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apache beam python dynamic query source
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bigquery.py
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# 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
#
# 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.
#
"""BigQuery sources and sinks.
This module implements reading from and writing to BigQuery tables. It relies
on several classes exposed by the BigQuery API: TableSchema, TableFieldSchema,
TableRow, and TableCell. The default mode is to return table rows read from a
BigQuery source as dictionaries. Similarly a Write transform to a BigQuerySink
accepts PCollections of dictionaries. This is done for more convenient
programming. If desired, the native TableRow objects can be used throughout to
represent rows (use an instance of TableRowJsonCoder as a coder argument when
creating the sources or sinks respectively).
Also, for programming convenience, instances of TableReference and TableSchema
have a string representation that can be used for the corresponding arguments:
- TableReference can be a PROJECT:DATASET.TABLE or DATASET.TABLE string.
- TableSchema can be a NAME:TYPE{,NAME:TYPE}* string
(e.g. 'month:STRING,event_count:INTEGER').
The syntax supported is described here:
https://cloud.google.com/bigquery/bq-command-line-tool-quickstart
BigQuery sources can be used as main inputs or side inputs. A main input
(common case) is expected to be massive and will be split into manageable chunks
and processed in parallel. Side inputs are expected to be small and will be read
completely every time a ParDo DoFn gets executed. In the example below the
lambda function implementing the DoFn for the Map transform will get on each
call *one* row of the main table and *all* rows of the side table. The runner
may use some caching techniques to share the side inputs between calls in order
to avoid excessive reading:::
main_table = pipeline | 'VeryBig' >> beam.io.Read(beam.io.BigQuerySource()
side_table = pipeline | 'NotBig' >> beam.io.Read(beam.io.BigQuerySource()
results = (
main_table
| 'ProcessData' >> beam.Map(
lambda element, side_input: ..., AsList(side_table)))
There is no difference in how main and side inputs are read. What makes the
side_table a 'side input' is the AsList wrapper used when passing the table
as a parameter to the Map transform. AsList signals to the execution framework
that its input should be made available whole.
The main and side inputs are implemented differently. Reading a BigQuery table
as main input entails exporting the table to a set of GCS files (currently in
JSON format) and then processing those files. Reading the same table as a side
input entails querying the table for all its rows. The coder argument on
BigQuerySource controls the reading of the lines in the export files (i.e.,
transform a JSON object into a PCollection element). The coder is not involved
when the same table is read as a side input since there is no intermediate
format involved. We get the table rows directly from the BigQuery service with
a query.
Users may provide a query to read from rather than reading all of a BigQuery
table. If specified, the result obtained by executing the specified query will
be used as the data of the input transform.::
query_results = pipeline | beam.io.Read(beam.io.BigQuerySource(
query='SELECT year, mean_temp FROM samples.weather_stations'))
When creating a BigQuery input transform, users should provide either a query
or a table. Pipeline construction will fail with a validation error if neither
or both are specified.
**Time partitioned tables**
BigQuery sink currently does not fully support writing to BigQuery
time partitioned tables. But writing to a *single* partition may work if
that does not involve creating a new table (for example, when writing to an
existing table with `create_disposition=CREATE_NEVER` and
`write_disposition=WRITE_APPEND`).
BigQuery source supports reading from a single time partition with the partition
decorator specified as a part of the table identifier.
*** Short introduction to BigQuery concepts ***
Tables have rows (TableRow) and each row has cells (TableCell).
A table has a schema (TableSchema), which in turn describes the schema of each
cell (TableFieldSchema). The terms field and cell are used interchangeably.
TableSchema: Describes the schema (types and order) for values in each row.
Has one attribute, 'field', which is list of TableFieldSchema objects.
TableFieldSchema: Describes the schema (type, name) for one field.
Has several attributes, including 'name' and 'type'. Common values for
the type attribute are: 'STRING', 'INTEGER', 'FLOAT', 'BOOLEAN'. All possible
values are described at:
https://cloud.google.com/bigquery/preparing-data-for-bigquery#datatypes
TableRow: Holds all values in a table row. Has one attribute, 'f', which is a
list of TableCell instances.
TableCell: Holds the value for one cell (or field). Has one attribute,
'v', which is a JsonValue instance. This class is defined in
apitools.base.py.extra_types.py module.
"""
from __future__ import absolute_import
import collections
import datetime
import json
import logging
import re
import time
import uuid
from builtins import object
from builtins import zip
from future.utils import iteritems
from future.utils import itervalues
from past.builtins import unicode
from apache_beam import coders
from apache_beam.internal.gcp import auth
from apache_beam.internal.gcp.json_value import from_json_value
from apache_beam.internal.gcp.json_value import to_json_value
from apache_beam.io.gcp.internal.clients import bigquery
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.runners.dataflow.native_io import iobase as dataflow_io
from apache_beam.transforms import DoFn
from apache_beam.transforms import ParDo
from apache_beam.transforms import PTransform
from apache_beam.transforms.display import DisplayDataItem
from apache_beam.utils import retry
# Protect against environments where bigquery library is not available.
# pylint: disable=wrong-import-order, wrong-import-position
try:
from apitools.base.py.exceptions import HttpError
except ImportError:
pass
# pylint: enable=wrong-import-order, wrong-import-position
__all__ = [
'TableRowJsonCoder',
'BigQueryDisposition',
'BigQuerySource',
'BigQuerySink',
'WriteToBigQuery',
]
JSON_COMPLIANCE_ERROR = 'NAN, INF and -INF values are not JSON compliant.'
MAX_RETRIES = 3
class RowAsDictJsonCoder(coders.Coder):
"""A coder for a table row (represented as a dict) to/from a JSON string.
This is the default coder for sources and sinks if the coder argument is not
specified.
"""
def encode(self, table_row):
# The normal error when dumping NAN/INF values is:
# ValueError: Out of range float values are not JSON compliant
# This code will catch this error to emit an error that explains
# to the programmer that they have used NAN/INF values.
try:
return json.dumps(table_row, allow_nan=False)
except ValueError as e:
raise ValueError('%s. %s' % (e, JSON_COMPLIANCE_ERROR))
def decode(self, encoded_table_row):
return json.loads(encoded_table_row)
class TableRowJsonCoder(coders.Coder):
"""A coder for a TableRow instance to/from a JSON string.
Note that the encoding operation (used when writing to sinks) requires the
table schema in order to obtain the ordered list of field names. Reading from
sources on the other hand does not need the table schema.
"""
def __init__(self, table_schema=None):
# The table schema is needed for encoding TableRows as JSON (writing to
# sinks) because the ordered list of field names is used in the JSON
# representation.
self.table_schema = table_schema
# Precompute field names since we need them for row encoding.
if self.table_schema:
self.field_names = tuple(fs.name for fs in self.table_schema.fields)
def encode(self, table_row):
if self.table_schema is None:
raise AttributeError(
'The TableRowJsonCoder requires a table schema for '
'encoding operations. Please specify a table_schema argument.')
try:
return json.dumps(
collections.OrderedDict(
zip(self.field_names,
[from_json_value(f.v) for f in table_row.f])),
allow_nan=False)
except ValueError as e:
raise ValueError('%s. %s' % (e, JSON_COMPLIANCE_ERROR))
def decode(self, encoded_table_row):
od = json.loads(
encoded_table_row, object_pairs_hook=collections.OrderedDict)
return bigquery.TableRow(
f=[bigquery.TableCell(v=to_json_value(e)) for e in itervalues(od)])
def parse_table_schema_from_json(schema_string):
"""Parse the Table Schema provided as string.
Args:
schema_string: String serialized table schema, should be a valid JSON.
Returns:
A TableSchema of the BigQuery export from either the Query or the Table.
"""
json_schema = json.loads(schema_string)
def _parse_schema_field(field):
"""Parse a single schema field from dictionary.
Args:
field: Dictionary object containing serialized schema.
Returns:
A TableFieldSchema for a single column in BigQuery.
"""
schema = bigquery.TableFieldSchema()
schema.name = field['name']
schema.type = field['type']
if 'mode' in field:
schema.mode = field['mode']
else:
schema.mode = 'NULLABLE'
if 'description' in field:
schema.description = field['description']
if 'fields' in field:
schema.fields = [_parse_schema_field(x) for x in field['fields']]
return schema
fields = [_parse_schema_field(f) for f in json_schema['fields']]
return bigquery.TableSchema(fields=fields)
class BigQueryDisposition(object):
"""Class holding standard strings used for create and write dispositions."""
CREATE_NEVER = 'CREATE_NEVER'
CREATE_IF_NEEDED = 'CREATE_IF_NEEDED'
WRITE_TRUNCATE = 'WRITE_TRUNCATE'
WRITE_APPEND = 'WRITE_APPEND'
WRITE_EMPTY = 'WRITE_EMPTY'
@staticmethod
def validate_create(disposition):
values = (BigQueryDisposition.CREATE_NEVER,
BigQueryDisposition.CREATE_IF_NEEDED)
if disposition not in values:
raise ValueError(
'Invalid create disposition %s. Expecting %s' % (disposition, values))
return disposition
@staticmethod
def validate_write(disposition):
values = (BigQueryDisposition.WRITE_TRUNCATE,
BigQueryDisposition.WRITE_APPEND,
BigQueryDisposition.WRITE_EMPTY)
if disposition not in values:
raise ValueError(
'Invalid write disposition %s. Expecting %s' % (disposition, values))
return disposition
def _parse_table_reference(table, dataset=None, project=None):
"""Parses a table reference into a (project, dataset, table) tuple.
Args:
table: The ID of the table. The ID must contain only letters
(a-z, A-Z), numbers (0-9), or underscores (_). If dataset argument is None
then the table argument must contain the entire table reference:
'DATASET.TABLE' or 'PROJECT:DATASET.TABLE'. This argument can be a
bigquery.TableReference instance in which case dataset and project are
ignored and the reference is returned as a result. Additionally, for date
partitioned tables, appending '$YYYYmmdd' to the table name is supported,
e.g. 'DATASET.TABLE$YYYYmmdd'.
dataset: The ID of the dataset containing this table or null if the table
reference is specified entirely by the table argument.
project: The ID of the project containing this table or null if the table
reference is specified entirely by the table (and possibly dataset)
argument.
Returns:
A bigquery.TableReference object. The object has the following attributes:
projectId, datasetId, and tableId.
Raises:
ValueError: if the table reference as a string does not match the expected
format.
"""
if isinstance(table, bigquery.TableReference):
return table
table_reference = bigquery.TableReference()
# If dataset argument is not specified, the expectation is that the
# table argument will contain a full table reference instead of just a
# table name.
if dataset is None:
match = re.match(
r'^((?P<project>.+):)?(?P<dataset>\w+)\.(?P<table>[\w\$]+)$', table)
if not match:
raise ValueError(
'Expected a table reference (PROJECT:DATASET.TABLE or '
'DATASET.TABLE) instead of %s.' % table)
table_reference.projectId = match.group('project')
table_reference.datasetId = match.group('dataset')
table_reference.tableId = match.group('table')
else:
table_reference.projectId = project
table_reference.datasetId = dataset
table_reference.tableId = table
return table_reference
# -----------------------------------------------------------------------------
# BigQuerySource, BigQuerySink.
class BigQuerySource(dataflow_io.NativeSource):
"""A source based on a BigQuery table."""
def __init__(self, table=None, dataset=None, project=None, query=None,
validate=False, coder=None, use_standard_sql=False,
flatten_results=True):
"""Initialize a :class:`BigQuerySource`.
Args:
table (str): The ID of a BigQuery table. If specified all data of the
table will be used as input of the current source. The ID must contain
only letters ``a-z``, ``A-Z``, numbers ``0-9``, or underscores
``_``. If dataset and query arguments are :data:`None` then the table
argument must contain the entire table reference specified as:
``'DATASET.TABLE'`` or ``'PROJECT:DATASET.TABLE'``.
dataset (str): The ID of the dataset containing this table or
:data:`None` if the table reference is specified entirely by the table
argument or a query is specified.
project (str): The ID of the project containing this table or
:data:`None` if the table reference is specified entirely by the table
argument or a query is specified.
query (str): A query to be used instead of arguments table, dataset, and
project.
validate (bool): If :data:`True`, various checks will be done when source
gets initialized (e.g., is table present?). This should be
:data:`True` for most scenarios in order to catch errors as early as
possible (pipeline construction instead of pipeline execution). It
should be :data:`False` if the table is created during pipeline
execution by a previous step.
coder (~apache_beam.coders.coders.Coder): The coder for the table
rows if serialized to disk. If :data:`None`, then the default coder is
:class:`~apache_beam.io.gcp.bigquery.RowAsDictJsonCoder`,
which will interpret every line in a file as a JSON serialized
dictionary. This argument needs a value only in special cases when
returning table rows as dictionaries is not desirable.
use_standard_sql (bool): Specifies whether to use BigQuery's standard SQL
dialect for this query. The default value is :data:`False`.
If set to :data:`True`, the query will use BigQuery's updated SQL
dialect with improved standards compliance.
This parameter is ignored for table inputs.
flatten_results (bool): Flattens all nested and repeated fields in the
query results. The default value is :data:`True`.
Raises:
~exceptions.ValueError: if any of the following is true:
1) the table reference as a string does not match the expected format
2) neither a table nor a query is specified
3) both a table and a query is specified.
"""
# Import here to avoid adding the dependency for local running scenarios.
try:
# pylint: disable=wrong-import-order, wrong-import-position
from apitools.base import py # pylint: disable=unused-variable
except ImportError:
raise ImportError(
'Google Cloud IO not available, '
'please install apache_beam[gcp]')
if table is not None and query is not None:
raise ValueError('Both a BigQuery table and a query were specified.'
' Please specify only one of these.')
elif table is None and query is None:
raise ValueError('A BigQuery table or a query must be specified')
elif table is not None:
self.table_reference = _parse_table_reference(table, dataset, project)
self.query = None
self.use_legacy_sql = True
else:
self.query = query
# TODO(BEAM-1082): Change the internal flag to be standard_sql
self.use_legacy_sql = not use_standard_sql
self.table_reference = None
self.validate = validate
self.flatten_results = flatten_results
self.coder = coder or RowAsDictJsonCoder()
def display_data(self):
if self.query is not None:
res = {'query': DisplayDataItem(self.query, label='Query')}
else:
if self.table_reference.projectId is not None:
tableSpec = '{}:{}.{}'.format(self.table_reference.projectId,
self.table_reference.datasetId,
self.table_reference.tableId)
else:
tableSpec = '{}.{}'.format(self.table_reference.datasetId,
self.table_reference.tableId)
res = {'table': DisplayDataItem(tableSpec, label='Table')}
res['validation'] = DisplayDataItem(self.validate,
label='Validation Enabled')
return res
@property
def format(self):
"""Source format name required for remote execution."""
return 'bigquery'
def reader(self, test_bigquery_client=None):
return BigQueryReader(
source=self,
test_bigquery_client=test_bigquery_client,
use_legacy_sql=self.use_legacy_sql,
flatten_results=self.flatten_results)
class BigQuerySink(dataflow_io.NativeSink):
"""A sink based on a BigQuery table."""
def __init__(self, table, dataset=None, project=None, schema=None,
create_disposition=BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=BigQueryDisposition.WRITE_EMPTY,
validate=False, coder=None):
"""Initialize a BigQuerySink.
Args:
table (str): The ID of the table. The ID must contain only letters
``a-z``, ``A-Z``, numbers ``0-9``, or underscores ``_``. If
**dataset** argument is :data:`None` then the table argument must
contain the entire table reference specified as: ``'DATASET.TABLE'`` or
``'PROJECT:DATASET.TABLE'``.
dataset (str): The ID of the dataset containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
project (str): The ID of the project containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
schema (str): The schema to be used if the BigQuery table to write has
to be created. This can be either specified as a
:class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema` object or a single string of the form
``'field1:type1,field2:type2,field3:type3'`` that defines a comma
separated list of fields. Here ``'type'`` should specify the BigQuery
type of the field. Single string based schemas do not support nested
fields, repeated fields, or specifying a BigQuery mode for fields (mode
will always be set to ``'NULLABLE'``).
create_disposition (BigQueryDisposition): A string describing what
happens if the table does not exist. Possible values are:
* :attr:`BigQueryDisposition.CREATE_IF_NEEDED`: create if does not
exist.
* :attr:`BigQueryDisposition.CREATE_NEVER`: fail the write if does not
exist.
write_disposition (BigQueryDisposition): A string describing what
happens if the table has already some data. Possible values are:
* :attr:`BigQueryDisposition.WRITE_TRUNCATE`: delete existing rows.
* :attr:`BigQueryDisposition.WRITE_APPEND`: add to existing rows.
* :attr:`BigQueryDisposition.WRITE_EMPTY`: fail the write if table not
empty.
validate (bool): If :data:`True`, various checks will be done when sink
gets initialized (e.g., is table present given the disposition
arguments?). This should be :data:`True` for most scenarios in order to
catch errors as early as possible (pipeline construction instead of
pipeline execution). It should be :data:`False` if the table is created
during pipeline execution by a previous step.
coder (~apache_beam.coders.coders.Coder): The coder for the
table rows if serialized to disk. If :data:`None`, then the default
coder is :class:`~apache_beam.io.gcp.bigquery.RowAsDictJsonCoder`,
which will interpret every element written to the sink as a dictionary
that will be JSON serialized as a line in a file. This argument needs a
value only in special cases when writing table rows as dictionaries is
not desirable.
Raises:
~exceptions.TypeError: if the schema argument is not a :class:`str` or a
:class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema` object.
~exceptions.ValueError: if the table reference as a string does not
match the expected format.
"""
# Import here to avoid adding the dependency for local running scenarios.
try:
# pylint: disable=wrong-import-order, wrong-import-position
from apitools.base import py # pylint: disable=unused-variable
except ImportError:
raise ImportError(
'Google Cloud IO not available, '
'please install apache_beam[gcp]')
self.table_reference = _parse_table_reference(table, dataset, project)
# Transform the table schema into a bigquery.TableSchema instance.
if isinstance(schema, (str, unicode)):
# TODO(silviuc): Should add a regex-based validation of the format.
table_schema = bigquery.TableSchema()
schema_list = [s.strip(' ') for s in schema.split(',')]
for field_and_type in schema_list:
field_name, field_type = field_and_type.split(':')
field_schema = bigquery.TableFieldSchema()
field_schema.name = field_name
field_schema.type = field_type
field_schema.mode = 'NULLABLE'
table_schema.fields.append(field_schema)
self.table_schema = table_schema
elif schema is None:
# TODO(silviuc): Should check that table exists if no schema specified.
self.table_schema = schema
elif isinstance(schema, bigquery.TableSchema):
self.table_schema = schema
else:
raise TypeError('Unexpected schema argument: %s.' % schema)
self.create_disposition = BigQueryDisposition.validate_create(
create_disposition)
self.write_disposition = BigQueryDisposition.validate_write(
write_disposition)
self.validate = validate
self.coder = coder or RowAsDictJsonCoder()
def display_data(self):
res = {}
if self.table_reference is not None:
tableSpec = '{}.{}'.format(self.table_reference.datasetId,
self.table_reference.tableId)
if self.table_reference.projectId is not None:
tableSpec = '{}:{}'.format(self.table_reference.projectId,
tableSpec)
res['table'] = DisplayDataItem(tableSpec, label='Table')
res['validation'] = DisplayDataItem(self.validate,
label="Validation Enabled")
return res
def schema_as_json(self):
"""Returns the TableSchema associated with the sink as a JSON string."""
def schema_list_as_object(schema_list):
"""Returns a list of TableFieldSchema objects as a list of dicts."""
fields = []
for f in schema_list:
fs = {'name': f.name, 'type': f.type}
if f.description is not None:
fs['description'] = f.description
if f.mode is not None:
fs['mode'] = f.mode
if f.type.lower() == 'record':
fs['fields'] = schema_list_as_object(f.fields)
fields.append(fs)
return fields
return json.dumps(
{'fields': schema_list_as_object(self.table_schema.fields)})
@property
def format(self):
"""Sink format name required for remote execution."""
return 'bigquery'
def writer(self, test_bigquery_client=None, buffer_size=None):
return BigQueryWriter(
sink=self, test_bigquery_client=test_bigquery_client,
buffer_size=buffer_size)
# -----------------------------------------------------------------------------
# BigQueryReader, BigQueryWriter.
class BigQueryReader(dataflow_io.NativeSourceReader):
"""A reader for a BigQuery source."""
def __init__(self, source, test_bigquery_client=None, use_legacy_sql=True,
flatten_results=True):
self.source = source
self.test_bigquery_client = test_bigquery_client
if auth.is_running_in_gce:
self.executing_project = auth.executing_project
elif hasattr(source, 'pipeline_options'):
self.executing_project = (
source.pipeline_options.view_as(GoogleCloudOptions).project)
else:
self.executing_project = None
# TODO(silviuc): Try to automatically get it from gcloud config info.
if not self.executing_project and test_bigquery_client is None:
raise RuntimeError(
'Missing executing project information. Please use the --project '
'command line option to specify it.')
self.row_as_dict = isinstance(self.source.coder, RowAsDictJsonCoder)
# Schema for the rows being read by the reader. It is initialized the
# first time something gets read from the table. It is not required
# for reading the field values in each row but could be useful for
# getting additional details.
self.schema = None
self.use_legacy_sql = use_legacy_sql
self.flatten_results = flatten_results
if self.source.query is None:
# If table schema did not define a project we default to executing
# project.
project_id = self.source.table_reference.projectId
if not project_id:
project_id = self.executing_project
self.query = 'SELECT * FROM [%s:%s.%s];' % (
project_id,
self.source.table_reference.datasetId,
self.source.table_reference.tableId)
else:
self.query = self.source.query
def _get_source_table_location(self):
tr = self.source.table_reference
if tr is None:
# TODO: implement location retrieval for query sources
return
if tr.projectId is None:
source_project_id = self.executing_project
else:
source_project_id = tr.projectId
source_dataset_id = tr.datasetId
source_table_id = tr.tableId
source_location = self.client.get_table_location(
source_project_id, source_dataset_id, source_table_id)
return source_location
def __enter__(self):
self.client = BigQueryWrapper(client=self.test_bigquery_client)
self.client.create_temporary_dataset(
self.executing_project, location=self._get_source_table_location())
return self
def __exit__(self, exception_type, exception_value, traceback):
self.client.clean_up_temporary_dataset(self.executing_project)
def __iter__(self):
for rows, schema in self.client.run_query(
project_id=self.executing_project, query=self.query,
use_legacy_sql=self.use_legacy_sql,
flatten_results=self.flatten_results):
if self.schema is None:
self.schema = schema
for row in rows:
if self.row_as_dict:
yield self.client.convert_row_to_dict(row, schema)
else:
yield row
class BigQueryWriter(dataflow_io.NativeSinkWriter):
"""The sink writer for a BigQuerySink."""
def __init__(self, sink, test_bigquery_client=None, buffer_size=None):
self.sink = sink
self.test_bigquery_client = test_bigquery_client
self.row_as_dict = isinstance(self.sink.coder, RowAsDictJsonCoder)
# Buffer used to batch written rows so we reduce communication with the
# BigQuery service.
self.rows_buffer = []
self.rows_buffer_flush_threshold = buffer_size or 1000
# Figure out the project, dataset, and table used for the sink.
self.project_id = self.sink.table_reference.projectId
# If table schema did not define a project we default to executing project.
if self.project_id is None and hasattr(sink, 'pipeline_options'):
self.project_id = (
sink.pipeline_options.view_as(GoogleCloudOptions).project)
assert self.project_id is not None
self.dataset_id = self.sink.table_reference.datasetId
self.table_id = self.sink.table_reference.tableId
def _flush_rows_buffer(self):
if self.rows_buffer:
logging.info('Writing %d rows to %s:%s.%s table.', len(self.rows_buffer),
self.project_id, self.dataset_id, self.table_id)
passed, errors = self.client.insert_rows(
project_id=self.project_id, dataset_id=self.dataset_id,
table_id=self.table_id, rows=self.rows_buffer)
self.rows_buffer = []
if not passed:
raise RuntimeError('Could not successfully insert rows to BigQuery'
' table [%s:%s.%s]. Errors: %s' %
(self.project_id, self.dataset_id,
self.table_id, errors))
def __enter__(self):
self.client = BigQueryWrapper(client=self.test_bigquery_client)
self.client.get_or_create_table(
self.project_id, self.dataset_id, self.table_id, self.sink.table_schema,
self.sink.create_disposition, self.sink.write_disposition)
return self
def __exit__(self, exception_type, exception_value, traceback):
self._flush_rows_buffer()
def Write(self, row):
self.rows_buffer.append(row)
if len(self.rows_buffer) > self.rows_buffer_flush_threshold:
self._flush_rows_buffer()
# -----------------------------------------------------------------------------
# BigQueryWrapper.
class BigQueryWrapper(object):
"""BigQuery client wrapper with utilities for querying.
The wrapper is used to organize all the BigQuery integration points and
offer a common place where retry logic for failures can be controlled.
In addition it offers various functions used both in sources and sinks
(e.g., find and create tables, query a table, etc.).
"""
TEMP_TABLE = 'temp_table_'
TEMP_DATASET = 'temp_dataset_'
def __init__(self, client=None):
self.client = client or bigquery.BigqueryV2(
credentials=auth.get_service_credentials())
self._unique_row_id = 0
# For testing scenarios where we pass in a client we do not want a
# randomized prefix for row IDs.
self._row_id_prefix = '' if client else uuid.uuid4()
self._temporary_table_suffix = uuid.uuid4().hex
@property
def unique_row_id(self):
"""Returns a unique row ID (str) used to avoid multiple insertions.
If the row ID is provided, BigQuery will make a best effort to not insert
the same row multiple times for fail and retry scenarios in which the insert
request may be issued several times. This comes into play for sinks executed
in a local runner.
Returns:
a unique row ID string
"""
self._unique_row_id += 1
return '%s_%d' % (self._row_id_prefix, self._unique_row_id)
def _get_temp_table(self, project_id):
return _parse_table_reference(
table=BigQueryWrapper.TEMP_TABLE + self._temporary_table_suffix,
dataset=BigQueryWrapper.TEMP_DATASET + self._temporary_table_suffix,
project=project_id)
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def _start_query_job(self, project_id, query, use_legacy_sql, flatten_results,
job_id, dry_run=False):
reference = bigquery.JobReference(jobId=job_id, projectId=project_id)
request = bigquery.BigqueryJobsInsertRequest(
projectId=project_id,
job=bigquery.Job(
configuration=bigquery.JobConfiguration(
dryRun=dry_run,
query=bigquery.JobConfigurationQuery(
query=query,
useLegacySql=use_legacy_sql,
allowLargeResults=True,
destinationTable=self._get_temp_table(project_id),
flattenResults=flatten_results)),
jobReference=reference))
response = self.client.jobs.Insert(request)
return response.jobReference.jobId
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def _get_query_results(self, project_id, job_id,
page_token=None, max_results=10000):
request = bigquery.BigqueryJobsGetQueryResultsRequest(
jobId=job_id, pageToken=page_token, projectId=project_id,
maxResults=max_results)
response = self.client.jobs.GetQueryResults(request)
return response
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_filter)
def _insert_all_rows(self, project_id, dataset_id, table_id, rows):
# The rows argument is a list of
# bigquery.TableDataInsertAllRequest.RowsValueListEntry instances as
# required by the InsertAll() method.
request = bigquery.BigqueryTabledataInsertAllRequest(
projectId=project_id, datasetId=dataset_id, tableId=table_id,
tableDataInsertAllRequest=bigquery.TableDataInsertAllRequest(
# TODO(silviuc): Should have an option for skipInvalidRows?
# TODO(silviuc): Should have an option for ignoreUnknownValues?
rows=rows))
response = self.client.tabledata.InsertAll(request)
# response.insertErrors is not [] if errors encountered.
return not response.insertErrors, response.insertErrors
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def _get_table(self, project_id, dataset_id, table_id):
request = bigquery.BigqueryTablesGetRequest(
projectId=project_id, datasetId=dataset_id, tableId=table_id)
response = self.client.tables.Get(request)
# The response is a bigquery.Table instance.
return response
def _create_table(self, project_id, dataset_id, table_id, schema):
table = bigquery.Table(
tableReference=bigquery.TableReference(
projectId=project_id, datasetId=dataset_id, tableId=table_id),
schema=schema)
request = bigquery.BigqueryTablesInsertRequest(
projectId=project_id, datasetId=dataset_id, table=table)
response = self.client.tables.Insert(request)
logging.debug("Created the table with id %s", table_id)
# The response is a bigquery.Table instance.
return response
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def get_or_create_dataset(self, project_id, dataset_id, location=None):
# Check if dataset already exists otherwise create it
try:
dataset = self.client.datasets.Get(bigquery.BigqueryDatasetsGetRequest(
projectId=project_id, datasetId=dataset_id))
return dataset
except HttpError as exn:
if exn.status_code == 404:
dataset_reference = bigquery.DatasetReference(
projectId=project_id, datasetId=dataset_id)
dataset = bigquery.Dataset(datasetReference=dataset_reference)
if location is not None:
dataset.location = location
request = bigquery.BigqueryDatasetsInsertRequest(
projectId=project_id, dataset=dataset)
response = self.client.datasets.Insert(request)
# The response is a bigquery.Dataset instance.
return response
else:
raise
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def _is_table_empty(self, project_id, dataset_id, table_id):
request = bigquery.BigqueryTabledataListRequest(
projectId=project_id, datasetId=dataset_id, tableId=table_id,
maxResults=1)
response = self.client.tabledata.List(request)
# The response is a bigquery.TableDataList instance.
return response.totalRows == 0
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def _delete_table(self, project_id, dataset_id, table_id):
request = bigquery.BigqueryTablesDeleteRequest(
projectId=project_id, datasetId=dataset_id, tableId=table_id)
try:
self.client.tables.Delete(request)
except HttpError as exn:
if exn.status_code == 404:
logging.warning('Table %s:%s.%s does not exist', project_id,
dataset_id, table_id)
return
else:
raise
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def _delete_dataset(self, project_id, dataset_id, delete_contents=True):
request = bigquery.BigqueryDatasetsDeleteRequest(
projectId=project_id, datasetId=dataset_id,
deleteContents=delete_contents)
try:
self.client.datasets.Delete(request)
except HttpError as exn:
if exn.status_code == 404:
logging.warning('Dataset %s:%s does not exist', project_id,
dataset_id)
return
else:
raise
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def get_table_location(self, project_id, dataset_id, table_id):
table = self._get_table(project_id, dataset_id, table_id)
return table.location
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def create_temporary_dataset(self, project_id, location=None):
# TODO: make location required, once "query" locations can be determined
dataset_id = BigQueryWrapper.TEMP_DATASET + self._temporary_table_suffix
# Check if dataset exists to make sure that the temporary id is unique
try:
self.client.datasets.Get(bigquery.BigqueryDatasetsGetRequest(
projectId=project_id, datasetId=dataset_id))
if project_id is not None:
# Unittests don't pass projectIds so they can be run without error
raise RuntimeError(
'Dataset %s:%s already exists so cannot be used as temporary.'
% (project_id, dataset_id))
except HttpError as exn:
if exn.status_code == 404:
logging.warning(
'Dataset %s:%s does not exist so we will create it as temporary '
'with location=%s',
project_id, dataset_id, location)
self.get_or_create_dataset(project_id, dataset_id, location=location)
else:
raise
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def clean_up_temporary_dataset(self, project_id):
temp_table = self._get_temp_table(project_id)
try:
self.client.datasets.Get(bigquery.BigqueryDatasetsGetRequest(
projectId=project_id, datasetId=temp_table.datasetId))
except HttpError as exn:
if exn.status_code == 404:
logging.warning('Dataset %s:%s does not exist', project_id,
temp_table.datasetId)
return
else:
raise
self._delete_dataset(temp_table.projectId, temp_table.datasetId, True)
@retry.with_exponential_backoff(
num_retries=MAX_RETRIES,
retry_filter=retry.retry_on_server_errors_and_timeout_filter)
def get_or_create_table(
self, project_id, dataset_id, table_id, schema,
create_disposition, write_disposition):
"""Gets or creates a table based on create and write dispositions.
The function mimics the behavior of BigQuery import jobs when using the
same create and write dispositions.
Args:
project_id: The project id owning the table.
dataset_id: The dataset id owning the table.
table_id: The table id.
schema: A bigquery.TableSchema instance or None.
create_disposition: CREATE_NEVER or CREATE_IF_NEEDED.
write_disposition: WRITE_APPEND, WRITE_EMPTY or WRITE_TRUNCATE.
Returns:
A bigquery.Table instance if table was found or created.
Raises:
RuntimeError: For various mismatches between the state of the table and
the create/write dispositions passed in. For example if the table is not
empty and WRITE_EMPTY was specified then an error will be raised since
the table was expected to be empty.
"""
found_table = None
try:
found_table = self._get_table(project_id, dataset_id, table_id)
except HttpError as exn:
if exn.status_code == 404:
if create_disposition == BigQueryDisposition.CREATE_NEVER:
raise RuntimeError(
'Table %s:%s.%s not found but create disposition is CREATE_NEVER.'
% (project_id, dataset_id, table_id))
else:
raise
# If table exists already then handle the semantics for WRITE_EMPTY and
# WRITE_TRUNCATE write dispositions.
if found_table:
table_empty = self._is_table_empty(project_id, dataset_id, table_id)
if (not table_empty and
write_disposition == BigQueryDisposition.WRITE_EMPTY):
raise RuntimeError(
'Table %s:%s.%s is not empty but write disposition is WRITE_EMPTY.'
% (project_id, dataset_id, table_id))
# Delete the table and recreate it (later) if WRITE_TRUNCATE was
# specified.
if write_disposition == BigQueryDisposition.WRITE_TRUNCATE:
self._delete_table(project_id, dataset_id, table_id)
# Create a new table potentially reusing the schema from a previously
# found table in case the schema was not specified.
if schema is None and found_table is None:
raise RuntimeError(
'Table %s:%s.%s requires a schema. None can be inferred because the '
'table does not exist.'
% (project_id, dataset_id, table_id))
if found_table and write_disposition != BigQueryDisposition.WRITE_TRUNCATE:
return found_table
else:
created_table = self._create_table(project_id=project_id,
dataset_id=dataset_id,
table_id=table_id,
schema=schema or found_table.schema)
# if write_disposition == BigQueryDisposition.WRITE_TRUNCATE we delete
# the table before this point.
if write_disposition == BigQueryDisposition.WRITE_TRUNCATE:
# BigQuery can route data to the old table for 2 mins max so wait
# that much time before creating the table and writing it
logging.warning('Sleeping for 150 seconds before the write as ' +
'BigQuery inserts can be routed to deleted table ' +
'for 2 mins after the delete and create.')
# TODO(BEAM-2673): Remove this sleep by migrating to load api
time.sleep(150)
return created_table
else:
return created_table
def run_query(self, project_id, query, use_legacy_sql, flatten_results,
dry_run=False):
job_id = self._start_query_job(project_id, query, use_legacy_sql,
flatten_results, job_id=uuid.uuid4().hex,
dry_run=dry_run)
if dry_run:
# If this was a dry run then the fact that we get here means the
# query has no errors. The start_query_job would raise an error otherwise.
return
page_token = None
while True:
response = self._get_query_results(project_id, job_id, page_token)
if not response.jobComplete:
# The jobComplete field can be False if the query request times out
# (default is 10 seconds). Note that this is a timeout for the query
# request not for the actual execution of the query in the service. If
# the request times out we keep trying. This situation is quite possible
# if the query will return a large number of rows.
logging.info('Waiting on response from query: %s ...', query)
time.sleep(1.0)
continue
# We got some results. The last page is signalled by a missing pageToken.
yield response.rows, response.schema
if not response.pageToken:
break
page_token = response.pageToken
def insert_rows(self, project_id, dataset_id, table_id, rows):
"""Inserts rows into the specified table.
Args:
project_id: The project id owning the table.
dataset_id: The dataset id owning the table.
table_id: The table id.
rows: A list of plain Python dictionaries. Each dictionary is a row and
each key in it is the name of a field.
Returns:
A tuple (bool, errors). If first element is False then the second element
will be a bigquery.InserttErrorsValueListEntry instance containing
specific errors.
"""
# Prepare rows for insertion. Of special note is the row ID that we add to
# each row in order to help BigQuery avoid inserting a row multiple times.
# BigQuery will do a best-effort if unique IDs are provided. This situation
# can happen during retries on failures.
# TODO(silviuc): Must add support to writing TableRow's instead of dicts.
final_rows = []
for row in rows:
json_object = bigquery.JsonObject()
for k, v in iteritems(row):
json_object.additionalProperties.append(
bigquery.JsonObject.AdditionalProperty(
key=k, value=to_json_value(v)))
final_rows.append(
bigquery.TableDataInsertAllRequest.RowsValueListEntry(
insertId=str(self.unique_row_id),
json=json_object))
result, errors = self._insert_all_rows(
project_id, dataset_id, table_id, final_rows)
return result, errors
def _convert_cell_value_to_dict(self, value, field):
if field.type == 'STRING':
# Input: "XYZ" --> Output: "XYZ"
return value
elif field.type == 'BOOLEAN':
# Input: "true" --> Output: True
return value == 'true'
elif field.type == 'INTEGER':
# Input: "123" --> Output: 123
return int(value)
elif field.type == 'FLOAT':
# Input: "1.23" --> Output: 1.23
return float(value)
elif field.type == 'TIMESTAMP':
# The UTC should come from the timezone library but this is a known
# issue in python 2.7 so we'll just hardcode it as we're reading using
# utcfromtimestamp.
# Input: 1478134176.985864 --> Output: "2016-11-03 00:49:36.985864 UTC"
dt = datetime.datetime.utcfromtimestamp(float(value))
return dt.strftime('%Y-%m-%d %H:%M:%S.%f UTC')
elif field.type == 'BYTES':
# Input: "YmJi" --> Output: "YmJi"
return value
elif field.type == 'DATE':
# Input: "2016-11-03" --> Output: "2016-11-03"
return value
elif field.type == 'DATETIME':
# Input: "2016-11-03T00:49:36" --> Output: "2016-11-03T00:49:36"
return value
elif field.type == 'TIME':
# Input: "00:49:36" --> Output: "00:49:36"
return value
elif field.type == 'RECORD':
# Note that a schema field object supports also a RECORD type. However
# when querying, the repeated and/or record fields are flattened
# unless we pass the flatten_results flag as False to the source
return self.convert_row_to_dict(value, field)
else:
raise RuntimeError('Unexpected field type: %s' % field.type)
def convert_row_to_dict(self, row, schema):
"""Converts a TableRow instance using the schema to a Python dict."""
result = {}
for index, field in enumerate(schema.fields):
value = None
if isinstance(schema, bigquery.TableSchema):
cell = row.f[index]
value = from_json_value(cell.v) if cell.v is not None else None
elif isinstance(schema, bigquery.TableFieldSchema):
cell = row['f'][index]
value = cell['v'] if 'v' in cell else None
if field.mode == 'REPEATED':
if value is None:
# Ideally this should never happen as repeated fields default to
# returning an empty list
result[field.name] = []
else:
result[field.name] = [self._convert_cell_value_to_dict(x['v'], field)
for x in value]
elif value is None:
if not field.mode == 'NULLABLE':
raise ValueError('Received \'None\' as the value for the field %s '
'but the field is not NULLABLE.' % field.name)
result[field.name] = None
else:
result[field.name] = self._convert_cell_value_to_dict(value, field)
return result
class BigQueryWriteFn(DoFn):
"""A ``DoFn`` that streams writes to BigQuery once the table is created.
"""
def __init__(self, table_id, dataset_id, project_id, batch_size, schema,
create_disposition, write_disposition, client):
"""Initialize a WriteToBigQuery transform.
Args:
table_id: The ID of the table. The ID must contain only letters
(a-z, A-Z), numbers (0-9), or underscores (_). If dataset argument is
None then the table argument must contain the entire table reference
specified as: 'DATASET.TABLE' or 'PROJECT:DATASET.TABLE'.
dataset_id: The ID of the dataset containing this table or null if the
table reference is specified entirely by the table argument.
project_id: The ID of the project containing this table or null if the
table reference is specified entirely by the table argument.
batch_size: Number of rows to be written to BQ per streaming API insert.
schema: The schema to be used if the BigQuery table to write has to be
created. This can be either specified as a 'bigquery.TableSchema' object
or a single string of the form 'field1:type1,field2:type2,field3:type3'
that defines a comma separated list of fields. Here 'type' should
specify the BigQuery type of the field. Single string based schemas do
not support nested fields, repeated fields, or specifying a BigQuery
mode for fields (mode will always be set to 'NULLABLE').
create_disposition: A string describing what happens if the table does not
exist. Possible values are:
- BigQueryDisposition.CREATE_IF_NEEDED: create if does not exist.
- BigQueryDisposition.CREATE_NEVER: fail the write if does not exist.
write_disposition: A string describing what happens if the table has
already some data. Possible values are:
- BigQueryDisposition.WRITE_TRUNCATE: delete existing rows.
- BigQueryDisposition.WRITE_APPEND: add to existing rows.
- BigQueryDisposition.WRITE_EMPTY: fail the write if table not empty.
For streaming pipelines WriteTruncate can not be used.
test_client: Override the default bigquery client used for testing.
"""
self.table_id = table_id
self.dataset_id = dataset_id
self.project_id = project_id
self.schema = schema
self.client = client
self.create_disposition = create_disposition
self.write_disposition = write_disposition
self._rows_buffer = []
# The default batch size is 500
self._max_batch_size = batch_size or 500
@staticmethod
def get_table_schema(schema):
"""Transform the table schema into a bigquery.TableSchema instance.
Args:
schema: The schema to be used if the BigQuery table to write has to be
created. This is a dictionary object created in the WriteToBigQuery
transform.
Returns:
table_schema: The schema to be used if the BigQuery table to write has
to be created but in the bigquery.TableSchema format.
"""
if schema is None:
return schema
elif isinstance(schema, dict):
return parse_table_schema_from_json(json.dumps(schema))
else:
raise TypeError('Unexpected schema argument: %s.' % schema)
def start_bundle(self):
self._rows_buffer = []
self.table_schema = self.get_table_schema(self.schema)
self.bigquery_wrapper = BigQueryWrapper(client=self.client)
self.bigquery_wrapper.get_or_create_table(
self.project_id, self.dataset_id, self.table_id, self.table_schema,
self.create_disposition, self.write_disposition)
def process(self, element, unused_create_fn_output=None):
self._rows_buffer.append(element)
if len(self._rows_buffer) >= self._max_batch_size:
self._flush_batch()
def finish_bundle(self):
if self._rows_buffer:
self._flush_batch()
self._rows_buffer = []
def _flush_batch(self):
# Flush the current batch of rows to BigQuery.
passed, errors = self.bigquery_wrapper.insert_rows(
project_id=self.project_id, dataset_id=self.dataset_id,
table_id=self.table_id, rows=self._rows_buffer)
if not passed:
raise RuntimeError('Could not successfully insert rows to BigQuery'
' table [%s:%s.%s]. Errors: %s'%
(self.project_id, self.dataset_id,
self.table_id, errors))
logging.debug("Successfully wrote %d rows.", len(self._rows_buffer))
self._rows_buffer = []
class WriteToBigQuery(PTransform):
def __init__(self, table, dataset=None, project=None, schema=None,
create_disposition=BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=BigQueryDisposition.WRITE_APPEND,
batch_size=None, test_client=None):
"""Initialize a WriteToBigQuery transform.
Args:
table (str): The ID of the table. The ID must contain only letters
``a-z``, ``A-Z``, numbers ``0-9``, or underscores ``_``. If dataset
argument is :data:`None` then the table argument must contain the
entire table reference specified as: ``'DATASET.TABLE'`` or
``'PROJECT:DATASET.TABLE'``.
dataset (str): The ID of the dataset containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
project (str): The ID of the project containing this table or
:data:`None` if the table reference is specified entirely by the table
argument.
schema (str): The schema to be used if the BigQuery table to write has to
be created. This can be either specified as a
:class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema`
object or a single string of the form
``'field1:type1,field2:type2,field3:type3'`` that defines a comma
separated list of fields. Here ``'type'`` should specify the BigQuery
type of the field. Single string based schemas do not support nested
fields, repeated fields, or specifying a BigQuery mode for fields
(mode will always be set to ``'NULLABLE'``).
create_disposition (BigQueryDisposition): A string describing what
happens if the table does not exist. Possible values are:
* :attr:`BigQueryDisposition.CREATE_IF_NEEDED`: create if does not
exist.
* :attr:`BigQueryDisposition.CREATE_NEVER`: fail the write if does not
exist.
write_disposition (BigQueryDisposition): A string describing what happens
if the table has already some data. Possible values are:
* :attr:`BigQueryDisposition.WRITE_TRUNCATE`: delete existing rows.
* :attr:`BigQueryDisposition.WRITE_APPEND`: add to existing rows.
* :attr:`BigQueryDisposition.WRITE_EMPTY`: fail the write if table not
empty.
For streaming pipelines WriteTruncate can not be used.
batch_size (int): Number of rows to be written to BQ per streaming API
insert.
test_client: Override the default bigquery client used for testing.
"""
self.table_reference = _parse_table_reference(table, dataset, project)
self.create_disposition = BigQueryDisposition.validate_create(
create_disposition)
self.write_disposition = BigQueryDisposition.validate_write(
write_disposition)
self.schema = schema
self.batch_size = batch_size
self.test_client = test_client
@staticmethod
def get_table_schema_from_string(schema):
"""Transform the string table schema into a
:class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema` instance.
Args:
schema (str): The sting schema to be used if the BigQuery table to write
has to be created.
Returns:
~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema:
The schema to be used if the BigQuery table to write has to be created
but in the :class:`~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema` format.
"""
table_schema = bigquery.TableSchema()
schema_list = [s.strip() for s in schema.split(',')]
for field_and_type in schema_list:
field_name, field_type = field_and_type.split(':')
field_schema = bigquery.TableFieldSchema()
field_schema.name = field_name
field_schema.type = field_type
field_schema.mode = 'NULLABLE'
table_schema.fields.append(field_schema)
return table_schema
@staticmethod
def table_schema_to_dict(table_schema):
"""Create a dictionary representation of table schema for serialization
"""
def get_table_field(field):
"""Create a dictionary representation of a table field
"""
result = {}
result['name'] = field.name
result['type'] = field.type
result['mode'] = getattr(field, 'mode', 'NULLABLE')
if hasattr(field, 'description') and field.description is not None:
result['description'] = field.description
if hasattr(field, 'fields') and field.fields:
result['fields'] = [get_table_field(f) for f in field.fields]
return result
if not isinstance(table_schema, bigquery.TableSchema):
raise ValueError("Table schema must be of the type bigquery.TableSchema")
schema = {'fields': []}
for field in table_schema.fields:
schema['fields'].append(get_table_field(field))
return schema
@staticmethod
def get_dict_table_schema(schema):
"""Transform the table schema into a dictionary instance.
Args:
schema (~apache_beam.io.gcp.internal.clients.bigquery.\
bigquery_v2_messages.TableSchema):
The schema to be used if the BigQuery table to write has to be created.
This can either be a dict or string or in the TableSchema format.
Returns:
Dict[str, Any]: The schema to be used if the BigQuery table to write has
to be created but in the dictionary format.
"""
if isinstance(schema, dict):
return schema
elif schema is None:
return schema
elif isinstance(schema, (str, unicode)):
table_schema = WriteToBigQuery.get_table_schema_from_string(schema)
return WriteToBigQuery.table_schema_to_dict(table_schema)
elif isinstance(schema, bigquery.TableSchema):
return WriteToBigQuery.table_schema_to_dict(schema)
else:
raise TypeError('Unexpected schema argument: %s.' % schema)
def expand(self, pcoll):
if self.table_reference.projectId is None:
self.table_reference.projectId = pcoll.pipeline.options.view_as(
GoogleCloudOptions).project
bigquery_write_fn = BigQueryWriteFn(
table_id=self.table_reference.tableId,
dataset_id=self.table_reference.datasetId,
project_id=self.table_reference.projectId,
batch_size=self.batch_size,
schema=self.get_dict_table_schema(self.schema),
create_disposition=self.create_disposition,
write_disposition=self.write_disposition,
client=self.test_client)
return pcoll | 'WriteToBigQuery' >> ParDo(bigquery_write_fn)
def display_data(self):
res = {}
if self.table_reference is not None:
tableSpec = '{}.{}'.format(self.table_reference.datasetId,
self.table_reference.tableId)
if self.table_reference.projectId is not None:
tableSpec = '{}:{}'.format(self.table_reference.projectId,
tableSpec)
res['table'] = DisplayDataItem(tableSpec, label='Table')
return res
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