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From "Arun C Murthy (JIRA)" <j...@apache.org>
Subject [jira] Updated: (MAPREDUCE-279) Map-Reduce 2.0
Date Tue, 15 Feb 2011 08:30:57 GMT

     [ https://issues.apache.org/jira/browse/MAPREDUCE-279?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Arun C Murthy updated MAPREDUCE-279:
------------------------------------

    Description: Re-factor MapReduce into a generic resource scheduler and a per-job, user-defined
component that manages the application execution.   (was: We, at Yahoo!, have been using Hadoop-On-Demand
as the resource provisioning/scheduling mechanism. 

With HoD the user uses a self-service system to ask-for a set of nodes. HoD allocates these
from a global pool and also provisions a private Map-Reduce cluster for the user. She then
runs her jobs and shuts the cluster down via HoD when done. All user-private clusters use
the same humongous, static HDFS (e.g. 2k node HDFS). 

More details about HoD are available here: HADOOP-1301.

----

h3. Motivation

The current deployment (Hadoop + HoD) has a couple of implications:

 * _Non-optimal Cluster Utilization_

   1. Job-private Map-Reduce clusters imply that the user-cluster potentially could be *idle*
for atleast a while before being detected and shut-down.

   2. Elastic Jobs: Map-Reduce jobs, typically, have lots of maps with much-smaller no. of
reduces; with maps being light and quick and reduces being i/o heavy and longer-running. Users
typically allocate clusters depending on the no. of maps (i.e. input size) which leads to
the scenario where all the maps are done (idle nodes in the cluster) and the few reduces are
chugging along. Right now, we do not have the ability to shrink the HoD'ed Map-Reduce clusters
which would alleviate this issue. 

 * _Impact on data-locality_

With the current setup of a static, large HDFS and much smaller (5/10/20/50 node) clusters
there is a good chance of losing one of Map-Reduce's primary features: ability to execute
tasks on the datanodes where the input splits are located. In fact, we have seen the data-local
tasks go down to 20-25 percent in the GridMix benchmarks, from the 95-98 percent we see on
the randomwriter+sort runs run as part of the hadoopqa benchmarks (admittedly a synthetic
benchmark, but yet). Admittedly, HADOOP-1985 (rack-aware Map-Reduce) helps significantly here.

----

Primarily, the notion of *job-level scheduling* leading to private clusers, as opposed to
*task-level scheduling*, is a good peg to hang-on the majority of the blame.

Keeping the above factors in mind, here are some thoughts on how to re-structure Hadoop Map-Reduce
to solve some of these issues.

----

h3. State of the Art

As it exists today, a large, static, Hadoop Map-Reduce cluster (forget HoD for a bit) does
provide task-level scheduling; however as it exists today, it's scalability to tens-of-thousands
of user-jobs, per-week, is in question.

Lets review it's current architecture and main components:

 * JobTracker: It does both *task-scheduling* and *task-monitoring* (tasktrackers send task-statuses
via periodic heartbeats), which implies it is fairly loaded. It is also a _single-point of
failure_ in the Map-Reduce framework i.e. its failure implies that all the jobs in the system
fail. This means a static, large Map-Reduce cluster is fairly susceptible and a definite suspect.
Clearly HoD solves this by having per-job clusters, albeit with the above drawbacks.
 * TaskTracker: The slave in the system which executes one task at-a-time under directions
from the JobTracker.
 * JobClient: The per-job client which just submits the job and polls the JobTracker for status.


----

h3. Proposal - Map-Reduce 2.0 

The primary idea is to move to task-level scheduling and static Map-Reduce clusters (so as
to maintain the same storage cluster and compute cluster paradigm) as a way to directly tackle
the two main issues illustrated above. Clearly, we will have to get around the existing problems,
especially w.r.t. scalability and reliability.

The proposal is to re-work Hadoop Map-Reduce to make it suitable for a large, static cluster.


Here is an overview of how its main components would look like:
 * JobTracker: Turn the JobTracker into a pure task-scheduler, a global one. Lets call this
the *JobScheduler* henceforth. Clearly (data-locality aware) Maui/Moab are  candidates for
being the scheduler, in which case, the JobScheduler is just a thin wrapper around them. 
 * TaskTracker: These stay as before, without some minor changes as illustrated later in the
piece.
 * JobClient: Fatten up the JobClient my putting a lot more intelligence into it. Enhance
it to talk to the JobTracker to ask for available TaskTrackers and then contact them to schedule
and monitor the tasks. So we'll have lots of per-job clients talking to the JobScheduler and
the relevant TaskTrackers for their respective jobs, a big change from today. Lets call this
the *JobManager* henceforth. 

A broad sketch of how things would work: 

h4. Deployment

There is a single, static, large Map-Reduce cluster, and no per-job clusters.

Essentially there is one global JobScheduler with thousands of independent TaskTrackers, each
running on one node.

As mentioned previously, the JobScheduler is a pure task-scheduler. When contacted by per-job
JobManagers querying for TaskTrackers to run their tasks on, the JobTracker takes into the
account the job priority, data-placements (HDFS blocks), current-load/capacity of the TaskTrackers
and gives the JobManager a free slot for the task(s) in question, if available.

Each TaskTracker periodically updates the master JobScheduler with information about the currently
running tasks and available free-slots. It waits for the per-job JobManager to contact it
for free-slots (which abide the JobScheduler's directives) and status for currently-running
tasks (of course, the JobManager knows exactly which TaskTrackers it needs to talk to).

The fact that the JobScheduler is no longer doing the heavy-lifting of monitoring tasks (like
the current JobTracker), and hence the jobs, is the key differentiator, which is why it should
be very light-weight. (Thus, it is even conceivable to imagine a hot-backup of the JobScheduler,
topic for another discussion.)

h4. Job Execution

Here is how the job-execution work-flow looks like:

    * User submits a job,
    * The JobClient, as today, validates inputs, computes the input splits etc.
    * Rather than submit the job to the JobTracker which then runs it, the JobClient now dons
the role of the JobManager as described above (of course they could be two independent processes
working in conjunction with the other... ). The JobManager pro-actively works with the JobScheduler
and the TaskTrackers to execute the job. While there are more tasks to run for the still-running
job, it contacts the JobScheduler to get 'n' free slots and schedules m tasks (m <= n)
on the given TaskTrackers (slots). The JobManager also monitors the tasks by contacting the
relevant TaskTrackers (it knows which of the TaskTrackers are running its tasks). 

h4. Brownie Points

 *  With Map-Reduce v2.0, we get reliability/scalability of the current (Map-Reduce + HoD)
architecture.
 * We get elastic jobs for free since there is no concept of private clusters and clearly
JobManagers do not need to hold on to the map-nodes when they are done.
 * We do get data-locality across all jobs, big or small, since there are no off-limit DataNodes
(i.e. DataNodes outside the private cluster) for a Map-Reduce cluster, as today.
 * From an architectural standpoint, each component in the system (sans the global scheduler)
is nicely independent and impervious of the other:
  ** A JobManager is responsible for one and only one job, loss of a JobManager affects only
one job.
  ** A TaskTracker manages only one node, it's loss affects only one node in the cluster.

  ** No user-code runs in the JobScheduler since it's a pure scheduler.
 * We can run all of the user-code (input/output formats, split calculation, task-output promotion
etc.) from the JobManager since it is, by definition, the user-client. 

h4. Points to Ponder

 * Given that the JobScheduler, is very light-weight, could we have a hot-backup for HA?
 * Discuss the notion of a rack-level aggregator of TaskTracker statuses i.e. rather than
have every TaskTracker update the JobScheduler, a rack-level aggregator could achieve the
same?
 * We could have the notion of a JobManager being the proxy process running inside the cluster
for the JobClient (the job-submitting program which is running outside the colo e.g. user's
dev box) ... in fact we can think of the JobManager being *another kind of task* which needs
to be scheduled to run at a TaskTracker. 
 * Task Isolation via separate vms (vmware/xen) rather than just separate jvms?

h4. How do we get to Map-Reduce 2.0?

At the risk of sounding hopelessly optimistic, we probably do not have to work too much to
get here.

 * Clearly the main changes come in the JobTracker/JobClient where we _move_ the pieces which
monitor the job's tasks' progress into the JobScheduler/JobManager.
 * We also need to enhance the JobClient (as the JobManager) to get it to talk to the JobTracker
(JobScheduler) to query for the empty slots, which might not be available!
 * Then we need to add RPCs to get the JobClient (JobManager) to talk to the given TaskTrackers
to get them to run the tasks, thus reversing the direction of current RPCs needed to start
a task (now the TaskTracker asks the JobTracker for tasks to run); we also need new RPCs for
the JobClient (JobManager) to talk to the TaskTracker to query it's tasks' statuses.
 * We leave the current heartbeat mechanism from the TaskTracker to the JobTracker (JobScheduler)
as-is, sans the task-statuses. 

h4. Glossary

 * JobScheduler - The global, task-scheduler which is today's JobTracker minus the code for
tracking/monitoring jobs and their tasks. A pure scheduler.
 * JobManager - The per-job manager which is wholly responsible for working with the JobScheduler
and TaskTrackers to schedule it's tasks and track their progress till job-completion (success/failure).
Simplistically it is the current JobClient plus the enhancements to enable it to talk to the
JobScheduler and TaskTrackers for running/monitoring the tasks. 

----

h3. Tickets for the Gravy-Train ride

Eric has started a discussion about generalizing Hadoop to support non-MR tasks, a discussion
which has surfaced a few times on our lists, at HADOOP-2491. 

He notes:

{quote}
Our primary goal in going this way would be to get better utilization out of map-reduce clusters
and support a richer scheduling model. The ability to support alternative job frameworks would
just be gravy!

Putting this in as a place holder. Hope to get folks talking about this to post some more
detail.
{quote}

This is the start of the path to the promised gravy-land. *smile*

We believe Map-Reduce 2.0 is a good start in moving most (if not all) of the Map-Reduce specific
code into the user-clients (i.e. JobManager) and taking a shot at generalizing the JobTracker
(as the JobScheduler) and the TaskTracker to handle more generic tasks via different (smarter/dumber)
user-clients.

----

Thoughts?)

The original description is too long... I've preserved this here:

----

We, at Yahoo!, have been using Hadoop-On-Demand as the resource provisioning/scheduling mechanism.


With HoD the user uses a self-service system to ask-for a set of nodes. HoD allocates these
from a global pool and also provisions a private Map-Reduce cluster for the user. She then
runs her jobs and shuts the cluster down via HoD when done. All user-private clusters use
the same humongous, static HDFS (e.g. 2k node HDFS). 

More details about HoD are available here: HADOOP-1301.

----

h3. Motivation

The current deployment (Hadoop + HoD) has a couple of implications:

 * _Non-optimal Cluster Utilization_

   1. Job-private Map-Reduce clusters imply that the user-cluster potentially could be *idle*
for atleast a while before being detected and shut-down.

   2. Elastic Jobs: Map-Reduce jobs, typically, have lots of maps with much-smaller no. of
reduces; with maps being light and quick and reduces being i/o heavy and longer-running. Users
typically allocate clusters depending on the no. of maps (i.e. input size) which leads to
the scenario where all the maps are done (idle nodes in the cluster) and the few reduces are
chugging along. Right now, we do not have the ability to shrink the HoD'ed Map-Reduce clusters
which would alleviate this issue. 

 * _Impact on data-locality_

With the current setup of a static, large HDFS and much smaller (5/10/20/50 node) clusters
there is a good chance of losing one of Map-Reduce's primary features: ability to execute
tasks on the datanodes where the input splits are located. In fact, we have seen the data-local
tasks go down to 20-25 percent in the GridMix benchmarks, from the 95-98 percent we see on
the randomwriter+sort runs run as part of the hadoopqa benchmarks (admittedly a synthetic
benchmark, but yet). Admittedly, HADOOP-1985 (rack-aware Map-Reduce) helps significantly here.

----

Primarily, the notion of *job-level scheduling* leading to private clusers, as opposed to
*task-level scheduling*, is a good peg to hang-on the majority of the blame.

Keeping the above factors in mind, here are some thoughts on how to re-structure Hadoop Map-Reduce
to solve some of these issues.

----

h3. State of the Art

As it exists today, a large, static, Hadoop Map-Reduce cluster (forget HoD for a bit) does
provide task-level scheduling; however as it exists today, it's scalability to tens-of-thousands
of user-jobs, per-week, is in question.

Lets review it's current architecture and main components:

 * JobTracker: It does both *task-scheduling* and *task-monitoring* (tasktrackers send task-statuses
via periodic heartbeats), which implies it is fairly loaded. It is also a _single-point of
failure_ in the Map-Reduce framework i.e. its failure implies that all the jobs in the system
fail. This means a static, large Map-Reduce cluster is fairly susceptible and a definite suspect.
Clearly HoD solves this by having per-job clusters, albeit with the above drawbacks.
 * TaskTracker: The slave in the system which executes one task at-a-time under directions
from the JobTracker.
 * JobClient: The per-job client which just submits the job and polls the JobTracker for status.


----

h3. Proposal - Map-Reduce 2.0 

The primary idea is to move to task-level scheduling and static Map-Reduce clusters (so as
to maintain the same storage cluster and compute cluster paradigm) as a way to directly tackle
the two main issues illustrated above. Clearly, we will have to get around the existing problems,
especially w.r.t. scalability and reliability.

The proposal is to re-work Hadoop Map-Reduce to make it suitable for a large, static cluster.


Here is an overview of how its main components would look like:
 * JobTracker: Turn the JobTracker into a pure task-scheduler, a global one. Lets call this
the *JobScheduler* henceforth. Clearly (data-locality aware) Maui/Moab are  candidates for
being the scheduler, in which case, the JobScheduler is just a thin wrapper around them. 
 * TaskTracker: These stay as before, without some minor changes as illustrated later in the
piece.
 * JobClient: Fatten up the JobClient my putting a lot more intelligence into it. Enhance
it to talk to the JobTracker to ask for available TaskTrackers and then contact them to schedule
and monitor the tasks. So we'll have lots of per-job clients talking to the JobScheduler and
the relevant TaskTrackers for their respective jobs, a big change from today. Lets call this
the *JobManager* henceforth. 

A broad sketch of how things would work: 

h4. Deployment

There is a single, static, large Map-Reduce cluster, and no per-job clusters.

Essentially there is one global JobScheduler with thousands of independent TaskTrackers, each
running on one node.

As mentioned previously, the JobScheduler is a pure task-scheduler. When contacted by per-job
JobManagers querying for TaskTrackers to run their tasks on, the JobTracker takes into the
account the job priority, data-placements (HDFS blocks), current-load/capacity of the TaskTrackers
and gives the JobManager a free slot for the task(s) in question, if available.

Each TaskTracker periodically updates the master JobScheduler with information about the currently
running tasks and available free-slots. It waits for the per-job JobManager to contact it
for free-slots (which abide the JobScheduler's directives) and status for currently-running
tasks (of course, the JobManager knows exactly which TaskTrackers it needs to talk to).

The fact that the JobScheduler is no longer doing the heavy-lifting of monitoring tasks (like
the current JobTracker), and hence the jobs, is the key differentiator, which is why it should
be very light-weight. (Thus, it is even conceivable to imagine a hot-backup of the JobScheduler,
topic for another discussion.)

h4. Job Execution

Here is how the job-execution work-flow looks like:

    * User submits a job,
    * The JobClient, as today, validates inputs, computes the input splits etc.
    * Rather than submit the job to the JobTracker which then runs it, the JobClient now dons
the role of the JobManager as described above (of course they could be two independent processes
working in conjunction with the other... ). The JobManager pro-actively works with the JobScheduler
and the TaskTrackers to execute the job. While there are more tasks to run for the still-running
job, it contacts the JobScheduler to get 'n' free slots and schedules m tasks (m <= n)
on the given TaskTrackers (slots). The JobManager also monitors the tasks by contacting the
relevant TaskTrackers (it knows which of the TaskTrackers are running its tasks). 

h4. Brownie Points

 *  With Map-Reduce v2.0, we get reliability/scalability of the current (Map-Reduce + HoD)
architecture.
 * We get elastic jobs for free since there is no concept of private clusters and clearly
JobManagers do not need to hold on to the map-nodes when they are done.
 * We do get data-locality across all jobs, big or small, since there are no off-limit DataNodes
(i.e. DataNodes outside the private cluster) for a Map-Reduce cluster, as today.
 * From an architectural standpoint, each component in the system (sans the global scheduler)
is nicely independent and impervious of the other:
  ** A JobManager is responsible for one and only one job, loss of a JobManager affects only
one job.
  ** A TaskTracker manages only one node, it's loss affects only one node in the cluster.

  ** No user-code runs in the JobScheduler since it's a pure scheduler.
 * We can run all of the user-code (input/output formats, split calculation, task-output promotion
etc.) from the JobManager since it is, by definition, the user-client. 

h4. Points to Ponder

 * Given that the JobScheduler, is very light-weight, could we have a hot-backup for HA?
 * Discuss the notion of a rack-level aggregator of TaskTracker statuses i.e. rather than
have every TaskTracker update the JobScheduler, a rack-level aggregator could achieve the
same?
 * We could have the notion of a JobManager being the proxy process running inside the cluster
for the JobClient (the job-submitting program which is running outside the colo e.g. user's
dev box) ... in fact we can think of the JobManager being *another kind of task* which needs
to be scheduled to run at a TaskTracker. 
 * Task Isolation via separate vms (vmware/xen) rather than just separate jvms?

h4. How do we get to Map-Reduce 2.0?

At the risk of sounding hopelessly optimistic, we probably do not have to work too much to
get here.

 * Clearly the main changes come in the JobTracker/JobClient where we _move_ the pieces which
monitor the job's tasks' progress into the JobScheduler/JobManager.
 * We also need to enhance the JobClient (as the JobManager) to get it to talk to the JobTracker
(JobScheduler) to query for the empty slots, which might not be available!
 * Then we need to add RPCs to get the JobClient (JobManager) to talk to the given TaskTrackers
to get them to run the tasks, thus reversing the direction of current RPCs needed to start
a task (now the TaskTracker asks the JobTracker for tasks to run); we also need new RPCs for
the JobClient (JobManager) to talk to the TaskTracker to query it's tasks' statuses.
 * We leave the current heartbeat mechanism from the TaskTracker to the JobTracker (JobScheduler)
as-is, sans the task-statuses. 

h4. Glossary

 * JobScheduler - The global, task-scheduler which is today's JobTracker minus the code for
tracking/monitoring jobs and their tasks. A pure scheduler.
 * JobManager - The per-job manager which is wholly responsible for working with the JobScheduler
and TaskTrackers to schedule it's tasks and track their progress till job-completion (success/failure).
Simplistically it is the current JobClient plus the enhancements to enable it to talk to the
JobScheduler and TaskTrackers for running/monitoring the tasks. 

----

h3. Tickets for the Gravy-Train ride

Eric has started a discussion about generalizing Hadoop to support non-MR tasks, a discussion
which has surfaced a few times on our lists, at HADOOP-2491. 

He notes:

{quote}
Our primary goal in going this way would be to get better utilization out of map-reduce clusters
and support a richer scheduling model. The ability to support alternative job frameworks would
just be gravy!

Putting this in as a place holder. Hope to get folks talking about this to post some more
detail.
{quote}

This is the start of the path to the promised gravy-land. *smile*

We believe Map-Reduce 2.0 is a good start in moving most (if not all) of the Map-Reduce specific
code into the user-clients (i.e. JobManager) and taking a shot at generalizing the JobTracker
(as the JobScheduler) and the TaskTracker to handle more generic tasks via different (smarter/dumber)
user-clients.

----

Thoughts?

> Map-Reduce 2.0
> --------------
>
>                 Key: MAPREDUCE-279
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-279
>             Project: Hadoop Map/Reduce
>          Issue Type: Improvement
>          Components: jobtracker, tasktracker
>            Reporter: Arun C Murthy
>            Assignee: Arun C Murthy
>             Fix For: 0.23.0
>
>
> Re-factor MapReduce into a generic resource scheduler and a per-job, user-defined component
that manages the application execution. 

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