What is Elixir and what are its top alternatives?
Top Alternatives to Elixir
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. ...
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
Go
- High-performance511
- Simple, minimal syntax375
- Fun to write343
- Easy concurrency support via goroutines289
- Fast compilation times261
- Goroutines183
- Statically linked binaries that are simple to deploy173
- Simple compile build/run procedures144
- Backed by google129
- Great community125
- Garbage collection built-in46
- Built-in Testing40
- Excellent tools - gofmt, godoc etc36
- Elegant and concise like Python, fast like C33
- Awesome to Develop28
- Flexible interface system22
- Used for Docker21
- Great concurrency pattern21
- Deploy as executable18
- Open-source Integration17
- Fun to write and so many feature out of the box14
- Its Simple and Heavy duty11
- Easy to read11
- Powerful and simple10
- Go is God9
- Safe GOTOs9
- Easy to deploy9
- Hassle free deployment7
- Rich standard library7
- Concurrency7
- Best language for concurrency7
- Easy setup7
- Used by Giants of the industry6
- Simplicity, Concurrency, Performance6
- Clean code, high performance6
- High performance6
- Single binary avoids library dependency issues6
- Simple, powerful, and great performance5
- Cross compiling5
- Garbage Collection4
- Excellent tooling4
- Very sophisticated syntax4
- Gofmt4
- WYSIWYG4
- Kubernetes written on Go3
- Keep it simple and stupid2
- Widely used1
- No generics0
- Operator goto0
- You waste time in plumbing code catching errors38
- Verbose23
- Packages and their path dependencies are braindead22
- Dependency management when working on multiple projects15
- Google's documentations aren't beginer friendly12
- Automatic garbage collection overheads10
- Uncommon syntax7
- Type system is lacking (no generics, etc)6
- Collection framework is lacking (list, set, map)2
related Go 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 Support57
- Real time, distributed applications57
- Fault tolerance53
- Soft real-time32
- Open source28
- Functional programming19
- Message passing17
- Immutable data12
- Works as expected10
- Facebook chat uses it at backend4
- Knowledgeable community2
- Practical2
- Bullets included1
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).
Postmates built a tool called Bazaar that helps onboard new partners and handles several routine tasks, like nightly emails to merchants alerting them about items that are out of stock.
Since they ran Bazaar across multiple instances, the team needed to avoid sending multiple emails to their partners by obtaining lock across multiple hosts. To solve their challenge, they created and open sourced ConsulMutEx, and an Elixir module for acquiring and releasing locks with Consul and other backends.
It works with Consul’s KV store, as well as other backends, including ets, Erlang’s in-memory database.
- It is a lisp111
- Persistent data structures97
- Concise syntax96
- jvm-based language85
- Concurrency85
- Interactive repl78
- Code is data74
- Open source60
- Lazy data structures56
- Macros54
- Functional45
- Simplistic21
- Immutable by default19
- Excellent collections18
- Fast-growing community17
- Multiple host languages14
- Simple (not easy!)12
- Practical Lisp12
- Addictive9
- Community9
- It creates Reusable code8
- Because it's really fun to use8
- Web friendly7
- Minimalist7
- Rapid development7
- Java interop5
- Programmable programming language5
- Regained interest in programming4
- Compiles to JavaScript3
- Share a lot of code with clojurescript/use on frontend2
- EDN2
- Cryptic stacktraces9
- Need to wrap basically every java lib4
- Toxic community3
- Good code heavily relies on local conventions3
- Tonns of abandonware2
- Slow application startup2
- Tricky profiling1
- Macros are overused by devs1
- Have no good and fast fmt1
- Bad documented libs1
- Usable only with REPL1
- LISP!!!!!!!!!!1
- Configuration bolierplate1
- Conservative community1
- IDE with high learning curve1
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 friendly598
- Quick to develop532
- Great community488
- Productivity466
- Simplicity430
- Open source272
- Meta-programming234
- Powerful203
- Blocks157
- Powerful one-liners138
- Flexible65
- Easy to learn56
- Easy to start48
- Maintainability40
- Lambdas36
- Procs30
- Fun to write19
- Diverse web frameworks19
- Reads like English11
- Rails8
- Makes me smarter and happier8
- Elegant syntax7
- Very Dynamic6
- Programmer happiness5
- Matz5
- Generally fun but makes you wanna cry sometimes4
- Fun and useful4
- Friendly3
- Object Oriented3
- There are so many ways to make it do what you want3
- Easy packaging and modules2
- Primitive types can be tampered with2
- Elegant code2
- 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 safety121
- Fast108
- Open source71
- Minimal runtime65
- Pattern matching56
- Type inference52
- Algebraic data types50
- Concurrent45
- Efficient C bindings42
- Practical37
- Best advances in languages in 20 years29
- Safe, fast, easy + friendly community21
- Fix for C/C++21
- Closures17
- Stablity16
- Zero-cost abstractions15
- Extensive compiler checks13
- Great community11
- No Garbage Collection8
- No NULL type8
- Completely cross platform: Windows, Linux, Android7
- Super fast7
- Async/await7
- Safety no runtime crashes6
- Great documentations6
- High performance5
- High-performance5
- Fearless concurrency5
- Guaranteed thread data race safety5
- RLS provides great IDE support5
- Generics5
- Painless dependency management4
- Prevents data races4
- Macros4
- Compiler can generate Webassembly4
- Easy Deployment4
- Helpful compiler3
- Support on Other Languages1
- Hard to learn21
- Ownership learning curve20
- Unfriendly, verbose syntax7
- Variable shadowing3
- Many type operations make it difficult to follow2
- High size of builded executable2
- No jobs2
related Rust posts
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).
In our company we have think a lot about languages that we're willing to use, there we have considering Java, Python and C++ . All of there languages are old and well developed at fact but that's not ideology of araclx. We've choose a edge technologies such as Node.js , Rust , Kotlin and Go as our programming languages which is some kind of fun. Node.js is one of biggest trends of 2019, same for Go. We want to grow in our company with growth of languages we have choose, and probably when we would choose Java that would be almost impossible because larger languages move on today's market slower, and cannot have big changes.
- Purely-functional programming82
- Statically typed63
- Type-safe56
- Open source38
- Great community37
- Composable29
- Built-in concurrency28
- Built-in parallelism27
- Referentially transparent21
- Generics18
- Intellectual satisfaction13
- Type inference13
- If it compiles, it's correct10
- Flexible7
- Monads6
- Great type system4
- Proposition testing with QuickCheck3
- Best in class thinking tool2
- Great maintainability of the code2
- Fun2
- One of the most powerful languages *(see blub paradox)*2
- Highly expressive, type-safe, fast development time2
- Type classes1
- Better type-safe than sorry1
- Pattern matching and completeness checking1
- Kind system1
- Purely-functional Programming1
- Reliable1
- Orthogonality0
- Predictable0
- Too much distraction in language extensions6
- Error messages can be very confusing5
- No best practices3
- No good ABI3
- Libraries have poor documentation3
- 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 code921
- Beautiful code815
- Rapid development764
- Large community669
- Open source414
- Elegant375
- Great community264
- Object oriented257
- Dynamic typing206
- Great standard library68
- Very fast51
- Functional programming47
- Scientific computing33
- Easy to learn31
- Great documentation29
- Matlab alternative25
- Productivity22
- Easy to read21
- Simple is better than complex19
- It's the way I think17
- Imperative17
- Very programmer and non-programmer friendly15
- Powerful14
- Free14
- Fast and simple13
- Powerfull language13
- Scripting12
- Explicit is better than implicit9
- Machine learning support9
- Unlimited power8
- Ease of development8
- Import antigravity7
- Clear and easy and powerfull7
- It's lean and fun to code6
- Print "life is short, use python"6
- Great for tooling5
- 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
- I love snakes5
- Although practicality beats purity5
- Flat is better than nested5
- Readability counts4
- Multiple Inheritence3
- Complex is better than complicated3
- Lists, tuples, dictionaries3
- Rapid Prototyping3
- Plotting3
- Socially engaged community3
- Great for analytics3
- Beautiful is better than ugly3
- CG industry needs3
- No cruft2
- Easy to learn and use2
- List comprehensions2
- Generators2
- Special cases aren't special enough to break the rules2
- Now is better than never2
- If the implementation is hard to explain, it's a bad id2
- If the implementation is easy to explain, it may be a g2
- Simple and easy to learn2
- Import this2
- It is Very easy , simple and will you be love programmi1
- Because of Netflix1
- Web scraping1
- Better outcome1
- Batteries included1
- Powerful language for AI1
- Easy to setup and run smooth1
- Pip install everything1
- Only one way to do it1
- A-to-Z1
- Many types of collections1
- Flexible and easy1
- Pro0
- Powerful0
- Still divided between python 2 and python 348
- Performance impact26
- Poor syntax for anonymous functions26
- Package management is a mess18
- GIL18
- Too imperative-oriented13
- Hard to understand12
- Dynamic typing10
- Very slow8
- Not everything is expression8
- Indentations matter a lot7
- Explicit self parameter in methods7
- Poor DSL capabilities6
- No anonymous functions6
- Requires C functions for dynamic modules6
- The "lisp style" whitespaces5
- Hard to obfuscate5
- The benevolent-dictator-for-life quit4
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- Threading4
- Fake object-oriented programming4
- Incredibly slow4
- Not suitable for autocomplete3
- Official documentation is unclear.3
- Circular import2
- Training wheels (forced indentation)1
- Meta classes1
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
- Jvm177
- Pattern-matching176
- Scala is fun169
- Types137
- Concurrency93
- Actor library88
- Solve functional problems83
- Open source83
- Solve concurrency in a safer way80
- Functional42
- Fast22
- Generics22
- It makes me a better engineer17
- Syntactic sugar15
- Scalable12
- First-class functions10
- Type safety10
- Interactive REPL9
- Expressive8
- SBT7
- Case classes6
- Implicit parameters6
- Rapid and Safe Development using Functional Programming4
- Object-oriented4
- JVM, OOP and Functional programming, and static typing4
- Used by Twitter4
- Functional Proframming3
- Beautiful Code2
- Safety2
- Spark2
- Growing Community2
- DSL1
- Rich Static Types System and great Concurrency support1
- Naturally enforce high code quality1
- Mill build tool1
- Akka Streams1
- Akka1
- Reactive Streams1
- Easy embedded DSLs1
- Freedom to choose the right tools for a job0
- Slow compilation time9
- Multiple ropes and styles to hang your self5
- Too few developers available3
- Complicated subtyping3
- My coworkers using scala are racist against other stuff1
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!