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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 outdated.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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退订所有邮件列表- 原始邮件 -发件人:Teoh, Hong发送时间:2023-06-15 05:19:57收件人:Lu Niu抄送:d...@flink.apache.org;user主 题:Re: AsyncFunction vs Async SinkHi 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 SinkWe 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 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