Scaling Early
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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 <>
  • 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.
  • 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.
  • 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
    • 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.