Re: Lifecycle of RDD in spark-streaming
Hi TD, We also struggled with this error for a long while. The recurring scenario is when the job takes longer to compute than the job interval and a backlog starts to pile up. Hint: Check If the DStream storage level is set to MEMORY_ONLY_SER and memory runs out, then you will get a 'Cannot compute split: Missing block ...'. What I don't know ATM is whether the new data is dropped or the LRU policy removes data in the system in favor for the incoming data. In any case, the DStream processing still thinks the data is there at the moment the job is scheduled to run and fails to run. In our case, changing storage to MEMORY_AND_DISK_SER solved the problem and our streaming job can get through tought times without issues. Regularly checking 'scheduling delay' and 'total delay' on the Streaming tab in the UI is a must. (And soon we will have that on the metrics report as well!! :-) ) -kr, Gerard. On Thu, Nov 27, 2014 at 8:14 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi TD, I am using Spark Streaming to consume data from Kafka and do some aggregation and ingest the results into RDS. I do use foreachRDD in the program. I am planning to use Spark streaming in our production pipeline and it performs well in generating the results. Unfortunately, we plan to have a production pipeline 24/7 and Spark streaming job usually fails after 8-20 hours due to the exception cannot compute split. In other cases, the Kafka receiver has failure and the program runs without producing any result. In my pipeline, the batch size is 1 minute and the data volume per minute from Kafka is 3G. I have been struggling with this issue for more than a month. It will be great if you can provide some solutions for this. Thanks! Bill On Wed, Nov 26, 2014 at 5:35 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Can you elaborate on the usage pattern that lead to cannot compute split ? Are you using the RDDs generated by DStream, outside the DStream logic? Something like running interactive Spark jobs (independent of the Spark Streaming ones) on RDDs generated by DStreams? If that is the case, what is happening is that Spark Streaming is not aware that some of the RDDs (and the raw input data that it will need) will be used by Spark jobs unrelated to Spark Streaming. Hence Spark Streaming will actively clear off the raw data, leading to failures in the unrelated Spark jobs using that data. In case this is your use case, the cleanest way to solve this, is by asking Spark Streaming remember stuff for longer, by using streamingContext.remember(duration). This will ensure that Spark Streaming will keep around all the stuff for at least that duration. Hope this helps. TD On Wed, Nov 26, 2014 at 5:07 PM, Bill Jay bill.jaypeter...@gmail.com wrote: Just add one more point. If Spark streaming knows when the RDD will not be used any more, I believe Spark will not try to retrieve data it will not use any more. However, in practice, I often encounter the error of cannot compute split. Based on my understanding, this is because Spark cleared out data that will be used again. In my case, the data volume is much smaller (30M/s, the batch size is 60 seconds) than the memory (20G each executor). If Spark will only keep RDD that are in use, I expect that this error may not happen. Bill On Wed, Nov 26, 2014 at 4:02 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Let me further clarify Lalit's point on when RDDs generated by DStreams are destroyed, and hopefully that will answer your original questions. 1. How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. This is isnt hard! By the time you call streamingContext.start(), you have already set up the output operations (foreachRDD, saveAs***Files, etc.) that you want to do with the DStream. There are RDD actions inside the DStream output oeprations that need to be done every batch interval. So all the systems does is this - after every batch interval, put all the output operations (that will call RDD actions) in a job queue, and then keep executing stuff in the queue. If there is any failure in running the jobs, the streaming context will stop. 2. How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Spark Streaming knows when the all the processing related to batch T has been completed. And also it keeps track of how much time of the previous RDDs does it need to remember and keep around in the cache based on what DStream operations have been done. For example, if you are using a window 1 minute, the system knows that it needs to keep around at least last 1 minute data in the memory. Accordingly, it cleans up the input data (actively unpersisted), and cached RDD (simply dereferenced from DStream metadata, and then
Re: Lifecycle of RDD in spark-streaming
Gerard, That is a good observation. However, the strange thing I meet is if I use MEMORY_AND_DISK_SER, the job even fails earlier. In my case, it takes 10 seconds to process my data of every batch, which is one minute. It fails after 10 hours with the cannot compute split error. Bill On Thu, Nov 27, 2014 at 3:31 AM, Gerard Maas gerard.m...@gmail.com wrote: Hi TD, We also struggled with this error for a long while. The recurring scenario is when the job takes longer to compute than the job interval and a backlog starts to pile up. Hint: Check If the DStream storage level is set to MEMORY_ONLY_SER and memory runs out, then you will get a 'Cannot compute split: Missing block ...'. What I don't know ATM is whether the new data is dropped or the LRU policy removes data in the system in favor for the incoming data. In any case, the DStream processing still thinks the data is there at the moment the job is scheduled to run and fails to run. In our case, changing storage to MEMORY_AND_DISK_SER solved the problem and our streaming job can get through tought times without issues. Regularly checking 'scheduling delay' and 'total delay' on the Streaming tab in the UI is a must. (And soon we will have that on the metrics report as well!! :-) ) -kr, Gerard. On Thu, Nov 27, 2014 at 8:14 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi TD, I am using Spark Streaming to consume data from Kafka and do some aggregation and ingest the results into RDS. I do use foreachRDD in the program. I am planning to use Spark streaming in our production pipeline and it performs well in generating the results. Unfortunately, we plan to have a production pipeline 24/7 and Spark streaming job usually fails after 8-20 hours due to the exception cannot compute split. In other cases, the Kafka receiver has failure and the program runs without producing any result. In my pipeline, the batch size is 1 minute and the data volume per minute from Kafka is 3G. I have been struggling with this issue for more than a month. It will be great if you can provide some solutions for this. Thanks! Bill On Wed, Nov 26, 2014 at 5:35 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Can you elaborate on the usage pattern that lead to cannot compute split ? Are you using the RDDs generated by DStream, outside the DStream logic? Something like running interactive Spark jobs (independent of the Spark Streaming ones) on RDDs generated by DStreams? If that is the case, what is happening is that Spark Streaming is not aware that some of the RDDs (and the raw input data that it will need) will be used by Spark jobs unrelated to Spark Streaming. Hence Spark Streaming will actively clear off the raw data, leading to failures in the unrelated Spark jobs using that data. In case this is your use case, the cleanest way to solve this, is by asking Spark Streaming remember stuff for longer, by using streamingContext.remember(duration). This will ensure that Spark Streaming will keep around all the stuff for at least that duration. Hope this helps. TD On Wed, Nov 26, 2014 at 5:07 PM, Bill Jay bill.jaypeter...@gmail.com wrote: Just add one more point. If Spark streaming knows when the RDD will not be used any more, I believe Spark will not try to retrieve data it will not use any more. However, in practice, I often encounter the error of cannot compute split. Based on my understanding, this is because Spark cleared out data that will be used again. In my case, the data volume is much smaller (30M/s, the batch size is 60 seconds) than the memory (20G each executor). If Spark will only keep RDD that are in use, I expect that this error may not happen. Bill On Wed, Nov 26, 2014 at 4:02 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Let me further clarify Lalit's point on when RDDs generated by DStreams are destroyed, and hopefully that will answer your original questions. 1. How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. This is isnt hard! By the time you call streamingContext.start(), you have already set up the output operations (foreachRDD, saveAs***Files, etc.) that you want to do with the DStream. There are RDD actions inside the DStream output oeprations that need to be done every batch interval. So all the systems does is this - after every batch interval, put all the output operations (that will call RDD actions) in a job queue, and then keep executing stuff in the queue. If there is any failure in running the jobs, the streaming context will stop. 2. How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Spark Streaming knows when the all the processing related to batch T has been completed. And also it keeps track of how much time of the previous RDDs does
Re: Lifecycle of RDD in spark-streaming
If it regularly fails after 8 hours then could you get me the log4j logs? To limit the size, set default log level to Warn and the level of logs for all classes in package o.a.s.streaming to Debug. Then I can take a look. On Nov 27, 2014 11:01 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Gerard, That is a good observation. However, the strange thing I meet is if I use MEMORY_AND_DISK_SER, the job even fails earlier. In my case, it takes 10 seconds to process my data of every batch, which is one minute. It fails after 10 hours with the cannot compute split error. Bill On Thu, Nov 27, 2014 at 3:31 AM, Gerard Maas gerard.m...@gmail.com wrote: Hi TD, We also struggled with this error for a long while. The recurring scenario is when the job takes longer to compute than the job interval and a backlog starts to pile up. Hint: Check If the DStream storage level is set to MEMORY_ONLY_SER and memory runs out, then you will get a 'Cannot compute split: Missing block ...'. What I don't know ATM is whether the new data is dropped or the LRU policy removes data in the system in favor for the incoming data. In any case, the DStream processing still thinks the data is there at the moment the job is scheduled to run and fails to run. In our case, changing storage to MEMORY_AND_DISK_SER solved the problem and our streaming job can get through tought times without issues. Regularly checking 'scheduling delay' and 'total delay' on the Streaming tab in the UI is a must. (And soon we will have that on the metrics report as well!! :-) ) -kr, Gerard. On Thu, Nov 27, 2014 at 8:14 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi TD, I am using Spark Streaming to consume data from Kafka and do some aggregation and ingest the results into RDS. I do use foreachRDD in the program. I am planning to use Spark streaming in our production pipeline and it performs well in generating the results. Unfortunately, we plan to have a production pipeline 24/7 and Spark streaming job usually fails after 8-20 hours due to the exception cannot compute split. In other cases, the Kafka receiver has failure and the program runs without producing any result. In my pipeline, the batch size is 1 minute and the data volume per minute from Kafka is 3G. I have been struggling with this issue for more than a month. It will be great if you can provide some solutions for this. Thanks! Bill On Wed, Nov 26, 2014 at 5:35 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Can you elaborate on the usage pattern that lead to cannot compute split ? Are you using the RDDs generated by DStream, outside the DStream logic? Something like running interactive Spark jobs (independent of the Spark Streaming ones) on RDDs generated by DStreams? If that is the case, what is happening is that Spark Streaming is not aware that some of the RDDs (and the raw input data that it will need) will be used by Spark jobs unrelated to Spark Streaming. Hence Spark Streaming will actively clear off the raw data, leading to failures in the unrelated Spark jobs using that data. In case this is your use case, the cleanest way to solve this, is by asking Spark Streaming remember stuff for longer, by using streamingContext.remember(duration). This will ensure that Spark Streaming will keep around all the stuff for at least that duration. Hope this helps. TD On Wed, Nov 26, 2014 at 5:07 PM, Bill Jay bill.jaypeter...@gmail.com wrote: Just add one more point. If Spark streaming knows when the RDD will not be used any more, I believe Spark will not try to retrieve data it will not use any more. However, in practice, I often encounter the error of cannot compute split. Based on my understanding, this is because Spark cleared out data that will be used again. In my case, the data volume is much smaller (30M/s, the batch size is 60 seconds) than the memory (20G each executor). If Spark will only keep RDD that are in use, I expect that this error may not happen. Bill On Wed, Nov 26, 2014 at 4:02 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Let me further clarify Lalit's point on when RDDs generated by DStreams are destroyed, and hopefully that will answer your original questions. 1. How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. This is isnt hard! By the time you call streamingContext.start(), you have already set up the output operations (foreachRDD, saveAs***Files, etc.) that you want to do with the DStream. There are RDD actions inside the DStream output oeprations that need to be done every batch interval. So all the systems does is this - after every batch interval, put all the output operations (that will call RDD actions) in a job queue, and then keep executing stuff in the queue. If there is any failure in running the jobs, the streaming context will stop. 2. How does spark determines
Re: Lifecycle of RDD in spark-streaming
When there is new data comes in a stream spark use streams classes to convert it into RDD and as you mention its follow with transformation and finally action. Till the time user doesn't destroy or application is alive All RDD remain in Memory as far as I experienced. On 26 November 2014 at 20:05, Mukesh Jha [via Apache Spark User List] ml-node+s1001560n19835...@n3.nabble.com wrote: Any pointers guys? On Tue, Nov 25, 2014 at 5:32 PM, Mukesh Jha [hidden email] http://user/SendEmail.jtp?type=nodenode=19835i=0 wrote: Hey Experts, I wanted to understand in detail about the lifecycle of rdd(s) in a streaming app. From my current understanding - rdd gets created out of the realtime input stream. - Transform(s) functions are applied in a lazy fashion on the RDD to transform into another rdd(s). - Actions are taken on the final transformed rdds to get the data out of the system. Also rdd(s) are stored in the clusters RAM (disc if configured so) and are cleaned in LRU fashion. So I have the following questions on the same. - How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. - How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Thanks in advance for all your help. Also, I'm relatively new to scala spark so pardon me in case these are naive questions/assumptions. -- Thanks Regards, *[hidden email] http://user/SendEmail.jtp?type=nodenode=19835i=1* -- Thanks Regards, *[hidden email] http://user/SendEmail.jtp?type=nodenode=19835i=2* -- If you reply to this email, your message will be added to the discussion below: http://apache-spark-user-list.1001560.n3.nabble.com/Lifecycle-of-RDD-in-spark-streaming-tp19749p19835.html To start a new topic under Apache Spark User List, email ml-node+s1001560n1...@n3.nabble.com To unsubscribe from Apache Spark User List, click here http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_codenode=1code=aG5haGFrQHd5bnlhcmRncm91cC5jb218MXwtMTgxOTE5MTkyOQ== . NAML http://apache-spark-user-list.1001560.n3.nabble.com/template/NamlServlet.jtp?macro=macro_viewerid=instant_html%21nabble%3Aemail.namlbase=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespacebreadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml -- Regards, Harihar Nahak BigData Developer Wynyard Email:hna...@wynyardgroup.com | Extn: 8019 - --Harihar -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Lifecycle-of-RDD-in-spark-streaming-tp19749p19987.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
Re: Lifecycle of RDD in spark-streaming
Hi Mukesh, Once you create a streming job, a DAG is created which contains your job plan i.e. all map transformation and all action operations to be performed on each batch of streaming application. So, once your job is started, the input dstream take the data input from specified source and all the transformations/actions are performed according to the DAG created. Once all the operations on dstream are performed, the dstream is destroyed in LRU fashion. - Lalit Yadav la...@sigmoidanalytics.com -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Lifecycle-of-RDD-in-spark-streaming-tp19749p19850.html Sent from the Apache Spark User List mailing list archive at Nabble.com. - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Lifecycle of RDD in spark-streaming
I have found this paper seems to answer most of questions about life duration.https://www.cs.berkeley.edu/~matei/papers/2012/hotcloud_spark_streaming.pdf Tian On Tuesday, November 25, 2014 4:02 AM, Mukesh Jha me.mukesh@gmail.com wrote: Hey Experts, I wanted to understand in detail about the lifecycle of rdd(s) in a streaming app. From my current understanding- rdd gets created out of the realtime input stream. - Transform(s) functions are applied in a lazy fashion on the RDD to transform into another rdd(s).- Actions are taken on the final transformed rdds to get the data out of the system. Also rdd(s) are stored in the clusters RAM (disc if configured so) and are cleaned in LRU fashion. So I have the following questions on the same. - How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. - How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Thanks in advance for all your help. Also, I'm relatively new to scala spark so pardon me in case these are naive questions/assumptions. -- Thanks Regards, Mukesh Jha
Re: Lifecycle of RDD in spark-streaming
Just add one more point. If Spark streaming knows when the RDD will not be used any more, I believe Spark will not try to retrieve data it will not use any more. However, in practice, I often encounter the error of cannot compute split. Based on my understanding, this is because Spark cleared out data that will be used again. In my case, the data volume is much smaller (30M/s, the batch size is 60 seconds) than the memory (20G each executor). If Spark will only keep RDD that are in use, I expect that this error may not happen. Bill On Wed, Nov 26, 2014 at 4:02 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Let me further clarify Lalit's point on when RDDs generated by DStreams are destroyed, and hopefully that will answer your original questions. 1. How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. This is isnt hard! By the time you call streamingContext.start(), you have already set up the output operations (foreachRDD, saveAs***Files, etc.) that you want to do with the DStream. There are RDD actions inside the DStream output oeprations that need to be done every batch interval. So all the systems does is this - after every batch interval, put all the output operations (that will call RDD actions) in a job queue, and then keep executing stuff in the queue. If there is any failure in running the jobs, the streaming context will stop. 2. How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Spark Streaming knows when the all the processing related to batch T has been completed. And also it keeps track of how much time of the previous RDDs does it need to remember and keep around in the cache based on what DStream operations have been done. For example, if you are using a window 1 minute, the system knows that it needs to keep around at least last 1 minute data in the memory. Accordingly, it cleans up the input data (actively unpersisted), and cached RDD (simply dereferenced from DStream metadata, and then Spark unpersists them as the RDD object gets GarbageCollected by the JVM). TD On Wed, Nov 26, 2014 at 10:10 AM, tian zhang tzhang...@yahoo.com.invalid wrote: I have found this paper seems to answer most of questions about life duration. https://www.cs.berkeley.edu/~matei/papers/2012/hotcloud_spark_streaming.pdf Tian On Tuesday, November 25, 2014 4:02 AM, Mukesh Jha me.mukesh@gmail.com wrote: Hey Experts, I wanted to understand in detail about the lifecycle of rdd(s) in a streaming app. From my current understanding - rdd gets created out of the realtime input stream. - Transform(s) functions are applied in a lazy fashion on the RDD to transform into another rdd(s). - Actions are taken on the final transformed rdds to get the data out of the system. Also rdd(s) are stored in the clusters RAM (disc if configured so) and are cleaned in LRU fashion. So I have the following questions on the same. - How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. - How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Thanks in advance for all your help. Also, I'm relatively new to scala spark so pardon me in case these are naive questions/assumptions. -- Thanks Regards, Mukesh Jha - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Lifecycle of RDD in spark-streaming
Can you elaborate on the usage pattern that lead to cannot compute split ? Are you using the RDDs generated by DStream, outside the DStream logic? Something like running interactive Spark jobs (independent of the Spark Streaming ones) on RDDs generated by DStreams? If that is the case, what is happening is that Spark Streaming is not aware that some of the RDDs (and the raw input data that it will need) will be used by Spark jobs unrelated to Spark Streaming. Hence Spark Streaming will actively clear off the raw data, leading to failures in the unrelated Spark jobs using that data. In case this is your use case, the cleanest way to solve this, is by asking Spark Streaming remember stuff for longer, by using streamingContext.remember(duration). This will ensure that Spark Streaming will keep around all the stuff for at least that duration. Hope this helps. TD On Wed, Nov 26, 2014 at 5:07 PM, Bill Jay bill.jaypeter...@gmail.com wrote: Just add one more point. If Spark streaming knows when the RDD will not be used any more, I believe Spark will not try to retrieve data it will not use any more. However, in practice, I often encounter the error of cannot compute split. Based on my understanding, this is because Spark cleared out data that will be used again. In my case, the data volume is much smaller (30M/s, the batch size is 60 seconds) than the memory (20G each executor). If Spark will only keep RDD that are in use, I expect that this error may not happen. Bill On Wed, Nov 26, 2014 at 4:02 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Let me further clarify Lalit's point on when RDDs generated by DStreams are destroyed, and hopefully that will answer your original questions. 1. How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. This is isnt hard! By the time you call streamingContext.start(), you have already set up the output operations (foreachRDD, saveAs***Files, etc.) that you want to do with the DStream. There are RDD actions inside the DStream output oeprations that need to be done every batch interval. So all the systems does is this - after every batch interval, put all the output operations (that will call RDD actions) in a job queue, and then keep executing stuff in the queue. If there is any failure in running the jobs, the streaming context will stop. 2. How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Spark Streaming knows when the all the processing related to batch T has been completed. And also it keeps track of how much time of the previous RDDs does it need to remember and keep around in the cache based on what DStream operations have been done. For example, if you are using a window 1 minute, the system knows that it needs to keep around at least last 1 minute data in the memory. Accordingly, it cleans up the input data (actively unpersisted), and cached RDD (simply dereferenced from DStream metadata, and then Spark unpersists them as the RDD object gets GarbageCollected by the JVM). TD On Wed, Nov 26, 2014 at 10:10 AM, tian zhang tzhang...@yahoo.com.invalid wrote: I have found this paper seems to answer most of questions about life duration. https://www.cs.berkeley.edu/~matei/papers/2012/hotcloud_spark_streaming.pdf Tian On Tuesday, November 25, 2014 4:02 AM, Mukesh Jha me.mukesh@gmail.com wrote: Hey Experts, I wanted to understand in detail about the lifecycle of rdd(s) in a streaming app. From my current understanding - rdd gets created out of the realtime input stream. - Transform(s) functions are applied in a lazy fashion on the RDD to transform into another rdd(s). - Actions are taken on the final transformed rdds to get the data out of the system. Also rdd(s) are stored in the clusters RAM (disc if configured so) and are cleaned in LRU fashion. So I have the following questions on the same. - How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. - How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Thanks in advance for all your help. Also, I'm relatively new to scala spark so pardon me in case these are naive questions/assumptions. -- Thanks Regards, Mukesh Jha - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Lifecycle of RDD in spark-streaming
Hi TD, I am using Spark Streaming to consume data from Kafka and do some aggregation and ingest the results into RDS. I do use foreachRDD in the program. I am planning to use Spark streaming in our production pipeline and it performs well in generating the results. Unfortunately, we plan to have a production pipeline 24/7 and Spark streaming job usually fails after 8-20 hours due to the exception cannot compute split. In other cases, the Kafka receiver has failure and the program runs without producing any result. In my pipeline, the batch size is 1 minute and the data volume per minute from Kafka is 3G. I have been struggling with this issue for more than a month. It will be great if you can provide some solutions for this. Thanks! Bill On Wed, Nov 26, 2014 at 5:35 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Can you elaborate on the usage pattern that lead to cannot compute split ? Are you using the RDDs generated by DStream, outside the DStream logic? Something like running interactive Spark jobs (independent of the Spark Streaming ones) on RDDs generated by DStreams? If that is the case, what is happening is that Spark Streaming is not aware that some of the RDDs (and the raw input data that it will need) will be used by Spark jobs unrelated to Spark Streaming. Hence Spark Streaming will actively clear off the raw data, leading to failures in the unrelated Spark jobs using that data. In case this is your use case, the cleanest way to solve this, is by asking Spark Streaming remember stuff for longer, by using streamingContext.remember(duration). This will ensure that Spark Streaming will keep around all the stuff for at least that duration. Hope this helps. TD On Wed, Nov 26, 2014 at 5:07 PM, Bill Jay bill.jaypeter...@gmail.com wrote: Just add one more point. If Spark streaming knows when the RDD will not be used any more, I believe Spark will not try to retrieve data it will not use any more. However, in practice, I often encounter the error of cannot compute split. Based on my understanding, this is because Spark cleared out data that will be used again. In my case, the data volume is much smaller (30M/s, the batch size is 60 seconds) than the memory (20G each executor). If Spark will only keep RDD that are in use, I expect that this error may not happen. Bill On Wed, Nov 26, 2014 at 4:02 PM, Tathagata Das tathagata.das1...@gmail.com wrote: Let me further clarify Lalit's point on when RDDs generated by DStreams are destroyed, and hopefully that will answer your original questions. 1. How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. This is isnt hard! By the time you call streamingContext.start(), you have already set up the output operations (foreachRDD, saveAs***Files, etc.) that you want to do with the DStream. There are RDD actions inside the DStream output oeprations that need to be done every batch interval. So all the systems does is this - after every batch interval, put all the output operations (that will call RDD actions) in a job queue, and then keep executing stuff in the queue. If there is any failure in running the jobs, the streaming context will stop. 2. How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Spark Streaming knows when the all the processing related to batch T has been completed. And also it keeps track of how much time of the previous RDDs does it need to remember and keep around in the cache based on what DStream operations have been done. For example, if you are using a window 1 minute, the system knows that it needs to keep around at least last 1 minute data in the memory. Accordingly, it cleans up the input data (actively unpersisted), and cached RDD (simply dereferenced from DStream metadata, and then Spark unpersists them as the RDD object gets GarbageCollected by the JVM). TD On Wed, Nov 26, 2014 at 10:10 AM, tian zhang tzhang...@yahoo.com.invalid wrote: I have found this paper seems to answer most of questions about life duration. https://www.cs.berkeley.edu/~matei/papers/2012/hotcloud_spark_streaming.pdf Tian On Tuesday, November 25, 2014 4:02 AM, Mukesh Jha me.mukesh@gmail.com wrote: Hey Experts, I wanted to understand in detail about the lifecycle of rdd(s) in a streaming app. From my current understanding - rdd gets created out of the realtime input stream. - Transform(s) functions are applied in a lazy fashion on the RDD to transform into another rdd(s). - Actions are taken on the final transformed rdds to get the data out of the system. Also rdd(s) are stored in the clusters RAM (disc if configured so) and are cleaned in LRU fashion. So I have the following questions on the
Lifecycle of RDD in spark-streaming
Hey Experts, I wanted to understand in detail about the lifecycle of rdd(s) in a streaming app. From my current understanding - rdd gets created out of the realtime input stream. - Transform(s) functions are applied in a lazy fashion on the RDD to transform into another rdd(s). - Actions are taken on the final transformed rdds to get the data out of the system. Also rdd(s) are stored in the clusters RAM (disc if configured so) and are cleaned in LRU fashion. So I have the following questions on the same. - How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. - How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Thanks in advance for all your help. Also, I'm relatively new to scala spark so pardon me in case these are naive questions/assumptions. -- Thanks Regards, *Mukesh Jha me.mukesh@gmail.com*
Re: Lifecycle of RDD in spark-streaming
Any pointers guys? On Tue, Nov 25, 2014 at 5:32 PM, Mukesh Jha me.mukesh@gmail.com wrote: Hey Experts, I wanted to understand in detail about the lifecycle of rdd(s) in a streaming app. From my current understanding - rdd gets created out of the realtime input stream. - Transform(s) functions are applied in a lazy fashion on the RDD to transform into another rdd(s). - Actions are taken on the final transformed rdds to get the data out of the system. Also rdd(s) are stored in the clusters RAM (disc if configured so) and are cleaned in LRU fashion. So I have the following questions on the same. - How spark (streaming) guarantees that all the actions are taken on each input rdd/batch. - How does spark determines that the life-cycle of a rdd is complete. Is there any chance that a RDD will be cleaned out of ram before all actions are taken on them? Thanks in advance for all your help. Also, I'm relatively new to scala spark so pardon me in case these are naive questions/assumptions. -- Thanks Regards, *Mukesh Jha me.mukesh@gmail.com* -- Thanks Regards, *Mukesh Jha me.mukesh@gmail.com*
