HA and Low Latency on EC2 for RESTful Apps

I just added a post to my blog condensing some of my experiences and thoughts about implementing HA, Low Latency on EC2: specifically for RESTful apps. I’d welcome any comments or suggestions anyone may have…
RESTful High Availability and Low Latency on AmazEC2 is not designed for low-latency if you have a non-trivial or distributed dataset. The only part of the topology with semi-consistent low-latency characteristics are the HPC nodes, which unfortunately are designed for applications where disk does not matter and offer little in the way of HA. Our engineers use the EC2 HPC nodes as a test platform for low-latency codes and that works well enough but it would not be efficient for production.

How low is “low-latency”? Throughput does not seem to be a concern, so it probably could be done for sufficiently high values of “low latency”.

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Andrew, that was my point. 🙂

If you want low latency, you’re talking fusionIO cards, 10GBe, and as little network between your rest server and your cluster. Then your client has to be as close as possible to your REST server.

Sort of violates the principle of Hadoop…;-)
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I would agree that “low-latency” and “Hadoop” are rarely used together in the same sentence and for good reason. 🙂

That said, the REST interface implies a moderate tolerance for latency so it might be possible to put something together that works. Our systems that run on EC2 are neither Hadoop nor REST; they are almost entirely custom but the workload necessitates it and there is no open source software that can scale the kinds of analytics we do.
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Low latency means different things to different people. In the world of trading, low latency means as fast as technically possible, damn the costs.

In the Hadoop world, low latency means as fast as possible using ‘commodity’ hardware. And even here commodity hardware is sometimes tough to define.

The real question is how fast is fast enough?

You can design low latency Hadoop systems. But if you look at the problem you are trying to solve, your budget for hardware, software, and man power … etc… Ask yourself how low do you need to go? Does it make more sense in paying a premium for faster hardware or expanding your cluster out?

EC2 is a different beast. It’s a public infrastructure and it’s not really designed for speed.
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Great article. Most of the advice including CDN, Geo-load balancing applies in all circumstances not only cloud computing. I am the #1 proponent of “Ground Computing”. Reading the article I get the “square peg in round hole” feeling. Technologies like Amazon SQS, Amazon Cloudfront, Amazon RDS, Amazon ELB, buzzword bingo anyone? All this just to get ALMOST as fast as real hardware doing it the “old fashioned way”.

If the primary fact is the Amazon network has latency and noisy neighbours, no amount of technology layered above that will help. You can not get around low latency with more layers.
5 days ago
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Are we talking 5 sec. or 50 ms ? (Assume 40 ms ping time from client to server.)

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If someone out there (admittedly, living in a box but interested in big data / low latency) now knows what they need is a CDN or to localize access to services based on GeoIP discovery then this article has helped.

Big data analytics at an elastic scale with minimized end to end latency is hard. There is no optimal commercial or open source solution that has a one size fits all form factor. The economy afforded by scale is not equivalent to the scale of the low latency economy. The former is relatively cheap. The latter is relatively expensive.

For example: GPU folk often talk about ‘speed’ when they really mean ‘high bandwidth’. Being able to do a truck load of processing in 50 millis is great, unless you just need to process a single event in a handful of microseconds. Just like 50 odd socks is a nightmare when you just want a single pair of socks. Context is key.

Last but not least: Latency, throughput, scale, volumetric, computational complexity… these aren’t numbers. We need the equivalence of a control structures (sequences, statements, selections) formalism for big data low latency analytic environments.

So my only constructive feedback would be to include a frame of reference and context in your postings so it’s clear (except to trolls and nitpickers, who are never happy anyway) to the audience what the non-functional characterstics are. The context.

An example with ‘low’ latency: If this is ‘human interaction time’ and it’s serialized (single human at a time) over the web then 5 updates a second aught to be enough, right, say to ‘select-then-click’ a button? Replace the wetware with, a web-spider or web-robot. Now replace the web-robot with a legion of DDOSsing zombie robots.
5 days ago
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MattStop Following Follow Matt

Matt Pouttu-Clarke • Well yes and no. I am talking about latency as a component of user
perceived performance in a RESTful service. Not sure what HPC architecture
has to do with that, unless the workload is extremely compute intensive I
get along fine with standard EC2 large instances. Then again, I am not
calculating Pi to the trillionth digit on every request either!

Right, you are correct, “out of the box” EC2 does not support sharding. If
you shard you can handle almost size of data with consistent low latency.

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• HPC nodes on EC2 have little to do with compute intensive loads in this context. In fact, I use them for applications that have very little CPU intensity. What HPC nodes *do* have is low latency variance and consistent cross-sectional bandwidth. This allows some latency sensitive applications to be run on HPC nodes that cannot be run on normal EC2 nodes.

Normal EC2 instances tend to have high latency variance that damages the performance of latency sensitive operations, particularly if there is any level of cooperation between nodes. As a consequence, many applications that are not usefully shardable on normal EC2 are at least somewhat shardable on the HPC nodes. The HPC nodes were designed for CPU-intensive loads but many people use them for their low, consistent latency characteristics.

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I agree, predictable low latency means [serious] dedicated hardware, and HA means private infrastructure. That pretty much rules out [budget] public utility computing providers including Amazon EC2.

But that’s if you want it all, no hostages taken. If you are ready to sacrifice predictability or availability, Amazon EC2 may be an option. But then, it will be neither low latency nor HA.
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The moment you have virtualization (like in case of AWS), you added to the latency. All the techniques mentioned in the blog post help in reducing the visible latency to the end user. But i guess we need to understand low latency with through put from the systems before making sense.

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Its one thing to reduce latency on a system like EC2 however you have latency built in because your environment is virtual. You have IO between system and disk, you have latency because your OS is virtual-ized and you have an added context switch.

To some here, low latency means removing as much of the delays as possible and in today’s climate, that means fast switches, fast CPU, fast Disk, and limit the amount of wire you have to travel.

To achieve low latency is not cheap.

Attempting to do low latency on EC2 is like amateur mechanics building a kit car in their home garage and expecting it to compete against a professional pit crew.

You can tune your home kit to a point, but its not going to be considered low latencyon EC2

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