Alternatives to Portworx logo

Alternatives to Portworx

ceph, Flocker, OpenEBS, Rook, and Minio are the most popular alternatives and competitors to Portworx.
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What is Portworx and what are its top alternatives?

It is the cloud native storage company that enterprises depend on to reduce the cost and complexity of rapidly deploying containerized applications across multiple clouds and on-prem environments.
Portworx is a tool in the Cloud Storage category of a tech stack.
Portworx is an open source tool with 251 GitHub stars and 43 GitHub forks. Here鈥檚 a link to Portworx's open source repository on GitHub

Top Alternatives to Portworx

  • ceph

    ceph

    In computing,It is a free-software storage platform, implements object storage on a single distributed computer cluster, and provides interfaces for object-, block- and file-level storage. ...

  • Flocker

    Flocker

    Flocker is a data volume manager and multi-host Docker cluster management tool. With it you can control your data using the same tools you use for your stateless applications. This means that you can run your databases, queues and key-value stores in Docker and move them around as easily as the rest of your app. ...

  • OpenEBS

    OpenEBS

    OpenEBS allows you to treat your persistent workload containers, such as DBs on containers, just like other containers. OpenEBS itself is deployed as just another container on your host. ...

  • Rook

    Rook

    It is an open source cloud-native storage orchestrator for Kubernetes, providing the platform, framework, and support for a diverse set of storage solutions to natively integrate with cloud-native environments. ...

  • Minio

    Minio

    Minio is an object storage server compatible with Amazon S3 and licensed under Apache 2.0 License ...

  • Amazon S3

    Amazon S3

    Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web ...

  • 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. ...

  • Docker Compose

    Docker Compose

    With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running. ...

Portworx alternatives & related posts

ceph logo

ceph

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192
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A free-software storage platform
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192
+ 1
1
PROS OF CEPH
  • 1
    Open source
CONS OF CEPH
    Be the first to leave a con

    related ceph posts

    Flocker logo

    Flocker

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    15
    Run your databases in Docker and make them as portable as the rest of your app
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    46
    + 1
    15
    PROS OF FLOCKER
    • 4
      Open-Source
    • 3
      Easily manage Docker containers with Data
    • 2
      Easy setup
    • 2
      Great support from their team
    • 2
      Multi-host docker-compose support
    • 2
      Only requires docker
    CONS OF FLOCKER
      Be the first to leave a con

      related Flocker posts

      OpenEBS logo

      OpenEBS

      19
      59
      40
      Cloud native storage for containerized workloads
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      PROS OF OPENEBS
      • 7
        Great support on Slack
      • 6
        Easy to use
      • 6
        Open source
      • 5
        Container attached storage
      • 5
        In user space
      • 3
        Cloud native storage
      • 3
        Large community
      • 3
        Everything in OpenEBS is a Kubernetes CR
      • 2
        CNCF Project
      CONS OF OPENEBS
        Be the first to leave a con

        related OpenEBS posts

        Rook logo

        Rook

        38
        68
        3
        Open source file, block and object storage for Kubernetes
        38
        68
        + 1
        3
        PROS OF ROOK
        • 2
          Minio Integration
        • 1
          Open Source
        CONS OF ROOK
        • 2
          Ceph is difficult
        • 1
          Slow

        related Rook posts

        Minio logo

        Minio

        261
        409
        36
        AWS S3 open source alternative written in Go
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        409
        + 1
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        PROS OF MINIO
        • 7
          Store and Serve Resumes & Job Description PDF, Backups
        • 5
          S3 Compatible
        • 4
          Simple
        • 4
          Open Source
        • 3
          Encryption and Tamper-Proof
        • 2
          Pluggable Storage Backend
        • 2
          Scalable
        • 2
          Lambda Compute
        • 2
          Private Cloud Storage
        • 2
          Data Protection
        • 2
          Highly Available
        • 1
          Performance
        CONS OF MINIO
        • 1
          Deletion of huge buckets is not possible

        related Minio posts

        Amazon S3 logo

        Amazon S3

        36.9K
        25.7K
        2K
        Store and retrieve any amount of data, at any time, from anywhere on the web
        36.9K
        25.7K
        + 1
        2K
        PROS OF AMAZON S3
        • 590
          Reliable
        • 492
          Scalable
        • 456
          Cheap
        • 328
          Simple & easy
        • 83
          Many sdks
        • 29
          Logical
        • 12
          Easy Setup
        • 11
          1000+ POPs
        • 10
          REST API
        • 5
          Secure
        • 2
          Plug and play
        • 2
          Web UI for uploading files
        • 2
          Easy
        • 1
          GDPR ready
        • 1
          Flexible
        • 1
          Faster on response
        • 1
          Plug-gable
        • 1
          Easy to use
        • 1
          Easy integration with CloudFront
        CONS OF AMAZON S3
        • 7
          Permissions take some time to get right
        • 6
          Takes time/work to organize buckets & folders properly
        • 5
          Requires a credit card
        • 3
          Complex to set up

        related Amazon S3 posts

        Ashish Singh
        Tech Lead, Big Data Platform at Pinterest | 35 upvotes 路 746K views

        To provide employees with the critical need of interactive querying, we鈥檝e worked with Presto, an open-source distributed SQL query engine, over the years. Operating Presto at Pinterest鈥檚 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
        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH | 28 upvotes 路 2.6M 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
        Kubernetes logo

        Kubernetes

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        28.2K
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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
        • 152
          Leading docker container management solution
        • 121
          Simple and powerful
        • 96
          Open source
        • 73
          Backed by google
        • 56
          The right abstractions
        • 24
          Scale services
        • 17
          Replication controller
        • 9
          Permission managment
        • 6
          Simple
        • 5
          Supports autoscaling
        • 5
          Cheap
        • 3
          Reliable
        • 3
          No cloud platform lock-in
        • 3
          Self-healing
        • 3
          Open, powerful, stable
        • 3
          Scalable
        • 3
          Promotes modern/good infrascture practice
        • 2
          Cloud Agnostic
        • 2
          Backed by Red Hat
        • 2
          Custom and extensibility
        • 2
          Quick cloud setup
        • 2
          Captain of Container Ship
        • 2
          A self healing environment with rich metadata
        • 1
          Everything of CaaS
        • 1
          Easy setup
        • 1
          Expandable
        • 1
          Runs on azure
        • 1
          Sfg
        • 1
          Golang
        • 1
          Gke
        CONS OF KUBERNETES
        • 13
          Poor workflow for development
        • 11
          Steep learning curve
        • 5
          Orchestrates only infrastructure
        • 2
          High resource requirements for on-prem clusters

        related Kubernetes posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber | 37 upvotes 路 3.7M 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
        Yshay Yaacobi

        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...

        See more
        Docker Compose logo

        Docker Compose

        13.1K
        9.5K
        473
        Define and run multi-container applications with Docker
        13.1K
        9.5K
        + 1
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        PROS OF DOCKER COMPOSE
        • 119
          Multi-container descriptor
        • 108
          Fast development environment setup
        • 75
          Easy linking of containers
        • 65
          Simple yaml configuration
        • 58
          Easy setup
        • 15
          Yml or yaml format
        • 11
          Use Standard Docker API
        • 7
          Open source
        • 4
          Go from template to application in minutes
        • 4
          Can choose Discovery Backend
        • 2
          Kubernetes integration
        • 2
          Easy configuration
        • 2
          Scalable
        • 1
          Quick and easy
        CONS OF DOCKER COMPOSE
        • 7
          Tied to single machine
        • 4
          Still very volatile, changing syntax often

        related Docker Compose posts

        Simon Reymann
        Senior Fullstack Developer at QUANTUSflow Software GmbH | 28 upvotes 路 2.6M 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

        Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.

        We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.

        See more