Trending Feed

Decision at CircleCI about Looker, PostgreSQL, Amplitude, Segment, Rollbar, Honeycomb, PagerDuty, Datadog

Avatar of z00b
CTO at CircleCI ·

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

6 upvotes·19.5K views
I’m about to accept a PR that will increase druid’s compile time about 3x and its executable size almost 2x. In this case, I think the tradeoff is worth it (without localization, a GUI toolkit is strictly a toy), but the bloat makes me unhappy and I think there is room for improvement in the Rust ecosystem …

Decision about GitHub, Scala, Ruby, TypeScript, Node.js, Python, Visual Studio Code

Avatar of mbnshtck
Principal Software Architect at Microsoft ·

I use Visual Studio Code because its the best IDE for my open source projects using Python, Node.js, TypeScript, Ruby and Scala. Extension exist for everything, great integration with GitHub. It makes development easy and fun.

2 upvotes·19.5K views

Decision at Mixmax about AWS Elastic Beanstalk, AWS Elastic Load Balancing (ELB), nginx, Go, Amazon EC2, Node.js, Meteor, Mixmax

Avatar of ttacon

As Mixmax began to scale super quickly, with more and more customers joining the platform, we started to see that the Meteor app was still having a lot of trouble scaling due to how it tried to provide its reactivity layer. To be honest, this led to a brutal summer of playing Galaxy container whack-a-mole as containers would saturate their CPU and become unresponsive. I’ll never forget hacking away at building a new microservice to relieve the load on the system so that we’d stop getting paged every 30-40 minutes. Luckily, we’ve never had to do that again! After stabilizing the system, we had to build out two more microservices to provide the necessary reactivity and authentication layers as we rebuilt our Meteor app from the ground up in Node.js. This also had the added benefit of being able to deploy the entire application in the same AWS VPCs. Thankfully, AWS had also released their ALB product so that we didn’t have to build and maintain our own websocket layer in Amazon EC2. All of our microservices, except for one special Go one, are now in Node with an nginx frontend on each instance, all behind AWS Elastic Load Balancing (ELB) or ALBs running in AWS Elastic Beanstalk.

5 upvotes·16.1K views

Decision at Intuit about Git, Karate DSL

Avatar of ptrthomas
Distinguished Engineer at Intuit ·

Karate DSL is extremely effective in those situations where you have a microservice still in development, but the "consumer" web-UI dev team needs to make progress. Just create a mock definition (feature) file, and since it is plain-text - it can easily be shared across teams via Git. Since Karate has a binary stand-alone executable, even teams that are not familiar with Java can use it to stand-up mock services. And the best part is that the mock serves as a "contract" - which the server-side team can use to practice test-driven development.

16 upvotes·2 comments·28.1K views

Decision at Atlassian about Azure Pipelines, jFrog, Octopus Deploy, AWS CodePipeline, CircleCI, Bitbucket, Jira

Avatar of ojburn
Architect at Atlassian ·

We recently added new APIs to Jira to associate information about Builds and Deployments to Jira issues.

The new APIs were developed using a spec-first API approach for speed and sanity. The details of this approach are described in this blog post, and we relied on using Swagger and associated tools like Swagger UI.

A new service was created for managing the data. It provides a REST API for external use, and an internal API based on GraphQL. The service is built using Kotlin for increased developer productivity and happiness, and the Spring-Boot framework. PostgreSQL was chosen for the persistence layer, as we have non-trivial requirements that cannot be easily implemented on top of a key-value store.

The front-end has been built using React and querying the back-end service using an internal GraphQL API. We have plans of providing a public GraphQL API in the future.

New Jira Integrations: Bitbucket CircleCI AWS CodePipeline Octopus Deploy jFrog Azure Pipelines

12 upvotes·50.3K views

Decision at Stitch Fix about Amazon EC2 Container Service, Docker, PyTorch, R, Python, Presto, Apache Spark, Amazon S3, PostgreSQL, Kafka, Data, DataStack, DataScience, ML, Etl, AWS

Avatar of ecolson
Chief Algorithms Officer at Stitch Fix ·

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

19 upvotes·127.9K views

Decision about Unity, PubNub

Avatar of jordanschuetz
Developer Advocate at MuleSoft ·

PubNub is a great tool for developers looking for an easy to use, real-time messaging service. PubNub's Publish/Subscribe APIs are some of the easiest to use in the industry, and their speed and reliability of service are unparrell. While many companies out there offer a wide range of pubsub and message queuing services, I've personally found that PubNub is the easiest to setup and get started with. When I was an indie game developer, I used PubNub as the realtime chat component in my application, and it also powered realtime drawing between players. The cost compared to spinning up my own servers globally was much cheaper, and I was happy that I decided to go with PubNub. While you could build it yourself, why when PubNub makes it so easy to get something up and running. Spend less time coding and more time marketing, that's always been my philosophy. PubNub Unity

6 upvotes·4.7K views

Decision at Gap about Visual Studio Code

Avatar of deepakk
Sr. DevOps Engineer ·
at

I use Visual Studio Code because of community support and popularity it gained in very short period and many more extensions that are being contributed by community every day. I like the Python Engine in VSCode makes my work life productive. My most fav extensions are

  • Gitlense
  • Kubernetes
  • Docker
  • Chef

Themes are always fun and make your development IDE productive especially with colors and error indicators etc..

7 upvotes·20.8K views

Decision at Rent the Runway about styled-components, PostCSS, Sass

Avatar of hcatlin
VP of Engineering at Rent The Runway ·

We use Sass because I invented it! No, that's not a joke at all! Well, let me explain. So, we used Sass before I started at Rent the Runway because it's the de-facto industry standard for pre-compiled and pre-processed CSS. We do also use PostCSS for stuff like vendor prefixing and various transformations, but Sass (specifically SCSS) is the main developer-focused language for describing our styling. Some internal apps use styled-components and @Aphrodite, but our main website is allllll Sassy. Oh, but the non-joking part is the inventing part. /shrug

4 upvotes·40K views

Decision at Segment about Swagger UI, ReadMe.io, Markdown, Postman, QA, Api, Documentation

Avatar of nzoschke
Engineering Manager at Segment ·

We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.

Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username, password and workspace_name so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.

Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.

This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.

Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct

Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.

Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.

29 upvotes·1 comment·94.7K views

Decision at Daily about Helm, Docker, CircleCI, GitHub, Kubernetes

Avatar of idosh
The Elegant Monkeys ·

Kubernetes powers our #backend services as it is very easy in terms of #devops (the managed version). We deploy everything using @helm charts as it provides us to manage deployments the same way we manage our code on GitHub . On every commit a CircleCI job is triggered to run the tests, build Docker images and deploy them to the registry. Finally on every master commit CircleCI also deploys the relevant service using Helm chart to our Kubernetes cluster

6 upvotes·14.1K views

Decision about Docker Compose, Docker, PostgreSQL, Gin Gonic, Go, axios, vuex, Vue.js, Quasar Framework

Avatar of valasek

I built a project using Quasar Framework with Vue.js, vuex and axios on the frontend and Go, Gin Gonic and PostgreSQL on the backend. Deployment was realized using Docker and Docker Compose. Now I can build the desktop and the mobile app using a single code base on the frontend. UI responsiveness and performance of this stack is amazing.

8 upvotes·14.2K views

Decision at Rainforest QA about Terraform, Helm, Google Cloud Build, CircleCI, Redis, Google Cloud Memorystore, PostgreSQL, Google Cloud SQL for PostgreSQL, Google Kubernetes Engine, Kubernetes, Heroku

Avatar of shosti
Senior Architect at Rainforest QA ·

We recently moved our main applications from Heroku to Kubernetes . The 3 main driving factors behind the switch were scalability (database size limits), security (the inability to set up PostgreSQL instances in private networks), and costs (GCP is cheaper for raw computing resources).

We prefer using managed services, so we are using Google Kubernetes Engine with Google Cloud SQL for PostgreSQL for our PostgreSQL databases and Google Cloud Memorystore for Redis . For our CI/CD pipeline, we are using CircleCI and Google Cloud Build to deploy applications managed with Helm . The new infrastructure is managed with Terraform .

Read the blog post to go more in depth.

12 upvotes·67.7K views

Decision at SparkPost about GitHub, AWS Lambda, Amazon EC2 Container Service, AWS CodeDeploy, AWS CodeBuild

Avatar of cristoirmac
VP, Engineering at SparkPost ·

The recent move of our CI/CD tooling to AWS CodeBuild / AWS CodeDeploy (with GitHub ) as well as moving to Amazon EC2 Container Service / AWS Lambda for our deployment architecture for most of our services has helped us significantly reduce our deployment times while improving both feature velocity and overall reliability. In one extreme case, we got one service down from 90 minutes to a very reasonable 15 minutes. Container-based build and deployments have made so many things simpler and easier and the integration between the tools has been helpful. There is still some work to do on our service mesh & API proxy approach to further simplify our environment.

9 upvotes·2 comments·20.1K views

Decision about Asana

Avatar of danielquinn
Senior Developer at Founders4Schools ·

I'm routinely frustrated by Asana because it's clearly a tool created by designers, imposed on developers.

  • No integration with code repositories.
    • No task numbers
    • You can't reference a ticket in a commit and expect any feedback in Asana
    • You certainly can't close a ticket with a commit.
    • There's no link between CI progress and a ticket.
  • No Markdown support (you can't even put links on text!)
  • Boards and task lists aren't linked.
  • It suppresses middle-click so you can't open more than one ticket at once.
  • It logs every last change to things like assignments, but then folds up the conversation to suppress past comments.

Here's what it does have:

  • Pretty backgrounds
  • Cute little creatures that appear at random

This is not a tool for engineering. Please don't force your nerds to use it.

13 upvotes·2 comments·9.7K views