Alternatives to OCaml logo

Alternatives to OCaml

Haskell, ReasonML, Java, Erlang, and Rust are the most popular alternatives and competitors to OCaml.
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What is OCaml and what are its top alternatives?

It is an industrial strength programming language supporting functional, imperative and object-oriented styles. It is the technology of choice in companies where a single mistake can cost millions and speed matters,
OCaml is a tool in the Languages category of a tech stack.

Top Alternatives to OCaml

  • Haskell

    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. ...

  • ReasonML

    ReasonML

    It lets you write simple, fast and quality type safe code while leveraging both the JavaScript & OCaml ecosystems.It is powerful, safe type inference means you rarely have to annotate types, but everything gets checked for you. ...

  • Java

    Java

    Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere! ...

  • Erlang

    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. ...

  • Rust

    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. ...

  • Python

    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

    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. ...

  • Clojure

    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. ...

OCaml alternatives & related posts

Haskell logo

Haskell

1K
1K
493
An advanced purely-functional programming language
1K
1K
+ 1
493
PROS OF HASKELL
  • 85
    Purely-functional programming
  • 65
    Statically typed
  • 57
    Type-safe
  • 38
    Open source
  • 38
    Great community
  • 29
    Built-in concurrency
  • 29
    Composable
  • 28
    Built-in parallelism
  • 22
    Referentially transparent
  • 19
    Generics
  • 14
    Intellectual satisfaction
  • 13
    Type inference
  • 11
    If it compiles, it's correct
  • 7
    Monads
  • 7
    Flexible
  • 4
    Great type system
  • 4
    Proposition testing with QuickCheck
  • 3
    One of the most powerful languages *(see blub paradox)*
  • 2
    Fun
  • 2
    Kind system
  • 2
    Reliable
  • 2
    Highly expressive, type-safe, fast development time
  • 2
    Type classes
  • 2
    Better type-safe than sorry
  • 2
    Pattern matching and completeness checking
  • 2
    Purely-functional Programming
  • 2
    Best in class thinking tool
  • 2
    Great maintainability of the code
  • 0
    Orthogonality
  • 0
    Predictable
CONS OF HASKELL
  • 7
    Too much distraction in language extensions
  • 6
    Error messages can be very confusing
  • 4
    Libraries have poor documentation
  • 3
    No best practices
  • 3
    No good ABI
  • 2
    Sometimes performance is unpredictable
  • 2
    Poor packaging for apps written in it for Linux distros
  • 1
    Slow compilation

related Haskell posts

Vadim Bakaev
Shared insights
on
HaskellHaskellScalaScala

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.

See more
ReasonML logo

ReasonML

68
77
9
A friendly programming language for JavaScript and OCaml
68
77
+ 1
9
PROS OF REASONML
  • 4
    Pattern Matching
  • 3
    Type System
  • 1
    Fun
  • 1
    React
CONS OF REASONML
  • 1
    Bindings

related ReasonML posts

Java logo

Java

93.2K
70.9K
3.6K
A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
93.2K
70.9K
+ 1
3.6K
PROS OF JAVA
  • 584
    Great libraries
  • 440
    Widely used
  • 399
    Excellent tooling
  • 386
    Huge amount of documentation available
  • 331
    Large pool of developers available
  • 203
    Open source
  • 200
    Excellent performance
  • 155
    Great development
  • 149
    Vast array of 3rd party libraries
  • 147
    Used for android
  • 60
    Compiled Language
  • 49
    Used for Web
  • 46
    Managed memory
  • 45
    High Performance
  • 44
    Native threads
  • 42
    Statically typed
  • 35
    Easy to read
  • 33
    Great Community
  • 29
    Reliable platform
  • 24
    JVM compatibility
  • 24
    Sturdy garbage collection
  • 21
    Cross Platform Enterprise Integration
  • 20
    Universal platform
  • 20
    Good amount of APIs
  • 18
    Great Support
  • 13
    Great ecosystem
  • 11
    Lots of boilerplate
  • 11
    Backward compatible
  • 10
    Everywhere
  • 9
    Excellent SDK - JDK
  • 7
    Static typing
  • 6
    Cross-platform
  • 6
    Long term language
  • 6
    Better than Ruby
  • 6
    Portability
  • 6
    It's Java
  • 6
    Mature language thus stable systems
  • 5
    Clojure
  • 5
    Used for Android development
  • 5
    Vast Collections Library
  • 4
    Old tech
  • 4
    Most developers favorite
  • 3
    Great Structure
  • 3
    History
  • 3
    Javadoc
  • 3
    Testable
  • 3
    Stable platform, which many new languages depend on
  • 3
    Best martial for design
  • 2
    Faster than python
CONS OF JAVA
  • 30
    Verbosity
  • 25
    NullpointerException
  • 16
    Overcomplexity is praised in community culture
  • 14
    Nightmare to Write
  • 11
    Boiler plate code
  • 8
    Classpath hell prior to Java 9
  • 6
    No REPL
  • 4
    No property
  • 2
    Non-intuitive generic implementation
  • 2
    There is not optional parameter
  • 2
    Code are too long
  • 2
    Floating-point errors
  • 1
    Returning Wildcard Types
  • 1
    Java's too statically, stronglly, and strictly typed
  • 1
    Terrbible compared to Python/Batch Perormence

related Java posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.4M 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
Kamil Kowalski
Lead Architect at Fresha · | 27 upvotes · 1.2M views

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.

See more
Erlang logo

Erlang

753
657
314
A programming language used to build massively scalable soft real-time systems with requirements on high availability
753
657
+ 1
314
PROS OF ERLANG
  • 59
    Concurrency Support
  • 59
    Real time, distributed applications
  • 55
    Fault tolerance
  • 34
    Soft real-time
  • 30
    Open source
  • 20
    Functional programming
  • 19
    Message passing
  • 14
    Immutable data
  • 12
    Works as expected
  • 4
    Facebook chat uses it at backend
  • 3
    Practical
  • 3
    Knowledgeable community
  • 2
    Bullets included
CONS OF ERLANG
    Be the first to leave a con

    related Erlang posts

    Sebastian Gębski

    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).

    See more
    Shared insights
    on
    ConsulConsulElixirElixirErlangErlang
    at

    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.

    See more
    Rust logo

    Rust

    3K
    3.3K
    1.1K
    A safe, concurrent, practical language
    3K
    3.3K
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    1.1K
    PROS OF RUST
    • 131
      Guaranteed memory safety
    • 118
      Fast
    • 76
      Open source
    • 72
      Minimal runtime
    • 65
      Pattern matching
    • 58
      Type inference
    • 53
      Algebraic data types
    • 50
      Concurrent
    • 44
      Efficient C bindings
    • 41
      Practical
    • 34
      Best advances in languages in 20 years
    • 27
      Fix for C/C++
    • 26
      Safe, fast, easy + friendly community
    • 20
      Stablity
    • 20
      Closures
    • 18
      Zero-cost abstractions
    • 16
      Extensive compiler checks
    • 16
      Great community
    • 13
      No NULL type
    • 12
      No Garbage Collection
    • 12
      Completely cross platform: Windows, Linux, Android
    • 11
      Async/await
    • 10
      Super fast
    • 10
      High-performance
    • 10
      Great documentations
    • 9
      Safety no runtime crashes
    • 9
      High performance
    • 8
      Generics
    • 8
      Fearless concurrency
    • 8
      Guaranteed thread data race safety
    • 7
      Helpful compiler
    • 7
      Easy Deployment
    • 7
      Compiler can generate Webassembly
    • 7
      Prevents data races
    • 6
      RLS provides great IDE support
    • 6
      Painless dependency management
    • 6
      Macros
    • 4
      Real multithreading
    • 3
      Support on Other Languages
    • 3
      Good package management
    CONS OF RUST
    • 24
      Hard to learn
    • 22
      Ownership learning curve
    • 8
      Unfriendly, verbose syntax
    • 3
      Many type operations make it difficult to follow
    • 3
      High size of builded executable
    • 3
      Variable shadowing
    • 2
      No jobs

    related Rust posts

    James Cunningham
    Operations Engineer at Sentry · | 18 upvotes · 113.6K views
    Shared insights
    on
    PythonPythonRustRust
    at

    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).

    See more
    Jakub Olan
    Node.js Software Engineer · | 17 upvotes · 271.1K views

    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.

    See more
    Python logo

    Python

    156.6K
    130.4K
    6.5K
    A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
    156.6K
    130.4K
    + 1
    6.5K
    PROS OF PYTHON
    • 1.1K
      Great libraries
    • 934
      Readable code
    • 826
      Beautiful code
    • 771
      Rapid development
    • 674
      Large community
    • 420
      Open source
    • 380
      Elegant
    • 270
      Great community
    • 262
      Object oriented
    • 209
      Dynamic typing
    • 71
      Great standard library
    • 53
      Very fast
    • 50
      Functional programming
    • 37
      Scientific computing
    • 36
      Easy to learn
    • 31
      Great documentation
    • 25
      Matlab alternative
    • 23
      Productivity
    • 23
      Easy to read
    • 20
      Simple is better than complex
    • 18
      It's the way I think
    • 17
      Imperative
    • 15
      Very programmer and non-programmer friendly
    • 15
      Free
    • 14
      Powerfull language
    • 14
      Powerful
    • 13
      Fast and simple
    • 12
      Scripting
    • 11
      Machine learning support
    • 9
      Explicit is better than implicit
    • 8
      Unlimited power
    • 8
      Ease of development
    • 8
      Clear and easy and powerfull
    • 7
      Import antigravity
    • 6
      It's lean and fun to code
    • 6
      Print "life is short, use python"
    • 5
      Great for tooling
    • 5
      Fast coding and good for competitions
    • 5
      There should be one-- and preferably only one --obvious
    • 5
      Python has great libraries for data processing
    • 5
      High Documented language
    • 5
      I love snakes
    • 5
      Although practicality beats purity
    • 5
      Flat is better than nested
    • 4
      Readability counts
    • 3
      Rapid Prototyping
    • 3
      Socially engaged community
    • 3
      Beautiful is better than ugly
    • 3
      CG industry needs
    • 3
      Great for analytics
    • 3
      Multiple Inheritence
    • 3
      Complex is better than complicated
    • 3
      Plotting
    • 3
      Now is better than never
    • 3
      Lists, tuples, dictionaries
    • 2
      List comprehensions
    • 2
      Web scraping
    • 2
      Many types of collections
    • 2
      Ys
    • 2
      Easy to setup and run smooth
    • 2
      Generators
    • 2
      Special cases aren't special enough to break the rules
    • 2
      If the implementation is hard to explain, it's a bad id
    • 2
      If the implementation is easy to explain, it may be a g
    • 2
      Simple and easy to learn
    • 2
      Import this
    • 2
      No cruft
    • 2
      Easy to learn and use
    • 1
      Better outcome
    • 1
      It is Very easy , simple and will you be love programmi
    • 1
      Powerful language for AI
    • 1
      Should START with this but not STICK with This
    • 1
      Flexible and easy
    • 1
      Batteries included
    • 1
      Good
    • 1
      A-to-Z
    • 1
      Only one way to do it
    • 1
      Because of Netflix
    • 1
      Pip install everything
    • 0
      Powerful
    • 0
      Pro
    CONS OF PYTHON
    • 51
      Still divided between python 2 and python 3
    • 29
      Performance impact
    • 26
      Poor syntax for anonymous functions
    • 21
      GIL
    • 19
      Package management is a mess
    • 14
      Too imperative-oriented
    • 12
      Dynamic typing
    • 12
      Hard to understand
    • 10
      Very slow
    • 8
      Not everything is expression
    • 7
      Indentations matter a lot
    • 7
      Explicit self parameter in methods
    • 6
      No anonymous functions
    • 6
      Poor DSL capabilities
    • 6
      Incredibly slow
    • 6
      Requires C functions for dynamic modules
    • 5
      The "lisp style" whitespaces
    • 5
      Fake object-oriented programming
    • 5
      Hard to obfuscate
    • 5
      Threading
    • 4
      Circular import
    • 4
      The benevolent-dictator-for-life quit
    • 4
      Official documentation is unclear.
    • 4
      Lack of Syntax Sugar leads to "the pyramid of doom"
    • 4
      Not suitable for autocomplete
    • 2
      Meta classes
    • 1
      Training wheels (forced indentation)

    related Python posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 39 upvotes · 4.4M 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
    Nick Parsons
    Director of Developer Marketing at Stream · | 35 upvotes · 1.5M views

    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

    See more
    Scala logo

    Scala

    7.7K
    5.9K
    1.5K
    A pure-bred object-oriented language that runs on the JVM
    7.7K
    5.9K
    + 1
    1.5K
    PROS OF SCALA
    • 188
      Static typing
    • 179
      Pattern-matching
    • 177
      Jvm
    • 172
      Scala is fun
    • 138
      Types
    • 95
      Concurrency
    • 88
      Actor library
    • 86
      Solve functional problems
    • 83
      Open source
    • 80
      Solve concurrency in a safer way
    • 44
      Functional
    • 23
      Generics
    • 23
      Fast
    • 18
      It makes me a better engineer
    • 17
      Syntactic sugar
    • 13
      Scalable
    • 10
      First-class functions
    • 10
      Type safety
    • 9
      Interactive REPL
    • 8
      Expressive
    • 7
      SBT
    • 6
      Implicit parameters
    • 6
      Case classes
    • 4
      Used by Twitter
    • 4
      JVM, OOP and Functional programming, and static typing
    • 4
      Rapid and Safe Development using Functional Programming
    • 4
      Object-oriented
    • 3
      Functional Proframming
    • 2
      Spark
    • 2
      Beautiful Code
    • 2
      Safety
    • 2
      Growing Community
    • 1
      DSL
    • 1
      Rich Static Types System and great Concurrency support
    • 1
      Naturally enforce high code quality
    • 1
      Akka Streams
    • 1
      Akka
    • 1
      Reactive Streams
    • 1
      Easy embedded DSLs
    • 1
      Mill build tool
    • 0
      Freedom to choose the right tools for a job
    CONS OF SCALA
    • 11
      Slow compilation time
    • 6
      Multiple ropes and styles to hang your self
    • 4
      Too few developers available
    • 3
      Complicated subtyping
    • 2
      My coworkers using scala are racist against other stuff

    related Scala posts

    Shared insights
    on
    JavaJavaScalaScalaApache SparkApache Spark

    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.

    See more
    Marc Bollinger
    Infra & Data Eng Manager at Thumbtack · | 5 upvotes · 477.2K views

    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!

    See more
    Clojure logo

    Clojure

    1.3K
    1.2K
    1.1K
    A dynamic programming language that targets the Java Virtual Machine
    1.3K
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    + 1
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    PROS OF CLOJURE
    • 116
      It is a lisp
    • 99
      Persistent data structures
    • 98
      Concise syntax
    • 88
      jvm-based language
    • 87
      Concurrency
    • 80
      Interactive repl
    • 75
      Code is data
    • 61
      Open source
    • 58
      Lazy data structures
    • 54
      Macros
    • 47
      Functional
    • 22
      Simplistic
    • 21
      Immutable by default
    • 19
      Excellent collections
    • 18
      Fast-growing community
    • 14
      Multiple host languages
    • 14
      Simple (not easy!)
    • 13
      Practical Lisp
    • 9
      Because it's really fun to use
    • 9
      Addictive
    • 9
      Community
    • 8
      It creates Reusable code
    • 8
      Web friendly
    • 8
      Rapid development
    • 7
      Minimalist
    • 5
      Java interop
    • 5
      Programmable programming language
    • 4
      Regained interest in programming
    • 3
      Compiles to JavaScript
    • 3
      EDN
    • 2
      Share a lot of code with clojurescript/use on frontend
    CONS OF CLOJURE
    • 9
      Cryptic stacktraces
    • 4
      Need to wrap basically every java lib
    • 3
      LISP!!!!!!!!
    • 3
      Good code heavily relies on local conventions
    • 3
      Toxic community
    • 2
      Slow application startup
    • 2
      Tonns of abandonware
    • 1
      Usable only with REPL
    • 1
      Hiring issues
    • 1
      Bad documented libs
    • 1
      Macros are overused by devs
    • 1
      Tricky profiling
    • 1
      IDE with high learning curve
    • 1
      Configuration bolierplate
    • 1
      Conservative community
    • 0
      Have no good and fast fmt

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    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.

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    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#.
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