Re: Mesos/Spark Deadlock
We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
BTW it seems to me that even without that patch, you should be getting tasks launched as long as you leave at least 32 MB of memory free on each machine (that is, the sum of the executor memory sizes is not exactly the same as the total size of the machine). Then Mesos will be able to re-offer that machine whenever CPUs free up. Matei On August 25, 2014 at 5:05:56 AM, Gary Malouf (malouf.g...@gmail.com) wrote: We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
This is kind of weird then, seems perhaps unrelated to this issue (or at least to the way I understood it). Is the problem maybe that Mesos saw 0 MB being freed and didn't re-offer the machine *even though there was more than 32 MB free overall*? Matei On August 25, 2014 at 12:59:59 PM, Cody Koeninger (c...@koeninger.org) wrote: I definitely saw a case where a. the only job running was a 256m shell b. I started a 2g job c. a little while later the same user as in a started another 256m shell My job immediately stopped making progress. Once user a killed his shells, it started again. This is on nodes with ~15G of memory, on which we have successfully run 8G jobs. On Mon, Aug 25, 2014 at 2:02 PM, Matei Zaharia matei.zaha...@gmail.com wrote: BTW it seems to me that even without that patch, you should be getting tasks launched as long as you leave at least 32 MB of memory free on each machine (that is, the sum of the executor memory sizes is not exactly the same as the total size of the machine). Then Mesos will be able to re-offer that machine whenever CPUs free up. Matei On August 25, 2014 at 5:05:56 AM, Gary Malouf (malouf.g...@gmail.com) wrote: We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
Anyway it would be good if someone from the Mesos side investigates this and proposes a solution. The 32 MB per task hack isn't completely foolproof either (e.g. people might allocate all the RAM to their executor and thus stop being able to launch tasks), so maybe we wait on a Mesos fix for this one. Matei On August 25, 2014 at 1:07:15 PM, Matei Zaharia (matei.zaha...@gmail.com) wrote: This is kind of weird then, seems perhaps unrelated to this issue (or at least to the way I understood it). Is the problem maybe that Mesos saw 0 MB being freed and didn't re-offer the machine *even though there was more than 32 MB free overall*? Matei On August 25, 2014 at 12:59:59 PM, Cody Koeninger (c...@koeninger.org) wrote: I definitely saw a case where a. the only job running was a 256m shell b. I started a 2g job c. a little while later the same user as in a started another 256m shell My job immediately stopped making progress. Once user a killed his shells, it started again. This is on nodes with ~15G of memory, on which we have successfully run 8G jobs. On Mon, Aug 25, 2014 at 2:02 PM, Matei Zaharia matei.zaha...@gmail.com wrote: BTW it seems to me that even without that patch, you should be getting tasks launched as long as you leave at least 32 MB of memory free on each machine (that is, the sum of the executor memory sizes is not exactly the same as the total size of the machine). Then Mesos will be able to re-offer that machine whenever CPUs free up. Matei On August 25, 2014 at 5:05:56 AM, Gary Malouf (malouf.g...@gmail.com) wrote: We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
I definitely saw a case where a. the only job running was a 256m shell b. I started a 2g job c. a little while later the same user as in a started another 256m shell My job immediately stopped making progress. Once user a killed his shells, it started again. This is on nodes with ~15G of memory, on which we have successfully run 8G jobs. On Mon, Aug 25, 2014 at 2:02 PM, Matei Zaharia matei.zaha...@gmail.com wrote: BTW it seems to me that even without that patch, you should be getting tasks launched as long as you leave at least 32 MB of memory free on each machine (that is, the sum of the executor memory sizes is not exactly the same as the total size of the machine). Then Mesos will be able to re-offer that machine whenever CPUs free up. Matei On August 25, 2014 at 5:05:56 AM, Gary Malouf (malouf.g...@gmail.com) wrote: We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
Hi Matei, I'm going to investigate from both Mesos and Spark side will hopefully have a good long term solution. In the mean time having a work around to start with is going to unblock folks. Tim On Mon, Aug 25, 2014 at 1:08 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Anyway it would be good if someone from the Mesos side investigates this and proposes a solution. The 32 MB per task hack isn't completely foolproof either (e.g. people might allocate all the RAM to their executor and thus stop being able to launch tasks), so maybe we wait on a Mesos fix for this one. Matei On August 25, 2014 at 1:07:15 PM, Matei Zaharia (matei.zaha...@gmail.com) wrote: This is kind of weird then, seems perhaps unrelated to this issue (or at least to the way I understood it). Is the problem maybe that Mesos saw 0 MB being freed and didn't re-offer the machine *even though there was more than 32 MB free overall*? Matei On August 25, 2014 at 12:59:59 PM, Cody Koeninger (c...@koeninger.org) wrote: I definitely saw a case where a. the only job running was a 256m shell b. I started a 2g job c. a little while later the same user as in a started another 256m shell My job immediately stopped making progress. Once user a killed his shells, it started again. This is on nodes with ~15G of memory, on which we have successfully run 8G jobs. On Mon, Aug 25, 2014 at 2:02 PM, Matei Zaharia matei.zaha...@gmail.com wrote: BTW it seems to me that even without that patch, you should be getting tasks launched as long as you leave at least 32 MB of memory free on each machine (that is, the sum of the executor memory sizes is not exactly the same as the total size of the machine). Then Mesos will be able to re-offer that machine whenever CPUs free up. Matei On August 25, 2014 at 5:05:56 AM, Gary Malouf (malouf.g...@gmail.com) wrote: We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
My problem is that I'm not sure this workaround would solve things, given the issue described here (where there was a lot of memory free but it didn't get re-offered). If you think it does, it would be good to explain why it behaves like that. Matei On August 25, 2014 at 2:28:18 PM, Timothy Chen (tnac...@gmail.com) wrote: Hi Matei, I'm going to investigate from both Mesos and Spark side will hopefully have a good long term solution. In the mean time having a work around to start with is going to unblock folks. Tim On Mon, Aug 25, 2014 at 1:08 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Anyway it would be good if someone from the Mesos side investigates this and proposes a solution. The 32 MB per task hack isn't completely foolproof either (e.g. people might allocate all the RAM to their executor and thus stop being able to launch tasks), so maybe we wait on a Mesos fix for this one. Matei On August 25, 2014 at 1:07:15 PM, Matei Zaharia (matei.zaha...@gmail.com) wrote: This is kind of weird then, seems perhaps unrelated to this issue (or at least to the way I understood it). Is the problem maybe that Mesos saw 0 MB being freed and didn't re-offer the machine *even though there was more than 32 MB free overall*? Matei On August 25, 2014 at 12:59:59 PM, Cody Koeninger (c...@koeninger.org) wrote: I definitely saw a case where a. the only job running was a 256m shell b. I started a 2g job c. a little while later the same user as in a started another 256m shell My job immediately stopped making progress. Once user a killed his shells, it started again. This is on nodes with ~15G of memory, on which we have successfully run 8G jobs. On Mon, Aug 25, 2014 at 2:02 PM, Matei Zaharia matei.zaha...@gmail.com wrote: BTW it seems to me that even without that patch, you should be getting tasks launched as long as you leave at least 32 MB of memory free on each machine (that is, the sum of the executor memory sizes is not exactly the same as the total size of the machine). Then Mesos will be able to re-offer that machine whenever CPUs free up. Matei On August 25, 2014 at 5:05:56 AM, Gary Malouf (malouf.g...@gmail.com) wrote: We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
I don't think it solves Cody's problem which still need more investigating, but I believe it does solve the problem you described earlier. I just confirmed with Mesos folks that we no longer need the minimum memory requirement so we'll be dropping that soon and the workaround might not be needed for the next mesos release. Tim On Mon, Aug 25, 2014 at 3:06 PM, Matei Zaharia matei.zaha...@gmail.com wrote: My problem is that I'm not sure this workaround would solve things, given the issue described here (where there was a lot of memory free but it didn't get re-offered). If you think it does, it would be good to explain why it behaves like that. Matei On August 25, 2014 at 2:28:18 PM, Timothy Chen (tnac...@gmail.com) wrote: Hi Matei, I'm going to investigate from both Mesos and Spark side will hopefully have a good long term solution. In the mean time having a work around to start with is going to unblock folks. Tim On Mon, Aug 25, 2014 at 1:08 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Anyway it would be good if someone from the Mesos side investigates this and proposes a solution. The 32 MB per task hack isn't completely foolproof either (e.g. people might allocate all the RAM to their executor and thus stop being able to launch tasks), so maybe we wait on a Mesos fix for this one. Matei On August 25, 2014 at 1:07:15 PM, Matei Zaharia (matei.zaha...@gmail.com) wrote: This is kind of weird then, seems perhaps unrelated to this issue (or at least to the way I understood it). Is the problem maybe that Mesos saw 0 MB being freed and didn't re-offer the machine *even though there was more than 32 MB free overall*? Matei On August 25, 2014 at 12:59:59 PM, Cody Koeninger (c...@koeninger.org) wrote: I definitely saw a case where a. the only job running was a 256m shell b. I started a 2g job c. a little while later the same user as in a started another 256m shell My job immediately stopped making progress. Once user a killed his shells, it started again. This is on nodes with ~15G of memory, on which we have successfully run 8G jobs. On Mon, Aug 25, 2014 at 2:02 PM, Matei Zaharia matei.zaha...@gmail.com wrote: BTW it seems to me that even without that patch, you should be getting tasks launched as long as you leave at least 32 MB of memory free on each machine (that is, the sum of the executor memory sizes is not exactly the same as the total size of the machine). Then Mesos will be able to re-offer that machine whenever CPUs free up. Matei On August 25, 2014 at 5:05:56 AM, Gary Malouf (malouf.g...@gmail.com) wrote: We have not tried the work-around because there are other bugs in there that affected our set-up, though it seems it would help. On Mon, Aug 25, 2014 at 12:54 AM, Timothy Chen tnac...@gmail.com wrote: +1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
+1 to have the work around in. I'll be investigating from the Mesos side too. Tim On Sun, Aug 24, 2014 at 9:52 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Yeah, Mesos in coarse-grained mode probably wouldn't work here. It's too bad that this happens in fine-grained mode -- would be really good to fix. I'll see if we can get the workaround in https://github.com/apache/spark/pull/1860 into Spark 1.1. Incidentally have you tried that? Matei On August 23, 2014 at 4:30:27 PM, Gary Malouf (malouf.g...@gmail.com) wrote: Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
Re: Mesos/Spark Deadlock
Hi Matei, We have an analytics team that uses the cluster on a daily basis. They use two types of 'run modes': 1) For running actual queries, they set the spark.executor.memory to something between 4 and 8GB of RAM/worker. 2) A shell that takes a minimal amount of memory on workers (128MB) for prototyping out a larger query. This allows them to not take up RAM on the cluster when they do not really need it. We see the deadlocks when there are a few shells in either case. From the usage patterns we have, coarse-grained mode would be a challenge as we have to constantly remind people to kill their shells as soon as their queries finish. Am I correct in viewing Mesos in coarse-grained mode as being similar to Spark Standalone's cpu allocation behavior? On Sat, Aug 23, 2014 at 7:16 PM, Matei Zaharia matei.zaha...@gmail.com wrote: Hey Gary, just as a workaround, note that you can use Mesos in coarse-grained mode by setting spark.mesos.coarse=true. Then it will hold onto CPUs for the duration of the job. Matei On August 23, 2014 at 7:57:30 AM, Gary Malouf (malouf.g...@gmail.com) wrote: I just wanted to bring up a significant Mesos/Spark issue that makes the combo difficult to use for teams larger than 4-5 people. It's covered in https://issues.apache.org/jira/browse/MESOS-1688. My understanding is that Spark's use of executors in fine-grained mode is a very different behavior than many of the other common frameworks for Mesos.
