Re: Use Spark Streaming to update result whenever data come
Tobias, Your help on the problems I have met have been very helpful. Thanks a lot! Bill On Wed, Jul 9, 2014 at 6:04 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, good to know you found your bottleneck. Unfortunately, I don't know how to solve this; until know, I have used Spark only with embarassingly parallel operations such as map or filter. I hope someone else might provide more insight here. Tobias On Thu, Jul 10, 2014 at 9:57 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Now I did the re-partition and ran the program again. I find a bottleneck of the whole program. In the streaming, there is a stage marked as *combineByKey at ShuffledDStream.scala:42 *in spark UI. This stage is repeatedly executed. However, during some batches, the number of executors allocated to this step is only 2 although I used 300 workers and specified the partition number as 300. In this case, the program is very slow although the data that are processed are not big. Do you know how to solve this issue? Thanks! On Wed, Jul 9, 2014 at 5:51 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, I haven't worked with Yarn, but I would try adding a repartition() call after you receive your data from Kafka. I would be surprised if that didn't help. On Thu, Jul 10, 2014 at 6:23 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, I was using Spark 0.9 before and the master I used was yarn-standalone. In Spark 1.0, the master will be either yarn-cluster or yarn-client. I am not sure whether it is the reason why more machines do not provide better scalability. What is the difference between these two modes in terms of efficiency? Thanks! On Tue, Jul 8, 2014 at 5:26 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, do the additional 100 nodes receive any tasks at all? (I don't know which cluster you use, but with Mesos you could check client logs in the web interface.) You might want to try something like repartition(N) or repartition(N*2) (with N the number of your nodes) after you receive your data. Tobias On Wed, Jul 9, 2014 at 3:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Thanks for the suggestion. I have tried to add more nodes from 300 to 400. It seems the running time did not get improved. On Wed, Jul 2, 2014 at 6:47 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, can't you just add more nodes in order to speed up the processing? Tobias On Thu, Jul 3, 2014 at 7:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi all, I have a problem of using Spark Streaming to accept input data and update a result. The input of the data is from Kafka and the output is to report a map which is updated by historical data in every minute. My current method is to set batch size as 1 minute and use foreachRDD to update this map and output the map at the end of the foreachRDD function. However, the current issue is the processing cannot be finished within one minute. I am thinking of updating the map whenever the new data come instead of doing the update when the whoe RDD comes. Is there any idea on how to achieve this in a better running time? Thanks! Bill
Re: Use Spark Streaming to update result whenever data come
Hi Tobias, I was using Spark 0.9 before and the master I used was yarn-standalone. In Spark 1.0, the master will be either yarn-cluster or yarn-client. I am not sure whether it is the reason why more machines do not provide better scalability. What is the difference between these two modes in terms of efficiency? Thanks! On Tue, Jul 8, 2014 at 5:26 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, do the additional 100 nodes receive any tasks at all? (I don't know which cluster you use, but with Mesos you could check client logs in the web interface.) You might want to try something like repartition(N) or repartition(N*2) (with N the number of your nodes) after you receive your data. Tobias On Wed, Jul 9, 2014 at 3:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Thanks for the suggestion. I have tried to add more nodes from 300 to 400. It seems the running time did not get improved. On Wed, Jul 2, 2014 at 6:47 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, can't you just add more nodes in order to speed up the processing? Tobias On Thu, Jul 3, 2014 at 7:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi all, I have a problem of using Spark Streaming to accept input data and update a result. The input of the data is from Kafka and the output is to report a map which is updated by historical data in every minute. My current method is to set batch size as 1 minute and use foreachRDD to update this map and output the map at the end of the foreachRDD function. However, the current issue is the processing cannot be finished within one minute. I am thinking of updating the map whenever the new data come instead of doing the update when the whoe RDD comes. Is there any idea on how to achieve this in a better running time? Thanks! Bill
Re: Use Spark Streaming to update result whenever data come
Bill, I haven't worked with Yarn, but I would try adding a repartition() call after you receive your data from Kafka. I would be surprised if that didn't help. On Thu, Jul 10, 2014 at 6:23 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, I was using Spark 0.9 before and the master I used was yarn-standalone. In Spark 1.0, the master will be either yarn-cluster or yarn-client. I am not sure whether it is the reason why more machines do not provide better scalability. What is the difference between these two modes in terms of efficiency? Thanks! On Tue, Jul 8, 2014 at 5:26 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, do the additional 100 nodes receive any tasks at all? (I don't know which cluster you use, but with Mesos you could check client logs in the web interface.) You might want to try something like repartition(N) or repartition(N*2) (with N the number of your nodes) after you receive your data. Tobias On Wed, Jul 9, 2014 at 3:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Thanks for the suggestion. I have tried to add more nodes from 300 to 400. It seems the running time did not get improved. On Wed, Jul 2, 2014 at 6:47 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, can't you just add more nodes in order to speed up the processing? Tobias On Thu, Jul 3, 2014 at 7:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi all, I have a problem of using Spark Streaming to accept input data and update a result. The input of the data is from Kafka and the output is to report a map which is updated by historical data in every minute. My current method is to set batch size as 1 minute and use foreachRDD to update this map and output the map at the end of the foreachRDD function. However, the current issue is the processing cannot be finished within one minute. I am thinking of updating the map whenever the new data come instead of doing the update when the whoe RDD comes. Is there any idea on how to achieve this in a better running time? Thanks! Bill
Re: Use Spark Streaming to update result whenever data come
Hi Tobias, Now I did the re-partition and ran the program again. I find a bottleneck of the whole program. In the streaming, there is a stage marked as *combineByKey at ShuffledDStream.scala:42 *in spark UI. This stage is repeatedly executed. However, during some batches, the number of executors allocated to this step is only 2 although I used 300 workers and specified the partition number as 300. In this case, the program is very slow although the data that are processed are not big. Do you know how to solve this issue? Thanks! On Wed, Jul 9, 2014 at 5:51 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, I haven't worked with Yarn, but I would try adding a repartition() call after you receive your data from Kafka. I would be surprised if that didn't help. On Thu, Jul 10, 2014 at 6:23 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, I was using Spark 0.9 before and the master I used was yarn-standalone. In Spark 1.0, the master will be either yarn-cluster or yarn-client. I am not sure whether it is the reason why more machines do not provide better scalability. What is the difference between these two modes in terms of efficiency? Thanks! On Tue, Jul 8, 2014 at 5:26 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, do the additional 100 nodes receive any tasks at all? (I don't know which cluster you use, but with Mesos you could check client logs in the web interface.) You might want to try something like repartition(N) or repartition(N*2) (with N the number of your nodes) after you receive your data. Tobias On Wed, Jul 9, 2014 at 3:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Thanks for the suggestion. I have tried to add more nodes from 300 to 400. It seems the running time did not get improved. On Wed, Jul 2, 2014 at 6:47 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, can't you just add more nodes in order to speed up the processing? Tobias On Thu, Jul 3, 2014 at 7:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi all, I have a problem of using Spark Streaming to accept input data and update a result. The input of the data is from Kafka and the output is to report a map which is updated by historical data in every minute. My current method is to set batch size as 1 minute and use foreachRDD to update this map and output the map at the end of the foreachRDD function. However, the current issue is the processing cannot be finished within one minute. I am thinking of updating the map whenever the new data come instead of doing the update when the whoe RDD comes. Is there any idea on how to achieve this in a better running time? Thanks! Bill
Re: Use Spark Streaming to update result whenever data come
Bill, good to know you found your bottleneck. Unfortunately, I don't know how to solve this; until know, I have used Spark only with embarassingly parallel operations such as map or filter. I hope someone else might provide more insight here. Tobias On Thu, Jul 10, 2014 at 9:57 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Now I did the re-partition and ran the program again. I find a bottleneck of the whole program. In the streaming, there is a stage marked as *combineByKey at ShuffledDStream.scala:42 *in spark UI. This stage is repeatedly executed. However, during some batches, the number of executors allocated to this step is only 2 although I used 300 workers and specified the partition number as 300. In this case, the program is very slow although the data that are processed are not big. Do you know how to solve this issue? Thanks! On Wed, Jul 9, 2014 at 5:51 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, I haven't worked with Yarn, but I would try adding a repartition() call after you receive your data from Kafka. I would be surprised if that didn't help. On Thu, Jul 10, 2014 at 6:23 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, I was using Spark 0.9 before and the master I used was yarn-standalone. In Spark 1.0, the master will be either yarn-cluster or yarn-client. I am not sure whether it is the reason why more machines do not provide better scalability. What is the difference between these two modes in terms of efficiency? Thanks! On Tue, Jul 8, 2014 at 5:26 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, do the additional 100 nodes receive any tasks at all? (I don't know which cluster you use, but with Mesos you could check client logs in the web interface.) You might want to try something like repartition(N) or repartition(N*2) (with N the number of your nodes) after you receive your data. Tobias On Wed, Jul 9, 2014 at 3:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Thanks for the suggestion. I have tried to add more nodes from 300 to 400. It seems the running time did not get improved. On Wed, Jul 2, 2014 at 6:47 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, can't you just add more nodes in order to speed up the processing? Tobias On Thu, Jul 3, 2014 at 7:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi all, I have a problem of using Spark Streaming to accept input data and update a result. The input of the data is from Kafka and the output is to report a map which is updated by historical data in every minute. My current method is to set batch size as 1 minute and use foreachRDD to update this map and output the map at the end of the foreachRDD function. However, the current issue is the processing cannot be finished within one minute. I am thinking of updating the map whenever the new data come instead of doing the update when the whoe RDD comes. Is there any idea on how to achieve this in a better running time? Thanks! Bill
Re: Use Spark Streaming to update result whenever data come
Hi Tobias, Thanks for the suggestion. I have tried to add more nodes from 300 to 400. It seems the running time did not get improved. On Wed, Jul 2, 2014 at 6:47 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, can't you just add more nodes in order to speed up the processing? Tobias On Thu, Jul 3, 2014 at 7:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi all, I have a problem of using Spark Streaming to accept input data and update a result. The input of the data is from Kafka and the output is to report a map which is updated by historical data in every minute. My current method is to set batch size as 1 minute and use foreachRDD to update this map and output the map at the end of the foreachRDD function. However, the current issue is the processing cannot be finished within one minute. I am thinking of updating the map whenever the new data come instead of doing the update when the whoe RDD comes. Is there any idea on how to achieve this in a better running time? Thanks! Bill
Re: Use Spark Streaming to update result whenever data come
Bill, do the additional 100 nodes receive any tasks at all? (I don't know which cluster you use, but with Mesos you could check client logs in the web interface.) You might want to try something like repartition(N) or repartition(N*2) (with N the number of your nodes) after you receive your data. Tobias On Wed, Jul 9, 2014 at 3:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi Tobias, Thanks for the suggestion. I have tried to add more nodes from 300 to 400. It seems the running time did not get improved. On Wed, Jul 2, 2014 at 6:47 PM, Tobias Pfeiffer t...@preferred.jp wrote: Bill, can't you just add more nodes in order to speed up the processing? Tobias On Thu, Jul 3, 2014 at 7:09 AM, Bill Jay bill.jaypeter...@gmail.com wrote: Hi all, I have a problem of using Spark Streaming to accept input data and update a result. The input of the data is from Kafka and the output is to report a map which is updated by historical data in every minute. My current method is to set batch size as 1 minute and use foreachRDD to update this map and output the map at the end of the foreachRDD function. However, the current issue is the processing cannot be finished within one minute. I am thinking of updating the map whenever the new data come instead of doing the update when the whoe RDD comes. Is there any idea on how to achieve this in a better running time? Thanks! Bill
