#
# 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.
# See the License for the specific language governing permissions and
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"""
A performance test for reading data from a BigQuery table.
Besides of the standard options, there are options with special meaning:
* input_dataset - BQ dataset id.
* input_table - BQ table id.
The table will be created and populated with data from Synthetic Source if it
does not exist.
* input_options - options for Synthetic Source:
num_records - number of rows to be inserted,
value_size - the length of a single row,
key_size - required option, but its value has no meaning.

Example test run on DataflowRunner:

python -m apache_beam.io.gcp.bigquery_read_perf_test \
    --test-pipeline-options="
    --runner=TestDataflowRunner
    --project=...
    --region=...
    --staging_location=gs://...
    --temp_location=gs://...
    --sdk_location=.../dist/apache-beam-x.x.x.dev0.tar.gz
    --publish_to_big_query=true
    --metrics_dataset=gs://...
    --metrics_table=...
    --input_dataset=...
    --input_table=...
    --input_options='{
    \"num_records\": 1024,
    \"key_size\": 1,
    \"value_size\": 1024,
    }'"
"""

# pytype: skip-file

import logging

from apache_beam import Map
from apache_beam import ParDo
from apache_beam.io import BigQueryDisposition
from apache_beam.io import BigQuerySource
from apache_beam.io import Read
from apache_beam.io import WriteToBigQuery
from apache_beam.io.gcp.bigquery_tools import BigQueryWrapper
from apache_beam.io.gcp.bigquery_tools import parse_table_schema_from_json
from apache_beam.testing.load_tests.load_test import LoadTest
from apache_beam.testing.load_tests.load_test_metrics_utils import CountMessages
from apache_beam.testing.load_tests.load_test_metrics_utils import MeasureTime
from apache_beam.testing.synthetic_pipeline import SyntheticSource
from apache_beam.testing.test_pipeline import TestPipeline
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import equal_to
from apache_beam.transforms.combiners import Count

# pylint: disable=wrong-import-order, wrong-import-position
try:
  from apitools.base.py.exceptions import HttpError
except ImportError:
  HttpError = None
# pylint: enable=wrong-import-order, wrong-import-position


class BigQueryReadPerfTest(LoadTest):
  def __init__(self):
    super().__init__()
    self.input_dataset = self.pipeline.get_option('input_dataset')
    self.input_table = self.pipeline.get_option('input_table')
    self._check_for_input_data()

  def _check_for_input_data(self):
    """Checks if a BQ table with input data exists and creates it if not."""
    wrapper = BigQueryWrapper()
    try:
      wrapper.get_table(self.project_id, self.input_dataset, self.input_table)
    except HttpError as exn:
      if exn.status_code == 404:
        self._create_input_data()

  def _create_input_data(self):
    """
    Runs an additional pipeline which creates test data and waits for its
    completion.
    """
    SCHEMA = parse_table_schema_from_json(
        '{"fields": [{"name": "data", "type": "BYTES"}]}')

    def format_record(record):
      # Since Synthetic Source returns data as a dictionary, we should skip one
      # of the part
      import base64
      return {'data': base64.b64encode(record[1])}

    with TestPipeline() as p:
      (  # pylint: disable=expression-not-assigned
          p
          | 'Produce rows' >> Read(
              SyntheticSource(self.parse_synthetic_source_options()))
          | 'Format' >> Map(format_record)
          | 'Write to BigQuery' >> WriteToBigQuery(
              dataset=self.input_dataset,
              table=self.input_table,
              schema=SCHEMA,
              create_disposition=BigQueryDisposition.CREATE_IF_NEEDED,
              write_disposition=BigQueryDisposition.WRITE_EMPTY))

  def test(self):
    output = (
        self.pipeline
        | 'Read from BigQuery' >> Read(
            BigQuerySource(dataset=self.input_dataset, table=self.input_table))
        | 'Count messages' >> ParDo(CountMessages(self.metrics_namespace))
        | 'Measure time' >> ParDo(MeasureTime(self.metrics_namespace))
        | 'Count' >> Count.Globally())
    assert_that(output, equal_to([self.input_options['num_records']]))


if __name__ == '__main__':
  logging.basicConfig(level=logging.INFO)
  BigQueryReadPerfTest().run()
