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.

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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.

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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?

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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.

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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?

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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.

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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. Read more...

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