#
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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#
"""Implements a source for reading Avro files."""
import cStringIO
import os
import zlib
import avro
from avro import datafile
from avro import io as avroio
from avro import schema
import apache_beam as beam
from apache_beam.io import filebasedsource
from apache_beam.io import filebasedsink
from apache_beam.io import iobase
from apache_beam.io.filesystem import CompressionTypes
from apache_beam.io.iobase import Read
from apache_beam.transforms import PTransform
__all__ = ['ReadFromAvro', 'WriteToAvro']
[docs]class ReadFromAvro(PTransform):
"""A ``PTransform`` for reading avro files."""
def __init__(self, file_pattern=None, min_bundle_size=0, validate=True):
"""Initializes ``ReadFromAvro``.
Uses source '_AvroSource' to read a set of Avro files defined by a given
file pattern.
If '/mypath/myavrofiles*' is a file-pattern that points to a set of Avro
files, a ``PCollection`` for the records in these Avro files can be created
in the following manner.
p = df.Pipeline(argv=pipeline_args)
records = p | 'Read' >> df.io.ReadFromAvro('/mypath/myavrofiles*')
Each record of this ``PCollection`` will contain a single record read from a
source. Records that are of simple types will be mapped into corresponding
Python types. Records that are of Avro type 'RECORD' will be mapped to
Python dictionaries that comply with the schema contained in the Avro file
that contains those records. In this case, keys of each dictionary
will contain the corresponding field names and will be of type ``string``
while the values of the dictionary will be of the type defined in the
corresponding Avro schema.
For example, if schema of the Avro file is the following.
{"namespace": "example.avro","type": "record","name": "User","fields":
[{"name": "name", "type": "string"},
{"name": "favorite_number", "type": ["int", "null"]},
{"name": "favorite_color", "type": ["string", "null"]}]}
Then records generated by ``AvroSource`` will be dictionaries of the
following form.
{u'name': u'Alyssa', u'favorite_number': 256, u'favorite_color': None}).
Args:
file_pattern: the set of files to be read.
min_bundle_size: the minimum size in bytes, to be considered when
splitting the input into bundles.
validate: flag to verify that the files exist during the pipeline
creation time.
"""
super(ReadFromAvro, self).__init__()
self._source = _AvroSource(file_pattern, min_bundle_size, validate=validate)
[docs] def expand(self, pvalue):
return pvalue.pipeline | Read(self._source)
[docs] def display_data(self):
return {'source_dd': self._source}
class _AvroUtils(object):
@staticmethod
def read_meta_data_from_file(f):
"""Reads metadata from a given Avro file.
Args:
f: Avro file to read.
Returns:
a tuple containing the codec, schema, and the sync marker of the Avro
file.
Raises:
ValueError: if the file does not start with the byte sequence defined in
the specification.
"""
if f.tell() > 0:
f.seek(0)
decoder = avroio.BinaryDecoder(f)
header = avroio.DatumReader().read_data(datafile.META_SCHEMA,
datafile.META_SCHEMA, decoder)
if header.get('magic') != datafile.MAGIC:
raise ValueError('Not an Avro file. File header should start with %s but'
'started with %s instead.', datafile.MAGIC,
header.get('magic'))
meta = header['meta']
if datafile.CODEC_KEY in meta:
codec = meta[datafile.CODEC_KEY]
else:
codec = 'null'
schema_string = meta[datafile.SCHEMA_KEY]
sync_marker = header['sync']
return codec, schema_string, sync_marker
@staticmethod
def read_block_from_file(f, codec, schema, expected_sync_marker):
"""Reads a block from a given Avro file.
Args:
f: Avro file to read.
codec: The codec to use for block-level decompression.
Supported codecs: 'null', 'deflate', 'snappy'
schema: Avro Schema definition represented as JSON string.
expected_sync_marker: Avro synchronization marker. If the block's sync
marker does not match with this parameter then ValueError is thrown.
Returns:
A single _AvroBlock.
Raises:
ValueError: If the block cannot be read properly because the file doesn't
match the specification.
"""
offset = f.tell()
decoder = avroio.BinaryDecoder(f)
num_records = decoder.read_long()
block_size = decoder.read_long()
block_bytes = decoder.read(block_size)
sync_marker = decoder.read(len(expected_sync_marker))
if sync_marker != expected_sync_marker:
raise ValueError('Unexpected sync marker (actual "%s" vs expected "%s"). '
'Maybe the underlying avro file is corrupted?',
sync_marker, expected_sync_marker)
size = f.tell() - offset
return _AvroBlock(block_bytes, num_records, codec, schema, offset, size)
@staticmethod
def advance_file_past_next_sync_marker(f, sync_marker):
buf_size = 10000
data = f.read(buf_size)
while data:
pos = data.find(sync_marker)
if pos >= 0:
# Adjusting the current position to the ending position of the sync
# marker.
backtrack = len(data) - pos - len(sync_marker)
f.seek(-1 * backtrack, os.SEEK_CUR)
return True
else:
if f.tell() >= len(sync_marker):
# Backtracking in case we partially read the sync marker during the
# previous read. We only have to backtrack if there are at least
# len(sync_marker) bytes before current position. We only have to
# backtrack (len(sync_marker) - 1) bytes.
f.seek(-1 * (len(sync_marker) - 1), os.SEEK_CUR)
data = f.read(buf_size)
class _AvroBlock(object):
"""Represents a block of an Avro file."""
def __init__(self, block_bytes, num_records, codec, schema_string,
offset, size):
# Decompress data early on (if needed) and thus decrease the number of
# parallel copies of the data in memory at any given in time during
# block iteration.
self._decompressed_block_bytes = self._decompress_bytes(block_bytes, codec)
self._num_records = num_records
self._schema = schema.parse(schema_string)
self._offset = offset
self._size = size
def size(self):
return self._size
def offset(self):
return self._offset
@staticmethod
def _decompress_bytes(data, codec):
if codec == 'null':
return data
elif codec == 'deflate':
# zlib.MAX_WBITS is the window size. '-' sign indicates that this is
# raw data (without headers). See zlib and Avro documentations for more
# details.
return zlib.decompress(data, -zlib.MAX_WBITS)
elif codec == 'snappy':
# Snappy is an optional avro codec.
# See Snappy and Avro documentation for more details.
try:
import snappy
except ImportError:
raise ValueError('Snappy does not seem to be installed.')
# Compressed data includes a 4-byte CRC32 checksum which we verify.
# We take care to avoid extra copies of data while slicing large objects
# by use of a buffer.
result = snappy.decompress(buffer(data)[:-4])
avroio.BinaryDecoder(cStringIO.StringIO(data[-4:])).check_crc32(result)
return result
else:
raise ValueError('Unknown codec: %r', codec)
def num_records(self):
return self._num_records
def records(self):
decoder = avroio.BinaryDecoder(
cStringIO.StringIO(self._decompressed_block_bytes))
reader = avroio.DatumReader(
writers_schema=self._schema, readers_schema=self._schema)
current_record = 0
while current_record < self._num_records:
yield reader.read(decoder)
current_record += 1
class _AvroSource(filebasedsource.FileBasedSource):
"""A source for reading Avro files.
``_AvroSource`` is implemented using the file-based source framework available
in module 'filebasedsource'. Hence please refer to module 'filebasedsource'
to fully understand how this source implements operations common to all
file-based sources such as file-pattern expansion and splitting into bundles
for parallel processing.
"""
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 iobase.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:
codec, schema_string, sync_marker = _AvroUtils.read_meta_data_from_file(
f)
# We have to start at current position if previous bundle ended at the
# end of a sync marker.
start_offset = max(0, start_offset - len(sync_marker))
f.seek(start_offset)
_AvroUtils.advance_file_past_next_sync_marker(f, sync_marker)
while range_tracker.try_claim(f.tell()):
block = _AvroUtils.read_block_from_file(f, codec, schema_string,
sync_marker)
next_block_start = block.offset() + block.size()
for record in block.records():
yield record
[docs]class WriteToAvro(beam.transforms.PTransform):
"""A ``PTransform`` for writing avro files."""
def __init__(self,
file_path_prefix,
schema,
codec='deflate',
file_name_suffix='',
num_shards=0,
shard_name_template=None,
mime_type='application/x-avro'):
"""Initialize a WriteToAvro transform.
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 returned by avro.schema.parse
codec: The codec to use for block-level compression. Any string supported
by the Avro specification is accepted (for example 'null').
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 WriteToAvro transform usable for writing.
"""
self._sink = _AvroSink(file_path_prefix, schema, codec, file_name_suffix,
num_shards, shard_name_template, mime_type)
[docs] def expand(self, pcoll):
return pcoll | beam.io.iobase.Write(self._sink)
[docs] def display_data(self):
return {'sink_dd': self._sink}
class _AvroSink(filebasedsink.FileBasedSink):
"""A sink to avro files."""
def __init__(self,
file_path_prefix,
schema,
codec,
file_name_suffix,
num_shards,
shard_name_template,
mime_type):
super(_AvroSink, 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
def open(self, temp_path):
file_handle = super(_AvroSink, self).open(temp_path)
return avro.datafile.DataFileWriter(
file_handle, avro.io.DatumWriter(), self._schema, self._codec)
def write_record(self, writer, value):
writer.append(value)
def display_data(self):
res = super(self.__class__, self).display_data()
res['codec'] = str(self._codec)
res['schema'] = str(self._schema)
return res