Source code for apache_beam.io.parquetio

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"""``PTransforms`` for reading from and writing to Parquet files.

Provides two read ``PTransform``\\s, ``ReadFromParquet`` and
``ReadAllFromParquet``, that produces a ``PCollection`` of records.
Each record of this ``PCollection`` will contain a single record read from
a Parquet file. Records that are of simple types will be mapped into
corresponding Python types. The actual parquet file operations are done by
pyarrow. Source splitting is supported at row group granularity.

Additionally, this module provides a write ``PTransform`` ``WriteToParquet``
that can be used to write a given ``PCollection`` of Python objects to a
Parquet file.
"""
# pytype: skip-file

from __future__ import absolute_import

from functools import partial

from apache_beam.io import filebasedsink
from apache_beam.io import filebasedsource
from apache_beam.io.filesystem import CompressionTypes
from apache_beam.io.iobase import RangeTracker
from apache_beam.io.iobase import Read
from apache_beam.io.iobase import Write
from apache_beam.transforms import DoFn
from apache_beam.transforms import ParDo
from apache_beam.transforms import PTransform

try:
  import pyarrow as pa
  import pyarrow.parquet as pq
except ImportError:
  pa = None
  pq = None

__all__ = [
    'ReadFromParquet',
    'ReadAllFromParquet',
    'ReadFromParquetBatched',
    'ReadAllFromParquetBatched',
    'WriteToParquet'
]


class _ArrowTableToRowDictionaries(DoFn):
  """ A DoFn that consumes an Arrow table and yields a python dictionary for
  each row in the table."""
  def process(self, table):
    num_rows = table.num_rows
    data_items = table.to_pydict().items()
    for n in range(num_rows):
      row = {}
      for column, values in data_items:
        row[column] = values[n]
      yield row


[docs]class ReadFromParquetBatched(PTransform): """A :class:`~apache_beam.transforms.ptransform.PTransform` for reading Parquet files as a `PCollection` of `pyarrow.Table`. This `PTransform` is currently experimental. No backward-compatibility guarantees.""" def __init__( self, file_pattern=None, min_bundle_size=0, validate=True, columns=None): """ Initializes :class:`~ReadFromParquetBatched` An alternative to :class:`~ReadFromParquet` that yields each row group from the Parquet file as a `pyarrow.Table`. These Table instances can be processed directly, or converted to a pandas DataFrame for processing. For more information on supported types and schema, please see the pyarrow documentation. .. testcode:: with beam.Pipeline() as p: dataframes = p \\ | 'Read' >> beam.io.ReadFromParquetBatched('/mypath/mypqfiles*') \\ | 'Convert to pandas' >> beam.Map(lambda table: table.to_pandas()) .. NOTE: We're not actually interested in this error; but if we get here, it means that the way of calling this transform hasn't changed. .. testoutput:: :hide: Traceback (most recent call last): ... OSError: No files found based on the file pattern See also: :class:`~ReadFromParquet`. Args: file_pattern (str): the file glob to read min_bundle_size (int): the minimum size in bytes, to be considered when splitting the input into bundles. validate (bool): flag to verify that the files exist during the pipeline creation time. columns (List[str]): list of columns that will be read from files. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e' """ super(ReadFromParquetBatched, self).__init__() self._source = _create_parquet_source( file_pattern, min_bundle_size, validate=validate, columns=columns, )
[docs] def expand(self, pvalue): return pvalue.pipeline | Read(self._source)
[docs] def display_data(self): return {'source_dd': self._source}
[docs]class ReadFromParquet(PTransform): """A :class:`~apache_beam.transforms.ptransform.PTransform` for reading Parquet files as a `PCollection` of dictionaries. This `PTransform` is currently experimental. No backward-compatibility guarantees.""" def __init__( self, file_pattern=None, min_bundle_size=0, validate=True, columns=None): """Initializes :class:`ReadFromParquet`. Uses source ``_ParquetSource`` to read a set of Parquet files defined by a given file pattern. If ``/mypath/myparquetfiles*`` is a file-pattern that points to a set of Parquet files, a :class:`~apache_beam.pvalue.PCollection` for the records in these Parquet files can be created in the following manner. .. testcode:: with beam.Pipeline() as p: records = p | 'Read' >> beam.io.ReadFromParquet('/mypath/mypqfiles*') .. NOTE: We're not actually interested in this error; but if we get here, it means that the way of calling this transform hasn't changed. .. testoutput:: :hide: Traceback (most recent call last): ... OSError: No files found based on the file pattern Each element of this :class:`~apache_beam.pvalue.PCollection` will contain a Python dictionary representing a single record. The keys will be of type :class:`str` and named after their corresponding column names. The values will be of the type defined in the corresponding Parquet schema. Records that are of simple types will be mapped into corresponding Python types. Records that are of complex types like list and struct will be mapped to Python list and dictionary respectively. For more information on supported types and schema, please see the pyarrow documentation. See also: :class:`~ReadFromParquetBatched`. Args: file_pattern (str): the file glob to read min_bundle_size (int): the minimum size in bytes, to be considered when splitting the input into bundles. validate (bool): flag to verify that the files exist during the pipeline creation time. columns (List[str]): list of columns that will be read from files. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e' """ super(ReadFromParquet, self).__init__() self._source = _create_parquet_source( file_pattern, min_bundle_size, validate=validate, columns=columns, )
[docs] def expand(self, pvalue): return pvalue | Read(self._source) | ParDo(_ArrowTableToRowDictionaries())
[docs] def display_data(self): return {'source_dd': self._source}
[docs]class ReadAllFromParquetBatched(PTransform): """A ``PTransform`` for reading ``PCollection`` of Parquet files. Uses source ``_ParquetSource`` to read a ``PCollection`` of Parquet files or file patterns and produce a ``PCollection`` of ``pyarrow.Table``, one for each Parquet file row group. This ``PTransform`` is currently experimental. No backward-compatibility guarantees. """ DEFAULT_DESIRED_BUNDLE_SIZE = 64 * 1024 * 1024 # 64MB def __init__( self, min_bundle_size=0, desired_bundle_size=DEFAULT_DESIRED_BUNDLE_SIZE, columns=None, label='ReadAllFiles'): """Initializes ``ReadAllFromParquet``. Args: min_bundle_size: the minimum size in bytes, to be considered when splitting the input into bundles. desired_bundle_size: the desired size in bytes, to be considered when splitting the input into bundles. columns: list of columns that will be read from files. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e' """ super(ReadAllFromParquetBatched, self).__init__() source_from_file = partial( _create_parquet_source, min_bundle_size=min_bundle_size, columns=columns) self._read_all_files = filebasedsource.ReadAllFiles( True, CompressionTypes.UNCOMPRESSED, desired_bundle_size, min_bundle_size, source_from_file) self.label = label
[docs] def expand(self, pvalue): return pvalue | self.label >> self._read_all_files
[docs]class ReadAllFromParquet(PTransform): def __init__(self, **kwargs): self._read_batches = ReadAllFromParquetBatched(**kwargs)
[docs] def expand(self, pvalue): return pvalue | self._read_batches | ParDo(_ArrowTableToRowDictionaries())
def _create_parquet_source( file_pattern=None, min_bundle_size=0, validate=False, columns=None): return \ _ParquetSource( file_pattern=file_pattern, min_bundle_size=min_bundle_size, validate=validate, columns=columns, ) class _ParquetUtils(object): @staticmethod def find_first_row_group_index(pf, start_offset): for i in range(_ParquetUtils.get_number_of_row_groups(pf)): row_group_start_offset = _ParquetUtils.get_offset(pf, i) if row_group_start_offset >= start_offset: return i return -1 @staticmethod def get_offset(pf, row_group_index): first_column_metadata =\ pf.metadata.row_group(row_group_index).column(0) if first_column_metadata.has_dictionary_page: return first_column_metadata.dictionary_page_offset else: return first_column_metadata.data_page_offset @staticmethod def get_number_of_row_groups(pf): return pf.metadata.num_row_groups class _ParquetSource(filebasedsource.FileBasedSource): """A source for reading Parquet files. """ def __init__(self, file_pattern, min_bundle_size, validate, columns): super(_ParquetSource, self).__init__( file_pattern=file_pattern, min_bundle_size=min_bundle_size, validate=validate) self._columns = columns def read_records(self, file_name, range_tracker): next_block_start = -1 def split_points_unclaimed(stop_position): if next_block_start >= stop_position: # Next block starts at or after the suggested stop position. Hence # there will not be split points to be claimed for the range ending at # suggested stop position. return 0 return RangeTracker.SPLIT_POINTS_UNKNOWN range_tracker.set_split_points_unclaimed_callback(split_points_unclaimed) start_offset = range_tracker.start_position() if start_offset is None: start_offset = 0 with self.open_file(file_name) as f: pf = pq.ParquetFile(f) # find the first dictionary page (or data page if there's no dictionary # page available) offset after the given start_offset. This offset is also # the starting offset of any row group since the Parquet specification # describes that the data pages always come first before the meta data in # each row group. index = _ParquetUtils.find_first_row_group_index(pf, start_offset) if index != -1: next_block_start = _ParquetUtils.get_offset(pf, index) else: next_block_start = range_tracker.stop_position() number_of_row_groups = _ParquetUtils.get_number_of_row_groups(pf) while range_tracker.try_claim(next_block_start): table = pf.read_row_group(index, self._columns) if index + 1 < number_of_row_groups: index = index + 1 next_block_start = _ParquetUtils.get_offset(pf, index) else: next_block_start = range_tracker.stop_position() yield table
[docs]class WriteToParquet(PTransform): """A ``PTransform`` for writing parquet files. This ``PTransform`` is currently experimental. No backward-compatibility guarantees. """ def __init__( self, file_path_prefix, schema, row_group_buffer_size=64 * 1024 * 1024, record_batch_size=1000, codec='none', use_deprecated_int96_timestamps=False, file_name_suffix='', num_shards=0, shard_name_template=None, mime_type='application/x-parquet'): """Initialize a WriteToParquet transform. Writes parquet files from a :class:`~apache_beam.pvalue.PCollection` of records. Each record is a dictionary with keys of a string type that represent column names. Schema must be specified like the example below. .. testsetup:: from tempfile import NamedTemporaryFile import glob import os import pyarrow filename = NamedTemporaryFile(delete=False).name .. testcode:: with beam.Pipeline() as p: records = p | 'Read' >> beam.Create( [{'name': 'foo', 'age': 10}, {'name': 'bar', 'age': 20}] ) _ = records | 'Write' >> beam.io.WriteToParquet(filename, pyarrow.schema( [('name', pyarrow.binary()), ('age', pyarrow.int64())] ) ) .. testcleanup:: for output in glob.glob('{}*'.format(filename)): os.remove(output) For more information on supported types and schema, please see the pyarrow document. Args: file_path_prefix: The file path to write to. The files written will begin with this prefix, followed by a shard identifier (see num_shards), and end in a common extension, if given by file_name_suffix. In most cases, only this argument is specified and num_shards, shard_name_template, and file_name_suffix use default values. schema: The schema to use, as type of ``pyarrow.Schema``. row_group_buffer_size: The byte size of the row group buffer. Note that this size is for uncompressed data on the memory and normally much bigger than the actual row group size written to a file. record_batch_size: The number of records in each record batch. Record batch is a basic unit used for storing data in the row group buffer. A higher record batch size implies low granularity on a row group buffer size. For configuring a row group size based on the number of records, set ``row_group_buffer_size`` to 1 and use ``record_batch_size`` to adjust the value. codec: The codec to use for block-level compression. Any string supported by the pyarrow specification is accepted. use_deprecated_int96_timestamps: Write nanosecond resolution timestamps to INT96 Parquet format. Defaults to False. file_name_suffix: Suffix for the files written. num_shards: The number of files (shards) used for output. If not set, the service will decide on the optimal number of shards. Constraining the number of shards is likely to reduce the performance of a pipeline. Setting this value is not recommended unless you require a specific number of output files. shard_name_template: A template string containing placeholders for the shard number and shard count. When constructing a filename for a particular shard number, the upper-case letters 'S' and 'N' are replaced with the 0-padded shard number and shard count respectively. This argument can be '' in which case it behaves as if num_shards was set to 1 and only one file will be generated. The default pattern used is '-SSSSS-of-NNNNN' if None is passed as the shard_name_template. mime_type: The MIME type to use for the produced files, if the filesystem supports specifying MIME types. Returns: A WriteToParquet transform usable for writing. """ super(WriteToParquet, self).__init__() self._sink = \ _create_parquet_sink( file_path_prefix, schema, codec, row_group_buffer_size, record_batch_size, use_deprecated_int96_timestamps, file_name_suffix, num_shards, shard_name_template, mime_type )
[docs] def expand(self, pcoll): return pcoll | Write(self._sink)
[docs] def display_data(self): return {'sink_dd': self._sink}
def _create_parquet_sink( file_path_prefix, schema, codec, row_group_buffer_size, record_batch_size, use_deprecated_int96_timestamps, file_name_suffix, num_shards, shard_name_template, mime_type): return \ _ParquetSink( file_path_prefix, schema, codec, row_group_buffer_size, record_batch_size, use_deprecated_int96_timestamps, file_name_suffix, num_shards, shard_name_template, mime_type ) class _ParquetSink(filebasedsink.FileBasedSink): """A sink for parquet files.""" def __init__( self, file_path_prefix, schema, codec, row_group_buffer_size, record_batch_size, use_deprecated_int96_timestamps, file_name_suffix, num_shards, shard_name_template, mime_type): super(_ParquetSink, self).__init__( file_path_prefix, file_name_suffix=file_name_suffix, num_shards=num_shards, shard_name_template=shard_name_template, coder=None, mime_type=mime_type, # Compression happens at the block level using the supplied codec, and # not at the file level. compression_type=CompressionTypes.UNCOMPRESSED) self._schema = schema self._codec = codec self._row_group_buffer_size = row_group_buffer_size self._use_deprecated_int96_timestamps = use_deprecated_int96_timestamps self._buffer = [[] for _ in range(len(schema.names))] self._buffer_size = record_batch_size self._record_batches = [] self._record_batches_byte_size = 0 self._file_handle = None def open(self, temp_path): self._file_handle = super(_ParquetSink, self).open(temp_path) return pq.ParquetWriter( self._file_handle, self._schema, compression=self._codec, use_deprecated_int96_timestamps=self._use_deprecated_int96_timestamps) def write_record(self, writer, value): if len(self._buffer[0]) >= self._buffer_size: self._flush_buffer() if self._record_batches_byte_size >= self._row_group_buffer_size: self._write_batches(writer) # reorder the data in columnar format. for i, n in enumerate(self._schema.names): self._buffer[i].append(value[n]) def close(self, writer): if len(self._buffer[0]) > 0: self._flush_buffer() if self._record_batches_byte_size > 0: self._write_batches(writer) writer.close() if self._file_handle: self._file_handle.close() self._file_handle = None def display_data(self): res = super(_ParquetSink, self).display_data() res['codec'] = str(self._codec) res['schema'] = str(self._schema) res['row_group_buffer_size'] = str(self._row_group_buffer_size) return res def _write_batches(self, writer): table = pa.Table.from_batches(self._record_batches) self._record_batches = [] self._record_batches_byte_size = 0 writer.write_table(table) def _flush_buffer(self): arrays = [[] for _ in range(len(self._schema.names))] for x, y in enumerate(self._buffer): arrays[x] = pa.array(y, type=self._schema.types[x]) self._buffer[x] = [] rb = pa.RecordBatch.from_arrays(arrays, self._schema.names) self._record_batches.append(rb) size = 0 for x in arrays: for b in x.buffers(): if b is not None: size = size + b.size self._record_batches_byte_size = self._record_batches_byte_size + size