#
# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# pytype: skip-file

from __future__ import absolute_import

import argparse
import logging
import time

from pymongo import MongoClient

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.testing.test_pipeline import TestPipeline
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import equal_to

_LOGGER = logging.getLogger(__name__)


class GenerateDocs(beam.DoFn):
  def process(self, num_docs, *args, **kwargs):
    for i in range(num_docs):
      yield {'number': i, 'number_mod_2': i % 2, 'number_mod_3': i % 3}


def run(argv=None):
  default_db = 'beam_mongodbio_it_db'
  default_coll = 'integration_test_%d' % time.time()
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--mongo_uri',
      default='mongodb://localhost:27017',
      help='mongo uri string for connection')
  parser.add_argument(
      '--mongo_db', default=default_db, help='mongo uri string for connection')
  parser.add_argument(
      '--mongo_coll',
      default=default_coll,
      help='mongo uri string for connection')
  parser.add_argument(
      '--num_documents',
      default=100000,
      help='The expected number of documents to be generated '
      'for write or read',
      type=int)
  parser.add_argument(
      '--batch_size',
      default=10000,
      type=int,
      help=('batch size for writing to mongodb'))
  known_args, pipeline_args = parser.parse_known_args(argv)

  # Test Write to MongoDB
  with TestPipeline(options=PipelineOptions(pipeline_args)) as p:
    start_time = time.time()
    _LOGGER.info('Writing %d documents to mongodb' % known_args.num_documents)

    _ = (
        p | beam.Create([known_args.num_documents])
        | 'Create documents' >> beam.ParDo(GenerateDocs())
        | 'WriteToMongoDB' >> beam.io.WriteToMongoDB(
            known_args.mongo_uri,
            known_args.mongo_db,
            known_args.mongo_coll,
            known_args.batch_size))
  elapsed = time.time() - start_time
  _LOGGER.info(
      'Writing %d documents to mongodb finished in %.3f seconds' %
      (known_args.num_documents, elapsed))

  # Test Read from MongoDB
  with TestPipeline(options=PipelineOptions(pipeline_args)) as p:
    start_time = time.time()
    _LOGGER.info(
        'Reading from mongodb %s:%s' %
        (known_args.mongo_db, known_args.mongo_coll))
    r = (
        p | 'ReadFromMongoDB' >> beam.io.ReadFromMongoDB(
            known_args.mongo_uri,
            known_args.mongo_db,
            known_args.mongo_coll,
            projection=['number'])
        | 'Map' >> beam.Map(lambda doc: doc['number'])
        | 'Combine' >> beam.CombineGlobally(sum))
    assert_that(r, equal_to([sum(range(known_args.num_documents))]))

  elapsed = time.time() - start_time
  _LOGGER.info(
      'Read %d documents from mongodb finished in %.3f seconds' %
      (known_args.num_documents, elapsed))
  with MongoClient(host=known_args.mongo_uri) as client:
    client.drop_database(known_args.mongo_db)


if __name__ == "__main__":
  logging.getLogger().setLevel(logging.INFO)
  run()