Distribution of sinks among the nodes
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Distribution of sinks among the nodes
 
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Hi, We try to deploy an application with the following “architecture” : 4 kafka sources => 16 maps => 4 kafka sinks, on 4 nodes, with 24 slots (we disabled operator chaining). So we’d like on each node : 1x source => 4x map => 1x sink That way there are no exchanges between different instances of flink and performances would be optimal. But we get (according to the flink GUI and the Host column when looking at the details of each task) : Node 1 : 1 source => 2 map Node 2 : 1 source => 1 map Node 3 : 1 source => 1 map Node 4 : 1 source => 12 maps => 4 sinks (I think no comments are needed
J) The the Web UI says that there are 24 slots and they are all used but they don’t seem evenly dispatched …
How could we make Flink deploy the tasks the way we want ? B.R. Gwen’ |
Re: Distribution of sinks among the nodes
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Hi Gwenhäel,
when you say 16 maps, are we talking about one mapper with parallelism 16 or 16 unique map operators? Regards, Aljoscha > On 03 Feb 2016, at 15:48, Gwenhael Pasquiers <[hidden email]> wrote: > > Hi, > > We try to deploy an application with the following “architecture” : > > 4 kafka sources => 16 maps => 4 kafka sinks, on 4 nodes, with 24 slots (we disabled operator chaining). > > So we’d like on each node : > 1x source => 4x map => 1x sink > > That way there are no exchanges between different instances of flink and performances would be optimal. > > But we get (according to the flink GUI and the Host column when looking at the details of each task) : > > Node 1 : 1 source => 2 map > Node 2 : 1 source => 1 map > Node 3 : 1 source => 1 map > Node 4 : 1 source => 12 maps => 4 sinks > > (I think no comments are needed J) > > The the Web UI says that there are 24 slots and they are all used but they don’t seem evenly dispatched … > > How could we make Flink deploy the tasks the way we want ? > > B.R. > > Gwen’ |
RE: Distribution of sinks among the nodes
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It is one type of mapper with a parallelism of 16
It's the same for the sinks and sources (parallelism of 4) The settings are Env.setParallelism(4) Mapper.setPrallelism(env.getParallelism() * 4) We mean to have X mapper tasks per source / sink The mapper is doing some heavy computation and we have only 4 kafka partitions. That's why we need more mappers than sources / sinks -----Original Message----- From: Aljoscha Krettek [mailto:[hidden email]] Sent: mercredi 3 février 2016 16:26 To: [hidden email] Subject: Re: Distribution of sinks among the nodes Hi Gwenhäel, when you say 16 maps, are we talking about one mapper with parallelism 16 or 16 unique map operators? Regards, Aljoscha > On 03 Feb 2016, at 15:48, Gwenhael Pasquiers <[hidden email]> wrote: > > Hi, > > We try to deploy an application with the following “architecture” : > > 4 kafka sources => 16 maps => 4 kafka sinks, on 4 nodes, with 24 slots (we disabled operator chaining). > > So we’d like on each node : > 1x source => 4x map => 1x sink > > That way there are no exchanges between different instances of flink and performances would be optimal. > > But we get (according to the flink GUI and the Host column when looking at the details of each task) : > > Node 1 : 1 source => 2 map > Node 2 : 1 source => 1 map > Node 3 : 1 source => 1 map > Node 4 : 1 source => 12 maps => 4 sinks > > (I think no comments are needed J) > > The the Web UI says that there are 24 slots and they are all used but they don’t seem evenly dispatched … > > How could we make Flink deploy the tasks the way we want ? > > B.R. > > Gwen’ |
Re: Distribution of sinks among the nodes
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Hi Gwenhäel, if you set the number of slots for each Cheers, On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <[hidden email]> wrote: It is one type of mapper with a parallelism of 16 |
RE: Distribution of sinks among the nodes
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Don’t we need to set the number of slots to 24 (4 sources + 16 mappers + 4 sinks) ? Or is there a way not to set the number of slots per TaskManager instead of globally so that they are at least equally dispatched among the
nodes ? As for the sink deployment : that’s not good news ; I mean we will have a non-negligible overhead : all the data generated by 3 of the 4 nodes
will be sent to a third node instead of being sent to the “local” sink. Network I/O have a price. Do you have some sort of “topology” feature coming in the roadmap ? Maybe a listener on the JobManager / env that would be trigerred, asking usk
on which node we would prefer each node to be deployed. That way you keep the standard behavior, don’t have to make a complicated generic-optimized algorithm, and let the user make it’s choices.
Should I create a JIRA ? For the time being we could start the application 4 time : one time per node, put that’s not pretty at all
J B.R. From: Till Rohrmann [mailto:[hidden email]]
Hi Gwenhäel, if you set the number of slots for each Cheers, On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <[hidden email]> wrote: It is one type of mapper with a parallelism of 16
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RE: Distribution of sinks among the nodes
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Sorry I was confused about the number of slots, it’s good now. However, is disableChaing or disableOperatorChaining working properly ? I called those methods everywhere I could but it still seems that some of my operators are being chained together I can’t go over 16 used slot
where I should be at 24 if there was no chaining … From: Gwenhael Pasquiers [mailto:[hidden email]]
Don’t we need to set the number of slots to 24 (4 sources + 16 mappers + 4 sinks) ? Or is there a way not to set the number of slots per TaskManager instead of globally so that they are at least equally dispatched among the
nodes ? As for the sink deployment : that’s not good news ; I mean we will have a non-negligible overhead : all the data generated by 3 of the 4 nodes
will be sent to a third node instead of being sent to the “local” sink. Network I/O have a price. Do you have some sort of “topology” feature coming in the roadmap ? Maybe a listener on the JobManager / env that would be trigerred, asking usk
on which node we would prefer each node to be deployed. That way you keep the standard behavior, don’t have to make a complicated generic-optimized algorithm, and let the user make it’s choices.
Should I create a JIRA ? For the time being we could start the application 4 time : one time per node, put that’s not pretty at all
J B.R. From: Till Rohrmann [[hidden email]]
Hi Gwenhäel, if you set the number of slots for each Cheers, On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <[hidden email]> wrote: It is one type of mapper with a parallelism of 16
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Re: Distribution of sinks among the nodes
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In reply to this post by Gwenhael Pasquiers
Hi Gwen! You actually need not 24 slots, but only as many as the highest parallelism is (16). Slots do not hold individual tasks, but "pipelines". Here is an illustration how that works. You can control whether a task can share the slot with the previous task with the function "startNewResourceGroup()" in the streaming API. Sharing lots makes a few things easier to reason about, especially when adding operators to a program, you need not immediately add new machines. How to solve your program case -------------------------------------------- We can actually make a pretty simple addition to Flink that will cause the tasks to be locally connected, which in turn will cause the scheduler to distribute them like you intend. Rather than let the 4 sources rebalance across all 16 mappers, each one should redistribute to 4 local mappers, and these 4 mappers should send data to one local sink each. We'll try and add that today and ping you once it is in. The following would be sample code to use this: env.setParallelism(4); env .addSource(kafkaSource) .partitionFan() .map(mapper).setParallelism(16); .partitionFan() .addSink(kafkaSink); A bit of background why the mechanism is the way that it is right now ---------------------------------------------------------------------------------------------- You can think of a slot as a slice of resources. In particular, an amount of memory from the memory manager, but also memory in the network stack. What we want to do quite soon is to make streaming programs more elastic. Consider for example the case that you have 16 slots on 4 machines, a machine fails, and you have no spare resources. In that case Flink should recognize that no spare resource can be acquired, and scale the job in. Since you have only 12 slots left, the parallelism of the mappers is reduced to 12, and the source task that was on the failed machine is moved to a slot on another machine. It is important that the guaranteed resources for each task do not change when scaling in, to keep behavior predictable. In this case, each slot will still at most host 1 source, 1 mapper, and 1 sink, as did some of the slots before. That is also the reason why the slots are per TaskManager, and not global, to associate them with a constant set of resources (mainly memory). Greetings, Stephan On Thu, Feb 4, 2016 at 9:54 AM, Gwenhael Pasquiers <[hidden email]> wrote:
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Re: Distribution of sinks among the nodes
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To your other question, there are two things in Flink: (1) Chaining. Tasks are folded together into one task, run by one thread. (2) Resource groups: Tasks stay separate, have separate threads, but share a slot (which means share memory resources). See the link in my previous mail for an explanation concerning those. Greetings, Stephan On Thu, Feb 4, 2016 at 3:10 PM, Stephan Ewen <[hidden email]> wrote:
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RE: Distribution of sinks among the nodes
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Okay ; Then I guess that the best we can do is to disable chaining (we really want one thread per operator since they are doing long operations) and have
the same parallelism for sinks as mapping : that way each map will have it’s own sink and there will be no exchanges between flink instances. From: [hidden email] [mailto:[hidden email]]
On Behalf Of Stephan Ewen To your other question, there are two things in Flink: (1) Chaining. Tasks are folded together into one task, run by one thread. (2) Resource groups: Tasks stay separate, have separate threads, but share a slot (which means share memory resources). See the link in my previous mail for an explanation concerning those. Greetings, Stephan On Thu, Feb 4, 2016 at 3:10 PM, Stephan Ewen <[hidden email]> wrote: Hi Gwen! You actually need not 24 slots, but only as many as the highest parallelism is (16). Slots do not hold individual tasks, but "pipelines". Here is an illustration how that works. You can control whether a task can share the slot with the previous task with the function "startNewResourceGroup()" in the streaming API. Sharing lots makes a few things easier to reason about, especially when adding operators to a program,
you need not immediately add new machines. How to solve your program case -------------------------------------------- We can actually make a pretty simple addition to Flink that will cause the tasks to be locally connected, which in turn will cause the scheduler to distribute them like you intend. Rather than let the 4 sources rebalance across all 16 mappers, each one should redistribute to 4 local mappers, and these 4 mappers should send data to one local sink each. We'll try and add that today and ping you once it is in. The following would be sample code to use this: env.setParallelism(4); env .addSource(kafkaSource) .partitionFan() .map(mapper).setParallelism(16); .partitionFan() .addSink(kafkaSink); A bit of background why the mechanism is the way that it is right now ---------------------------------------------------------------------------------------------- You can think of a slot as a slice of resources. In particular, an amount of memory from the memory manager, but also memory in the network stack. What we want to do quite soon is to make streaming programs more elastic. Consider for example the case that you have 16 slots on 4 machines, a machine fails, and you have no spare resources. In that case Flink should recognize that no
spare resource can be acquired, and scale the job in. Since you have only 12 slots left, the parallelism of the mappers is reduced to 12, and the source task that was on the failed machine is moved to a slot on another machine. It is important that the guaranteed resources for each task do not change when scaling in, to keep behavior predictable. In this case, each slot will still at most host 1 source, 1 mapper, and 1 sink, as did some of the slots before. That
is also the reason why the slots are per TaskManager, and not global, to associate them with a constant set of resources (mainly memory). Greetings, Stephan On Thu, Feb 4, 2016 at 9:54 AM, Gwenhael Pasquiers <[hidden email]> wrote: Don’t we need to set the number of slots to 24 (4 sources + 16 mappers + 4 sinks) ? Or is there a way not to set the number of slots per TaskManager instead of globally
so that they are at least equally dispatched among the nodes ? As for the sink deployment : that’s not good news ; I mean we will have a non-negligible
overhead : all the data generated by 3 of the 4 nodes will be sent to a third node instead of being sent to the “local” sink. Network I/O have a price. Do you have some sort of “topology” feature coming in the roadmap ? Maybe a listener
on the JobManager / env that would be trigerred, asking usk on which node we would prefer each node to be deployed. That way you keep the standard behavior, don’t have to make a complicated generic-optimized algorithm, and let the user make it’s choices.
Should I create a JIRA ? For the time being we could start the application 4 time : one time per node, put that’s
not pretty at all J B.R. From: Till
Rohrmann [mailto:[hidden email]]
Hi Gwenhäel, if you set the number of slots for each Cheers, On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <[hidden email]> wrote: It is one type of mapper with a parallelism of 16
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Re: Distribution of sinks among the nodes
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I added a new Ticket: https://issues.apache.org/jira/browse/FLINK-3336
This will implement the data shipping pattern that you mentioned in your initial mail. I also have the Pull request almost ready. > On 04 Feb 2016, at 16:25, Gwenhael Pasquiers <[hidden email]> wrote: > > Okay ; > > Then I guess that the best we can do is to disable chaining (we really want one thread per operator since they are doing long operations) and have the same parallelism for sinks as mapping : that way each map will have it’s own sink and there will be no exchanges between flink instances. > > From: [hidden email] [mailto:[hidden email]] On Behalf Of Stephan Ewen > Sent: jeudi 4 février 2016 15:13 > To: [hidden email] > Subject: Re: Distribution of sinks among the nodes > > To your other question, there are two things in Flink: > > (1) Chaining. Tasks are folded together into one task, run by one thread. > > (2) Resource groups: Tasks stay separate, have separate threads, but share a slot (which means share memory resources). See the link in my previous mail for an explanation concerning those. > > Greetings, > Stephan > > > On Thu, Feb 4, 2016 at 3:10 PM, Stephan Ewen <[hidden email]> wrote: > Hi Gwen! > > You actually need not 24 slots, but only as many as the highest parallelism is (16). Slots do not hold individual tasks, but "pipelines". > > Here is an illustration how that works. > https://ci.apache.org/projects/flink/flink-docs-release-0.10/setup/config.html#configuring-taskmanager-processing-slots > > You can control whether a task can share the slot with the previous task with the function "startNewResourceGroup()" in the streaming API. Sharing lots makes a few things easier to reason about, especially when adding operators to a program, you need not immediately add new machines. > > > How to solve your program case > -------------------------------------------- > > We can actually make a pretty simple addition to Flink that will cause the tasks to be locally connected, which in turn will cause the scheduler to distribute them like you intend. > Rather than let the 4 sources rebalance across all 16 mappers, each one should redistribute to 4 local mappers, and these 4 mappers should send data to one local sink each. > > We'll try and add that today and ping you once it is in. > > The following would be sample code to use this: > > env.setParallelism(4); > > env > .addSource(kafkaSource) > .partitionFan() > .map(mapper).setParallelism(16); > .partitionFan() > .addSink(kafkaSink); > > > > A bit of background why the mechanism is the way that it is right now > ---------------------------------------------------------------------------------------------- > > You can think of a slot as a slice of resources. In particular, an amount of memory from the memory manager, but also memory in the network stack. > > What we want to do quite soon is to make streaming programs more elastic. Consider for example the case that you have 16 slots on 4 machines, a machine fails, and you have no spare resources. In that case Flink should recognize that no spare resource can be acquired, and scale the job in. Since you have only 12 slots left, the parallelism of the mappers is reduced to 12, and the source task that was on the failed machine is moved to a slot on another machine. > > It is important that the guaranteed resources for each task do not change when scaling in, to keep behavior predictable. In this case, each slot will still at most host 1 source, 1 mapper, and 1 sink, as did some of the slots before. That is also the reason why the slots are per TaskManager, and not global, to associate them with a constant set of resources (mainly memory). > > > Greetings, > Stephan > > > > On Thu, Feb 4, 2016 at 9:54 AM, Gwenhael Pasquiers <[hidden email]> wrote: > Don’t we need to set the number of slots to 24 (4 sources + 16 mappers + 4 sinks) ? > > Or is there a way not to set the number of slots per TaskManager instead of globally so that they are at least equally dispatched among the nodes ? > > As for the sink deployment : that’s not good news ; I mean we will have a non-negligible overhead : all the data generated by 3 of the 4 nodes will be sent to a third node instead of being sent to the “local” sink. Network I/O have a price. > > Do you have some sort of “topology” feature coming in the roadmap ? Maybe a listener on the JobManager / env that would be trigerred, asking usk on which node we would prefer each node to be deployed. That way you keep the standard behavior, don’t have to make a complicated generic-optimized algorithm, and let the user make it’s choices. Should I create a JIRA ? > > For the time being we could start the application 4 time : one time per node, put that’s not pretty at all J > > B.R. > > From: Till Rohrmann [mailto:[hidden email]] > Sent: mercredi 3 février 2016 17:58 > > To: [hidden email] > Subject: Re: Distribution of sinks among the nodes > > Hi Gwenhäel, > > if you set the number of slots for each TaskManager to 4, then all of your mapper will be evenly spread out. The sources should also be evenly spread out. However, for the sinks since they depend on all mappers, it will be most likely random where they are deployed. So you might end up with 4 sink tasks on one machine. > > Cheers, > Till > > > > On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <[hidden email]> wrote: > It is one type of mapper with a parallelism of 16 > It's the same for the sinks and sources (parallelism of 4) > > The settings are > Env.setParallelism(4) > Mapper.setPrallelism(env.getParallelism() * 4) > > We mean to have X mapper tasks per source / sink > > The mapper is doing some heavy computation and we have only 4 kafka partitions. That's why we need more mappers than sources / sinks > > > -----Original Message----- > From: Aljoscha Krettek [mailto:[hidden email]] > Sent: mercredi 3 février 2016 16:26 > To: [hidden email] > Subject: Re: Distribution of sinks among the nodes > > Hi Gwenhäel, > when you say 16 maps, are we talking about one mapper with parallelism 16 or 16 unique map operators? > > Regards, > Aljoscha > > On 03 Feb 2016, at 15:48, Gwenhael Pasquiers <[hidden email]> wrote: > > > > Hi, > > > > We try to deploy an application with the following “architecture” : > > > > 4 kafka sources => 16 maps => 4 kafka sinks, on 4 nodes, with 24 slots (we disabled operator chaining). > > > > So we’d like on each node : > > 1x source => 4x map => 1x sink > > > > That way there are no exchanges between different instances of flink and performances would be optimal. > > > > But we get (according to the flink GUI and the Host column when looking at the details of each task) : > > > > Node 1 : 1 source => 2 map > > Node 2 : 1 source => 1 map > > Node 3 : 1 source => 1 map > > Node 4 : 1 source => 12 maps => 4 sinks > > > > (I think no comments are needed J) > > > > The the Web UI says that there are 24 slots and they are all used but they don’t seem evenly dispatched … > > > > How could we make Flink deploy the tasks the way we want ? > > > > B.R. > > > > Gwen’ > |
Re: Distribution of sinks among the nodes
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Hi,
I just merged the new feature, so once this makes it into the 1.0-SNAPSHOT builds you should be able to use: env.setParallelism(4); env .addSource(kafkaSource) .rescale() .map(mapper).setParallelism(16); .rescale() .addSink(kafkaSink); to get your desired behavior. For this to work, the parallelism should be set to 16, with 4 nodes. Then each node will have one source, 4 mappers and 1 sink. The source will only be connected to the 4 mappers while the 4 mappers will be the only ones connected to the sink. Cheers, Aljoscha > On 04 Feb 2016, at 18:29, Aljoscha Krettek <[hidden email]> wrote: > > I added a new Ticket: https://issues.apache.org/jira/browse/FLINK-3336 > > This will implement the data shipping pattern that you mentioned in your initial mail. I also have the Pull request almost ready. > >> On 04 Feb 2016, at 16:25, Gwenhael Pasquiers <[hidden email]> wrote: >> >> Okay ; >> >> Then I guess that the best we can do is to disable chaining (we really want one thread per operator since they are doing long operations) and have the same parallelism for sinks as mapping : that way each map will have it’s own sink and there will be no exchanges between flink instances. >> >> From: [hidden email] [mailto:[hidden email]] On Behalf Of Stephan Ewen >> Sent: jeudi 4 février 2016 15:13 >> To: [hidden email] >> Subject: Re: Distribution of sinks among the nodes >> >> To your other question, there are two things in Flink: >> >> (1) Chaining. Tasks are folded together into one task, run by one thread. >> >> (2) Resource groups: Tasks stay separate, have separate threads, but share a slot (which means share memory resources). See the link in my previous mail for an explanation concerning those. >> >> Greetings, >> Stephan >> >> >> On Thu, Feb 4, 2016 at 3:10 PM, Stephan Ewen <[hidden email]> wrote: >> Hi Gwen! >> >> You actually need not 24 slots, but only as many as the highest parallelism is (16). Slots do not hold individual tasks, but "pipelines". >> >> Here is an illustration how that works. >> https://ci.apache.org/projects/flink/flink-docs-release-0.10/setup/config.html#configuring-taskmanager-processing-slots >> >> You can control whether a task can share the slot with the previous task with the function "startNewResourceGroup()" in the streaming API. Sharing lots makes a few things easier to reason about, especially when adding operators to a program, you need not immediately add new machines. >> >> >> How to solve your program case >> -------------------------------------------- >> >> We can actually make a pretty simple addition to Flink that will cause the tasks to be locally connected, which in turn will cause the scheduler to distribute them like you intend. >> Rather than let the 4 sources rebalance across all 16 mappers, each one should redistribute to 4 local mappers, and these 4 mappers should send data to one local sink each. >> >> We'll try and add that today and ping you once it is in. >> >> The following would be sample code to use this: >> >> env.setParallelism(4); >> >> env >> .addSource(kafkaSource) >> .partitionFan() >> .map(mapper).setParallelism(16); >> .partitionFan() >> .addSink(kafkaSink); >> >> >> >> A bit of background why the mechanism is the way that it is right now >> ---------------------------------------------------------------------------------------------- >> >> You can think of a slot as a slice of resources. In particular, an amount of memory from the memory manager, but also memory in the network stack. >> >> What we want to do quite soon is to make streaming programs more elastic. Consider for example the case that you have 16 slots on 4 machines, a machine fails, and you have no spare resources. In that case Flink should recognize that no spare resource can be acquired, and scale the job in. Since you have only 12 slots left, the parallelism of the mappers is reduced to 12, and the source task that was on the failed machine is moved to a slot on another machine. >> >> It is important that the guaranteed resources for each task do not change when scaling in, to keep behavior predictable. In this case, each slot will still at most host 1 source, 1 mapper, and 1 sink, as did some of the slots before. That is also the reason why the slots are per TaskManager, and not global, to associate them with a constant set of resources (mainly memory). >> >> >> Greetings, >> Stephan >> >> >> >> On Thu, Feb 4, 2016 at 9:54 AM, Gwenhael Pasquiers <[hidden email]> wrote: >> Don’t we need to set the number of slots to 24 (4 sources + 16 mappers + 4 sinks) ? >> >> Or is there a way not to set the number of slots per TaskManager instead of globally so that they are at least equally dispatched among the nodes ? >> >> As for the sink deployment : that’s not good news ; I mean we will have a non-negligible overhead : all the data generated by 3 of the 4 nodes will be sent to a third node instead of being sent to the “local” sink. Network I/O have a price. >> >> Do you have some sort of “topology” feature coming in the roadmap ? Maybe a listener on the JobManager / env that would be trigerred, asking usk on which node we would prefer each node to be deployed. That way you keep the standard behavior, don’t have to make a complicated generic-optimized algorithm, and let the user make it’s choices. Should I create a JIRA ? >> >> For the time being we could start the application 4 time : one time per node, put that’s not pretty at all J >> >> B.R. >> >> From: Till Rohrmann [mailto:[hidden email]] >> Sent: mercredi 3 février 2016 17:58 >> >> To: [hidden email] >> Subject: Re: Distribution of sinks among the nodes >> >> Hi Gwenhäel, >> >> if you set the number of slots for each TaskManager to 4, then all of your mapper will be evenly spread out. The sources should also be evenly spread out. However, for the sinks since they depend on all mappers, it will be most likely random where they are deployed. So you might end up with 4 sink tasks on one machine. >> >> Cheers, >> Till >> >> >> >> On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <[hidden email]> wrote: >> It is one type of mapper with a parallelism of 16 >> It's the same for the sinks and sources (parallelism of 4) >> >> The settings are >> Env.setParallelism(4) >> Mapper.setPrallelism(env.getParallelism() * 4) >> >> We mean to have X mapper tasks per source / sink >> >> The mapper is doing some heavy computation and we have only 4 kafka partitions. That's why we need more mappers than sources / sinks >> >> >> -----Original Message----- >> From: Aljoscha Krettek [mailto:[hidden email]] >> Sent: mercredi 3 février 2016 16:26 >> To: [hidden email] >> Subject: Re: Distribution of sinks among the nodes >> >> Hi Gwenhäel, >> when you say 16 maps, are we talking about one mapper with parallelism 16 or 16 unique map operators? >> >> Regards, >> Aljoscha >>> On 03 Feb 2016, at 15:48, Gwenhael Pasquiers <[hidden email]> wrote: >>> >>> Hi, >>> >>> We try to deploy an application with the following “architecture” : >>> >>> 4 kafka sources => 16 maps => 4 kafka sinks, on 4 nodes, with 24 slots (we disabled operator chaining). >>> >>> So we’d like on each node : >>> 1x source => 4x map => 1x sink >>> >>> That way there are no exchanges between different instances of flink and performances would be optimal. >>> >>> But we get (according to the flink GUI and the Host column when looking at the details of each task) : >>> >>> Node 1 : 1 source => 2 map >>> Node 2 : 1 source => 1 map >>> Node 3 : 1 source => 1 map >>> Node 4 : 1 source => 12 maps => 4 sinks >>> >>> (I think no comments are needed J) >>> >>> The the Web UI says that there are 24 slots and they are all used but they don’t seem evenly dispatched … >>> >>> How could we make Flink deploy the tasks the way we want ? >>> >>> B.R. >>> >>> Gwen’ >> > |
RE: Distribution of sinks among the nodes
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Thanks,
One more thing to expect from the next version ! -----Original Message----- From: Aljoscha Krettek [mailto:[hidden email]] Sent: lundi 8 février 2016 13:18 To: [hidden email] Subject: Re: Distribution of sinks among the nodes Hi, I just merged the new feature, so once this makes it into the 1.0-SNAPSHOT builds you should be able to use: env.setParallelism(4); env .addSource(kafkaSource) .rescale() .map(mapper).setParallelism(16); .rescale() .addSink(kafkaSink); to get your desired behavior. For this to work, the parallelism should be set to 16, with 4 nodes. Then each node will have one source, 4 mappers and 1 sink. The source will only be connected to the 4 mappers while the 4 mappers will be the only ones connected to the sink. Cheers, Aljoscha > On 04 Feb 2016, at 18:29, Aljoscha Krettek <[hidden email]> wrote: > > I added a new Ticket: https://issues.apache.org/jira/browse/FLINK-3336 > > This will implement the data shipping pattern that you mentioned in your initial mail. I also have the Pull request almost ready. > >> On 04 Feb 2016, at 16:25, Gwenhael Pasquiers <[hidden email]> wrote: >> >> Okay ; >> >> Then I guess that the best we can do is to disable chaining (we really want one thread per operator since they are doing long operations) and have the same parallelism for sinks as mapping : that way each map will have it’s own sink and there will be no exchanges between flink instances. >> >> From: [hidden email] [mailto:[hidden email]] On Behalf >> Of Stephan Ewen >> Sent: jeudi 4 février 2016 15:13 >> To: [hidden email] >> Subject: Re: Distribution of sinks among the nodes >> >> To your other question, there are two things in Flink: >> >> (1) Chaining. Tasks are folded together into one task, run by one thread. >> >> (2) Resource groups: Tasks stay separate, have separate threads, but share a slot (which means share memory resources). See the link in my previous mail for an explanation concerning those. >> >> Greetings, >> Stephan >> >> >> On Thu, Feb 4, 2016 at 3:10 PM, Stephan Ewen <[hidden email]> wrote: >> Hi Gwen! >> >> You actually need not 24 slots, but only as many as the highest parallelism is (16). Slots do not hold individual tasks, but "pipelines". >> >> Here is an illustration how that works. >> https://ci.apache.org/projects/flink/flink-docs-release-0.10/setup/co >> nfig.html#configuring-taskmanager-processing-slots >> >> You can control whether a task can share the slot with the previous task with the function "startNewResourceGroup()" in the streaming API. Sharing lots makes a few things easier to reason about, especially when adding operators to a program, you need not immediately add new machines. >> >> >> How to solve your program case >> -------------------------------------------- >> >> We can actually make a pretty simple addition to Flink that will cause the tasks to be locally connected, which in turn will cause the scheduler to distribute them like you intend. >> Rather than let the 4 sources rebalance across all 16 mappers, each one should redistribute to 4 local mappers, and these 4 mappers should send data to one local sink each. >> >> We'll try and add that today and ping you once it is in. >> >> The following would be sample code to use this: >> >> env.setParallelism(4); >> >> env >> .addSource(kafkaSource) >> .partitionFan() >> .map(mapper).setParallelism(16); >> .partitionFan() >> .addSink(kafkaSink); >> >> >> >> A bit of background why the mechanism is the way that it is right now >> --------------------------------------------------------------------- >> ------------------------- >> >> You can think of a slot as a slice of resources. In particular, an amount of memory from the memory manager, but also memory in the network stack. >> >> What we want to do quite soon is to make streaming programs more elastic. Consider for example the case that you have 16 slots on 4 machines, a machine fails, and you have no spare resources. In that case Flink should recognize that no spare resource can be acquired, and scale the job in. Since you have only 12 slots left, the parallelism of the mappers is reduced to 12, and the source task that was on the failed machine is moved to a slot on another machine. >> >> It is important that the guaranteed resources for each task do not change when scaling in, to keep behavior predictable. In this case, each slot will still at most host 1 source, 1 mapper, and 1 sink, as did some of the slots before. That is also the reason why the slots are per TaskManager, and not global, to associate them with a constant set of resources (mainly memory). >> >> >> Greetings, >> Stephan >> >> >> >> On Thu, Feb 4, 2016 at 9:54 AM, Gwenhael Pasquiers <[hidden email]> wrote: >> Don’t we need to set the number of slots to 24 (4 sources + 16 mappers + 4 sinks) ? >> >> Or is there a way not to set the number of slots per TaskManager instead of globally so that they are at least equally dispatched among the nodes ? >> >> As for the sink deployment : that’s not good news ; I mean we will have a non-negligible overhead : all the data generated by 3 of the 4 nodes will be sent to a third node instead of being sent to the “local” sink. Network I/O have a price. >> >> Do you have some sort of “topology” feature coming in the roadmap ? Maybe a listener on the JobManager / env that would be trigerred, asking usk on which node we would prefer each node to be deployed. That way you keep the standard behavior, don’t have to make a complicated generic-optimized algorithm, and let the user make it’s choices. Should I create a JIRA ? >> >> For the time being we could start the application 4 time : one time >> per node, put that’s not pretty at all J >> >> B.R. >> >> From: Till Rohrmann [mailto:[hidden email]] >> Sent: mercredi 3 février 2016 17:58 >> >> To: [hidden email] >> Subject: Re: Distribution of sinks among the nodes >> >> Hi Gwenhäel, >> >> if you set the number of slots for each TaskManager to 4, then all of your mapper will be evenly spread out. The sources should also be evenly spread out. However, for the sinks since they depend on all mappers, it will be most likely random where they are deployed. So you might end up with 4 sink tasks on one machine. >> >> Cheers, >> Till >> >> >> >> On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <[hidden email]> wrote: >> It is one type of mapper with a parallelism of 16 It's the same for >> the sinks and sources (parallelism of 4) >> >> The settings are >> Env.setParallelism(4) >> Mapper.setPrallelism(env.getParallelism() * 4) >> >> We mean to have X mapper tasks per source / sink >> >> The mapper is doing some heavy computation and we have only 4 kafka >> partitions. That's why we need more mappers than sources / sinks >> >> >> -----Original Message----- >> From: Aljoscha Krettek [mailto:[hidden email]] >> Sent: mercredi 3 février 2016 16:26 >> To: [hidden email] >> Subject: Re: Distribution of sinks among the nodes >> >> Hi Gwenhäel, >> when you say 16 maps, are we talking about one mapper with parallelism 16 or 16 unique map operators? >> >> Regards, >> Aljoscha >>> On 03 Feb 2016, at 15:48, Gwenhael Pasquiers <[hidden email]> wrote: >>> >>> Hi, >>> >>> We try to deploy an application with the following “architecture” : >>> >>> 4 kafka sources => 16 maps => 4 kafka sinks, on 4 nodes, with 24 slots (we disabled operator chaining). >>> >>> So we’d like on each node : >>> 1x source => 4x map => 1x sink >>> >>> That way there are no exchanges between different instances of flink and performances would be optimal. >>> >>> But we get (according to the flink GUI and the Host column when looking at the details of each task) : >>> >>> Node 1 : 1 source => 2 map >>> Node 2 : 1 source => 1 map >>> Node 3 : 1 source => 1 map >>> Node 4 : 1 source => 12 maps => 4 sinks >>> >>> (I think no comments are needed J) >>> >>> The the Web UI says that there are 24 slots and they are all used >>> but they don’t seem evenly dispatched … >>> >>> How could we make Flink deploy the tasks the way we want ? >>> >>> B.R. >>> >>> Gwen’ >> > |
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