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"""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.
Writing Data to BigQuery
========================
The `WriteToBigQuery` transform is the recommended way of writing data to
BigQuery. It supports a large set of parameters to customize how you'd like to
write to BigQuery.
Table References
----------------
This transform allows you to provide static `project`, `dataset` and `table`
parameters which point to a specific BigQuery table to be created. The `table`
parameter can also be a dynamic parameter (i.e. a callable), which receives an
element to be written to BigQuery, and returns the table that that element
should be sent to.
You may also provide a tuple of PCollectionView elements to be passed as side
inputs to your callable. For example, suppose that one wishes to send
events of different types to different tables, and the table names are
computed at pipeline runtime, one may do something like the following::
with Pipeline() as p:
elements = (p | beam.Create([
{'type': 'error', 'timestamp': '12:34:56', 'message': 'bad'},
{'type': 'user_log', 'timestamp': '12:34:59', 'query': 'flu symptom'},
]))
table_names = (p | beam.Create([
('error', 'my_project.dataset1.error_table_for_today'),
('user_log', 'my_project.dataset1.query_table_for_today'),
])
table_names_dict = beam.pvalue.AsDict(table_names)
elements | beam.io.gcp.WriteToBigQuery(
table=lambda row, table_dict: table_dict[row['type']],
table_side_inputs=(table_names_dict,))
In the example above, the `table_dict` argument passed to the function in
`table_dict` is the side input coming from `table_names_dict`, which is passed
as part of the `table_side_inputs` argument.
Schemas
---------
This transform also allows you to provide a static or dynamic `schema`
parameter (i.e. a callable).
If providing a callable, this should take in a table reference (as returned by
the `table` parameter), and return the corresponding schema for that table.
This allows to provide different schemas for different tables::
def compute_table_name(row):
...
errors_schema = {'fields': [
{'name': 'type', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'message', 'type': 'STRING', 'mode': 'NULLABLE'}]}
queries_schema = {'fields': [
{'name': 'type', 'type': 'STRING', 'mode': 'NULLABLE'},
{'name': 'query', 'type': 'STRING', 'mode': 'NULLABLE'}]}
with Pipeline() as p:
elements = (p | beam.Create([
{'type': 'error', 'timestamp': '12:34:56', 'message': 'bad'},
{'type': 'user_log', 'timestamp': '12:34:59', 'query': 'flu symptom'},
]))
elements | beam.io.gcp.WriteToBigQuery(
table=compute_table_name,
schema=lambda table: (errors_schema
if 'errors' in table
else queries_schema))
It may be the case that schemas are computed at pipeline runtime. In cases
like these, one can also provide a `schema_side_inputs` parameter, which is
a tuple of PCollectionViews to be passed to the schema callable (much like
the `table_side_inputs` parameter).
Additional Parameters for BigQuery Tables
-----------------------------------------
This sink is able to create tables in BigQuery if they don't already exist. It
also relies on creating temporary tables when performing file loads.
The WriteToBigQuery transform creates tables using the BigQuery API by
inserting a load job (see the API reference [1]), or by inserting a new table
(see the API reference for that [2][3]).
When creating a new BigQuery table, there are a number of extra parameters
that one may need to specify. For example, clustering, partitioning, data
encoding, etc. It is possible to provide these additional parameters by
passing a Python dictionary as `additional_bq_parameters` to the transform.
As an example, to create a table that has specific partitioning, and
clustering properties, one would do the following::
additional_bq_parameters = {
'timePartitioning': {'type': 'DAY'},
'clustering': {'fields': ['country']}}
with Pipeline() as p:
elements = (p | beam.Create([
{'country': 'mexico', 'timestamp': '12:34:56', 'query': 'acapulco'},
{'country': 'canada', 'timestamp': '12:34:59', 'query': 'influenza'},
]))
elements | beam.io.gcp.WriteToBigQuery(
table='project_name1.dataset_2.query_events_table',
additional_bq_parameters=additional_bq_parameters)
Much like the schema case, the parameter with `additional_bq_parameters` can
also take a callable that receives a table reference.
[1] https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#\
configuration.load
[2] https://cloud.google.com/bigquery/docs/reference/rest/v2/tables/insert
[3] https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#resource
*** 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', 'NUMERIC',
'GEOGRAPHY'.
All possible values are described at:
https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
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.
As of Beam 2.7.0, the NUMERIC data type is supported. This data type supports
high-precision decimal numbers (precision of 38 digits, scale of 9 digits).
The GEOGRAPHY data type works with Well-Known Text (See
https://en.wikipedia.org/wiki/Well-known_text) format for reading and writing
to BigQuery.
BigQuery IO requires values of BYTES datatype to be encoded using base64
encoding when writing to BigQuery. When bytes are read from BigQuery they are
returned as base64-encoded bytes.
"""
from __future__ import absolute_import
import collections
import itertools
import json
import logging
import time
import uuid
from builtins import object
from builtins import zip
from future.utils import itervalues
from past.builtins import unicode
import apache_beam as beam
from apache_beam import coders
from apache_beam import pvalue
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 import bigquery_tools
from apache_beam.io.gcp.internal.clients import bigquery
from apache_beam.options import value_provider as vp
from apache_beam.options.pipeline_options import DebugOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import StandardOptions
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.transforms.window import GlobalWindows
from apache_beam.utils import retry
from apache_beam.utils.annotations import deprecated
__all__ = [
'TableRowJsonCoder',
'BigQueryDisposition',
'BigQuerySource',
'BigQuerySink',
'WriteToBigQuery',
'SCHEMA_AUTODETECT',
]
_LOGGER = logging.getLogger(__name__)
@deprecated(since='2.11.0', current="bigquery_tools.parse_table_reference")
def _parse_table_reference(table, dataset=None, project=None):
return bigquery_tools.parse_table_reference(table, dataset, project)
@deprecated(since='2.11.0',
current="bigquery_tools.parse_table_schema_from_json")
def parse_table_schema_from_json(schema_string):
return bigquery_tools.parse_table_schema_from_json(schema_string)
@deprecated(since='2.11.0', current="bigquery_tools.default_encoder")
def default_encoder(obj):
return bigquery_tools.default_encoder(obj)
@deprecated(since='2.11.0', current="bigquery_tools.RowAsDictJsonCoder")
def RowAsDictJsonCoder(*args, **kwargs):
return bigquery_tools.RowAsDictJsonCoder(*args, **kwargs)
@deprecated(since='2.11.0', current="bigquery_tools.BigQueryReader")
def BigQueryReader(*args, **kwargs):
return bigquery_tools.BigQueryReader(*args, **kwargs)
@deprecated(since='2.11.0', current="bigquery_tools.BigQueryWriter")
def BigQueryWriter(*args, **kwargs):
return bigquery_tools.BigQueryWriter(*args, **kwargs)
@deprecated(since='2.11.0', current="bigquery_tools.BigQueryWrapper")
def BigQueryWrapper(*args, **kwargs):
return bigquery_tools.BigQueryWrapper(*args, **kwargs)
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)
self.field_types = tuple(fs.type 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,
default=bigquery_tools.default_encoder)
except ValueError as e:
raise ValueError('%s. %s' % (e, bigquery_tools.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)])
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
# -----------------------------------------------------------------------------
# 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, kms_key=None):
"""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_tools.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`.
kms_key (str): Experimental. Optional Cloud KMS key name for use when
creating new tables.
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-import
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 = bigquery_tools.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 bigquery_tools.RowAsDictJsonCoder()
self.kms_key = kms_key
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 bigquery_tools.BigQueryReader(
source=self,
test_bigquery_client=test_bigquery_client,
use_legacy_sql=self.use_legacy_sql,
flatten_results=self.flatten_results,
kms_key=self.kms_key)
@deprecated(since='2.11.0', current="WriteToBigQuery")
class BigQuerySink(dataflow_io.NativeSink):
"""A sink based on a BigQuery table.
This BigQuery sink triggers a Dataflow native sink for BigQuery
that only supports batch pipelines.
Instead of using this sink directly, please use WriteToBigQuery
transform that works for both batch and streaming pipelines.
"""
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, kms_key=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_tools.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.
kms_key (str): Experimental. Optional Cloud KMS key name for use when
creating new tables.
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-import
except ImportError:
raise ImportError(
'Google Cloud IO not available, '
'please install apache_beam[gcp]')
self.table_reference = bigquery_tools.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 bigquery_tools.RowAsDictJsonCoder()
self.kms_key = kms_key
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 bigquery_tools.BigQueryWriter(
sink=self, test_bigquery_client=test_bigquery_client,
buffer_size=buffer_size)
class BigQueryWriteFn(DoFn):
"""A ``DoFn`` that streams writes to BigQuery once the table is created."""
DEFAULT_MAX_BUFFERED_ROWS = 2000
DEFAULT_MAX_BATCH_SIZE = 500
FAILED_ROWS = 'FailedRows'
def __init__(
self,
batch_size,
schema=None,
create_disposition=None,
write_disposition=None,
kms_key=None,
test_client=None,
max_buffered_rows=None,
retry_strategy=None,
additional_bq_parameters=None):
"""Initialize a WriteToBigQuery transform.
Args:
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.
kms_key: Experimental. Optional Cloud KMS key name for use when creating
new tables.
test_client: Override the default bigquery client used for testing.
max_buffered_rows: The maximum number of rows that are allowed to stay
buffered when running dynamic destinations. When destinations are
dynamic, it is important to keep caches small even when a single
batch has not been completely filled up.
retry_strategy: The strategy to use when retrying streaming inserts
into BigQuery. Options are shown in bigquery_tools.RetryStrategy attrs.
additional_bq_parameters (dict, callable): A set of additional parameters
to be passed when creating a BigQuery table. These are passed when
triggering a load job for FILE_LOADS, and when creating a new table for
STREAMING_INSERTS.
"""
self.schema = schema
self.test_client = test_client
self.create_disposition = create_disposition
self.write_disposition = write_disposition
self._rows_buffer = []
self._reset_rows_buffer()
self._observed_tables = set()
self._total_buffered_rows = 0
self.kms_key = kms_key
self._max_batch_size = batch_size or BigQueryWriteFn.DEFAULT_MAX_BATCH_SIZE
self._max_buffered_rows = (max_buffered_rows
or BigQueryWriteFn.DEFAULT_MAX_BUFFERED_ROWS)
self._retry_strategy = (
retry_strategy or bigquery_tools.RetryStrategy.RETRY_ON_TRANSIENT_ERROR)
self.additional_bq_parameters = additional_bq_parameters or {}
def display_data(self):
return {'max_batch_size': self._max_batch_size,
'max_buffered_rows': self._max_buffered_rows,
'retry_strategy': self._retry_strategy,
'create_disposition': str(self.create_disposition),
'write_disposition': str(self.write_disposition),
'additional_bq_parameters': str(self.additional_bq_parameters)}
def _reset_rows_buffer(self):
self._rows_buffer = collections.defaultdict(lambda: [])
@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, (str, unicode)):
return bigquery_tools.parse_table_schema_from_json(schema)
elif isinstance(schema, dict):
return bigquery_tools.parse_table_schema_from_json(json.dumps(schema))
else:
raise TypeError('Unexpected schema argument: %s.' % schema)
def start_bundle(self):
self._reset_rows_buffer()
self.bigquery_wrapper = bigquery_tools.BigQueryWrapper(
client=self.test_client)
self._observed_tables = set()
self._backoff_calculator = iter(retry.FuzzedExponentialIntervals(
initial_delay_secs=0.2,
num_retries=10000,
max_delay_secs=1500))
def _create_table_if_needed(self, table_reference, schema=None):
str_table_reference = '%s:%s.%s' % (
table_reference.projectId,
table_reference.datasetId,
table_reference.tableId)
if str_table_reference in self._observed_tables:
return
if self.create_disposition == BigQueryDisposition.CREATE_NEVER:
# If we never want to create the table, we assume it already exists,
# and avoid the get-or-create step.
return
_LOGGER.debug('Creating or getting table %s with schema %s.',
table_reference, schema)
table_schema = self.get_table_schema(schema)
if table_reference.projectId is None:
table_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
self.bigquery_wrapper.get_or_create_table(
table_reference.projectId,
table_reference.datasetId,
table_reference.tableId,
table_schema,
self.create_disposition,
self.write_disposition,
additional_create_parameters=self.additional_bq_parameters)
self._observed_tables.add(str_table_reference)
def process(self, element, *schema_side_inputs):
destination = element[0]
if callable(self.schema):
schema = self.schema(destination, *schema_side_inputs)
elif isinstance(self.schema, vp.ValueProvider):
schema = self.schema.get()
else:
schema = self.schema
self._create_table_if_needed(
bigquery_tools.parse_table_reference(destination),
schema)
destination = bigquery_tools.get_hashable_destination(destination)
row_and_insert_id = element[1]
self._rows_buffer[destination].append(row_and_insert_id)
self._total_buffered_rows += 1
if len(self._rows_buffer[destination]) >= self._max_batch_size:
return self._flush_batch(destination)
elif self._total_buffered_rows >= self._max_buffered_rows:
return self._flush_all_batches()
def finish_bundle(self):
return self._flush_all_batches()
def _flush_all_batches(self):
_LOGGER.debug('Attempting to flush to all destinations. Total buffered: %s',
self._total_buffered_rows)
return itertools.chain(*[self._flush_batch(destination)
for destination in list(self._rows_buffer.keys())
if self._rows_buffer[destination]])
def _flush_batch(self, destination):
# Flush the current batch of rows to BigQuery.
rows_and_insert_ids = self._rows_buffer[destination]
table_reference = bigquery_tools.parse_table_reference(destination)
if table_reference.projectId is None:
table_reference.projectId = vp.RuntimeValueProvider.get_value(
'project', str, '')
_LOGGER.debug('Flushing data to %s. Total %s rows.',
destination, len(rows_and_insert_ids))
rows = [r[0] for r in rows_and_insert_ids]
insert_ids = [r[1] for r in rows_and_insert_ids]
while True:
passed, errors = self.bigquery_wrapper.insert_rows(
project_id=table_reference.projectId,
dataset_id=table_reference.datasetId,
table_id=table_reference.tableId,
rows=rows,
insert_ids=insert_ids,
skip_invalid_rows=True)
_LOGGER.debug("Passed: %s. Errors are %s", passed, errors)
failed_rows = [rows[entry.index] for entry in errors]
should_retry = any(
bigquery_tools.RetryStrategy.should_retry(
self._retry_strategy, entry.errors[0].reason)
for entry in errors)
rows = failed_rows
if not should_retry:
break
else:
retry_backoff = next(self._backoff_calculator)
_LOGGER.info('Sleeping %s seconds before retrying insertion.',
retry_backoff)
time.sleep(retry_backoff)
self._total_buffered_rows -= len(self._rows_buffer[destination])
del self._rows_buffer[destination]
return [pvalue.TaggedOutput(BigQueryWriteFn.FAILED_ROWS,
GlobalWindows.windowed_value(
(destination, row))) for row in failed_rows]
class _StreamToBigQuery(PTransform):
def __init__(self,
table_reference,
table_side_inputs,
schema_side_inputs,
schema,
batch_size,
create_disposition,
write_disposition,
kms_key,
retry_strategy,
additional_bq_parameters,
test_client=None):
self.table_reference = table_reference
self.table_side_inputs = table_side_inputs
self.schema_side_inputs = schema_side_inputs
self.schema = schema
self.batch_size = batch_size
self.create_disposition = create_disposition
self.write_disposition = write_disposition
self.kms_key = kms_key
self.retry_strategy = retry_strategy
self.test_client = test_client
self.additional_bq_parameters = additional_bq_parameters
class InsertIdPrefixFn(DoFn):
def start_bundle(self):
self.prefix = str(uuid.uuid4())
self._row_count = 0
def process(self, element):
key = element[0]
value = element[1]
insert_id = '%s-%s' % (self.prefix, self._row_count)
self._row_count += 1
yield (key, (value, insert_id))
def expand(self, input):
bigquery_write_fn = BigQueryWriteFn(
schema=self.schema,
batch_size=self.batch_size,
create_disposition=self.create_disposition,
write_disposition=self.write_disposition,
kms_key=self.kms_key,
retry_strategy=self.retry_strategy,
test_client=self.test_client,
additional_bq_parameters=self.additional_bq_parameters)
return (input
| 'AppendDestination' >> beam.ParDo(
bigquery_tools.AppendDestinationsFn(self.table_reference),
*self.table_side_inputs)
| 'AddInsertIds' >> beam.ParDo(_StreamToBigQuery.InsertIdPrefixFn())
| 'CommitInsertIds' >> beam.Reshuffle()
| 'StreamInsertRows' >> ParDo(
bigquery_write_fn, *self.schema_side_inputs).with_outputs(
BigQueryWriteFn.FAILED_ROWS, main='main'))
# Flag to be passed to WriteToBigQuery to force schema autodetection
SCHEMA_AUTODETECT = 'SCHEMA_AUTODETECT'
class WriteToBigQuery(PTransform):
"""Write data to BigQuery.
This transform receives a PCollection of elements to be inserted into BigQuery
tables. The elements would come in as Python dictionaries, or as `TableRow`
instances.
"""
class Method(object):
DEFAULT = 'DEFAULT'
STREAMING_INSERTS = 'STREAMING_INSERTS'
FILE_LOADS = 'FILE_LOADS'
def __init__(self,
table,
dataset=None,
project=None,
schema=None,
create_disposition=BigQueryDisposition.CREATE_IF_NEEDED,
write_disposition=BigQueryDisposition.WRITE_APPEND,
kms_key=None,
batch_size=None,
max_file_size=None,
max_files_per_bundle=None,
test_client=None,
custom_gcs_temp_location=None,
method=None,
insert_retry_strategy=None,
additional_bq_parameters=None,
table_side_inputs=None,
schema_side_inputs=None,
triggering_frequency=None,
validate=True):
"""Initialize a WriteToBigQuery transform.
Args:
table (str, callable, ValueProvider): The ID of the table, or a callable
that returns it. 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'``. If it's a callable, it must receive one
argument representing an element to be written to BigQuery, and return
a TableReference, or a string table name as specified above.
Multiple destinations are only supported on Batch pipelines at the
moment.
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,dict,ValueProvider,callable): 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`. or a `ValueProvider` that has a JSON string,
or a python dictionary, or the string or dictionary itself,
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'``).
If a callable, then it should receive a destination (in the form of
a TableReference or a string, and return a str, dict or TableSchema.
One may also pass ``SCHEMA_AUTODETECT`` here, and BigQuery will try to
infer the schema for the files that are being loaded.
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.
kms_key (str): Experimental. Optional Cloud KMS key name for use when
creating new tables.
batch_size (int): Number of rows to be written to BQ per streaming API
insert. The default is 500.
insert.
test_client: Override the default bigquery client used for testing.
max_file_size (int): The maximum size for a file to be written and then
loaded into BigQuery. The default value is 4TB, which is 80% of the
limit of 5TB for BigQuery to load any file.
max_files_per_bundle(int): The maximum number of files to be concurrently
written by a worker. The default here is 20. Larger values will allow
writing to multiple destinations without having to reshard - but they
increase the memory burden on the workers.
custom_gcs_temp_location (str): A GCS location to store files to be used
for file loads into BigQuery. By default, this will use the pipeline's
temp_location, but for pipelines whose temp_location is not appropriate
for BQ File Loads, users should pass a specific one.
method: The method to use to write to BigQuery. It may be
STREAMING_INSERTS, FILE_LOADS, or DEFAULT. An introduction on loading
data to BigQuery: https://cloud.google.com/bigquery/docs/loading-data.
DEFAULT will use STREAMING_INSERTS on Streaming pipelines and
FILE_LOADS on Batch pipelines.
insert_retry_strategy: The strategy to use when retrying streaming inserts
into BigQuery. Options are shown in bigquery_tools.RetryStrategy attrs.
additional_bq_parameters (callable): A function that returns a dictionary
with additional parameters to pass to BQ when creating / loading data
into a table. These can be 'timePartitioning', 'clustering', etc. They
are passed directly to the job load configuration. See
https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.load
table_side_inputs (tuple): A tuple with ``AsSideInput`` PCollections to be
passed to the table callable (if one is provided).
schema_side_inputs: A tuple with ``AsSideInput`` PCollections to be
passed to the schema callable (if one is provided).
triggering_frequency (int): Every triggering_frequency duration, a
BigQuery load job will be triggered for all the data written since
the last load job. BigQuery has limits on how many load jobs can be
triggered per day, so be careful not to set this duration too low, or
you may exceed daily quota. Often this is set to 5 or 10 minutes to
ensure that the project stays well under the BigQuery quota.
See https://cloud.google.com/bigquery/quota-policy for more information
about BigQuery quotas.
validate: Indicates whether to perform validation checks on
inputs. This parameter is primarily used for testing.
"""
self.table_reference = bigquery_tools.parse_table_reference(
table, dataset, project)
self.create_disposition = BigQueryDisposition.validate_create(
create_disposition)
self.write_disposition = BigQueryDisposition.validate_write(
write_disposition)
if schema == SCHEMA_AUTODETECT:
self.schema = schema
else:
self.schema = WriteToBigQuery.get_dict_table_schema(schema)
self.batch_size = batch_size
self.kms_key = kms_key
self.test_client = test_client
# TODO(pabloem): Consider handling ValueProvider for this location.
self.custom_gcs_temp_location = custom_gcs_temp_location
self.max_file_size = max_file_size
self.max_files_per_bundle = max_files_per_bundle
self.method = method or WriteToBigQuery.Method.DEFAULT
self.triggering_frequency = triggering_frequency
self.insert_retry_strategy = insert_retry_strategy
self._validate = validate
self.additional_bq_parameters = additional_bq_parameters or {}
self.table_side_inputs = table_side_inputs or ()
self.schema_side_inputs = schema_side_inputs or ()
@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, vp.ValueProvider)) or
callable(schema) or
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 _compute_method(self, experiments, is_streaming_pipeline):
# If the new BQ sink is not activated for experiment flags, then we use
# streaming inserts by default (it gets overridden in dataflow_runner.py).
if 'use_beam_bq_sink' not in experiments:
return self.Method.STREAMING_INSERTS
elif self.method == self.Method.DEFAULT and is_streaming_pipeline:
return self.Method.STREAMING_INSERTS
elif self.method == self.Method.DEFAULT and not is_streaming_pipeline:
return self.Method.FILE_LOADS
else:
return self.method
def expand(self, pcoll):
p = pcoll.pipeline
if (isinstance(self.table_reference, bigquery.TableReference)
and self.table_reference.projectId is None):
self.table_reference.projectId = pcoll.pipeline.options.view_as(
GoogleCloudOptions).project
experiments = p.options.view_as(DebugOptions).experiments or []
# TODO(pabloem): Use a different method to determine if streaming or batch.
is_streaming_pipeline = p.options.view_as(StandardOptions).streaming
method_to_use = self._compute_method(experiments, is_streaming_pipeline)
if (method_to_use == WriteToBigQuery.Method.STREAMING_INSERTS
and self.schema == SCHEMA_AUTODETECT):
raise ValueError('Schema auto-detection is not supported for streaming '
'inserts into BigQuery. Only for File Loads.')
if method_to_use == WriteToBigQuery.Method.STREAMING_INSERTS:
if self.triggering_frequency:
raise ValueError('triggering_frequency can only be used with '
'FILE_LOADS method of writing to BigQuery.')
outputs = pcoll | _StreamToBigQuery(self.table_reference,
self.table_side_inputs,
self.schema_side_inputs,
self.schema,
self.batch_size,
self.create_disposition,
self.write_disposition,
self.kms_key,
self.insert_retry_strategy,
self.additional_bq_parameters,
test_client=self.test_client)
return {BigQueryWriteFn.FAILED_ROWS: outputs[BigQueryWriteFn.FAILED_ROWS]}
else:
from apache_beam.io.gcp import bigquery_file_loads
return pcoll | bigquery_file_loads.BigQueryBatchFileLoads(
destination=self.table_reference,
schema=self.schema,
create_disposition=self.create_disposition,
write_disposition=self.write_disposition,
triggering_frequency=self.triggering_frequency,
max_file_size=self.max_file_size,
max_files_per_bundle=self.max_files_per_bundle,
custom_gcs_temp_location=self.custom_gcs_temp_location,
test_client=self.test_client,
table_side_inputs=self.table_side_inputs,
schema_side_inputs=self.schema_side_inputs,
additional_bq_parameters=self.additional_bq_parameters,
validate=self._validate,
is_streaming_pipeline=is_streaming_pipeline)
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