Thanks, the logs were very helpful!
TL:DR - The offset committing to ZooKeeper is very slow and prevents proper
starting of checkpoints.
Here is what is happening in detail:
- Between the point when the TaskManager receives the "trigger
checkpoint" message and when the point when the KafkaSource actually starts
the checkpoint is a long time (many seconds) - for one of the Kafka Inputs
(the other is fine).
- The only way this delayed can be introduced is if another checkpoint
related operation (such as trigger() or notifyComplete() ) is still in
progress when the checkpoint is started. Flink does not perform concurrent
checkpoint operations on a single operator, to ease the concurrency model
for users.
- The operation that is still in progress must be the committing of the
offsets (to ZooKeeper or Kafka). That also explains why this only happens
once one side receives the first record. Before that, there is nothing to
commit.
What Flink should fix:
- The KafkaConsumer should run the commit operations asynchronously, to
not block the "notifyCheckpointComplete()" method.
What you can fix:
- Have a look at your Kafka/ZooKeeper setup. One Kafka Input works well,
the other does not. Do they go against different sets of brokers, or
different ZooKeepers? Is the metadata for one input bad?
- In the next Flink version, you may opt-out of committing offsets to
Kafka/ZooKeeper all together. It is not important for Flink's checkpoints
anyways.
Greetings,
Stephan
On Mon, Sep 26, 2016 at 5:13 PM, Chakravarthy varaga <
chakravarth...@gmail.com> wrote:
> Hi Stefan,
>
> Please find my responses below.
>
> - What source are you using for the slow input?
> * [CVP] - Both stream as pointed out in my first mail, are Kafka
> Streams*
> - How large is the state that you are checkpointing?
>
> *[CVP] - I have enabled checkpointing on the StreamEnvironment as below.*
>
>
>
> * final StreamExecutionEnvironment streamEnv =
> StreamExecutionEnvironment.getExecutionEnvironment();
> streamEnv.setStateBackend(new
> FsStateBackend("file:///tmp/flink/checkpoints"));
> streamEnv.enableCheckpointing(10000);*
>
>
> * In terms of the state stored, the KS1 stream has payload of 100K
> events/second, while KS2 have about 1 event / 10 minutes... basically the
> operators perform flatmaps on 8 fields of tuple (all fields are
> primitives). If you look at the states' sizes in dashboard they are in
> Kb...*
> - Can you try to see in the log if actually the state snapshot takes
> that long, or if it simply takes long for the checkpoint barriers to
> travel through the stream due to a lot of backpressure?
> [CVP] -There are no back pressure atleast from the sample computation
> in the flink dashboard. 100K/second is low load for flink's benchmarks. I
> could not quite get the barriers vs snapshot state. I have attached the
> Task Manager log (DEBUG) info if that will interest you.
>
> I have attached the checkpoints times' as .png from the dashboard.
> Basically if you look at checkpoint IDs 28 & 29 &30- you'd see that the
> checkpoints take more than a minute in each case. Before these checkpoints,
> the KS2 stream did not have any events. As soon as an event(should be in
> bytes) was generated, the checkpoints went slow and subsequently a minute
> more for every checkpoint thereafter.
>
> This log was collected from the standalone flink cluster with 1 job
> manager & 2 TMs. 1 TM was running this application with checkpointing
> (parallelism=1)
>
> Please let me know if you need further info.,
>
>
>
> On Fri, Sep 23, 2016 at 6:26 PM, Stephan Ewen <se...@apache.org> wrote:
>
>> Hi!
>>
>> Let's try to figure that one out. Can you give us a bit more information?
>>
>> - What source are you using for the slow input?
>> - How large is the state that you are checkpointing?
>> - Can you try to see in the log if actually the state snapshot takes
>> that long, or if it simply takes long for the checkpoint barriers to travel
>> through the stream due to a lot of backpressure?
>>
>> Greetings,
>> Stephan
>>
>>
>>
>> On Fri, Sep 23, 2016 at 3:35 PM, Fabian Hueske <fhue...@gmail.com> wrote:
>>
>>> Hi CVP,
>>>
>>> I'm not so much familiar with the internals of the checkpointing system,
>>> but maybe Stephan (in CC) has an idea what's going on here.
>>>
>>> Best, Fabian
>>>
>>> 2016-09-23 11:33 GMT+02:00 Chakravarthy varaga <chakravarth...@gmail.com
>>> >:
>>>
>>>> Hi Aljoscha & Fabian,
>>>>
>>>> I have a stream application that has 2 stream source as below.
>>>>
>>>> KeyedStream<String, String> *ks1* = ds1.keyBy("*") ;
>>>> KeyedStream<Tuple2<String, V>, String> *ks2* = ds2.flatMap(split
>>>> T into k-v pairs).keyBy(0);
>>>>
>>>> ks1.connect(ks2).flatMap(X);
>>>> //X is a CoFlatMapFunction that inserts and removes elements from
>>>> ks2 into a key-value state member. Elements from ks1 are matched against
>>>> that state. the CoFlatMapFunction operator maintains
>>>> ValueState<Tuple2<Long, Long>>;
>>>>
>>>> //ks1 is streaming about 100K events/sec from kafka topic
>>>> //ks2 is streaming about 1 event every 10 minutes... Precisely
>>>> when the 1st event is consumed from this stream, checkpoint takes 2 minutes
>>>> straight away.
>>>>
>>>> The version of flink is 1.1.2.
>>>>
>>>> I tried to use checkpoint every 10 Secs using a FsStateBackend... What
>>>> I notice is that the checkpoint duration is almost 2 minutes for many
>>>> cases, while for the other cases it varies from 100 ms to 1.5 minutes
>>>> frequently. I'm attaching the snapshot of the dashboard for your reference.
>>>>
>>>> Is this an issue with flink checkpointing?
>>>>
>>>> Best Regards
>>>> CVP
>>>>
>>>
>>>
>>
>
