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

1.4K
527
An advanced purely-functional programming language
1.4K
527
PROS OF HASKELL
  • 90
    Purely-functional programming
  • 66
    Statically typed
  • 59
    Type-safe
  • 39
    Open source
  • 38
    Great community
  • 31
    Built-in concurrency
  • 30
    Built-in parallelism
  • 30
    Composable
  • 24
    Referentially transparent
  • 20
    Generics
  • 15
    Type inference
  • 15
    Intellectual satisfaction
  • 12
    If it compiles, it's correct
  • 8
    Flexible
  • 8
    Monads
  • 5
    Great type system
  • 4
    Proposition testing with QuickCheck
  • 4
    One of the most powerful languages *(see blub paradox)*
  • 4
    Purely-functional Programming
  • 3
    Highly expressive, type-safe, fast development time
  • 3
    Pattern matching and completeness checking
  • 3
    Great maintainability of the code
  • 3
    Fun
  • 3
    Reliable
  • 2
    Best in class thinking tool
  • 2
    Kind system
  • 2
    Better type-safe than sorry
  • 2
    Type classes
  • 1
    Predictable
  • 1
    Orthogonality
CONS OF HASKELL
  • 9
    Too much distraction in language extensions
  • 8
    Error messages can be very confusing
  • 5
    Libraries have poor documentation
  • 3
    No good ABI
  • 3
    No best practices
  • 2
    Poor packaging for apps written in it for Linux distros
  • 2
    Sometimes performance is unpredictable
  • 1
    Slow compilation
  • 1
    Monads are hard to understand

related Haskell posts

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

75
8
A friendly programming language for JavaScript and OCaml
75
8
PROS OF REASONML
  • 4
    Pattern Matching
  • 3
    Type System
  • 1
    React
CONS OF REASONML
  • 1
    Bindings

related ReasonML posts

Java logo

Java

135.3K
3.7K
A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
135.3K
3.7K
PROS OF JAVA
  • 603
    Great libraries
  • 446
    Widely used
  • 401
    Excellent tooling
  • 396
    Huge amount of documentation available
  • 334
    Large pool of developers available
  • 208
    Open source
  • 203
    Excellent performance
  • 158
    Great development
  • 150
    Used for android
  • 148
    Vast array of 3rd party libraries
  • 60
    Compiled Language
  • 52
    Used for Web
  • 46
    Managed memory
  • 46
    High Performance
  • 45
    Native threads
  • 43
    Statically typed
  • 35
    Easy to read
  • 33
    Great Community
  • 29
    Reliable platform
  • 24
    Sturdy garbage collection
  • 24
    JVM compatibility
  • 22
    Cross Platform Enterprise Integration
  • 20
    Good amount of APIs
  • 20
    Universal platform
  • 18
    Great Support
  • 14
    Great ecosystem
  • 11
    Backward compatible
  • 11
    Lots of boilerplate
  • 10
    Everywhere
  • 9
    Excellent SDK - JDK
  • 7
    Cross-platform
  • 7
    It's Java
  • 7
    Static typing
  • 6
    Portability
  • 6
    Mature language thus stable systems
  • 6
    Better than Ruby
  • 6
    Long term language
  • 5
    Used for Android development
  • 5
    Clojure
  • 5
    Vast Collections Library
  • 4
    Best martial for design
  • 4
    Most developers favorite
  • 4
    Old tech
  • 3
    Testable
  • 3
    History
  • 3
    Javadoc
  • 3
    Stable platform, which many new languages depend on
  • 3
    Great Structure
  • 2
    Faster than python
  • 2
    Type Safe
  • 0
    Job
CONS OF JAVA
  • 33
    Verbosity
  • 27
    NullpointerException
  • 17
    Nightmare to Write
  • 16
    Overcomplexity is praised in community culture
  • 12
    Boiler plate code
  • 8
    Classpath hell prior to Java 9
  • 6
    No REPL
  • 4
    No property
  • 3
    Code are too long
  • 2
    Non-intuitive generic implementation
  • 2
    There is not optional parameter
  • 2
    Floating-point errors
  • 1
    Java's too statically, stronglly, and strictly typed
  • 1
    Returning Wildcard Types
  • 1
    Terrbible compared to Python/Batch Perormence

related Java posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 12.7M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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Kamil Kowalski
Lead Architect at Fresha · | 28 upvotes · 4.1M 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

1.3K
345
A programming language used to build massively scalable soft real-time systems with requirements on high availability
1.3K
345
PROS OF ERLANG
  • 62
    Real time, distributed applications
  • 62
    Concurrency Support
  • 58
    Fault tolerance
  • 36
    Soft real-time
  • 32
    Open source
  • 22
    Message passing
  • 22
    Functional programming
  • 16
    Immutable data
  • 14
    Works as expected
  • 6
    Facebook chat uses it at backend
  • 5
    Practical
  • 5
    Knowledgeable community
  • 4
    Bullets included
  • 1
    WhatsApp uses it at backend
CONS OF ERLANG
  • 1
    Languange is not popular demand

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

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.

See more
Rust logo

Rust

5.8K
1.2K
A safe, concurrent, practical language
5.8K
1.2K
PROS OF RUST
  • 145
    Guaranteed memory safety
  • 132
    Fast
  • 88
    Open source
  • 75
    Minimal runtime
  • 72
    Pattern matching
  • 63
    Type inference
  • 57
    Algebraic data types
  • 57
    Concurrent
  • 47
    Efficient C bindings
  • 43
    Practical
  • 37
    Best advances in languages in 20 years
  • 32
    Safe, fast, easy + friendly community
  • 30
    Fix for C/C++
  • 25
    Stablity
  • 24
    Zero-cost abstractions
  • 23
    Closures
  • 20
    Extensive compiler checks
  • 20
    Great community
  • 18
    Async/await
  • 18
    No NULL type
  • 15
    Completely cross platform: Windows, Linux, Android
  • 15
    No Garbage Collection
  • 14
    Great documentations
  • 14
    High-performance
  • 12
    Generics
  • 12
    Super fast
  • 12
    High performance
  • 11
    Safety no runtime crashes
  • 11
    Fearless concurrency
  • 11
    Compiler can generate Webassembly
  • 11
    Macros
  • 11
    Guaranteed thread data race safety
  • 10
    Helpful compiler
  • 9
    RLS provides great IDE support
  • 9
    Prevents data races
  • 9
    Easy Deployment
  • 8
    Real multithreading
  • 8
    Painless dependency management
  • 7
    Good package management
  • 5
    Support on Other Languages
  • 1
    Type System
CONS OF RUST
  • 28
    Hard to learn
  • 24
    Ownership learning curve
  • 12
    Unfriendly, verbose syntax
  • 4
    High size of builded executable
  • 4
    Many type operations make it difficult to follow
  • 4
    No jobs
  • 4
    Variable shadowing
  • 1
    Use it only for timeoass not in production

related Rust posts

Caue Carvalho
Shared insights
on
RustRustGolangGolangPythonPythonRubyRubyC#C#

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.

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James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 321.9K 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
Python logo

Python

245K
6.9K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
245K
6.9K
PROS OF PYTHON
  • 1.2K
    Great libraries
  • 963
    Readable code
  • 847
    Beautiful code
  • 788
    Rapid development
  • 691
    Large community
  • 438
    Open source
  • 393
    Elegant
  • 282
    Great community
  • 273
    Object oriented
  • 221
    Dynamic typing
  • 77
    Great standard library
  • 60
    Very fast
  • 55
    Functional programming
  • 50
    Easy to learn
  • 46
    Scientific computing
  • 35
    Great documentation
  • 29
    Productivity
  • 28
    Matlab alternative
  • 28
    Easy to read
  • 24
    Simple is better than complex
  • 20
    It's the way I think
  • 19
    Imperative
  • 18
    Very programmer and non-programmer friendly
  • 18
    Free
  • 17
    Machine learning support
  • 17
    Powerfull language
  • 16
    Fast and simple
  • 14
    Scripting
  • 12
    Explicit is better than implicit
  • 11
    Ease of development
  • 10
    Clear and easy and powerfull
  • 9
    Unlimited power
  • 8
    Import antigravity
  • 8
    It's lean and fun to code
  • 7
    Print "life is short, use python"
  • 7
    Python has great libraries for data processing
  • 6
    High Documented language
  • 6
    I love snakes
  • 6
    Readability counts
  • 6
    Rapid Prototyping
  • 6
    Now is better than never
  • 6
    Although practicality beats purity
  • 6
    Flat is better than nested
  • 6
    Great for tooling
  • 6
    There should be one-- and preferably only one --obvious
  • 6
    Fast coding and good for competitions
  • 5
    Web scraping
  • 5
    Lists, tuples, dictionaries
  • 5
    Great for analytics
  • 4
    Beautiful is better than ugly
  • 4
    Easy to learn and use
  • 4
    Easy to setup and run smooth
  • 4
    Multiple Inheritence
  • 4
    CG industry needs
  • 4
    Socially engaged community
  • 4
    Complex is better than complicated
  • 4
    Plotting
  • 4
    Simple and easy to learn
  • 3
    List comprehensions
  • 3
    Powerful language for AI
  • 3
    Flexible and easy
  • 3
    It is Very easy , simple and will you be love programmi
  • 3
    Many types of collections
  • 3
    If the implementation is easy to explain, it may be a g
  • 3
    If the implementation is hard to explain, it's a bad id
  • 3
    Special cases aren't special enough to break the rules
  • 3
    Pip install everything
  • 3
    No cruft
  • 3
    Generators
  • 3
    Import this
  • 2
    Batteries included
  • 2
    Securit
  • 2
    Can understand easily who are new to programming
  • 2
    Should START with this but not STICK with This
  • 2
    A-to-Z
  • 2
    Because of Netflix
  • 2
    Only one way to do it
  • 2
    Better outcome
  • 2
    Good for hacking
  • 1
    Best friend for NLP
  • 1
    Sexy af
  • 1
    Procedural programming
  • 1
    Automation friendly
  • 1
    Slow
  • 0
    Keep it simple
  • 0
    Powerful
  • 0
    Ni
CONS OF PYTHON
  • 53
    Still divided between python 2 and python 3
  • 28
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 22
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Hard to understand
  • 12
    Dynamic typing
  • 12
    Very slow
  • 8
    Indentations matter a lot
  • 8
    Not everything is expression
  • 7
    Incredibly slow
  • 7
    Explicit self parameter in methods
  • 6
    Requires C functions for dynamic modules
  • 6
    Poor DSL capabilities
  • 6
    No anonymous functions
  • 5
    Fake object-oriented programming
  • 5
    Threading
  • 5
    The "lisp style" whitespaces
  • 5
    Official documentation is unclear.
  • 5
    Hard to obfuscate
  • 5
    Circular import
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    The benevolent-dictator-for-life quit
  • 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 · | 44 upvotes · 12.7M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

See more
Nick Parsons
Building cool things on the internet 🛠️ at Stream · | 35 upvotes · 4.3M 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

10.9K
1.5K
A pure-bred object-oriented language that runs on the JVM
10.9K
1.5K
PROS OF SCALA
  • 188
    Static typing
  • 178
    Pattern-matching
  • 175
    Jvm
  • 172
    Scala is fun
  • 138
    Types
  • 95
    Concurrency
  • 88
    Actor library
  • 86
    Solve functional problems
  • 81
    Open source
  • 80
    Solve concurrency in a safer way
  • 44
    Functional
  • 24
    Fast
  • 23
    Generics
  • 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
    Case classes
  • 6
    Implicit parameters
  • 4
    Rapid and Safe Development using Functional Programming
  • 4
    JVM, OOP and Functional programming, and static typing
  • 4
    Object-oriented
  • 4
    Used by Twitter
  • 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
  • 7
    Multiple ropes and styles to hang your self
  • 6
    Too few developers available
  • 4
    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 · 1.9M 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

1.9K
1.1K
A dynamic programming language that targets the Java Virtual Machine
1.9K
1.1K
PROS OF CLOJURE
  • 117
    It is a lisp
  • 100
    Persistent data structures
  • 100
    Concise syntax
  • 90
    jvm-based language
  • 89
    Concurrency
  • 81
    Interactive repl
  • 76
    Code is data
  • 61
    Open source
  • 61
    Lazy data structures
  • 57
    Macros
  • 49
    Functional
  • 23
    Simplistic
  • 22
    Immutable by default
  • 20
    Excellent collections
  • 19
    Fast-growing community
  • 15
    Multiple host languages
  • 15
    Simple (not easy!)
  • 15
    Practical Lisp
  • 10
    Because it's really fun to use
  • 10
    Addictive
  • 9
    Community
  • 9
    Web friendly
  • 9
    Rapid development
  • 9
    It creates Reusable code
  • 8
    Minimalist
  • 6
    Programmable programming language
  • 6
    Java interop
  • 5
    Regained interest in programming
  • 4
    Compiles to JavaScript
  • 3
    Share a lot of code with clojurescript/use on frontend
  • 3
    EDN
  • 1
    Clojurescript
CONS OF CLOJURE
  • 11
    Cryptic stacktraces
  • 5
    Need to wrap basically every java lib
  • 4
    Toxic community
  • 3
    Good code heavily relies on local conventions
  • 3
    Tonns of abandonware
  • 3
    Slow application startup
  • 1
    Usable only with REPL
  • 1
    Hiring issues
  • 1
    It's a lisp
  • 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

related Clojure posts

Jake Stein

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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Robert Zuber
Shared insights
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CircleCICircleCIClojureClojureRailsRails
at

Most of CircleCI is written in Clojure and it has been this way since almost the beginning. Early development included Rails, but by the time that CircleCI was released to the public, it was written entirely in Clojure. Clojure is still at our platform’s core. It helps having a common language across much of our stack to allow for our engineers to move between layers of the stack without much overhead.

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