Hi, It seems to me that there are issues related to below
*<Prem> I think when a task failed in between and retry task started and completed it may create duplicate as failed task has some data + retry task has full data. but my question is why spark keeps delta data or according to you if speculative and original task completes generally spark kills one of the tasks to get rid of dups data. when a data node is rebooted then spark fault tolerant should go to other nodes isn't it ? then why it has missing data.* Spark is designed to be fault-tolerant through lineage and recomputation. However, there are scenarios where speculative execution or task retries might lead to duplicated or missing data. So what are these? - Task Failure and Retry: You are correct that a failed task might have processed some data before encountering the FetchFailedException. If a retry succeeds, it would process the entire data partition again, leading to duplicates. When a task fails, Spark may recompute the lost data by recomputing the lost task on another node. The output of the retried task is typically combined with the output of the original task during the final stage of the computation. This combination is done to handle scenarios where the original task partially completed and generated some output before failing. Spark does not intentionally store partially processed data. However, due to retries and speculative execution, duplicate processing can occur. To the best of my knowledge, Spark itself doesn't have a mechanism to identify and eliminate duplicates automatically. While Spark might sometimes kill speculative tasks if the original one finishes, it is not a guaranteed behavior. This depends on various factors like scheduling and task dependencies. - Speculative Execution: Spark supports speculative execution, where the same task is launched on multiple executors simultaneously. The result of the first completed task is used, and the others are usually killed to avoid duplicated results. However, speculative execution might introduce some duplication in the final output if tasks on different executors complete successfully. - Node Reboots and Fault Tolerance: If the data node reboot leads to data corruption or loss, that data might be unavailable to Spark. Even with fault tolerance, Spark cannot recover completely missing data. Fault tolerance focuses on recovering from issues like executor failures, not data loss on storage nodes. Overall, Spark's fault tolerance is designed to handle executor failures by rescheduling tasks on other available executors and temporary network issues by retrying fetches based on configuration. Here are some stuff to consider: - Minimize retries: Adjust spark.shuffle.io.maxRetries to a lower value such as 1 or 2 to reduce the chance of duplicate processing attempts, if retries are suspected to be a source. - Disable speculative execution if needed: Consider disabling speculative execution (spark.speculation=false) if duplicates are a major concern. However, this might impact performance. - Data persistence: As mentioned in the previous reply, persist intermediate data to reliable storage (HDFS, GCS, etc.) if data integrity is critical. This ensures data availability even during node failures. - Data validation checks: Implement data validation checks after processing to identify potential duplicates or missing data. HTH Mich Talebzadeh, Dad | Technologist | Solutions Architect | Engineer London United Kingdom view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)". On Sat, 2 Mar 2024 at 01:43, Prem Sahoo <prem.re...@gmail.com> wrote: > Hello Mich, > thanks for your reply. > > As an engineer I can chip in. You may have partial execution and retries > meaning when spark encounters a *FetchFailedException*, it may retry > fetching the data from the unavailable (the one being rebooted) node a few > times before marking it permanently unavailable. However, if the rebooted > node recovers quickly within this retry window, some executors might > successfully fetch the data after a retry. *This leads to duplicate > processing of the same data partition*. > > <Prem> data node reboot is taking more than 20 mins and our config > spark.network.timeout=300s so we don't have dupls for the above reason. > I am not sure this one applies to your spark version but spark may > speculatively execute tasks on different executors to improve > performance. If a task fails due to the *FetchFailedException*, a > speculative task might be launched on another executor. This is where fun > and games start. If the unavailable node recovers before the speculative > task finishes, both the original and speculative tasks might complete > successfully,* resulting in duplicates*. With regard to missing data, if > the data node reboot leads to data corruption or loss, some data partitions > might be completely unavailable. In this case, spark may skip processing > that missing data, leading to missing data in the final output. > > <Prem> I think when a task failed in between and retry task started and > completed it may create duplicate as failed task has some data + retry task > has full data. but my question is why spark keeps delta data or > according to you if speculative and original task completes generally spark > kills one of the tasks to get rid of dups data. when a data node is > rebooted then spark fault tolerant should go to other nodes isn't it ? then > why it has missing data. > Potential remedies: Spark offers some features to mitigate these issues, > but it might not guarantee complete elimination of duplicates or data > loss:. You can adjust parameters like *spark.shuffle.retry.wa*it and > *spark.speculation* to control retry attempts and speculative execution > behavior. Lineage tracking is there to help. Spark can track data lineage, > allowing you to identify potentially corrupted or missing data in some > cases. You can consider persisting intermediate data results to a reliable > storage (like HDFS or GCS or another cloud storage) to avoid data loss in > case of node failures. Your mileage varies as it adds additional > processing overhead but can ensure data integrity. > > <Prem> How spark will handle these without a checkpoint as it will slow > down the process . I have data loss or duplication is due to > fetchFailedException as a part of data node reboot. > I have few config to minimize fetchFailedException > spark.network.timeout=300s > spark.reducer.maxReqsInFlight=4 > spark.shuffle.io.retryWait=30s > spark.shuffle.io.maxRetries=3 > > When we get a fetchFailedException due to data node reboot then spark > should handle it gracefully isn't it ? > or how to handle it ? > > > > > > On Fri, Mar 1, 2024 at 5:35 PM Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > >> Hi, >> >> Your point -> "When Spark job shows FetchFailedException it creates few >> duplicate data and we see few data also missing , please explain why. We >> have scenario when spark job complains *FetchFailedException as one of >> the data node got ** rebooted middle of job running ."* >> >> As an engineer I can chip in. You may have partial execution and retries >> meaning when spark encounters a *FetchFailedException*, it may retry >> fetching the data from the unavailable (the one being rebooted) node a few >> times before marking it permanently unavailable. However, if the rebooted >> node recovers quickly within this retry window, some executors might >> successfully fetch the data after a retry. *This leads to duplicate >> processing of the same data partition*. >> >> I am not sure this one applies to your spark version but spark may >> speculatively execute tasks on different executors to improve >> performance. If a task fails due to the *FetchFailedException*, a >> speculative task might be launched on another executor. This is where fun >> and games start. If the unavailable node recovers before the speculative >> task finishes, both the original and speculative tasks might complete >> successfully,* resulting in duplicates*. With regard to missing data, if >> the data node reboot leads to data corruption or loss, some data partitions >> might be completely unavailable. In this case, spark may skip processing >> that missing data, leading to missing data in the final output. >> >> Potential remedies: Spark offers some features to mitigate these issues, >> but it might not guarantee complete elimination of duplicates or data >> loss:. You can adjust parameters like *spark.shuffle.retry.wa*it and >> *spark.speculation* to control retry attempts and speculative execution >> behavior. Lineage tracking is there to help. Spark can track data lineage, >> allowing you to identify potentially corrupted or missing data in some >> cases. You can consider persisting intermediate data results to a reliable >> storage (like HDFS or GCS or another cloud storage) to avoid data loss in >> case of node failures. Your mileage varies as it adds additional >> processing overhead but can ensure data integrity. >> >> HTH >> >> Mich Talebzadeh, >> Dad | Technologist >> London >> United Kingdom >> >> >> view my Linkedin profile >> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> >> >> >> https://en.everybodywiki.com/Mich_Talebzadeh >> >> >> >> *Disclaimer:* The information provided is correct to the best of my >> knowledge but of course cannot be guaranteed . It is essential to note >> that, as with any advice, quote "one test result is worth one-thousand >> expert opinions (Werner >> <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von Braun >> <https://en.wikipedia.org/wiki/Wernher_von_Braun>)". >> >> >> On Fri, 1 Mar 2024 at 20:56, Prem Sahoo <prem.re...@gmail.com> wrote: >> >>> Hello All, >>> in the list of JIRAs i didn't find anything related to >>> fetchFailedException. >>> >>> as mentioned above >>> >>> "When Spark job shows FetchFailedException it creates few duplicate data >>> and we see few data also missing , please explain why. We have a scenario >>> when spark job complains FetchFailedException as one of the data nodes got >>> rebooted in the middle of job running . >>> Now due to this we have few duplicate data and few missing data . Why is >>> spark not handling this scenario correctly ? kind of we shouldn't miss any >>> data and we shouldn't create duplicate data . " >>> >>> We have to rerun the job again to fix this data quality issue . Please >>> let me know why this case is not handled properly by Spark ? >>> >>> On Thu, Feb 29, 2024 at 9:50 PM Dongjoon Hyun <dongjoon.h...@gmail.com> >>> wrote: >>> >>>> Please use the url as thr full string including '()' part. >>>> >>>> Or you can seach directly at ASF Jira with 'Spark' project and three >>>> labels, 'Correctness', 'correctness' and 'data-loss'. >>>> >>>> Dongjoon >>>> >>>> On Thu, Feb 29, 2024 at 11:54 Prem Sahoo <prem.re...@gmail.com> wrote: >>>> >>>>> Hello Dongjoon, >>>>> Thanks for emailing me. >>>>> Could you please share a list of fixes as the link provided by you is >>>>> not working. >>>>> >>>>> On Thu, Feb 29, 2024 at 11:27 AM Dongjoon Hyun <dongj...@apache.org> >>>>> wrote: >>>>> >>>>>> Hi, >>>>>> >>>>>> If you are observing correctness issues, you may hit some old (and >>>>>> fixed) correctness issues. >>>>>> >>>>>> For example, from Apache Spark 3.2.1 to 3.2.4, we fixed 31 >>>>>> correctness issues. >>>>>> >>>>>> >>>>>> https://issues.apache.org/jira/issues/?filter=12345390&jql=project%20%3D%20SPARK%20AND%20fixVersion%20in%20(3.2.1%2C%203.2.2%2C%203.2.3%2C%203.2.4)%20AND%20labels%20in%20(Correctness%2C%20correctness%2C%20data-loss) >>>>>> >>>>>> There are more fixes in 3.3 and 3.4 and 3.5, too. >>>>>> >>>>>> Please use the latest version, Apache Spark 3.5.1, because Apache >>>>>> Spark 3.2 and 3.3 are in the End-Of-Support status of the community. >>>>>> >>>>>> It would be help if you can report any correctness issues with Apache >>>>>> Spark 3.5.1. >>>>>> >>>>>> Thanks, >>>>>> Dongjoon. >>>>>> >>>>>> On 2024/02/29 15:04:41 Prem Sahoo wrote: >>>>>> > When Spark job shows FetchFailedException it creates few duplicate >>>>>> data and >>>>>> > we see few data also missing , please explain why. We have scenario >>>>>> when >>>>>> > spark job complains FetchFailedException as one of the data node got >>>>>> > rebooted middle of job running . >>>>>> > >>>>>> > Now due to this we have few duplicate data and few missing data . >>>>>> Why spark >>>>>> > is not handling this scenario correctly ? kind of we shouldn't miss >>>>>> any >>>>>> > data and we shouldn't create duplicate data . >>>>>> > >>>>>> > >>>>>> > >>>>>> > I am using spark3.2.0 version. >>>>>> > >>>>>> >>>>>> --------------------------------------------------------------------- >>>>>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>>>>> >>>>>>
