[ 
https://issues.apache.org/jira/browse/BEAM-4862?focusedWorklogId=128305&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-128305
 ]

ASF GitHub Bot logged work on BEAM-4862:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 27/Jul/18 21:34
            Start Date: 27/Jul/18 21:34
    Worklog Time Spent: 10m 
      Work Description: Ardagan commented on a change in pull request #6077: 
BEAM-4862 Fixes bug in Spanner's MutationGroupEncoder by converting timestamps 
into Long and not Int.
URL: https://github.com/apache/beam/pull/6077#discussion_r205904597
 
 

 ##########
 File path: 
sdks/java/io/google-cloud-platform/src/test/java/org/apache/beam/sdk/io/gcp/spanner/MutationGroupEncoderTest.java
 ##########
 @@ -528,6 +528,32 @@ public void dateKeys() throws Exception {
     verifyEncodedOrdering(schema, "test", keys);
   }
 
+  @Test
+  // Test for Jira issue BEAM-4862
+  public void decodeTimestampMutationGroup() {
 
 Review comment:
   Please, split this test into two:
   decodeTimestampCanDecodeTimestampMinValue and 
decodeTimestampCanDecodeTimestampMaxValue.
   
   These names will give detailed description on what this test does. Splitting 
will allow to easily understand failing setup by reading test name only.

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Issue Time Tracking
-------------------

            Worklog Id:     (was: 128305)
            Time Spent: 0.5h  (was: 20m)
    Remaining Estimate: 23.5h  (was: 23h 40m)

> varint overflow -62135596800 exception with Cloud Spanner Timestamp 
> 0001-01-01T00:00:00Z
> ----------------------------------------------------------------------------------------
>
>                 Key: BEAM-4862
>                 URL: https://issues.apache.org/jira/browse/BEAM-4862
>             Project: Beam
>          Issue Type: Bug
>          Components: io-java-gcp
>    Affects Versions: 2.5.0
>            Reporter: Eric Beach
>            Assignee: Chamikara Jayalath
>            Priority: Minor
>   Original Estimate: 24h
>          Time Spent: 0.5h
>  Remaining Estimate: 23.5h
>
> tl;dr - If you try to write a Timestamp of value "0001-01-01T00:00:00Z" as a 
> Spanner Mutation, you get an overflow error.
>  
> The crux of the issue appears to be that 0001-01-01T00:00:00Z, which is a 
> valid Timestamp per 
> [https://cloud.google.com/spanner/docs/data-types#timestamp-type], is too 
> large for an integer. See the two lines of code below. 
> [https://github.com/apache/beam/blob/release-2.5.0/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/MutationGroupEncoder.java#L453]
> [https://github.com/apache/beam/blob/279a05604b83a54e8e5a79e13d8761f94841f326/sdks/java/core/src/main/java/org/apache/beam/sdk/util/VarInt.java#L58]
>  
>  
> Stack Trade
> {{Caused by: java.io.IOException: varint overflow -62135596800 at 
> org.apache.beam.sdk.util.VarInt.decodeInt(VarInt.java:65) at 
> org.apache.beam.sdk.io.gcp.spanner.MutationGroupEncoder.decodePrimitive(MutationGroupEncoder.java:453)
>  at 
> org.apache.beam.sdk.io.gcp.spanner.MutationGroupEncoder.decodeModification(MutationGroupEncoder.java:326)
>  at 
> org.apache.beam.sdk.io.gcp.spanner.MutationGroupEncoder.decodeMutation(MutationGroupEncoder.java:280)
>  at 
> org.apache.beam.sdk.io.gcp.spanner.MutationGroupEncoder.decode(MutationGroupEncoder.java:264)
>  at 
> org.apache.beam.sdk.io.gcp.spanner.SpannerIO$BatchFn.processElement(SpannerIO.java:1030)
>  at 
> org.apache.beam.sdk.io.gcp.spanner.SpannerIO$BatchFn$DoFnInvoker.invokeProcessElement(Unknown
>  Source) at 
> org.apache.beam.runners.core.SimpleDoFnRunner.invokeProcessElement(SimpleDoFnRunner.java:185)
>  at 
> org.apache.beam.runners.core.SimpleDoFnRunner.processElement(SimpleDoFnRunner.java:146)
>  at 
> com.google.cloud.dataflow.worker.SimpleParDoFn.processElement(SimpleParDoFn.java:323)
>  at 
> com.google.cloud.dataflow.worker.util.common.worker.ParDoOperation.process(ParDoOperation.java:43)
>  at 
> com.google.cloud.dataflow.worker.util.common.worker.OutputReceiver.process(OutputReceiver.java:48)
>  at 
> com.google.cloud.dataflow.worker.GroupAlsoByWindowsParDoFn$1.output(GroupAlsoByWindowsParDoFn.java:181)
>  at 
> com.google.cloud.dataflow.worker.GroupAlsoByWindowFnRunner$1.outputWindowedValue(GroupAlsoByWindowFnRunner.java:102)
>  at 
> com.google.cloud.dataflow.worker.util.BatchGroupAlsoByWindowViaIteratorsFn.processElement(BatchGroupAlsoByWindowViaIteratorsFn.java:124)
>  at 
> com.google.cloud.dataflow.worker.util.BatchGroupAlsoByWindowViaIteratorsFn.processElement(BatchGroupAlsoByWindowViaIteratorsFn.java:53)
>  at 
> com.google.cloud.dataflow.worker.GroupAlsoByWindowFnRunner.invokeProcessElement(GroupAlsoByWindowFnRunner.java:115)
>  at 
> com.google.cloud.dataflow.worker.GroupAlsoByWindowFnRunner.processElement(GroupAlsoByWindowFnRunner.java:73)
>  at 
> com.google.cloud.dataflow.worker.GroupAlsoByWindowsParDoFn.processElement(GroupAlsoByWindowsParDoFn.java:113)
>  at 
> com.google.cloud.dataflow.worker.util.common.worker.ParDoOperation.process(ParDoOperation.java:43)
>  at 
> com.google.cloud.dataflow.worker.util.common.worker.OutputReceiver.process(OutputReceiver.java:48)
>  at 
> com.google.cloud.dataflow.worker.util.common.worker.ReadOperation.runReadLoop(ReadOperation.java:200)
>  at 
> com.google.cloud.dataflow.worker.util.common.worker.ReadOperation.start(ReadOperation.java:158)
>  at 
> com.google.cloud.dataflow.worker.util.common.worker.MapTaskExecutor.execute(MapTaskExecutor.java:75)
>  at 
> com.google.cloud.dataflow.worker.BatchDataflowWorker.executeWork(BatchDataflowWorker.java:391)
>  at 
> com.google.cloud.dataflow.worker.BatchDataflowWorker.doWork(BatchDataflowWorker.java:360)
>  at 
> com.google.cloud.dataflow.worker.BatchDataflowWorker.getAndPerformWork(BatchDataflowWorker.java:288)
>  at 
> com.google.cloud.dataflow.worker.DataflowBatchWorkerHarness$WorkerThread.doWork(DataflowBatchWorkerHarness.java:134)
>  at 
> com.google.cloud.dataflow.worker.DataflowBatchWorkerHarness$WorkerThread.call(DataflowBatchWorkerHarness.java:114)
>  at 
> com.google.cloud.dataflow.worker.DataflowBatchWorkerHarness$WorkerThread.call(DataflowBatchWorkerHarness.java:101)
>  at java.util.concurrent.FutureTask.run(FutureTask.java:266) at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>  at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>  at java.lang.Thread.run(Thread.java:745)}}
>  



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