Re: Multitenancy in Spark - within/across spark context
Ashwin, What is your motivation for needing to share RDDs between jobs? Optimizing for reusing data across jobs? If so, you may want to look into Tachyon. My understanding is that Tachyon acts like a caching layer and you can designate when data will be reused in multiple jobs so it know to keep that in memory or local disk for faster access. But my knowledge of tachyon is second hand so forgive me if I have it wrong :) RJ On Friday, October 24, 2014, Evan Chan velvia.git...@gmail.com wrote: Ashwin, I would say the strategies in general are: 1) Have each user submit separate Spark app (each its own Spark Context), with its own resource settings, and share data through HDFS or something like Tachyon for speed. 2) Share a single spark context amongst multiple users, using fair scheduler. This is sort of like having a Hadoop resource pool.It has some obvious HA/SPOF issues, namely that if the context dies then every user using it is also dead. Also, sharing RDDs in cached memory has the same resiliency problems, namely that if any executor dies then Spark must recompute / rebuild the RDD (it tries to only rebuild the missing part, but sometimes it must rebuild everything). Job server can help with 1 or 2, 2 in particular. If you have any questions about job server, feel free to ask at the spark-jobserver google group. I am the maintainer. -Evan On Thu, Oct 23, 2014 at 1:06 PM, Marcelo Vanzin van...@cloudera.com javascript:; wrote: You may want to take a look at https://issues.apache.org/jira/browse/SPARK-3174. On Thu, Oct 23, 2014 at 2:56 AM, Jianshi Huang jianshi.hu...@gmail.com javascript:; wrote: Upvote for the multitanency requirement. I'm also building a data analytic platform and there'll be multiple users running queries and computations simultaneously. One of the paint point is control of resource size. Users don't really know how much nodes they need, they always use as much as possible... The result is lots of wasted resource in our Yarn cluster. A way to 1) allow multiple spark context to share the same resource or 2) add dynamic resource management for Yarn mode is very much wanted. Jianshi On Thu, Oct 23, 2014 at 5:36 AM, Marcelo Vanzin van...@cloudera.com javascript:; wrote: On Wed, Oct 22, 2014 at 2:17 PM, Ashwin Shankar ashwinshanka...@gmail.com javascript:; wrote: That's not something you might want to do usually. In general, a SparkContext maps to a user application My question was basically this. In this page in the official doc, under Scheduling within an application section, it talks about multiuser and fair sharing within an app. How does multiuser within an application work(how users connect to an app,run their stuff) ? When would I want to use this ? I see. The way I read that page is that Spark supports all those scheduling options; but Spark doesn't give you the means to actually be able to submit jobs from different users to a running SparkContext hosted on a different process. For that, you'll need something like the job server that I referenced before, or write your own framework for supporting that. Personally, I'd use the information on that page when dealing with concurrent jobs in the same SparkContext, but still restricted to the same user. I'd avoid trying to create any application where a single SparkContext is trying to be shared by multiple users in any way. As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. I basically wanted to find out if there were any gotchas related to preemption on Spark. Things like say half of an application's executors got preempted say while doing reduceByKey, will the application progress with the remaining resources/fair share ? Jobs should still make progress as long as at least one executor is available. The gotcha would be the one I mentioned, where Spark will fail your job after x executors failed, which might be a common occurrence when preemption is enabled. That being said, it's a configurable option, so you can set x to a very large value and your job should keep on chugging along. The options you'd want to take a look at are: spark.task.maxFailures and spark.yarn.max.executor.failures -- Marcelo - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org javascript:; For additional commands, e-mail: user-h...@spark.apache.org javascript:; -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github Blog: http://huangjs.github.com/ -- Marcelo - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org javascript:; For additional
Re: Multitenancy in Spark - within/across spark context
Upvote for the multitanency requirement. I'm also building a data analytic platform and there'll be multiple users running queries and computations simultaneously. One of the paint point is control of resource size. Users don't really know how much nodes they need, they always use as much as possible... The result is lots of wasted resource in our Yarn cluster. A way to 1) allow multiple spark context to share the same resource or 2) add dynamic resource management for Yarn mode is very much wanted. Jianshi On Thu, Oct 23, 2014 at 5:36 AM, Marcelo Vanzin van...@cloudera.com wrote: On Wed, Oct 22, 2014 at 2:17 PM, Ashwin Shankar ashwinshanka...@gmail.com wrote: That's not something you might want to do usually. In general, a SparkContext maps to a user application My question was basically this. In this page in the official doc, under Scheduling within an application section, it talks about multiuser and fair sharing within an app. How does multiuser within an application work(how users connect to an app,run their stuff) ? When would I want to use this ? I see. The way I read that page is that Spark supports all those scheduling options; but Spark doesn't give you the means to actually be able to submit jobs from different users to a running SparkContext hosted on a different process. For that, you'll need something like the job server that I referenced before, or write your own framework for supporting that. Personally, I'd use the information on that page when dealing with concurrent jobs in the same SparkContext, but still restricted to the same user. I'd avoid trying to create any application where a single SparkContext is trying to be shared by multiple users in any way. As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. I basically wanted to find out if there were any gotchas related to preemption on Spark. Things like say half of an application's executors got preempted say while doing reduceByKey, will the application progress with the remaining resources/fair share ? Jobs should still make progress as long as at least one executor is available. The gotcha would be the one I mentioned, where Spark will fail your job after x executors failed, which might be a common occurrence when preemption is enabled. That being said, it's a configurable option, so you can set x to a very large value and your job should keep on chugging along. The options you'd want to take a look at are: spark.task.maxFailures and spark.yarn.max.executor.failures -- Marcelo - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github Blog: http://huangjs.github.com/
Re: Multitenancy in Spark - within/across spark context
You may want to take a look at https://issues.apache.org/jira/browse/SPARK-3174. On Thu, Oct 23, 2014 at 2:56 AM, Jianshi Huang jianshi.hu...@gmail.com wrote: Upvote for the multitanency requirement. I'm also building a data analytic platform and there'll be multiple users running queries and computations simultaneously. One of the paint point is control of resource size. Users don't really know how much nodes they need, they always use as much as possible... The result is lots of wasted resource in our Yarn cluster. A way to 1) allow multiple spark context to share the same resource or 2) add dynamic resource management for Yarn mode is very much wanted. Jianshi On Thu, Oct 23, 2014 at 5:36 AM, Marcelo Vanzin van...@cloudera.com wrote: On Wed, Oct 22, 2014 at 2:17 PM, Ashwin Shankar ashwinshanka...@gmail.com wrote: That's not something you might want to do usually. In general, a SparkContext maps to a user application My question was basically this. In this page in the official doc, under Scheduling within an application section, it talks about multiuser and fair sharing within an app. How does multiuser within an application work(how users connect to an app,run their stuff) ? When would I want to use this ? I see. The way I read that page is that Spark supports all those scheduling options; but Spark doesn't give you the means to actually be able to submit jobs from different users to a running SparkContext hosted on a different process. For that, you'll need something like the job server that I referenced before, or write your own framework for supporting that. Personally, I'd use the information on that page when dealing with concurrent jobs in the same SparkContext, but still restricted to the same user. I'd avoid trying to create any application where a single SparkContext is trying to be shared by multiple users in any way. As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. I basically wanted to find out if there were any gotchas related to preemption on Spark. Things like say half of an application's executors got preempted say while doing reduceByKey, will the application progress with the remaining resources/fair share ? Jobs should still make progress as long as at least one executor is available. The gotcha would be the one I mentioned, where Spark will fail your job after x executors failed, which might be a common occurrence when preemption is enabled. That being said, it's a configurable option, so you can set x to a very large value and your job should keep on chugging along. The options you'd want to take a look at are: spark.task.maxFailures and spark.yarn.max.executor.failures -- Marcelo - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github Blog: http://huangjs.github.com/ -- Marcelo - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Multitenancy in Spark - within/across spark context
Ashwin, I would say the strategies in general are: 1) Have each user submit separate Spark app (each its own Spark Context), with its own resource settings, and share data through HDFS or something like Tachyon for speed. 2) Share a single spark context amongst multiple users, using fair scheduler. This is sort of like having a Hadoop resource pool.It has some obvious HA/SPOF issues, namely that if the context dies then every user using it is also dead. Also, sharing RDDs in cached memory has the same resiliency problems, namely that if any executor dies then Spark must recompute / rebuild the RDD (it tries to only rebuild the missing part, but sometimes it must rebuild everything). Job server can help with 1 or 2, 2 in particular. If you have any questions about job server, feel free to ask at the spark-jobserver google group. I am the maintainer. -Evan On Thu, Oct 23, 2014 at 1:06 PM, Marcelo Vanzin van...@cloudera.com wrote: You may want to take a look at https://issues.apache.org/jira/browse/SPARK-3174. On Thu, Oct 23, 2014 at 2:56 AM, Jianshi Huang jianshi.hu...@gmail.com wrote: Upvote for the multitanency requirement. I'm also building a data analytic platform and there'll be multiple users running queries and computations simultaneously. One of the paint point is control of resource size. Users don't really know how much nodes they need, they always use as much as possible... The result is lots of wasted resource in our Yarn cluster. A way to 1) allow multiple spark context to share the same resource or 2) add dynamic resource management for Yarn mode is very much wanted. Jianshi On Thu, Oct 23, 2014 at 5:36 AM, Marcelo Vanzin van...@cloudera.com wrote: On Wed, Oct 22, 2014 at 2:17 PM, Ashwin Shankar ashwinshanka...@gmail.com wrote: That's not something you might want to do usually. In general, a SparkContext maps to a user application My question was basically this. In this page in the official doc, under Scheduling within an application section, it talks about multiuser and fair sharing within an app. How does multiuser within an application work(how users connect to an app,run their stuff) ? When would I want to use this ? I see. The way I read that page is that Spark supports all those scheduling options; but Spark doesn't give you the means to actually be able to submit jobs from different users to a running SparkContext hosted on a different process. For that, you'll need something like the job server that I referenced before, or write your own framework for supporting that. Personally, I'd use the information on that page when dealing with concurrent jobs in the same SparkContext, but still restricted to the same user. I'd avoid trying to create any application where a single SparkContext is trying to be shared by multiple users in any way. As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. I basically wanted to find out if there were any gotchas related to preemption on Spark. Things like say half of an application's executors got preempted say while doing reduceByKey, will the application progress with the remaining resources/fair share ? Jobs should still make progress as long as at least one executor is available. The gotcha would be the one I mentioned, where Spark will fail your job after x executors failed, which might be a common occurrence when preemption is enabled. That being said, it's a configurable option, so you can set x to a very large value and your job should keep on chugging along. The options you'd want to take a look at are: spark.task.maxFailures and spark.yarn.max.executor.failures -- Marcelo - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org -- Jianshi Huang LinkedIn: jianshi Twitter: @jshuang Github Blog: http://huangjs.github.com/ -- Marcelo - To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Multitenancy in Spark - within/across spark context
Hi Spark devs/users, One of the things we are investigating here at Netflix is if Spark would suit us for our ETL needs, and one of requirements is multi tenancy. I did read the official doc http://spark.apache.org/docs/latest/job-scheduling.html and the book, but I'm still not clear on certain things. Here are my questions : 1. *Sharing spark context* : How exactly multiple users can share the cluster using same spark context ? UserA wants to run AppA, UserB wants to run AppB. How do they talk to same context ? How exactly are each of their jobs scheduled and run in same context? Is preemption supported in this scenario ? How are user names passed on to the spark context ? 2. *Different spark context in YARN*: assuming I have a YARN cluster with queues and preemption configured. Are there problems if executors/containers of a spark app are preempted to allow a high priority spark app to execute ? Would the preempted app get stuck or would it continue to make progress? How are user names passed on from spark to yarn(say I'm using nested user queues feature in fair scheduler) ? 3. Sharing RDDs in 1 and 2 above ? 4. Anything else about user/job isolation ? I know I'm asking a lot of questions. Thanks in advance :) ! -- Thanks, Ashwin Netflix
Re: Multitenancy in Spark - within/across spark context
Hi Ashwin, Let me try to answer to the best of my knowledge. On Wed, Oct 22, 2014 at 11:47 AM, Ashwin Shankar ashwinshanka...@gmail.com wrote: Here are my questions : 1. Sharing spark context : How exactly multiple users can share the cluster using same spark context ? That's not something you might want to do usually. In general, a SparkContext maps to a user application, so each user would submit their own job which would create its own SparkContext. If you want to go outside of Spark, there are project which allow you to manage SparkContext instances outside of applications and potentially share them, such as https://github.com/spark-jobserver/spark-jobserver. But be sure you actually need it - since you haven't really explained the use case, it's not very clear. 2. Different spark context in YARN: assuming I have a YARN cluster with queues and preemption configured. Are there problems if executors/containers of a spark app are preempted to allow a high priority spark app to execute ? As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. Yarn mode does have a configurable upper limit on the number of executor failures, so if your jobs keeps getting preempted it will eventually fail (unless you tweak the settings). I don't recall whether Yarn has an API to cleanly allow clients to stop executors when preempted, but even if it does, I don't think that's supported in Spark at the moment. How are user names passed on from spark to yarn(say I'm using nested user queues feature in fair scheduler) ? Spark will try to run the job as the requesting user; if you're not using Kerberos, that means the process themselves will be run as whatever user runs the Yarn daemons, but the Spark app will be run inside a UserGroupInformation.doAs() call as the requesting user. So technically nested queues should work as expected. 3. Sharing RDDs in 1 and 2 above ? I'll assume you don't mean actually sharing RDDs in the same context, but between different SparkContext instances. You might (big might here) be able to checkpoint an RDD from one context and load it from another context; that's actually like some HA-like features for Spark drivers are being addressed. The job server I mentioned before, which allows different apps to share the same Spark context, has a feature to share RDDs by name, also, without having to resort to checkpointing. Hope this helps! -- Marcelo - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Re: Multitenancy in Spark - within/across spark context
Thanks Marcelo, that was helpful ! I had some follow up questions : That's not something you might want to do usually. In general, a SparkContext maps to a user application My question was basically this. In this http://spark.apache.org/docs/latest/job-scheduling.html page in the official doc, under Scheduling within an application section, it talks about multiuser and fair sharing within an app. How does multiuser within an application work(how users connect to an app,run their stuff) ? When would I want to use this ? As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. I basically wanted to find out if there were any gotchas related to preemption on Spark. Things like say half of an application's executors got preempted say while doing reduceByKey, will the application progress with the remaining resources/fair share ? I'm new to spark, sry if I'm asking something very obvious :). Thanks, Ashwin On Wed, Oct 22, 2014 at 12:07 PM, Marcelo Vanzin van...@cloudera.com wrote: Hi Ashwin, Let me try to answer to the best of my knowledge. On Wed, Oct 22, 2014 at 11:47 AM, Ashwin Shankar ashwinshanka...@gmail.com wrote: Here are my questions : 1. Sharing spark context : How exactly multiple users can share the cluster using same spark context ? That's not something you might want to do usually. In general, a SparkContext maps to a user application, so each user would submit their own job which would create its own SparkContext. If you want to go outside of Spark, there are project which allow you to manage SparkContext instances outside of applications and potentially share them, such as https://github.com/spark-jobserver/spark-jobserver. But be sure you actually need it - since you haven't really explained the use case, it's not very clear. 2. Different spark context in YARN: assuming I have a YARN cluster with queues and preemption configured. Are there problems if executors/containers of a spark app are preempted to allow a high priority spark app to execute ? As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. Yarn mode does have a configurable upper limit on the number of executor failures, so if your jobs keeps getting preempted it will eventually fail (unless you tweak the settings). I don't recall whether Yarn has an API to cleanly allow clients to stop executors when preempted, but even if it does, I don't think that's supported in Spark at the moment. How are user names passed on from spark to yarn(say I'm using nested user queues feature in fair scheduler) ? Spark will try to run the job as the requesting user; if you're not using Kerberos, that means the process themselves will be run as whatever user runs the Yarn daemons, but the Spark app will be run inside a UserGroupInformation.doAs() call as the requesting user. So technically nested queues should work as expected. 3. Sharing RDDs in 1 and 2 above ? I'll assume you don't mean actually sharing RDDs in the same context, but between different SparkContext instances. You might (big might here) be able to checkpoint an RDD from one context and load it from another context; that's actually like some HA-like features for Spark drivers are being addressed. The job server I mentioned before, which allows different apps to share the same Spark context, has a feature to share RDDs by name, also, without having to resort to checkpointing. Hope this helps! -- Marcelo -- Thanks, Ashwin
Re: Multitenancy in Spark - within/across spark context
On Wed, Oct 22, 2014 at 2:17 PM, Ashwin Shankar ashwinshanka...@gmail.com wrote: That's not something you might want to do usually. In general, a SparkContext maps to a user application My question was basically this. In this page in the official doc, under Scheduling within an application section, it talks about multiuser and fair sharing within an app. How does multiuser within an application work(how users connect to an app,run their stuff) ? When would I want to use this ? I see. The way I read that page is that Spark supports all those scheduling options; but Spark doesn't give you the means to actually be able to submit jobs from different users to a running SparkContext hosted on a different process. For that, you'll need something like the job server that I referenced before, or write your own framework for supporting that. Personally, I'd use the information on that page when dealing with concurrent jobs in the same SparkContext, but still restricted to the same user. I'd avoid trying to create any application where a single SparkContext is trying to be shared by multiple users in any way. As far as I understand, this will cause executors to be killed, which means that Spark will start retrying tasks to rebuild the data that was held by those executors when needed. I basically wanted to find out if there were any gotchas related to preemption on Spark. Things like say half of an application's executors got preempted say while doing reduceByKey, will the application progress with the remaining resources/fair share ? Jobs should still make progress as long as at least one executor is available. The gotcha would be the one I mentioned, where Spark will fail your job after x executors failed, which might be a common occurrence when preemption is enabled. That being said, it's a configurable option, so you can set x to a very large value and your job should keep on chugging along. The options you'd want to take a look at are: spark.task.maxFailures and spark.yarn.max.executor.failures -- Marcelo - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
