IMO, using any kind of machine learning or AI for DRA is overkill. The effort involved would be considerable and likely counterproductive, compared to a more conventional approach of comparing the rate of incoming stream data with the effort of handling previous data rates. ________________________________ From: Mich Talebzadeh <mich.talebza...@gmail.com> Sent: Tuesday, August 8, 2023 19:59 To: Pavan Kotikalapudi <pkotikalap...@twilio.com> Cc: dev@spark.apache.org <dev@spark.apache.org> Subject: Re: Dynamic resource allocation for structured streaming [SPARK-24815]
EXTERNAL SENDER. Do not click links or open attachments unless you recognize the sender and know the content is safe. DO NOT provide your username or password. I am currently contemplating and sharing my thoughts openly. Considering our reliance on previously collected statistics (as mentioned earlier), it raises the question of why we couldn't integrate certain machine learning elements into Spark Structured Streaming? While this might slightly deviate from our current topic, I am not an expert in machine learning. However, there are individuals who possess the expertise to assist us in exploring this avenue. HTH Mich Talebzadeh, Solutions Architect/Engineering Lead London United Kingdom [https://ci3.googleusercontent.com/mail-sig/AIorK4zholKucR2Q9yMrKbHNn-o1TuS4mYXyi2KO6Xmx6ikHPySa9MLaLZ8t2hrA6AUcxSxDgHIwmKE] view my Linkedin profile<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh Disclaimer: Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Tue, 8 Aug 2023 at 18:01, Pavan Kotikalapudi <pkotikalap...@twilio.com<mailto:pkotikalap...@twilio.com>> wrote: Listeners are the best resources to the allocation manager afaik... It already has SparkListener<https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala#L640> that it utilizes. We can use it to extract more information (like processing times). The one with more information regarding streaming query resides in sql module<https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/streaming/StreamingQueryListener.scala> though. Thanks Pavan On Tue, Aug 8, 2023 at 5:43 AM Mich Talebzadeh <mich.talebza...@gmail.com<mailto:mich.talebza...@gmail.com>> wrote: Hi Pavan or anyone else Is there any way one access the matrix displayed on SparkGUI? For example the readings for processing time? Can these be acessed? Thanks For example, Mich Talebzadeh, Solutions Architect/Engineering Lead London United Kingdom [https://ci3.googleusercontent.com/mail-sig/AIorK4zholKucR2Q9yMrKbHNn-o1TuS4mYXyi2KO6Xmx6ikHPySa9MLaLZ8t2hrA6AUcxSxDgHIwmKE] view my Linkedin profile<https://urldefense.com/v3/__https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/__;!!NCc8flgU!d-qX4RylsnHucGkE4OdsO8agaKMFV59tVQnWZL1FbbZLVLWVUWgWmiiKC1Mvyy-796X-uP5XZfjLEbrVfe771d6VrCySTg$> https://en.everybodywiki.com/Mich_Talebzadeh<https://urldefense.com/v3/__https://en.everybodywiki.com/Mich_Talebzadeh__;!!NCc8flgU!d-qX4RylsnHucGkE4OdsO8agaKMFV59tVQnWZL1FbbZLVLWVUWgWmiiKC1Mvyy-796X-uP5XZfjLEbrVfe771d4r4xOqSg$> Disclaimer: Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Tue, 8 Aug 2023 at 06:44, Pavan Kotikalapudi <pkotikalap...@twilio.com<mailto:pkotikalap...@twilio.com>> wrote: Thanks for the review Mich, Yes, the configuration parameters we end up setting would be based on the trigger interval. > If you are going to have additional indicators why not look at scheduling > delay as well Yes. The implementation is based on scheduling delays, not for pending tasks of the current stage but rather pending tasks of all the stages in a micro-batch<https://urldefense.com/v3/__https://github.com/apache/spark/pull/42352/files*diff-fdddb0421641035be18233c212f0e3ccd2d6a49d345bd0cd4eac08fc4d911e21R1025__;Iw!!NCc8flgU!d-qX4RylsnHucGkE4OdsO8agaKMFV59tVQnWZL1FbbZLVLWVUWgWmiiKC1Mvyy-796X-uP5XZfjLEbrVfe771d6feoFH2Q$> (hence trigger interval). > we ought to utilise the historical statistics collected under the > checkpointing directory to get more accurate statistics You are right! This is just a simple implementation based on one factor, we should also look into other indicators as well If that would help build a better scaling algorithm. Thank you, Pavan On Mon, Aug 7, 2023 at 9:55 PM Mich Talebzadeh <mich.talebza...@gmail.com<mailto:mich.talebza...@gmail.com>> wrote: Hi, I glanced over the design doc. You are providing certain configuration parameters plus some settings based on static values. For example: spark.dynamicAllocation.schedulerBacklogTimeout": 54s I cannot see any use of <processing time> which ought to be at least half of the batch interval to have the correct margins (confidence level). If you are going to have additional indicators why not look at scheduling delay as well. Moreover most of the needed statistics are also available to set accurate values. My inclination is that this is a great effort but we ought to utilise the historical statistics collected under checkpointing directory to get more accurate statistics. I will review the design document in duew course HTH Mich Talebzadeh, Solutions Architect/Engineering Lead London United Kingdom [https://ci3.googleusercontent.com/mail-sig/AIorK4zholKucR2Q9yMrKbHNn-o1TuS4mYXyi2KO6Xmx6ikHPySa9MLaLZ8t2hrA6AUcxSxDgHIwmKE] view my Linkedin profile<https://urldefense.com/v3/__https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/__;!!NCc8flgU!blQ5zGotPbReMPXKaZw50BES4V_1AKqHv6bIxHVlc0QfY9iisFjT-u0be3CR6C6-41dtKLX5Ija0-EmAYfkcxLFr9YSZnw$> https://en.everybodywiki.com/Mich_Talebzadeh<https://urldefense.com/v3/__https://en.everybodywiki.com/Mich_Talebzadeh__;!!NCc8flgU!blQ5zGotPbReMPXKaZw50BES4V_1AKqHv6bIxHVlc0QfY9iisFjT-u0be3CR6C6-41dtKLX5Ija0-EmAYfkcxLEPx44C1w$> Disclaimer: Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Tue, 8 Aug 2023 at 01:30, Pavan Kotikalapudi <pkotikalap...@twilio.com.invalid> wrote: Hi Spark Dev, I have extended traditional DRA to work for structured streaming use-case. Here is an initial Implementation draft PR https://github.com/apache/spark/pull/42352<https://urldefense.com/v3/__https://github.com/apache/spark/pull/42352__;!!NCc8flgU!blQ5zGotPbReMPXKaZw50BES4V_1AKqHv6bIxHVlc0QfY9iisFjT-u0be3CR6C6-41dtKLX5Ija0-EmAYfkcxLHLe7WCUw$> and design doc: https://docs.google.com/document/d/1_YmfCsQQb9XhRdKh0ijbc-j8JKGtGBxYsk_30NVSTWo/edit?usp=sharing<https://urldefense.com/v3/__https://docs.google.com/document/d/1_YmfCsQQb9XhRdKh0ijbc-j8JKGtGBxYsk_30NVSTWo/edit?usp=sharing__;!!NCc8flgU!blQ5zGotPbReMPXKaZw50BES4V_1AKqHv6bIxHVlc0QfY9iisFjT-u0be3CR6C6-41dtKLX5Ija0-EmAYfkcxLFAjJfilg$> Please review and let me know what you think. Thank you, Pavan
