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High Performance Political Revolutions RailsCon...

High Performance Political Revolutions RailsConf 2017

Presentation video: https://confreaks.tv/videos/railsconf2017-high-performance-political-revolutions

Bernie Sanders has popularized small dollar donations as a mean to fund political campaigns, but having to process millions of small contributions brings technical challenges. The best example is from New Hampshire primary night, when Sanders told a national TV audience to go to berniesanders.com and donate $27. Web traffic peaked at 320,000 requests per minute and 2,600 credit card payments per minute.

ActBlue is a nonprofit tech organization that builds fundraising software for Democratic campaigns and committees, advocacy groups, and nonprofit organizations. Used by the Sanders campaign and 17,000 other organizations since 2004.

This presentation is about what we have learned in ActBlue building a high performance web application. The original version was written in 2004 and migrated to Rails in 2005.

Caching is one of the first steps to address performance, and we will discuss how we are using it. But we will cover other topics as well, like software architecture, which is sometimes overlooked, in spite of being more important.

For each solution, we will explain how it works, the complexities it introduces and how to address them.

We are keeping explanations as simple as possible and we are making few assumptions about the skill level of the audience. We start with basic concepts and build from there, so that even beginners can follow along and take away something useful.

Braulio Carreno

April 27, 2017
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  1. Political Revolutions • ActBlue is a nonprofit that builds fundraising

    software for: • Democratic campaigns and committees • advocacy groups • nonprofit organization • Bernie most popular, but 17,500 organizations since 2004 • 3,000 organizations Q1 2017 alone • using Rails since 2005
  2. • process donations (payments) • technology + compliance • fundraise

    tools, stats, A/B tests • 3.8 million ActBlue Express users • $1.6 billion in 31 million contributions ($50 avg) • empower small-dollar donors
  3. Scaling Challenge • approach • implementation • cost / solution

    because lots of small-dollar donations Presenting
  4. Metrics # statsd-ruby statsd = Statsd.new(‘chicago-1’) statsd.gauge(‘pending.auths’, pending_auths.count) statsd.timing(‘latency.auths’, Time.now

    - auth.created_at) • Graphite renders the graphs • collectd has plugins for PostgreSQL, Postfix, … • logs!
  5. Multiple Servers # NGINX load balancer configuration http { upstream

    backend { server 192.168.1.101; server 192.168.1.102; server 192.168.1.103; } server { listen 80; location / { proxy_pass http://backend; }}}
  6. Multiple Servers Cost • no file system • handle persistence:

    Rails sessions, Redis, … • increases required DB connections • LB is single point of failure
  7. Caching • general term, exists at multiple levels • highest

    value for the effort • Fastly CDN - huge! • stress tested with DDoS
  8. Caching Control • HTTP headers • Varnish Configuration Language (VCL)

    • API Cache-Control: max-age=400 Surrogate-Control: max-age=3600 Vary: Accept-Encoding Surrogate-Key: key1 key2 if (req.url ~ "^/videos") { set req.backend = F_VIDEO_LB; }
  9. Caching Cost • expensive • complexity if you want flexibility

    • intricate SSL termination • contribution form filled by JS
  10. Separation of Concerns • main app in Rails • credit

    card tokenizer in Node • multiple DB instances • separation useful for compliance as well
  11. Deferred Tasks • CC processing = multiple calls to web

    services • fault isolation • increased reliability • deferred batch settlements - huge!
  12. Sidekiq class SettlementProcessor include Sidekiq::Worker def perform(id) Settlement.find(id).settle! end end

    SettlementProcessor.perform_async(settlement.id) ActiveJob ReceiptMailer.receipt(contribution).deliver_later
  13. Scalable Architecture • mindset, i.e. most important • being fast

    + being scalable • most difficult to fix if you get it wrong • concurrency instead of central process • avoid premature optimization
  14. • interested in what we do? • come work with

    us • talk to me directly • slides on speakerdeck.com • stickers! Braulio Carreño @bcarreno