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https://issues.apache.org/jira/browse/BEAM-2953?focusedWorklogId=152498&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-152498
]
ASF GitHub Bot logged work on BEAM-2953:
----------------------------------------
Author: ASF GitHub Bot
Created on: 09/Oct/18 03:07
Start Date: 09/Oct/18 03:07
Worklog Time Spent: 10m
Work Description: rezarokni commented on a change in pull request #6540:
[BEAM-2953] Advanced Timeseries examples.
URL: https://github.com/apache/beam/pull/6540#discussion_r223549405
##########
File path:
examples/java/src/main/java/org/apache/beam/examples/timeseries/TimeSeriesExampleToFile.java
##########
@@ -0,0 +1,153 @@
+/*
+ * 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.
+ */
+
+package org.apache.beam.examples.timeseries;
+
+import com.google.protobuf.util.Timestamps;
+import java.util.ArrayList;
+import java.util.List;
+import org.apache.beam.examples.timeseries.configuration.TSConfiguration;
+import org.apache.beam.examples.timeseries.io.tf.TFSequenceExampleToBytes;
+import
org.apache.beam.examples.timeseries.io.tf.TSAccumSequenceToTFSequencExample;
+import org.apache.beam.examples.timeseries.protos.TimeSeriesData;
+import org.apache.beam.examples.timeseries.transforms.DebugSortedResult;
+import org.apache.beam.examples.timeseries.transforms.ExtractAggregates;
+import org.apache.beam.examples.timeseries.transforms.OrderOutput;
+import org.apache.beam.examples.timeseries.transforms.TSAccumToFixedWindowSeq;
+import org.apache.beam.examples.timeseries.utils.TSMultiVariateDataPoints;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.annotations.Experimental;
+import org.apache.beam.sdk.io.TFRecordIO;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.transforms.Create;
+import org.apache.beam.sdk.transforms.ParDo;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+
+/**
+ * This example pipeline is used to illustrate an advanced use of Keyed state
and timers. The
+ * pipeline extracts interesting information from timeseries data. One of the
key elements, is the
+ * transfer of data between fixed windows for a given key, as well as backfill
when a key does not
+ * have any new data within a time boundary. This sample should not be used in
production.
+ *
+ * <p>The output of this pipeline is to a File path.
+ */
+@Experimental
+public class TimeSeriesExampleToFile {
+
+ private static final String FILE_LOCATION = "/tmp/tf/";
+
+ public static void main(String[] args) {
+
+ // Create pipeline
+ TimeSeriesOptions options =
+
PipelineOptionsFactory.fromArgs(args).withValidation().as(TimeSeriesOptions.class);
+
+ TSConfiguration configuration =
+ TSConfiguration.builder()
+ .downSampleDuration(Duration.standardSeconds(1))
+ .timeToLive(Duration.standardMinutes(1))
+ .fillOption(TSConfiguration.BFillOptions.LAST_KNOWN_VALUE);
+
+ Pipeline p = Pipeline.create(options);
+
+ // ------------ READ DATA ------------
+
+ // Read some dummy timeseries data
+ PCollection<KV<TimeSeriesData.TSKey, TimeSeriesData.TSDataPoint>>
readL1Data =
+ p.apply(Create.of(SinWaveSample.generateSinWave()))
+ .apply(ParDo.of(new TSMultiVariateDataPoints.ExtractTimeStamp()))
+ .apply(ParDo.of(new
TSMultiVariateDataPoints.ConvertMultiToUniDataPoint()));
+
+ // ------------ Create perfect rectangles of data--------
+
+ PCollection<KV<TimeSeriesData.TSKey, TimeSeriesData.TSAccum>> downSampled =
+ readL1Data.apply(new ExtractAggregates(configuration));
+
+ PCollection<KV<TimeSeriesData.TSKey, TimeSeriesData.TSAccum>> weHaveOrder =
+ downSampled.apply(new OrderOutput(configuration));
+
+ // ------------ OutPut Data as Logs and TFRecords--------
+
+ // This transform is purely to allow logged debug output, it will fail
with OOM if large dataset is used.
+ weHaveOrder.apply(new DebugSortedResult());
+
+ // Create 3 different window lengths for the TFSequenceExample
+ weHaveOrder
+ .apply(new TSAccumToFixedWindowSeq(configuration,
Duration.standardMinutes(1)))
+ .apply(ParDo.of(new TSAccumSequenceToTFSequencExample()))
+ .apply(ParDo.of(new TFSequenceExampleToBytes()))
+ .apply(TFRecordIO.write().to(FILE_LOCATION +
"1min/tfSequenceExamplerecords"));
+
+ weHaveOrder
+ .apply(new TSAccumToFixedWindowSeq(configuration,
Duration.standardMinutes(5)))
+ .apply(ParDo.of(new TSAccumSequenceToTFSequencExample()))
+ .apply(ParDo.of(new TFSequenceExampleToBytes()))
+ .apply(TFRecordIO.write().to(FILE_LOCATION +
"5min/tfSequenceExamplerecords"));
+
+ weHaveOrder
+ .apply(new TSAccumToFixedWindowSeq(configuration,
Duration.standardMinutes(10)))
+ .apply(ParDo.of(new TSAccumSequenceToTFSequencExample()))
+ .apply(ParDo.of(new TFSequenceExampleToBytes()))
+ .apply(TFRecordIO.write().to(FILE_LOCATION +
"10min/tfSequenceExamplerecords"));
+
+ p.run();
+ }
+
+ /** Simple data generator that creates some dummy test data for the
timeseries examples. */
Review comment:
Done.
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Issue Time Tracking
-------------------
Worklog Id: (was: 152498)
Time Spent: 3h (was: 2h 50m)
> Create more advanced Timeseries processing examples using state API
> -------------------------------------------------------------------
>
> Key: BEAM-2953
> URL: https://issues.apache.org/jira/browse/BEAM-2953
> Project: Beam
> Issue Type: Improvement
> Components: examples-java
> Affects Versions: 2.1.0
> Reporter: Reza ardeshir rokni
> Assignee: Reuven Lax
> Priority: Minor
> Time Spent: 3h
> Remaining Estimate: 0h
>
> As described in the phase 1 portion of this solution outline:
> https://cloud.google.com/solutions/correlating-time-series-dataflow
> BEAM can be used to build out some very interesting pre-processing stages for
> time series data. Some examples that will be useful:
> - Downsampling time series based on simple AVG, MIN, MAX
> - Creating a value for each time window using generatesequence as a seed
> - Loading the value of a downsample with the previous value (used in FX with
> previous close being brought into current open value)
> This will show some concrete examples of keyed state as well as the use of
> combiners.
> The samples can also be used to show how you can create a ordered list of
> values per key from a unbounded topic which has multiple time series keys.
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