -> _SampleInfo: # Sample the first rows batch from file fragment `serialized_fragment`. - # Return the encoding ratio calculated from the sampled rows. fragment = _deserialize_fragments_with_retry([file_fragment])[0] # Only sample the first row group. @@ -616,23 +525,57 @@ def _sample_fragment( schema=schema, batch_size=batch_size, ) - # Use first batch in-memory size as ratio estimation. + # Use first batch in-memory size for estimation. try: batch = next(batches) except StopIteration: - ratio = PARQUET_ENCODING_RATIO_ESTIMATE_LOWER_BOUND + sample_data = _SampleInfo(actual_bytes_per_row=None, estimated_bytes_per_row=None) else: if batch.num_rows > 0: - in_memory_size = batch.nbytes / batch.num_rows metadata = fragment.metadata total_size = 0 for idx in range(metadata.num_row_groups): total_size += metadata.row_group(idx).total_byte_size - file_size = total_size / metadata.num_rows - ratio = in_memory_size / file_size + sample_data = _SampleInfo( + actual_bytes_per_row=batch.nbytes / batch.num_rows, + estimated_bytes_per_row=total_size / metadata.num_rows, + ) else: - ratio = PARQUET_ENCODING_RATIO_ESTIMATE_LOWER_BOUND - _logger.debug( - f"Estimated Parquet encoding ratio is {ratio} for fragment {fragment} " f"with batch size {batch_size}." - ) - return ratio + sample_data = _SampleInfo(actual_bytes_per_row=None, estimated_bytes_per_row=None) + return sample_data + + +def _estimate_files_encoding_ratio(sample_infos: list[_SampleInfo]) -> float: + """Return an estimate of the Parquet files encoding ratio. + + To avoid OOMs, it is safer to return an over-estimate than an underestimate. + """ + if not DataContext.get_current().decoding_size_estimation: + return PARQUET_ENCODING_RATIO_ESTIMATE_DEFAULT + + def compute_encoding_ratio(sample_info: _SampleInfo) -> float: + if sample_info.actual_bytes_per_row is None or sample_info.estimated_bytes_per_row is None: + return PARQUET_ENCODING_RATIO_ESTIMATE_LOWER_BOUND + else: + return sample_info.actual_bytes_per_row / sample_info.estimated_bytes_per_row + + ratio = np.mean(list(map(compute_encoding_ratio, sample_infos))) + _logger.debug(f"Estimated Parquet encoding ratio from sampling is {ratio}.") + return max(ratio, PARQUET_ENCODING_RATIO_ESTIMATE_LOWER_BOUND) # type: ignore[return-value] + + +def _estimate_default_read_batch_size_rows(sample_infos: list[_SampleInfo]) -> int: + def compute_batch_size_rows(sample_info: _SampleInfo) -> int: + if sample_info.actual_bytes_per_row is None: + return PARQUET_READER_ROW_BATCH_SIZE + else: + max_parquet_reader_row_batch_size_bytes = DataContext.get_current().target_max_block_size // 10 + return max( + 1, + min( + PARQUET_READER_ROW_BATCH_SIZE, + max_parquet_reader_row_batch_size_bytes // sample_info.actual_bytes_per_row, + ), + ) + + return np.mean(list(map(compute_batch_size_rows, sample_infos))) # type: ignore[return-value] diff --git a/awswrangler/dynamodb/_utils.py b/awswrangler/dynamodb/_utils.py index 6934dcd62..c22ab1203 100644 --- a/awswrangler/dynamodb/_utils.py +++ b/awswrangler/dynamodb/_utils.py @@ -23,7 +23,7 @@ ExecuteStatementOutputTypeDef, KeySchemaElementTypeDef, TableAttributeValueTypeDef, - WriteRequestTypeDef, + WriteRequestOutputTypeDef, ) @@ -214,7 +214,7 @@ def __init__( ): self._table_name = table_name self._client = client - self._items_buffer: list["WriteRequestTypeDef"] = [] + self._items_buffer: list["WriteRequestOutputTypeDef"] = [] self._flush_amount = flush_amount self._overwrite_by_pkeys = overwrite_by_pkeys @@ -240,14 +240,16 @@ def delete_item(self, key: dict[str, "AttributeValueTypeDef"]) -> None: """ self._add_request_and_process({"DeleteRequest": {"Key": key}}) - def _add_request_and_process(self, request: "WriteRequestTypeDef") -> None: + def _add_request_and_process(self, request: "WriteRequestOutputTypeDef") -> None: if self._overwrite_by_pkeys: self._remove_dup_pkeys_request_if_any(request, self._overwrite_by_pkeys) self._items_buffer.append(request) self._flush_if_needed() - def _remove_dup_pkeys_request_if_any(self, request: "WriteRequestTypeDef", overwrite_by_pkeys: list[str]) -> None: + def _remove_dup_pkeys_request_if_any( + self, request: "WriteRequestOutputTypeDef", overwrite_by_pkeys: list[str] + ) -> None: pkey_values_new = self._extract_pkey_values(request, overwrite_by_pkeys) for item in self._items_buffer: if self._extract_pkey_values(item, overwrite_by_pkeys) == pkey_values_new: @@ -257,7 +259,9 @@ def _remove_dup_pkeys_request_if_any(self, request: "WriteRequestTypeDef", overw item, ) - def _extract_pkey_values(self, request: "WriteRequestTypeDef", overwrite_by_pkeys: list[str]) -> list[Any] | None: + def _extract_pkey_values( + self, request: "WriteRequestOutputTypeDef", overwrite_by_pkeys: list[str] + ) -> list[Any] | None: if request.get("PutRequest"): return [request["PutRequest"]["Item"][key] for key in overwrite_by_pkeys] if request.get("DeleteRequest"): diff --git a/poetry.lock b/poetry.lock index 0596b64a8..5e588019b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. [[package]] name = "aenum" @@ -149,13 +149,13 @@ files = [ [[package]] name = "annotated-types" -version = "0.6.0" +version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" files = [ - {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, - {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] [package.dependencies] @@ -163,13 +163,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] name = "anyio" -version = "4.3.0" +version = "4.4.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"}, - {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"}, + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, ] [package.dependencies] @@ -345,13 +345,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p [[package]] name = "babel" -version = "2.14.0" +version = "2.15.0" description = "Internationalization utilities" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"}, - {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"}, + {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"}, + {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"}, ] [package.dependencies] @@ -412,17 +412,17 @@ css = ["tinycss2 (>=1.1.0,<1.3)"] [[package]] name = "boto3" -version = "1.34.122" +version = "1.34.131" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.34.122-py3-none-any.whl", hash = "sha256:b2d7400ff84fa547e53b3d9acfa3c95d65d45b5886ba1ede1f7df4768d1cc0b1"}, - {file = "boto3-1.34.122.tar.gz", hash = "sha256:56840d8ce91654d182f1c113f0791fa2113c3aa43230c50b4481f235348a6037"}, + {file = "boto3-1.34.131-py3-none-any.whl", hash = "sha256:05e388cb937e82be70bfd7eb0c84cf8011ff35cf582a593873ac21675268683b"}, + {file = "boto3-1.34.131.tar.gz", hash = "sha256:dab8f72a6c4e62b4fd70da09e08a6b2a65ea2115b27dd63737142005776ef216"}, ] [package.dependencies] -botocore = ">=1.34.122,<1.35.0" +botocore = ">=1.34.131,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -862,13 +862,13 @@ xray = ["mypy-boto3-xray (>=1.34.0,<1.35.0)"] [[package]] name = "botocore" -version = "1.34.127" +version = "1.34.131" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" files = [ - {file = "botocore-1.34.127-py3-none-any.whl", hash = "sha256:e14fa28c8bb141de965e700f88b196d17c67a703c7f0f5c7e14f7dd1cf636011"}, - {file = "botocore-1.34.127.tar.gz", hash = "sha256:a377871742c40603d559103f19acb7bc93cfaf285e68f21b81637ec396099877"}, + {file = "botocore-1.34.131-py3-none-any.whl", hash = "sha256:13b011d7b206ce00727dcee26548fa3b550db9046d5a0e90ac25a6e6c8fde6ef"}, + {file = 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python-versions = ">=3.8" files = [ - {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"}, - {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"}, + {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"}, + {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"}, ] [package.extras] -docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"] +docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"] optional = ["python-socks", "wsaccel"] test = ["websockets"] @@ -6995,18 +7071,18 @@ multidict = ">=4.0" [[package]] name = "zipp" -version = "3.17.0" +version = "3.19.2" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = 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(>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] deltalake = ["deltalake"] @@ -7028,4 +7104,4 @@ sqlserver = ["pyodbc"] [metadata] lock-version = "2.0" python-versions = ">=3.8, <4.0" -content-hash = "1d35cb10819a27f2df59c24cc103ab2f044170e6c45f5b4a72f7e8e0a91bad06" +content-hash = "758354151014f0bb89a1bdd58d85550a142af44623de6153f74ccd7400c0cc56" diff --git a/pyproject.toml b/pyproject.toml index 8a67ab112..710547b2c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -79,7 +79,7 @@ modin = [ { version = "0.23.1post0", markers = "python_version < \"3.9\"", optional = true }, { version = "^0.26.0", markers = "python_version >= \"3.9\"", optional = true } ] -ray = { version = "^2.20.0", extras = ["default", "data"], optional = true } +ray = { version = "^2.30.0", extras = ["default", "data"], optional = true } [tool.poetry.extras] redshift = ["redshift-connector"] diff --git a/tests/unit/test_s3_parquet.py b/tests/unit/test_s3_parquet.py index c1cbf14d6..ddda82e30 100644 --- a/tests/unit/test_s3_parquet.py +++ b/tests/unit/test_s3_parquet.py @@ -410,6 +410,12 @@ def test_index_recovery_simple_str(path, use_threads): assert_pandas_equals(df, df2) +@pytest.mark.modin_index +@pytest.mark.xfail( + raises=AssertionError, + reason="https://github.com/ray-project/ray/issues/37771", + condition=is_ray_modin, +) @pytest.mark.parametrize("use_threads", [True, False, 2]) def test_index_recovery_partitioned_str(path, use_threads): df = pd.DataFrame( @@ -530,6 +536,11 @@ def test_index_schema_validation(path, glue_database, glue_table, index): @pytest.mark.modin_index +@pytest.mark.xfail( + raises=AssertionError, + reason="https://github.com/ray-project/ray/issues/37771", + condition=is_ray_modin, +) @pytest.mark.parametrize("index", [["c0"], ["c0", "c1"]]) @pytest.mark.parametrize("partition_cols", [["c0"], ["c0", "c1"]]) def test_index_partition(path, glue_database, glue_table, index, partition_cols): @@ -679,6 +690,12 @@ def test_validate_columns(path, partition_cols) -> None: wr.s3.read_parquet(path, columns=["a", "b", "c"], dataset=True, validate_schema=True) +@pytest.mark.modin_index +@pytest.mark.xfail( + raises=AssertionError, + reason="https://github.com/ray-project/ray/issues/37771", + condition=is_ray_modin, +) @pytest.mark.parametrize("use_threads", [True, False, 2]) def test_empty_column(path, use_threads): df = pd.DataFrame({"c0": [1, 2, 3], "c1": [None, None, None], "par": ["a", "b", "c"]}) diff --git a/tests/unit/test_s3_text.py b/tests/unit/test_s3_text.py index 14f7a7647..7b2d90113 100644 --- a/tests/unit/test_s3_text.py +++ b/tests/unit/test_s3_text.py @@ -205,6 +205,12 @@ def test_json(path): assert df1.equals(wr.s3.read_json(path=[path0, path1], use_threads=True)) +@pytest.mark.modin_index +@pytest.mark.xfail( + raises=AssertionError, + reason="https://github.com/ray-project/ray/issues/37771", + condition=is_ray_modin, +) def test_json_lines(path): df0 = pd.DataFrame({"id": [1, 2, 3]}) path0 = f"{path}test_json0.json"