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From Steve Loughran <ste...@hortonworks.com>
Subject Re: HDFS read/write data throttling
Date Wed, 13 Nov 2013 10:54:51 GMT
this is interesting -I've moved my comments over to the JIRA and it would
be good for yours to go there too.

is there a URL for your paper?


On 13 November 2013 06:27, Andrew Wang <andrew.wang@cloudera.com> wrote:

> Hey Steve,
>
> My research project (Cake, published at SoCC '12) was trying to provide
> SLAs for mixed workloads of latency-sensitive and throughput-bound
> applications, e.g. HBase running alongside MR. This was challenging because
> seeks are a real killer. Basically, we had to strongly limit MR I/O to keep
> worst-case seek latency down, and did so by putting schedulers on the RPC
> queues in HBase and HDFS to restrict queuing in the OS and disk where we
> lacked preemption.
>
> Regarding citations of note, most academics consider throughput-sharing to
> be a solved problem. It's not dissimilar from normal time slicing, you try
> to ensure fairness over some coarse timescale. I think cgroups [1] and
> ioprio_set [2] essentially provide this.
>
> Mixing throughput and latency though is difficult, and my conclusion is
> that there isn't a really great solution for spinning disks besides
> physical isolation. As we all know, you can get either IOPS or bandwidth,
> but not both, and it's not a linear tradeoff between the two. If you're
> interested in this though, I can dig up some related work from my Cake
> paper.
>
> However, since it seems that we're more concerned with throughput-bound
> apps, we might be okay just using cgroups and ioprio_set to do
> time-slicing. I actually hacked up some code a while ago which passed a
> client-provided priority byte to the DN, which used it to set the I/O
> priority of the handling DataXceiver accordingly. This isn't the most
> outlandish idea, since we've put QoS fields in our RPC protocol for
> instance; this would just be another byte. Short-circuit reads are outside
> this paradigm, but then you can use cgroup controls instead.
>
> My casual conversations with Googlers indicate that there isn't any special
> Borg/Omega sauce either, just that they heavily prioritize DFS I/O over
> non-DFS. Maybe that's another approach: if we can separate block management
> in HDFS, MR tasks could just write their output to a raw HDFS block, thus
> bringing a lot of I/O back into the fold of "datanode as I/O manager" for a
> machine.
>
> Overall, I strongly agree with you that it's important to first define what
> our goals are regarding I/O QoS. The general case is a tarpit, so it'd be
> good to carve off useful things that can be done now (like Lohit's
> direction of per-stream/FS throughput throttling with trusted clients) and
> then carefully grow the scope as we find more usecases we can confidently
> solve.
>
> Best,
> Andrew
>
> [1] cgroups blkio controller
> https://www.kernel.org/doc/Documentation/cgroups/blkio-controller.txt
> [2] ioprio_set http://man7.org/linux/man-pages/man2/ioprio_set.2.html
>
>
> On Tue, Nov 12, 2013 at 1:38 AM, Steve Loughran <stevel@hortonworks.com
> >wrote:
>
> > I've looked at it a bit within the context of YARN.
> >
> > YARN containers are where this would be ideal, as then you'd be able to
> > request IO capacity as well as CPU and RAM. For that to work, the
> > throttling would have to be outside the App, as you are trying to limit
> > code whether or not it wants to be, and because you probably (*) want to
> > give it more bandwidth if the system is otherwise idle. Self-throttling
> > doesn't pick up spare IO
> >
> >
> >    1. you can use cgroups in YARN to throttle local disk IO through the
> >    file:// URLs or the java filesystem APIs -such as for MR temp data
> >    2. you can't c-group throttle HDFS per YARN container, which would be
> >    the ideal use case for it. The IO is taking place in the DN, and
> cgroups
> >    only limits IO in the throttled process group.
> >    3. implementing it in the DN would require a lot more complex code
> there
> >    to prioritise work based on block ID (sole identifier that goes around
> >    everywhere) or input source (local sockets for HBase IO vs TCP stack)
> >    4. One you go to a heterogenous filesystem you need to think about IO
> >    load per storage layer as well as/alongside per-volume
> >    5. There's also generic RPC request throttle to prevent DoS against
> the
> >    NN and other HDFS services. That would need to be server side, but
> once
> >    implemented in the RPC code be universal.
> >
> > You also need to define what is the load you are trying to throttle, pure
> > RPCs/second, read bandwidth, write bandwidth, seeks or IOPs. Once a file
> is
> > lined up for sequential reading, you'd almost want it to stream through
> the
> > next blocks until a high priority request came through, but operations
> like
> > a seek which would involve a disk head movement backwards would be
> > something to throttle (hence you need to be storage type aware as SSD
> seeks
> > costs less). You also need to consider that although the cost of writes
> is
> > high, it's usually being done with the goal of preserving data -and you
> > don't want to impact durability.
> >
> > (*) probably, because that's one of the issues that causes debates in
> other
> > datacentre platforms, such as Google Omega: do you want max cluster
> > utilisation vs max determinism of workload.
> >
> > If someone were to do IOP throttling in the 3.x+ timeline,
> >
> >    1. It needs clear use cases, YARN containers being #1 for me
> >    2. We'd have to look at all the research done on this in the past to
> see
> >    what works, doesn't
> >
> > Andrew, what citations of relevance do you have?
> >
> > -steve
> >
> >
> > On 12 November 2013 04:24, lohit <lohit.vijayarenu@gmail.com> wrote:
> >
> > > 2013/11/11 Andrew Wang <andrew.wang@cloudera.com>
> > >
> > > > Hey Lohit,
> > > >
> > > > This is an interesting topic, and something I actually worked on in
> > grad
> > > > school before coming to Cloudera. It'd help if you could outline some
> > of
> > > > your usecases and how per-FileSystem throttling would help. For what
> I
> > > was
> > > > doing, it made more sense to throttle on the DN side since you have a
> > > > better view over all the I/O happening on the system, and you have
> > > > knowledge of different volumes so you can set limits per-disk. This
> > still
> > > > isn't 100% reliable though since normally a portion of each disk is
> > used
> > > > for MR scratch space, which the DN doesn't have control over. I tried
> > > > playing with thread I/O priorities here, but didn't see much
> > improvement.
> > > > Maybe the newer cgroups stuff can help out.
> > > >
> > >
> > > Thanks. Yes, we also thought about having something on DataNode. This
> > would
> > > also mean one could easily throttle client who access from outside the
> > > cluster, for example distcp or hftp copies. Clients need not worry
> about
> > > throttle configs and each cluster can control how much much throughput
> > can
> > > be achieved. We do want to have something like this.
> > >
> > > >
> > > > I'm sure per-FileSystem throttling will have some benefits (and
> > probably
> > > be
> > > > easier than some DN-side implementation) but again, it'd help to
> better
> > > > understand the problem you are trying to solve.
> > > >
> > >
> > > One idea was flexibility for client to override and have value they can
> > > set. For on trusted cluster we could allow clients to go beyond default
> > > value for some usecases. Alternatively we also thought about having
> > default
> > > value and max value where clients could change default, but not go
> beyond
> > > default. Another problem with DN side config is having different values
> > for
> > > different clients and easily changing those for selective clients.
> > >
> > > As, Haosong also suggested we could wrap FSDataOutputStream/FSDataInput
> > > stream with ThrottleInputStream. But we might have to be careful of any
> > > code which uses FileSystem APIs and accidentally throttling itself.
> (like
> > > reducer copy,  distributed cache and such...)
> > >
> > >
> > >
> > > > Best,
> > > > Andrew
> > > >
> > > >
> > > > On Mon, Nov 11, 2013 at 6:16 PM, Haosong Huang <haosdent@gmail.com>
> > > wrote:
> > > >
> > > > > Hi, lohit. There is a Class named
> > > > > ThrottledInputStream<
> > > > >
> > > >
> > >
> >
> http://svn.apache.org/repos/asf/hadoop/common/trunk/hadoop-tools/hadoop-distcp/src/main/java/org/apache/hadoop/tools/util/ThrottledInputStream.java
> > > > > >
> > > > >  in hadoop-distcp, you could check it out and find more details.
> > > > >
> > > > > In addition to this, I am working on this and try to achieve
> > resources
> > > > > control(include CPU, Network, Disk IO) in JVM. But my
> implementation
> > is
> > > > > depends on cgroup, which only could run in Linux. I would push my
> > > > > library(java-cgroup) to github in the next several months. If you
> are
> > > > > interested at it, give my any advices and help me improve it
> please.
> > > :-)
> > > > >
> > > > >
> > > > > On Tue, Nov 12, 2013 at 3:47 AM, lohit <lohit.vijayarenu@gmail.com
> >
> > > > wrote:
> > > > >
> > > > > > Hi Adam,
> > > > > >
> > > > > > Thanks for the reply. The changes I was referring was in
> > > > FileSystem.java
> > > > > > layer which should not affect HDFS Replication/NameNode
> operations.
> > > > > > To give better idea this would affect clients something like
this
> > > > > >
> > > > > > Configuration conf = new Configuration();
> > > > > > conf.setInt("read.bandwitdh.mbpersec", 20); // 20MB/s
> > > > > > FileSystem fs = FileSystem.get(conf);
> > > > > >
> > > > > > FSDataInputStream fis = fs.open("/path/to/file.xt");
> > > > > > fis.read(); // <-- This would be max of 20MB/s
> > > > > >
> > > > > >
> > > > > >
> > > > > >
> > > > > > 2013/11/11 Adam Muise <amuise@hortonworks.com>
> > > > > >
> > > > > > > See https://issues.apache.org/jira/browse/HDFS-3475
> > > > > > >
> > > > > > > Please note that this has met with many unexpected impacts
on
> > > > workload.
> > > > > > Be
> > > > > > > careful and be mindful of your Datanode memory and network
> > > capacity.
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > On Mon, Nov 11, 2013 at 1:59 PM, lohit <
> > lohit.vijayarenu@gmail.com
> > > >
> > > > > > wrote:
> > > > > > >
> > > > > > > > Hello Devs,
> > > > > > > >
> > > > > > > > Wanted to reach out and see if anyone has thought
about
> ability
> > > to
> > > > > > > throttle
> > > > > > > > data transfer within HDFS. One option we have been
thinking
> is
> > to
> > > > > > > throttle
> > > > > > > > on a per FileSystem basis, similar to Statistics in
> FileSystem.
> > > > This
> > > > > > > would
> > > > > > > > mean anyone with handle to HDFS/Hftp will be throttled
> globally
> > > > > within
> > > > > > > JVM.
> > > > > > > > Right value to come up for this would be based on
type of
> > > hardware
> > > > we
> > > > > > use
> > > > > > > > and how many tasks/clients we allow.
> > > > > > > >
> > > > > > > > On the other hand doing something like this at FileSystem
> layer
> > > > would
> > > > > > > mean
> > > > > > > > many other tasks such as Job jar copy, DistributedCache
copy
> > and
> > > > any
> > > > > > > hidden
> > > > > > > > data movement would also be throttled. We wanted to
know if
> > > anyone
> > > > > has
> > > > > > > had
> > > > > > > > such requirement on their clusters in the past and
what was
> the
> > > > > > thinking
> > > > > > > > around it. Appreciate your inputs/comments
> > > > > > > >
> > > > > > > > --
> > > > > > > > Have a Nice Day!
> > > > > > > > Lohit
> > > > > > > >
> > > > > > >
> > > > > > >
> > > > > > >
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