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Scaling Early

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by Mark Maunder

Published in: Economy & Finance, Technology
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Scaling Early

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

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