What is Elixir and what are its top alternatives?
Top Alternatives to Elixir
- Golang
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. ...
- Erlang
Some of Erlang's uses are in telecoms, banking, e-commerce, computer telephony and instant messaging. Erlang's runtime system has built-in support for concurrency, distribution and fault tolerance. OTP is set of Erlang libraries and design principles providing middle-ware to develop these systems. ...
- Clojure
Clojure is designed to be a general-purpose language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming. Clojure is a compiled language - it compiles directly to JVM bytecode, yet remains completely dynamic. Clojure is a dialect of Lisp, and shares with Lisp the code-as-data philosophy and a powerful macro system. ...
- Ruby
Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming. ...
- Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory. ...
- Haskell
It is a general purpose language that can be used in any domain and use case, it is ideally suited for proprietary business logic and data analysis, fast prototyping and enhancing existing software environments with correct code, performance and scalability. ...
- 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. ...
- Scala
Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them. ...
Elixir alternatives & related posts
Golang
- High-performance534
- Simple, minimal syntax389
- Fun to write356
- Easy concurrency support via goroutines297
- Fast compilation times269
- Goroutines191
- Statically linked binaries that are simple to deploy178
- Simple compile build/run procedures149
- Backed by google135
- Great community132
- Garbage collection built-in51
- Built-in Testing43
- Excellent tools - gofmt, godoc etc42
- Elegant and concise like Python, fast like C38
- Awesome to Develop35
- Used for Docker25
- Flexible interface system24
- Deploy as executable22
- Great concurrency pattern22
- Open-source Integration19
- Go is God16
- Fun to write and so many feature out of the box16
- Easy to read15
- Its Simple and Heavy duty14
- Powerful and simple13
- Easy to deploy13
- Best language for concurrency12
- Concurrency11
- Safe GOTOs10
- Rich standard library10
- Clean code, high performance9
- Easy setup9
- High performance8
- Hassle free deployment8
- Simplicity, Concurrency, Performance8
- Used by Giants of the industry7
- Single binary avoids library dependency issues7
- Cross compiling6
- Simple, powerful, and great performance6
- Gofmt5
- Garbage Collection5
- Very sophisticated syntax5
- Excellent tooling5
- WYSIWYG5
- Keep it simple and stupid4
- Widely used4
- Kubernetes written on Go4
- No generics2
- Operator goto1
- You waste time in plumbing code catching errors41
- Verbose25
- Packages and their path dependencies are braindead23
- Dependency management when working on multiple projects15
- Google's documentations aren't beginer friendly15
- Automatic garbage collection overheads10
- Uncommon syntax8
- Type system is lacking (no generics, etc)6
- Collection framework is lacking (list, set, map)3
- Best programming language2
related Golang posts











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
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
Erlang
- Concurrency Support60
- Real time, distributed applications60
- Fault tolerance56
- Soft real-time35
- Open source31
- Functional programming21
- Message passing20
- Immutable data15
- Works as expected13
- Facebook chat uses it at backend5
- Practical4
- Knowledgeable community4
- Bullets included3
related Erlang posts
Another major decision was to adopt Elixir and Phoenix Framework - the DX (Developer eXperience) is pretty similar to what we know from RoR, but this tech is running on the top of rock-solid Erlang platform which is powering planet-scale telecom solutions for 20+ years. So we're getting pretty much the best from both worlds: minimum friction & smart conventions that eliminate the excessive boilerplate AND highly concurrent EVM (Erlang's Virtual Machine) that makes all the scalability problems vanish. The transition was very smooth - none of Ruby developers we had decided to leave because of Elixir. What is more, we kept recruiting Ruby developers w/o any requirement regarding Elixir proficiency & we still were able to educate them internally in almost no time. Obviously Elixir comes with some more tools in the stack: Credo , Hex , AppSignal (required to properly monitor BEAM apps).
Hello everyone, I plan on building a platform that supports 100s of forums out of the box, it would give the user the ability to create forums, where other users can comment, post images, and videos (the size of videos would be limited). Each forum would have the ability to trend. I have been doing a lot of research and I have arrived at Golang and Erlang as the backend languages and PostgreSQL as the DB. Erlang would be used for the routing of chats and messages, while Go would be used to manage the forums. We would also be implementing a one on one chat system like WhatsApp chat, where users can add contacts.
Please I would like to know if the languages picked are appropriate for this project. Suggestions would be appreciated.
Clojure
- It is a lisp118
- Persistent data structures101
- Concise syntax100
- jvm-based language89
- Concurrency88
- Interactive repl82
- Code is data76
- Open source61
- Lazy data structures60
- Macros55
- Functional48
- Simplistic22
- Immutable by default21
- Excellent collections19
- Fast-growing community18
- Simple (not easy!)14
- Multiple host languages14
- Practical Lisp14
- Because it's really fun to use9
- Community9
- Addictive9
- It creates Reusable code8
- Web friendly8
- Rapid development8
- Minimalist7
- Programmable programming language5
- Java interop5
- Regained interest in programming4
- EDN3
- Compiles to JavaScript3
- Share a lot of code with clojurescript/use on frontend2
- Cryptic stacktraces9
- Need to wrap basically every java lib4
- Toxic community4
- Good code heavily relies on local conventions3
- Slow application startup2
- Tonns of abandonware2
- Usable only with REPL1
- Hiring issues1
- Bad documented libs1
- Macros are overused by devs1
- Tricky profiling1
- IDE with high learning curve1
- Configuration bolierplate1
- Conservative community1
- Have no good and fast fmt0
related Clojure posts










Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).
The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.
While we continue to be happy with Clojure for our internal services, we felt that its relatively narrow adoption could impede Singer's growth. We chose Python both because it is well suited to the task, and it seems to have reached critical mass among data engineers. All that being said, the Singer spec is language agnostic, and integrations and libraries have been developed in JavaScript, Go, and Clojure.
I adopted Clojure and ClojureScript because:
- it's 1 language, multiple platforms.
- Simple syntax.
- Designed to avoid unwanted side effects and bugs.
- Immutable data-structures.
- Compact code, very expressive.
- Source code is data.
- It has super-flexible macro.
- Has metadata.
- Interoperability with JavaScript, Java and C#.
Ruby
- Programme friendly604
- Quick to develop536
- Great community488
- Productivity467
- Simplicity430
- Open source272
- Meta-programming233
- Powerful204
- Blocks155
- Powerful one-liners138
- Flexible67
- Easy to learn57
- Easy to start50
- Maintainability41
- Lambdas36
- Procs30
- Fun to write21
- Diverse web frameworks19
- Reads like English13
- Makes me smarter and happier10
- Rails9
- Elegant syntax8
- Very Dynamic7
- Matz6
- Object Oriented5
- Programmer happiness5
- Fun and useful4
- Generally fun but makes you wanna cry sometimes4
- Friendly4
- Elegant code3
- There are so many ways to make it do what you want3
- Easy packaging and modules3
- Primitive types can be tampered with2
- Memory hog7
- Really slow if you're not really careful7
- Nested Blocks can make code unreadable3
- Encouraging imperative programming2
- Ambiguous Syntax, such as function parentheses1
related Ruby posts
When you think about test automation, it’s crucial to make it everyone’s responsibility (not just QA Engineers'). We started with Selenium and Java, but with our platform revolving around Ruby, Elixir and JavaScript, QA Engineers were left alone to automate tests. Cypress was the answer, as we could switch to JS and simply involve more people from day one. There's a downside too, as it meant testing on Chrome only, but that was "good enough" for us + if really needed we can always cover some specific cases in a different way.














I needed to choose a full stack of tools for cross platform mobile application design & development. After much research and trying different tools, these are what I came up with that work for me today:
For the client coding I chose Framework7 because of its performance, easy learning curve, and very well designed, beautiful UI widgets. I think it's perfect for solo development or small teams. I didn't like React Native. It felt heavy to me and rigid. Framework7 allows the use of #CSS3, which I think is the best technology to come out of the #WWW movement. No other tech has been able to allow designers and developers to develop such flexible, high performance, customisable user interface elements that are highly responsive and hardware accelerated before. Now #CSS3 includes variables and flexboxes it is truly a powerful language and there is no longer a need for preprocessors such as #SCSS / #Sass / #less. React Native contains a very limited interpretation of #CSS3 which I found very frustrating after using #CSS3 for some years already and knowing its powerful features. The other very nice feature of Framework7 is that you can even build for the browser if you want your app to be available for desktop web browsers. The latest release also includes the ability to build for #Electron so you can have MacOS, Windows and Linux desktop apps. This is not possible with React Native yet.
Framework7 runs on top of Apache Cordova. Cordova and webviews have been slated as being slow in the past. Having a game developer background I found the tweeks to make it run as smooth as silk. One of those tweeks is to use WKWebView. Another important one was using srcset on images.
I use #Template7 for the for the templating system which is a no-nonsense mobile-centric #HandleBars style extensible templating system. It's easy to write custom helpers for, is fast and has a small footprint. I'm not forced into a new paradigm or learning some new syntax. It operates with standard JavaScript, HTML5 and CSS 3. It's written by the developer of Framework7 and so dovetails with it as expected.
I configured TypeScript to work with the latest version of Framework7. I consider TypeScript to be one of the best creations to come out of Microsoft in some time. They must have an amazing team working on it. It's very powerful and flexible. It helps you catch a lot of bugs and also provides code completion in supporting IDEs. So for my IDE I use Visual Studio Code which is a blazingly fast and silky smooth editor that integrates seamlessly with TypeScript for the ultimate type checking setup (both products are produced by Microsoft).
I use Webpack and Babel to compile the JavaScript. TypeScript can compile to JavaScript directly but Babel offers a few more options and polyfills so you can use the latest (and even prerelease) JavaScript features today and compile to be backwards compatible with virtually any browser. My favorite recent addition is "optional chaining" which greatly simplifies and increases readability of a number of sections of my code dealing with getting and setting data in nested objects.
I use some Ruby scripts to process images with ImageMagick and pngquant to optimise for size and even auto insert responsive image code into the HTML5. Ruby is the ultimate cross platform scripting language. Even as your scripts become large, Ruby allows you to refactor your code easily and make it Object Oriented if necessary. I find it the quickest and easiest way to maintain certain aspects of my build process.
For the user interface design and prototyping I use Figma. Figma has an almost identical user interface to #Sketch but has the added advantage of being cross platform (MacOS and Windows). Its real-time collaboration features are outstanding and I use them a often as I work mostly on remote projects. Clients can collaborate in real-time and see changes I make as I make them. The clickable prototyping features in Figma are also very well designed and mean I can send clickable prototypes to clients to try user interface updates as they are made and get immediate feedback. I'm currently also evaluating the latest version of #AdobeXD as an alternative to Figma as it has the very cool auto-animate feature. It doesn't have real-time collaboration yet, but I heard it is proposed for 2019.
For the UI icons I use Font Awesome Pro. They have the largest selection and best looking icons you can find on the internet with several variations in styles so you can find most of the icons you want for standard projects.
For the backend I was using the #GraphCool Framework. As I later found out, #GraphQL still has some way to go in order to provide the full power of a mature graph query language so later in my project I ripped out #GraphCool and replaced it with CouchDB and Pouchdb. Primarily so I could provide good offline app support. CouchDB with Pouchdb is very flexible and efficient combination and overcomes some of the restrictions I found in #GraphQL and hence #GraphCool also. The most impressive and important feature of CouchDB is its replication. You can configure it in various ways for backups, fault tolerance, caching or conditional merging of databases. CouchDB and Pouchdb even supports storing, retrieving and serving binary or image data or other mime types. This removes a level of complexity usually present in database implementations where binary or image data is usually referenced through an #HTML5 link. With CouchDB and Pouchdb apps can operate offline and sync later, very efficiently, when the network connection is good.
I use PhoneGap when testing the app. It auto-reloads your app when its code is changed and you can also install it on Android phones to preview your app instantly. iOS is a bit more tricky cause of Apple's policies so it's not available on the App Store, but you can build it and install it yourself to your device.
So that's my latest mobile stack. What tools do you use? Have you tried these ones?
- Guaranteed memory safety138
- Fast125
- Open source83
- Minimal runtime75
- Pattern matching69
- Type inference61
- Concurrent55
- Algebraic data types55
- Efficient C bindings45
- Practical43
- Best advances in languages in 20 years36
- Fix for C/C++29
- Safe, fast, easy + friendly community29
- Stablity23
- Zero-cost abstractions22
- Closures22
- Extensive compiler checks19
- Great community18
- No NULL type16
- Completely cross platform: Windows, Linux, Android14
- Async/await14
- No Garbage Collection13
- Great documentations12
- High-performance12
- High performance11
- Super fast11
- Safety no runtime crashes10
- Fearless concurrency10
- Generics10
- Guaranteed thread data race safety10
- Compiler can generate Webassembly9
- Helpful compiler9
- Macros8
- Prevents data races8
- Easy Deployment8
- Painless dependency management7
- RLS provides great IDE support7
- Real multithreading6
- Good package management4
- Support on Other Languages4
- Hard to learn26
- Ownership learning curve23
- Unfriendly, verbose syntax11
- Variable shadowing4
- High size of builded executable4
- Many type operations make it difficult to follow4
- No jobs3
related Rust posts
Hello!
I'm a developer for over 9 years, and most of this time I've been working with C# and it is paying my bills until nowadays. But I'm seeking to learn other languages and expand the possibilities for the next years.
Now the question... I know Ruby is far from dead but is it still worth investing time in learning it? Or would be better to take Python, Golang, or even Rust? Or maybe another language.
Thanks in advance.
Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.
For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).
- Purely-functional programming89
- Statically typed66
- Type-safe59
- Open source39
- Great community38
- Built-in concurrency30
- Composable29
- Built-in parallelism29
- Referentially transparent23
- Generics19
- Intellectual satisfaction14
- Type inference14
- If it compiles, it's correct11
- Flexible7
- Monads7
- Proposition testing with QuickCheck4
- Great type system4
- Purely-functional Programming3
- One of the most powerful languages *(see blub paradox)*3
- Highly expressive, type-safe, fast development time2
- Reliable2
- Kind system2
- Pattern matching and completeness checking2
- Better type-safe than sorry2
- Type classes2
- Great maintainability of the code2
- Fun2
- Best in class thinking tool2
- Orthogonality0
- Predictable0
- Error messages can be very confusing8
- Too much distraction in language extensions8
- Libraries have poor documentation4
- No best practices3
- No good ABI3
- Sometimes performance is unpredictable2
- Poor packaging for apps written in it for Linux distros2
- Slow compilation1
related Haskell posts
Why I am using Haskell in my free time?
I have 3 reasons for it. I am looking for:
Fun.
Improve functional programming skill.
Improve problem-solving skill.
Laziness and mathematical abstractions behind Haskell makes it a wonderful language.
It is Pure functional, it helps me to write better Scala code.
Highly expressive language gives elegant ways to solve coding puzzle.
Python
- Great libraries1.1K
- Readable code947
- Beautiful code835
- Rapid development780
- Large community682
- Open source426
- Elegant385
- Great community278
- Object oriented268
- Dynamic typing214
- Great standard library75
- Very fast56
- Functional programming51
- Scientific computing43
- Easy to learn43
- Great documentation33
- Matlab alternative26
- Productivity25
- Easy to read25
- Simple is better than complex21
- It's the way I think18
- Imperative17
- Very programmer and non-programmer friendly15
- Free15
- Machine learning support14
- Powerful14
- Powerfull language14
- Fast and simple13
- Scripting12
- Explicit is better than implicit9
- Unlimited power8
- Clear and easy and powerfull8
- Ease of development8
- Import antigravity7
- It's lean and fun to code6
- Print "life is short, use python"6
- I love snakes5
- Fast coding and good for competitions5
- There should be one-- and preferably only one --obvious5
- Python has great libraries for data processing5
- High Documented language5
- Although practicality beats purity5
- Flat is better than nested5
- Great for tooling5
- Readability counts4
- Rapid Prototyping4
- Lists, tuples, dictionaries3
- Socially engaged community3
- Now is better than never3
- Web scraping3
- Complex is better than complicated3
- Multiple Inheritence3
- Plotting3
- Beautiful is better than ugly3
- CG industry needs3
- Great for analytics3
- Easy to setup and run smooth2
- Generators2
- If the implementation is easy to explain, it may be a g2
- Special cases aren't special enough to break the rules2
- If the implementation is hard to explain, it's a bad id2
- Simple and easy to learn2
- Import this2
- Many types of collections2
- No cruft2
- Easy to learn and use2
- List comprehensions2
- Can understand easily who are new to programming1
- Because of Netflix1
- A-to-Z1
- Only one way to do it1
- It is Very easy , simple and will you be love programmi1
- Powerful language for AI1
- Flexible and easy1
- Better outcome1
- Batteries included1
- Pip install everything1
- Should START with this but not STICK with This1
- Good for hacking1
- Powerful0
- Still divided between python 2 and python 351
- Performance impact28
- Poor syntax for anonymous functions26
- GIL21
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow10
- Not everything is expression8
- Explicit self parameter in methods7
- Indentations matter a lot7
- Poor DSL capabilities6
- Incredibly slow6
- No anonymous functions6
- Requires C functions for dynamic modules6
- Hard to obfuscate5
- Threading5
- Fake object-oriented programming5
- The "lisp style" whitespaces5
- Official documentation is unclear.4
- Circular import4
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- Not suitable for autocomplete4
- The benevolent-dictator-for-life quit4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts











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
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
- Static typing186
- Pattern-matching179
- Jvm177
- Scala is fun172
- Types138
- Concurrency95
- Actor library88
- Solve functional problems86
- Open source83
- Solve concurrency in a safer way80
- Functional44
- Fast24
- Generics23
- It makes me a better engineer18
- Syntactic sugar17
- Scalable13
- First-class functions10
- Type safety10
- Interactive REPL9
- Expressive8
- SBT7
- Implicit parameters6
- Case classes6
- Used by Twitter4
- JVM, OOP and Functional programming, and static typing4
- Rapid and Safe Development using Functional Programming4
- Object-oriented4
- Functional Proframming3
- Spark2
- Beautiful Code2
- Safety2
- Growing Community2
- DSL1
- Rich Static Types System and great Concurrency support1
- Naturally enforce high code quality1
- Akka Streams1
- Akka1
- Reactive Streams1
- Easy embedded DSLs1
- Mill build tool1
- Freedom to choose the right tools for a job0
- Slow compilation time11
- Multiple ropes and styles to hang your self7
- Too few developers available6
- Complicated subtyping4
- My coworkers using scala are racist against other stuff2
related Scala posts
I am new to Apache Spark and Scala both. I am basically a Java developer and have around 10 years of experience in Java.
I wish to work on some Machine learning or AI tech stacks. Please assist me in the tech stack and help make a clear Road Map. Any feedback is welcome.
Technologies apart from Scala and Spark are also welcome. Please note that the tools should be relevant to Machine Learning or Artificial Intelligence.
Lumosity is home to the world's largest cognitive training database, a responsibility we take seriously. For most of the company's history, our analysis of user behavior and training data has been powered by an event stream--first a simple Node.js pub/sub app, then a heavyweight Ruby app with stronger durability. Both supported decent throughput and latency, but they lacked some major features supported by existing open-source alternatives: replaying existing messages (also lacking in most message queue-based solutions), scaling out many different readers for the same stream, the ability to leverage existing solutions for reading and writing, and possibly most importantly: the ability to hire someone externally who already had expertise.
We ultimately migrated to Kafka in early- to mid-2016, citing both industry trends in companies we'd talked to with similar durability and throughput needs, the extremely strong documentation and community. We pored over Kyle Kingsbury's Jepsen post (https://aphyr.com/posts/293-jepsen-Kafka), as well as Jay Kreps' follow-up (http://blog.empathybox.com/post/62279088548/a-few-notes-on-kafka-and-jepsen), talked at length with Confluent folks and community members, and still wound up running parallel systems for quite a long time, but ultimately, we've been very, very happy. Understanding the internals and proper levers takes some commitment, but it's taken very little maintenance once configured. Since then, the Confluent Platform community has grown and grown; we've gone from doing most development using custom Scala consumers and producers to being 60/40 Kafka Streams/Connects.
We originally looked into Storm / Heron , and we'd moved on from Redis pub/sub. Heron looks great, but we already had a programming model across services that was more akin to consuming a message consumers than required a topology of bolts, etc. Heron also had just come out while we were starting to migrate things, and the community momentum and direction of Kafka felt more substantial than the older Storm. If we were to start the process over again today, we might check out Pulsar , although the ecosystem is much younger.
To find out more, read our 2017 engineering blog post about the migration!