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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.

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Update: How CircleCI Processes Over 30 Million Builds Per Month - CircleCI Tech Stack (stackshare.io)
8 upvotes·264.5K views
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

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4 upvotes·181K views

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.

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How Mixmax Uses Node and Go to Process 250M Events a day - Mixmax Tech Stack (stackshare.io)
5 upvotes·138.8K views
Avatar of ptrthomas
Distinguished Engineer at Intuit ·
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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.

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The World's Smallest Micro Service - ptrthomas Tech Stack (stackshare.io)
16 upvotes·2 comments·78.2K views
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

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6 integrations every Jira Software Cloud team NEED... - Atlassian Community (community.atlassian.com)
12 upvotes·179.9K views
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

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Stitch Fix Algorithms Tour (algorithms-tour.stitchfix.com)
19 upvotes·589.5K views
Avatar of jordanschuetz
Developer Advocate at MuleSoft ·
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PubNubPubNubUnityUnity

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

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6 upvotes·41.1K views
Avatar of deepakk
Sr. DevOps Engineer ·
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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..

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7 upvotes·50.8K views

I love Python and JavaScript . You can do the same JavaScript async operations in Python by using asyncio. This is particularly useful when you need to do socket programming in Python. With streaming sockets, data can be sent or received at any time. In case your Python program is in the middle of executing some code, other threads can handle the new socket data. Libraries like asyncio implement multiple threads, so your Python program can work in an asynchronous fashion. PubNub makes bi-directional data streaming between devices even easier.

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Socket Programming with Python and PubNub - PubNub Tech Stack (stackshare.io)
20 upvotes·2 comments·78.8K views
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.

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Announcing Config API: convenient and extensible workspace configuration · Segment Blog (segment.com)
29 upvotes·1 comment·555.7K views
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

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6 upvotes·133.6K views

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.

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Migrating from Vuetify to Quasar - Quasar Framework - Medium (medium.com)
8 upvotes·66.4K views
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.

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Why Rainforest QA Moved from Heroku to Google Kubernetes Engine (rainforestqa.com)
12 upvotes·1 comment·259K views
Avatar of Yshayy
Software Engineer ·

Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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GitHub - Soluto/tweek: Tweek - an open source feature management solution (github.com)
29 upvotes·2 comments·642K views
Avatar of Joseph-Irving
DevOps Engineer at uSwitch ·
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At uSwitch we wanted a way to load balance between our multiple Kubernetes clusters in AWS to give us added redundancy. We already had ingresses defined for all our applications so we wanted to build on top of that, instead of creating a new system that would require our various teams to change code/config etc.

Envoy seemed to tick a lot of boxes:

  • Loadbalancing capabilities right out of the box: health checks, circuit breaking, retries etc.
  • Tracing and prometheus metrics support
  • Lightweight
  • Good community support

This was all good but what really sold us was the api that supported dynamic configuration. This would allow us to dynamically configure envoy to route to ingresses and clusters as they were created or destroyed.

To do this we built a tool called Yggdrasil using their Go sdk. Yggdrasil effectively just creates envoy configuration from Kubernetes ingress objects, so you point Yggdrasil at your kube clusters, it generates config from the ingresses and then envoy can loadbalance between your clusters for you. This is all done dynamically so as soon as new ingress is created the envoy nodes get updated with the new config. Importantly this all worked with what we already had, no need to create new config for every application, we just put this on top of it.

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(medium.com)
7 upvotes·17K views
Avatar of tim_nolet
Founder, Engineer & Dishwasher at Checkly ·

PostgreSQL Heroku Heroku Postgres Node.js Knex.js

Last week we rolled out a simple patch that decimated the response time of a Postgres query crucial to Checkly. It quite literally went from an average of ~100ms with peaks to 1 second to a steady 1ms to 10ms.

However, that patch was just the last step of a longer journey:

  1. I looked at what API endpoints were using which queries and how their response time grew over time. Specifically the customer facing API endpoints that are directly responsible for rendering the first dashboard page of the product are crucial.

  2. I looked at the Heroku metrics such as those reported by heroku pg:outlier and cross references that with "slowest response time" statistics.

  3. I reproduced the production situation as best as possible on a local development machine and test my hypothesis that an composite index on a uuid field and a timestampz field would reduce response times.

This method secured the victory and we rolled out a new index last week. Response times plummeted. Read the full story in the blog post.

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How I decimated Postgres response times for my SaaS (blog.checklyhq.com)
10 upvotes·89.4K views

At Kong while building an internal tool, we struggled to route metrics to Prometheus and logs to Logstash without incurring too much latency in our metrics collection.

We replaced nginx with OpenResty on the edge of our tool which allowed us to use the lua-nginx-module to run Lua code that captures metrics and records telemetry data during every request’s log phase. Our code then pushes the metrics to a local aggregator process (written in Go) which in turn exposes them in Prometheus Exposition Format for consumption by Prometheus. This solution reduced the number of components we needed to maintain and is fast thanks to NGINX and LuaJIT.

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3 upvotes·168.1K views
Avatar of tim_nolet
Founder, Engineer & Dishwasher at Checkly ·

PostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

In a nutshell:cPostgreSQL Heroku Node.js MongoDB Amazon DynamoDB

When I started building Checkly, one of the first things on the agenda was how to actually structure our SaaS database model: think accounts, users, subscriptions etc. Weirdly, there is not a lot of information on this on the "blogopshere" (cringe...). After research and some false starts with MongoDB and Amazon DynamoDB we ended up with PostgreSQL and a schema consisting of just four tables that form the backbone of all generic "Saasy" stuff almost any B2B SaaS bumps into.

In a nutshell:

  • We use Postgres on Heroku.
  • We use a "one database, on schema" approach for partitioning customer data.
  • We use an accounts, memberships and users table to create a many-to-many relation between users and accounts.
  • We completely decouple prices, payments and the exact ingredients for a customer's plan.

All the details including a database schema diagram are in the linked blog post.

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Building a multi-tenant SaaS data model (blog.checklyhq.com)
8 upvotes·74.2K views
Avatar of conor
Tech Brand Mgr, Office of CTO at Uber ·

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

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Meet Horovod: Uber's Open Source Distributed Deep Learning Framework (eng.uber.com)
6 upvotes·593.4K views
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.

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9 upvotes·2 comments·65K views
Avatar of NickCraver
Architecture Lead at Stack Overflow ·
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We use .NET Core for our web socket servers, mail relays, and scheduling applications. Soon, it will power all of Stack Overflow. The ability to run on any platform, further extend and plug especially the ASP.NET bits and treat almost everything as a building block you can move around has been a huge win. We're headed towards an appliance model and with .NET Core we can finally put everything in box...on Linux. We can re-use more code, fit all our deployment scenarios both during the move and after, and also ditch a lot of performance workarounds we had to scale...they're in-box now.

And testing. The ability to fire up a web server and request and access both in a single method is an orders of magnitude improvement over ASP.NET 5. We're looking forward to tremendously improving our automated test coverage in places it's finally reasonable in both time and effort for devs to do so. In short: we're getting a lot more for the same dev time spent in .NET Core.

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4 upvotes·1 comment·59.5K views
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Senior Software Engineer at Netflix ·
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JavaScriptJavaScript
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I use JavaScript because it is important to understand the fundamentals of any language you are using. jQuery was a revolutionary JS library for making DOM access and manipulation easier, but native APIs have been implemented that make it just as easy to do without a library.

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6 upvotes·198.2K views
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Web Solutions Architect at Adthena ·
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Node.jsNode.js
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We use Node.js because it is a really powerful Javascript runtime for building network applications. There is a large ecosystem of tools and packages available to help engineers build effective solutions to their problems . We have built robust and flexible server and client side solutions using Javascript and Node.js.

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5 upvotes·12.5K views