What is Travis CI?
Who uses Travis CI?
Travis CI Integrations
Why developers like Travis CI?
Here are some stack decisions, common use cases and reviews by members of with Travis CI in their tech stack.
I initially chose CircleCI for a personal project because I was not satisified with using Travis CI in the past. When it came time to develop my CI/CD config on Circle, I was pleasantly surprised with the fantastic documentation, invaluable collection of example configs and helpful support provided. The free tier they provide is quite robust for most small projects and the platform is updated frequently with nice features.
Areas where CircleCI could improve:
- the UI is a bit slow (you can feel the local machine straining to load all the code) and it is not as intuitive as it could be
- many UI elements receive updates and/or changes that are not always reflected in the current docs
Travis CI builds and tests every commit. It's also used to deploy Buildtime Trend as a Service to Heroku and the Buildtime Trend Python library to the PyPi repository. Travis CI
Here are some stack decisions, common use cases and reviews by companies and developers who chose Travis CI in their tech stack.
We actually started out on Travis CI, but we've migrated our main builds to CircleCI, and it's been a huge improvement.
The reason it's been a huge improvement is that Travis CI has a fundamentally bad design for their images, where they start with a standard base Linux image containing tons of packages (several versions of postgres, every programming language environment, etc). This is potentially nice for the "get builds for a small project running quickly" use case, but it's a total disaster for a larger project that needs a decent number of dependencies and cares about the performance and reliability of their build.
This issue is exacerbated by their networking infrastructure being unreliable; we usually saw over 1% of builds failing due to transient networking errors in Travis CI, even after we added retries to the most frequently failing operations like
apt update or
pip install. And they never install Ubuntu's point release updates to their images. So doing an
apt install, or especially
apt upgrade would take forever. We ended up writing code to actually uninstall many of their base packages and pin the versions of hundreds of others to get a semi-fast, semi-reliable build. It was infuriating.
The CircleCI v2.0 system has the right design for a CI system: we can customize the base image to start with any expensive-to-install packages we need for our build, and we can update that image if and when we want to. The end result is that when migrating, we were able to delete all the hacky optimizations mentioned above, while still ending up with a 50% faster build latency. And we've also had 5-10x fewer issues with networking-related flakes, which means one doesn't have to constantly check whether a build failure is actually due to an issue with the code under test or "just another networking flake".
We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.
To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas
To build #Webapps we decided to use Angular 2 with RxJS
#Devops - GitHub , Travis CI , Terraform , Docker , Serverless
We are using React Native in #SmartHome to share the business logic between Android and iOS team and approach users with a unique brand experience. The drawback is that we require lots of native Android SDK and Objective-C modules, so a good part of the invested time is there. The gain for a app that relies less on native communication, sensors and OS tools should be even higher.
We use a microservices structure on top of Zeit's @now that read from firebase. We use JWT auth to authenticate requests among services and from users, following GitHub philosophy of using the same infrastructure than its API consumers. Firebase is used mainly as a key-value store between services and as a backup database for users. We also use its authentication mechanisms.
You can be super locked-in if you also rely on it's analytics, but we use Amplitude for that, which offers us great insights. Intercom for communications with end-user and Mailjet for marketing.
I recommend using Travis CI and/or Appveyor in all projects.
Projects using these tools have given me confidence to know that I don't cause any breaking changes. Travis CI and Appveyor have functionality to test components of a project across multiple installation projects to ensure that modifications don't break a project. These tools integrate easily with GitHub and are useful in open source projects that must review contributions from many different people.
In 2010 we made the very difficult decision to entirely re-engineer our existing monolithic LAMP application from the ground up in order to address some growing concerns about it's long term viability as a platform.
Full application re-write is almost always never the answer, because of the risks involved. However the situation warranted drastic action as it was clear that the existing product was going to face severe scaling issues. We felt it better address these sooner rather than later and also take the opportunity to improve the international architecture and also to refactor the database in. order that it better matched the changes in core functionality.
PostgreSQL was chosen for its reputation as being solid ACID compliant database backend, it was available as an offering AWS RDS service which reduced the management overhead of us having to configure it ourselves. In order to reduce read load on the primary database we implemented an Elasticsearch layer for fast and scalable search operations. Synchronisation of these indexes was to be achieved through the use of Sidekiq's Redis based background workers on Amazon ElastiCache. Again the AWS solution here looked to be an easy way to keep our involvement in managing this part of the platform at a minimum. Allowing us to focus on our core business.
Rails ls was chosen for its ability to quickly get core functionality up and running, its MVC architecture and also its focus on Test Driven Development using RSpec and Selenium with Travis CI providing continual integration. We also liked Ruby for its terse, clean and elegant syntax. Though YMMV on that one!
Unicorn was chosen for its continual deployment and reputation as a reliable application server, nginx for its reputation as a fast and stable reverse-proxy. We also took advantage of the Amazon CloudFront CDN here to further improve performance by caching static assets globally.
We tried to strike a balance between having control over management and configuration of our core application with the convenience of being able to leverage AWS hosted services for ancillary functions (Amazon SES , Amazon SQS Amazon Route 53 all hosted securely inside Amazon VPC of course!).
Whilst there is some compromise here with potential vendor lock in, the tasks being performed by these ancillary services are no particularly specialised which should mitigate this risk. Furthermore we have already containerised the stack in our development using Docker environment, and looking to how best to bring this into production - potentially using Amazon EC2 Container Service
As the maintainer of the Karate DSL open-source project - I found Travis CI very easy to integrate into the GitHub workflow and it has been steady sailing for more than 2 years now ! It works well for Java / Apache Maven projects and we were able to configure it to use the latest Oracle JDK as per our needs. Thanks to the Travis CI team for this service to the open-source community !
Travis CI's Features
- Easy Setup- Getting started with Travis CI is as easy as enabling a project, adding basic build instructions to your project and committing code.
- Supports Your Platform- Lots of databases and services are pre-installed and can simply be enabled in your build configuration, we'll launch them for you automatically. MySQL, PostgreSQL, ElasticSearch, Redis, Riak, RabbitMQ, Memcached are available by default.
- Deploy With Confidence- Deploying to production after a successful build is as easy as setting up a bit of configuration, and we'll deploy your code to Heroku, Engine Yard Cloud, Nodejitsu, cloudControl, OpenShift, and CloudFoundry.