Alternatives to Celery logo

Alternatives to Celery

RabbitMQ, Kafka, Airflow, Cucumber, and JavaScript are the most popular alternatives and competitors to Celery.
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What is Celery and what are its top alternatives?

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
Celery is a tool in the Message Queue category of a tech stack.
Celery is an open source tool with 23.7K GitHub stars and 4.6K GitHub forks. Here’s a link to Celery's open source repository on GitHub

Top Alternatives to Celery

  • RabbitMQ
    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Kafka
    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • Airflow
    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • Cucumber
    Cucumber

    Cucumber is a tool that supports Behaviour-Driven Development (BDD) - a software development process that aims to enhance software quality and reduce maintenance costs. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • Git
    Git

    Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. ...

  • GitHub
    GitHub

    GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. ...

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

Celery alternatives & related posts

RabbitMQ logo

RabbitMQ

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Open source multiprotocol messaging broker
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PROS OF RABBITMQ
  • 234
    It's fast and it works with good metrics/monitoring
  • 79
    Ease of configuration
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    I like the admin interface
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    Easy to set-up and start with
  • 21
    Durable
  • 18
    Intuitive work through python
  • 18
    Standard protocols
  • 10
    Written primarily in Erlang
  • 8
    Simply superb
  • 6
    Completeness of messaging patterns
  • 3
    Scales to 1 million messages per second
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    Reliable
  • 2
    Distributed
  • 2
    Supports MQTT
  • 2
    Better than most traditional queue based message broker
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    Supports AMQP
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    Clusterable
  • 1
    Clear documentation with different scripting language
  • 1
    Great ui
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    Inubit Integration
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    Better routing system
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    High performance
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    Runs on Open Telecom Platform
  • 1
    Delayed messages
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    Reliability
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    Open-source
CONS OF RABBITMQ
  • 9
    Too complicated cluster/HA config and management
  • 6
    Needs Erlang runtime. Need ops good with Erlang runtime
  • 5
    Configuration must be done first, not by your code
  • 4
    Slow

related RabbitMQ posts

James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 1.7M views
Shared insights
on
CeleryCeleryRabbitMQRabbitMQ
at

As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

#MessageQueue

See more

Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.

See more
Kafka logo

Kafka

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
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    High-throughput
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    Distributed
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    Scalable
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    High-Performance
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    Durable
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    Publish-Subscribe
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    Simple-to-use
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    Open source
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    Written in Scala and java. Runs on JVM
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    Message broker + Streaming system
  • 4
    KSQL
  • 4
    Avro schema integration
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    Robust
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    Suport Multiple clients
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    Extremely good parallelism constructs
  • 2
    Partioned, replayable log
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Fun
  • 1
    Flexible
CONS OF KAFKA
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    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging

related Kafka posts

Nick Rockwell
SVP, Engineering at Fastly · | 46 upvotes · 3.5M views

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

See more
Ashish Singh
Tech Lead, Big Data Platform at Pinterest · | 38 upvotes · 3M views

To provide employees with the critical need of interactive querying, we’ve worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest’s scale has involved resolving quite a few challenges like, supporting deeply nested and huge thrift schemas, slow/ bad worker detection and remediation, auto-scaling cluster, graceful cluster shutdown and impersonation support for ldap authenticator.

Our infrastructure is built on top of Amazon EC2 and we leverage Amazon S3 for storing our data. This separates compute and storage layers, and allows multiple compute clusters to share the S3 data.

We have hundreds of petabytes of data and tens of thousands of Apache Hive tables. Our Presto clusters are comprised of a fleet of 450 r4.8xl EC2 instances. Presto clusters together have over 100 TBs of memory and 14K vcpu cores. Within Pinterest, we have close to more than 1,000 monthly active users (out of total 1,600+ Pinterest employees) using Presto, who run about 400K queries on these clusters per month.

Each query submitted to Presto cluster is logged to a Kafka topic via Singer. Singer is a logging agent built at Pinterest and we talked about it in a previous post. Each query is logged when it is submitted and when it finishes. When a Presto cluster crashes, we will have query submitted events without corresponding query finished events. These events enable us to capture the effect of cluster crashes over time.

Each Presto cluster at Pinterest has workers on a mix of dedicated AWS EC2 instances and Kubernetes pods. Kubernetes platform provides us with the capability to add and remove workers from a Presto cluster very quickly. The best-case latency on bringing up a new worker on Kubernetes is less than a minute. However, when the Kubernetes cluster itself is out of resources and needs to scale up, it can take up to ten minutes. Some other advantages of deploying on Kubernetes platform is that our Presto deployment becomes agnostic of cloud vendor, instance types, OS, etc.

#BigData #AWS #DataScience #DataEngineering

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Airflow logo

Airflow

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A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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PROS OF AIRFLOW
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    Features
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    Task Dependency Management
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    Beautiful UI
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    Cluster of workers
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    Extensibility
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    Open source
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    Complex workflows
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    Python
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    Good api
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    Apache project
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    Custom operators
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    Dashboard
CONS OF AIRFLOW
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    Observability is not great when the DAGs exceed 250
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    Running it on kubernetes cluster relatively complex
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    Open source - provides minimum or no support
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    Logical separation of DAGs is not straight forward

related Airflow posts

Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

See more

We are a young start-up with 2 developers and a team in India looking to choose our next ETL tool. We have a few processes in Azure Data Factory but are looking to switch to a better platform. We were debating Trifacta and Airflow. Or even staying with Azure Data Factory. The use case will be to feed data to front-end APIs.

See more
Cucumber logo

Cucumber

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Simple, human collaboration.
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PROS OF CUCUMBER
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    Simple Syntax
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    Simple usage
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    Huge community
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    Nice report
CONS OF CUCUMBER
    Be the first to leave a con

    related Cucumber posts

    Benjamin Poon
    QA Manager - Engineering at HBC Digital · | 8 upvotes · 1.9M views

    For our digital QA organization to support a complex hybrid monolith/microservice architecture, our team took on the lofty goal of building out a commonized UI test automation framework. One of the primary requisites included a technical minimalist threshold such that an engineer or analyst with fundamental knowledge of JavaScript could automate their tests with greater ease. Just to list a few: - Nightwatchjs - Selenium - Cucumber - GitHub - Go.CD - Docker - ExpressJS - React - PostgreSQL

    With this structure, we're able to combine the automation efforts of each team member into a centralized repository while also providing new relevant metrics to business owners.

    See more

    I am a QA heading to a new company where they all generally use Visual Studio Code, my experience is with IntelliJ IDEA and PyCharm. The language they use is JavaScript and so I will be writing my test framework in javaScript so the devs can more easily write tests without context switching.

    My 2 questions: Does VS Code have Cucumber Plugins allowing me to write behave tests? And more importantly, does VS Code have the same refactoring tools that IntelliJ IDEA has? I love that I have easy access to a range of tools that allow me to refactor and simplify my code, making code writing really easy.

    See more
    JavaScript logo

    JavaScript

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    PROS OF JAVASCRIPT
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      Lots of great frameworks
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      Fast
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      Light weight
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      Flexible
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      You can't get a device today that doesn't run js
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      Non-blocking i/o
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      Ubiquitousness
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      Expressive
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      Extended functionality to web pages
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      Relatively easy language
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      Executed on the client side
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      Relatively fast to the end user
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      Pure Javascript
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      Functional programming
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      Async
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      Full-stack
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      Setup is easy
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      Future Language of The Web
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      Its everywhere
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      Because I love functions
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      JavaScript is the New PHP
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      Like it or not, JS is part of the web standard
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      Expansive community
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      Everyone use it
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      Can be used in backend, frontend and DB
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      Easy
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      Most Popular Language in the World
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      Powerful
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      Can be used both as frontend and backend as well
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      For the good parts
    • 8
      No need to use PHP
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      Easy to hire developers
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      Agile, packages simple to use
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      Love-hate relationship
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      Photoshop has 3 JS runtimes built in
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      Evolution of C
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      It's fun
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      Hard not to use
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      Versitile
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      Its fun and fast
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      Nice
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      Popularized Class-Less Architecture & Lambdas
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      Supports lambdas and closures
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      It let's me use Babel & Typescript
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      Can be used on frontend/backend/Mobile/create PRO Ui
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      1.6K Can be used on frontend/backend
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      Client side JS uses the visitors CPU to save Server Res
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      Easy to make something
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      Clojurescript
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      Promise relationship
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      Stockholm Syndrome
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      Function expressions are useful for callbacks
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      Scope manipulation
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      Everywhere
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      Client processing
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      What to add
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      Because it is so simple and lightweight
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      Only Programming language on browser
    • 1
      Test
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      Hard to learn
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      Test2
    • 1
      Not the best
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      Easy to understand
    • 1
      Subskill #4
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      Easy to learn
    • 0
      Hard 彤
    CONS OF JAVASCRIPT
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      A constant moving target, too much churn
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      Horribly inconsistent
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      Javascript is the New PHP
    • 9
      No ability to monitor memory utilitization
    • 8
      Shows Zero output in case of ANY error
    • 7
      Thinks strange results are better than errors
    • 6
      Can be ugly
    • 3
      No GitHub
    • 2
      Slow

    related JavaScript posts

    Zach Holman

    Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

    But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

    But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

    Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

    See more
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

    See more
    Git logo

    Git

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    PROS OF GIT
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      Distributed version control system
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      Efficient branching and merging
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      Fast
    • 845
      Open source
    • 726
      Better than svn
    • 368
      Great command-line application
    • 306
      Simple
    • 291
      Free
    • 232
      Easy to use
    • 222
      Does not require server
    • 27
      Distributed
    • 22
      Small & Fast
    • 18
      Feature based workflow
    • 15
      Staging Area
    • 13
      Most wide-spread VSC
    • 11
      Role-based codelines
    • 11
      Disposable Experimentation
    • 7
      Frictionless Context Switching
    • 6
      Data Assurance
    • 5
      Efficient
    • 4
      Just awesome
    • 3
      Github integration
    • 3
      Easy branching and merging
    • 2
      Compatible
    • 2
      Flexible
    • 2
      Possible to lose history and commits
    • 1
      Rebase supported natively; reflog; access to plumbing
    • 1
      Light
    • 1
      Team Integration
    • 1
      Fast, scalable, distributed revision control system
    • 1
      Easy
    • 1
      Flexible, easy, Safe, and fast
    • 1
      CLI is great, but the GUI tools are awesome
    • 1
      It's what you do
    • 0
      Phinx
    CONS OF GIT
    • 16
      Hard to learn
    • 11
      Inconsistent command line interface
    • 9
      Easy to lose uncommitted work
    • 7
      Worst documentation ever possibly made
    • 5
      Awful merge handling
    • 3
      Unexistent preventive security flows
    • 3
      Rebase hell
    • 2
      When --force is disabled, cannot rebase
    • 2
      Ironically even die-hard supporters screw up badly
    • 1
      Doesn't scale for big data

    related Git posts

    Simon Reymann
    Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.3M views

    Our whole DevOps stack consists of the following tools:

    • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
    • Respectively Git as revision control system
    • SourceTree as Git GUI
    • Visual Studio Code as IDE
    • CircleCI for continuous integration (automatize development process)
    • Prettier / TSLint / ESLint as code linter
    • SonarQube as quality gate
    • Docker as container management (incl. Docker Compose for multi-container application management)
    • VirtualBox for operating system simulation tests
    • Kubernetes as cluster management for docker containers
    • Heroku for deploying in test environments
    • nginx as web server (preferably used as facade server in production environment)
    • SSLMate (using OpenSSL) for certificate management
    • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
    • PostgreSQL as preferred database system
    • Redis as preferred in-memory database/store (great for caching)

    The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

    • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
    • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
    • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
    • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
    • Scalability: All-in-one framework for distributed systems.
    • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
    See more
    Tymoteusz Paul
    Devops guy at X20X Development LTD · | 23 upvotes · 8.3M views

    Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

    It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

    I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

    We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

    If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

    The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

    Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

    See more
    GitHub logo

    GitHub

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    Powerful collaboration, review, and code management for open source and private development projects
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      Open source friendly
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      Great for team collaboration
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      Easy setup
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      Issue tracker
    • 486
      Great community
    • 482
      Remote team collaboration
    • 451
      Great way to share
    • 442
      Pull request and features planning
    • 147
      Just works
    • 132
      Integrated in many tools
    • 121
      Free Public Repos
    • 116
      Github Gists
    • 112
      Github pages
    • 83
      Easy to find repos
    • 62
      Open source
    • 60
      It's free
    • 60
      Easy to find projects
    • 56
      Network effect
    • 49
      Extensive API
    • 43
      Organizations
    • 42
      Branching
    • 34
      Developer Profiles
    • 32
      Git Powered Wikis
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      Great for collaboration
    • 24
      It's fun
    • 23
      Clean interface and good integrations
    • 22
      Community SDK involvement
    • 20
      Learn from others source code
    • 16
      Because: Git
    • 14
      It integrates directly with Azure
    • 10
      Standard in Open Source collab
    • 10
      Newsfeed
    • 8
      It integrates directly with Hipchat
    • 8
      Fast
    • 8
      Beautiful user experience
    • 7
      Easy to discover new code libraries
    • 6
      Smooth integration
    • 6
      Cloud SCM
    • 6
      Nice API
    • 6
      Graphs
    • 6
      Integrations
    • 6
      It's awesome
    • 5
      Quick Onboarding
    • 5
      Reliable
    • 5
      Remarkable uptime
    • 5
      CI Integration
    • 5
      Hands down best online Git service available
    • 4
      Uses GIT
    • 4
      Version Control
    • 4
      Simple but powerful
    • 4
      Unlimited Public Repos at no cost
    • 4
      Free HTML hosting
    • 4
      Security options
    • 4
      Loved by developers
    • 4
      Easy to use and collaborate with others
    • 3
      Ci
    • 3
      IAM
    • 3
      Nice to use
    • 3
      Easy deployment via SSH
    • 2
      Easy to use
    • 2
      Leads the copycats
    • 2
      All in one development service
    • 2
      Free private repos
    • 2
      Free HTML hostings
    • 2
      Easy and efficient maintainance of the projects
    • 2
      Beautiful
    • 2
      Easy source control and everything is backed up
    • 2
      IAM integration
    • 2
      Very Easy to Use
    • 2
      Good tools support
    • 2
      Issues tracker
    • 2
      Never dethroned
    • 2
      Self Hosted
    • 1
      Dasf
    • 1
      Profound
    CONS OF GITHUB
    • 53
      Owned by micrcosoft
    • 37
      Expensive for lone developers that want private repos
    • 15
      Relatively slow product/feature release cadence
    • 10
      API scoping could be better
    • 8
      Only 3 collaborators for private repos
    • 3
      Limited featureset for issue management
    • 2
      GitHub Packages does not support SNAPSHOT versions
    • 2
      Does not have a graph for showing history like git lens
    • 1
      No multilingual interface
    • 1
      Takes a long time to commit
    • 1
      Expensive

    related GitHub posts

    Johnny Bell

    I was building a personal project that I needed to store items in a real time database. I am more comfortable with my Frontend skills than my backend so I didn't want to spend time building out anything in Ruby or Go.

    I stumbled on Firebase by #Google, and it was really all I needed. It had realtime data, an area for storing file uploads and best of all for the amount of data I needed it was free!

    I built out my application using tools I was familiar with, React for the framework, Redux.js to manage my state across components, and styled-components for the styling.

    Now as this was a project I was just working on in my free time for fun I didn't really want to pay for hosting. I did some research and I found Netlify. I had actually seen them at #ReactRally the year before and deployed a Gatsby site to Netlify already.

    Netlify was very easy to setup and link to my GitHub account you select a repo and pretty much with very little configuration you have a live site that will deploy every time you push to master.

    With the selection of these tools I was able to build out my application, connect it to a realtime database, and deploy to a live environment all with $0 spent.

    If you're looking to build out a small app I suggest giving these tools a go as you can get your idea out into the real world for absolutely no cost.

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    Russel Werner
    Lead Engineer at StackShare · | 32 upvotes · 2.2M views

    StackShare Feed is built entirely with React, Glamorous, and Apollo. One of our objectives with the public launch of the Feed was to enable a Server-side rendered (SSR) experience for our organic search traffic. When you visit the StackShare Feed, and you aren't logged in, you are delivered the Trending feed experience. We use an in-house Node.js rendering microservice to generate this HTML. This microservice needs to run and serve requests independent of our Rails web app. Up until recently, we had a mono-repo with our Rails and React code living happily together and all served from the same web process. In order to deploy our SSR app into a Heroku environment, we needed to split out our front-end application into a separate repo in GitHub. The driving factor in this decision was mostly due to limitations imposed by Heroku specifically with how processes can't communicate with each other. A new SSR app was created in Heroku and linked directly to the frontend repo so it stays in-sync with changes.

    Related to this, we need a way to "deploy" our frontend changes to various server environments without building & releasing the entire Ruby application. We built a hybrid Amazon S3 Amazon CloudFront solution to host our Webpack bundles. A new CircleCI script builds the bundles and uploads them to S3. The final step in our rollout is to update some keys in Redis so our Rails app knows which bundles to serve. The result of these efforts were significant. Our frontend team now moves independently of our backend team, our build & release process takes only a few minutes, we are now using an edge CDN to serve JS assets, and we have pre-rendered React pages!

    #StackDecisionsLaunch #SSR #Microservices #FrontEndRepoSplit

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    Python logo

    Python

    239.8K
    195.7K
    6.9K
    A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
    239.8K
    195.7K
    + 1
    6.9K
    PROS OF PYTHON
    • 1.2K
      Great libraries
    • 961
      Readable code
    • 846
      Beautiful code
    • 787
      Rapid development
    • 689
      Large community
    • 435
      Open source
    • 393
      Elegant
    • 282
      Great community
    • 272
      Object oriented
    • 220
      Dynamic typing
    • 77
      Great standard library
    • 59
      Very fast
    • 55
      Functional programming
    • 49
      Easy to learn
    • 45
      Scientific computing
    • 35
      Great documentation
    • 29
      Productivity
    • 28
      Easy to read
    • 28
      Matlab alternative
    • 23
      Simple is better than complex
    • 20
      It's the way I think
    • 19
      Imperative
    • 18
      Free
    • 18
      Very programmer and non-programmer friendly
    • 17
      Powerfull language
    • 17
      Machine learning support
    • 16
      Fast and simple
    • 14
      Scripting
    • 12
      Explicit is better than implicit
    • 11
      Ease of development
    • 10
      Clear and easy and powerfull
    • 9
      Unlimited power
    • 8
      It's lean and fun to code
    • 8
      Import antigravity
    • 7
      Print "life is short, use python"
    • 7
      Python has great libraries for data processing
    • 6
      Although practicality beats purity
    • 6
      Flat is better than nested
    • 6
      Great for tooling
    • 6
      Rapid Prototyping
    • 6
      Readability counts
    • 6
      High Documented language
    • 6
      I love snakes
    • 6
      Fast coding and good for competitions
    • 6
      There should be one-- and preferably only one --obvious
    • 6
      Now is better than never
    • 5
      Great for analytics
    • 5
      Lists, tuples, dictionaries
    • 4
      Easy to learn and use
    • 4
      Simple and easy to learn
    • 4
      Easy to setup and run smooth
    • 4
      Web scraping
    • 4
      CG industry needs
    • 4
      Socially engaged community
    • 4
      Complex is better than complicated
    • 4
      Multiple Inheritence
    • 4
      Beautiful is better than ugly
    • 4
      Plotting
    • 3
      If the implementation is hard to explain, it's a bad id
    • 3
      Special cases aren't special enough to break the rules
    • 3
      Pip install everything
    • 3
      List comprehensions
    • 3
      No cruft
    • 3
      Generators
    • 3
      Import this
    • 3
      It is Very easy , simple and will you be love programmi
    • 3
      Many types of collections
    • 3
      If the implementation is easy to explain, it may be a g
    • 2
      Batteries included
    • 2
      Should START with this but not STICK with This
    • 2
      Powerful language for AI
    • 2
      Can understand easily who are new to programming
    • 2
      Flexible and easy
    • 2
      Good for hacking
    • 2
      A-to-Z
    • 2
      Because of Netflix
    • 2
      Only one way to do it
    • 2
      Better outcome
    • 1
      Sexy af
    • 1
      Slow
    • 1
      Securit
    • 0
      Ni
    • 0
      Powerful
    CONS OF PYTHON
    • 53
      Still divided between python 2 and python 3
    • 28
      Performance impact
    • 26
      Poor syntax for anonymous functions
    • 22
      GIL
    • 19
      Package management is a mess
    • 14
      Too imperative-oriented
    • 12
      Hard to understand
    • 12
      Dynamic typing
    • 12
      Very slow
    • 8
      Indentations matter a lot
    • 8
      Not everything is expression
    • 7
      Incredibly slow
    • 7
      Explicit self parameter in methods
    • 6
      Requires C functions for dynamic modules
    • 6
      Poor DSL capabilities
    • 6
      No anonymous functions
    • 5
      Fake object-oriented programming
    • 5
      Threading
    • 5
      The "lisp style" whitespaces
    • 5
      Official documentation is unclear.
    • 5
      Hard to obfuscate
    • 5
      Circular import
    • 4
      Lack of Syntax Sugar leads to "the pyramid of doom"
    • 4
      The benevolent-dictator-for-life quit
    • 4
      Not suitable for autocomplete
    • 2
      Meta classes
    • 1
      Training wheels (forced indentation)

    related Python posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.1M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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    Nick Parsons
    Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 3.5M views

    Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

    We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

    We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

    Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

    #FrameworksFullStack #Languages

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