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ceph vs Go: What are the differences?

ceph: A free-software storage platform. 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; Go: An open source programming language that makes it easy to build simple, reliable, and efficient software. Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

ceph and Go are primarily classified as "File Storage" and "Languages" tools respectively.

Go is an open source tool with 60.5K GitHub stars and 8.37K GitHub forks. Here's a link to Go's open source repository on GitHub.

According to the StackShare community, Go has a broader approval, being mentioned in 903 company stacks & 609 developers stacks; compared to ceph, which is listed in 13 company stacks and 3 developer stacks.

- No public GitHub repository available -

What is 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.

What is Go?

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.
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Why do developers choose ceph?
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        What are some alternatives to ceph and Go?
        Minio
        Minio is an object storage server compatible with Amazon S3 and licensed under Apache 2.0 License
        Swift
        Writing code is interactive and fun, the syntax is concise yet expressive, and apps run lightning-fast. Swift is ready for your next iOS and OS X project — or for addition into your current app — because Swift code works side-by-side with Objective-C.
        FreeNAS
        It is the simplest way to create a centralized and easily accessible place for your data. Use it with ZFS to protect, store, backup, all of your data. It is used everywhere, for the home, small business, and the enterprise.
        Portworx
        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.
        PHP
        Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.
        See all alternatives
        Decisions about ceph and Go
        StackShare Editors
        StackShare Editors
        Kotlin
        Kotlin
        gRPC
        gRPC
        Go
        Go
        Ruby
        Ruby

        As the WeWork footprint continued to expand, in mid-2018 the team began to explore the next generation of identity management to handle the global scale of the business.

        The team decided to vet three languages for building microservices: Go, Kotlin, and Ruby. They compared the three by building a component of an identity system in each, and assessing the performance apples-to-apples.

        After building out the systems and load testing each one, the team decided to implement the new system in Go for a few reasons. In addition to better performance under heavy loads, Go, according to the team, is a simpler language that will constrain developers to simpler code. Additionally, the development lifecycle is simpler with Go, since “there is little difference between running a service directly on a dev machine, to running it in a container, to running clustered instances of the service.”

        In the implementation, they the Go grpc framework to handle various common infrastructure patterns, resulting in “in a clean common server pattern that we can reuse across our microservices.”

        See more
        PagerDuty
        PagerDuty
        Slack
        Slack
        Go
        Go
        PHP
        PHP
        Java
        Java
        Python
        Python
        Ruby
        Ruby
        Node.js
        Node.js
        Sqreen
        Sqreen

        I chose Sqreen because it provides an out-of-the-box Security as a Service solution to protect my customer data. I get full visibility over my application security in real-time and I reduce my risk against the most common threats. My customers are happy and I don't need to spend any engineering resources or time on this. We're only alerted when our attention is required and the data that is provided helps engineering teams easily remediate vulnerabilities. The platform grows with us and will allow us to have all the right tools in place when our first security engineer joins the company. Advanced security protections against business logic threats can then be implemented.

        Installation was super easy on my Node.js and Ruby apps. But Sqreen also supports Python , Java , PHP and soon Go .

        It integrates well with the tools I'm using every day Slack , PagerDuty and more.

        See more
        Yshay Yaacobi
        Yshay Yaacobi
        Software Engineer · | 27 upvotes · 206.6K views
        atSolutoSoluto
        Docker Swarm
        Docker Swarm
        Kubernetes
        Kubernetes
        Visual Studio Code
        Visual Studio Code
        Go
        Go
        TypeScript
        TypeScript
        JavaScript
        JavaScript
        C#
        C#
        F#
        F#
        .NET
        .NET

        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
        Tim Abbott
        Tim Abbott
        Founder at Zulip · | 7 upvotes · 10K views
        atZulipZulip
        Go
        Go
        Python
        Python

        We've been a big fan of Python ever since we adopted it for my first startup, Ksplice. But it's been an absolutely ideal tool for Zulip, which is now one of the leading alternatives to Slack. Zulip is 100% open source software, with ~10K stars on GItHub. And being written in idiomatic Python has been really helpful for our open source project, because it's such an accessible language: Any programmer can learn Python quickly. And that means we're not restricted to e.g. "folks who are excited about contributing to Zulip and ALSO know Go".

        I've linked to a blog post I wrote on Python's awesome new static type system, which fixes the main complaint one might have about using Python for a large codebase, which has a lot more perspective, as well as some commentary on our Python 3 migration.

        See more
        Antonio Sanchez
        Antonio Sanchez
        CEO at Kokoen GmbH · | 10 upvotes · 70.1K views
        atKokoen GmbHKokoen GmbH
        ExpressJS
        ExpressJS
        Node.js
        Node.js
        JavaScript
        JavaScript
        MongoDB
        MongoDB
        Go
        Go
        MySQL
        MySQL
        Laravel
        Laravel
        PHP
        PHP

        Back at the start of 2017, we decided to create a web-based tool for the SEO OnPage analysis of our clients' websites. We had over 2.000 websites to analyze, so we had to perform thousands of requests to get every single page from those websites, process the information and save the big amounts of data somewhere.

        Very soon we realized that the initial chosen script language and database, PHP, Laravel and MySQL, was not going to be able to cope efficiently with such a task.

        By that time, we were doing some experiments for other projects with a language we had recently get to know, Go , so we decided to get a try and code the crawler using it. It was fantastic, we could process much more data with way less CPU power and in less time. By using the concurrency abilites that the language has to offers, we could also do more Http requests in less time.

        Unfortunately, I have no comparison numbers to show about the performance differences between Go and PHP since the difference was so clear from the beginning and that we didn't feel the need to do further comparison tests nor document it. We just switched fully to Go.

        There was still a problem: despite the big amount of Data we were generating, MySQL was performing very well, but as we were adding more and more features to the software and with those features more and more different type of data to save, it was a nightmare for the database architects to structure everything correctly on the database, so it was clear what we had to do next: switch to a NoSQL database. So we switched to MongoDB, and it was also fantastic: we were expending almost zero time in thinking how to structure the Database and the performance also seemed to be better, but again, I have no comparison numbers to show due to the lack of time.

        We also decided to switch the website from PHP and Laravel to JavaScript and Node.js and ExpressJS since working with the JSON Data that we were saving now in the Database would be easier.

        As of now, we don't only use the tool intern but we also opened it for everyone to use for free: https://tool-seo.com

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        Nitzan Shapira
        Nitzan Shapira
        at Epsagon · | 10 upvotes · 92.8K views
        atEpsagonEpsagon
        AWS Lambda
        AWS Lambda
        GitHub
        GitHub
        Java
        Java
        Go
        Go
        Node.js
        Node.js
        npm
        npm
        Serverless
        Serverless
        Python
        Python

        At Epsagon, we use hundreds of AWS Lambda functions, most of them are written in Python, and the Serverless Framework to pack and deploy them. One of the issues we've encountered is the difficulty to package external libraries into the Lambda environment using the Serverless Framework. This limitation is probably by design since the external code your Lambda needs can be usually included with a package manager.

        In order to overcome this issue, we've developed a tool, which we also published as open-source (see link below), which automatically packs these libraries using a simple npm package and a YAML configuration file. Support for Node.js, Go, and Java will be available soon.

        The GitHub respoitory: https://github.com/epsagon/serverless-package-external

        See more
        Omar Mehilba
        Omar Mehilba
        Co-Founder and COO at Magalix · | 11 upvotes · 41.4K views
        atMagalixMagalix
        Python
        Python
        Go
        Go
        Amazon EC2
        Amazon EC2
        Google Kubernetes Engine
        Google Kubernetes Engine
        Microsoft Azure
        Microsoft Azure
        Kubernetes
        Kubernetes
        #Autopilot

        We are hardcore Kubernetes users and contributors. We loved the automation it provides. However, as our team grew and added more clusters and microservices, capacity and resources management becomes a massive pain to us. We started suffering from a lot of outages and unexpected behavior as we promote our code from dev to production environments. Luckily we were working on our AI-powered tools to understand different dependencies, predict usage, and calculate the right resources and configurations that should be applied to our infrastructure and microservices. We dogfooded our agent (http://github.com/magalixcorp/magalix-agent) and were able to stabilize as the #autopilot continuously recovered any miscalculations we made or because of unexpected changes in workloads. We are open sourcing our agent in a few days. Check it out and let us know what you think! We run workloads on Microsoft Azure Google Kubernetes Engine and Amazon EC2 and we're all about Go and Python!

        See more
        Conor Myhrvold
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 13 upvotes · 646K views
        atUber TechnologiesUber Technologies
        Apache Spark
        Apache Spark
        C#
        C#
        OpenShift
        OpenShift
        JavaScript
        JavaScript
        Kubernetes
        Kubernetes
        C++
        C++
        Go
        Go
        Node.js
        Node.js
        Java
        Java
        Python
        Python
        Jaeger
        Jaeger

        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
        Go
        Go
        Lua
        Lua
        OpenResty
        OpenResty
        nginx
        nginx
        Logstash
        Logstash
        Prometheus
        Prometheus

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

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

        See more
        StackShare Editors
        StackShare Editors
        Kubernetes
        Kubernetes
        Go
        Go
        Python
        Python

        Following its migration from vanilla instances with autoscaling groups to Kubernetes, Postmates began facing challenges while “migrating workloads that needed to scale up very quickly.”

        The built-in Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization. But the challenges for Postmates is that there’s no way to configure the scale velocity of one particular cluster with an HPA.

        For Postmates, which runs at least three different types of applications with distinct performance and scaling characteristics, this proved problematic.

        To overcome these challenges, the team created and open sourced the Configurable Horizontal Pod Autoscaler, which allows for fine-grained tuning on a per-HPA object basis. The result is that “you can configure critical services to scale down very slowly, while every other service could be configured to scale down instantly to reduce costs.”

        See more
        Git
        Git
        Docker
        Docker
        NATS
        NATS
        JavaScript
        JavaScript
        TypeScript
        TypeScript
        PostgreSQL
        PostgreSQL
        Python
        Python
        Go
        Go

        Go is a high performance language with simple syntax / semantics. Although it is not as expressive as some other languages, it's still a great language for backend development.

        Python is expressive and battery-included, and pre-installed in most linux distros, making it a great language for scripting.

        PostgreSQL: Rock-solid RDBMS with NoSQL support.

        TypeScript saves you from all nonsense semantics of JavaScript , LOL.

        NATS: fast message queue and easy to deploy / maintain.

        Docker makes deployment painless.

        Git essential tool for collaboration and source management.

        See more
        Vishwa Bhat
        Vishwa Bhat
        Fullstack Developer at Sequoia · | 10 upvotes · 4.3K views
        atSequoia Consulting GroupSequoia Consulting Group
        Java
        Java
        Go
        Go
        Node.js
        Node.js

        Our new backend micro services are primarily written in Node.js and Go and legacy systems are written in Java. For our new stack decision, we aimed to achieve greater developer productivity, low IO latency and good community so we had couple of technologies in hand to choose but finally we concluded to go for Node.js for API layer and Go for CPU/IO intensive tasks. Currently the inter-services communication is happening via REST but soon to be moved to RPC-based communication.

        See more
        Robert Zuber
        Robert Zuber
        CTO at CircleCI · | 4 upvotes · 6.9K views
        atCircleCICircleCI
        Slack
        Slack
        Go
        Go
        Hubot
        Hubot
        CoffeeScript
        CoffeeScript

        We have added very little to the CoffeeScript Hubot application – just enough to allow it to talk to our Hubot workers. The Hubot workers implement our operational management functionality and expose it to Hubot so we can get chat integration for free. We’ve also tailored the authentication and authorization code of Hubot to meet the needs of roles within our team.

        For larger tasks, we’ve got an internal #CLI written in Go that talks to the same #API as Hubot, giving access to the same functionality we have in Slack, with the addition of scripting, piping, and all of our favorite #Unix tools. When the Hubot worker recognizes the CLI is in use, it logs the commands to Slack to maintain visibility of operational changes.

        See more
        John Datserakis
        John Datserakis
        Node.js
        Node.js
        PHP
        PHP
        Go
        Go

        For the backend of https://www.rsvpkeeper.com I went with Go.

        My past few project have been built with Go and I'm really loving it. It was my first statically typed language after many years with PHP and Node.js - and honestly I couldn't be happier to have made the switch.

        The biggest thing for me, is that with the forced declaration of types - it's made me feel like I've made a more solid backend. Sometimes with PHP I felt like a stiff breeze could knock the whole thing down. I know that's an exaggeration - but it's kinda how it feels.

        Anyways, everyone knows that it almost doesn't even matter what an app is actually made with - what really matters are the design decisions you make a long the way.

        See more
        Dan Larsen
        Dan Larsen
        CTO at FlowStack · | 7 upvotes · 12.7K views
        atFlowStack ApSFlowStack ApS
        C++
        C++
        C
        C
        Rust
        Rust
        Go
        Go

        At FlowStack we write most of our backend in Go. Go is a well thought out language, with all the right compromises for speedy development of speedy and robust software. It's tooling is part of what makes Go such a great language. Testing and benchmarking is built into the language, in a way that makes it easy to ensure correctness and high performance. In most cases you can get more performance out of Rust and C or C++, but getting everything right is more cumbersome.

        See more
        Interest over time
        Reviews of ceph and Go
        Avatar of tschellenbach
        CEO at Stream
        Review ofGoGo

        Go has been a joy to work with. Performance is often 30x of what we used to see with Python. It's a performant and productive programming language: https://getstream.io/blog/switched-python-go/

        How developers use ceph and Go
        Avatar of Karma
        Karma uses GoGo

        The first time I actually started using Go was for software on our devices. So on our hotspots we have some custom software running in the firmware. For the first device, that was actually completely built by our manufacturer. But for the second generation most of the parts are built by us in-house and we needed a way to quickly develop software for the device. But we don't have any C programmers in-house, so we were actually looking for something that basically sits in between the friendliness of Ruby, but the performance and the ability to be deployed on an embedded system which you get with C. That's basically what led us to Go and it's been awesome for that. It works so well and so great. Since it works so great, it pushed us into looking into whether we should start using this for some backend services as well.

        Avatar of Flutter Health Inc.
        Flutter Health Inc. uses GoGo

        The following basic API endpoints are implemented on the server written in Go:

        • Authorization (Sign Up, Sign In)
        • Update user profile
        • Community: add post, like post, add comment, delete post, add reply to comment
        • Self-diagnosis: send data from the app to the server
        • Journal: send user data from the app to the server
        • Add groups of community
        • Report post, report comment, report reply
        • Block user
        Avatar of Zinc
        Zinc uses GoGo

        We wrote our own image processing, resizing, and snapshotting service in Go to allow our clients to send photos and GIFs to each other. Files are stored in S3, resized on the fly using OpenCV, and then cached in GroupCache before being served to clients.

        Go allows it all to be quite fast and efficient, and entirely non-blocking on uploads!

        Avatar of Diggernaut LLC
        Diggernaut LLC uses GoGo

        Our main web scraping engine is built usign Golang because of the way how efficiently and fast this language is. Also out compilation facility let people who dont know Golang build fast as flash scrapers to run ourside of our platform without any knowledge in programming in Golang.

        Avatar of Refractal
        Refractal uses GoGo

        For some of our more taxing parts of our applications, something able to handle high I/O load quickly and with fast processing is needed. Go has completely filled that gap, allowing us to break down walls that would've been completely impossible with other languages.

        How much does ceph cost?
        How much does Go cost?
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