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Faster Rails: Indexing Large Database Tables Without Downtime

This article is part of our Faster Rails series. Check out the previous article about proper database indexing.

As the scope and size of a Rails project grows, actions that were blazingly fast can become slow, and even downright unacceptable. The cause behind this issue can be an exponential growth of your database tables that makes lookups and updates much slower. If this is the case, adding missing indexes to your database is a cheap and easy way to drastically improve the performance of your application.

Faster Rails: Indexing Large Database Tables Without Downtime

However, adding a new index to a database table that’s already big can be dangerous. Don’t forget, index creation on a database table is a synchronous action that prevents INSERT, UPDATE, and DELETE operations until the full index is created. If the system is a live production database, this can have severe effects. Indexing very large tables can take many hours. For a system like Semaphore, even short periods are unacceptable. If this happens during deployment, we can potentially cause an unwanted downtime for the whole system.

note: There might be a database vendor that doesn’t lock the table by default. We are mostly familiar with PostgreSQL and MySQL. Both of them lock write access on your table while the index is being created.

Building Indexes Concurrently

PostgreSQL – our database of choice while developing Semaphore – has a handy option that enables us to build indexes concurrently without locking up our database.

For example, let’s build an index concurrently for branches on the build model:

CREATE INDEX CONCURRENTLY idx_builds_branch ON builds USING btree (branch_id);

The main benefit of concurrent index creation is that it does not require a lock on the table to build the index tree so we can avoid the issue of accidental downtimes.

Keep in mind that while concurrent index building is a safe option for your production system, the build itself takes up to several times longer to complete. The database must perform two scans of the table, and it must wait for all existing transactions that could modify or use the index to terminate. The concurrent index build also imposes extra CPU and I/O load that might slow down other database operations.

Concurrent Index Creation in Rails

In Rails Migrations, you can use the algorithm option to trigger a concurrent index build on your database table.

For example, we recently noticed that we miss a database index for accessing our build_metrics database table from our build models, which in a snowball effect slowed down job creation on Semaphore.

Our build_metrics table is huge, counting many millions of elements, and it’s also accessed very frequently. We could not risk introducing a migration that would lock this table and potentially block build processing on Semaphore.

We used the safe route, and triggered a concurrent index build:

def change
  add_index :builds, :build_metric_id, :algorithm => :concurrently
end

However, we immediately learned that you can’t run the above from inside of a transaction. Active Record creates a transition around every migration step. To avoid this, we used the disable_ddl_transaction! introduced in Rails 4 to run this one migration without a transaction wrapper:

class AddIndexToBuildMetricIdOnBuilds < ActiveRecord::Migration
  disable_ddl_transaction!

  def change
    add_index :builds, :build_metric_id, :algorithm => :concurrently
  end
end

The results were phenomenal. With this simple little tweak, our job processing capabilities got around 2.5 times faster.

2.5x Job Processing

Small tweaks can sometimes bring great improvements. Premature optimization can be a huge anti-pattern, however investing in metrics and gaining a deep understanding of your system never is.

Keep building and tweaking!

At Semaphore, we’re all about speed. We’re on a mission to make continuous integration fast and easy. Driven by numerous conversations with our customers and our own experiences, we’ve built a new CI feature that can automatically parallelize any test suite and cut its runtime to just a few minutes - Semaphore Boosters. Learn more and try it out.

Speeding Up Rendering Rails Pages with render_async

Adding new code to Rails controllers can bring a couple of problems with it. Sometimes controller actions get really big, and they tend to do a lot of things. Another common problem is an increase in data over time, which can lead to slow page loading time. Adding new code to controller actions can also sometimes block the rendering of some actions if it fails, breaking user experience and user hapiness.

Here at Semaphore, we came across these types of problems a couple of times. We usually resolved them by splitting controller actions into smaller actions, and rendering them asynchrounously using plain Javascript.

After some time, we saw that this can be extracted to render_async, a gem that speeds up Rails pages for you - it loads content to your HTML asynchrounously by making an AJAX call to your Rails server.

Speeding Up Rendering Rails Pages with render_async

Problem no. 1: Slowness accumulates over time

As new code gets added, Rails controller actions can get “fat”. If we’re not careful, page load time slowly increases as the amount of code and data rises.

Problem no. 2: Dealing with code that blocks your actions

As we add new code to our controllers, we sometimes need to load extra data in the controller action in order to render the complete view.

Let’s take a look at an example of code that blocks the rendering of an action.

Let’s say we have a movies_controller.rb, and in the show action we want to fetch a movie from the database, but we also want to get the movie rating from IMDB.

class MoviesController < ApplicationController
  def show
    @movie = Movies.find_by_id(params[:id])

    @movie_rating = IMDB.movie_rating(@movie)
  end
end

Getting the movie by find_by_id is a normal line that tries to find a movie in our database that we can control.

However, the line where we fetch the movie rating makes an external request to an IMDB service that is expected to return the answer. The problem starts when an external service is not available or is experiencing downtime. Now our MoviesController#show is down and cannot be loaded to the user that wants the movie.

The solution

Both problems can be solved or relieved by splitting your code using the renderasync gem. renderasync loads content asynchronously to your Rails pages after they’ve rendered.

Why choose renderasync over traditional Javascript code that does async requests and adds HTML to the page? Because renderasync does the boring Javascript fetch and replace for you.

How render_async works

Let’s say you have an app/views/movies/show.html.erb file that shows details about a specified movie and ratings it fetches from an external service.

Here’s the code before using render_async:

# app/views/movies/show.html.erb

<h1>Information about <%= @movie.title %></h1>

<p><%= @movie.description %></p>

<p>Movie rating on IMDB: <%= @movie_rating %></p>

And this is what it looks like after using render_async:

# app/views/movies/show.html.erb

<h1>Information about <%= @movie.title %></h1>

<p><%= @movie.description %></p>

<%= render_async movie_rating_path(@movie.id) %>

With renderasync, the section with the movie rating is loaded after the show.html.erb loads. The page makes an AJAX request using jQuery to `movierating_path`, and it renders the contents of the AJAX response in the HTML of the page.

Since render_async makes a request to the specified path, we need to add it to config/routes.rb:

# config/routes.rb

get :movie_rating, :controller => :movies

We also need to add a proper action in the controller we set in the routes, so we’ll add the movie_rating action inside movies_controller.rb:

# app/controllers/movies_controller.rb

def movie_rating
  @movie_rating = IMDB.movie_ratings(@movie)

  render :partial => "movie_ratings"
end

Since our movie_rating is rendering a partial, we need to create a partial for it to render:

# app/views/movies/_movie_rating.html.erb

<p>Movie rating on IMDB: <%= @movie_rating %></p>

The most important part is to add the content_for tag to your application layout, because render_async will put the code for fetching AJAX responses there. It’s best is to put it just before the footer in your layout.

# app/views/layouts/application.html.erb

<%= content_for :render_async %>

If the IMDB service is down or not responding, our show page will load without the movie rating. The movie page now cannot be broken by an external service, leaving the rest of the page fully usable.

Wrapping Up

In this example we managed to speed up rendering Rails pages with render_async by:

  1. Simplifying the MoviesController show action, thus making it easier to test and load.
  2. Splitting our movie rating markup into a partial, which is a good pattern in the Rails world, and
  3. Freeing up show action from being blocked by an external service.

If you have ideas on what else could be added or improved in render_async, you can submit a pull request or an issue at render_async.

Also, feel free to leave any comments or questions you may have in the section below. If you find this article useful, or think someone else would find it useful, please share it with the world.

At Semaphore, we’re all about speed. We’re on a mission to make continuous integration fast and easy. Driven by numerous conversations with our customers and our own experiences, we’ve built a new CI feature that can automatically parallelize any test suite and cut its runtime to just a few minutes - Semaphore Boosters. Learn more and try it out.

Faster Rails: Is Your Database Properly Indexed?

This article is part of our Faster Rails series. Check out the previous article about fast existence checks.

My Rails app used to be fast and snappy, and everything was working just fine for several months. Then, slowly, as my product grew and users started to flock in, web requests become slow and my database’s CPU usage started hitting the roof. I hadn’t changed anything, why was my app getting slower?

Is there any cure for the issues I’m having with my application, or is Rails simply not able to scale?

Faster Rails: Is Your Database Properly Indexed?

What makes your Rails application slow?

While there can be many reasons behind an application’s slowness, database queries usually play the biggest role in an application’s performance footprint. Loading too much data into memory, N+1 queries, lack of cached values, and the lack of proper databases indexes are the biggest culprits that can cause slow requests.

Missing database indexes on foreign keys and commonly searched columns or values that need to be sorted can make a huge difference. The missing index is an issue that is not even noticeable for tables with several thousand records. However, when you start hitting millions of records, the lookups in the table become painfully slow.

The role of database indexes

When you create a database column, it’s vital to consider if you will need to find and retrieve records based on that column.

For example, let’s take a look at the internals of Semaphore. We have a Project model, and every project has a name attribute. When someone visits a project on Semaphore, e.g. https://semaphoreci.com/renderedtext/test-boosters, the first thing we need to do in the projects controller is to find the project based on its name — test-boosters.

project = Project.find_by_name(params[:name])

Without an index, the database engine would need to check every record in the projects table, one by one, until a match is found.

However, if we introduce an index on the ‘projects’ table, as in the following example, the lookup will be much, much faster.

class IndexProjectsOnName < ActiveRecord::Migration
  def change
    add_index :projects, :name
  end
end

A good way to think about indexes is to imagine them as the index section at the end of a book. If you want to find a word in a book, you can either read the whole book and find the word, or your can open the index section that contains a alphabetically sorted list of important words with a locator that points to the page that defines the word.

What needs to be indexed?

A good rule of thumb is to create database indexes for everything that is referenced in the WHERE, HAVING and ORDER BY parts of your SQL queries.

Indexes for unique lookups

Any lookup based on a unique column value should have an index.

For example, the following queries:

User.find_by_username("shiroyasha")
User.find_by_email("support@semaphoreci.com")

will benefit from an index of the username and email fields:

add_index :users, :username
add_index :users, :email

Indexes for foreign keys

If you have belongs_to or has_many relationships, you will need to index the foreign keys to optimize for fast lookup.

For example, we have the branches that belong to projects:

class Project < ActiveRecord::Base
  has_many :branches
end

class Branch < ActiveRecord::Base
  belongs_to :project
end

For fast lookup, we need to add the following index:

add_index :branches, :project_id

For polymorphic associations, the owner of the project can either be a User or an Organization:

class Organization < ActiveRecord::Base
  has_many :projects, :as => :owner
end

class User < ActiveRecord::Base
  has_many :projects, :as => :owner
end

class Project < ActiveRecord::Base
  belongs_to :owner, :polymorphic => true
end

We need to make sure that we create a double index:

# Bad: This will not improve the lookup speed

add_index :projects, :owner_id
add_index :projects, :owner_type

# Good: This will create the proper index

add_index :projects, [:owner_id, :owner_type]

Indexes for ordered values

Any frequently used sorting can be improved by using a dedicated index.

For example:

Build.order(:updated_at).take(10)

can be improved with a dedicated index:

add_index :updated_at

Should I always use indexes?

While using indexes for important fields can immensely improve the performance of your application, sometimes the effect can be negligible, or it can even make your application slower.

For example, tables that have elements that are frequently deleted can negatively impact the performance of your database. Huge tables with many millions of records also require more storage for your indexes.

Always be concious about the changes you introduce in your database, and if in doubt, be sure to base your decisions on real world data and measurements.

At Semaphore, we’re all about speed. We’re on a mission to make continuous integration fast and easy. Driven by numerous conversations with our customers and our own experiences, we’ve built a new CI feature that can automatically parallelize any test suite and cut its runtime to just a few minutes - Semaphore Boosters. Learn more and try it out.

Perfection is Useless

One of the most important things we teach the junior programmers who join the Semaphore team is the mindset of shipping in small iterations. This is a simple concept, however there’s an inevitable misunderstanding that stems from the subjective ideas of “small”. Thus, in practice we need to teach by example what we really mean by small.

When you’re inexperienced, the desire to do and show your best work often leads to perfectionism. In programming, perfectionism manifests itself as “I haven’t submitted my pull request because I haven’t completed everything yet”.

Perfectionism is at odds with the goals of developing business software — giving something useful to users, preferably sooner rather than later. Perfectionists create imaginary obstacles and never end up building anything.

Recently, a pair of junior programmers was building a new reporting screen for our marketing team. The screen needed to combine two sources of data for a given time range and present a paginated view of results. The team that needed the report has never seen the data this screen would provide. Would it hurt if the first version of the report did not include a date picker and pagination of results beyond the top 25? Hell no. So, we encouraged them to ship the screen without the date range and pagination. The initial results provided more than enough value and ideas for improvement. The marketing team had some data they could work with while the developers continued working on the remaining tasks.

The crux of the matter lies in decomposing a task into minimal useful pieces. Next, you estimate the complexity of each piece and communicate expectations with the “stakeholder” (customer, client, product manager, or feature user).

Say a designer has recently updated several details that affect four distinct screens. Would it be best to integrate these changes in four separate pull requests, or one? This is where complexity, i.e. the time it would take to complete each one, needs to be considered. If they would take a day each, four separate pull requests are probably best. If all of them together would take you less than an hour to complete, go ahead and combine them all into one pull request. Are three tasks really easy, but the fourth one requires additional input from the designer who’s having a day off, as well as more time than all others combined? Best to please your users with what you can finish soon, and then do the last thing separately.

Shipping early will often provide you with surprising feedback. Perhaps the initial version is so good that nobody really needs the stuff that’s “missing”. Or, the whole idea didn’t really deliver what was expected and needs to be reconsidered. The goal is to learn and help others. Just keep moving.

Flaky Tests: Are You Sure You Want to Rerun Them?

Flaky Tests: Are You Sure You Want to Rerun Them?

Different teams have different approaches to dealing with flaky tests. Some even go as far as using the “Let’s run each test 10 times and if it passes at least once, it passed” approach.

I personally think that rerunning failed tests is poisonous — it legitimizes and encourages entropy, and rots the test suite in the long run.

The half-cocked approach

Some teams see rerunning failed tests as a very convenient short-term solution. In my experience, there is unfortunately no such thing as ‘a short-term solution’. All temporary solutions tend to become permanent.

Along with some other techniques that are efficient in the short term, but are otherwise devastating, rerunning tests is very popular with a certain category of managers. It’s particularly common in corporate environments: there are company goals, and then there are personal goals (ladders to climb). In such environments, some people tend to focus only on what needs to happen until the end of the current quarter or year. What happens later is often seen as someone else’s concern. Looking from that perspective, test rerunning is both fast and efficient, which makes it a desirable and convenient solution.

Keeping flaky tests and brute-forcing them to pass defeats the purpose of testing. There is an unspoken assumption that something is wrong with the tests, and that it’s fine to just rerun them. This assumption is dangerous. Who’s to say that the race or the time-out that causes the flakiness is in the test, and not in the production code? And that it’s not affecting the customer?

The sustainable solution

The long-term solution is to either fix or replace the flaky tests. If one developer cannot fix them, another one should try. If a test cannot be fixed, it should be deleted and written from scratch, preferably by somebody who didn’t see the flaky one. Test coverage tools can be used as a kind of a safety net, showing if some tests have been deleted without being adequately replaced.

Not being able to develop stable tests for some part of the code usually means one of these two things — either that something is wrong with the test and/or the testing approach, or that something is wrong with the code being tested. If we are reasonably certain that the tests are fine, it’s time to take a deeper look at the code itself.

Our position on flaky tests

Deleting and fixing flaky tests is a pretty aggressive measure, and rewriting tests can be time consuming. However, not taking care of flaky tests leads to certain long-term test suite degradation.

On the other hand, there are some legitimate use-cases for flaky test reruns. For example, when time-to-market is of essential importance, and when technical debt is deliberately accumulated with the intention and a clear plan on paying it off in the near future.

As a CI/CD tool vendor, we feel that our choice whether to support rerunning failing flaky tests affects numerous customers. Not just the way they work, but, much more importantly, the way they perceive flaky tests and the testing process itself. At this point, we are choosing not to support rerunning failed tests, since our position is that this approach is harmful much more often than it is useful.

The Cracking Monolith: The Forces That Call for Microservices

The microservice architecture has recently been gaining traction, with many companies sharing their positive experiences with applying it. The early adopters have been tech behemoths such as Amazon and Netflix, or companies with huge user bases like SoundCloud. Based on the profiles of these companies and the assumption that there’s more complexity to running and deploying many things than to deploying a single application, many people understand microservices as an interesting idea that does not apply to them. It’s something that mere mortals could qualify for in the far distant future, if ever.

However, obsessing about “being ready” is rarely a good strategy in life. I think that it’s far more useful to first learn how to detect when the opposite approach — a monolithic application — is no longer optimal. The knowledge that helps us to recognize the need enables us to start taking action when the time comes for us to make the change. This and future posts on our blog will be based on our experience of scaling up Semaphore to manage tens of thousands of private CI jobs on a daily basis.

Overweight monoliths exhibit two classes of problems: degrading system performance and stability, and slow development cycles. So, whatever we do comes from the desire to escape these technical and consequently social challenges.

The single point of fragility

Today’s typical large monolithic systems started off as web applications written in an MVC framework, such as Ruby on Rails. These systems are characterized by either being a single point of failure, or having severe bottlenecks under pressure.

Of course, having potential bottlenecks, or having an entire system that is a single point of failure is not inherently a problem. When you’re in month 3 of your MVP, this is fine. When you’re working in a team of a few developers on a client project which serves 100 customers, this is fine. When most of your app’s functionality are well-designed CRUD operations based on human input with a linear increase of load, things are probably going to be fine for a long time.

Also, there’s nothing inherently wrong about big apps. If you have one and you’re not experiencing any of these issues, there’s absolutely no reason to change your approach. You shouldn’t build microservices solely in the service of making the app smaller — it makes no sense to replace the parts that are doing their job well.

Problems begin to arise after your single point of failure has actually started failing under heavy load.

At that point, having a large attack surface can start keeping the team in a perpetual state of emergency. For example:

  • An outage in non-critical data processing brings down your entire website. With Semaphore, we had events where the monolith was handling callbacks from many CI servers, and when that part of the system failed, it brought the entire service down.
  • You moved all time-intensive tasks to one huge group of background workers, and keeping them stable gradually becomes a full-time job for a small team.
  • Changing one part of the system unexpectedly affects some other parts even though they’re logically unrelated, which leads to some nasty surprises.

As a consequence, your team spends more time solving technical issues than building cool and useful stuff for your users.

Slow development cycles

The second big problem is when making any change happen begins to take too much time.

There are some technical factors that are not difficult to measure. A good question to consider is how much time it takes your team to ship a hotfix to production. Not having a fast delivery pipeline is painfully obvious to your users in the case of an outage.

What’s less obvious is how much the slow development cycles are affecting your company over a longer period of time. How long does it take your team to get from an idea to something that customers can use in production? If the answer is weeks or months, then your company is vulnerable to being outplayed by competition.

Nobody wants that, but that’s where the compound effects of monolithic, complex code bases lead to.

Slow CI builds: anything longer than a few minutes leads to too much unproductive time and task switching. As a standard for web apps we recommend setting the bar at 10 minutes and we actually draw the line for you. Slow CI builds are one of the first symptoms of an overweight monolith, but the good news is that a good CI tool can help you fix it. For example, on Semaphore you can split your test suite into parallel jobs, or let Semaphore do the work for you automatically, regardless of the sequential runtime of your build.

Slow deployment: this issue is typical for monoliths that have accumulated many dependencies and assets. There are often multiple app instances, and we need to replace each one without having downtime. Moving to container-based deployment can make things even worse, by adding the time needed to build and copy the container image.

High bus factor on the old guard, long onboarding for the newcomers: it takes months for someone new to become comfortable with making a non-trivial contribution in a large code base. And yet, all new code is just a small percentile of the code that has already been written. The idiosyncrasies of old code affect and constrain all new code that is layered on top of the old one. This leaves those who have watched the app grow with an ever-expanding responsibility. For example, having 5 developers that are waiting for a single person to review their pull requests is an indicator of this.

Emergency-driven context switching: we may have begun working on a new feature, but an outage has just exposed a vulnerability in our system. So, healing it becomes a top priority, and the team needs to react and switch to solving that issue. By the time they return to the initial project, internal or external circumstances can change and reduce its impact, perhaps even make it obsolete. A badly designed distributed system can make this even worse — hence one of the requirements for making one is having solid design skills. However, if all code is part of a single runtime hitting one database, our options for avoiding contention and downtime are very limited.

Change of technology is difficult: our current framework and tooling might not be the best match for the new use cases and the problems we face. It’s also common for monoliths to depend on outdated software. For example, GitHub upgraded to Rails 3 four years after it was released. Such latency can either limit our design choices, or generate additional maintenance work. For example, when the library version that you’re using is no longer receiving security updates, you need to find a way to patch it yourself.

Decomposition for fun and profit

While product success certainly helps, a development team that’s experiencing all of these issues won’t have the highest morale. Nor will its people be able to develop their true potential.

All this can happen regardless of code quality. Practicing behavior-driven development is not a vaccine against scaling issues.

The root cause is simple. A monolithic application grows multiple applications within itself, and it meets high traffic and large volumes of data.

Big problems are best solved by breaking them up into many smaller ones that are easier to handle. This basic engineering idea is what leads teams to start decomposing large monoliths into smaller services, and eventually into microservices. The ultimate goal is to go back to being creative and successful by enabling the team to develop useful products as quickly as possible.

Faster Rails: How to Check if a Record Exists

Ruby and Rails are slow — this argument is often used to downplay the worth of the language and the framework. This statement in itself is not false. Generally speaking, Ruby is slower than its direct competitors such as Node.js and Python. Yet, many businesses from small startups to platforms with millions of users use it as the backbone of their operations. How can we explain these contradictions?

Faster Rails: How to Check if a Record Exists

What makes your application slow?

While there can be many reasons behind making your application slow, database queries usually play the biggest role in an application’s performance footprint. Loading too much data into memory, N+1 queries, lack of cached values, and the lack of proper databases indexes are the biggest culprits that cause slow requests.

There are some legitimate domains where Ruby is simply too slow. However, most of the slow responses in our applications usually boil down to unoptimized database calls and the lack of proper caching.

Even if your application is blazing fast today, it can become much slower in only several months. API calls that worked just fine can suddenly start killing your service with a dreaded HTTP 502 response. After all, working with a database table with several hundred records is very different from working with a table that has millions of records.

Existence checks in Rails

Existence checks are probably the most common calls that you send to your database. Every request handler in your application probably starts with a lookup, followed by a policy check that uses multiple dependent lookups in the database.

However, there are multiple ways to check the existence of a database record in Rails. We have present?, empty?, any?, exists?, and various other counting-based approaches, and they all have vastly different performance implications.

In general, when working on Semaphore, I always prefer to use .exists?.

I’ll use our production database to illustrate why I prefer .exists? over the alternatives. We will try to look up if there has been a passed build in the last 7 days.

Let’s observe the database calls produced by our calls.

Build.where(:created_at => 7.days.ago..1.day.ago).passed.present?

# SELECT "builds".* FROM "builds" WHERE ("builds"."created_at" BETWEEN
# '2017-02-22 21:22:27.133402' AND '2017-02-28 21:22:27.133529') AND
# "builds"."result" = $1  [["result", "passed"]]


Build.where(:created_at => 7.days.ago..1.day.ago).passed.any?

# SELECT COUNT(*) FROM "builds" WHERE ("builds"."created_at" BETWEEN
# '2017-02-22 21:22:16.885942' AND '2017-02-28 21:22:16.886077') AND
# "builds"."result" = $1  [["result", "passed"]]


Build.where(:created_at => 7.days.ago..1.day.ago).passed.empty?

# SELECT COUNT(*) FROM "builds" WHERE ("builds"."created_at" BETWEEN
# '2017-02-22 21:22:16.885942' AND '2017-02-28 21:22:16.886077') AND
# "builds"."result" = $1  [["result", "passed"]]


Build.where(:created_at => 7.days.ago..1.day.ago).passed.exists?

# SELECT 1 AS one FROM "builds" WHERE ("builds"."created_at" BETWEEN
# '2017-02-22 21:23:04.066301' AND '2017-02-28 21:23:04.066443') AND
# "builds"."result" = $1 LIMIT 1  [["result", "passed"]]

The first call that uses .present? is very inefficient. It loads all the records from the database into memory, constructs the Active Record objects, and then finds out if the array is empty or not. In a huge database table, this can cause havoc and potentially load millions of records, that can even lead to downtimes in your service.

The second and third approaches, any? and empty?, are optimized in Rails and load only COUNT(*) into the memory. COUNT(*) queries are usually efficient, and you can use them even on semi-large tables without any dangerous side effects.

The third approach, exists?, is even more optimized, and it should be your first choice when checking the existence of a record. It uses the SELECT 1 ... LIMIT 1 approach, which is very fast.

Here are some numbers from our production database for the above queries:

present? =>  2892.7 ms
any?     =>   400.9 ms
empty?   =>   403.9 ms
exists   =>     1.1 ms

This small tweak can make your code up to 400 times faster in some cases.

If you take into account that 200ms is considered the upper limit for an acceptable response time, you will realize that this tweak can spell the difference between a good, sluggish, and bad user experience.

Should I always use exists?

I consider exists? a good sane default that usually has the best performance footprint. However, there are some exceptions.

For example, if we are checking for the existence of an association record without any scope, any? and empty? will also produce a very optimized query that uses SELECT 1 FROM ... LIMIT 1 form, but any? fill not hit the database again if the records are already loaded into memory.

This makes any? faster by one whole database call when the records are already loaded into memory:

project = Project.find_by_name("semaphore")

project.builds.load    # eager loads all the builds into the association cache

project.builds.any?    # no database hit
project.builds.exists? # hits the database

# if we bust the association cache
project.builds(true).any?    # hits the database
project.builds(true).exists? # hits the database

As a conclusion, my general advice is to always use exists? and improve the code based on metrics.

At Semaphore, we’re all about speed. We’re on a mission to make continuous integration fast and easy. Driven by numerous conversations with our customers and our own experiences, we’ve built a new CI feature that can automatically parallelize any test suite and cut its runtime to just a few minutes - Semaphore Boosters. Learn more and try it out.

Making a Mailing Microservice with Elixir and RabbitMQ

At Rendered Text, we like to decompose our applications into microservices. These days, when we have an idea, we think of its implementation as a composition of multiple small, self-sustaining services, rather than an addition to a big monolith.

In a recent Semaphore product experiment, we wanted to create a service that gathers information from multiple sources, and then emails a report that summarizes the data in a useful way to our customers. It’s a good use case for a microservice, so let’s dive into how we did it.

Our microservices stack includes Elixir, RabbitMQ for communication, and Apache Thrift for message serialization.

We designed a mailing system that consists of three parts:

  1. the main user-facing application that contains the data,
  2. a data-processing microservice, and
  3. a service for composing and sending the emails.

Asynchronous messaging with RabbitMQ

For asynchronous messaging, we decided to use RabbitMQ, a platform for sending and receiving messages.

First, we run the cron job inside our main application which gathers the data. Then, we encode that data and send it to the Elixir mailing microservice using RabbitMQ.

Every RabbitMQ communication pipeline consists of a producer and a consumer. For publishing messages from our main application, we have a Ruby producer that sends a message to a predefined channel and queue.

def self.publish(message)
  options = {
    :exchange => "exchange_name",
    :routing_key => "routing_key",
    :url => "amqp_url"
    }
  Tackle.publish(message, options)
end

In the last line above, we are using our open source tool called Tackle to publish a message. Tackle tackles the problem of processing asynchronous jobs in a reliable manner by relying on RabbitMQ. It serves as an abstraction around RabbitMQ’s API.

The consumer is a microservice written in Elixir, so we use ex-tackle, which is an Elixir port of Tackle:

defmodule MailingService.Consumer do
  use Tackle.Consumer,
  url: "amqp_url",
  exchange: "exchange_name",
  routing_key: "routing_key",
  service: "service_name",
  retry_limit: 10,
  retry_delay: 10

  def handle_message(message) do
    message
    |> Poison.decode!
    |> Mail.compose
  end
end

We connect to the specified exchange and wait for encoded messages to arrive. Options like retry_limit and retry_delay are there to allow us to specify how many times we want to retry message handling before the message is sent to the dead letter queue. The delay is there to set the timespan between each retry. This ensures the stability and reliability of our publish-subscribe system.

In our case, we use the decoded message to request data from the data processing microservice, and later use that response to send a message to the mailing service.

HTML template rendering in Elixir with EEx

Once our data is received and successfully decoded, we start composing the email by inserting the received data into the email templates. Similar to Ruby’s ERB and Java’s JSPs, Elixir has EEx, or Embedded Elixir. EEx allows us to embed and evaluate Elixir inside strings.

While using EEx, we go through three main phases. The first one is evaluation, the second is definition, and the third is compilation. EEx rules apply when a filename contains an extension html.eex.

Our email consists of multiple sections, all of which are using different datasets. Because of this, we divided our HTML email into partials for easier composing and improved code readability.

In order to evaluate the data inside a partial, we call the eval_file function and pass the data to partials:

<div>
  <%= EEx.eval_file "data/_introduction.html.eex",
                    [data1: template_data.data1,
                     data2: template_data.data2] %>

  <%= EEx.eval_file "data/_information.html.eex",
                    [data2: template_data.data2,
                     data3: template_data.data3] %>
</div>

Once we have all partials in place, we can combine them by evaluating them inside an entry point HTML template.

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
 <%= EEx.eval_file "data/_header.html.eex" %>
 <%= EEx.eval_file "data/_content.html.eex", [template_data: template_data] %>
</html>

Sending email with the SparkPost API

For email delivery, we rely on SparkPost. It provides email delivery services for apps, and it also provides useful email analytics. In our case, we used the Elixir SparkPost API client. We used it by creating a Mailer module that is very easy to use for email sending when instanced.

defmodule MailingService.Mailer do
  alias SparkPost.{Content, Recipient, Transmission}

  @return_path "semaphore+notifications@renderedtext.com"

  def send_message(recipient_email, content) do
    Transmission.send(%Transmission{
                      recipients: [ recipient_email ],
                      return_path: @return_path,
                      content: content,
                      campaign_id: "Campaign Name"
                    })
  end
end

Once we’ve defined this module, we can easily use it anywhere as long as we pass the correct data structure. For example, we have a function that creates a data structure for the email template and passes it to the send_message function with desired recipients.

def compose_and_deliver(data1, data2) do
  mail = %Content.Inline{
    subject: "[Semaphore] #{TimeFormatter.today} Mail subject",
    from: "Semaphore <semaphore+notifications@renderedtext.com>",
    text: template_data(data1, data2) |> text,
    html: template_data(data1, data2) |> html
  }
  @mailer.send_message(data2["email"], mail)
end

SparkPost also enables us to send to multiple recipients at the same time, as well as send both HTML and plain text versions of an email. Bear in mind that you also need to provide .txt templates in order to send a plain text email.

As a final step in this iteration, we have developed a preview email that service owners receive a few hours before the production reports go out to customers.

Wrapping up

We now have a mailing microservice, made using Elixir, SparkPost, and RabbitMQ. Combining these three has allowed us to create a microservice that takes less than 4 seconds to gather data, send it, receive it on the other end, compose the emails, and dispatch them to customers.

What is Proper Continuous Integration?

Standard continuous integration time

Continuous integration (CI) is confusing. As with all ideas, everybody does their own version of it in practice.

CI is a solution to the problems we face while writing, testing and delivering software to end users. Its core promise is reliability.

A prerequisite for continuous integration is having an automated test suite. This is not a light requirement. Learning to write automated tests and mastering test-driven development takes years of practice. And yet, in a growing app, the tests we’ve developed can become an impediment to our productivity.

Are We Doing CI?

Let’s take two development teams, both writing tests, as an example. The first one’s CI build runs for about 3 minutes. The second team clocks at 45 minutes. They both use a CI server or a hosted CI service like Semaphore that runs tests on feature branches. They both release reliable software in predictable cycles. But are they both doing proper continuous integration?

Martin Fowler recently shared a description of an informal CI certification process performed by Jez Humble:

He usually begins the certification process by asking his [conference] audience to raise their hands if they do Continuous Integration. Usually most of the audience raise their hands.

He then asks them to keep their hands up if everyone on their team commits and pushes to a shared mainline (usually shared master in git) at least daily.

Over half the hands go down.

He then asks them to keep their hands up if each such commit causes an automated build and test. Half the remaining hands are lowered.

Finally he asks if, when the build fails, it’s usually back to green within ten minutes.

With that last question only a few hands remain. Those are the people who pass his certification test.

Software Development or a Sword Fight?

If a CI build takes long enough for us to have time to go practice swordmanship while we wait, we approach our work defensively. We tend to keep branches on the local computer longer, and thus every developer’s code is in a significantly different state. Merges are rarer, and they become big and risky events. Refactoring becomes hard to do on the scale that the system needs to stay healthy.

With a slow build, every “git push” sends us to Limbo. We either wait, or look for something else to do to avoid being completely idle. And if we context-switch to something else, we know that we’ll need to switch back again when the build is finished. The catch is that every task switch in programming is hard and it sucks up our energy.

The point of continuous in continuous integration is speed. Speed drives high productivity: we want feedback as soon as possible. Fast feedback loops keep us in a state of flow, which is the source of our happiness at work.

So, it’s helpful to establish criteria for what proper continuous integration really means and how it’s done.

The 10 Minutes Test

It’s simple: does it take you less than 10 minutes from pushing new code to getting results? If so, congratulations. Your team is equipped for high performance. If not, your workflow only has elements of a CI process, for lack of a better term. But, this slowness develops wrong habits and hurts the productivity of all developers in a team. This ultimately inhibits the performance of the company as a whole.

Nobody sets out to build an unproductive delivery pipeline. Yet, we’re busy enough writing code until we feel like a boiling frog — we don’t notice the change until we accept it as the way things just are. Of course our build takes long, we have over 10,000 lines of code!

The Light at the End of the Tunnel

But, things don’t have to be this way. Regardless of how big your test suite is, parallelizing tests can cut waiting time down to just a couple of minutes or less. A fast hosted CI service that allows you to easily configure jobs to run in parallel and run as many jobs as you need can be a good solution. By parallelizing tests, you’ll reduce the time you spend deciding what to do while you wait, and keep your team in a state of flow.

We are building Semaphore, which has been proven to be the fastest hosted CI service on the market. We’re on a mission to make CI fast and easy. Driven by numerous conversations with our customers and our own experiences, we’ve built a new CI feature that can automatically parallelize any test suite and cut its runtime to just a few minutes - Semaphore Boosters. Learn more and try it out.

Lightweight Docker Images in 5 Steps

Lightweight Docker Images

Lightweight Docker Images Speed Up Deployment

Deploying your services packaged in lightweight Docker images has many practical benefits. In a container, your service usually comes with all the dependencies it needs to run, it’s isolated from the rest of the system, and deployment is as simple as running a docker run command on the target system.

However, most of the benefits of dockerized services can be negated if your Docker images are several gigabytes in size and/or they take several minutes to boot up. Caching Docker layers can help, but ideally you want to have small and fast containers that can be deployed and booted in a mater of minutes, or even seconds.

The first time we used Docker at Rendered Text to package one of Semaphore services, we made many mistakes that resulted in a huge Docker image that was painful for deployment and maintenance. However, we didn’t give up, and, step by step, we improved our images.

We’ve managed to make a lot of improvements since our first encounter with Docker, and we’ve successfully reduced the footprint of our images from several gigabytes to around 20 megabytes for our latest microservices, with boot times that are always under 3 seconds.

Our First Docker Service

You might be wondering how a Docker image can possibly be larger than a gigabyte. When you take a standard Rails application — with gems, assets, background workers and cron jobs — and package it using a base image that comes with everything but the kitchen sink preinstalled, you will surely cross the 1 GB threshold.

We started our Docker journey with a service that used Capistrano for deployment. To make our transition easy, we started out with a base Docker image that resembled our old workflow. The phusion/passenger-full image was a great candidate, and we managed to package up our application very quickly.

A big downside of using passenger-full was that it’s around 300 MB in size. When you add all of your application’s dependency gems, which can easily be around 300 MB in size, you are already starting at around 600 MB.

The deployment of that image took around 20 minutes, which is an unacceptable time frame if you want to be happy with your continuous delivery pipeline. However, this was a good first step.

We knew that we could do better.

Step 1: Use Fewer Layers

One of the first things you learn when building your Docker images is that you should squash multiple Docker layers into one big layer.

Let’s take a look at the following Dockerfile, and demonstrate why it’s better to use fewer layers in a Docker image:

FROM ubuntu:14.04

RUN apt-get update -y

# Install packages
RUN apt-get install -y curl
RUN apt-get install -y postgresql
RUN apt-get install -y postgresql-client

# Remove apt cache to make the image smaller
RUN rm -rf /var/lib/apt/lists/*

CMD bash

When we build the image with docker build -t my-image ., we get an image that is 279 MB in size. With docker history my-image we can list the layers of our Docker image:

$ docker history my-image

IMAGE               CREATED             CREATED BY                                      SIZE
47f6bd778b89        7 minutes ago       /bin/sh -c #(nop)  CMD ["/bin/sh" "-c" "bash"   0 B
3650b449ca91        7 minutes ago       /bin/sh -c rm -rf /var/lib/apt/lists/*          0 B
0c43b2bf2d13        7 minutes ago       /bin/sh -c apt-get install -y postgresql-client 1.101 MB
ce8e5465213b        7 minutes ago       /bin/sh -c apt-get install -y postgresql        56.72 MB
b3061ed9d53a        7 minutes ago       /bin/sh -c apt-get install -y curl              11.38 MB
ee62ceeafb06        8 minutes ago       /bin/sh -c apt-get update -y                    22.16 MB
ff6011336327        3 weeks ago         /bin/sh -c #(nop) CMD ["/bin/bash"]             0 B
<missing>           3 weeks ago         /bin/sh -c sed -i 's/^#\s*\(deb.*universe\)$/   1.895 kB
<missing>           3 weeks ago         /bin/sh -c rm -rf /var/lib/apt/lists/*          0 B
<missing>           3 weeks ago         /bin/sh -c set -xe   && echo '#!/bin/sh' > /u   194.6 kB
<missing>           3 weeks ago         /bin/sh -c #(nop) ADD file:4f5a660d3f5141588d   187.8 MB

There are several things to note in the output above:

  1. Every RUN command creates a new Docker layer
  2. The apt-get update command increases the image size by 23 MB
  3. The rm -rf /var/lib/apt/lists/* command doesn’t reduce the size of the image

When working with Docker, we need to keep in mind that any layer added to the image is never removed. In other words, it’s smarter to update the apt cache, install some packages, and remove the cache in a single Docker RUN command.

Let’s see if we can reduce the size of our image with this technique:

FROM ubuntu:14.04

RUN apt-get update -y && \
    apt-get install -y curl postgresql postgresql-client && \
    rm -rf /var/lib/apt/lists/*

CMD bash

Hooray! After the successful build, the size of our image dropped to 250 megabytes. We’ve just reduced the size by 25 MB just by joining the installation commands in our Dockerfile.

Step 2: Make Container Boot Time Predictable

This step describes an anti-pattern that you should avoid in your deployment pipeline.

When working on a Rails-based application, the biggest portion of your Docker images will be gems and assets. To circumvent this, you can try to be clever and place your gems outside of the container.

For example, you can run the Docker image by mounting a directory on the host machines, and cache the gems between two subsequent runs of your Docker image.

FROM ruby

WORKDIR /home/app
ADD . /home/app

CMD bundle install --path vendor/bundle && bundle exec rails server

Let’s build such an image. Notice that we use the CMD keyword, which means that our gems will be installed every time we run our Docker image. The build step only pushes the source code into the container.

docker build -t rails-image .

When we start our image this time around, it will first install the gems, and then start our Rails server.

docker run -tdi rails-image

Now, let’s use a volume to cache the gems between each run of our image. We will achieve this by mounting an external folder in our Docker image with -v /tmp/gems:vendor/bundle option.

docker run -v /tmp/gems:vendor/bundle -tdi rails-image

Hooray! Or is it?

The technique above looks promising, but in practice, it turns out to be a bad idea. Here are some reasons why:

  1. Your Docker images are not stateless. If you run the image twice, you can experience different behaviour. This is not ideal because it makes your deployment cycle more exciting than it should be.

  2. Your boot time can differ vastly depending on the content of your cache directory. For bigger Rails projects, the boot time can range from several seconds up to 20 minutes.

We have tried to build our images with this technique, but we ultimately had to drop this idea because of the above drawbacks. As a rule of thumb, predictable boot time and immutability of your images outweigh any speed improvement you may gain by extracting dependencies from your containers.

Step 3: Understand and Use Docker Cache Effectively

When creating your first Docker image, the most obvious choice is to use the same commands you would use in your development environment.

For example, if you’re working on a Rails project, you would probably want to use the following:

FROM ruby

WORKDIR /home/app
ADD . /home/app

RUN bundle install --path vendor/bundle
RUN bundle exec rake asset:precompile

CMD bundle exec rails server

However, by doing this, you will effectively wipe every cached layer, and start from scratch on every build.

New Docker layers are created for every ADD, RUN and COPY command. When you build a new image, Docker first checks if a layer with the same content and history exists on your machine. If it already exists, Docker reuses it. If it doesn’t exist, Docker needs to create a new layer.

In the above example, ADD . /home/app creates a new layer even if you make the smallest change in your source code. Then, the next command RUN bundle install --path vendor/bundle always needs to do a fresh install of every gem because the history of your cached layers has changed.

To avoid this, it’s better to just add the Gemfile first, since it changes rarely compared to the source code. Then, you should install all the gems, and add your source code on top of it.

FROM ruby

WORKDIR /tmp/gems
ADD Gemfile /tmp/gems/Gemfile
RUN bundle install --path vendor/bundle

WORKDIR /home/app
ADD . /home/app
RUN bundle exec rake asset:precompile

RUN mv /tmp/gems/vendor/bundle vendor/bundle

CMD bundle exec rails server

With the above technique, you can shorten the build time of your image and reduce the number of layers that need to be uploaded on every deploy.

Step 4: Use a Small Base Image

Big base images are great when you’re starting out with Docker, but you’ll eventually want to move on to smaller images that contain only the packages that are essential for your application.

For example, if you start with phusion/passenger-full, a logical next step would be to try out phusion/baseimage-docker and enable only the packages that are necessary. We followed this path too, and we successfully reduced the size of our Docker images by 200 megabytes.

But why stop there? You can also try to run your image on a base ubuntu image. Then, as a next step, go and try out debian that’s only around 80 MB in size.

You will notice that every time you reduce the image size, some of the dependencies will be missing, and you will probably need to spend some time on figuring out how to install them manually. However, this is only a one-time issue, and once you’ve resolved it, you can most likely enjoy faster deployment for several months to follow.

We used ubuntu for several months too. However, as we moved away from Ruby as our primary language and started using Elixir, we knew that we could go even lighter.

Being a compiled language, Elixir has a nice property that the resulting compiled binaries are self-contained, and can pretty much run on any Linux distribution. This is when the alpine image becomes an awesome candidate.

The base image is only 5 MB, and if you compile your Elixir application, you can achieve images that are only around 25 MB in size. This is awesome comparing to the 1.5 GB beast from the beginning of our Docker journey.

With Go, which we also use occasionally, we could go even further and build an image FROM scratch, and achieve 5 MB-sized images.

Step 5: Build Your Own Base Image

Using lightweight images can be a great way to improve build and deployment performance, but bootstrapping a new microservice can be painful if you need to remember to install a bunch of packages before you can use it.

If you create new services frequently, building your own customized base Docker image can bring great improvement. By building your own custom Docker image and publishing it on DockerHub, you can have small images, that are also easy to use.

Keep Learning, Docker is Great

Switching to a Docker-based development and deployment environment can be tricky at first. You can even get frustrated and think that Docker is not for you. However, if you persist and do your best to learn some good practices including how to make and keep your Docker images lightweight, the Docker ecosystem will reward you with speed, stability and reliability you’ve never experienced before.

P.S. If you’re looking for a continuous integration and deployment solution that works great with Docker — including fully-featured toolchain support, registry integrations, image caching, and fast image builds — try Semaphore for free.

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