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https://issues.apache.org/jira/browse/BEAM-2855?focusedWorklogId=116263&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-116263
]
ASF GitHub Bot logged work on BEAM-2855:
----------------------------------------
Author: ASF GitHub Bot
Created on: 27/Jun/18 02:09
Start Date: 27/Jun/18 02:09
Worklog Time Spent: 10m
Work Description: aaltay commented on a change in pull request #5738:
[BEAM-2855] Add Python Nexmark suite
URL: https://github.com/apache/beam/pull/5738#discussion_r198347814
##########
File path:
sdks/python/apache_beam/testing/benchmarks/nexmark/nexmark_launcher.py
##########
@@ -0,0 +1,242 @@
+#
+# 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
+#
+# Unless required by applicable law or agreed to in writing, software
+# 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
+# limitations under the License.
+#
+
+"""Nexmark launcher.
+
+The Nexmark suite is a series of queries (streaming pipelines) performed
+on a simulation of auction events. The launcher orchestrates the generation
+and parsing of streaming events and the running of queries.
+
+Model
+ - Person: Author of an auction or a bid.
+ - Auction: Item under auction.
+ - Bid: A bid for an item under auction.
+
+Events
+ - Create Person
+ - Create Auction
+ - Create Bid
+
+Queries
+ - Query0: Pass through (send and receive auction events).
+
+Usage
+ - DirectRunner
+ python nexmark_launcher.py \
+ --query/q <query number> \
+ --project <project id> \
+ --loglevel=DEBUG (optional) \
+
+ - DataflowRunner
+ python nexmark_launcher.py \
+ --query/q <query number> \
+ --project <project id> \
+ --loglevel=DEBUG (optional) \
+ --sdk_location <apache_beam tar.gz> \
+ --staging_location=gs://... \
+ --temp_location=gs://
+
+"""
+
+from __future__ import absolute_import
+from __future__ import print_function
+
+import argparse
+import collections
+import logging
+import sys
+import uuid
+
+from google.cloud import pubsub
+
+import apache_beam as beam
+from apache_beam.options.pipeline_options import GoogleCloudOptions
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.options.pipeline_options import StandardOptions
+from apache_beam.options.pipeline_options import TestOptions
+from apache_beam.testing.benchmarks.nexmark.nexmark_util import Command
+from apache_beam.testing.benchmarks.nexmark.queries import query0
+
+
+class NexmarkLauncher(object):
+ def __init__(self):
+ self.parse_args()
+ self.uuid = str(uuid.uuid4())
+ self.topic_name = self.args.topic_name + self.uuid
+ self.subscription_name = self.args.subscription_name + self.uuid
+
+ def parse_args(self):
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument('--query', '-q',
+ type=int,
+ action='append',
+ required=True,
+ choices=[0, 1, 2],
+ help='Query to run')
+
+ parser.add_argument('--subscription_name',
+ type=str,
+ help='Pub/Sub subscription to read from')
+
+ parser.add_argument('--topic_name',
+ type=str,
+ help='Pub/Sub topic to read from')
+
+ parser.add_argument('--loglevel',
+ choices=['DEBUG', 'INFO', 'WARNING',
+ 'ERROR', 'CRITICAL'],
+ default='INFO',
+ help='Set logging level to debug')
+ parser.add_argument('--input',
+ type=str,
+ required=True,
+ help='Path to the data file containing nexmark
events.')
+
+ self.args, self.pipeline_args = parser.parse_known_args()
+ logging.basicConfig(level=getattr(logging, self.args.loglevel, None),
+ format='(%(threadName)-10s) %(message)s')
+
+ self.pipeline_options = PipelineOptions(self.pipeline_args)
+ logging.debug('args, pipeline_args: %s, %s', self.args, self.pipeline_args)
+
+ # Usage with Dataflow requires a project to be supplied.
+ self.project = self.pipeline_options.view_as(GoogleCloudOptions).project
+ if self.project is None:
+ parser.print_usage()
+ print(sys.argv[0] + ': error: argument --project is required')
+ sys.exit(1)
+
+ # Pub/Sub is currently available for use only in streaming pipelines.
+ self.streaming = self.pipeline_options.view_as(StandardOptions).streaming
+ if self.streaming is None:
+ parser.print_usage()
+ print(sys.argv[0] + ': error: argument --streaming is required')
+ sys.exit(1)
+
+ # wait_until_finish ensures that the streaming job is canceled.
+ self.wait_until_finish_duration = (
+ self.pipeline_options.view_as(TestOptions).wait_until_finish_duration
+ )
+ if self.wait_until_finish_duration is None:
+ parser.print_usage()
+ print(sys.argv[0] + ': error: argument --wait_until_finish_duration is
required') # pylint: disable=line-too-long
+ sys.exit(1)
+
+ # We use the save_main_session option because one or more DoFn's in this
+ # workflow rely on global context (e.g., a module imported at module
level).
+ self.pipeline_options.view_as(SetupOptions).save_main_session = True
+
+ def generate_events(self):
+ publish_client = pubsub.Client(project=self.project)
+ topic = publish_client.topic(self.topic_name)
+ if topic.exists():
+ topic.delete()
+ topic.create()
+ sub = topic.subscription(self.subscription_name)
+ if sub.exists():
+ sub.delete()
+ sub.create()
+
+ logging.info('Generating auction events to topic %s', topic.name)
+
+ if self.args.input.startswith('gs://'):
+ from apache_beam.io.gcp.gcsfilesystem import GCSFileSystem
+ fs = GCSFileSystem(self.pipeline_options)
+ with fs.open(self.args.input) as infile:
+ for line in infile:
+ topic.publish(line)
+ else:
+ with open(self.args.input) as infile:
+ for line in infile:
+ topic.publish(line)
+
+ logging.info('Finished event generation.')
+
+ # Read from PubSub into a PCollection.
+ if self.args.subscription_name:
+ raw_events = self.pipeline | 'ReadPubSub' >> beam.io.ReadFromPubSub(
+ subscription=sub.full_name)
+ else:
+ raw_events = self.pipeline | 'ReadPubSub' >> beam.io.ReadFromPubSub(
+ topic=topic.full_name)
+
+ return raw_events
+
+ def run_query(self, query, query_error):
+ try:
+ self.pipeline = beam.Pipeline(options=self.pipeline_options)
+ raw_events = self.generate_events()
+ query.load(raw_events)
+ result = self.pipeline.run()
+ job_duration = (
+ self.pipeline_options.view_as(TestOptions).wait_until_finish_duration
+ )
+ if self.pipeline_options.view_as(StandardOptions).runner ==
'DataflowRunner': # pylint: disable=line-too-long
+ result.wait_until_finish(duration=job_duration)
+ result.cancel()
+ else:
+ result.wait_until_finish()
+ except Exception as exc:
+ query_error.append(str(exc))
+ raise
+
+ def cleanup(self):
+ publish_client = pubsub.Client(project=self.project)
+ topic = publish_client.topic(self.topic_name)
+ if topic.exists():
+ topic.delete()
+ sub = topic.subscription(self.subscription_name)
+ if sub.exists():
+ sub.delete()
+
+ def run(self):
+ queries = {
+ 0: query0,
+ # TODO(mariagh): Add more queries.
+ }
+
+ for i in self.args.query:
+ logging.info('Running query %d', i)
+
+ # The DirectRunner is the default runner, and it needs
+ # special handling to cancel streaming jobs.
+ launch_from_direct_runner = self.pipeline_options.view_as(
+ StandardOptions).runner in [None, 'DirectRunner']
+
+ if launch_from_direct_runner:
+ query_errors = collections.defaultdict(list)
+ command = Command(self.run_query, args=[queries[i], query_errors[i]])
Review comment:
Maybe I am missing something. Please explain if that is the case.
`query_errors = collections.defaultdict(list)` is in the same loop as all of
its uses. I do not understand using a dictionary and keying it on i, instead of
just making it a list and passing that to run_query.
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Issue Time Tracking
-------------------
Worklog Id: (was: 116263)
Time Spent: 9h 50m (was: 9h 40m)
> Implement a python version of the nexmark queries
> -------------------------------------------------
>
> Key: BEAM-2855
> URL: https://issues.apache.org/jira/browse/BEAM-2855
> Project: Beam
> Issue Type: Improvement
> Components: examples-nexmark, sdk-py-core, testing
> Reporter: Ismaël Mejía
> Assignee: María GH
> Priority: Minor
> Labels: newbie, nexmark, starter
> Time Spent: 9h 50m
> Remaining Estimate: 0h
>
> Currently we have a Java only implementation of Nexmark, a python based
> implementation would be nice to have to validate the direct and dataflow
> runners, but also to validate the new support of multiple SDKs in multiple
> runners via the runner/fn API.
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