[
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 a MinorFragment sender of one query
occurs , it will notice the FragmentScheduler. The FragmentScheduler is
instanced by the Foreman.It holds the whole PlanFragment execution graph.It
will allocate a new corresponding 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!
[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.
> 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 a MinorFragment sender of one query
> occurs , it will notice the FragmentScheduler. The FragmentScheduler is
> instanced by the Foreman.It holds the whole PlanFragment execution graph.It
> will allocate a new corresponding 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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