Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems. Learn about such fundamental distributed computing "concepts" for cloud computing. Some of these concepts include: clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more! Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required. The course also features interviews with leading researchers and managers, from both industry and academia.
3.4. MapReduce Fault-Tolerance
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From the course by University of Illinois at Urbana-Champaign
Cloud Computing Concepts, Part 1
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University of Illinois at Urbana-Champaign
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Course 1 of 6 in the Specialization Cloud Computing
From the lesson
Week 1: Orientation, Introduction to Clouds, MapReduce
This course is oriented towards learners with similar backgrounds as juniors and seniors in a CS undergraduate curriculum. Since learners come from various backgrounds, it is critical you view this lecture AND pass the prerequisite test. This will ensure you have many of the assumed prerequisite pieces of knowledge required to enjoy this course.
Meet the Instructors
Indranil GuptaProfessor
Department of Computer Science
So in this last lecture
on the MapReduce, uh, uh, lecture series
we'll see how MapReduce deals with, uh, failures.
Uh, failures, as you know, are very common,
and the norm rather than the exception
in, uh, cloud computing clusters.
The most common failure is a failure of the server itself,
uh, and the server failure might need to, uh, lead to multiple,
uh, components of the Hadoop YARN scheduler failing.
Remember that the servers are running the node manager, uh,
they're running tasks, they're running, uh,
one of the servers is running the resource manager,
and also the Application Master might be running, uh,
on a server for each job.
So, uh, in order to deal with server failures,
there are heartbeats.
The node manager, each per-server node manager
sends heartbeats, periodic heartbeats,
to the central resource manager.
If a server fails and these heartbeats stop,
the RM times are waiting
for the next heartbeat from that node manager,
and it knows the node manager has failed.
It lets all the affected application masters know,
and the AMs, uh,
then have the responsibility of uh, rescheduling their tasks.
The, uh, um, node manager also keeps track of each task
running at, uh, its server,
so if one of the tasks fails, for instance, uh,
due to an auto memory exception,
then, uh, the task, uh, is marked as idle,
and, uh, either the node manager could restart it,
or it could inform, uh,
the resource manager or the application master, uh,
that, uh, the task has failed.
Finally, the application master
also sends heartbeats periodically
to the resource manager.
If, uh, the AM fails and the RM would restart the AM,
which then syncs up with, uh, its running tasks,
and this might get complicated,
uh, depending on how many tasks are running, uh, at the AM.
The RM itself might fail, and there is no one, uh,
detecting the failure of the RM, uh, so far.
However, one way to deal with this
is to maintain a secondary, uh, hard backup, uh, RM,
which, uh, takes over immediately
a-after the RM failure.
Now the heartbeat messages that I've described so far
are also useful to send, uh, actual container requests
and other, uh, requests.
So these are, uh, the container requests
that we saw in the previous lecture
of this MapReduce series
are in fact always piggybacked
on top of the heartbeat messages, um,
and this avoids sending extra messages, uh,
in, uh, the underlying network.
This might lead to a smaller, more delay, uh,
in sending the next request across,
because heartbeat messages are sent only periodically.
Uh, however, uh, this, uh, does avoid
extra communication overhead.
Now, other than, uh, failures, we might also have some nodes
that are slow in, uh, the, um, uh, cluster, uh,
just because they might be having slower CPUs
or, uh, or, uh, uh, or, memory,
or because over time these nodes have, um, uh, incurred, uh,
more failures and have more errors
in their-in their, uh, memory banks than other servers.
In any case, this is problem in, uh, in MapReduce
because the slowest machine slows down their entire job.
Why is this?
Well, remember that, uh, the, um, uh,
Map phase has a bad ear on the end of it,
which means that none of the Reduce tasks can start
until all the Map tasks are done,
and this means that the slowest Map jo-the slowest, uh, Map task
will slow down the entire Reduce task.
Similarly, the slowest Reduce task
will cause the job completion time to be delayed even more.
Remember, the job is not considered to be completed
until all of its Reduce tasks are done.
Okay, once again, uh, this slowness
may be due to a disk,
maybe due to, uh, network bandwidth being bad
near that server, or maybe to a bad CPU or bad memory.
The way, uh, this is tackled is by keeping, uh, track
of the progress of each task.
This is essentially the percentage of input
that has already been processed by that task,
and, uh, for tasks that are slow,
meaning they have a very slow progress rate,
uh, by replicating them.
The way this works is suppose I have three tasks
which are currently- which had been started
all at the same time, um, uh,
and these are tasks one, two, and three of the same job,
and suppose, uh, these tasks have completion,
uh, progress rates of 90%, 50% and 10%.
Clearly the 10% task is in trouble,
has been making the slowest progress.
What happens here is what is known as speculative execution.
And the MapReduce scheduler, uh, in this case the AM, uh,
might spin up another replica of the task, number three,
which has only a 10% progress rate,
but on a different server than where Task three is running.
In essence now you have two copies of the same task running,
and because these are identical copies
and have identical inputs,
they will generate identical outputs,
and they are racing with each other;
whichever one finishes fastest
will lead to the task being marked as completed.
So the second replica of the- of that same task number three,
which we started on a different server, gets lucky
and finishes fairly quickly,
then, uh, the first replica will be killed, uh,
and the task will be marked as having completed.
This is known as speculative execution.
Uh, it's a somewhat misleading term, uh,
because, uh, you don't really speculate anything.
Instead you look at the progress rate and you say,
"Well, you know, uh, the progress rate is slow
for this particular task, and so I'm going to, uh, replicate it."
Uh, so really it should be called replicated execution,
but the technical term is speculative execution.
The final thing is locality.
Um, so a cloud, uh, typically has a hierarchical topology.
For instance, a cloud might have multiple racks,
and communication inside a rack is typically much faster
than across racks, which goes across the core switch.
Uh, the file system that is underlying, h-uh,
MapReduce or Hadoop, which might be GFS or HDFS,
typically stores three replicas of each of the chunks or blocks.
For instance, the blocks might be 64 megabytes in size.
Uh, typically these are stored on two different racks.
There might be two stored on one rack,
two replicas of the block stored on one rack,
and one stored on a different rack.
These are stored on two different racks
so that if one of the racks fails, for instance,
the top of the rack switch goes down,
then you at least have one copy of the file around, uh,
or-or the block around.
So given this, uh, MapReduce or Hadoop attempts to schedule
a Map task on preferably a machine or server
that contains a replica of the corresponding input data,
so that, uh, the input to that task,
this is typically a Map task,
uh, would, uh, be, uh, uh, fetched
without incurring any network overhead.
However, this may not be possible
because all the servers on which-all the three servers
on which this Map input is located
might already have all their containers, um, uh, full,
meaning they're already running tasks.
In this case, the scheduler tries to schedule, uh,
the Map task on the same rack
as a server that contains the input.
Uh, this has the advantage that the inter, uh, the intrarack,
or the inside rack bandwidth can be used,
which is typically much faster than going across racks,
in order to transfer, uh, the input block,
and this is going to be fairly fast,
not as fast as running on the same machine
as where the data is,
but faster than going across, uh, the core switch.
However, this may not be possible either,
because all the servers or all the racks where, uh,
this input block is located may be using up
all of their containers.
In this case, uh, the, uh, MapReduce scheduler
would then schedule the task anywhere
where a free container is available.
So essentially, uh, these three
are, uh, orders of, uh, preferring
where to schedule a Map task.
For scheduling Reduce tasks
really, um, you don't have much of a choice
because you don't really know up front
where the data from these Reduce tasks is gonna be coming from.
Remember the data for a Reduce task comes from one
or potentially all of the Map tasks in that job.
Uh, and so typically you want to schedule the Reduce tasks
on the same racks as where the Map tasks are scheduled uh,
but in terms of which machines
you want to prefer in those racks, uh,
you may want to prefer at least those machines
where Map tasks are scheduled
but you, uh, really don't know much about
uh, which particular Map tasks
you want to collocate your Reduce tasks with.
So, uh, in summary, in this MapReduce lecture series
we have seen how MapReduce, uh,
uses parallelization plus aggregation in the Map phase
and in the Reduce phase
to schedule applications across clusters.
You have seen many examples
of, uh, applications that use MapReduce.
There are many more out there,
and, uh, there are many more out there that are simple,
there are many more out there, uh, of applications
that are fairly complex and have chains of MapReduce, uh, jobs,
one after another, and that leverage these chains, uh,
to run fairly complex applications.
Uh, these applications
need to schedule, uh, these tasks across jobs,
we have seen the Hadoop YARN scheduler at work,
and they also need to deal with, uh, failure.
This is a fairly active area of research,
and there's been a lot of work in the last few years, uh,
for both scheduling-efficient scheduling in MapReduce
and in Hadoop,
and also for fault tolerance in MapReduce and in Hadoop,
uh, and I would encourage you
to look at a lot of this literature that is out there.
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