Map-Reduce 2.0
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Key: HADOOP-2510
URL: https://issues.apache.org/jira/browse/HADOOP-2510
Project: Hadoop
Issue Type: Improvement
Components: mapred
Reporter: Arun C Murthy
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.
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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.
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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.
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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.
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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.
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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.
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Thoughts?
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