Unknown producer for value SingletonPCollectionView - google-cloud-dataflow

In the interest of providing a minimal example of my problem, I'm trying to implement a simple Beam job that takes in a String as a side input and applies it to a PCollection which is read from a csv file in Cloud Storage. The result is then output to a .txt file in Cloud Storage.
So far, I have tried: Experimenting with PipelineResult.waitUntilFinish (as in (p.run().waitUntilFinish()), altering the placement of the two p.run() commands, and simplifying as much as possible by just using a string as my side input, always with the same result. Searching on Stack and Google just led me to the PR on the Beam repo which implemented the error message.
SideInputTest.java:
public class SideInputTest {
public static void main(String[] arg) throws IOException {
// Build a pipeline to read in string
DataflowPipelineOptions options1 = PipelineOptionsFactory.as(DataflowPipelineOptions.class);
options1.setRunner(DataflowRunner.class);
Pipeline p = Pipeline.create(options1);
// Build really simple side input
PCollectionView<String> sideInputView = p.apply(Create.of("foo"))
.apply(View.<String>asSingleton());
// Run p
p.run();
// Build main pipeline to read csv data
DataflowPipelineOptions options2 = PipelineOptionsFactory.as(DataflowPipelineOptions.class);
options2.setProject(PROJECT_NAME);
options2.setStagingLocation(STAGING_LOCATION);
options2.setRunner(DataflowRunner.class);
Pipeline p2 = Pipeline.create(options2);
p2.apply(TextIO.Read.from(INPUT_DATA))
.apply(ParDo.withSideInputs(sideInputView).of(new DoFn<String, String>() {
#ProcessElement
public void processElement(ProcessContext c) {
String[] rowData = c.element().split(",");
String sideInput = c.sideInput(sideInputView);
c.output(rowData[0] + sideInput);
}
}))
.apply(TextIO.Write
.to(OUTPUT_DATA));
p2.run();
}
}
Full stack trace:
Caused by: java.lang.NullPointerException: Unknown producer for value SingletonPCollectionView{tag=Tag<org.apache.beam.sdk.util.PCollectionViews$SimplePCollectionView.<init>:435#3d93cb799b3970be>} while translating step ParDo(Anonymous)
at org.apache.beam.runners.dataflow.repackaged.com.google.common.base.Preconditions.checkNotNull(Preconditions.java:1079)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator$Translator.getProducer(DataflowPipelineTranslator.java:508)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator.translateSideInputs(DataflowPipelineTranslator.java:926)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator.translateInputs(DataflowPipelineTranslator.java:913)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator.access$1100(DataflowPipelineTranslator.java:112)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator$7.translateSingleHelper(DataflowPipelineTranslator.java:863)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator$7.translate(DataflowPipelineTranslator.java:856)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator$7.translate(DataflowPipelineTranslator.java:853)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator$Translator.visitPrimitiveTransform(DataflowPipelineTranslator.java:415)
at org.apache.beam.sdk.runners.TransformHierarchy$Node.visit(TransformHierarchy.java:486)
at org.apache.beam.sdk.runners.TransformHierarchy$Node.visit(TransformHierarchy.java:481)
at org.apache.beam.sdk.runners.TransformHierarchy$Node.access$400(TransformHierarchy.java:231)
at org.apache.beam.sdk.runners.TransformHierarchy.visit(TransformHierarchy.java:206)
at org.apache.beam.sdk.Pipeline.traverseTopologically(Pipeline.java:321)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator$Translator.translate(DataflowPipelineTranslator.java:365)
at org.apache.beam.runners.dataflow.DataflowPipelineTranslator.translate(DataflowPipelineTranslator.java:154)
at org.apache.beam.runners.dataflow.DataflowRunner.run(DataflowRunner.java:514)
at org.apache.beam.runners.dataflow.DataflowRunner.run(DataflowRunner.java:151)
at org.apache.beam.sdk.Pipeline.run(Pipeline.java:210)
at com.xpw.SideInputTest.main(SideInputTest.java:63)
Currently using org.apache.beam packages #0.6.0

This code is taking a PCollectionView created in one pipeline (p.apply(Create.of("foo")).apply(View.<String>asSingleton()‌​);) and using it in another pipeline (p2).
PCollection's and PCollectionView's belong to a particular pipeline and reuse of them in a different pipeline is not supported.
You can create an analogous PCollectionView in p2.
I'm also confused as to what your pipeline p is trying to accomplish: the only transform it has is creating the view?.. so there's no data being processed in it. I think you should get rid of p entirely and just use p2.

Related

Apache beam seems to be truncating pub sub message payload

We've created a pretty simple pipeline for pub sub event processing. The pub sub message payload itself is tab separated csv data.
After the message is read, the payload data is being truncated when inflating back into the event object. Using the direct runner and running locally the pipeline is working end to end.
Its only when running within the Google Cloud Dataflow runner where we are seeing this message data truncated?
// Create the pipeline
Pipeline pipeline = Pipeline.create(options);
LOG.info("Reading from subscription: " + options.getInputSubscription());
//Step #1: Read from a PubSub subscription.
PCollection<PubsubMessage> pubsubMessages = pipeline.apply(
"ReadPubSubSubscription",
PubsubIO.readMessagesWithMessageId()
.fromSubscription(options.getInputSubscription())
);
//Step #2: Transform the PubsubMessages into snowplow events.
PCollection<Event> rawEvents = pubsubMessages.apply(
"ConvertMessageToEvent",
ParDo.of(new PubsubMessageEventFn())
);
// other pipeline functions.....
Here the conversion function, where for every pub sub message were falling into the error case. Note that Event.parse() is actually a scala library but I don't see how that could affect this as the message data itself is what has been truncated between the two stages of the pipeline.
Perhaps there is an encoding issue?
public static class PubsubMessageEventFn extends DoFn<PubsubMessage, Event> {
#ProcessElement
public void processElement(ProcessContext context) {
PubsubMessage message = context.element();
Validated<ParsingError, Event> event = Event.parse(new String(message.getPayload()));
Either<ParsingError, Event> condition = event.toEither();
if (condition.isLeft()) {
ParsingError err = condition.left().get();
LOG.error("Event parsing error: " + err.toString() + " for message: " + new String(message.getPayload()));
} else {
Event e = condition.right().get();
context.output(e);
}
}
}
Here is a sample of the data that is emitted in the log message:
Event parsing error: FieldNumberMismatch(5) for message: 4f6ec25-67a7-4edf-972a-29e80320f67f web 2020-04-14 21:26:40.034 2020-04-14 21:26:39.884 2020-04-1
Note that the Pub/Sub implementation for DirectRunner is different from the implementation in Dataflow Runner as documented here - https://cloud.google.com/dataflow/docs/concepts/streaming-with-cloud-pubsub#integration-features.
I believe the issue is related to encoding because message.getPayload is of type bytes and the code might need to be modified as new String(message.getPayload(), StandardCharsets.UTF_8) in the below line
Validated<ParsingError, Event> event = Event.parse(new String(message.getPayload(), StandardCharsets.UTF_8));
Using readMessagesWithAttributesAndMessageId instead of readMessagesWithMessageId is the workaround according to this bug issue https://issues.apache.org/jira/browse/BEAM-9483.
It does not appear to have been fixed yet.

Move files to another GCS folder and perform actions after an apache beam pipeline has been executed

I created a streaming apache beam pipeline that read files from GCS folders and insert them in BigQuery, it works perfectly but it re-process all the files when i stop and run the job,so all the data will be replicated again.
So my idea is to move files from the scanned directory to another one but i don't how technically do it with apache beam.
Thank you
public static PipelineResult run(Options options) {
// Create the pipeline.
Pipeline pipeline = Pipeline.create(options);
/*
* Steps:
* 1) Read from the text source.
* 2) Write each text record to Pub/Sub
*/
LOG.info("Running pipeline");
LOG.info("Input : " + options.getInputFilePattern());
LOG.info("Output : " + options.getOutputTopic());
PCollection<String> collection = pipeline
.apply("Read Text Data", TextIO.read()
.from(options.getInputFilePattern())
.watchForNewFiles(Duration.standardSeconds(60), Watch.Growth.<String>never()))
.apply("Write logs", ParDo.of(new DoFn<String, String>() {
#ProcessElement
public void processElement(ProcessContext c) throws Exception {
LOG.info(c.element());
c.output(c.element());
}
}));
collection.apply("Write to PubSub", PubsubIO.writeStrings().to(options.getOutputTopic()));
return pipeline.run();
}
A couple tips:
You are normally not expected to stop and rerun a streaming pipeline. Streaming pipelines are more meant to run forever, and be updated sometimes if you want to make changes to the logic.
Nonetheless, it is possible to use FileIO to match a number of files, and move them after they have been processed.
You would write a DoFn class like so: ReadWholeFileThenMoveToAnotherBucketDoFn, which would read the whole file, and then move it to a new bucket.
Pipeline pipeline = Pipeline.create(options);
PCollection<FileIO.Match> matches = pipeline
.apply("Read Text Data", FileIO.match()
.filepattern(options.getInputFilePattern())
.continuously(Duration.standardSeconds(60),
Watch.Growth.<String>never()));
matches.apply(FileIO.readMatches())
.apply(ParDo.of(new ReadWholeFileThenMoveToAnotherBucketDoFn()))
.apply("Write logs", ParDo.of(new DoFn<String, String>() {
#ProcessElement
public void processElement(ProcessContext c) throws Exception {
LOG.info(c.element());
c.output(c.element());
}
}));
....

Apache Beam PubSubIO with GroupByKey

I'm trying with Apache Beam 2.1.0 to consume simple data (key,value) from google PubSub and group by key to be able to treat batches of data.
With default trigger my code after "GroupByKey" never fires (I waited 30min).
If I defined custom trigger, code is executed but I would like to understand why default trigger is never fired. I tried to define my own timestamp with "withTimestampLabel" but same issue. I tried to change duration of windows but same issue too (1second, 10seconds, 30seconds etc).
I used command line for this test to insert data
gcloud beta pubsub topics publish test A,1
gcloud beta pubsub topics publish test A,2
gcloud beta pubsub topics publish test B,1
gcloud beta pubsub topics publish test B,2
From documentation it says that we can do one or the other but not necessarily both
If you are using unbounded PCollections, you must use either
non-global windowing OR an aggregation trigger in order to perform a
GroupByKey or CoGroupByKey
It looks to be similar to
Consuming unbounded data in windows with default trigger
Scio: groupByKey doesn't work when using Pub/Sub as collection source
My code
static class Compute extends DoFn<KV<String, Iterable<Integer>>, Void> {
#ProcessElement
public void processElement(ProcessContext c) {
// Code never fires
System.out.println("KEY:" + c.element().getKey());
System.out.println("NB:" + c.element().getValue().spliterator().getExactSizeIfKnown());
}
}
public static void main(String[] args) {
Pipeline p = Pipeline.create(PipelineOptionsFactory.create());
p.apply(PubsubIO.readStrings().fromSubscription("projects/" + args[0] + "/subscriptions/test"))
.apply(Window.into(FixedWindows.of(Duration.standardMinutes(1))))
.apply(
MapElements
.into(TypeDescriptors.kvs(TypeDescriptors.strings(), TypeDescriptors.integers()))
.via((String row) -> {
String[] parts = row.split(",");
System.out.println(Arrays.toString(parts)); // Code fires
return KV.of(parts[0], Integer.parseInt(parts[1]));
})
)
.apply(GroupByKey.create())
.apply(ParDo.of(new Compute()));
p.run();
}

Consuming unbounded data in windows with default trigger

I have a Pub/Sub topic + subscription and want to consume and aggregate the unbounded data from the subscription in a Dataflow. I use a fixed window and write the aggregates to BigQuery.
Reading and writing (without windowing and aggregation) works fine. But when I pipe the data into a fixed window (to count the elements in each window) the window is never triggered. And thus the aggregates are not written.
Here is my word publisher (it uses kinglear.txt from the examples as input file):
public static class AddCurrentTimestampFn extends DoFn<String, String> {
#ProcessElement public void processElement(ProcessContext c) {
c.outputWithTimestamp(c.element(), new Instant(System.currentTimeMillis()));
}
}
public static class ExtractWordsFn extends DoFn<String, String> {
#ProcessElement public void processElement(ProcessContext c) {
String[] words = c.element().split("[^a-zA-Z']+");
for (String word:words){ if(!word.isEmpty()){ c.output(word); }}
}
}
// main:
Pipeline p = Pipeline.create(o); // 'o' are the pipeline options
p.apply("ReadLines", TextIO.Read.from(o.getInputFile()))
.apply("Lines2Words", ParDo.of(new ExtractWordsFn()))
.apply("AddTimestampFn", ParDo.of(new AddCurrentTimestampFn()))
.apply("WriteTopic", PubsubIO.Write.topic(o.getTopic()));
p.run();
Here is my windowed word counter:
Pipeline p = Pipeline.create(o); // 'o' are the pipeline options
BigQueryIO.Write.Bound tablePipe = BigQueryIO.Write.to(o.getTable(o))
.withSchema(o.getSchema())
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND);
Window.Bound<String> w = Window
.<String>into(FixedWindows.of(Duration.standardSeconds(1)));
p.apply("ReadTopic", PubsubIO.Read.subscription(o.getSubscription()))
.apply("FixedWindow", w)
.apply("CountWords", Count.<String>perElement())
.apply("CreateRows", ParDo.of(new WordCountToRowFn()))
.apply("WriteRows", tablePipe);
p.run();
The above subscriber will not work, since the window does not seem to trigger using the default trigger. However, if I manually define a trigger the code works and the counts are written to BigQuery.
Window.Bound<String> w = Window.<String>into(FixedWindows.of(Duration.standardSeconds(1)))
.triggering(AfterProcessingTime
.pastFirstElementInPane()
.plusDelayOf(Duration.standardSeconds(1)))
.withAllowedLateness(Duration.ZERO)
.discardingFiredPanes();
I like to avoid specifying custom triggers if possible.
Questions:
Why does my solution not work with Dataflow's default trigger?
How do I have to change my publisher or subscriber to trigger windows using the default trigger?
How are you determining the trigger never fires?
Your PubSubIO.Write and PubSubIO.Read transforms should both specify a timestamp label using withTimestampLabel, otherwise the timestamps you've added will not be written to PubSub and the publish times will be used.
Either way, the input watermark of the pipeline will be derived from the timestamps of the elements waiting in PubSub. Once all inputs have been processed, it will stay back for a few minutes (in case there was a delay in the publisher) before advancing to real time.
What you are likely seeing is that all the elements are published in the same ~1 second window (since the input file is pretty small). These are all read and processed relatively quickly, but the 1-second window they are put in will not trigger until after the input watermark has advanced, indicating that all data in that 1-second window has been consumed.
This won't happen until several minutes, which may make it look like the trigger isn't working. The trigger you wrote fired after 1 second of processing time, which would fire much earlier, but there is no guarantee all the data has been processed.
Steps to get better behavior from the default trigger:
Use withTimestampLabel on both the write and read pubsub steps.
Have the publisher spread the timestamps out further (eg., run for several minutes and spread the timestamps out across that range)

How can I name a MapElements step in a Cloud Dataflow pipeline

Following the dataflow docs, I can name each step of a Google Cloud Dataflow pipeline using ParDo.named:
PCollection<Integer> wordLengths = words.apply(
ParDo
.named("ComputeWordLengths") // the transform name
.of(new DoFn<String, Integer>() {
#Override
public void processElement(ProcessContext c) {
c.output(c.element().length());
}
}));
If I use MapElements instead, however, the example in the documentation does not name the step:
PCollection<Integer> wordLengths = words.apply(
MapElements.via((String word) -> word.length())
.withOutputType(new TypeDescriptor<Integer>() {});
How can I name this MapElements step?
I have several MapElements steps and I'm getting errors like this:
Mar 01, 2016 1:36:39 PM com.google.cloud.dataflow.sdk.Pipeline applyInternal
WARNING: Transform MapElements2 does not have a stable unique name. This will prevent updating of pipelines.
You can specify the name when you apply it. For instance:
words.apply("name", MapElements.via(...))
// instead of
words.apply(MapElements.via(...))
See the JavaDoc on the named apply method for more details.

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