|go ahead... be a heretic|
Re^9: Async DNS with LWPby BrowserUk (Pope)
|on Oct 07, 2010 at 09:56 UTC||Need Help??|
Now, I've taken a quick look at your example code but notice that you are not actually doing anything with LWP. You've consumed 1/2 GB with only 100 threads that are not performing any TCP communication. The moment you start doing TCP the TCP/IP stack of whatever OS you are using will start consuming even more memory resources. (Note that the small webbook I am developing on has about 1/2 GB to work with).
Yes. But the point is, I wouldn't use anything like 100 threads.
Not unless I had 100 cores anyway, and not even then because I would reserve at least 50% of my cores for digesting and link extraction. I would not be doing that within my crawler. Why? Because--from experience of writing a high-throughput crawler--it doesn't make sense to go through the process of extracting links from a page until you've digested it so that you can check whether it has already been processed by another leg of the crawler. It just wastes cycles.
It also doesn't make sense to cache urls in memory. When crashes happen--and they always do, especially if you are running on remote, hosted boxes with arbitrarily enforced management limits(*)--then you will inevitably loose work. And that costs time and money.
(*) We had many crashes because the hoster had management software that would terminate processes if they variously: exceeded memory limits; exceeded diskIO limits; exceeded bandwidth limits; exceeded runtime limits. They deemed all of these to be likely to be "ran-away processes" and terminated them with prejudice. Retaining flow information in memory means loosing work and time.
And using urls (alone) as the basis of your duplication elimination also doesn't work. Take PerlMonks for instance. There are (to my knowledge) at least 3 domain names for this place, and all pages are accessible via all the domains. Add the underlying IP and that makes 4 copies of every page that you'd fetch, parse and store unless you do something to eliminate the duplicates. And then 4 copies of every link on each of those pages; and then 4 copies of the links on each of those...
You see where that is going.
I don't know how much (per box) bandwidth your ISP is capable of providing you with, but throwing more than low multiples of threads per core at the problem is not the solution. Far better to use 1 thread per core (that you allocate to crawling) and run a parallel useragent in each thread fetching (say) 10 urls concurrently on each thread. That will easily max your bandwidth without total sucking up either memory or cpu.
The crawler process digests (say MD5) the content and writes it to disk under the digest. It also writes the digest to a db-fetched queue table. Another process, read that queue, extracts links from the content and adds them to a to to-fetch-queue table. This is where the crawler gets its urls from.
At each stage, the work is committed to the DB, so if the box goes down, it can pick up right from where it left off when it comes back up. By separating out the concerns of fetching, and processing, and de-duplication, you avoid doing make-work. And to balance the system you can adjust the number of threads in the crawler; then number of concurrent fetches in each of those threads; and the number of link extractor processes that you run. With a little ingenuity, you can even automate that process by having a management process that monitors the size of the inbound and outbound DB-Q tables and starts or kills link extractor processes to compensate.
For a serious scale crawler, you;d need to be looking at multiple boxes each with it's own direct link to the network backbone--to avoid all the boxes being limited to the through put of some upstream choke point.
But if you're looking for a single-box threaded solution, it still makes considerable sense to separate the concerns of fetching and link extraction. And to ensure that the ongoing work-flow state is committed to disk on-the-fly rather than a periodic points which will cost you time and work if processes or the whole box fails. Note. That doesn't necessarily mean a RDBMS, they have their own concurrency limitations unless your pocket stretches to a distributed setup.
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