bigflow/bigflow_python/python/bigflow/input.py at master · himdd/bigflow · GitHub

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#!/usr/bin/env python

# -*- coding: utf-8 -*-

########################################################################

# Licensed 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

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

########################################################################

"""

定义所有的数据源(Source)，用于Pipeline.read()方法

实现一个Source需要实现四个接口：

1. 有一个input_format属性，是一个flume::Loader

2. 有一个objector属性，是一个Objector

3. 有一个uris属性，返回一个uri列表

4. 有一个transform_from_node方法，把一个Node变换成一个PType

5. 有一个get_size方法，计算本文件读出数据量有多少。可以返回-1(?)表示未知大小。

"""

import subprocess

from bigflow import error

from bigflow import pcollection

from bigflow import serde

from bigflow.core import entity

from bigflow.core.serde import record_objector

from bigflow.core.serde import cloudpickle

from bigflow.util import path_util

from bigflow.util import hadoop_client

from flume.proto import entity_pb2

class UserInputBase(object):

""" 用户输入抽象基类

用户需要按以下方法重写split/load函数

Eg. ::

class LocalFileInput(UserInputBase):

def __init__(self, dir):

self._dir = dir

def split(self):

return [os.path.join(self._dir, filename) for filename in os.listdir(self._dir)]

def load(split):

with open(split) as f:

for line in f.readline():

yield line.strip()

用户可以重写post_process以实现一些后处理，

post_process有一个传入参数，是一个PTable，这个PTable的key是split string,

value是这个split上的数据。

默认post_process方法是`bigflow.transforms.flatten_values`.

"""

def split(self):

"""

splits urls as some splits. User should override this method.

"""

raise NotImplementedError()

def load(self, split):

"""

Load data from a split.

The return value will be flattened into a PCollection.

"""

raise NotImplementedError()

def post_process(self, ptable):

"""

User can override post_process method to do some post_process.

"""

return ptable.flatten_values()

def get_serde(self):

""" User can override this method to set the serde """

import serde

return serde.any()

def get_size(self):

""" user can override this method to calculate the size of the input data """

return -1

class _TextInputFormat(entity.EntitiedBySelf):

def __init__(self, config=""):

self._config = config

pass

def get_entity_name(self):

return "TextInputFormat"

def get_entity_config(self):

return self._config

class _TextInputFormatWithUgi(entity.EntitiedBySelf):

def __init__(self, config=""):

self._config = config

def get_entity_name(self):

return "TextInputFormatWithUgi"

def get_entity_config(self):

return self._config

class _TextFromRecord(entity.EntitiedBySelf):

def __init__(self):

pass

def get_entity_name(self):

return "PythonFromRecordProcessor"

def get_entity_config(self):

return cloudpickle.dumps(self)

class _SequenceFileAsBinaryInputFormat(entity.EntitiedBySelf):

def __init__(self, config=""):

self._config = config

pass

def get_entity_name(self):

return "SequenceFileAsBinaryInputFormat"

def get_entity_config(self):

return self._config

class _SequenceFileAsBinaryInputFormatWithUgi(entity.EntitiedBySelf):

def __init__(self, config=""):

self._config = config

def get_entity_name(self):

return "SequenceFileAsBinaryInputFormatWithUgi"

def get_entity_config(self):

return self._config

class _KVFromBinaryRecord(entity.EntitiedBySelf):

def __init__(self):

pass

def get_entity_name(self):

return "PythonKVFromRecordProcessor"

def get_entity_config(self):

return cloudpickle.dumps(self)

class FileBase(object):

"""

用于Pipeline.read()方法读取文件的基类

Args:

*path: 读取文件的path，必须均为str或unicode类型

"""

def __init__(self, *path, **options):

self.uris = map(lambda p: p.replace(",", "\,"), path)

self.objector = record_objector.RecordObjector()

self.ugi = options.get("ugi", None)

def get_size(self, pipeline):

"""

获得所有读取文件在文件系统中的大小

Returns:

int: 文件大小，以字节为单位

"""

def _get_file_size(uri):

cmd = list()

if uri.startswith("hdfs://"):

fs_name_from_path = hadoop_client.extract_fs_name_from_path(uri)

replace_explicit_fs_name = False

config = pipeline.config()

cmd.append(config.hadoop_client_path)

cmd.append("fs")

for kv in config.hadoop_job_conf:

if kv.key == "fs.defaultFS" and fs_name_from_path is not None:

cmd.extend(["-D", kv.key + "=" + fs_name_from_path])

else:

cmd.extend(["-D", kv.key + "=" + kv.value])

if not replace_explicit_fs_name and fs_name_from_path is not None:

cmd.extend(["-D fs.defaultFS=" + fs_name_from_path])

cmd.append("-conf %s" % config.hadoop_config_path)

cmd.append("-dus %s | cut -f 2" % uri)

else:

cmd.append("du -s -b %s | cut -f 1" % uri)

process = subprocess.Popen(" ".join(cmd), stdout=subprocess.PIPE, shell=True)

ret = process.wait()

if ret != 0:

raise error.BigflowRPCException("Error getting file size for uri: %s" % uri)

size = 0

try:

for line in process.stdout.readlines():

size += int(line.strip())

except Exception as e:

raise error.BigflowPlanningException("Cannot get input size", e)

return size

return sum(map(_get_file_size, self.uris))

def _use_dce_combine(self, options):

combine_multi_file = options.get("combine_multi_file", None)

if combine_multi_file is not None:

return combine_multi_file

return options.get('use_dce_combine', True)

def user_define_format(user_input_base):

""" return a FileBase object from a UserInputBase"""

assert isinstance(user_input_base, UserInputBase)

class _LoaderImpl(object):

def __init__(self, user_input_base):

self._user_input_base = user_input_base

def split(self, uri):

""" inner """

return self._user_input_base.split()

def load(self, split):

""" inner """

return self._user_input_base.load(split)

class _UserDefineFileBase(FileBase):

def __init__(self, user_input_base):

super(_UserDefineFileBase, self).__init__('user_define_format')

self.input_format = entity.Entity.of(

entity.Entity.loader,

cloudpickle.dumps(_LoaderImpl(user_input_base))

)

self.objector = user_input_base.get_serde()

self._user_input_base = user_input_base

def get_size(self, pipeline):

""" get file size """

return self._user_input_base.get_size()

def transform_from_node(self, load_node, pipeline):

""" inner func """

from bigflow import ptable

transformed_pcollection = pcollection.PCollection(load_node, pipeline)

before_post_process = \

ptable.PTable(transformed_pcollection, key_serde=serde.CPickleSerde())

return self._user_input_base.post_process(before_post_process)

return _UserDefineFileBase(user_input_base)

class TextFile(FileBase):

"""

表示读取的文本文件的数据源

Args:

*path: 读取文件的path，必须均为str类型

读取文件数据示例：::

>>> lines1 = _pipeline.read(input.TextFile('hdfs:///my_hdfs_dir/'))

>>> lines2 = _pipeline.read(input.TextFile('hdfs://host:port/my_hdfs_file'))

>>> lines3 = _pipeline.read(input.TextFile('hdfs:///multi_path1', 'hdfs:///multi_path2'))

>>> lines4 = _pipeline.read(input.TextFile('./local_file_by_rel_path/'))

>>> lines5 = _pipeline.read(input.TextFile('/home/work/local_file_by_abs_path/'))

>>> lines6 = _pipeline.read(input.TextFile(*['hdfs:///multi_path1', 'hdfs:///multi_path2']))

**options: 其中关键参数：

combine_multi_file: 是否可以合并多个文件到一个mapper中处理。默认为True。

partitioned: 默认为False，如果置为True，则返回的数据集为一个ptable，

ptable的key是split_info，value这个split上的全部数据所组成的pcollection。::

>>> f1 = open('data1.txt', 'w')

>>> f1.write('1 2 1')

>>> f1.close()

>>> f2 = open('data2.txt', 'w')

>>> f2.write('1 2 2')

>>> f2.close()

>>> table = _pipeline.read(input.TextFile('./data1.txt', './data2.txt', partitioned = True))

>>> def wordcount(p):

return p.flat_map(lambda line: line.split()) \\

.group_by(lambda word: word) \\

.apply_values(transforms.count)

>>> table.apply_values(wordcount).get()

{'/home/data1.txt': {'1': 2, '2': 1}, '/home/data2.txt': {'1': 1, '2', 2}}

# 需要注意，MR模式下，key可能不是这个格式的，切分方法也不一定是按照文件来的。

"""

def __init__(self, *path, **options):

super(TextFile, self).__init__(*path, **options)

self.repeatedly = options.get("repeatedly", False)

input_format = _TextInputFormat if not self.ugi else _TextInputFormatWithUgi

if self.repeatedly:

from flume.proto import entity_pb2

pb = entity_pb2.PbInputFormatEntityConfig()

pb.repeatedly = True

pb.max_record_num_per_round = options.get('max_record_num_per_round', 1000)

pb.timeout_per_round = options.get('timeout_per_round', 30)

self.input_format = input_format(pb.SerializeToString())

elif self._use_dce_combine(options):

self.input_format = input_format("use_dce_combine")

else:

self.input_format = input_format()

self._options = options

def transform_from_node(self, load_node, pipeline):

"""

内部接口

"""

from bigflow import ptable

if self.repeatedly:

transformed = load_node.repeatedly() \

.process_by(_TextFromRecord()) \

.as_type(serde.StrSerde()) \

.set_effective_key_num(0) \

.input(0).allow_partial_processing() \

.done()

else:

transformed = load_node \

.process_by(_TextFromRecord()) \

.as_type(serde.StrSerde()) \

.set_effective_key_num(0) \

.input(0).allow_partial_processing() \

.done()

transformed.set_size(load_node.size())

if self._options.get('partitioned', False):

transformed_pcollection = pcollection.PCollection(transformed, pipeline)

return ptable.PTable(transformed_pcollection, key_serde=serde.StrSerde())

return pcollection.PCollection(transformed.leave_scope(), pipeline)

class SchemaTextFile(TextFile):

"""

读取文本文件生成支持字段操作的SchemaPCollection

Args:

*path: 读取文件的path, 必须均为str类型

**options: Arbitrary keyword arguments, 其中关键参数，

(1). 若columns(list), 每一项为字段名，则生成SchemaPCollection的元素是dict，dict中的value类型都是str;

(2). 若columns(list), 每一项为(字段名，类型)，则生成SchemaPCollection的元素是dict，

dict中的值类型是字段对应的类型;

(3). 若columns(int)，表示分割的列数，则生成SchemaPCollection的元素是tuple，tuple中的每个元素的类型都是str，

separator(str)表示每行数据字段分隔符，默认分隔符是Tab("\t");

(4). 若columns(list), 每一项为python基本类型(int, str, float)，则生成SchemaPcollection的元素是tuple，

每个tuple中的元素的类型和columns中的类型一一对应；separator(str)表示每行数据字段分隔符，默认分隔符是Tab("\t");

ignore_overflow(bool)表示如果文件有多余的列，是否可以忽略掉。默认为False，即出现多余的列时即会报错。

ignore_illegal_line(bool): 表示当文件某一行的列数少于提供的字段数时，是否可以忽略该文件行。若不设置，则抛出异常

Example:

>>> open("input-data", "w").write("XiaoA\\t20\\nXiaoB\\t21\\n")

>>> persons = _pipeline.read(input.SchemaTextFile("input-data", columns = ['name', 'age']))

>>> persons.get()

[{'age': '20', 'name': 'XiaoA'}, {'age': '21', 'name': 'XiaoB'}]

>>> open("input-data", "w").write("XiaoA\\t20\\nXiaoB\\t21\\n")

>>> persons = _pipeline.read(input.SchemaTextFile("input-data", columns = [('name', str), ('age', int)]))

>>> persons.get()

[{'age': 20, 'name': 'XiaoA'}, {'age': 21, 'name': 'XiaoB'}]

>>> open("temp_data.txt", "w").write("1\\t2.0\\tbiflow\\n10\\t20.10\\tinf")

>>> data = p.read(input.SchemaTextFile("./temp_data.txt", columns=3))

>>> data.get()

[('1', '2.0', 'biflow'), ('10', '20.1', 'inf')]

>>> open("temp_data.txt", "w").write("1\\t2.0\\tbiflow\\n10\\t20.10\\tinf")

>>> data = p.read(input.SchemaTextFile("./temp_data.txt", columns=[int, float, str]))

>>> data.get()

[(1, 2.0, 'biflow'), (10, 20.1, 'inf')]

"""

def __init__(self, *path, **options):

super(SchemaTextFile, self).__init__(*path, **options)

self.fields = options.get('columns', None)

self.sep = options.get('separator', "\t")

self.ignore_overflow = options.get('ignore_overflow', False)

self.ignore_illegal_line = options.get('ignore_illegal_line', False)

def transform_from_node(self, load_node, pipeline):

"""

内部接口

"""

from bigflow import schema

if self.fields is None:

raise ValueError('''columns is necessary，(1) columns(list)，

each item in columns is string, SchemaPCollection's element

is dict, (2) columns(int)，SchemaPCollection's element is tuple. eg.

columns=3 or columns=[(xx, int), (yy, str)] or columns=[xx, yy],

(3) columns(list), each item in columns is base type in [int, float, str]''')

if isinstance(self.fields, tuple):

self.fields = list(self.fields)

fields_type = []

ignore_overflow = self.ignore_overflow

ignore_illegal_line = self.ignore_illegal_line

if isinstance(self.fields, list):

def get_fields_type(fields):

"""内部函数"""

fields_type = []

for field in fields:

if isinstance(field, tuple):

if field[1] in [int, str, float]:

fields_type.append(field[1])

else:

raise ValueError('''columns is list(field name or data type),

data type(int/str/float)''')

elif field in [int, str, float]:

fields_type.append(field)

elif isinstance(field, str):

fields_type.append(str)

else:

raise ValueError('''columns is list(field name or data type),

data type(int/str/float)''')

return fields_type

fields_type = get_fields_type(self.fields)

ret = super(SchemaTextFile, self)\

.transform_from_node(load_node, pipeline)\

.flat_map(entity.SplitStringToTypes(self.sep,

fields_type,

ignore_overflow,

ignore_illegal_line),

serde=serde.of(tuple(fields_type)))

if self.fields[0] in [int, float, str]:

return ret

else:

ret = ret.apply(schema.tuple_to_dict, self.fields)

return ret

elif isinstance(self.fields, int):

from bigflow import schema_pcollection

return schema_pcollection.SchemaPCollection(super(SchemaTextFile, self)

.transform_from_node(load_node, pipeline)\

.flat_map(entity.SplitStringToTypes(self.sep,

[str for _ in xrange(self.fields)],

True,

ignore_illegal_line),

serde=serde.of(tuple(serde.StrSerde() for index in xrange(self.fields)))))

else:

raise ValueError("columns is list(field name)，or int(row number)")

class SequenceFile(FileBase):

"""

表示读取SequenceFile的数据源，SequenceFile的(Key, Value)必须均为BytesWritable，并由用户自行解析

Args:

*path: 读取文件的path，必须均为str类型

**options: 其中关键参数：

combine_multi_file: 是否可以合并多个文件到一个mapper中处理。默认为True。

partitioned: 默认为False，如果置为True，则返回的数据集为一个ptable，

ptable的key是split_info，value这个split上的全部数据所组成的pcollection。

key_serde: key如何反序列化

value_serde: value如何反序列化

如果未设定key_serde/value_serde，则会忽略掉key，只把value用默认序列化器反序列化并返回。

Example:

>>> from bigflow import serde

>>> StrSerde = serde.StrSerde

>>> lines = _pipeline.read(

input.SequenceFile('path', key_serde=StrSerde(), value_serde=StrSerde()))

>>> lines.get()

[("key1", "value1"), ("key2", "value2")]

>>> import mytest_proto_pb2

>>> msg_type = mytest_proto_pb2.MyTestPbType

>>> _pipeline.add_file("mytest_proto_pb2.py", "mytest_proto_pb2.py")

>>> pbs = _pipeline.read(input.SequenceFile('path2', serde=serde.ProtobufSerde(msg_type)))

>>> pbs.get() # 如果未设置key_serde/value_serde，则key会被丢弃。

>>> [<mytest_proto_pb2.MyTestPbType at 0x7fa9e262a870>,

<mytest_proto_pb2.MyTestPbType at 0x7fa9e262a870>]

有时，Pb包在本地没有，例如，py文件在hdfs，则可以使用下边的方法：

>>> _pipeline.add_archive("hdfs:///proto.tar.gz", "proto") #add_archive暂时不支持本地模式

>>> def get_pb_msg_creator(module_name, class_name):

... import importlib

... return lambda: importlib.import_module(module_name).__dict__[class_name]()

>>> pbs = _pipeline.read(input.SequenceFile('path2', serde=serde.ProtobufSerde(get_pb_msg_creator("proto.mytest_proto_pb2", "MyTestPbType"))))

>>> pbs.get()

>>> [<mytest_proto_pb2.MyTestPbType at 0x7fa9e262a870>,

<mytest_proto_pb2.MyTestPbType at 0x7fa9e262a870>]

如果需要自定义Serde，参见：:class:`bigflow.serde.Serde`。

"""

def __init__(self, *path, **options):

super(SequenceFile, self).__init__(*path, **options)

self.repeatedly = options.get("repeatedly", False)

input_format = _SequenceFileAsBinaryInputFormat if not self.ugi \

else _SequenceFileAsBinaryInputFormatWithUgi

if self.repeatedly:

from flume.proto import entity_pb2

pb = entity_pb2.PbInputFormatEntityConfig()

pb.repeatedly = True

pb.max_record_num_per_round = options.get('max_record_num_per_round', 1000)

pb.timeout_per_round = options.get('timeout_per_round', 30)

self.input_format = input_format(pb.SerializeToString())

elif self._use_dce_combine(options):

self.input_format = input_format("use_dce_combine")

else:

self.input_format = input_format()

# 只有当用户把value_serde和key_serde都设置或者都不设置时才会生效

# 否则抛出错误

k_serde = options.get("key_serde", None)

v_serde = options.get("value_serde", None)

if (not k_serde) != (not v_serde):

raise error.InvalidSeqSerdeException("key and value serde should be both set or not.")

elif (k_serde is not None) and (v_serde is not None):

self.kv_deserializer = entity.KVDeserializeFn(k_serde, v_serde)

else:

self.kv_deserializer = None

self._options = options

def as_type(self, kv_deserializer):

"""

通过kv_deserializer反序列化读取的(Key, Value)

kv_deserializer的期望签名为:

kv_deserializer(key: str, value: str) => object

"""

self.kv_deserializer = kv_deserializer

return self

def transform_from_node(self, load_node, pipeline):

"""

内部接口

"""

from bigflow import ptable

if self.repeatedly:

transformed = load_node.repeatedly() \

.process_by(_KVFromBinaryRecord()) \

.as_type(serde.tuple_of(serde.StrSerde(), serde.StrSerde())) \

.set_effective_key_num(0) \

.input(0).allow_partial_processing() \

.done()

else:

transformed = load_node \

.process_by(_KVFromBinaryRecord()) \

.as_type(serde.tuple_of(serde.StrSerde(), serde.StrSerde())) \

.set_effective_key_num(0) \

.ignore_group() \

.input(0).allow_partial_processing() \

.done()

transformed.set_size(load_node.size())

transformed = pcollection.PCollection(transformed, pipeline)

tserde = self._options.get('serde', pipeline.default_objector())

if self.kv_deserializer is not None:

transformed = transformed.map(self.kv_deserializer, serde = tserde)

else:

is_serialize = False

deserialize = entity.SerdeWrapper(tserde, is_serialize, 1)

transformed = transformed.map(deserialize, serde = tserde)

if self._options.get('partitioned'):

return ptable.PTable(transformed, key_serde=serde.StrSerde())

return pcollection.PCollection(transformed.node().leave_scope(), pipeline)

class _TextStreamInputFormat(entity.EntitiedBySelf):

def __init__(self, config):

""" 内部方法 """

self._config = config

def get_entity_name(self):

""" 内部方法 """

return "TextStreamInputFormat"

def get_entity_config(self):

""" 内部方法 """

return self._config

class TextFileStream(FileBase):

"""

表示读取的文本文件的无穷数据源。

Args:

*path: 读取文件目录的path，必须均为str类型

读取文件数据示例：::

>>> lines1 = _pipeline.read(input.TextFileStream('hdfs:///my_hdfs_dir/'))

>>> lines2 = _pipeline.read(input.TextFileStream('hdfs://host:port/my_hdfs_dir/'))

>>> lines3 = _pipeline.read(input.TextFileStream('hdfs:///multi_path1', 'hdfs:///multi_path2'))

>>> lines4 = _pipeline.read(input.TextFileStream('./local_file_by_rel_path/'))

>>> lines5 = _pipeline.read(input.TextFileStream('/home/work/local_file_by_abs_path/'))

>>> lines6 = _pipeline.read(input.TextFileStream(*['hdfs:///multi_path1', 'hdfs:///multi_path2']))

**options: 可选的参数。

[Hint] max_record_num_per_round: 用于指定每轮订阅的日志条数，默认值为1000

[Hint] timeout_per_round: 用于指定每轮订阅的超时时间（单位为s），默认为10s

Note:

1. 如果path中含有子目录，则以子目录作为数据源；如果path中没有子目录，则以path作为数据源

2. 目录中有效的文件名为从0开始的正整数；如果文件不存在，会一直等待该文件

3. 目录中的文件不允许被修改，添加需要保证原子（可以先写成其他文件名，然后进行mv）

"""

def __init__(self, *path, **options):

""" 内部方法 """

super(TextFileStream, self).__init__(*path)

from flume.proto import entity_pb2

pb = entity_pb2.PbInputFormatEntityConfig()

pb.repeatedly = True

pb.max_record_num_per_round = options.get('max_record_num_per_round', 1000)

pb.timeout_per_round = options.get('timeout_per_round', 30)

pb.file_stream.filename_pattern = options.get('filename_pattern', 'default').lower()

self.input_format = _TextStreamInputFormat(pb.SerializeToString())

def transform_from_node(self, load_node, pipeline):

""" 内部接口 """

transformed = load_node.repeatedly() \

.process_by(_TextFromRecord()) \

.as_type(serde.StrSerde()) \

.set_effective_key_num(0) \

.input(0).allow_partial_processing() \

.done()

transformed.set_size(load_node.size())

return pcollection.PCollection(transformed.leave_scope(), pipeline)

class _SequenceStreamInputFormat(entity.EntitiedBySelf):

def __init__(self, config):

""" 内部方法 """

self._config = config

def get_entity_name(self):

""" 内部方法 """

return "SequenceStreamInputFormat"

def get_entity_config(self):

""" 内部方法 """

return self._config

class SequenceFileStream(FileBase):

"""

表示读取SequenceFile的无穷数据源，SequenceFile的(Key, Value)必须均为BytesWritable，并由用户自行解析

Args:

*path: 读取文件的path，必须均为str类型

**options: 可选的参数。

[Hint] max_record_num_per_round: 用于指定每轮订阅的日志条数，默认值为1000

[Hint] timeout_per_round: 用于指定每轮订阅的超时时间（单位为s），默认为10s

key_serde: key如何反序列化

value_serde: value如何反序列化

如果未设定key_serde/value_serde，则会忽略掉key，只把value用默认序列化器反序列化并返回。

Note:

1. 如果path中含有子目录，则以子目录作为数据源；如果path中没有子目录，则以path作为数据源

2. 目录中有效的文件名为从0开始的正整数；如果文件不存在，会一直等待该文件

3. 目录中的文件不允许被修改，添加需要保证原子（可以先写成其他文件名，然后进行mv）

"""

def __init__(self, *path, **options):

""" 内部方法 """

super(SequenceFileStream, self).__init__(*path)

from flume.proto import entity_pb2

pb = entity_pb2.PbInputFormatEntityConfig()

pb.repeatedly = True

pb.max_record_num_per_round = options.get('max_record_num_per_round', 1000)

pb.timeout_per_round = options.get('timeout_per_round', 30)

pb.file_stream.filename_pattern = options.get('filename_pattern', 'default').lower()

self.input_format = _SequenceStreamInputFormat(pb.SerializeToString())

# 只有当用户把value_serde和key_serde都设置或者都不设置时才会生效

# 否则抛出错误

k_serde = options.get("key_serde", None)

v_serde = options.get("value_serde", None)

if (not k_serde) != (not v_serde):

raise error.InvalidSeqSerdeException("key and value serde should be both set or not.")

elif (k_serde is not None) and (v_serde is not None):

self.kv_deserializer = entity.KVDeserializeFn(k_serde, v_serde)

else:

self.kv_deserializer = None

self._options = options

def as_type(self, kv_deserializer):

"""

通过kv_deserializer反序列化读取的(Key, Value)

kv_deserializer的期望签名为:

kv_deserializer(key: str, value: str) => object

"""

self.kv_deserializer = kv_deserializer

return self

def transform_from_node(self, load_node, pipeline):

""" 内部方法 """

transformed = load_node.repeatedly() \

.process_by(_KVFromBinaryRecord()) \

.as_type(serde.tuple_of(serde.StrSerde(), serde.StrSerde())) \

.set_effective_key_num(0) \

.input(0).allow_partial_processing() \

.done()

transformed.set_size(load_node.size())

transformed = pcollection.PCollection(transformed, pipeline)

tserde = self._options.get('serde', pipeline.default_objector())

if self.kv_deserializer is not None:

transformed = transformed.map(self.kv_deserializer, serde = tserde)

else:

is_serialize = False

deserialize = entity.SerdeWrapper(tserde, is_serialize, 1)

transformed = transformed.map(deserialize, serde = tserde)

return pcollection.PCollection(transformed.node().leave_scope(), pipeline)

bigflow/bigflow_python/python/bigflow/input.py at master · himdd/bigflow · GitHub

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