Re: Issue with KafkaIO for list of topics

[Maulik Soneji]

Hello Rahul,

Thanks again for the detailed explanation.

I require some guidance on what values to be set for maxDelay and
previousWatermark for CustomTimestampPolicyWithLimitedDelay.

Currently, I was providing maxDelay as Duration.ZERO and previousWatermark
as Optional.empty().
With these values I see that the getWatermark function always goes to else
block(code link) and always returns TIMESTAMP_MIN_VALUE.
So with this case as well, I see that the watermark is returned as
TIMESTAMP_MIN_VALUE for zero throughput topics.

Please share your observations on how to tune the Timestamp Policy.

Thanks and regards,
Maulik


On Fri, Feb 28, 2020 at 8:46 PM rahul patwari 
wrote:

> Hi Maulik,
>
> Currently, I don't think it is possible to filter topics based on whether
> data is being produced to the topic (or) not.
> But, the Watermark logic can be changed to make the Pipeline work.
>
> Since the timestamps of the records are the time when the events are
> pushed to Kafka, every record will have monotonically increasing timestamps
> except for out of order events.
> Instead of assigning the Watermark as BoundedWindow.TIMESTAMP_MIN_VALUE
> by default, we can assign [current_timestamp - some_delay] as default and
> the same can be done in getWatermark() method, in which case, even if the
> partition is idle, Watermark will advance.
>
> Make sure that the timestamp of the Watermark is monotonically increasing
> and choose the delay carefully in order to avoid discarding out of order
> events.
>
> Refer
> https://github.com/apache/beam/blob/master/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/CustomTimestampPolicyWithLimitedDelay.java
> for an example.
>
> Regards,
> Rahul
>
>
> On Fri, Feb 28, 2020 at 6:54 PM Maulik Soneji 
> wrote:
>
>> Hi Rahul,
>>
>> Thank you very much for the detailed explanation.
>>
>> Since we don't know which are the topics that have zero throughputs, is
>> there a way in which we can filter out such topics in KafkaIO?
>>
>> Since KafkaIO doesn't support passing a regex to consume data from, I am
>> getting a list of topics from kafka and passing it.
>>
>> Is there a way to filter out such topics? Also, it can happen that when
>> the job has started the topic might have no data for a few windows and
>> after that, it can get some data. This filter should be dynamic as well.
>>
>> Please share some ideas on how we can make this work.
>>
>> Community members, please share your thoughts as well on how we can
>> achieve this.
>>
>> Thanks and regards,
>> Maulik
>>
>> On Fri, Feb 28, 2020 at 3:03 PM rahul patwari 
>> wrote:
>>
>>> Hi Maulik,
>>>
>>> This seems like an issue with Watermark.
>>> According to
>>> https://github.com/apache/beam/blob/f0930f958d47042948c06041e074ef9f1b0872d9/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaUnboundedReader.java#L240
>>> ,
>>>
>>> If there are multiple partitions (or) multiple topics, Watermark will be
>>> calculated for each of the partition and the minimum watermark is
>>> considered as the current Watermark.
>>> Assuming that no message is pushed to the topic with 0 throughput,
>>> according to your logic for the watermark calculation, the watermark of
>>> each partition for this topic will be BoundedWindow.TIMESTAMP_MIN_VALUE
>>> (the smallest representable timestamp of an element -
>>> https://github.com/apache/beam/blob/f0930f958d47042948c06041e074ef9f1b0872d9/model/pipeline/src/main/proto/beam_runner_api.proto#L44
>>> ).
>>>
>>> As the result will be emitted from GroupByKey when the Watermark crosses
>>> the window and as the watermark is BoundedWindow.TIMESTAMP_MIN_VALUE,
>>> you are not seeing the results from GroupByKey.
>>>
>>> Regards,
>>> Rahul
>>>
>>> On Fri, Feb 28, 2020 at 12:39 PM Maulik Soneji 
>>> wrote:
>>>
>>>> *Observations:*
>>>> If we read using KafkaIO for a list of topics where one of the topics
>>>> has zero throughputs,
>>>> and KafkaIO is followed by GroupByKey stage, then:
>>>> a. No data is output from GroupByKey stage for all the topics and not
>>>> just the zero throughput topic.
>>>>
>>>> If all topics have some throughput coming in, then it works fine and we
>>>> get some output from GroupByKey stage.
>>>>
>>>> Is this an issue?
>>>>
>>>> *Points:*
>>>> a. The output from GroupByKey is only when all topics have some
>>>> throughput
>

Re: Issue with KafkaIO for list of topics

[Maulik Soneji]

Hi Rahul,

Thank you very much for the detailed explanation.

Since we don't know which are the topics that have zero throughputs, is
there a way in which we can filter out such topics in KafkaIO?

Since KafkaIO doesn't support passing a regex to consume data from, I am
getting a list of topics from kafka and passing it.

Is there a way to filter out such topics? Also, it can happen that when the
job has started the topic might have no data for a few windows and after
that, it can get some data. This filter should be dynamic as well.

Please share some ideas on how we can make this work.

Community members, please share your thoughts as well on how we can achieve
this.

Thanks and regards,
Maulik

On Fri, Feb 28, 2020 at 3:03 PM rahul patwari 
wrote:

> Hi Maulik,
>
> This seems like an issue with Watermark.
> According to
> https://github.com/apache/beam/blob/f0930f958d47042948c06041e074ef9f1b0872d9/sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/KafkaUnboundedReader.java#L240
> ,
>
> If there are multiple partitions (or) multiple topics, Watermark will be
> calculated for each of the partition and the minimum watermark is
> considered as the current Watermark.
> Assuming that no message is pushed to the topic with 0 throughput,
> according to your logic for the watermark calculation, the watermark of
> each partition for this topic will be BoundedWindow.TIMESTAMP_MIN_VALUE
> (the smallest representable timestamp of an element -
> https://github.com/apache/beam/blob/f0930f958d47042948c06041e074ef9f1b0872d9/model/pipeline/src/main/proto/beam_runner_api.proto#L44
> ).
>
> As the result will be emitted from GroupByKey when the Watermark crosses
> the window and as the watermark is BoundedWindow.TIMESTAMP_MIN_VALUE, you
> are not seeing the results from GroupByKey.
>
> Regards,
> Rahul
>
> On Fri, Feb 28, 2020 at 12:39 PM Maulik Soneji 
> wrote:
>
>> *Observations:*
>> If we read using KafkaIO for a list of topics where one of the topics has
>> zero throughputs,
>> and KafkaIO is followed by GroupByKey stage, then:
>> a. No data is output from GroupByKey stage for all the topics and not
>> just the zero throughput topic.
>>
>> If all topics have some throughput coming in, then it works fine and we
>> get some output from GroupByKey stage.
>>
>> Is this an issue?
>>
>> *Points:*
>> a. The output from GroupByKey is only when all topics have some throughput
>> b. This is a problem with KafkaIO + GroupByKey, for case where I have
>> FileIO + GroupByKey, this issue doesn't arise. GroupByKey outputs some data
>> even if there is no data for one of the files.
>> c. Not a runner issue, since I ran it with FlinkRunner and DataflowRunner
>> d. Even if lag is different for each topic on the list, we still get some
>> output from GroupByKey.
>>
>>
>> *Debugging:*While Debugging this issue I found that in split function of
>> KafkaUnboundedSource we create KafkaUnboundedSource where partition list is
>> one partition for each topic.
>>
>> I am not sure if this is some issue with watermark, since watermark for
>> the topic with no throughput will not advance. But this looks like the most
>> likely cause to me.
>>
>> *Please help me in figuring out whether this is an issue or if there is
>> something wrong with my pipeline.*
>>
>> Attaching detailed pipeline information for more details:
>>
>> *Context:*
>> I am currently using KafkaIO to read data from kafka for a list of topics
>> with a custom timestamp policy.
>>
>> Below is how I am constructing KafkaIO reader:
>>
>> return KafkaIO.read()
>> .withBootstrapServers(brokers)
>> .withTopics(topics)
>> .withKeyDeserializer(ByteArrayDeserializer.class)
>> .withValueDeserializer(ByteArrayDeserializer.class)
>> .withTimestampPolicyFactory((partition, previousWatermark) -> new 
>> EventTimestampPolicy(godataService, previousWatermark))
>> .commitOffsetsInFinalize();
>>
>> *Pipeline Information:
>> *Pipeline Consists of six steps:
>> a. Read From Kafka with custom timestamp policy
>> b. Convert KafkaRecord to Message object
>> c. Window based on FixedWindow of 10 minutes triggering AfterWatermark
>> d. PCollection to PCollection> where Topic is 
>> Keye. GroupByKey.create() to get PCollection>f. 
>> PCollection> to PCollection 
>> for each topicg. Write output to kafka
>>
>> *Detailed Pipeline Information*
>> a. Read data from kafka to get KafkaRecord
>> Here I am using my own timestamp policy which looks like below:
>>
>> public EventTim

Issue with KafkaIO for list of topics

[Maulik Soneji]

*Observations:*
If we read using KafkaIO for a list of topics where one of the topics has
zero throughputs,
and KafkaIO is followed by GroupByKey stage, then:
a. No data is output from GroupByKey stage for all the topics and not just
the zero throughput topic.

If all topics have some throughput coming in, then it works fine and we get
some output from GroupByKey stage.

Is this an issue?

*Points:*
a. The output from GroupByKey is only when all topics have some throughput
b. This is a problem with KafkaIO + GroupByKey, for case where I have
FileIO + GroupByKey, this issue doesn't arise. GroupByKey outputs some data
even if there is no data for one of the files.
c. Not a runner issue, since I ran it with FlinkRunner and DataflowRunner
d. Even if lag is different for each topic on the list, we still get some
output from GroupByKey.


*Debugging:*While Debugging this issue I found that in split function of
KafkaUnboundedSource we create KafkaUnboundedSource where partition list is
one partition for each topic.

I am not sure if this is some issue with watermark, since watermark for the
topic with no throughput will not advance. But this looks like the most
likely cause to me.

*Please help me in figuring out whether this is an issue or if there is
something wrong with my pipeline.*

Attaching detailed pipeline information for more details:

*Context:*
I am currently using KafkaIO to read data from kafka for a list of topics
with a custom timestamp policy.

Below is how I am constructing KafkaIO reader:

return KafkaIO.read()
.withBootstrapServers(brokers)
.withTopics(topics)
.withKeyDeserializer(ByteArrayDeserializer.class)
.withValueDeserializer(ByteArrayDeserializer.class)
.withTimestampPolicyFactory((partition, previousWatermark) ->
new EventTimestampPolicy(godataService, previousWatermark))
.commitOffsetsInFinalize();

*Pipeline Information:
*Pipeline Consists of six steps:
a. Read From Kafka with custom timestamp policy
b. Convert KafkaRecord to Message object
c. Window based on FixedWindow of 10 minutes triggering AfterWatermark
d. PCollection to PCollection> where
Topic is Keye. GroupByKey.create() to get PCollection>f. PCollection> to
PCollection for each topicg. Write output to kafka

*Detailed Pipeline Information*
a. Read data from kafka to get KafkaRecord
Here I am using my own timestamp policy which looks like below:

public EventTimestampPolicy(MyService myService, Optional
previousWatermark) {
this.myService = myService;
this.currentWatermark =
previousWatermark.orElse(BoundedWindow.TIMESTAMP_MIN_VALUE);
}

@Override
public Instant getTimestampForRecord(PartitionContext context,
KafkaRecord record) {
Instant eventTimestamp;
try {
eventTimestamp = Deserializer.getEventTimestamp(record, myService);
} catch (InvalidProtocolBufferException e) {
statsClient.increment("io.proto.buffer.exception");
throw new RuntimeException(e);
}
this.currentWatermark = eventTimestamp;
return this.currentWatermark;
}

@Override
public Instant getWatermark(PartitionContext ctx) {
return this.currentWatermark;
}

Event timestamp is one of the fields in the kafka message. It is the time
when the event was pushed to kafka.

b. DoFn to transform KafkaRecord to Message class.The
Message class contains properties like offset, topic, partition,
offset and timestamp

c. Windowing on 10 minute fixed window triggering at
AfterWatermark.pastEndOfWindow()

d. PCollection to PCollection>
Here Key is the kafka topic.

e. GroupByKey to get PCollection>

f. PCollection> to
PCollection for each topic

g. Write output to kafka

KafkaIO to read from regex topic

[Maulik Soneji]

Hello All,

I am currently using Beam version 2.19.0 to read data from Kafka using
KafkaIO.
Kafka version: 0.11.0.0
My use case is to read all topics from the kafka cluster.
Below is the code that reads data from kafka using KafkaIO.

KafkaIO.read()
.withBootstrapServers(brokers)
.withTopic(topic)
.withKeyDeserializer(ByteArrayDeserializer.class)
.withValueDeserializer(ByteArrayDeserializer.class)
.updateConsumerProperties(props)
.commitOffsetsInFinalize();

If I provide topic as a regex like topic-.*,the request fails with:

Exception in thread "main"
org.apache.beam.sdk.Pipeline$PipelineExecutionException:
org.apache.kafka.common.errors.InvalidTopicException: Topic topic-.*  is invalid

By looking at the code, I saw that there is a call to fetch partition
information for the topics at KafkaUnboundSource here
.
Because we are fetching partitions for only the topic mentioned in the
builder, it considers the regex as a topic and tries to fetch partition
information for it even when it is not a topic but a regex. My requirement
is that I should read from all topics in kafka cluster and if there are new
tpoics which are added, they should be considered as well dynamically
without any process restart. Can someone please share details about how I
can read data from multiple topics by using a regex. Thanks in advance.
Thanks and regards, Maulik

Intermittent No FileSystem found exception

[Maulik Soneji]

Hi everyone,

We are running a Batch job on flink that reads data from GCS and does some
aggregation on this data.
We are intermittently getting issue: `No filesystem found for scheme gs`

We are running Beam version 2.15.0 with FlinkRunner, Flink version: 1.6.4

On remote debugging the task managers, we found that in a few task
managers, the *GcsFileSystemRegistrar is not added to the list of
FileSystem Schemes*. In these task managers, we get this issue.

The collection *SCHEME_TO_FILESYSTEM* is getting modified only in
*setDefaultPipelineOptions* function call in
org.apache.beam.sdk.io.FileSystems class and this function is not getting
called and thus the GcsFileSystemRegistrar is not added to
*SCHEME_TO_FILESYSTEM*.

*Detailed stacktrace:*


java.lang.IllegalArgumentException: No filesystem found for scheme gs
at 
org.apache.beam.sdk.io.FileSystems.getFileSystemInternal(FileSystems.java:463)
at 
org.apache.beam.sdk.io.FileSystems.matchNewResource(FileSystems.java:533)
at 
org.apache.beam.sdk.io.fs.ResourceIdCoder.decode(ResourceIdCoder.java:49)
at 
org.apache.beam.sdk.io.fs.MetadataCoder.decodeBuilder(MetadataCoder.java:62)
at org.apache.beam.sdk.io.fs.MetadataCoder.decode(MetadataCoder.java:58)
at org.apache.beam.sdk.io.fs.MetadataCoder.decode(MetadataCoder.java:36)
at org.apache.beam.sdk.coders.Coder.decode(Coder.java:159)
at org.apache.beam.sdk.coders.KvCoder.decode(KvCoder.java:82)
at org.apache.beam.sdk.coders.KvCoder.decode(KvCoder.java:36)
at 
org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.decode(WindowedValue.java:592)
at 
org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.decode(WindowedValue.java:583)
at 
org.apache.beam.sdk.util.WindowedValue$FullWindowedValueCoder.decode(WindowedValue.java:529)
at 
org.apache.beam.runners.flink.translation.types.CoderTypeSerializer.deserialize(CoderTypeSerializer.java:92)
at 
org.apache.flink.runtime.plugable.NonReusingDeserializationDelegate.read(NonReusingDeserializationDelegate.java:55)
at 
org.apache.flink.runtime.io.network.api.serialization.SpillingAdaptiveSpanningRecordDeserializer.getNextRecord(SpillingAdaptiveSpanningRecordDeserializer.java:106)
at 
org.apache.flink.runtime.io.network.api.reader.AbstractRecordReader.getNextRecord(AbstractRecordReader.java:72)
at 
org.apache.flink.runtime.io.network.api.reader.MutableRecordReader.next(MutableRecordReader.java:47)
at 
org.apache.flink.runtime.operators.util.ReaderIterator.next(ReaderIterator.java:73)
at org.apache.flink.runtime.operators.NoOpDriver.run(NoOpDriver.java:94)
at org.apache.flink.runtime.operators.BatchTask.run(BatchTask.java:503)
at 
org.apache.flink.runtime.operators.BatchTask.invoke(BatchTask.java:368)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:711)
at java.lang.Thread.run(Thread.java:748)

Inorder to resolve this issue, we tried calling the following in
PTransform's expand function:

FileSystems.setDefaultPipelineOptions(PipelineOptionsFactory.create());

This function call is to make sure that the GcsFileSystemRegistrar is added
to the list, but this hasn't solved the issue.

Can someone please help in checking why this might be happening and what
can be done to resolve this issue.

Thanks and Regards,
Maulik