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> 在 2015年12月3日，上午1:41，Maximilian Michels  写道：
> 
> Great. Here is the commit to try out:
> https://github.com/mxm/flink/commit/f49b9635bec703541f19cb8c615f302a07ea88b3
> 
> If you already have the Flink repository, check it out using
> 
> git fetch https://github.com/mxm/flink/
> f49b9635bec703541f19cb8c615f302a07ea88b3 && git checkout FETCH_HEAD
> 
> Alternatively, here's a direct download link to the sources with the
> fix included:
> https://github.com/mxm/flink/archive/f49b9635bec703541f19cb8c615f302a07ea88b3.zip
> 
> Thanks a lot,
> Max
> 
>> On Wed, Dec 2, 2015 at 5:44 PM, Niels Basjes  wrote:
>> Sure, just give me the git repo url to build and I'll give it a try.
>> 
>> Niels
>> 
>>> On Wed, Dec 2, 2015 at 4:28 PM, Maximilian Michels  wrote:
>>> 
>>> I mentioned that the exception gets thrown when requesting container
>>> status information. We need this to send a heartbeat to YARN but it is
>>> not very crucial if this fails once for the running job. Possibly, we
>>> could work around this problem by retrying N times in case of an
>>> exception.
>>> 
>>> Would it be possible for you to deploy a custom Flink 0.10.1 version
>>> we provide and test again?
>>> 
 On Wed, Dec 2, 2015 at 4:03 PM, Niels Basjes  wrote:
 No, I was just asking.
 No upgrade is possible for the next month or two.
 
 This week is our busiest day of the year ...
 Our shop is doing about 10 orders per second these days ...
 
 So they won't upgrade until next January/February
 
 Niels
 
 On Wed, Dec 2, 2015 at 3:59 PM, Maximilian Michels 
 wrote:
> 
> Hi Niels,
> 
> You mentioned you have the option to update Hadoop and redeploy the
> job. Would be great if you could do that and let us know how it turns
> out.
> 
> Cheers,
> Max
> 
>> On Wed, Dec 2, 2015 at 3:45 PM, Niels Basjes  wrote:
>> Hi,
>> 
>> I posted the entire log from the first log line at the moment of
>> failure
>> to
>> the very end of the logfile.
>> This is all I have.
>> 
>> As far as I understand the Kerberos and Keytab mechanism in Hadoop
>> Yarn
>> is
>> that it catches the "Invalid Token" and then (if keytab) gets a new
>> Kerberos
>> ticket (or tgt?).
>> When the new ticket has been obtained it retries the call that
>> previously
>> failed.
>> To me it seemed that this call can fail over the invalid Token yet it
>> cannot
>> be retried.
>> 
>> At this moment I'm thinking a bug in Hadoop.
>> 
>> Niels
>> 
>> On Wed, Dec 2, 2015 at 2:51 PM, Maximilian Michels 
>> wrote:
>>> 
>>> Hi Niels,
>>> 
>>> Sorry for hear you experienced this exception. From a first glance,
>>> it
>>> looks like a bug in Hadoop to me.
>>> 
 "Not retrying because the invoked method is not idempotent, and
 unable
 to determine whether it was invoked"
>>> 
>>> That is nothing to worry about. This is Hadoop's internal retry
>>> mechanism that re-attempts to do actions which previously failed if
>>> that's possible. Since the action is not idempotent (it cannot be
>>> executed again without risking to change the state of the execution)
>>> and it also doesn't track its execution states, it won't be retried
>>> again.
>>> 
>>> The main issue is this exception:
>>> 
 org.apache.hadoop.security.token.SecretManager$InvalidToken:
 Invalid
 AMRMToken from >appattempt_1443166961758_163901_01
>>> 
>>> From the stack trace it is clear that this exception occurs upon
>>> requesting container status information from the Resource Manager:
>>> 
 at
 
 
 org.apache.flink.yarn.YarnJobManager$$anonfun$handleYarnMessage$1.applyOrElse(YarnJobManager.scala:259)
>>> 
>>> Are there any more exceptions in the log? Do you have the complete
>>> logs available and could you share them?
>>> 
>>> 
>>> Best regards,
>>> Max
>>> 
>>> On Wed, Dec 2, 2015 at 11:47 AM, Niels Basjes 
>>> wrote:
 Hi,
 
 
 We have a Kerberos secured Yarn cluster here and I'm experimenting
 with
 Apache Flink on top of that.
 
 A few days ago I started a very simple Flink application (just
 stream
 the
 time as a String into HBase 10 times per second).
 
 I (deliberately) asked our IT-ops guys to make my account have a
 max
 ticket
 time of 5 minutes and a max renew time of 10 minutes (yes,
 ridiculously
 low
 timeout values because I needed to validate this
 https://issues.apache.org/jira/browse/FLINK-2977).
 
 This job is started with a keytab file and after running for 31
 hours
 it
 suddenly failed with the exception you see b

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Unsubscribe- 原始邮件 -发件人：Lu Niu发送时间：2023-06-15 07:26:34收件人：Teoh, Hong抄送：d...@flink.apache.org;user主 题：Re: AsyncFunction vs Async SinkThanks, Hong!I understand that if the user case is to simply write sth to an external service, Async Sink is a good option that provides features like batching, state management and rate limiting. I have some follow up questions:1. Is there any problem if we use Async Function for such a user case? We can simply drop the output and use Unordered mode.2. For AsyncFunction and  Async Sink. does it make sense that both could share the same underlying implementation and the features like batching and rate limiting can benefit both?BestLu
  On Wed, Jun 14, 2023 at 2:20 PM Teoh, Hong <lian...@amazon.co.uk> wrote:Hi Lu,
  Thanks for your question. See below for my understanding.
  I would recommend using the Async Sink if you are writing to the external service as the final output of your job graph, and if you don’t have the ordered requirement that updates to the external system must be done before updates to some other external system within the same job graph. (More explained later).
  The abstraction of the Async Sink is a sink, meaning it is a terminal operator in the job graph. The abstraction is intended to simplify the writing of a sink - meaning the base implementation will handle batching, state management and rate limiting. You only need to provide the client and request structure to be used to interact with the external service. This makes writing and maintaining the sink easier (if you simply want to write to a destination with at least once processing). 
  The AsyncFunction, as I understand it is more used for data enrichment, and is not a terminal operator in the job graph. This means the return value from the external service will continue to be passed on down the Flink Job graph. This is useful for data enrichment using the external service, or if we want to ensure the system being called in the AsyncFunction is updated BEFORE any data is written to the sinks further down the job graph.
  For example:
  Kinesis Source -> Map -> AsyncFunction (Updates DynamoDB) -> Kinesis Sink
  We can be sure that the updates to DynamoDB for a particular record happens before the record is written to the Kinesis Sink.
   
  Hope the above clarifies your question!
  Regards,Hong
    
    On 14 Jun 2023, at 19:27, Lu Niu <qqib...@gmail.com> wrote:
      CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you can confirm the sender and know the content is safe.
        Hi, Flink dev and usersIf I want to async write to an external service, which API shall I use, AsyncFunction or Async Sink?My understanding after checking the code are:Both APIs guarantee at least once write to external service. As both API internally stores in-flight requests in the checkpoint.Async Sink provides a batching request feature. This can be implemented with Map + AsyncFunction. Map function groups requests in batches and pass it to AsyncFunction.The batching implementation can refer to AbstractMapBundleOperator if don’t want to use state.Async Sink supports retry on failed requests. AsyncFunction also supports retry in latest flink version.Async Sink supports rate limiting, AsyncFunction doesn’t.AsyncFunction can be used to implement read-update-write. Async Sink cannot.BestLu
   

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Unsubscribe- 原始邮件 -发件人：Robin Cassan via user发送时间：2023-06-14 23:13:09收件人：Gyula Fóra抄送：user主 题：Re: Kubernetes operator: config for taskmanager.memory.process.size ignoredThanks again, maybe the jvm overhead param will act as the margin I want, I'll try that :)RobinLe mer. 14 juin 2023 à 15:28, Gyula Fóra <gyula.f...@gmail.com> a écrit :Again, this has absolutely nothing to do with the Kubernetes Operator, but simply how Flink Kubernetes Memory configs work:https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/memory/mem_tuning/#configure-memory-for-containers
  
  You can probably play around with:  jobmanager.memory.jvm-overhead.fraction
  You can set a larger memory size in the TM spec and increase the jvm overhead fraction.
  Gyula
  On Wed, Jun 14, 2023 at 2:46 PM Robin Cassan <robin.cas...@contentsquare.com> wrote:
  Thanks Gyula for your answer! I'm wondering about your claim:
      > In Flink kubernetes the process is the pod so pod memory is always equal to process memoryWhy should the flink TM process use the whole container (and so, the whole pod) memory?
    Before migrating to the k8s operator, we still used Flink on kubernetes (without the operator) and left a little bit of margin between the process memory and the pod memory, which helped stability. It looks like it cannot be done with the k8s operator though and I wonder why the choice of removing this granularity in the settings
    Robin
    Le mer. 14 juin 2023 à 12:20, Gyula Fóra <gyula.f...@gmail.com> a écrit :
    Basically what happens is that whatever you set to the spec.taskManager.resource.memory will be set in the config as process memory.In Flink kubernetes the process is the pod so pod memory is always equal to process memory.
        So basically the spec is a config shorthand, there is no reason to override it as you won't get a different behaviour at the end of the day.
        Gyula
        On Wed, Jun 14, 2023 at 11:55 AM Robin Cassan via user <user@flink.apache.org> wrote:
        Hello all!
          I am using the flink kubernetes operator and I would like to set the value for `taskmanager.memory.process.size`. I set the desired value in the flinkdeployment resource specs (here, I want 55gb), however it looks like the value that is effectively passed to the taskmanager is the same as the pod memory setting (which is set to 59gb).
          For example, this flinkdeployment configuration:
          ```Spec:
              Flink Configuration:
              taskmanager.memory.process.size:                                  55gb
            Task Manager:
                Resource:
                  Cpu:     6
                  Memory:  59Gb
          ```will create a pod with 59Gb total memory (as expected) but will also give 59Gb to the memory.process.size instead of 55Gb, as seen in this TM log: `Loading configuration property: taskmanager.memory.process.size, 59Gb`
          
          Maybe this part of the flink k8s operator code is responsible:
          https://github.com/apache/flink-kubernetes-operator/blob/d43e1ca9050e83b492b2e16b0220afdba4ffa646/flink-kubernetes-operator/src/main/java/org/apache/flink/kubernetes/operator/config/FlinkConfigBuilder.java#L393
          
          If so, I wonder what is the rationale for forcing the flink process memory to be the same as the pod memory?
            Is there a way to bypass that, for example by setting the desired process.memory configuration differently?
          Thanks! 

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- 原始邮件 -
发件人：Ganesh Walse 
收件人：user@flink.apache.org
主题：Hikari data source and jdbc connection issue
日期：2024年09月04日 09点05分

Hi All,
I am using Hikari data source with ojdbc 8 to connect with database in flink 
application.
I have series of jobs which other java application submit to my jobmanager.And 
on each job submission I will connect to database after that my job manager and 
task manager metaspace increases by 10mb and it will never get released.
Due to above scenario my application gets oom error after certain number of 
jobs.

Please help me here to get out of this situation.
Thanks and regards,Ganesh walse

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[1] https://flink.apache.org/community.html#mailing-lists

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