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HDFS >> mail # dev >> HDFS read/write data throttling

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Re: HDFS read/write data throttling
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 <[EMAIL PROTECTED]> 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 <[EMAIL PROTECTED]
> >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)

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NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB