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Scaling Early

Scaling Early



by Mark Maunder

by Mark Maunder



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    Scaling Early Scaling Early Presentation Transcript

    • Scaling an early stage startup by Mark Maunder <mark@feedjit.com>
    • Why does performance and scaling quickly matter?
      • Slow performance could cost you 20% of your revenue according to Google.
      • Any reduction in hosting costs goes directly to your bottom line as profit or can accelerate growth.
      • In a viral business, slow performance can damage your viral growth.
    • My first missteps
      • Misconfiguration. Web server and DB configured to grab too much RAM.
      • As traffic builds, the server swaps and slows down drastically.
      • Easy to fix – just a quick config change on web server and/or DB.
    • Traffic at this stage
      • 2 Widgets per second
      • 10 HTTP requests per second.
      • 1 Widget = 1 Pageview
      • We serve as many pages as our users do, combined.
    • Keepalive – Good for clients, bad for servers.
      • As http requests increased to 10 per second, I ran out of server threads to handle connections.
      • Keepalive was on and Keepalive Timeout was set to 300.
      • Turned Keepalive off.
    • Traffic at this stage
      • 4 Widgets per second
      • 20 HTTP requests per second
    • Cache as much DB data as possible
      • I used Perl’s Cache::FileCache to cache either DB data or rendered HTML on disk.
      • MemCacheD, developed for LiveJournal, caches across servers.
      • YMMV – How dynamic is your data?
    • MySQL not fast enough
      • High number of writes & deletes on a large single table caused severe slowness.
      • Writes blow away the query cache.
      • MySQL doesn’t support a large number of small tables (over 10,000).
      • MySQL is memory hungry if you want to cache large indexes.
      • I maxed out at about 200 concurrent read/write queries per second with over 1 million records (and that’s not large enough).
    • Perl’s Tie::File to the early rescue
      • Tie::File is a very simple flat-file API.
      • Lots of files/tables.
      • Faster – 500 to 1000 concurrent read/writes per second.
      • Prepending requires reading and rewriting the whole file.
    • BerkeleyDB is very very fast!
      • I’m also experimenting with BerkeleyDB for some small intensive tasks.
      • Data From Oracle who owns BDB: Just over 90,000 transactional writes per second.
      • Over 1 Million non-transactional writes per second in memory.
      • Oracle’s machine: Linux on an AMD Athlon™ 64 processor 3200+ at 1GHz system with 1GB of RAM. 7200RPM Drive with 8MB cache RAM.
      Source: http://www.oracle.com/technology/products/berkeley-db/pdf/berkeley-db-perf.pdf
    • Traffic at this stage
      • 7 Widgets per second
      • 35 HTTP requests per second
    • Created a separate image and CSS server
      • Enabled Keepalive on the Image server to be nice to clients.
      • Static content requires very little memory per thread/process.
      • Kept Keepalive off on the App server to reduce memory.
      • Added benefit of higher browser concurrency with 2 hostnames.
      Source: http://www.die.net/musings/page_load_time/
    • Now using Home Grown Fixed Length Records
      • A lot like ISAM or MyISAM
      • Fixed length records mean we seek directly to the data. No more file slurping.
      • Sequential records mean sequential reads which are fast.
      • Still using file level locking.
      • Benchmarked at 20,000+ concurrent reads/writes/deletes.
    • Traffic at this stage
      • 12 Widgets per second
      • 50 to 60 HTTP requests per second
      • Load average spiking to 12 or more about 3 times per day for unknown reason.
    • Blocking Content Thieves
      • Content thieves were aggressively crawling our site on pages that are CPU intensive.
      • Robots.txt is irrelevant.
      • Reverse DNS lookup with ‘dig –x’
      • Firewall the &^%$@’s with ‘iptables’
    • Moved to httpd.prefork
      • Httpd.worker consumes more memory than prefork because worker doesn’t share memory.
      • Tuning the number of Perl interpreters vs number of threads didn’t improve things.
      • Prefork with no keepalive on the app server uses less RAM and works well – for Mod_Perl.
    • The amazing Linux Filesystem Cache
      • Linux uses spare memory to cache files on disk.
      • Lots of spare memory == Much faster I/O.
      • Prefork freed lots of memory. 1.3 Gigs out of 2 Gigs is used as cache.
      • I’ve noticed a roughly 20% performance increase since using it.
    • Tools
      • httperf for benchmarking your server
      • Websitepulse.com for perf monitoring.
    • Summary
      • Make content as static as possible.
      • Cache as much of your dynamic content as possible.
      • Separate serving app requests and serving static content.
      • Don’t underestimate the speed of lightweight file access API’s .
      • Only serve real users and search engines you care about.