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https://issues.apache.org/jira/browse/BEAM-2953?focusedWorklogId=150818&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-150818
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ASF GitHub Bot logged work on BEAM-2953:
----------------------------------------
Author: ASF GitHub Bot
Created on: 03/Oct/18 16:29
Start Date: 03/Oct/18 16:29
Worklog Time Spent: 10m
Work Description: akedin commented on a change in pull request #6540:
[BEAM-2953] Advanced Timeseries examples.
URL: https://github.com/apache/beam/pull/6540#discussion_r222141398
##########
File path:
examples/java/src/main/java/org/apache/beam/examples/timeseries/Configuration/TSConfiguration.java
##########
@@ -0,0 +1,94 @@
+/*
+ * 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.configuration;
+
+import com.google.auto.value.AutoValue;
+import java.io.Serializable;
+import javax.annotation.Nullable;
+import org.apache.beam.sdk.annotations.Experimental;
+import org.apache.beam.sdk.coders.AvroCoder;
+import org.apache.beam.sdk.coders.DefaultCoder;
+import org.joda.time.Duration;
+
+/** configuration options for the timeseries pipeline. */
+@SuppressWarnings("serial")
+@Experimental
+public class TSConfiguration implements Serializable{
+
+ /**
+ * Used to determine the backfill operation type. NONE : Do not backfill
NULL : Do not set a value
+ * LAST_KNOWN_VALUE : Use the last known value
+ */
+ public enum BFillOptions {
+ NONE,
+ NULL,
+ LAST_KNOWN_VALUE
+ }
+
+ private BFillOptions fillOption = BFillOptions.LAST_KNOWN_VALUE;
+
+ // The down sample period which must be set.
+ private Duration downSampleDuration = Duration.standardSeconds(1);
+
+ // Once a key is observed we will generate a value during periods when the
key has not been observed if the
+ // fillOption is set to anything other than NONE.
+ private Duration timeToLive = Duration.ZERO;
+
+ // Set if this is a streaming pipeline.
+ private boolean isStreaming = false;
+
+ public BFillOptions fillOption() {return fillOption;};
+
+ // The down sample period which must be set.
+ public Duration downSampleDuration() { return downSampleDuration;};
+
+ // Once a key is observed we will generate a value during periods when the
key has not been observed if the
+ // fillOption is set to anything other than NONE.
+ public Duration timeToLive() {return timeToLive;};
+
+ // Set if this is a streaming pipeline.
+ public boolean isStreaming() {return this.isStreaming;};
+
+ public static TSConfiguration builder(){
Review comment:
We usually use `@AutoValue` and `@AutoValue.Builder` for these cases
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Issue Time Tracking
-------------------
Worklog Id: (was: 150818)
Time Spent: 2h 10m (was: 2h)
> 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: 2h 10m
> 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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