Alternatives to NGINX Unit logo

Alternatives to NGINX Unit

NGINX, Docker, uWSGI, Gunicorn, and PHP-FPM are the most popular alternatives and competitors to NGINX Unit.
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What is NGINX Unit and what are its top alternatives?

NGINX Unit is a dynamic web application server, designed to run applications in multiple languages. Unit is lightweight, polyglot, and dynamically configured via API. The design of the server allows reconfiguration of specific application parameters as needed by the engineering or operations.
NGINX Unit is a tool in the Web Servers category of a tech stack.
NGINX Unit is an open source tool with 5.2K GitHub stars and 317 GitHub forks. Here’s a link to NGINX Unit's open source repository on GitHub

Top Alternatives to NGINX Unit

  • NGINX
    NGINX

    nginx [engine x] is an HTTP and reverse proxy server, as well as a mail proxy server, written by Igor Sysoev. According to Netcraft nginx served or proxied 30.46% of the top million busiest sites in Jan 2018. ...

  • Docker
    Docker

    The Docker Platform is the industry-leading container platform for continuous, high-velocity innovation, enabling organizations to seamlessly build and share any application — from legacy to what comes next — and securely run them anywhere ...

  • uWSGI
    uWSGI

    The uWSGI project aims at developing a full stack for building hosting services. ...

  • Gunicorn
    Gunicorn

    Gunicorn is a pre-fork worker model ported from Ruby's Unicorn project. The Gunicorn server is broadly compatible with various web frameworks, simply implemented, light on server resources, and fairly speedy. ...

  • PHP-FPM
    PHP-FPM

    It is an alternative PHP FastCGI implementation with some additional features useful for sites of any size, especially busier sites. It includes Adaptive process spawning, Advanced process management with graceful stop/start, Emergency restart in case of accidental opcode cache destruction etc. ...

  • Apache Tomcat
    Apache Tomcat

    Apache Tomcat powers numerous large-scale, mission-critical web applications across a diverse range of industries and organizations. ...

  • Puma
    Puma

    Unlike other Ruby Webservers, Puma was built for speed and parallelism. Puma is a small library that provides a very fast and concurrent HTTP 1.1 server for Ruby web applications. ...

  • Kubernetes
    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

NGINX Unit alternatives & related posts

NGINX logo

NGINX

112.4K
60.2K
5.5K
A high performance free open source web server powering busiest sites on the Internet.
112.4K
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PROS OF NGINX
  • 1.4K
    High-performance http server
  • 893
    Performance
  • 730
    Easy to configure
  • 607
    Open source
  • 530
    Load balancer
  • 289
    Free
  • 288
    Scalability
  • 226
    Web server
  • 175
    Simplicity
  • 136
    Easy setup
  • 30
    Content caching
  • 21
    Web Accelerator
  • 15
    Capability
  • 14
    Fast
  • 12
    High-latency
  • 12
    Predictability
  • 8
    Reverse Proxy
  • 7
    The best of them
  • 7
    Supports http/2
  • 5
    Great Community
  • 5
    Lots of Modules
  • 5
    Enterprise version
  • 4
    High perfomance proxy server
  • 3
    Embedded Lua scripting
  • 3
    Streaming media delivery
  • 3
    Streaming media
  • 3
    Reversy Proxy
  • 2
    Blash
  • 2
    GRPC-Web
  • 2
    Lightweight
  • 2
    Fast and easy to set up
  • 2
    Slim
  • 2
    saltstack
  • 1
    Virtual hosting
  • 1
    Narrow focus. Easy to configure. Fast
  • 1
    Along with Redis Cache its the Most superior
  • 1
    Ingress controller
CONS OF NGINX
  • 10
    Advanced features require subscription

related NGINX posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M 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
John-Daniel Trask
Co-founder & CEO at Raygun · | 19 upvotes · 261K views

We chose AWS because, at the time, it was really the only cloud provider to choose from.

We tend to use their basic building blocks (EC2, ELB, Amazon S3, Amazon RDS) rather than vendor specific components like databases and queuing. We deliberately decided to do this to ensure we could provide multi-cloud support or potentially move to another cloud provider if the offering was better for our customers.

We’ve utilized c3.large nodes for both the Node.js deployment and then for the .NET Core deployment. Both sit as backends behind an nginx instance and are managed using scaling groups in Amazon EC2 sitting behind a standard AWS Elastic Load Balancing (ELB).

While we’re satisfied with AWS, we do review our decision each year and have looked at Azure and Google Cloud offerings.

#CloudHosting #WebServers #CloudStorage #LoadBalancerReverseProxy

See more
Docker logo

Docker

171.4K
137.7K
3.9K
Enterprise Container Platform for High-Velocity Innovation.
171.4K
137.7K
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PROS OF DOCKER
  • 823
    Rapid integration and build up
  • 691
    Isolation
  • 521
    Open source
  • 505
    Testa­bil­i­ty and re­pro­ducibil­i­ty
  • 460
    Lightweight
  • 218
    Standardization
  • 185
    Scalable
  • 106
    Upgrading / down­grad­ing / ap­pli­ca­tion versions
  • 88
    Security
  • 85
    Private paas environments
  • 34
    Portability
  • 26
    Limit resource usage
  • 17
    Game changer
  • 16
    I love the way docker has changed virtualization
  • 14
    Fast
  • 12
    Concurrency
  • 8
    Docker's Compose tools
  • 6
    Easy setup
  • 6
    Fast and Portable
  • 5
    Because its fun
  • 4
    Makes shipping to production very simple
  • 3
    Highly useful
  • 3
    It's dope
  • 2
    Very easy to setup integrate and build
  • 2
    HIgh Throughput
  • 2
    Package the environment with the application
  • 2
    Does a nice job hogging memory
  • 2
    Open source and highly configurable
  • 2
    Simplicity, isolation, resource effective
  • 2
    MacOS support FAKE
  • 2
    Its cool
  • 2
    Docker hub for the FTW
  • 2
    Super
  • 0
    Asdfd
CONS OF DOCKER
  • 8
    New versions == broken features
  • 6
    Unreliable networking
  • 6
    Documentation not always in sync
  • 4
    Moves quickly
  • 3
    Not Secure

related Docker posts

Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 9.7M 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 · 8.7M 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
uWSGI logo

uWSGI

257
312
12
uWSGI application server container
257
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PROS OF UWSGI
  • 6
    Faster
  • 4
    Simple
  • 2
    Powerful
CONS OF UWSGI
    Be the first to leave a con

    related uWSGI posts

    I find I really like using GitHub because its issue tracker integrates really well into my project flow and the projects feature allows me to organize different efforts into boards. The automation features allow my issues to automatically progress through some states on the boards when I merge pull requests.

    My Python / Django app is deployed on Heroku with PostgreSQL database and uWSGI webserver.

    See more

    I use Gunicorn because does one thing - it’s a WSGI HTTP server - and it does it well. Deploy it quickly and easily, and let the rest of your stack do what the rest of your stack does well, wherever that may be.

    uWSGI “aims at developing a full stack for building hosting services” - if that’s a thing you need then ok, but I like the principle of doing one thing well, and I deploy to platforms like Heroku and AWS Elastic Beanstalk where the rest of the “hosting service” is provided and managed for me.

    See more
    Gunicorn logo

    Gunicorn

    1.1K
    900
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    A Python WSGI HTTP Server for UNIX
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      Python
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    • 8
      Reliable
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    CONS OF GUNICORN
      Be the first to leave a con

      related Gunicorn posts

      Pierre Chapuis

      Unlike our frontend, we chose Flask, a microframework, for our backend. We use it with Python 3 and Gunicorn.

      One of the reasons was that I have significant experience with this framework. However, it also was a rather straightforward choice given that our backend almost only serves REST APIs, and that most of the work is talking to the database with SQLAlchemy .

      We could have gone with something like Hug but it is kind of early. We might revisit that decision for new services later on.

      See more
      Greg Smethells
      CTO and Software Architect at Medstrat · | 3 upvotes · 114.1K views

      We use AppOptics. I am curious what are the current leaders for APM for small companies (50 employees) that use Python, MariaDB, RabbitMQ, and Google Cloud Storage. We run both Celery and Gunicorn services. We are considering Datadog or some other deep code profiling tool that can spot I/O, DB, or other response time/request rate issues

      See more
      PHP-FPM logo

      PHP-FPM

      117
      118
      0
      An alternative FastCGI daemon for PHP
      117
      118
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      0
      PROS OF PHP-FPM
        Be the first to leave a pro
        CONS OF PHP-FPM
          Be the first to leave a con

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          Apache Tomcat logo

          Apache Tomcat

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          An open source software implementation of the Java Servlet and JavaServer Pages technologies
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          • 72
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          Остап Комплікевич

          I need some advice to choose an engine for generation web pages from the Spring Boot app. Which technology is the best solution today? 1) JSP + JSTL 2) Apache FreeMarker 3) Thymeleaf Or you can suggest even other perspective tools. I am using Spring Boot, Spring Web, Spring Data, Spring Security, PostgreSQL, Apache Tomcat in my project. I have already tried to generate pages using jsp, jstl, and it went well. However, I had huge problems via carrying already created static pages, to jsp format, because of syntax. Thanks.

          See more
          Tomas Zeman
          product manager at Broadcom · | 3 upvotes · 18.9K views
          Shared insights
          on
          PlayPlayApache TomcatApache Tomcat
          at

          I use Play as the best Java framewrk for web development. It is easy to use and I was able to learn it quickly. Before I was using Apache Tomcat , but I would never go back. Play is preselecting for you popular and usefull libraries, you can use templating with Twirl, JPA, Injections and much more.

          See more
          Puma logo

          Puma

          833
          262
          20
          A Modern, Concurrent Web Server for Ruby
          833
          262
          + 1
          20
          PROS OF PUMA
          • 4
            Free
          • 3
            Convenient
          • 3
            Easy
          • 2
            Multithreaded
          • 2
            Consumes less memory than Unicorn
          • 2
            Default Rails server
          • 2
            First-class support for WebSockets
          • 1
            Lightweight
          • 1
            Fast
          CONS OF PUMA
          • 0
            Uses `select` (limited client count)

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          Jerome Dalbert
          Principal Backend Software Engineer at StackShare · | 6 upvotes · 167.5K views
          Shared insights
          on
          UnicornUnicornPumaPumaRailsRails
          at

          We switched from Unicorn (process model) to Puma (threaded model) to decrease the memory footprint of our Rails production web server. Memory indeed dropped from 6GB to only 1GB!

          We just had to decrease our worker count and increase our thread count instead. Performance (response time and throughput) remained the same, if not slightly better. We had no thread-safety errors, which was good.

          Free bonus points are:

          • Requests are blazing fast on our dev and staging environments!
          • Puma has first-class support for WebSockets, so we know for sure that Rails ActionCable or GraphQL subscriptions will work great.
          • Being on Puma makes us even more "default Rails"-compliant since it is the default Rails web server these days.
          See more
          Mark Ndungu
          Software Developer at Nouveta · | 4 upvotes · 28.8K views
          Shared insights
          on
          UnicornUnicornPumaPumaRubyRubyRailsRails

          I have an integration service that pulls data from third party systems saves it and returns it to the user of the service. We can pull large data sets with the service and response JSON can go up to 5MB with gzip compression. I currently use Rails 6 and Ruby 2.7.2 and Puma web server. Slow clients tend to prevent other users from accessing the system. Am considering a switch to Unicorn.

          See more
          Kubernetes logo

          Kubernetes

          58.9K
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          Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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          PROS OF KUBERNETES
          • 164
            Leading docker container management solution
          • 128
            Simple and powerful
          • 106
            Open source
          • 76
            Backed by google
          • 58
            The right abstractions
          • 25
            Scale services
          • 20
            Replication controller
          • 11
            Permission managment
          • 9
            Supports autoscaling
          • 8
            Cheap
          • 8
            Simple
          • 6
            Self-healing
          • 5
            No cloud platform lock-in
          • 5
            Promotes modern/good infrascture practice
          • 5
            Open, powerful, stable
          • 5
            Reliable
          • 4
            Scalable
          • 4
            Quick cloud setup
          • 3
            Cloud Agnostic
          • 3
            Captain of Container Ship
          • 3
            A self healing environment with rich metadata
          • 3
            Runs on azure
          • 3
            Backed by Red Hat
          • 3
            Custom and extensibility
          • 2
            Sfg
          • 2
            Gke
          • 2
            Everything of CaaS
          • 2
            Golang
          • 2
            Easy setup
          • 2
            Expandable
          CONS OF KUBERNETES
          • 16
            Steep learning curve
          • 15
            Poor workflow for development
          • 8
            Orchestrates only infrastructure
          • 4
            High resource requirements for on-prem clusters
          • 2
            Too heavy for simple systems
          • 1
            Additional vendor lock-in (Docker)
          • 1
            More moving parts to secure
          • 1
            Additional Technology Overhead

          related Kubernetes posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.9M 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
          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

          See more