#
# 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.
#
"""BigTable connector
This module implements writing to BigTable tables.
The default mode is to set row data to write to BigTable tables.
The syntax supported is described here:
https://cloud.google.com/bigtable/docs/quickstart-cbt
BigTable connector can be used as main outputs. A main output
(common case) is expected to be massive and will be split into
manageable chunks and processed in parallel. In the example below
we created a list of rows then passed to the GeneratedDirectRows
DoFn to set the Cells and then we call the BigTableWriteFn to insert
those generated rows in the table.
main_table = (p
| beam.Create(self._generate())
| WriteToBigTable(project_id,
instance_id,
table_id))
"""
# pytype: skip-file
import logging
import struct
from typing import Dict
from typing import List
import apache_beam as beam
from apache_beam.internal.metrics.metric import ServiceCallMetric
from apache_beam.io.gcp import resource_identifiers
from apache_beam.metrics import Metrics
from apache_beam.metrics import monitoring_infos
from apache_beam.transforms import PTransform
from apache_beam.transforms.display import DisplayDataItem
from apache_beam.transforms.external import BeamJarExpansionService
from apache_beam.transforms.external import SchemaAwareExternalTransform
from apache_beam.typehints.row_type import RowTypeConstraint
_LOGGER = logging.getLogger(__name__)
try:
from google.cloud.bigtable import Client
from google.cloud.bigtable.row import Cell, PartialRowData
from google.cloud.bigtable.batcher import MutationsBatcher
FLUSH_COUNT = 1000
MAX_ROW_BYTES = 5242880 # 5MB
except ImportError:
_LOGGER.warning(
'ImportError: from google.cloud.bigtable import Client', exc_info=True)
__all__ = ['WriteToBigTable', 'ReadFromBigtable']
class _BigTableWriteFn(beam.DoFn):
""" Creates the connector can call and add_row to the batcher using each
row in beam pipe line
Args:
project_id(str): GCP Project ID
instance_id(str): GCP Instance ID
table_id(str): GCP Table ID
"""
def __init__(self, project_id, instance_id, table_id):
""" Constructor of the Write connector of Bigtable
Args:
project_id(str): GCP Project of to write the Rows
instance_id(str): GCP Instance to write the Rows
table_id(str): GCP Table to write the `DirectRows`
"""
super().__init__()
self.beam_options = {
'project_id': project_id,
'instance_id': instance_id,
'table_id': table_id
}
self.table = None
self.batcher = None
self.service_call_metric = None
self.written = Metrics.counter(self.__class__, 'Written Row')
def __getstate__(self):
return self.beam_options
def __setstate__(self, options):
self.beam_options = options
self.table = None
self.batcher = None
self.service_call_metric = None
self.written = Metrics.counter(self.__class__, 'Written Row')
def write_mutate_metrics(self, status_list):
for status in status_list:
code = status.code if status else None
grpc_status_string = (
ServiceCallMetric.bigtable_error_code_to_grpc_status_string(code))
self.service_call_metric.call(grpc_status_string)
def start_service_call_metrics(self, project_id, instance_id, table_id):
resource = resource_identifiers.BigtableTable(
project_id, instance_id, table_id)
labels = {
monitoring_infos.SERVICE_LABEL: 'BigTable',
# TODO(JIRA-11985): Add Ptransform label.
monitoring_infos.METHOD_LABEL: 'google.bigtable.v2.MutateRows',
monitoring_infos.RESOURCE_LABEL: resource,
monitoring_infos.BIGTABLE_PROJECT_ID_LABEL: (
self.beam_options['project_id']),
monitoring_infos.INSTANCE_ID_LABEL: self.beam_options['instance_id'],
monitoring_infos.TABLE_ID_LABEL: self.beam_options['table_id']
}
return ServiceCallMetric(
request_count_urn=monitoring_infos.API_REQUEST_COUNT_URN,
base_labels=labels)
def start_bundle(self):
if self.table is None:
client = Client(project=self.beam_options['project_id'])
instance = client.instance(self.beam_options['instance_id'])
self.table = instance.table(self.beam_options['table_id'])
self.service_call_metric = self.start_service_call_metrics(
self.beam_options['project_id'],
self.beam_options['instance_id'],
self.beam_options['table_id'])
self.batcher = MutationsBatcher(
self.table,
batch_completed_callback=self.write_mutate_metrics,
flush_count=FLUSH_COUNT,
max_row_bytes=MAX_ROW_BYTES)
def process(self, row):
self.written.inc()
# You need to set the timestamp in the cells in this row object,
# when we do a retry we will mutating the same object, but, with this
# we are going to set our cell with new values.
# Example:
# direct_row.set_cell('cf1',
# 'field1',
# 'value1',
# timestamp=datetime.now())
self.batcher.mutate(row)
def finish_bundle(self):
if self.batcher:
self.batcher.close()
self.batcher = None
def display_data(self):
return {
'projectId': DisplayDataItem(
self.beam_options['project_id'], label='Bigtable Project Id'),
'instanceId': DisplayDataItem(
self.beam_options['instance_id'], label='Bigtable Instance Id'),
'tableId': DisplayDataItem(
self.beam_options['table_id'], label='Bigtable Table Id')
}
[docs]class WriteToBigTable(beam.PTransform):
"""A transform that writes rows to a Bigtable table.
Takes an input PCollection of `DirectRow` objects containing un-committed
mutations. For more information about this row object, visit
https://cloud.google.com/python/docs/reference/bigtable/latest/row#class-googlecloudbigtablerowdirectrowrowkey-tablenone
If flag `use_cross_language` is set to true, this transform will use the
multi-language transforms framework to inject the Java native write transform
into the pipeline.
"""
URN = "beam:schematransform:org.apache.beam:bigtable_write:v1"
def __init__(
self,
project_id,
instance_id,
table_id,
use_cross_language=False,
expansion_service=None):
"""Initialize an WriteToBigTable transform.
:param table_id:
The ID of the table to write to.
:param instance_id:
The ID of the instance where the table resides.
:param project_id:
The GCP project ID.
:param use_cross_language:
If set to True, will use the Java native transform via cross-language.
:param expansion_service:
The address of the expansion service in the case of using cross-language.
If no expansion service is provided, will attempt to run the default GCP
expansion service.
"""
super().__init__()
self._table_id = table_id
self._instance_id = instance_id
self._project_id = project_id
self._use_cross_language = use_cross_language
if use_cross_language:
self._expansion_service = (
expansion_service or BeamJarExpansionService(
'sdks:java:io:google-cloud-platform:expansion-service:build'))
self.schematransform_config = (
SchemaAwareExternalTransform.discover_config(
self._expansion_service, self.URN))
[docs] def expand(self, input):
if self._use_cross_language:
external_write = SchemaAwareExternalTransform(
identifier=self.schematransform_config.identifier,
expansion_service=self._expansion_service,
rearrange_based_on_discovery=True,
table_id=self._table_id,
instance_id=self._instance_id,
project_id=self._project_id)
return (
input
| beam.ParDo(self._DirectRowMutationsToBeamRow()).with_output_types(
RowTypeConstraint.from_fields(
[("key", bytes), ("mutations", List[Dict[str, bytes]])]))
| external_write)
else:
return (
input
| beam.ParDo(
_BigTableWriteFn(
self._project_id, self._instance_id, self._table_id)))
class _DirectRowMutationsToBeamRow(beam.DoFn):
def process(self, direct_row):
args = {"key": direct_row.row_key, "mutations": []}
# start accumulating mutations in a list
for mutation in direct_row._get_mutations():
if mutation.__contains__("set_cell"):
mutation_dict = {
"type": b'SetCell',
"family_name": mutation.set_cell.family_name.encode('utf-8'),
"column_qualifier": mutation.set_cell.column_qualifier,
"value": mutation.set_cell.value,
"timestamp_micros": struct.pack(
'>q', mutation.set_cell.timestamp_micros)
}
elif mutation.__contains__("delete_from_column"):
mutation_dict = {
"type": b'DeleteFromColumn',
"family_name": mutation.delete_from_column.family_name.encode(
'utf-8'),
"column_qualifier": mutation.delete_from_column.column_qualifier
}
time_range = mutation.delete_from_column.time_range
if time_range.start_timestamp_micros:
mutation_dict['start_timestamp_micros'] = struct.pack(
'>q', time_range.start_timestamp_micros)
if time_range.end_timestamp_micros:
mutation_dict['end_timestamp_micros'] = struct.pack(
'>q', time_range.end_timestamp_micros)
elif mutation.__contains__("delete_from_family"):
mutation_dict = {
"type": b'DeleteFromFamily',
"family_name": mutation.delete_from_family.family_name.encode(
'utf-8')
}
elif mutation.__contains__("delete_from_row"):
mutation_dict = {"type": b'DeleteFromRow'}
else:
raise ValueError("Unexpected mutation")
args["mutations"].append(mutation_dict)
yield beam.Row(**args)
[docs]class ReadFromBigtable(PTransform):
"""Reads rows from Bigtable.
Returns a PCollection of PartialRowData objects, each representing a
Bigtable row. For more information about this row object, visit
https://cloud.google.com/python/docs/reference/bigtable/latest/row#class-googlecloudbigtablerowpartialrowdatarowkey
"""
URN = "beam:schematransform:org.apache.beam:bigtable_read:v1"
def __init__(self, project_id, instance_id, table_id, expansion_service=None):
"""Initialize a ReadFromBigtable transform.
:param table_id:
The ID of the table to read from.
:param instance_id:
The ID of the instance where the table resides.
:param project_id:
The GCP project ID.
:param expansion_service:
The address of the expansion service. If no expansion service is
provided, will attempt to run the default GCP expansion service.
"""
super().__init__()
self._table_id = table_id
self._instance_id = instance_id
self._project_id = project_id
self._expansion_service = (
expansion_service or BeamJarExpansionService(
'sdks:java:io:google-cloud-platform:expansion-service:build'))
self.schematransform_config = SchemaAwareExternalTransform.discover_config(
self._expansion_service, self.URN)
[docs] def expand(self, input):
external_read = SchemaAwareExternalTransform(
identifier=self.schematransform_config.identifier,
expansion_service=self._expansion_service,
rearrange_based_on_discovery=True,
table_id=self._table_id,
instance_id=self._instance_id,
project_id=self._project_id)
return (
input.pipeline
| external_read
| beam.ParDo(self._BeamRowToPartialRowData()))
# PartialRowData has some useful methods for querying data within a row.
# To make use of those methods and to give Python users a more familiar
# object, we process each Beam Row and return a PartialRowData equivalent.
class _BeamRowToPartialRowData(beam.DoFn):
def process(self, row):
key = row.key
families = row.column_families
# initialize PartialRowData object
partial_row: PartialRowData = PartialRowData(key)
for fam_name, col_fam in families.items():
if fam_name not in partial_row.cells:
partial_row.cells[fam_name] = {}
for col_qualifier, cells in col_fam.items():
# store column qualifier as bytes to follow PartialRowData behavior
col_qualifier_bytes = col_qualifier.encode()
if col_qualifier not in partial_row.cells[fam_name]:
partial_row.cells[fam_name][col_qualifier_bytes] = []
for cell in cells:
value = cell.value
timestamp_micros = cell.timestamp_micros
partial_row.cells[fam_name][col_qualifier_bytes].append(
Cell(value, timestamp_micros))
yield partial_row