Source code for dask.dataframe.io.parquet.core

from __future__ import annotations

import importlib
import math
import warnings

import tlz as toolz
from fsspec.core import get_fs_token_paths
from fsspec.utils import stringify_path
from packaging.version import parse as parse_version

from dask.base import tokenize
from dask.blockwise import BlockIndex
from dask.dataframe.core import DataFrame, Scalar
from dask.dataframe.io.io import from_map
from dask.dataframe.io.parquet.utils import Engine, _sort_and_analyze_paths
from dask.dataframe.io.utils import DataFrameIOFunction, _is_local_fs
from dask.dataframe.methods import concat
from dask.delayed import Delayed
from dask.highlevelgraph import HighLevelGraph
from dask.utils import apply, import_required, natural_sort_key, parse_bytes

__all__ = ("read_parquet", "to_parquet")

NONE_LABEL = "__null_dask_index__"

# ----------------------------------------------------------------------
# User API


class ParquetFunctionWrapper(DataFrameIOFunction):
    """
    Parquet Function-Wrapper Class
    Reads parquet data from disk to produce a partition
    (given a `part` argument).
    """

    def __init__(
        self,
        engine,
        fs,
        meta,
        columns,
        index,
        kwargs,
        common_kwargs,
    ):
        self.engine = engine
        self.fs = fs
        self.meta = meta
        self._columns = columns
        self.index = index

        # `kwargs` = user-defined kwargs to be passed
        #            identically for all partitions.
        #
        # `common_kwargs` = kwargs set by engine to be
        #                   passed identically for all
        #                   partitions.
        self.common_kwargs = toolz.merge(common_kwargs, kwargs or {})

    @property
    def columns(self):
        return self._columns

    def project_columns(self, columns):
        """Return a new ParquetFunctionWrapper object
        with a sub-column projection.
        """
        if columns == self.columns:
            return self
        return ParquetFunctionWrapper(
            self.engine,
            self.fs,
            self.meta,
            columns,
            self.index,
            None,  # Already merged into common_kwargs
            self.common_kwargs,
        )

    def __call__(self, part):

        if not isinstance(part, list):
            part = [part]

        return read_parquet_part(
            self.fs,
            self.engine,
            self.meta,
            [(p["piece"], p.get("kwargs", {})) for p in part],
            self.columns,
            self.index,
            self.common_kwargs,
        )


class ToParquetFunctionWrapper:
    """
    Parquet Function-Wrapper Class

    Writes a DataFrame partition into a distinct parquet
    file. When called, the function also requires the
    current block index (via ``blockwise.BlockIndex``).
    """

    def __init__(
        self,
        engine,
        path,
        fs,
        partition_on,
        write_metadata_file,
        i_offset,
        name_function,
        kwargs_pass,
    ):
        self.engine = engine
        self.path = path
        self.fs = fs
        self.partition_on = partition_on
        self.write_metadata_file = write_metadata_file
        self.i_offset = i_offset
        self.name_function = name_function
        self.kwargs_pass = kwargs_pass

        # NOTE: __name__ must be with "to-parquet"
        # for the name of the resulting `Blockwise`
        # layer to begin with "to-parquet"
        self.__name__ = "to-parquet"

    def __dask_tokenize__(self):
        return (
            self.engine,
            self.path,
            self.fs,
            self.partition_on,
            self.write_metadata_file,
            self.i_offset,
            self.name_function,
            self.kwargs_pass,
        )

    def __call__(self, df, block_index: tuple[int]):
        # Get partition index from block index tuple
        part_i = block_index[0]
        filename = (
            f"part.{part_i + self.i_offset}.parquet"
            if self.name_function is None
            else self.name_function(part_i + self.i_offset)
        )

        # Write out data
        return self.engine.write_partition(
            df,
            self.path,
            self.fs,
            filename,
            self.partition_on,
            self.write_metadata_file,
            **(dict(self.kwargs_pass, head=True) if part_i == 0 else self.kwargs_pass),
        )


[docs]def read_parquet( path, columns=None, filters=None, categories=None, index=None, storage_options=None, engine="auto", calculate_divisions=None, ignore_metadata_file=False, metadata_task_size=None, split_row_groups=False, chunksize=None, aggregate_files=None, parquet_file_extension=(".parq", ".parquet", ".pq"), **kwargs, ): """ Read a Parquet file into a Dask DataFrame This reads a directory of Parquet data into a Dask.dataframe, one file per partition. It selects the index among the sorted columns if any exist. Parameters ---------- path : str or list Source directory for data, or path(s) to individual parquet files. Prefix with a protocol like ``s3://`` to read from alternative filesystems. To read from multiple files you can pass a globstring or a list of paths, with the caveat that they must all have the same protocol. columns : str or list, default None Field name(s) to read in as columns in the output. By default all non-index fields will be read (as determined by the pandas parquet metadata, if present). Provide a single field name instead of a list to read in the data as a Series. filters : Union[List[Tuple[str, str, Any]], List[List[Tuple[str, str, Any]]]], default None List of filters to apply, like ``[[('col1', '==', 0), ...], ...]``. Using this argument will NOT result in row-wise filtering of the final partitions unless ``engine="pyarrow"`` is also specified. For other engines, filtering is only performed at the partition level, that is, to prevent the loading of some row-groups and/or files. For the "pyarrow" engine, predicates can be expressed in disjunctive normal form (DNF). This means that the inner-most tuple describes a single column predicate. These inner predicates are combined with an AND conjunction into a larger predicate. The outer-most list then combines all of the combined filters with an OR disjunction. Predicates can also be expressed as a ``List[Tuple]``. These are evaluated as an AND conjunction. To express OR in predictates, one must use the (preferred for "pyarrow") ``List[List[Tuple]]`` notation. Note that the "fastparquet" engine does not currently support DNF for the filtering of partitioned columns (``List[Tuple]`` is required). index : str, list or False, default None Field name(s) to use as the output frame index. By default will be inferred from the pandas parquet file metadata, if present. Use ``False`` to read all fields as columns. categories : list or dict, default None For any fields listed here, if the parquet encoding is Dictionary, the column will be created with dtype category. Use only if it is guaranteed that the column is encoded as dictionary in all row-groups. If a list, assumes up to 2**16-1 labels; if a dict, specify the number of labels expected; if None, will load categories automatically for data written by dask/fastparquet, not otherwise. storage_options : dict, default None Key/value pairs to be passed on to the file-system backend, if any. open_file_options : dict, default None Key/value arguments to be passed along to ``AbstractFileSystem.open`` when each parquet data file is open for reading. Experimental (optimized) "precaching" for remote file systems (e.g. S3, GCS) can be enabled by adding ``{"method": "parquet"}`` under the ``"precache_options"`` key. Also, a custom file-open function can be used (instead of ``AbstractFileSystem.open``), by specifying the desired function under the ``"open_file_func"`` key. engine : {'auto', 'fastparquet', 'pyarrow'}, default 'auto' Parquet library to use. Options include: 'auto', 'fastparquet', and 'pyarrow'. Defaults to 'auto', which uses ``fastparquet`` if it is installed, and falls back to ``pyarrow`` otherwise. Note that in the future this default ordering for 'auto' will switch, with ``pyarrow`` being used if it is installed, and falling back to ``fastparquet``. calculate_divisions : bool, default False Whether to use min/max statistics from the footer metadata (or global ``_metadata`` file) to calculate divisions for the output DataFrame collection. Divisions will not be calculated if statistics are missing. This option will be ignored if ``index`` is not specified and there is no physical index column specified in the custom "pandas" Parquet metadata. Note that ``calculate_divisions=True`` may be extremely slow on some systems, and should be avoided when reading from remote storage. ignore_metadata_file : bool, default False Whether to ignore the global ``_metadata`` file (when one is present). If ``True``, or if the global ``_metadata`` file is missing, the parquet metadata may be gathered and processed in parallel. Parallel metadata processing is currently supported for ``ArrowDatasetEngine`` only. metadata_task_size : int, default configurable If parquet metadata is processed in parallel (see ``ignore_metadata_file`` description above), this argument can be used to specify the number of dataset files to be processed by each task in the Dask graph. If this argument is set to ``0``, parallel metadata processing will be disabled. The default values for local and remote filesystems can be specified with the "metadata-task-size-local" and "metadata-task-size-remote" config fields, respectively (see "dataframe.parquet"). split_row_groups : bool or int, default False If True, then each output dataframe partition will correspond to a single parquet-file row-group. If False, each partition will correspond to a complete file. If a positive integer value is given, each dataframe partition will correspond to that number of parquet row-groups (or fewer). chunksize : int or str, default None WARNING: The ``chunksize`` argument will be deprecated in the future. Please use ``split_row_groups`` to specify how many row-groups should be mapped to each output partition. If you strongly oppose the deprecation of ``chunksize``, please comment at https://github.com/dask/dask/issues/9043". The desired size of each output ``DataFrame`` partition in terms of total (uncompressed) parquet storage space. If specified, adjacent row-groups and/or files will be aggregated into the same output partition until the cumulative ``total_byte_size`` parquet-metadata statistic reaches this value. Use `aggregate_files` to enable/disable inter-file aggregation. aggregate_files : bool or str, default None WARNING: The ``aggregate_files`` argument will be deprecated in the future. Please consider using ``from_map`` to create a DataFrame collection with a custom file-to-partition mapping. If you strongly oppose the deprecation of ``aggregate_files``, comment at https://github.com/dask/dask/issues/9051". Whether distinct file paths may be aggregated into the same output partition. This parameter is only used when `chunksize` is specified or when `split_row_groups` is an integer >1. A setting of True means that any two file paths may be aggregated into the same output partition, while False means that inter-file aggregation is prohibited. For "hive-partitioned" datasets, a "partition"-column name can also be specified. In this case, we allow the aggregation of any two files sharing a file path up to, and including, the corresponding directory name. For example, if ``aggregate_files`` is set to ``"section"`` for the directory structure below, ``03.parquet`` and ``04.parquet`` may be aggregated together, but ``01.parquet`` and ``02.parquet`` cannot be. If, however, ``aggregate_files`` is set to ``"region"``, ``01.parquet`` may be aggregated with ``02.parquet``, and ``03.parquet`` may be aggregated with ``04.parquet``:: dataset-path/ ├── region=1/ │ ├── section=a/ │ │ └── 01.parquet │ ├── section=b/ │ └── └── 02.parquet └── region=2/ ├── section=a/ │ ├── 03.parquet └── └── 04.parquet Note that the default behavior of ``aggregate_files`` is ``False``. parquet_file_extension: str, tuple[str], or None, default (".parq", ".parquet", ".pq") A file extension or an iterable of extensions to use when discovering parquet files in a directory. Files that don't match these extensions will be ignored. This argument only applies when ``paths`` corresponds to a directory and no ``_metadata`` file is present (or ``ignore_metadata_file=True``). Passing in ``parquet_file_extension=None`` will treat all files in the directory as parquet files. The purpose of this argument is to ensure that the engine will ignore unsupported metadata files (like Spark's '_SUCCESS' and 'crc' files). It may be necessary to change this argument if the data files in your parquet dataset do not end in ".parq", ".parquet", or ".pq". **kwargs: dict (of dicts) Passthrough key-word arguments for read backend. The top-level keys correspond to the appropriate operation type, and the second level corresponds to the kwargs that will be passed on to the underlying ``pyarrow`` or ``fastparquet`` function. Supported top-level keys: 'dataset' (for opening a ``pyarrow`` dataset), 'file' or 'dataset' (for opening a ``fastparquet.ParquetFile``), 'read' (for the backend read function), 'arrow_to_pandas' (for controlling the arguments passed to convert from a ``pyarrow.Table.to_pandas()``). Any element of kwargs that is not defined under these top-level keys will be passed through to the `engine.read_partitions` classmethod as a stand-alone argument (and will be ignored by the engine implementations defined in ``dask.dataframe``). Examples -------- >>> df = dd.read_parquet('s3://bucket/my-parquet-data') # doctest: +SKIP See Also -------- to_parquet pyarrow.parquet.ParquetDataset """ # "Pre-deprecation" warning for `chunksize` if chunksize: warnings.warn( "The `chunksize` argument will be deprecated in the future. " "Please use `split_row_groups` to specify how many row-groups " "should be mapped to each output partition.\n\n" "If you strongly oppose the deprecation of `chunksize`, please " "comment at https://github.com/dask/dask/issues/9043", FutureWarning, ) # "Pre-deprecation" warning for `aggregate_files` if aggregate_files: warnings.warn( "The `aggregate_files` argument will be deprecated in the future. " "Please consider using `from_map` to create a DataFrame collection " "with a custom file-to-partition mapping.\n\n" "If you strongly oppose the deprecation of `aggregate_files`, " "please comment at https://github.com/dask/dask/issues/9051", FutureWarning, ) if "read_from_paths" in kwargs: kwargs.pop("read_from_paths") warnings.warn( "`read_from_paths` is no longer supported and will be ignored.", FutureWarning, ) # Handle gather_statistics deprecation if "gather_statistics" in kwargs: if calculate_divisions is None: calculate_divisions = kwargs.pop("gather_statistics") warnings.warn( "``gather_statistics`` is deprecated and will be removed in a " "future release. Please use ``calculate_divisions`` instead.", FutureWarning, ) else: warnings.warn( f"``gather_statistics`` is deprecated. Ignoring this option " f"in favor of ``calculate_divisions={calculate_divisions}``", FutureWarning, ) calculate_divisions = bool(calculate_divisions) # We support a top-level `parquet_file_extension` kwarg, but # must check if the deprecated `require_extension` option is # being passed to the engine. If `parquet_file_extension` is # set to the default value, and `require_extension` was also # specified, we will use `require_extension` but warn the user. if ( "dataset" in kwargs and "require_extension" in kwargs["dataset"] and parquet_file_extension == (".parq", ".parquet", ".pq") ): parquet_file_extension = kwargs["dataset"].pop("require_extension") warnings.warn( "require_extension is deprecated, and will be removed from " "read_parquet in a future release. Please use the top-level " "parquet_file_extension argument instead.", FutureWarning, ) # Store initial function arguments input_kwargs = { "columns": columns, "filters": filters, "categories": categories, "index": index, "storage_options": storage_options, "engine": engine, "calculate_divisions": calculate_divisions, "ignore_metadata_file": ignore_metadata_file, "metadata_task_size": metadata_task_size, "split_row_groups": split_row_groups, "chunksize": chunksize, "aggregate_files": aggregate_files, "parquet_file_extension": parquet_file_extension, **kwargs, } if isinstance(columns, str): input_kwargs["columns"] = [columns] df = read_parquet(path, **input_kwargs) return df[columns] if columns is not None: columns = list(columns) if isinstance(engine, str): engine = get_engine(engine, bool(kwargs)) if hasattr(path, "name"): path = stringify_path(path) # Update input_kwargs input_kwargs.update({"columns": columns, "engine": engine}) fs, _, paths = get_fs_token_paths(path, mode="rb", storage_options=storage_options) paths = sorted(paths, key=natural_sort_key) # numeric rather than glob ordering auto_index_allowed = False if index is None: # User is allowing auto-detected index auto_index_allowed = True if index and isinstance(index, str): index = [index] read_metadata_result = engine.read_metadata( fs, paths, categories=categories, index=index, gather_statistics=calculate_divisions, filters=filters, split_row_groups=split_row_groups, chunksize=chunksize, aggregate_files=aggregate_files, ignore_metadata_file=ignore_metadata_file, metadata_task_size=metadata_task_size, parquet_file_extension=parquet_file_extension, **kwargs, ) # In the future, we may want to give the engine the # option to return a dedicated element for `common_kwargs`. # However, to avoid breaking the API, we just embed this # data in the first element of `parts` for now. # The logic below is inteded to handle backward and forward # compatibility with a user-defined engine. meta, statistics, parts, index = read_metadata_result[:4] common_kwargs = {} aggregation_depth = False if len(parts): # For now, `common_kwargs` and `aggregation_depth` # may be stored in the first element of `parts` common_kwargs = parts[0].pop("common_kwargs", {}) aggregation_depth = parts[0].pop("aggregation_depth", aggregation_depth) # Parse dataset statistics from metadata (if available) parts, divisions, index, index_in_columns = process_statistics( parts, statistics, filters, index, chunksize, split_row_groups, fs, aggregation_depth, ) # Account for index and columns arguments. # Modify `meta` dataframe accordingly meta, index, columns = set_index_columns( meta, index, columns, index_in_columns, auto_index_allowed ) if meta.index.name == NONE_LABEL: meta.index.name = None # Set the index that was previously treated as a column if index_in_columns: meta = meta.set_index(index) if meta.index.name == NONE_LABEL: meta.index.name = None if len(divisions) < 2: # empty dataframe - just use meta divisions = (None, None) io_func = lambda x: x parts = [meta] else: # Use IO function wrapper io_func = ParquetFunctionWrapper( engine, fs, meta, columns, index, {}, # All kwargs should now be in `common_kwargs` common_kwargs, ) # Construct the output collection with from_map return from_map( io_func, parts, meta=meta, divisions=divisions, label="read-parquet", token=tokenize(path, **input_kwargs), enforce_metadata=False, creation_info={ "func": read_parquet, "args": (path,), "kwargs": input_kwargs, }, )
def check_multi_support(engine): # Helper function to check that the engine # supports a multi-partition read return hasattr(engine, "multi_support") and engine.multi_support() def read_parquet_part(fs, engine, meta, part, columns, index, kwargs): """Read a part of a parquet dataset This function is used by `read_parquet`.""" if isinstance(part, list): if len(part) == 1 or part[0][1] or not check_multi_support(engine): # Part kwargs expected func = engine.read_partition dfs = [ func(fs, rg, columns.copy(), index, **toolz.merge(kwargs, kw)) for (rg, kw) in part ] df = concat(dfs, axis=0) if len(dfs) > 1 else dfs[0] else: # No part specific kwargs, let engine read # list of parts at once df = engine.read_partition( fs, [p[0] for p in part], columns.copy(), index, **kwargs ) else: # NOTE: `kwargs` are the same for all parts, while `part_kwargs` may # be different for each part. rg, part_kwargs = part df = engine.read_partition( fs, rg, columns, index, **toolz.merge(kwargs, part_kwargs) ) if meta.columns.name: df.columns.name = meta.columns.name columns = columns or [] index = index or [] df = df[[c for c in columns if c not in index]] if index == [NONE_LABEL]: df.index.name = None return df
[docs]def to_parquet( df, path, engine="auto", compression="snappy", write_index=True, append=False, overwrite=False, ignore_divisions=False, partition_on=None, storage_options=None, custom_metadata=None, write_metadata_file=None, compute=True, compute_kwargs=None, schema=None, name_function=None, **kwargs, ): """Store Dask.dataframe to Parquet files Notes ----- Each partition will be written to a separate file. Parameters ---------- df : dask.dataframe.DataFrame path : string or pathlib.Path Destination directory for data. Prepend with protocol like ``s3://`` or ``hdfs://`` for remote data. engine : {'auto', 'fastparquet', 'pyarrow'}, default 'auto' Parquet library to use. Options include: 'auto', 'fastparquet', and 'pyarrow'. Defaults to 'auto', which uses ``fastparquet`` if it is installed, and falls back to ``pyarrow`` otherwise. Note that in the future this default ordering for 'auto' will switch, with ``pyarrow`` being used if it is installed, and falling back to ``fastparquet``. compression : string or dict, default 'snappy' Either a string like ``"snappy"`` or a dictionary mapping column names to compressors like ``{"name": "gzip", "values": "snappy"}``. Defaults to ``"snappy"``. write_index : boolean, default True Whether or not to write the index. Defaults to True. append : bool, default False If False (default), construct data-set from scratch. If True, add new row-group(s) to an existing data-set. In the latter case, the data-set must exist, and the schema must match the input data. overwrite : bool, default False Whether or not to remove the contents of `path` before writing the dataset. The default is False. If True, the specified path must correspond to a directory (but not the current working directory). This option cannot be set to True if `append=True`. NOTE: `overwrite=True` will remove the original data even if the current write operation fails. Use at your own risk. ignore_divisions : bool, default False If False (default) raises error when previous divisions overlap with the new appended divisions. Ignored if append=False. partition_on : list, default None Construct directory-based partitioning by splitting on these fields' values. Each dask partition will result in one or more datafiles, there will be no global groupby. storage_options : dict, default None Key/value pairs to be passed on to the file-system backend, if any. custom_metadata : dict, default None Custom key/value metadata to include in all footer metadata (and in the global "_metadata" file, if applicable). Note that the custom metadata may not contain the reserved b"pandas" key. write_metadata_file : bool or None, default None Whether to write the special ``_metadata`` file. If ``None`` (the default), a ``_metadata`` file will only be written if ``append=True`` and the dataset already has a ``_metadata`` file. compute : bool, default True If :obj:`True` (default) then the result is computed immediately. If :obj:`False` then a ``dask.dataframe.Scalar`` object is returned for future computation. compute_kwargs : dict, default True Options to be passed in to the compute method schema : Schema object, dict, or {"infer", None}, default None Global schema to use for the output dataset. Alternatively, a `dict` of pyarrow types can be specified (e.g. `schema={"id": pa.string()}`). For this case, fields excluded from the dictionary will be inferred from `_meta_nonempty`. If "infer", the first non-empty and non-null partition will be used to infer the type for "object" columns. If None (default), we let the backend infer the schema for each distinct output partition. If the partitions produce inconsistent schemas, pyarrow will throw an error when writing the shared _metadata file. Note that this argument is ignored by the "fastparquet" engine. name_function : callable, default None Function to generate the filename for each output partition. The function should accept an integer (partition index) as input and return a string which will be used as the filename for the corresponding partition. Should preserve the lexicographic order of partitions. If not specified, files will created using the convention ``part.0.parquet``, ``part.1.parquet``, ``part.2.parquet``, ... and so on for each partition in the DataFrame. **kwargs : Extra options to be passed on to the specific backend. Examples -------- >>> df = dd.read_csv(...) # doctest: +SKIP >>> df.to_parquet('/path/to/output/', ...) # doctest: +SKIP By default, files will be created in the specified output directory using the convention ``part.0.parquet``, ``part.1.parquet``, ``part.2.parquet``, ... and so on for each partition in the DataFrame. To customize the names of each file, you can use the ``name_function=`` keyword argument. The function passed to ``name_function`` will be used to generate the filename for each partition and should expect a partition's index integer as input and return a string which will be used as the filename for the corresponding partition. Strings produced by ``name_function`` must preserve the order of their respective partition indices. For example: >>> name_function = lambda x: f"data-{x}.parquet" >>> df.to_parquet('/path/to/output/', name_function=name_function) # doctest: +SKIP will result in the following files being created:: /path/to/output/ ├── data-0.parquet ├── data-1.parquet ├── data-2.parquet └── ... See Also -------- read_parquet: Read parquet data to dask.dataframe """ compute_kwargs = compute_kwargs or {} if compression == "default": warnings.warn( "compression='default' is deprecated and will be removed in a " "future version, the default for all engines is 'snappy' now.", FutureWarning, ) compression = "snappy" partition_on = partition_on or [] if isinstance(partition_on, str): partition_on = [partition_on] if set(partition_on) - set(df.columns): raise ValueError( "Partitioning on non-existent column. " "partition_on=%s ." "columns=%s" % (str(partition_on), str(list(df.columns))) ) if df.columns.inferred_type not in {"string", "empty"}: raise ValueError("parquet doesn't support non-string column names") if isinstance(engine, str): engine = get_engine(engine, bool(kwargs)) if hasattr(path, "name"): path = stringify_path(path) fs, _, _ = get_fs_token_paths(path, mode="wb", storage_options=storage_options) # Trim any protocol information from the path before forwarding path = fs._strip_protocol(path) if overwrite: if _is_local_fs(fs): working_dir = fs.expand_path(".")[0] if path.rstrip("/") == working_dir.rstrip("/"): raise ValueError( "Cannot clear the contents of the current working directory!" ) if append: raise ValueError("Cannot use both `overwrite=True` and `append=True`!") if fs.exists(path) and fs.isdir(path): # Only remove path contents if # (1) The path exists # (2) The path is a directory # (3) The path is not the current working directory fs.rm(path, recursive=True) # Always skip divisions checks if divisions are unknown if not df.known_divisions: ignore_divisions = True # Save divisions and corresponding index name. This is necessary, # because we may be resetting the index to write the file division_info = {"divisions": df.divisions, "name": df.index.name} if division_info["name"] is None: # As of 0.24.2, pandas will rename an index with name=None # when df.reset_index() is called. The default name is "index", # but dask will always change the name to the NONE_LABEL constant if NONE_LABEL not in df.columns: division_info["name"] = NONE_LABEL elif write_index: raise ValueError( "Index must have a name if __null_dask_index__ is a column." ) else: warnings.warn( "If read back by Dask, column named __null_dask_index__ " "will be set to the index (and renamed to None)." ) # There are some "resrved" names that may be used as the default column # name after resetting the index. However, we don't want to treat it as # a "special" name if the string is already used as a "real" column name. reserved_names = [] for name in ["index", "level_0"]: if name not in df.columns: reserved_names.append(name) # If write_index==True (default), reset the index and record the # name of the original index in `index_cols` (we will set the name # to the NONE_LABEL constant if it is originally `None`). # `fastparquet` will use `index_cols` to specify the index column(s) # in the metadata. `pyarrow` will revert the `reset_index` call # below if `index_cols` is populated (because pyarrow will want to handle # index preservation itself). For both engines, the column index # will be written to "pandas metadata" if write_index=True index_cols = [] if write_index: real_cols = set(df.columns) none_index = list(df._meta.index.names) == [None] df = df.reset_index() if none_index: df.columns = [ c if c not in reserved_names else NONE_LABEL for c in df.columns ] index_cols = [c for c in set(df.columns) - real_cols] else: # Not writing index - might as well drop it df = df.reset_index(drop=True) if custom_metadata and b"pandas" in custom_metadata.keys(): raise ValueError( "User-defined key/value metadata (custom_metadata) can not " "contain a b'pandas' key. This key is reserved by Pandas, " "and overwriting the corresponding value can render the " "entire dataset unreadable." ) # Engine-specific initialization steps to write the dataset. # Possibly create parquet metadata, and load existing stuff if appending i_offset, fmd, metadata_file_exists, extra_write_kwargs = engine.initialize_write( df, fs, path, append=append, ignore_divisions=ignore_divisions, partition_on=partition_on, division_info=division_info, index_cols=index_cols, schema=schema, custom_metadata=custom_metadata, **kwargs, ) # By default we only write a metadata file when appending if one already # exists if append and write_metadata_file is None: write_metadata_file = metadata_file_exists # Check that custom name_function is valid, # and that it will produce unique names if name_function is not None: if not callable(name_function): raise ValueError("``name_function`` must be a callable with one argument.") filenames = [name_function(i + i_offset) for i in range(df.npartitions)] if len(set(filenames)) < len(filenames): raise ValueError("``name_function`` must produce unique filenames.") # Create Blockwise layer for parquet-data write data_write = df.map_partitions( ToParquetFunctionWrapper( engine, path, fs, partition_on, write_metadata_file, i_offset, name_function, toolz.merge( kwargs, {"compression": compression, "custom_metadata": custom_metadata}, extra_write_kwargs, ), ), BlockIndex((df.npartitions,)), # Pass in the original metadata to avoid # metadata emulation in `map_partitions`. # This is necessary, because we are not # expecting a dataframe-like output. meta=df._meta, enforce_metadata=False, transform_divisions=False, align_dataframes=False, ) # Collect metadata and write _metadata. # TODO: Use tree-reduction layer (when available) if write_metadata_file: final_name = "metadata-" + data_write._name dsk = { (final_name, 0): ( apply, engine.write_metadata, [ data_write.__dask_keys__(), fmd, fs, path, ], {"append": append, "compression": compression}, ) } else: # NOTE: We still define a single task to tie everything together # when we are not writing a _metadata file. We do not want to # return `data_write` (or a `data_write.to_bag()`), because calling # `compute()` on a multi-partition collection requires the overhead # of trying to concatenate results on the client. final_name = "store-" + data_write._name dsk = {(final_name, 0): (lambda x: None, data_write.__dask_keys__())} # Convert data_write + dsk to computable collection graph = HighLevelGraph.from_collections(final_name, dsk, dependencies=(data_write,)) out = Scalar(graph, final_name, "") if compute: out = out.compute(**compute_kwargs) # Invalidate the filesystem listing cache for the output path after write. # We do this before returning, even if `compute=False`. This helps ensure # that reading files that were just written succeeds. fs.invalidate_cache(path) return out
def create_metadata_file( paths, root_dir=None, out_dir=None, engine="pyarrow", storage_options=None, split_every=32, compute=True, compute_kwargs=None, fs=None, ): """Construct a global _metadata file from a list of parquet files. Dask's read_parquet function is designed to leverage a global _metadata file whenever one is available. The to_parquet function will generate this file automatically by default, but it may not exist if the dataset was generated outside of Dask. This utility provides a mechanism to generate a _metadata file from a list of existing parquet files. NOTE: This utility is not yet supported for the "fastparquet" engine. Parameters ---------- paths : list(string) List of files to collect footer metadata from. root_dir : string, optional Root directory of dataset. The `file_path` fields in the new _metadata file will relative to this directory. If None, a common root directory will be inferred. out_dir : string or False, optional Directory location to write the final _metadata file. By default, this will be set to `root_dir`. If False is specified, the global metadata will be returned as an in-memory object (and will not be written to disk). engine : str or Engine, default 'pyarrow' Parquet Engine to use. Only 'pyarrow' is supported if a string is passed. storage_options : dict, optional Key/value pairs to be passed on to the file-system backend, if any. split_every : int, optional The final metadata object that is written to _metadata can be much smaller than the list of footer metadata. In order to avoid the aggregation of all metadata within a single task, a tree reduction is used. This argument specifies the maximum number of metadata inputs to be handled by any one task in the tree. Defaults to 32. compute : bool, optional If True (default) then the result is computed immediately. If False then a ``dask.delayed`` object is returned for future computation. compute_kwargs : dict, optional Options to be passed in to the compute method fs : fsspec object, optional File-system instance to use for file handling. If prefixes have been removed from the elements of ``paths`` before calling this function, an ``fs`` argument must be provided to ensure correct behavior on remote file systems ("naked" paths cannot be used to infer file-system information). """ # Get engine. # Note that "fastparquet" is not yet supported if isinstance(engine, str): if engine not in ("pyarrow", "arrow"): raise ValueError( f"{engine} is not a supported engine for create_metadata_file " "Try engine='pyarrow'." ) engine = get_engine(engine) # Process input path list if fs is None: # Only do this if an fsspec file-system object is not # already defined. The prefixes may already be stripped. fs, _, paths = get_fs_token_paths( paths, mode="rb", storage_options=storage_options ) ap_kwargs = {"root": root_dir} if root_dir else {} paths, root_dir, fns = _sort_and_analyze_paths(paths, fs, **ap_kwargs) out_dir = root_dir if out_dir is None else out_dir # Start constructing a raw graph dsk = {} name = "gen-metadata-" + tokenize(paths, fs) collect_name = "collect-" + name agg_name = "agg-" + name # Define a "collect" task for each file in the input list. # Each tasks will: # 1. Extract the footer metadata from a distinct file # 2. Populate the `file_path` field in the metadata # 3. Return the extracted/modified metadata for p, (fn, path) in enumerate(zip(fns, paths)): key = (collect_name, p, 0) dsk[key] = (engine.collect_file_metadata, path, fs, fn) # Build a reduction tree to aggregate all footer metadata # into a single metadata object. Each task in the tree # will take in a list of metadata objects as input, and will # usually output a single (aggregated) metadata object. # The final task in the tree will write the result to disk # instead of returning it (this behavior is triggered by # passing a file path to `engine.aggregate_metadata`). parts = len(paths) widths = [parts] while parts > 1: parts = math.ceil(parts / split_every) widths.append(parts) height = len(widths) for depth in range(1, height): for group in range(widths[depth]): p_max = widths[depth - 1] lstart = split_every * group lstop = min(lstart + split_every, p_max) dep_task_name = collect_name if depth == 1 else agg_name node_list = [(dep_task_name, p, depth - 1) for p in range(lstart, lstop)] if depth == height - 1: assert group == 0 dsk[name] = (engine.aggregate_metadata, node_list, fs, out_dir) else: dsk[(agg_name, group, depth)] = ( engine.aggregate_metadata, node_list, None, None, ) # There will be no aggregation tasks if there is only one file if len(paths) == 1: dsk[name] = (engine.aggregate_metadata, [(collect_name, 0, 0)], fs, out_dir) # Convert the raw graph to a `Delayed` object graph = HighLevelGraph.from_collections(name, dsk, dependencies=[]) out = Delayed(name, graph) # Optionally compute the result if compute: if compute_kwargs is None: compute_kwargs = dict() out = out.compute(**compute_kwargs) return out _ENGINES: dict[str, Engine] = {} # TODO: remove _warn_engine_default_changing once the default has changed to # pyarrow. def get_engine(engine, _warn_engine_default_changing=False): """Get the parquet engine backend implementation. Parameters ---------- engine : str, default 'auto' Parquet library to use. Options include: 'auto', 'fastparquet', and 'pyarrow'. Defaults to 'auto', which uses ``fastparquet`` if it is installed, and falls back to ``pyarrow`` otherwise. Note that in the future this default ordering for 'auto' will switch, with ``pyarrow`` being used if it is installed, and falling back to ``fastparquet``. Returns ------- A dict containing a ``'read'`` and ``'write'`` function. """ if engine in _ENGINES: return _ENGINES[engine] if engine == "auto": try: engine = get_engine("fastparquet") if _warn_engine_default_changing and importlib.util.find_spec("pyarrow"): warnings.warn( "engine='auto' will switch to using pyarrow by default in " "a future version. To continue using fastparquet even if " "pyarrow is installed in the future please explicitly " "specify engine='fastparquet'.", FutureWarning, ) return engine except RuntimeError: pass try: return get_engine("pyarrow") except RuntimeError: raise RuntimeError("Please install either fastparquet or pyarrow") from None elif engine == "fastparquet": import_required("fastparquet", "`fastparquet` not installed") from dask.dataframe.io.parquet.fastparquet import FastParquetEngine _ENGINES["fastparquet"] = eng = FastParquetEngine return eng elif engine in ("pyarrow", "arrow", "pyarrow-dataset"): pa = import_required("pyarrow", "`pyarrow` not installed") pa_version = parse_version(pa.__version__) if engine in ("pyarrow-dataset", "arrow"): engine = "pyarrow" if engine == "pyarrow": if pa_version.major < 1: raise ImportError( f"pyarrow-{pa_version.major} does not support the " f"pyarrow.dataset API. Please install pyarrow>=1." ) from dask.dataframe.io.parquet.arrow import ArrowDatasetEngine _ENGINES[engine] = eng = ArrowDatasetEngine return eng else: raise ValueError( f'Unsupported engine: "{engine}".' ' Valid choices include "pyarrow" and "fastparquet".' ) ##################### # Utility Functions # ##################### def sorted_columns(statistics): """Find sorted columns given row-group statistics This finds all columns that are sorted, along with appropriate divisions values for those columns Returns ------- out: List of {'name': str, 'divisions': List[str]} dictionaries """ if not statistics: return [] out = [] for i, c in enumerate(statistics[0]["columns"]): if not all( "min" in s["columns"][i] and "max" in s["columns"][i] for s in statistics ): continue divisions = [c["min"]] max = c["max"] success = c["min"] is not None for stats in statistics[1:]: c = stats["columns"][i] if c["min"] is None: success = False break if c["min"] >= max: divisions.append(c["min"]) max = c["max"] else: success = False break if success: divisions.append(max) assert divisions == sorted(divisions) out.append({"name": c["name"], "divisions": divisions}) return out def apply_filters(parts, statistics, filters): """Apply filters onto parts/statistics pairs Parameters ---------- parts: list Tokens corresponding to row groups to read in the future statistics: List[dict] List of statistics for each part, including min and max values filters: Union[List[Tuple[str, str, Any]], List[List[Tuple[str, str, Any]]]] List of filters to apply, like ``[[('x', '=', 0), ...], ...]``. This implements partition-level (hive) filtering only, i.e., to prevent the loading of some row-groups and/or files. Predicates can be expressed in disjunctive normal form (DNF). This means that the innermost tuple describes a single column predicate. These inner predicates are combined with an AND conjunction into a larger predicate. The outer-most list then combines all of the combined filters with an OR disjunction. Predicates can also be expressed as a List[Tuple]. These are evaluated as an AND conjunction. To express OR in predictates, one must use the (preferred) List[List[Tuple]] notation. Note that the "fastparquet" engine does not currently support DNF for the filtering of partitioned columns (List[Tuple] is required). Returns ------- parts, statistics: the same as the input, but possibly a subset """ def apply_conjunction(parts, statistics, conjunction): for column, operator, value in conjunction: if operator == "in" and not isinstance(value, (list, set, tuple)): raise TypeError("Value of 'in' filter must be a list, set, or tuple.") out_parts = [] out_statistics = [] for part, stats in zip(parts, statistics): if "filter" in stats and stats["filter"]: continue # Filtered by engine try: c = toolz.groupby("name", stats["columns"])[column][0] min = c["min"] max = c["max"] except KeyError: out_parts.append(part) out_statistics.append(stats) else: if ( operator in ("==", "=") and min <= value <= max or operator == "!=" and (min != value or max != value) or operator == "<" and min < value or operator == "<=" and min <= value or operator == ">" and max > value or operator == ">=" and max >= value or operator == "in" and any(min <= item <= max for item in value) ): out_parts.append(part) out_statistics.append(stats) parts, statistics = out_parts, out_statistics return parts, statistics conjunction, *disjunction = filters if isinstance(filters[0], list) else [filters] out_parts, out_statistics = apply_conjunction(parts, statistics, conjunction) for conjunction in disjunction: for part, stats in zip(*apply_conjunction(parts, statistics, conjunction)): if part not in out_parts: out_parts.append(part) out_statistics.append(stats) return out_parts, out_statistics def process_statistics( parts, statistics, filters, index, chunksize, split_row_groups, fs, aggregation_depth, ): """Process row-group column statistics in metadata Used in read_parquet. """ index_in_columns = False if statistics and len(parts) != len(statistics): # It is up to the Engine to guarantee that these # lists are the same length (if statistics are defined). # This misalignment may be indicative of a bug or # incorrect read_parquet usage, so throw a warning. warnings.warn( f"Length of partition statistics ({len(statistics)}) " f"does not match the partition count ({len(parts)}). " f"This may indicate a bug or incorrect read_parquet " f"usage. We must ignore the statistics and disable: " f"filtering, divisions, and/or file aggregation." ) statistics = [] if statistics: result = list( zip( *[ (part, stats) for part, stats in zip(parts, statistics) if stats["num-rows"] > 0 ] ) ) parts, statistics = result or [[], []] if filters: parts, statistics = apply_filters(parts, statistics, filters) # Aggregate parts/statistics if we are splitting by row-group if chunksize or (split_row_groups and int(split_row_groups) > 1): parts, statistics = aggregate_row_groups( parts, statistics, chunksize, split_row_groups, fs, aggregation_depth ) out = sorted_columns(statistics) if index and isinstance(index, str): index = [index] if index and out: # Only one valid column out = [o for o in out if o["name"] in index] if index is not False and len(out) == 1: # Use only sorted column with statistics as the index divisions = out[0]["divisions"] if index is None: index_in_columns = True index = [out[0]["name"]] elif index != [out[0]["name"]]: raise ValueError(f"Specified index is invalid.\nindex: {index}") elif index is not False and len(out) > 1: if any(o["name"] == NONE_LABEL for o in out): # Use sorted column matching NONE_LABEL as the index [o] = [o for o in out if o["name"] == NONE_LABEL] divisions = o["divisions"] if index is None: index = [o["name"]] index_in_columns = True elif index != [o["name"]]: raise ValueError(f"Specified index is invalid.\nindex: {index}") else: # Multiple sorted columns found, cannot autodetect the index warnings.warn( "Multiple sorted columns found %s, cannot\n " "autodetect index. Will continue without an index.\n" "To pick an index column, use the index= keyword; to \n" "silence this warning use index=False." "" % [o["name"] for o in out], RuntimeWarning, ) index = False divisions = [None] * (len(parts) + 1) else: divisions = [None] * (len(parts) + 1) else: divisions = [None] * (len(parts) + 1) return parts, divisions, index, index_in_columns def set_index_columns(meta, index, columns, index_in_columns, auto_index_allowed): """Handle index/column arguments, and modify `meta` Used in read_parquet. """ ignore_index_column_intersection = False if columns is None: # User didn't specify columns, so ignore any intersection # of auto-detected values with the index (if necessary) ignore_index_column_intersection = True # Do not allow "un-named" fields to be read in as columns. # These were intended to be un-named indices at write time. _index = index or [] columns = [ c for c in meta.columns if c not in (None, NONE_LABEL) or c in _index ] if not set(columns).issubset(set(meta.columns)): raise ValueError( "The following columns were not found in the dataset %s\n" "The following columns were found %s" % (set(columns) - set(meta.columns), meta.columns) ) if index: if isinstance(index, str): index = [index] if isinstance(columns, str): columns = [columns] if ignore_index_column_intersection: columns = [col for col in columns if col not in index] if set(index).intersection(columns): if auto_index_allowed: raise ValueError( "Specified index and column arguments must not intersect" " (set index=False or remove the detected index from columns).\n" "index: {} | column: {}".format(index, columns) ) else: raise ValueError( "Specified index and column arguments must not intersect.\n" "index: {} | column: {}".format(index, columns) ) # Leaving index as a column in `meta`, because the index # will be reset below (in case the index was detected after # meta was created) if index_in_columns: meta = meta[columns + index] else: meta = meta[columns] else: meta = meta[list(columns)] return meta, index, columns def aggregate_row_groups( parts, stats, chunksize, split_row_groups, fs, aggregation_depth ): if not stats or not stats[0].get("file_path_0", None): return parts, stats parts_agg = [] stats_agg = [] use_row_group_criteria = split_row_groups and int(split_row_groups) > 1 use_chunksize_criteria = bool(chunksize) if use_chunksize_criteria: chunksize = parse_bytes(chunksize) next_part, next_stat = [parts[0].copy()], stats[0].copy() for i in range(1, len(parts)): stat, part = stats[i], parts[i] # Criteria #1 for aggregating parts: parts are within the same file same_path = stat["file_path_0"] == next_stat["file_path_0"] multi_path_allowed = False if aggregation_depth: # Criteria #2 for aggregating parts: The part does not include # row-group information, or both parts include the same kind # of row_group aggregation (all None, or all indices) multi_path_allowed = len(part["piece"]) == 1 if not (same_path or multi_path_allowed): rgs = set(list(part["piece"][1]) + list(next_part[-1]["piece"][1])) multi_path_allowed = (rgs == {None}) or (None not in rgs) # Criteria #3 for aggregating parts: The parts share a # directory at the "depth" allowed by `aggregation_depth` if not same_path and multi_path_allowed: if aggregation_depth is True: multi_path_allowed = True elif isinstance(aggregation_depth, int): # Make sure files share the same directory root = stat["file_path_0"].split(fs.sep)[:-aggregation_depth] next_root = next_stat["file_path_0"].split(fs.sep)[ :-aggregation_depth ] multi_path_allowed = root == next_root else: raise ValueError( f"{aggregation_depth} not supported for `aggregation_depth`" ) def _check_row_group_criteria(stat, next_stat): if use_row_group_criteria: return (next_stat["num-row-groups"] + stat["num-row-groups"]) <= int( split_row_groups ) else: return False def _check_chunksize_criteria(stat, next_stat): if use_chunksize_criteria: return ( next_stat["total_byte_size"] + stat["total_byte_size"] ) <= chunksize else: return False stat["num-row-groups"] = stat.get("num-row-groups", 1) next_stat["num-row-groups"] = next_stat.get("num-row-groups", 1) if (same_path or multi_path_allowed) and ( _check_row_group_criteria(stat, next_stat) or _check_chunksize_criteria(stat, next_stat) ): # Update part list next_part.append(part) # Update Statistics next_stat["total_byte_size"] += stat["total_byte_size"] next_stat["num-rows"] += stat["num-rows"] next_stat["num-row-groups"] += stat["num-row-groups"] for col, col_add in zip(next_stat["columns"], stat["columns"]): if col["name"] != col_add["name"]: raise ValueError("Columns are different!!") if "min" in col: col["min"] = min(col["min"], col_add["min"]) if "max" in col: col["max"] = max(col["max"], col_add["max"]) else: parts_agg.append(next_part) stats_agg.append(next_stat) next_part, next_stat = [part.copy()], stat.copy() parts_agg.append(next_part) stats_agg.append(next_stat) return parts_agg, stats_agg DataFrame.to_parquet.__doc__ = to_parquet.__doc__