Re: Using Spark on Data size larger than Memory size
Thanks. We've run into timeout issues at scale as well. We were able to workaround them by setting the following JVM options: -Dspark.akka.askTimeout=300 -Dspark.akka.timeout=300 -Dspark.worker.timeout=300 NOTE: these JVM options *must* be set on worker nodes (and not just the driver/master) for the settings to take. Allen -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Using-Spark-on-Data-size-larger-than-Memory-size-tp6589p7435.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
Re: Using Spark on Data size larger than Memory size
Thanks for the clarification. What is the proper way to configure RDDs when your aggregate data size exceeds your available working memory size? In particular, in additional to typical operations, I'm performing cogroups, joins, and coalesces/shuffles. I see that the default storage level for RDDs is MEMORY_ONLY. Do I just need to set all the storage level for all of my RDDs to something like MEMORY_AND_DISK? Do I need to do anything else to get graceful behavior in the presence of coalesces/shuffles, cogroups, and joins? Thanks, Allen -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Using-Spark-on-Data-size-larger-than-Memory-size-tp6589p7364.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
Re: Using Spark on Data size larger than Memory size
Aaron, Thank You for your response and clarifying things. -Vibhor On Sun, Jun 1, 2014 at 11:40 AM, Aaron Davidson ilike...@gmail.com wrote: There is no fundamental issue if you're running on data that is larger than cluster memory size. Many operations can stream data through, and thus memory usage is independent of input data size. Certain operations require an entire *partition* (not dataset) to fit in memory, but there are not many instances of this left (sorting comes to mind, and this is being worked on). In general, one problem with Spark today is that you *can* OOM under certain configurations, and it's possible you'll need to change from the default configuration if you're using doing very memory-intensive jobs. However, there are very few cases where Spark would simply fail as a matter of course *-- *for instance, you can always increase the number of partitions to decrease the size of any given one. or repartition data to eliminate skew. Regarding impact on performance, as Mayur said, there may absolutely be an impact depending on your jobs. If you're doing a join on a very large amount of data with few partitions, then we'll have to spill to disk. If you can't cache your working set of data in memory, you will also see a performance degradation. Spark enables the use of memory to make things fast, but if you just don't have enough memory, it won't be terribly fast. On Sat, May 31, 2014 at 12:14 AM, Mayur Rustagi mayur.rust...@gmail.com wrote: Clearly thr will be impact on performance but frankly depends on what you are trying to achieve with the dataset. Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi https://twitter.com/mayur_rustagi On Sat, May 31, 2014 at 11:45 AM, Vibhor Banga vibhorba...@gmail.com wrote: Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
Re: Using Spark on Data size larger than Memory size
Andrew, Thank you. I'm using mapPartitions() but as you say, it requires that every partition fit in memory. This will work for now but may not always work so I was wondering about another way. Thanks, Roger On Thu, Jun 5, 2014 at 5:26 PM, Andrew Ash and...@andrewash.com wrote: Hi Roger, You should be able to sort within partitions using the rdd.mapPartitions() method, and that shouldn't require holding all data in memory at once. It does require holding the entire partition in memory though. Do you need the partition to never be held in memory all at once? As far as the work that Aaron mentioned is happening, I think he might be referring to the discussion and code surrounding https://issues.apache.org/jira/browse/SPARK-983 Cheers! Andrew On Thu, Jun 5, 2014 at 5:16 PM, Roger Hoover roger.hoo...@gmail.com wrote: I think it would very handy to be able to specify that you want sorting during a partitioning stage. On Thu, Jun 5, 2014 at 4:42 PM, Roger Hoover roger.hoo...@gmail.com wrote: Hi Aaron, When you say that sorting is being worked on, can you elaborate a little more please? If particular, I want to sort the items within each partition (not globally) without necessarily bringing them all into memory at once. Thanks, Roger On Sat, May 31, 2014 at 11:10 PM, Aaron Davidson ilike...@gmail.com wrote: There is no fundamental issue if you're running on data that is larger than cluster memory size. Many operations can stream data through, and thus memory usage is independent of input data size. Certain operations require an entire *partition* (not dataset) to fit in memory, but there are not many instances of this left (sorting comes to mind, and this is being worked on). In general, one problem with Spark today is that you *can* OOM under certain configurations, and it's possible you'll need to change from the default configuration if you're using doing very memory-intensive jobs. However, there are very few cases where Spark would simply fail as a matter of course *-- *for instance, you can always increase the number of partitions to decrease the size of any given one. or repartition data to eliminate skew. Regarding impact on performance, as Mayur said, there may absolutely be an impact depending on your jobs. If you're doing a join on a very large amount of data with few partitions, then we'll have to spill to disk. If you can't cache your working set of data in memory, you will also see a performance degradation. Spark enables the use of memory to make things fast, but if you just don't have enough memory, it won't be terribly fast. On Sat, May 31, 2014 at 12:14 AM, Mayur Rustagi mayur.rust...@gmail.com wrote: Clearly thr will be impact on performance but frankly depends on what you are trying to achieve with the dataset. Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi https://twitter.com/mayur_rustagi On Sat, May 31, 2014 at 11:45 AM, Vibhor Banga vibhorba...@gmail.com wrote: Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
Re: Using Spark on Data size larger than Memory size
Hi Aaron, When you say that sorting is being worked on, can you elaborate a little more please? If particular, I want to sort the items within each partition (not globally) without necessarily bringing them all into memory at once. Thanks, Roger On Sat, May 31, 2014 at 11:10 PM, Aaron Davidson ilike...@gmail.com wrote: There is no fundamental issue if you're running on data that is larger than cluster memory size. Many operations can stream data through, and thus memory usage is independent of input data size. Certain operations require an entire *partition* (not dataset) to fit in memory, but there are not many instances of this left (sorting comes to mind, and this is being worked on). In general, one problem with Spark today is that you *can* OOM under certain configurations, and it's possible you'll need to change from the default configuration if you're using doing very memory-intensive jobs. However, there are very few cases where Spark would simply fail as a matter of course *-- *for instance, you can always increase the number of partitions to decrease the size of any given one. or repartition data to eliminate skew. Regarding impact on performance, as Mayur said, there may absolutely be an impact depending on your jobs. If you're doing a join on a very large amount of data with few partitions, then we'll have to spill to disk. If you can't cache your working set of data in memory, you will also see a performance degradation. Spark enables the use of memory to make things fast, but if you just don't have enough memory, it won't be terribly fast. On Sat, May 31, 2014 at 12:14 AM, Mayur Rustagi mayur.rust...@gmail.com wrote: Clearly thr will be impact on performance but frankly depends on what you are trying to achieve with the dataset. Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi https://twitter.com/mayur_rustagi On Sat, May 31, 2014 at 11:45 AM, Vibhor Banga vibhorba...@gmail.com wrote: Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
Re: Using Spark on Data size larger than Memory size
I think it would very handy to be able to specify that you want sorting during a partitioning stage. On Thu, Jun 5, 2014 at 4:42 PM, Roger Hoover roger.hoo...@gmail.com wrote: Hi Aaron, When you say that sorting is being worked on, can you elaborate a little more please? If particular, I want to sort the items within each partition (not globally) without necessarily bringing them all into memory at once. Thanks, Roger On Sat, May 31, 2014 at 11:10 PM, Aaron Davidson ilike...@gmail.com wrote: There is no fundamental issue if you're running on data that is larger than cluster memory size. Many operations can stream data through, and thus memory usage is independent of input data size. Certain operations require an entire *partition* (not dataset) to fit in memory, but there are not many instances of this left (sorting comes to mind, and this is being worked on). In general, one problem with Spark today is that you *can* OOM under certain configurations, and it's possible you'll need to change from the default configuration if you're using doing very memory-intensive jobs. However, there are very few cases where Spark would simply fail as a matter of course *-- *for instance, you can always increase the number of partitions to decrease the size of any given one. or repartition data to eliminate skew. Regarding impact on performance, as Mayur said, there may absolutely be an impact depending on your jobs. If you're doing a join on a very large amount of data with few partitions, then we'll have to spill to disk. If you can't cache your working set of data in memory, you will also see a performance degradation. Spark enables the use of memory to make things fast, but if you just don't have enough memory, it won't be terribly fast. On Sat, May 31, 2014 at 12:14 AM, Mayur Rustagi mayur.rust...@gmail.com wrote: Clearly thr will be impact on performance but frankly depends on what you are trying to achieve with the dataset. Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi https://twitter.com/mayur_rustagi On Sat, May 31, 2014 at 11:45 AM, Vibhor Banga vibhorba...@gmail.com wrote: Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
Re: Using Spark on Data size larger than Memory size
Hi Roger, You should be able to sort within partitions using the rdd.mapPartitions() method, and that shouldn't require holding all data in memory at once. It does require holding the entire partition in memory though. Do you need the partition to never be held in memory all at once? As far as the work that Aaron mentioned is happening, I think he might be referring to the discussion and code surrounding https://issues.apache.org/jira/browse/SPARK-983 Cheers! Andrew On Thu, Jun 5, 2014 at 5:16 PM, Roger Hoover roger.hoo...@gmail.com wrote: I think it would very handy to be able to specify that you want sorting during a partitioning stage. On Thu, Jun 5, 2014 at 4:42 PM, Roger Hoover roger.hoo...@gmail.com wrote: Hi Aaron, When you say that sorting is being worked on, can you elaborate a little more please? If particular, I want to sort the items within each partition (not globally) without necessarily bringing them all into memory at once. Thanks, Roger On Sat, May 31, 2014 at 11:10 PM, Aaron Davidson ilike...@gmail.com wrote: There is no fundamental issue if you're running on data that is larger than cluster memory size. Many operations can stream data through, and thus memory usage is independent of input data size. Certain operations require an entire *partition* (not dataset) to fit in memory, but there are not many instances of this left (sorting comes to mind, and this is being worked on). In general, one problem with Spark today is that you *can* OOM under certain configurations, and it's possible you'll need to change from the default configuration if you're using doing very memory-intensive jobs. However, there are very few cases where Spark would simply fail as a matter of course *-- *for instance, you can always increase the number of partitions to decrease the size of any given one. or repartition data to eliminate skew. Regarding impact on performance, as Mayur said, there may absolutely be an impact depending on your jobs. If you're doing a join on a very large amount of data with few partitions, then we'll have to spill to disk. If you can't cache your working set of data in memory, you will also see a performance degradation. Spark enables the use of memory to make things fast, but if you just don't have enough memory, it won't be terribly fast. On Sat, May 31, 2014 at 12:14 AM, Mayur Rustagi mayur.rust...@gmail.com wrote: Clearly thr will be impact on performance but frankly depends on what you are trying to achieve with the dataset. Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi https://twitter.com/mayur_rustagi On Sat, May 31, 2014 at 11:45 AM, Vibhor Banga vibhorba...@gmail.com wrote: Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
Re: Using Spark on Data size larger than Memory size
There is no fundamental issue if you're running on data that is larger than cluster memory size. Many operations can stream data through, and thus memory usage is independent of input data size. Certain operations require an entire *partition* (not dataset) to fit in memory, but there are not many instances of this left (sorting comes to mind, and this is being worked on). In general, one problem with Spark today is that you *can* OOM under certain configurations, and it's possible you'll need to change from the default configuration if you're using doing very memory-intensive jobs. However, there are very few cases where Spark would simply fail as a matter of course *-- *for instance, you can always increase the number of partitions to decrease the size of any given one. or repartition data to eliminate skew. Regarding impact on performance, as Mayur said, there may absolutely be an impact depending on your jobs. If you're doing a join on a very large amount of data with few partitions, then we'll have to spill to disk. If you can't cache your working set of data in memory, you will also see a performance degradation. Spark enables the use of memory to make things fast, but if you just don't have enough memory, it won't be terribly fast. On Sat, May 31, 2014 at 12:14 AM, Mayur Rustagi mayur.rust...@gmail.com wrote: Clearly thr will be impact on performance but frankly depends on what you are trying to achieve with the dataset. Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi https://twitter.com/mayur_rustagi On Sat, May 31, 2014 at 11:45 AM, Vibhor Banga vibhorba...@gmail.com wrote: Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
Re: Using Spark on Data size larger than Memory size
Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
Re: Using Spark on Data size larger than Memory size
Clearly thr will be impact on performance but frankly depends on what you are trying to achieve with the dataset. Mayur Rustagi Ph: +1 (760) 203 3257 http://www.sigmoidanalytics.com @mayur_rustagi https://twitter.com/mayur_rustagi On Sat, May 31, 2014 at 11:45 AM, Vibhor Banga vibhorba...@gmail.com wrote: Some inputs will be really helpful. Thanks, -Vibhor On Fri, May 30, 2014 at 7:51 PM, Vibhor Banga vibhorba...@gmail.com wrote: Hi all, I am planning to use spark with HBase, where I generate RDD by reading data from HBase Table. I want to know that in the case when the size of HBase Table grows larger than the size of RAM available in the cluster, will the application fail, or will there be an impact in performance ? Any thoughts in this direction will be helpful and are welcome. Thanks, -Vibhor -- Vibhor Banga Software Development Engineer Flipkart Internet Pvt. Ltd., Bangalore
