Semaphore Blog

News and updates from your friendly continuous integration and deployment service.

Fast Failing: Introducing Faster Feedback on Failing Builds

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Fast feedback on the work we’ve done minimizes developer context switching and keeps us in the state of flow. Waiting for all the jobs to finish in order to see that a job has failed can waste a lot of time. If a job fails, the developer should have the option to be notified right away, rather than wait until all the tests are run.

In order to make you more productive when building on Semaphore, we bring you fast failing as a feature.

The fast failing approach

On Semaphore, fast failing means that the developers get instant feedback when a job fails. All the running jobs of a build are stopped as soon as one of the jobs fails. This means that you don’t need to wait for all the other jobs to finish in order to get build feedback.

For example, if a build takes 10 minutes, the fast failing approach gives you feedback in 1 minute, and fixing issues takes 1 minute, the entire process along with re-building takes 12 minutes in total. With the approach that does not allow fast failing, the entire process would take 21 minutes.

The fast failing approach also minimizes developer context switch due to faster feedback cycles.

How to enable fast failing on Semaphore

You can select the type of fast failing in the branch settings of your project. You can either enable it for all branches, or for all branches except the default one.

Branch settings for Fast Failing


Happy fast failing!

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.

Customizable Command Timeouts

For a long time, Semaphore has been limiting your build command execution time to a fixed 60 minutes. This restriction worked great for the majority of builds on Semaphore, however there are some cases when this limit is simply not good enough.

Tasks such as compiling large binaries, provisioning big infrastructures, or running tests that are difficult to parallelize sometimes require more than 60 minutes to complete.

On the other hand, developers like to restrict build duration to prevent their test suites from getting stuck because of an accidental debug statement or a network call that will never complete.

For this reason, we have introduced a new configuration option in the admin section of your project’s configuration that allows you to choose a best suited timeout for your project:

Customizable command timeouts on Semaphore


Happy building!

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.

Measure and Improve Your CI Speed with Semaphore

Measuring CI speed on Semaphore

We’re happy to announce a new Semaphore feature that will help you monitor and improve your CI speed over time. We’re calling it CI Speed Insights, and it’s available to all Semaphore users as of today.

Here’s how it works. On the project page, below all of your recently active branches, you’ll find a chart. In it, you’ll be able to see the runtime duration of recent builds and an indicator of your average CI speed, in minutes:

Fast CI speed indicator on Semaphore

The chart will also include a line indicating the 10 minute mark, in case your build runs around or longer than that.

Slow continuous integration speed indicator on Semaphore

Why 10 minutes?

We’re convinced that having a build slower than 10 minutes is not proper continuous integration. When a build takes longer than 10 minutes, we waste too much precious time and energy waiting, or context switching back and forth. We merge rarely, making every deploy more risky. Refactoring is hard to do well. You can read our full rationale about this in our recent blog post — What is Proper Continuous Integration?.

There’s more to CI Speed Insights if you click to view the details.

Continuous Integration Speed Insights on Semaphore

Measuring CI speed

We’ve defined CI speed as the average time that elapses from pushing new code to getting the build status report. In many cases this is how long it takes for your build setup and tests to run on Semaphore. However, another possibility is that your build is blocked waiting for available resources, which are being used by other builds. When this happens, you’ll see hints on Semaphore that point you to the Activity Monitor.

So, if your build runs for 4 minutes on average, but is waiting for other builds to finish for 5 minutes on average, then the CI speed for that project is reported as 9 minutes. This is what the screenshot above illustrates.

At the bottom of the screen, you’ll find an interactive chart of your recent builds that shows both the build runtime and your waiting time. You can click on each one to view full build details.

Viewing build details on CI Speed Insights chart

If your build lasts too long, Semaphore will recommend running tests in parallel. You can set this up by manually configuring parallel jobs. This is a great way to cut down your build time.

Automatic test parallelisation

Still, wouldn’t it be great if Semaphore could automatically parallelise all your tests for you, without any effort needed on your side? We’ve been hard at work on making that possible. A recent beta customer managed to reduce build time from 2 hours to 8 minutes(!).

The initial release of automatic parallelisation will support Ruby — RSpec and Cucumber to be precise. If you’re using these tools and are interested in setting up automatic parallelisation in your project, please get in touch with us to schedule a personal demo. We’re excited to show you how Semaphore can optimize the way your team runs tests.

Happy building!

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.

Celebrate Continuous Delivery in Slack with New Semaphore Notifications

By popular demand, we’re happy to announce that we’ve made some tweaks to Slack notifications coming from Semaphore.

Here’s what our build and deployment notifications now look like:

We experimented with different approaches, ranging from minimal to multi-line, rich format messages, and used them for our projects for several weeks. In the end, we settled on a minimal, yet informative single-line format that includes all the key information (what, where) and links to relevant resources.

Deployment is everyone’s success

If you’re using Slack, and you haven’t had the chance to set up your Semaphore notifications, now is a great time to do that. All it takes is a few clicks, and it can help keep everyone in the team informed about what everyone else is working on. Learn how to set up Semaphore Slack notifications here.

We recommend setting up deployment notifications for production to a common Slack channel, such as #engineering or #general. Regardless of whether you ship weekly or continuously, every time you deliver something to your users, it’s a moment of accomplishment that’s worth sharing with the entire team.

Our internal settings are currently set to send notifications when build status of master branch changes (when it fails and recovers) and after deployment to production. Watch this quick video to see what our notifications look like in action:

If your team is not yet practicing continuous deployment, here’s an overview of how it works, and if you’re ready you can look into options for setting up deployment on Semaphore. Even just starting with a central manual trigger and history of deployment within your CI process is a great first step towards more complete automation.

Happy building!

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. And we’re getting ready to take CI speed to the next level.

Moving Platform Updates to Changelog

Quick heads up that we’ll be publishing future platform updates on the Semaphore changelog.

This will help us focus the blog on important product updates and helpful engineering posts, while the changelog will contain a more complete timeline of evolutionary changes that we’re making to the product.

We’ll be tweeting every new post from the changelog, and you can subscribe to the changelog’s RSS feed here

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