Alternatives to Haskell logo

Alternatives to Haskell

Scala, Clojure, Erlang, Rust, and Python are the most popular alternatives and competitors to Haskell.
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What is Haskell and what are its top alternatives?

Haskell is a tool in the Languages category of a tech stack.
Top Alternatives

Haskell alternatives & related posts

Scala logo

Scala

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A pure-bred object-oriented language that runs on the JVM
Scala logo
Scala
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Haskell logo
Haskell

related Scala posts

Marc Bollinger
Marc Bollinger
Infra & Data Eng Manager at Thumbtack · | 4 upvotes · 157.2K views
atLumosityLumosity
Node.js
Node.js
Ruby
Ruby
Kafka
Kafka
Scala
Scala
Apache Storm
Apache Storm
Heron
Heron
Redis
Redis
Pulsar
Pulsar

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!

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Tobias Widmer
Tobias Widmer
CTO at Onedot · | 4 upvotes · 79.3K views
atOnedotOnedot
React
React
Redux
Redux
Scala
Scala
TypeScript
TypeScript
Cassandra
Cassandra
Apache Spark
Apache Spark
Amazon S3
Amazon S3
Blueprint
Blueprint
npm
npm

Onedot is building an automated data preparation service using probabilistic and statistical methods including artificial intelligence (AI). From the beginning, having a stable foundation while at the same time being able to iterate quickly was very important to us. Due to the nature of compute workloads we face, the decision for a functional programming paradigm and a scalable cluster model was a no-brainer. We started playing with Apache Spark very early on, when the platform was still in its infancy. As a storage backend, we first used Cassandra, but found out that it was not the optimal choice for our workloads (lots of rather smallish datasets, data pipelines with considerable complexity, etc.). In the end, we migrated dataset storage to Amazon S3 which proved to be much more adequate to our case. In the frontend, we bet on more traditional frameworks like React/Redux.js, Blueprint and a number of common npm packages of our universe. Because of the very positive experience with Scala (in particular the ability to write things very expressively, use immutability across the board, etc.) we settled with TypeScript in the frontend. In our opinion, a very good decision. Nowadays, transpiling is a common thing, so we thought why not introduce the same type-safety and mathematical rigour to the user interface?

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related Clojure posts

Jake Stein
Jake Stein
CEO at Stitch · | 13 upvotes · 114.8K views
atStitchStitch
Go
Go
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Amazon Redshift
Amazon Redshift
Amazon EC2
Amazon EC2
AWS OpsWorks
AWS OpsWorks
Kubernetes
Kubernetes
Python
Python
JavaScript
JavaScript
Clojure
Clojure

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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Clojure
Clojure
ClojureScript
ClojureScript
JavaScript
JavaScript
Java
Java
C#
C#

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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Erlang logo

Erlang

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A programming language used to build massively scalable soft real-time systems with requirements on high availability
Erlang logo
Erlang
VS
Haskell logo
Haskell

related Erlang posts

Sebastian Gębski
Sebastian Gębski
CTO at Shedul/Fresha · | 7 upvotes · 63.6K views
atFresha EngineeringFresha Engineering
Elixir
Elixir
Phoenix Framework
Phoenix Framework
Erlang
Erlang
Credo
Credo
Hex
Hex
AppSignal
AppSignal

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).

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StackShare Editors
StackShare Editors
Consul
Consul
Elixir
Elixir
Erlang
Erlang

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.

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related Rust posts

James Cunningham
James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 61.3K views
atSentrySentry
Python
Python
Rust
Rust

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).

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marcoalmeida
marcoalmeida
Go
Go
C
C
Python
Python
Rust
Rust

One important decision for delivering a platform independent solution with low memory footprint and minimal dependencies was the choice of the programming language. We considered a few from Python (there was already a reasonably large Python code base at Thumbtack), to Go (we were taking our first steps with it), and even Rust (too immature at the time).

We ended up writing it in C. It was easy to meet all requirements with only one external dependency for implementing the web server, clearly no challenges running it on any of the Linux distributions we were maintaining, and arguably the implementation with the smallest memory footprint given the choices above.

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Python logo

Python

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A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
Python logo
Python
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Haskell logo
Haskell

related Python posts

Nick Parsons
Nick Parsons
Director of Developer Marketing at Stream · | 34 upvotes · 539.3K views
atStreamStream
Stream
Stream
Go
Go
JavaScript
JavaScript
ES6
ES6
Node.js
Node.js
Babel
Babel
Yarn
Yarn
Python
Python
#FrameworksFullStack
#Languages

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
Jeyabalaji Subramanian
Jeyabalaji Subramanian
CTO at FundsCorner · | 24 upvotes · 740.9K views
atFundsCornerFundsCorner
MongoDB
MongoDB
PostgreSQL
PostgreSQL
MongoDB Stitch
MongoDB Stitch
Node.js
Node.js
Amazon SQS
Amazon SQS
Python
Python
SQLAlchemy
SQLAlchemy
AWS Lambda
AWS Lambda
Zappa
Zappa

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

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OCaml logo

OCaml

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28
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A general purpose industrial-strength programming language
    Be the first to leave a pro
    OCaml logo
    OCaml
    VS
    Haskell logo
    Haskell
    Elixir logo

    Elixir

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    Dynamic, functional language designed for building scalable and maintainable applications
    Elixir logo
    Elixir
    VS
    Haskell logo
    Haskell

    related Elixir posts

    Kamil Kowalski
    Kamil Kowalski
    Engineering Manager at Fresha · | 26 upvotes · 135.7K views
    atFresha EngineeringFresha Engineering
    Cypress
    Cypress
    JavaScript
    JavaScript
    Elixir
    Elixir
    Ruby
    Ruby
    Java
    Java
    Selenium
    Selenium

    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
    Sebastian Gębski
    Sebastian Gębski
    CTO at Shedul/Fresha · | 7 upvotes · 63.6K views
    atFresha EngineeringFresha Engineering
    Elixir
    Elixir
    Phoenix Framework
    Phoenix Framework
    Erlang
    Erlang
    Credo
    Credo
    Hex
    Hex
    AppSignal
    AppSignal

    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

    related JavaScript posts

    Nick Parsons
    Nick Parsons
    Director of Developer Marketing at Stream · | 34 upvotes · 539.3K views
    atStreamStream
    Stream
    Stream
    Go
    Go
    JavaScript
    JavaScript
    ES6
    ES6
    Node.js
    Node.js
    Babel
    Babel
    Yarn
    Yarn
    Python
    Python
    #FrameworksFullStack
    #Languages

    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
    Yshay Yaacobi
    Yshay Yaacobi
    Software Engineer · | 29 upvotes · 656.7K views
    atSolutoSoluto
    Docker Swarm
    Docker Swarm
    .NET
    .NET
    F#
    F#
    C#
    C#
    JavaScript
    JavaScript
    TypeScript
    TypeScript
    Go
    Go
    Visual Studio Code
    Visual Studio Code
    Kubernetes
    Kubernetes

    Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

    Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

    After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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