Source code for apache_beam.io.gcp.bigtableio

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"""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.metrics.metric import Lineage
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
      # Report Lineage metrics on write
      Lineage.sinks().add(
          'bigtable',
          self.beam_options['project_id'],
          self.beam_options['instance_id'],
          self.beam_options['table_id'])

  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