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# The ASF licenses this file to You under the Apache License, Version 2.0
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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

from __future__ import absolute_import

import apache_beam as beam
from apache_beam.metrics import Metrics
from apache_beam.transforms.display import DisplayDataItem

try:
  from google.cloud.bigtable import Client
except ImportError:
  pass

__all__ = ['WriteToBigTable']


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(_BigTableWriteFn, self).__init__()
    self.beam_options = {
        'project_id': project_id,
        'instance_id': instance_id,
        'table_id': table_id
    }
    self.table = None
    self.batcher = 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.written = Metrics.counter(self.__class__, 'Written Row')

  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.batcher = self.table.mutations_batcher()

  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.datetime.now())
    self.batcher.mutate(row)

  def finish_bundle(self):
    self.batcher.flush()
    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')
    }


class WriteToBigTable(beam.PTransform):
  """ A transform to write to the Bigtable Table.

  A PTransform that write a list of `DirectRow` into the Bigtable Table

  """
  def __init__(self, project_id=None, instance_id=None, table_id=None):
    """ The PTransform to access the Bigtable Write connector
    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(WriteToBigTable, self).__init__()
    self.beam_options = {
        'project_id': project_id,
        'instance_id': instance_id,
        'table_id': table_id
    }

  def expand(self, pvalue):
    beam_options = self.beam_options
    return (
        pvalue
        | beam.ParDo(
            _BigTableWriteFn(
                beam_options['project_id'],
                beam_options['instance_id'],
                beam_options['table_id'])))
