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All right.
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Okay, hello everyone! Thank you, thank you! Welcome to RailsConf.
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Let me be the first to welcome you to our talk today about Heroku: 2014 A Year in Review.
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This presentation will be a play in six acts featuring Terence Lee and Richard Schneeman.
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As many of you might know, Heroku measures their years by RailsConf.
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So this review will cover our journey from the Portland RailsConf to the Chicago RailsConf this past year.
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We are members of the Ruby task force, and this year has been significant for us.
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We will discuss app performance, some Heroku features, and community features.
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First up to the stage, I would like to introduce the one and only Mr. Terence Lee.
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You might recognize him from his other roles. He hails from Austin, Texas, known for having the best tacos in the world.
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Now, those are fighting words, my friend!
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He’s also known as the Chief Taco Officer—or CTO, depending on the context.
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Something interesting about Terence is that he was recently inducted into Ruby core. So, congratulations to Terence!
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Without further ado, let's move on to Act One: Deploy Speed.
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Thank you, Richard. At the beginning of the Rails standard year, we focused on improving deployment speed.
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We received a lot of feedback indicating that deployment was not as fast as it could be, and we were determined to enhance it.
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Our first step was measurement and profiling to identify slow areas and find ways to improve them.
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We aimed to gauge the before and after states to set a clear endpoint before moving on to other enhancements.
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As you know, you can never be completely done with performance improvements.
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After about six months of work, we succeeded in cutting down deployment speeds across the platform for Ruby by 40%.
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We approached this by focusing on three key strategies for improvement.
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Firstly, we implemented parallel code execution, allowing multiple processes to run at the same time.
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With this strategy, if you catch something once, you won’t have to do it again.
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Additionally, we worked closely with the Bundler team on Bundler 1.5 to add a parallel installation feature.
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If you haven't upgraded yet, I highly recommend moving to Bundler 1.5 or later.
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Bundler 1.5 introduced the '-j' option, which allows users to specify the number of jobs to run in parallel.
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This option utilizes system resources effectively during bundle installation.
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Dependencies are now downloaded in parallel, improving efficiency significantly.
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This enhancement is especially beneficial when dealing with native extensions like Nokogiri.
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In previous versions, such installations could stall the process.
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Furthermore, Bundler 1.5 added a function that automatically retries failed commands.
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Previously, if a command failed due to a transient network issue, users had to restart the entire process.
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By default, Bundler now retries clones and gem installs for up to three attempts.
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Next, I want to talk about the Pixie command.
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Has anyone here heard of Pixie?
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Pixie is a parallel gzip command that our build and packaging team implemented.
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When you push an app to Heroku, it compiles the app into what we call "slugs."
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Originally, we used a method that involved just tar files, but we found compression was a bottleneck.
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By using Pixie, we significantly improved the slug compression time, speeding up the overall build process.
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The trade-off was slightly larger slug sizes, but the performance gains made it worth the change.
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Next, we turned our attention to caching.
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In Rails 4, we were able to cache assets between deploys, a feature that was not reliable in Rails 3.
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In Rails 3, caches would often become corrupted, necessitating their removal between deploys.
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This inconsistency made it difficult to rely on cached assets.
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Fortunately, Rails 4 fixed many of these issues, allowing reliable caching that improved deployment speed.
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On average, deploying a Rails 3 app took about 32 seconds, while Rails 4 reduced this to approximately 14 seconds.
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This significant improvement came from both caching and optimizations introduced in Rails 4.
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We also focused on eliminating unnecessary code to speed up the build process.
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One of our first improvements involved preventing Bundler from downloading itself multiple times.
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Previously, we would download and install Bundler twice during the build process.
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To address this, we implemented caching for the Bundler gem, reducing network I/O.
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We also eliminated duplicate checks in determining the type of application being deployed.
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Richard took the lead on refactoring the detection process, simplifying it significantly.
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This allowed us to perform checks only once, leading to further efficiency.
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For those interested, Richard has given a previous talk about testing and improving the build pack.
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Now, let me introduce Richard, who will present the next section.
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Richard loves Ruby so much that he married it—almost literally! He got married right before our last RailsConf.
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He's also a member of the Rails issues team and is recognized as a top Rails contributor.
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Many of you might know him for creating a gem called Sextant, which simplifies route verification.
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Both Richard and I live in Austin, and we have Ruby meetups at Franklin's BBQ. If you're ever in town, let us know!
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Now, for the first part of this act, we will discuss app speed.
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Before we dive into that, let's talk about document dimensions.
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Originally, our slides were designed for widescreen, but the displays here are standard.
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You will be able to see all the slides without any cut-offs.
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In talking about app speed, I first want to discuss tail latencies.
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Is anyone familiar with tail latencies? Some of you are, great!
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This graph shows a normalized distribution with the number of requests on one side and response time on the other.
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The further out you go, the slower the response becomes.
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Customers on either side of this distribution have very different experiences.
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We can see the average response, which can be misleading.
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We estimate that roughly fifty percent of your customers get a response time at or below this average.
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When we move up to Percentile 95, we can see that 95% of customers receive responses faster.
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While the average may look good, having an understanding of the distribution is crucial for app performance.
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We must consider not only average response times, but also the consistency of each request.
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So, how can we address these tail latencies?
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One solution launched this year was PX Dynos.
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Whereas typical dynos only have 512 MB of RAM, a PX dyno offers six gigabytes of RAM and eight CPU cores.
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This allows for better management of high traffic loads and improved processing speeds.
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Scaling can be done both horizontally and vertically, which adds to the flexibility.
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When faced with more requests than can be processed, simply scaling up by adding more dynos is one approach.
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However, effective use of resources is key.
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In the previous 512 MB dyno, you might think you’re utilizing space well by adding a couple of Unicorn workers.
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In a PX dyno, adding two Unicorn workers might not utilize the resources effectively.
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I’ve fallen in love with Puma, a web server designed by Evan Phoenix.
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It was originally designed for Rubinius, but it also works quite well with MRI.
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Puma manages requests by utilizing multiple processes or threads and can run in a hybrid mode.
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If one process crashes, it doesn't affect the entire server.
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However, even with MRI's Global Interpreter Lock (GIL), many I/O operations can still efficiently run in threads.
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By implementing multiple threads, we can maximize resource utilization.
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Scaling effectively within a dyno ensures that we take advantage of the greater RAM capacity.
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Nonetheless, we must be careful to avoid exceeding memory limits, as this would slow down our application.
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It’s also critical to consider slow clients.
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A client connecting via a slow connection may consume resources unnecessarily.
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Puma can buffer these requests, offering a safeguard against performance degradation.
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To clarify threading concerns: Ruby is not commonly viewed as a thread-safe environment.
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However, you can configure Puma to utilize only one thread if desired.
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You can gradually adapt your application to be thread-safe.
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Working within the context of application speed, we’ve done significant work to improve routing efficiency.
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Shared distributed state across multiple machines can be inefficient.
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In-memory state within a single machine is inherently faster.
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The Heroku router is designed to quickly route requests to the appropriate dyno.
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It employs a random algorithm to deliver requests efficiently, minimizing delays.
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Having intelligent in-memory routing allows for faster processing.
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A graph produced by the Rap Genius team illustrates request queuing.
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The goal is to minimize request queuing, ensuring minimal wait times for customers.
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The top line in the graph illustrates current performance, while mythological scenarios display ideal response times.
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As we improve our request handling, we aim to reduce queuing times and enhance customer satisfaction.
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With additional resources, we can push our performance further.
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Ruby 2.0 introduced optimizations such as garbage collection improvements that help make processes more efficient.
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As a community, it’s essential to address memory allocation concerns.
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Enhanced performance means increased consistency across application experiences.
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If you experience slow moments in your application, we rolled out a platform feature called HTTP request ID.
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Each incoming request receives a unique identifier, which you can track in your logs.
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This helps trace specific errors and debug runtime metrics in your Rails app.
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If you’re encountering any performance issues, you can check the request ID to pinpoint bottlenecks.
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Now, let's transition to Act Two, where Terence will discuss Ruby on the Heroku stack and community updates.
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Thank you for your attention, everyone!
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I will dive into some Ruby updates now.
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Who's still using Ruby 1.8.7? Raise your hands!
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Just one person; please consider upgrading.
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And Ruby 1.9.2? A few more hands—good!
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What about Ruby 1.9.3? I see a decent number of you.
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Let’s not forget that Ruby 1.8.7 and 1.9.2 have reached end-of-life, and I strongly recommend upgrading!
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As of this year, Ruby 1.9.3 will also reach end-of-life in February 2015, which is quite soon.
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The minimum recommended Ruby version for Heroku apps is now 2.0.
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You can declare your Ruby version in the Gemfile on Heroku to use this version.
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We're committed to supporting the latest Ruby versions as they are released.
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We roll out updates on the same day as new Ruby versions are released.
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If any security updates are released, we ensure they are available as promptly as possible.
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Only push your application again after updates; we avoid automatic upgrades to maintain your control over changes.
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We publish all Ruby updates, especially security ones, through our changelog.
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If you'd like to stay informed, consider subscribing to the dev center changelog.
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You won’t receive excessive emails; it's low traffic.
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Now, let’s discuss the Ruby team we have built.
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In 2012, we hired three prominent members from Ruby core: Matsuda, Koichi, and Nobu.
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Many attendees may not know who these figures are, so let me clarify their contributions.
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If you look at the number of commits made to Ruby since 2012, you'll see significant activity from Nobu.
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In fact, he’s the top committer, followed closely by Koichi.
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I mistakenly included an entry for an unknown entity called 'svn' which isn't a real person.
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I learned about this during my early contributions to Ruby.
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Nobu, known as the 'Patch Monster,' is essential for fixing errors within Ruby.
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Nobu consistently addresses various issues, making Ruby run much better.
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To illustrate his impact, let’s discuss a couple of bugs he's resolved.
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For example, the 'time.now equals an empty string' bug.
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Nobu is also responsible for fixing bugs that involve segfaults in the Rational library.
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The other key member is Koichi, who has made substantial contributions to Ruby's performance.
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His work on the YARV virtual machine and performance optimization is commendable.
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Recently, he has been focusing on the new garbage collection process.
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The introduction of shorter GC pauses helps improve execution speed.
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We appreciate the continuous enhancements to Ruby's performance.
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Koichi has also been heavily involved in profiling the Ruby application experience.
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Another important aspect is the infrastructure to support faster Ruby releases.
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Improving release speed has been a high priority for us.
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With that said, we are eagerly anticipating Ruby 2.2, and we’re keen to hear your ideas for enhancements.
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We are now entering Act Three, where we will cover Rails 4.1 updates.
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I hope everyone is enjoying RailsConf so far!
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In Rails 4.1, we’ve made significant enhancements to Heroku.
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We are focused on security by default.
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The new secrets.yml file utilizes environment variables for configurations.
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Upon deploying, this setup automatically generates a secure key.
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You can modify the secret key base environment variable at any time.
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Another essential improvement is support for the database URL environment variable.
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This has been a long-standing feature but lacked reliable functionality.
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Now, Rails 4.1 supports this feature out of the box, simplifying database connections.
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If both the database URL and the database yaml file are present, their values will merge.
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So now, ensuring proper database configuration has become more straightforward.
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Additionally, we have made improvements to the Rails asset pipeline.
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The asset pipeline has historically generated numerous support tickets for us.
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To reduce confusion, Rails 4.1 will provide warnings in development if you forget to compile assets.
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Rails will prompt you if you haven't added necessary files to the pre-compile list.
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Thank you all for joining us today!
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We'll have community office hours later where you can ask any Rails-related questions.
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Richard will also be available for a book signing of his new Heroku book.
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Thank you, everyone, and enjoy the rest of RailsConf!
