#
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# 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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"""
This is CoGroupByKey load test with Synthetic Source. Besides of the standard
input options there are additional options:
* project (optional) - the gcp project in case of saving
metrics in Big Query (in case of Dataflow Runner
it is required to specify project of runner),
* publish_to_big_query - if metrics should be published in big query,
* metrics_namespace (optional) - name of BigQuery dataset where metrics
will be stored,
* metrics_table (optional) - name of BigQuery table where metrics
will be stored,
* input_options - options for Synthetic Sources,
* co_input_options - options for  Synthetic Sources.

Example test run on DirectRunner:

python setup.py nosetests \
    --test-pipeline-options="
      --project=big-query-project
      --publish_to_big_query=true
      --metrics_dataset=python_load_tests
      --metrics_table=co_gbk
      --input_options='{
        \"num_records\": 1000,
        \"key_size\": 5,
        \"value_size\":15,
        \"bundle_size_distribution_type\": \"const\",
        \"bundle_size_distribution_param\": 1,
        \"force_initial_num_bundles\":0}'
        --co_input_options='{
        \"num_records\": 1000,
        \"key_size\": 5,
        \"value_size\":15,
        \"bundle_size_distribution_type\": \"const\",
        \"bundle_size_distribution_param\": 1,
        \"force_initial_num_bundles\":0}'" \
    --tests apache_beam.testing.load_tests.co_group_by_key_test

or:

./gradlew -PloadTest.args='
    --publish_to_big_query=true
    --project=...
    --metrics_dataset=python_load_tests
    --metrics_table=co_gbk
    --input_options=\'
      {"num_records": 1,
      "key_size": 1,
      "value_size":1,
      "bundle_size_distribution_type": "const",
      "bundle_size_distribution_param": 1,
      "force_initial_num_bundles": 1}\'
     --co_input_options=\'{
        "num_records": 1,
        "key_size": 1,
        "value_size": 1,
        "bundle_size_distribution_type": "const",
        "bundle_size_distribution_param": 1,
        "force_initial_num_bundles":0}\'
    --runner=DirectRunner' \
-PloadTest.mainClass=
apache_beam.testing.load_tests.co_group_by_key_test \
-Prunner=DirectRunner :sdks:python:apache_beam:testing:load-tests:run

To run test on other runner (ex. Dataflow):

python setup.py nosetests \
    --test-pipeline-options="
        --runner=TestDataflowRunner
        --project=...
        --staging_location=gs://...
        --temp_location=gs://...
        --sdk_location=./dist/apache-beam-x.x.x.dev0.tar.gz
        --publish_to_big_query=true
        --metrics_dataset=python_load_tests
        --metrics_table=co_gbk
        --input_options='{
        \"num_records\": 1000,
        \"key_size\": 5,
        \"value_size\":15,
        \"bundle_size_distribution_type\": \"const\",
        \"bundle_size_distribution_param\": 1,
        \"force_initial_num_bundles\":0
        }'
        --co_input_options='{
        \"num_records\": 1000,
        \"key_size\": 5,
        \"value_size\":15,
        \"bundle_size_distribution_type\": \"const\",
        \"bundle_size_distribution_param\": 1,
        \"force_initial_num_bundles\":0
        }'" \
    --tests apache_beam.testing.load_tests.co_group_by_key_test

or:

./gradlew -PloadTest.args='
    --publish_to_big_query=true
    --project=...
    --metrics_dataset=python_load_tests
    --metrics_table=co_gbk
    --temp_location=gs://...
    --input_options=\'
      {"num_records": 1,
      "key_size": 1,
      "value_size":1,
      "bundle_size_distribution_type": "const",
      "bundle_size_distribution_param": 1,
      "force_initial_num_bundles": 1}\'
    --co_input_options=\'{
      "num_records": 1,
      "key_size": 1,
      "value_size": 1,
      "bundle_size_distribution_type": "const",
      "bundle_size_distribution_param": 1,
      "force_initial_num_bundles":0}\'
    --runner=TestDataflowRunner' \
-PloadTest.mainClass=
apache_beam.testing.load_tests.co_group_by_key_test \
-Prunner=TestDataflowRunner :sdks:python:apache_beam:testing:load-tests:run
"""

from __future__ import absolute_import

import json
import logging
import os
import unittest

import apache_beam as beam
from apache_beam.testing import synthetic_pipeline
from apache_beam.testing.load_tests.load_test import LoadTest
from apache_beam.testing.load_tests.load_test_metrics_utils import MeasureTime

INPUT_TAG = 'pc1'
CO_INPUT_TAG = 'pc2'

load_test_enabled = False
if os.environ.get('LOAD_TEST_ENABLED') == 'true':
  load_test_enabled = True


@unittest.skipIf(not load_test_enabled, 'Enabled only for phrase triggering.')
class CoGroupByKeyTest(LoadTest):
  def setUp(self):
    super(CoGroupByKeyTest, self).setUp()
    self.co_input_options = json.loads(
        self.pipeline.get_option('co_input_options'))

  class _Ungroup(beam.DoFn):
    def process(self, element):
      values = element[1]
      inputs = values.get(INPUT_TAG)
      co_inputs = values.get(CO_INPUT_TAG)
      for i in inputs:
        yield i
      for i in co_inputs:
        yield i

  def testCoGroupByKey(self):
    pc1 = (self.pipeline
           | 'Read ' + INPUT_TAG >> beam.io.Read(
               synthetic_pipeline.SyntheticSource(
                   self.parseTestPipelineOptions(self.input_options)))
           | 'Make ' + INPUT_TAG + ' iterable' >> beam.Map(lambda x: (x, x))
           | 'Measure time: Start pc1' >> beam.ParDo(
               MeasureTime(self.metrics_namespace))
          )

    pc2 = (self.pipeline
           | 'Read ' + CO_INPUT_TAG >> beam.io.Read(
               synthetic_pipeline.SyntheticSource(
                   self.parseTestPipelineOptions(self.co_input_options)))
           | 'Make ' + CO_INPUT_TAG + ' iterable' >> beam.Map(
               lambda x: (x, x))
           | 'Measure time: Start pc2' >> beam.ParDo(
               MeasureTime(self.metrics_namespace))
          )
    # pylint: disable=expression-not-assigned
    ({INPUT_TAG: pc1, CO_INPUT_TAG: pc2}
     | 'CoGroupByKey: ' >> beam.CoGroupByKey()
     | 'Consume Joined Collections' >> beam.ParDo(self._Ungroup())
     | 'Measure time: End' >> beam.ParDo(MeasureTime(self.metrics_namespace))
    )


if __name__ == '__main__':
  logging.getLogger().setLevel(logging.INFO)
  unittest.main()