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Please send email to user-unsubscr...@flink.apache.org if you want to unsubscribe the mail from user@flink.apache.org, and you can refer [1][2] for more details. 请发送任意内容的邮件到 user-unsubscr...@flink.apache.org 地址来取消订阅来自 user@flink.apache.org 邮件组的邮件,你可以参考[1][2] 管理你的邮件订阅。 Best, Leonard [1] https://flink.apache.org/zh/community/#%e9%82%ae%e4%bb%b6%e5%88%97%e8%a1%a8 [2] https://flink.apache.org/community.html#mailing-lists > On Jun 15, 2023, at 10:40 AM, yanglele via user wrote: > > > > 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 > ignored > > Thanks again, maybe the jvm overhead param will act as the margin I want, > I'll try that :) > Robin > > > Le mer. 14 juin 2023 à 15:28, Gyula Fóra <mailto: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 > > <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 <mailto: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 memory > Why 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 <mailto: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 <mailto: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 > > <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! > > > > <https://a.app.qq.com/o/simple.jsp?pkgname=com.sohu.mail.client.cordova> > >
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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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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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退订请发送任意消息至user-unsubscr...@flink.apache.org In order to unsubscribe, please send an email to user-unsubscr...@flink.apache.org Thanks Best regards, Jing From: liangzai Date: Sun, Jun 19, 2022 at 4:37 AM Subject: Re: New KafkaSource API: Change in default behavior regarding starting offset To: bastien dine Cc: Martijn Visser , Jing Ge , user 请问这个邮件咋退订? Replied Message >From bastien dine Date 06/15/2022 17:50 To Martijn Visser Cc Jing Ge , user Subject Re: New KafkaSource API : Change in default behavior regarding starting offset Hello Martijn, Thanks for the link to the release note, especially : "When resuming from the savepoint, please use setStartingOffsets(OffsetsInitializer.committedOffsets()) in the new KafkaSourceBuilder to transfer the offsets to the new source." So earliest is the new default We use for sure .committedOffsets - we have it by default in our custom KafkaSource builder to be sure we do not read all the previous data (earliest) What bother me is just this change in starting offset default behavior from FlinkKafkaConsumer to KafkaSource (this can lead to mistake) In fact it happens that we drop some of our kafka source state to read again from kafka committed offset, but maybe nodoby does that ^^ Anyway thanks for the focus on the release note ! Best Regards, -- Bastien DINE Data Architect / Software Engineer / Sysadmin bastiendine.io Le mer. 15 juin 2022 à 10:58, Martijn Visser a écrit : > Hi Bastien, > > When the FlinkKafkaConsumer was deprecated in 1.14.0, the release notes > included the instruction how to migrate from FlinkKafkaConsumer to > KafkaConsumer [1]. Looking at the Kafka documentation [2], there is a > section on how to upgrade to the latest connector version that I think is > outdated. I'm leaning towards copying the migration instructions to the > generic documentation. Do you think that would have sufficed? > > Best regards, > > Martijn > > [1] > https://nightlies.apache.org/flink/flink-docs-master/release-notes/flink-1.14/#deprecate-flinkkafkaconsumer > [2] > https://nightlies.apache.org/flink/flink-docs-master/docs/connectors/datastream/kafka/#upgrading-to-the-latest-connector-version > > Op wo 15 jun. 2022 om 09:22 schreef bastien dine : > >> Hello jing, >> >> This was the previous method in old Kafka consumer API, it has been >> removed in 1.15, so source code is not in master anymore, >> Yes I know for the new Offset initializer, committed offset + earliest as >> fallback can be used to have the same behavior as before >> I just wanted to know whether this is a changed behavior or I am missing >> something >> >> >> >> Bastien DINE >> Freelance >> Data Architect / Software Engineer / Sysadmin >> http://bastiendine.io >> >> >> >> Le mar. 14 juin 2022 à 23:08, Jing Ge a écrit : >> >>> Hi Bastien, >>> >>> Thanks for asking. I didn't find any call of setStartFromGroupOffsets() >>> within >>> Flink in the master branch. Could you please point out the code that >>> committed offset is used as default? >>> >>> W.r.t. the new KafkaSource, if OffsetsInitializer.committedOffsets() >>> is used, an exception will be thrown at runtime in case there is no >>> committed offset, which is useful if the user is intended to read from the >>> committed offset but something is wrong. It might feel weird if it is used >>> as default, because an exception will be thrown when users start new jobs >>> with default settings. >>> >>> Best regards, >>> Jing >>> >>> On Tue, Jun 14, 2022 at 4:15 PM bastien dine >>> wrote: >>> >>>> Hello everyone, >>>> >>>> Does someone know why the starting offset behaviour has changed in the >>>> new Kafka Source ? >>>> >>>> This is now from earliest (code in KafkaSourceBuilder), doc says : >>>> "If offsets initializer is not specified, OffsetsInitializer.earliest() >>>> will >>>> be used by default." from : >>>> https://nightlies.apache.org/flink/flink-docs-master/docs/connectors/datastream/kafka/#starting-offset >>>> >>>> Before in old FlinkKafkaConsumer it was from committed offset (i.e : >>>> setStartFromGroupOffsets() >>>> method) >>>> >>>> which match with this behaviour in new KafkaSource : : >>>> OffsetsInitializer.committedOffsets(OffsetResetStrategy.EARLIEST >>>> >>>> This change can lead to big troubles if user pay no attention to this >>>> point when migrating from old KafkaConsumer to new KafkaSource, >>>> >>>> Regards, >>>> Bastien >>>> >>>> -- >>>> >>>> Bastien DINE >>>> Data Architect / Software Engineer / Sysadmin >>>> bastiendine.io >>>> >>>
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Hi, In order to unsubscribe, please send an email to user-unsubscr...@flink.apache.org Best regards, Martijn Op vr 10 jun. 2022 om 17:23 schreef : > Unsubscribe >
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I did this, but I am still getting emails from the flink user group. On Wed, May 11, 2022 at 6:30 PM yuxia wrote: > To unsubscribe, you can send email to user-unsubscr...@flink.apache.org > with any object. > > Best regards, > Yuxia > > -- > *发件人: *"Henry Cai" > *收件人: *"User" > *发送时间: *星期四, 2022年 5 月 12日 上午 1:14:43 > *主题: *unsubscribe > > unsubscribe > >
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Hi, In order to unsubscribe, please send an email to user-unsubscr...@flink.apache.org as documented on the Flink community page [1]. Best regards, Martijn Visser https://twitter.com/MartijnVisser82 https://github.com/MartijnVisser [1] https://flink.apache.org/community.html On Thu, 5 May 2022 at 08:44, Henry Cai wrote: > unsubscribe > > On Wed, May 4, 2022 at 4:27 AM Nishant Gupta > wrote: > >> Unsubscribe >> >
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