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Posted to issues@drill.apache.org by "weijie.tong (JIRA)" <ji...@apache.org> on 2017/11/19 05:41:00 UTC
[jira] [Updated] (DRILL-5975) Resource utilization
[ https://issues.apache.org/jira/browse/DRILL-5975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
weijie.tong updated DRILL-5975:
-------------------------------
Description:
h1. Motivation
Now the resource utilization radio of Drill's cluster is not too good. Most of the cluster resource is wasted. We can not afford too much concurrent queries. Once the system accepted more queries with a not high cpu load, the query which originally is very quick will become slower and slower.
The reason is Drill does not supply a scheduler . It just assume all the nodes have enough calculation resource. Once a query comes, it will schedule the related fragments to random nodes not caring about the node's load. Some nodes will suffer more cpu context switch to satisfy the coming query. The profound causes to this is that the runtime minor fragments construct a runtime tree whose nodes spread different drillbits. The runtime tree is a memory pipeline that is all the nodes will stay alone the whole lifecycle of a query by sending out data to upper nodes successively, even though some node could run quickly and quit immediately.What's more the runtime tree is constructed before actual running. The schedule target to Drill will become the whole runtime tree nodes.
h1. Design
It will be hard to schedule the runtime tree nodes as a whole. So I try to solve this by breaking the runtime cascade nodes. The graph below describes the initial design. !https://raw.githubusercontent.com/wiki/weijietong/drill/images/design.png! [graph link|https://raw.githubusercontent.com/wiki/weijietong/drill/images/design.png]
Every Drillbit instance will have a RecordBatchManager which will accept all the RecordBatchs written by the senders of local different MinorFragments. The RecordBatchManager will hold the RecordBatchs in memory firstly then disk storage . Once the first RecordBatch of one MinorFragment sender of one query occur , it will notice the FragmentScheduler. The FragmentScheduler is instanced by the Foreman.It holds the whole PlanFragment execution graph.It will allocate a new FragmentExecutor to run the generated RecordBatch. The allocated FragmentExecutor will then notify the corresponding FragmentManager to indicate that I am ready to receive the data. Then the FragmentManger will send out the RecordBatch one by one to the corresponding FragmentExecutor's receiver like what the current Sender does by throttling the data stream.
What we can gain from this design is :
a. The computation leaf node does not to wait for the consumer's speed to end its life to release the resource.
b. The sending data logic will be isolated from the computation nodes and shared by different FragmentManagers.
c. We can schedule the MajorFragments according to Drillbit's actual resource capacity at runtime.
d. Drill's pipeline data processing characteristic is also retained.
h1. Plan
This will be a large PR ,so I plan to divide it into some small ones.
a. to implement the RecordManager.
b. to implement a simple random FragmentScheduler and the whole event flow.
c. to implement a primitive FragmentScheduler which may reference the Sparrow project.
was:
h1. Motivation
Now the resource utilization radio of Drill's cluster is not too good. Most of the cluster resource is wasted. We can not afford too much concurrent queries. Once the system accepted more queries with a not high cpu load, the query which originally is very quick will become slower and slower.
The reason is Drill does not supply a scheduler . It just assume all the nodes have enough calculation resource. Once a query comes, it will schedule the related fragments to random nodes not caring about the node's load. Some nodes will suffer more cpu context switch to satisfy the coming query. The profound causes to this is that the runtime minor fragments construct a runtime tree whose nodes spread different drillbits. The runtime tree is a memory pipeline that is all the nodes will stay alone the whole lifecycle of a query by sending out data to upper nodes successively, even though some node could run quickly and quit immediately.What's more the runtime tree is constructed before actual running. The schedule target to Drill will become the whole runtime tree nodes.
h1. Design
It will be hard to schedule the runtime tree nodes as a whole. So I try to solve this by breaking the runtime cascade nodes. The graph below describes the initial design. !https://raw.githubusercontent.com/wiki/weijietong/drill/images/design.png!
Every Drillbit instance will have a RecordBatchManager which will accept all the RecordBatchs written by the senders of local different MinorFragments. The RecordBatchManager will hold the RecordBatchs in memory firstly then disk storage . Once the first RecordBatch of one MinorFragment sender of one query occur , it will notice the FragmentScheduler. The FragmentScheduler is instanced by the Foreman.It holds the whole PlanFragment execution graph.It will allocate a new FragmentExecutor to run the generated RecordBatch. The allocated FragmentExecutor will then notify the corresponding FragmentManager to indicate that I am ready to receive the data. Then the FragmentManger will send out the RecordBatch one by one to the corresponding FragmentExecutor's receiver like what the current Sender does by throttling the data stream.
h1. Plan
This will be a large PR ,so I plan to divide it into some small ones.
a. to implement the RecordManager.
b. to implement a simple random FragmentScheduler and the whole event flow.
c. to implement a primitive FragmentScheduler which may reference the Sparrow project.
> Resource utilization
> --------------------
>
> Key: DRILL-5975
> URL: https://issues.apache.org/jira/browse/DRILL-5975
> Project: Apache Drill
> Issue Type: New Feature
> Affects Versions: 2.0.0
> Reporter: weijie.tong
> Assignee: weijie.tong
>
> h1. Motivation
> Now the resource utilization radio of Drill's cluster is not too good. Most of the cluster resource is wasted. We can not afford too much concurrent queries. Once the system accepted more queries with a not high cpu load, the query which originally is very quick will become slower and slower.
> The reason is Drill does not supply a scheduler . It just assume all the nodes have enough calculation resource. Once a query comes, it will schedule the related fragments to random nodes not caring about the node's load. Some nodes will suffer more cpu context switch to satisfy the coming query. The profound causes to this is that the runtime minor fragments construct a runtime tree whose nodes spread different drillbits. The runtime tree is a memory pipeline that is all the nodes will stay alone the whole lifecycle of a query by sending out data to upper nodes successively, even though some node could run quickly and quit immediately.What's more the runtime tree is constructed before actual running. The schedule target to Drill will become the whole runtime tree nodes.
> h1. Design
> It will be hard to schedule the runtime tree nodes as a whole. So I try to solve this by breaking the runtime cascade nodes. The graph below describes the initial design. !https://raw.githubusercontent.com/wiki/weijietong/drill/images/design.png! [graph link|https://raw.githubusercontent.com/wiki/weijietong/drill/images/design.png]
> Every Drillbit instance will have a RecordBatchManager which will accept all the RecordBatchs written by the senders of local different MinorFragments. The RecordBatchManager will hold the RecordBatchs in memory firstly then disk storage . Once the first RecordBatch of one MinorFragment sender of one query occur , it will notice the FragmentScheduler. The FragmentScheduler is instanced by the Foreman.It holds the whole PlanFragment execution graph.It will allocate a new FragmentExecutor to run the generated RecordBatch. The allocated FragmentExecutor will then notify the corresponding FragmentManager to indicate that I am ready to receive the data. Then the FragmentManger will send out the RecordBatch one by one to the corresponding FragmentExecutor's receiver like what the current Sender does by throttling the data stream.
> What we can gain from this design is :
> a. The computation leaf node does not to wait for the consumer's speed to end its life to release the resource.
> b. The sending data logic will be isolated from the computation nodes and shared by different FragmentManagers.
> c. We can schedule the MajorFragments according to Drillbit's actual resource capacity at runtime.
> d. Drill's pipeline data processing characteristic is also retained.
> h1. Plan
> This will be a large PR ,so I plan to divide it into some small ones.
> a. to implement the RecordManager.
> b. to implement a simple random FragmentScheduler and the whole event flow.
> c. to implement a primitive FragmentScheduler which may reference the Sparrow project.
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