def _ret_dict_handler(ret_dict, record_val):
""" 内部函数 """
inter_type_keys = []
inter_type_values = []
inter_type_flag = False
ptype_keys = []
ptype_values = []
ptype_flag = False
for key, value in ret_dict.items():
if isinstance(value, ptype.PType):
ptype_keys.append((key, value.serde()))
ptype_values.append(value)
ptype_flag = True
else:
inter_type_keys.append((key, type(value)))
inter_type_values.append(value)
inter_type_flag = True
if ptype_flag and inter_type_flag:
ptype_keys.extend(inter_type_keys)
return tuple_to_dict(transforms.cartesian(*ptype_values)\
.apply(transforms.map, lambda record: record + tuple(inter_type_values)),
ptype_keys)
elif not ptype_flag and inter_type_flag:
from bigflow import schema_pcollection
return schema_pcollection.SchemaPCollection(record_val.apply(transforms.map,
lambda record: dict(zip(tuple(key_sd[0] for key_sd in inter_type_keys),
inter_type_values)), serde=of(dict(inter_type_keys))))
else:
return tuple_to_dict(transforms.cartesian(*ptype_values), ptype_keys)
def _ret_tuple_handler(ret_tuple, record_val):
""" 内部函数 """
inter_type_sds = []
inter_type_values = []
inter_type_flag = False
ptype_sds = []
ptype_values = []
ptype_flag = False
for item in ret_tuple:
if isinstance(item, ptype.PType):
ptype_sds.append((item.serde()))
ptype_values.append(item)
ptype_flag = True
else:
inter_type_sds.append((type(item)))
inter_type_values.append(item)
inter_type_flag = True
if ptype_flag and inter_type_flag:
ptype_sds.extend(inter_type_sds)
return transforms.cartesian(*ptype_values)\
.apply(transforms.map, lambda record: record + tuple(inter_type_values),
serde=serde.of(tuple(ptype_sds)))
elif not ptype_flag and inter_type_flag:
return record_val.apply(transforms.map,
lambda record: tuple(inter_type_values), serde=serde.of(tuple(inter_type_sds)))
else:
return transforms.cartesian(*ptype_values)
def _ret_tuple_handler(ret_tuple, record_val):
""" 内部函数 """
inter_type_sds = []
inter_type_values = []
inter_type_flag = False
ptype_sds = []
ptype_values = []
ptype_flag = False
for item in ret_tuple:
if isinstance(item, ptype.PType):
ptype_sds.append((item.serde()))
ptype_values.append(item)
ptype_flag = True
else:
inter_type_sds.append((type(item)))
inter_type_values.append(item)
inter_type_flag = True
if ptype_flag and inter_type_flag:
ptype_sds.extend(inter_type_sds)
return transforms.cartesian(*ptype_values)\
.apply(transforms.map, lambda record: record + tuple(inter_type_values),
serde=serde.of(tuple(ptype_sds)))
elif not ptype_flag and inter_type_flag:
return record_val.apply(transforms.map,
lambda record: tuple(inter_type_values),
serde=serde.of(tuple(inter_type_sds)))
else:
return transforms.cartesian(*ptype_values)
def _ret_dict_handler(ret_dict, record_val):
""" 内部函数 """
inter_type_keys = []
inter_type_values = []
inter_type_flag = False
ptype_keys = []
ptype_values = []
ptype_flag = False
for key, value in ret_dict.items():
if isinstance(value, ptype.PType):
ptype_keys.append((key, value.serde()))
ptype_values.append(value)
ptype_flag = True
else:
inter_type_keys.append((key, type(value)))
inter_type_values.append(value)
inter_type_flag = True
if ptype_flag and inter_type_flag:
ptype_keys.extend(inter_type_keys)
return tuple_to_dict(transforms.cartesian(*ptype_values)\
.apply(transforms.map, lambda record: record + tuple(inter_type_values)),
ptype_keys)
elif not ptype_flag and inter_type_flag:
from bigflow import schema_pcollection
return schema_pcollection.SchemaPCollection(
record_val.apply(
transforms.map,
lambda record: dict(
zip(tuple(key_sd[0] for key_sd in inter_type_keys),
inter_type_values)),
serde=of(dict(inter_type_keys))))
else:
return tuple_to_dict(transforms.cartesian(*ptype_values), ptype_keys)
def agg(p, io_description, fn, *args, **kargs):
"""
选择一些字段去做一些聚合操作。
Args:
p (pcollection): 输入数据集，需要是一个每个元素都是dict的pcollection
io_description (str): 格式为： a,b=>c,d,e 即，输入字段=>输出字段
fn (callable) : 函数原型为 (*input_pcollections) => (*output_pcollection_or_pobjects)
即，该函数的输入参数为多个pcollection，
每个pcollection表示数据的一个字段的全部行所拼成的一个pcollection。
该函数的返回值是一些pobject或
pcollection所组成的tuple（如果只有一个元素可以不必返回tuple）。
Returns:
返回一个每个元素是一个dict的pcollection。
这个pcollection中所有元素输出的几个pcollection进行笛卡尔积并添加字段名后的结果。
例如：::
>>> x = _pipeline.parallelize([{'a' : 1, 'b': 2.0}, {'a': 2, 'b': 3.0}])
>>> print x.apply(fields.agg,
>>> 'a, b => c, d, e',
>>> lambda a, b: (
>>> a.count(),
>>> b.sum(),
>>> a.flat_map(lambda x: xrange(x))
>>> )
>>> ).get()
[{'c': 2, 'd': 5.0, 'e': 0}, {'c': 2, 'd': 5.0, 'e': 0}, {'c': 2, 'd': 5.0, 'e': 1}]
"""
io_fields = ''.join(io_description.split()).split('=>')
assert len(io_fields) >= 1
assert len(io_fields) <= 2
select_fields = io_fields[0].split(',')
io_fields.append(io_fields[0])
out_fields = io_fields[1].split(',')
fields = list(p.apply(select_cols, select_fields))
fields.extend(args)
ret = fn(*fields, **kargs)
if isinstance(ret, ptype.PType):
ret = (ret, )
tp = transforms.cartesian(*ret)
return tp.apply(transforms.map,
lambda tp: dict(zip(out_fields, tp)),
serde=get_out_fields_serde(tp.serde(), out_fields))
def agg(p, io_description, fn, *args, **kargs):
"""
选择一些字段去做一些聚合操作。
Args:
p (pcollection): 输入数据集，需要是一个每个元素都是dict的pcollection
io_description (str): 格式为： a,b=>c,d,e 即，输入字段=>输出字段
fn (callable) : 函数原型为 (*input_pcollections) => (*output_pcollection_or_pobjects)
即，该函数的输入参数为多个pcollection，
每个pcollection表示数据的一个字段的全部行所拼成的一个pcollection。
该函数的返回值是一些pobject或
pcollection所组成的tuple（如果只有一个元素可以不必返回tuple）。
Returns:
返回一个每个元素是一个dict的pcollection。
这个pcollection中所有元素输出的几个pcollection进行笛卡尔积并添加字段名后的结果。
例如：::
>>> x = _pipeline.parallelize([{'a' : 1, 'b': 2.0}, {'a': 2, 'b': 3.0}])
>>> print x.apply(fields.agg,
>>> 'a, b => c, d, e',
>>> lambda a, b: (
>>> a.count(),
>>> b.sum(),
>>> a.flat_map(lambda x: xrange(x))
>>> )
>>> ).get()
[{'c': 2, 'd': 5.0, 'e': 0}, {'c': 2, 'd': 5.0, 'e': 0}, {'c': 2, 'd': 5.0, 'e': 1}]
"""
io_fields = ''.join(io_description.split()).split('=>')
assert len(io_fields) >= 1
assert len(io_fields) <= 2
select_fields = io_fields[0].split(',')
io_fields.append(io_fields[0])
out_fields = io_fields[1].split(',')
fields = list(p.apply(select_cols, select_fields))
fields.extend(args)
ret = fn(*fields, **kargs)
if isinstance(ret, ptype.PType):
ret = (ret,)
tp = transforms.cartesian(*ret)
return tp.apply(transforms.map, lambda tp: dict(zip(out_fields, tp)),
serde=get_out_fields_serde(tp.serde(), out_fields))
def cartesian(self, other, *others, **options):
"""
与其他的PCollection做笛卡尔积
Args:
other (PCollection): 其他的PCollection
*others: 更多的PCollection
Returns:
PCollection: 表示结果的PCollection
>>> _p1 = _pipeline.parallelize([1, 2, 3])
>>> _p2 = _pipeline.parallelize([4, 5])
>>> _p1.cartesian(_p2).get()
[(1, 4), (1, 5), (2, 4), (2, 5), (3, 4), (3, 5)]
"""
return transforms.cartesian(self, other, *others, **options)
def cartesian(self, other, *others, **options):
"""
与其他的PCollection做笛卡尔积
Args:
other (PCollection): 其他的PCollection
*others: 更多的PCollection
Returns:
PCollection: 表示结果的PCollection
>>> _p1 = _pipeline.parallelize([1, 2, 3])
>>> _p2 = _pipeline.parallelize([4, 5])
>>> _p1.cartesian(_p2).get()
[(1, 4), (1, 5), (2, 4), (2, 5), (3, 4), (3, 5)]
"""
return transforms.cartesian(self, other, *others, **options)
def cartesian(self, *pvalues, **options):
"""
求当前算子与pvalues的笛卡尔积。
等价于 :func:`bigflow.transforms.cartesian(self, *pvalues, **options)
<bigflow.transforms.cartesian>`
Args: *pvalues (PObject/PCollection)
Returns:
PCollection: 此PObject与所有参数的笛卡尔积。结果PCollection中的每条记录是一个tuple。
每个tuple的第n个元素是第n个输入ptype对象的记录。
>>> _p1 = _pipeline.parallelize(1)
>>> _p2 = _pipeline.parallelize(2)
>>> _p1.cartesian(_p2).get()
[(1, 2)]
>>> _p3 = _pipeline.parallelize([3, 4])
>>> _p1.cartesian(_p3).get()
[(1, 3), (1, 4)]
>>> _p1.cartesian(_p2, _p3).get()
[(1, 2, 3), (1, 2, 4)]
"""
from bigflow import transforms
return transforms.cartesian(self, *pvalues, **options)
def cartesian(self, *pvalues, **options):
"""
求当前算子与pvalues的笛卡尔积。
等价于 :func:`bigflow.transforms.cartesian(self, *pvalues, **options)
<bigflow.transforms.cartesian>`
Args: *pvalues (PObject/PCollection)
Returns:
PCollection: 此PObject与所有参数的笛卡尔积。结果PCollection中的每条记录是一个tuple。
每个tuple的第n个元素是第n个输入ptype对象的记录。
>>> _p1 = _pipeline.parallelize(1)
>>> _p2 = _pipeline.parallelize(2)
>>> _p1.cartesian(_p2).get()
[(1, 2)]
>>> _p3 = _pipeline.parallelize([3, 4])
>>> _p1.cartesian(_p3).get()
[(1, 3), (1, 4)]
>>> _p1.cartesian(_p2, _p3).get()
[(1, 2, 3), (1, 2, 4)]
"""
from bigflow import transforms
return transforms.cartesian(self, *pvalues, **options)
def check_cartesian(self, expect, *pvalues):
self.passertEqual(expect, pvalues[0].cartesian(*pvalues[1:]))
self.passertEqual(expect, transforms.cartesian(*pvalues))
