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From Amandeep Khurana <ama...@gmail.com>
Subject Re: scaling a low latency service with HBase
Date Sat, 20 Oct 2012 00:22:35 GMT
Answers inline

On Fri, Oct 19, 2012 at 4:31 PM, Dave Latham <latham@davelink.net> wrote:

> I need to scale an internal service / datastore that is currently hosted on
> an HBase cluster and wanted to ask for advice from anyone out there who may
> have some to share.  The service does simple key value lookups on 20 byte
> keys to 20-40 byte values.  It currently has about 5 billion entries
> (200GB), and processes about 40k random reads per second, and about 2k
> random writes per second.  It currently delivers a median response at 2ms,
> 90% at 20ms, 99% at 200ms, 99.5% at 5000ms - but the mean is 58ms which is
> no longer meeting our needs very well.  It is persistent and highly
> available.  I need to measure its working set more closely, but I believe
> that around 20-30% (randomly distributed) of the data is accessed each
> day.  I want a system that can scale to at least 10x current levels (50
> billion entries - 2TB, 400k requests per second) and achieve a mean < 5ms
> (ideally 1-2ms) and 99.5% < 50ms response time for reads while maintaining
> persistence and reasonably high availability (99.9%).  Writes would ideally
> be in the same as range but we could probably tolerate a mean more in the
> 20-30ms range.
> Clearly for that latency, spinning disks won't cut it.  The current service
> is running out of an hbase cluster that is shared with many other things
> and when those other things hit the disk and network hard is when it
> degrades.  The cluster has hundreds of nodes and this data is fits in a
> small slice of block cache across most of them.  The concerns are that its
> performance is impacted by other loads and that as it continues to grow
> there may not be enough space in the current cluster's shared block cache.
> So I'm looking for something that will serve out of memory (backed by disk
> for persistence) or from SSDs.  A few questions that I would love to hear
> answers for:
>  - Does HBase sound like a good match as this grows?

Yes. The key to get more predictable performance is to separate out
workloads. What are the other things that are using the same physical
hardware and impacting performance? Have you measure performance when
nothing else is running on the cluster?

>  - Does anyone have experience running HBase over SSDs?  What sort of
> latency and requests per second have you been able to achieve?

I don't believe many people are actually running this in production yet.
Some folks have done some research on this topic and posted blogs (eg:
but there's not a whole lot more than that to go by at this point.

>  - Is anyone using a row cache on top of (or built into) HBase?  I think
> there's been a bit of discussion on occasion but it hasn't gone very far.
> There would be some overhead for each row.  It seems that if we were to
> continue to rely on memory + disks this could reduce the memory required.
>  - Does anyone have alternate suggestions for such a service?

The biggest recommendation is to separate out the workloads and then start
planning for more hardware or additional components to get better

> Dave

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