Slashdot Mirror
Learning High-Availability Server-Side Development?
fmoidu writes "I am a developer for a mid-size company, and I work primarily on internal applications. The users of our apps are business professionals who are forced to use them, so they are are more tolerant of access times being a second or two slower than they could be. Our apps' total potential user base is about 60,000 people, although we normally experience only 60-90 concurrent users during peak usage. The type of work being done is generally straightforward reads or updates that typically hit two or three DB tables per transaction. So this isn't a complicated site and the usage is pretty low. The types of problems we address are typically related to maintainability and dealing with fickle users. From what I have read in industry papers and from conversations with friends, the apps I have worked on just don't address scaling issues. Our maximum load during typical usage is far below the maximum potential load of the system, so we never spend time considering what would happen when there is an extreme load on the system. What papers or projects are available for an engineer who wants to learn to work in a high-availability environment but isn't in one?"
start by being clear about what you want to achieve. If it is HA then you want to look at clustering, failover, network topology, DR plans etc. If it is HP then look for the bottlenecks in the process, don't waste time shaving nanoseconds off something that wasn't bothering anyone. At infrastructure level you might think about cacheing some stuff, or putting a reverse proxy in front of a cluster of responding servers. In general disk reads are expensive but easily cached, disk writes are very expensive and normally you don't want to cache them, at least not for very long. Network bandwidth may be fast or slow, latency might be an issue if you have a chatty application.
I've worked in multiple extremely super-scaled applications (including ones sustaining 70,000 connections at any one time, 10,000 new connections each minute, and 15,000 concurrent throttled file transfers at any one time - all in one application instance on one machine).
:)
The biggest problem I have seen is people don't know how to properly define their thread's purpose and requirements, and don't know how to decouple tasks that have in-built latency or avoid thread blocking (and locking).
For example, often in a high-performance network app, you will have some kind of multiplexor (or more than one) for your connections, so you don't have a thread per connection. But people often make the mistake of doing too much in the multiplexor's thread. The multiplexor should ideally only exist to be able to pull data off the socket, chop it up into packets that make sense, and hand it off to some kind of thread pool to do actual processing. Anything more and your multiplexor can't get back to retrieving the next bit of data fast enough.
Similarly, when moving data from a multiplexor to a thread pool, you should be a) moving in bulk (lock the queue once, not once per message), AND you should be using the Least Loaded pattern - where each thread in the pool has its OWN queue, and you move the entire batch of messages to the thread that is least loaded, and next time the multiplexor has another batch, it will move it to a different thread because IT is least loaded. Assuming your processing takes longer than the data takes to be split into packets (IT SHOULD!), then all your threads will still be busy, but there will be no lock contention between them, and occasional lock contention ONCE when they get a new batch of messages to process.
Finally, decouple your I/O-bound processes. Make your I/O bound things (eg. reporting via. socket back to some kind of stats/reporting system) happen in their own thread if they are allowed to block. And make sure your worker threads aren't waiting to give the I/O bound thread data - in this case, a similar pattern to the above in reverse works well - where each thread PUSHING to the I/O bound thread has its own queue, and your I/O bound thread has its own queue, and when it is empty, it just collects the swaps from all the worker queues (or just the next one in a round-robin fashion), so the workers can put data onto those queues at its leisure again, without lock contention with each other.
Never underestimate the value of your memory - if you are doing something like reporting to a stats/reporting server via. socket, you should implement some kind of Store and Forward system. This is both for integrity (if your app crashes, you still have the data to send), and so you don't blow your memory. This is also true if you are doing SQL inserts to an off-system database server - spool it out to local disk (local solid-state is even better!) and then just have a thread continually reading from disk and doing the inserts - in a thread not touched by anything else. And make sure your SAF uses *CYCLING FILES* that cycle on max size AND time - you don't want to keep appending to a file that can never be erased - and preferably, make that file a memory mapped file. Similarly, when sending data to your end-users, make sure you can overflow the data to disk so you don't have 3mb data sitting in memory for a single client, who happens to be too slow to take it fast enough.
And last thing, make sure you have architected things in a way that you can simply start up a new instance on another machine, and both machines can work IN TANDEM, allowing you to just throw hardware at the problem once you reach your hardware's limit. I've personally scaled up an app from about 20 machines to over 650 by ensuring the collector could handle multiple collections - and even making sure I could run multiple collectors side-by-side for when the data is too much for one collector to crunch.
I don't know of any papers on this, but this is my experience writing extremely high performance network apps
you work for Skype, don't you?
Feed the need: Digitaladdiction.net
This is a bit like saying "auto mechanics is a matter of turning a wrench," and when someone points out to them that there's a lot more to it, saying "well then maybe you could teach me to be a mechanic in your reply." If scalability was an issue simple enough to explain in a slashdot post, people wouldn't have trouble with it. Scalability isn't a problem; it's a family of problems. Suggesting that a single algorithm from a single library magically waves away the issues involved in a heavily parallel server is simply an exposition that you aren't aware what goes into scalable servers.
The Macy's Door Problem is a great example of a Scalability 101 problem that map_reduce has no way to address. In the early 30s, when most department stores were making big, flashy front entrances to their stores with big glass walls and paths for 12 groups of people at a time, doormen, signage, the whole lot, Macy's elected to take a different approach. They set up a small door with a sign above it. The idea was simple: if there was just the one door, it would be a hassle to get in and out of the store; thus, it would always look like there was a crowd struggling to get in - as if the store was just so popular that they couldn't keep up with customer foot traffic. The idea worked famously well.
In server design, we use that as a metaphor for near-redline usage. There's a problem that's common in naïve server design, where the server will perform just fine right up to 99%. Then, there'll be a tiny usage spike, and it'll hit 101% very briefly. However, the act of queueing and disqueueing withheld users is more expensive than processing a user, meaning that even though the usage drops back to 99%, by the time those 2% overqueue have been processed, a new 3% overqueue has formed, and performance progressively drops through the floor on a load the application ought to be able to handle. I should point out that Apache has this problem, and that until six years ago, so did the Linux TCP stack. It's a much more common scalability flaw than most people expect.
Now, that's just one issue in scalability; there are dozens of others. However, map_reduce has literally nothing to say to that problem. Do I need to rattle off others too, or maybe is that good enough? I mean, we have the exponential growth of client interconnections (Metcalfe's Law, which is easily solved with a hub process;) we have making sure that processing workloads is linear growth (that is, o(1) as opposed to o(lg n) or worse), which means no std::map, no std::set, no std::list, only pre-sized std::vector and very careful use of hash tables; we have packet fragmentation throttling; we have making sure that you process all clients in order, to prevent response-time clustering (like when you load an apache site and it sits there for five seconds, so you hit reload and it comes up instantly,) all sorts of stuff. Most scalability issues are hard to explain, but maybe that brief list will give you the idea that scalability is a whole lot bigger of an issue than some silly little google library.
Talk to someone who's tried to write an IRC server. Those things hit lots of scalability problems very early on. That community knows the basics very, very well.
StoneCypher is Full of BS