[GitHub] spark issue #20997: [SPARK-19185] [DSTREAMS] Avoid concurrent use of cached ...

[vanzin]

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That being the case, merging to master.


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[koeninger]

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I'm fine as well.


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[vanzin]

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retest this please


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[vanzin]

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I'm fine with it. Unless Cody beats me to it or has more comments, I'll 
push this after the long weekend.


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[gaborgsomogyi]

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Do I need to do any further changes?


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[gaborgsomogyi]

Github user gaborgsomogyi commented on the issue:

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In the meantime found a small glitch in the SQL part. Namely if reattempt 
happens this line

https://github.com/apache/spark/blob/1d758dc73b54e802fdc92be204185fe7414e6553/external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaDataConsumer.scala#L445
removes the consumer from cache which will end up in this log message:

```
13:27:07.556 INFO org.apache.spark.sql.kafka010.KafkaDataConsumer: Released 
a supposedly cached consumer that was not found in the cache
```

I've solved this here by removing only the closed consumer. The marked for 
close will be removed in `release`.



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[gaborgsomogyi]

Github user gaborgsomogyi commented on the issue:

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Taken a look at the pool options I have the feeling it requires more time 
to come up with a proper solution. Switching back to the SQL code provided one 
cached consumer approach...


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[koeninger]

Github user koeninger commented on the issue:

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I think if we can't come up with a pool design now that solves most of the
issues, we should switch back to the one cached consumer approach that the
SQL code is using.

On Mon, Apr 16, 2018 at 3:25 AM, Gabor Somogyi 
wrote:

> *@gaborgsomogyi* commented on this pull request.
> --
>
> In external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/
> KafkaDataConsumer.scala
> :
>
> > +   * If matching consumer doesn't already exist, will be created using 
kafkaParams.
> +   * The returned consumer must be released explicitly using 
[[KafkaDataConsumer.release()]].
> +   *
> +   * Note: This method guarantees that the consumer returned is not 
currently in use by anyone
> +   * else. Within this guarantee, this method will make a best effort 
attempt to re-use consumers by
> +   * caching them and tracking when they are in use.
> +   */
> +  def acquire[K, V](
> +  groupId: String,
> +  topicPartition: TopicPartition,
> +  kafkaParams: ju.Map[String, Object],
> +  context: TaskContext,
> +  useCache: Boolean): KafkaDataConsumer[K, V] = synchronized {
> +val key = new CacheKey(groupId, topicPartition)
> +val existingInternalConsumers = Option(cache.get(key))
> +  .getOrElse(new ju.LinkedList[InternalKafkaConsumer[_, _]])
>
> That's correct, the SQL part isn't keeping a linked list pool but a single
> cached consumer. I was considering your suggestion and came to the same
> conclusion:
>
> Can you clarify why you want to allow only 1 cached consumer per 
topicpartition, closing any others at task end?
>
> It seems like opening and closing consumers would be less efficient than 
allowing a pool of more than one consumer per topicpartition.
>
> Though limiting the number of cached consumers per groupId/TopicPartition
> is a must as you've pointed out. On the other side if we go the SQL way
> it's definitely less risky. Do you think we should switch back to the one
> cached consumer approach?
>
> —
> You are receiving this because you were mentioned.
> Reply to this email directly, view it on GitHub
> , or 
mute
> the thread
> 

> .
>



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[gaborgsomogyi]

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@koeninger 

> I don't see an upper bound on the number of consumers per key, nor a way 
of reaping idle consumers. If the SQL equivalent code is likely to be modified 
to use pooling of some kind, seems better to make a consistent decision.

When do you think the decision will happen?



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[koeninger]

Github user koeninger commented on the issue:

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In general, 2 things about this make me uncomfortable:

- It's basically a cut-and-paste of the SQL equivalent PR, 
https://github.com/apache/spark/pull/20767, but it is different from both that 
PR and the existing DStream code.

- I don't see an upper bound on the number of consumers per key, nor a way 
of reaping idle consumers.  If the SQL equivalent code is likely to be modified 
to use pooling of some kind, seems better to make a consistent decision.




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cc @tdas @zsxwing @koeninger 


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[GitHub] spark issue #20997: [SPARK-19185] [DSTREAMS] Avoid concurrent use of cached ...

[SparkQA]

Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/20997
  
**[Test build #88990 has 
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/88990/testReport)**
 for PR 20997 at commit 
[`0fe456b`](https://github.com/apache/spark/commit/0fe456b48d93ed24cc59446b79ccfb32694295bc).
 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark issue #20997: [SPARK-19185] [DSTREAMS] Avoid concurrent use of cached ...

[SparkQA]

Github user SparkQA commented on the issue:

https://github.com/apache/spark/pull/20997
  
**[Test build #88990 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/88990/testReport)**
 for PR 20997 at commit 
[`0fe456b`](https://github.com/apache/spark/commit/0fe456b48d93ed24cc59446b79ccfb32694295bc).


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[GitHub] spark issue #20997: [SPARK-19185] [DSTREAMS] Avoid concurrent use of cached ...

[AmplabJenkins]

Github user AmplabJenkins commented on the issue:

https://github.com/apache/spark/pull/20997
  
Can one of the admins verify this patch?


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