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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2021/06/02 20:35:54 UTC
[GitHub] [beam] ibzib commented on a change in pull request #14398: [BEAM-12024] Add flight delay groupby.apply example
ibzib commented on a change in pull request #14398:
URL: https://github.com/apache/beam/pull/14398#discussion_r644279752
##########
File path: sdks/python/apache_beam/examples/dataframe/README.md
##########
@@ -143,3 +143,40 @@ Queens,81138
Staten Island,531
Unknown,28527
```
+
+## Flight Delay pipeline (added in 2.31.0)
+[`flight_delays.py`](./flight_delays.py) contains an implementation of
+a pipeline that processes the flight ontime data from
+`bigquery-samples.airline_ontime_data.flights`. It uses a conventional Beam
+pipeline to read from BigQuery, apply a 24-hour rolling window, and define a
+Beam schema for the data. Then it converts to DataFrames in order to perform
+a complex aggregation using `GroupBy.apply`, and write the result out with
+`to_csv`. Note that the DataFrame computation respects the 24-hour window
+applied above, and results are partitioned into separate files per day.
+
+### Running the pipeline
+To run the pipeline locally:
+
+```sh
+python -m apache_beam.examples.dataframe.flight_delays \
+ --start_date 2012-12-24 \
+ --end_date 2012-12-25 \
+ --output gs://<bucket>/<dir>/delays.csv \
+ --project <gcp-project> \
+ --temp_location gs://<bucket>/<dir>
+```
+
+Note a GCP `project` and `temp_location` are required for reading from BigQuery.
+
+This will produce files like
+`gs://<bucket>/<dir>/delays.csv-2012-12-23T00:00:00-2012-12-24T00:00:00-XXXXX-of-YYYYY`
Review comment:
I'm curious why it's `2012-12-23` when the start date is `2012-12-24`?
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays.py
##########
@@ -0,0 +1,129 @@
+#
+# 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.
+#
+
+"""A word-counting workflow using dataframes."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import argparse
+import logging
+
+import apache_beam as beam
+from apache_beam.dataframe.convert import to_dataframe
+from apache_beam.options.pipeline_options import PipelineOptions
+
+
+def get_delay_at_top_airports(aa):
Review comment:
Replace `aa` with a meaningful variable name.
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays.py
##########
@@ -0,0 +1,129 @@
+#
+# 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.
+#
+
+"""A word-counting workflow using dataframes."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import argparse
+import logging
+
+import apache_beam as beam
+from apache_beam.dataframe.convert import to_dataframe
+from apache_beam.options.pipeline_options import PipelineOptions
+
+
+def get_delay_at_top_airports(aa):
Review comment:
This function name could be more specific.
```suggestion
def get_mean_delay_at_top_airports(aa):
```
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays.py
##########
@@ -0,0 +1,129 @@
+#
+# 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.
+#
+
+"""A word-counting workflow using dataframes."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import argparse
+import logging
+
+import apache_beam as beam
+from apache_beam.dataframe.convert import to_dataframe
+from apache_beam.options.pipeline_options import PipelineOptions
+
+
+def get_delay_at_top_airports(aa):
+ arr = aa.rename(columns={'arrival_airport': 'airport'}).airport.value_counts()
+ dep = aa.rename(columns={
+ 'departure_airport': 'airport'
+ }).airport.value_counts()
+ total = arr + dep
+ # Note we keep all to include duplicates.
+ # This ensures the result is deterministic
+ top_airports = total.nlargest(10, keep='all')
+ return aa[aa['arrival_airport'].isin(top_airports.index.values)].mean()
+
+
+def input_date(date):
+ import datetime
+ parsed = datetime.datetime.strptime(date, '%Y-%m-%d')
+ if parsed > datetime.datetime(2012, 12, 31):
+ raise ValueError("There's no data after 2012-12-31")
+ return date
+
+
+def run_flight_delay_pipeline(
+ pipeline, start_date=None, end_date=None, output=None):
+ query = f"""
+ SELECT
+ date,
+ airline,
+ departure_airport,
+ arrival_airport,
+ departure_delay,
+ arrival_delay
+ FROM `bigquery-samples.airline_ontime_data.flights`
+ WHERE date >= '{start_date}' AND date <= '{end_date}'
+ """
+
+ # Import this here to avoid pickling the main session.
+ import time
+ import datetime
+ from apache_beam import window
+
+ def to_unixtime(s):
+ return time.mktime(datetime.datetime.strptime(s, "%Y-%m-%d").timetuple())
+
+ # The pipeline will be run on exiting the with block.
+ with pipeline as p:
+ tbl = (
+ p
+ | 'read table' >> beam.io.ReadFromBigQuery(
+ query=query, use_standard_sql=True)
+ | 'assign ts' >>
Review comment:
Nit: no need for abbreviation here.
```suggestion
| 'assign timestamp' >>
```
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays_it_test.py
##########
@@ -0,0 +1,138 @@
+# -*- coding: utf-8 -*-
+#
+# 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.
+#
+
+"""Test for the wordcount example."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import logging
+import os
+import unittest
+import uuid
+
+import pandas as pd
+from nose.plugins.attrib import attr
+
+from apache_beam.examples.dataframe import flight_delays
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.testing.test_pipeline import TestPipeline
+
+
+class FlightDelaysTest(unittest.TestCase):
+ EXPECTED = {
+ '2012-12-23': [
+ ('AA', 20.082559339525282, 12.825593395252838),
+ ('EV', 10.01901901901902, 4.431431431431432),
+ ('HA', -1.0829015544041452, 0.010362694300518135),
+ ('UA', 19.142555438225976, 11.07180570221753),
+ ('MQ', 8.902255639097744, 3.6676691729323307),
+ ('OO', 31.148883374689827, 31.90818858560794),
+ ('US', 3.092541436464088, -2.350828729281768),
+ ('WN', 12.074298711144806, 6.717968157695224),
+ ('AS', 5.0456273764258555, 1.0722433460076046),
+ ('B6', 20.646569646569645, 16.405405405405407),
+ ('DL', 5.2559923298178335, -3.214765100671141),
+ ('F9', 23.823529411764707, 25.455882352941178),
+ ('FL', 4.492877492877493, -0.8005698005698005),
+ ('VX', 62.755102040816325, 62.61224489795919),
+ ('YV', 16.155844155844157, 13.376623376623376),
+ ],
+ '2012-12-24': [
+ ('AS', 0.5917602996254682, -2.2659176029962547),
+ ('B6', 8.070993914807302, 2.73630831643002),
+ ('DL', 3.7171824973319105, -2.2358591248665953),
+ ('F9', 14.111940298507463, 15.888059701492537),
+ ('FL', 2.4210526315789473, 2.242690058479532),
+ ('VX', 3.841666666666667, -2.4166666666666665),
+ ('YV', 0.32, 0.78),
+ ('MQ', 15.869642857142857, 9.992857142857142),
+ ('OO', 11.048517520215633, 10.138814016172507),
+ ('US', 1.369281045751634, -1.4101307189542485),
+ ('WN', 7.515952597994531, 0.7028258887876025),
+ ('AA', 7.049086757990867, -1.5970319634703196),
+ ('EV', 7.297101449275362, 2.2693236714975846),
+ ('HA', -2.6785714285714284, -2.4744897959183674),
+ ('UA', 10.935406698564593, -1.3337320574162679),
+ ],
+ '2012-12-25': [
+ ('AS', 3.4816326530612245, 0.27346938775510204),
+ ('B6', 9.10590631364562, 3.989816700610998),
+ ('DL', 2.3022170361726952, -3.6709451575262544),
+ ('F9', 19.38255033557047, 21.845637583892618),
+ ('FL', 1.3982300884955752, 0.9380530973451328),
+ ('VX', 23.62878787878788, 23.636363636363637),
+ ('YV', 11.256302521008404, 11.659663865546218),
+ ('MQ', 32.6, 44.28666666666667),
+ ('OO', 16.2275960170697, 17.11948790896159),
+ ('US', 2.7953216374269005, 0.2236842105263158),
+ ('WN', 14.405783582089553, 10.111940298507463),
+ ('AA', 23.551581843191197, 35.62585969738652),
+ ('EV', 17.368638239339752, 16.43191196698762),
+ ('HA', -4.725806451612903, -3.9946236559139785),
+ ('UA', 16.663145539906104, 10.772300469483568),
+ ],
+ }
+
+ def setUp(self):
+ self.test_pipeline = TestPipeline(is_integration_test=True)
+ self.outdir = (
+ self.test_pipeline.get_option('temp_location') + '/flight_delays_it-' +
+ str(uuid.uuid4()))
+ self.output_path = os.path.join(self.outdir, 'output.csv')
+
+ def tearDown(self):
+ FileSystems.delete([self.outdir + '/'])
+
+ @attr('IT')
+ def test_flight_delays(self):
+ flight_delays.run_flight_delay_pipeline(
+ self.test_pipeline,
+ start_date='2012-12-23',
+ end_date='2012-12-25',
+ output=self.output_path)
+
+ def read_csv(path):
+ with FileSystems.open(path) as fp:
+ return pd.read_csv(fp)
+
+ # Parse result file and compare.
+ for date, expectation in self.EXPECTED.items():
+ result_df = pd.concat(
+ read_csv(metadata.path) for metadata in FileSystems.match(
+ [f'{self.output_path}-{date}*'])[0].metadata_list)
+ result_df = result_df.sort_values('airline').reset_index(drop=True)
Review comment:
For my education: why do we need `reset_index(drop=True) `?
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays.py
##########
@@ -0,0 +1,129 @@
+#
+# 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.
+#
+
+"""A word-counting workflow using dataframes."""
Review comment:
Change this.
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays.py
##########
@@ -0,0 +1,129 @@
+#
+# 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.
+#
+
+"""A word-counting workflow using dataframes."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import argparse
+import logging
+
+import apache_beam as beam
+from apache_beam.dataframe.convert import to_dataframe
+from apache_beam.options.pipeline_options import PipelineOptions
+
+
+def get_delay_at_top_airports(aa):
+ arr = aa.rename(columns={'arrival_airport': 'airport'}).airport.value_counts()
+ dep = aa.rename(columns={
+ 'departure_airport': 'airport'
+ }).airport.value_counts()
+ total = arr + dep
+ # Note we keep all to include duplicates.
+ # This ensures the result is deterministic
+ top_airports = total.nlargest(10, keep='all')
+ return aa[aa['arrival_airport'].isin(top_airports.index.values)].mean()
+
+
+def input_date(date):
+ import datetime
+ parsed = datetime.datetime.strptime(date, '%Y-%m-%d')
+ if parsed > datetime.datetime(2012, 12, 31):
Review comment:
Is there a minimum date as well?
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays_it_test.py
##########
@@ -0,0 +1,138 @@
+# -*- coding: utf-8 -*-
+#
+# 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.
+#
+
+"""Test for the wordcount example."""
Review comment:
Change this.
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays.py
##########
@@ -0,0 +1,129 @@
+#
+# 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.
+#
+
+"""A word-counting workflow using dataframes."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import argparse
+import logging
+
+import apache_beam as beam
+from apache_beam.dataframe.convert import to_dataframe
+from apache_beam.options.pipeline_options import PipelineOptions
+
+
+def get_delay_at_top_airports(aa):
+ arr = aa.rename(columns={'arrival_airport': 'airport'}).airport.value_counts()
+ dep = aa.rename(columns={
+ 'departure_airport': 'airport'
+ }).airport.value_counts()
+ total = arr + dep
+ # Note we keep all to include duplicates.
+ # This ensures the result is deterministic
+ top_airports = total.nlargest(10, keep='all')
+ return aa[aa['arrival_airport'].isin(top_airports.index.values)].mean()
+
+
+def input_date(date):
+ import datetime
+ parsed = datetime.datetime.strptime(date, '%Y-%m-%d')
+ if parsed > datetime.datetime(2012, 12, 31):
+ raise ValueError("There's no data after 2012-12-31")
+ return date
+
+
+def run_flight_delay_pipeline(
+ pipeline, start_date=None, end_date=None, output=None):
+ query = f"""
+ SELECT
+ date,
+ airline,
+ departure_airport,
+ arrival_airport,
+ departure_delay,
+ arrival_delay
+ FROM `bigquery-samples.airline_ontime_data.flights`
+ WHERE date >= '{start_date}' AND date <= '{end_date}'
+ """
+
+ # Import this here to avoid pickling the main session.
+ import time
+ import datetime
+ from apache_beam import window
+
+ def to_unixtime(s):
+ return time.mktime(datetime.datetime.strptime(s, "%Y-%m-%d").timetuple())
+
+ # The pipeline will be run on exiting the with block.
+ with pipeline as p:
+ tbl = (
+ p
+ | 'read table' >> beam.io.ReadFromBigQuery(
+ query=query, use_standard_sql=True)
+ | 'assign ts' >>
+ beam.Map(lambda x: window.TimestampedValue(x, to_unixtime(x['date'])))
+ | 'set schema' >> beam.Select(
+ date=lambda x: str(x['date']),
Review comment:
Do we actually need all these casts, or can we just use the values as they are i.e. `beam.Select('date', ...)`?
##########
File path: sdks/python/apache_beam/examples/dataframe/flight_delays.py
##########
@@ -0,0 +1,129 @@
+#
+# 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.
+#
+
+"""A word-counting workflow using dataframes."""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import argparse
+import logging
+
+import apache_beam as beam
+from apache_beam.dataframe.convert import to_dataframe
+from apache_beam.options.pipeline_options import PipelineOptions
+
+
+def get_delay_at_top_airports(aa):
+ arr = aa.rename(columns={'arrival_airport': 'airport'}).airport.value_counts()
+ dep = aa.rename(columns={
+ 'departure_airport': 'airport'
+ }).airport.value_counts()
+ total = arr + dep
+ # Note we keep all to include duplicates.
+ # This ensures the result is deterministic
+ top_airports = total.nlargest(10, keep='all')
+ return aa[aa['arrival_airport'].isin(top_airports.index.values)].mean()
+
+
+def input_date(date):
+ import datetime
+ parsed = datetime.datetime.strptime(date, '%Y-%m-%d')
+ if parsed > datetime.datetime(2012, 12, 31):
+ raise ValueError("There's no data after 2012-12-31")
+ return date
+
+
+def run_flight_delay_pipeline(
+ pipeline, start_date=None, end_date=None, output=None):
+ query = f"""
+ SELECT
+ date,
+ airline,
+ departure_airport,
+ arrival_airport,
+ departure_delay,
+ arrival_delay
+ FROM `bigquery-samples.airline_ontime_data.flights`
+ WHERE date >= '{start_date}' AND date <= '{end_date}'
+ """
+
+ # Import this here to avoid pickling the main session.
+ import time
+ import datetime
+ from apache_beam import window
+
+ def to_unixtime(s):
+ return time.mktime(datetime.datetime.strptime(s, "%Y-%m-%d").timetuple())
+
+ # The pipeline will be run on exiting the with block.
+ with pipeline as p:
+ tbl = (
+ p
+ | 'read table' >> beam.io.ReadFromBigQuery(
+ query=query, use_standard_sql=True)
+ | 'assign ts' >>
+ beam.Map(lambda x: window.TimestampedValue(x, to_unixtime(x['date'])))
+ | 'set schema' >> beam.Select(
+ date=lambda x: str(x['date']),
+ airline=lambda x: str(x['airline']),
+ departure_airport=lambda x: str(x['departure_airport']),
+ arrival_airport=lambda x: str(x['arrival_airport']),
+ departure_delay=lambda x: float(x['departure_delay']),
+ arrival_delay=lambda x: float(x['arrival_delay'])))
+ daily = tbl | 'daily windows' >> beam.WindowInto(
+ beam.window.FixedWindows(60 * 60 * 24))
+
+ # group the flights data by carrier
+ df = to_dataframe(daily)
+ result = df.groupby('airline').apply(get_delay_at_top_airports)
+ result.to_csv(output)
+
+
+def run(argv=None):
+ """Main entry point; defines and runs the wordcount pipeline."""
Review comment:
Change this.
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