What is Scala and what are its top alternatives?
Top Alternatives to Scala
- Kotlin
Kotlin is a statically typed programming language for the JVM, Android and the browser, 100% interoperable with Java ...
- 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. ...
- 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. ...
- 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! ...
- Golang
Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language. ...
- Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...
- 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. ...
- Groovy
It is a powerful multi-faceted programming language for the JVM platform. It supports a spectrum of programming styles incorporating features from dynamic languages such as optional and duck typing, but also static compilation and static type checking at levels similar to or greater than Java through its extensible static type checker. It aims to greatly increase developer productivity with many powerful features but also a concise, familiar and easy to learn syntax. ...
Scala alternatives & related posts
Kotlin
- Interoperable with Java72
- Functional Programming support54
- Null Safety49
- Official Android support45
- Backed by JetBrains43
- Concise36
- Modern Multiplatform Applications35
- Expressive Syntax27
- Target to JVM26
- Coroutines25
- Open Source23
- Practical elegance18
- Statically Typed18
- Android support16
- Type Inference16
- Readable code13
- Better Java12
- Powerful as Scala, simple as Python, plus coroutines <312
- Pragmatic10
- Lambda9
- Better language for android8
- Expressive DSLs8
- Target to JavaScript8
- Used for Android6
- Less boilerplate code6
- Fast Programming language5
- Less code5
- Functional Programming Language4
- Friendly community4
- Less boiler plate code4
- Native3
- Official Google Support2
- Spring2
- Latest version of Java2
- Java interop makes users write Java in Kotlin7
- Frequent use of {} keys4
- Hard to make teams adopt the Kotlin style2
- Nonullpointer Exception2
- Friendly community1
- Slow compiler1
- No boiler plate code1
related Kotlin posts
Hi Community! Trust everyone is keeping safe. I am exploring the idea of building a #Neobank (App) with end-to-end banking capabilities. In the process of exploring this space, I have come across multiple Apps (N26, Revolut, Monese, etc) and explored their stacks in detail. The confusion remains to be the Backend Tech to be used?
What would you go with considering all of the languages such as Node.js Java Rails Python are suggested by some person or the other. As a general trend, I have noticed the usage of Node with React on the front or Node with a combination of Kotlin and Swift. Please suggest what would be the right approach!
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.
Python
- Great libraries1.2K
- Readable code948
- Beautiful code835
- Rapid development780
- Large community682
- Open source426
- Elegant385
- Great community278
- Object oriented268
- Dynamic typing214
- Great standard library75
- Very fast56
- Functional programming51
- Scientific computing43
- Easy to learn43
- Great documentation33
- Matlab alternative26
- Productivity25
- Easy to read25
- Simple is better than complex21
- It's the way I think18
- Imperative17
- Free15
- Very programmer and non-programmer friendly15
- Powerful14
- Machine learning support14
- Powerfull language14
- Fast and simple13
- Scripting12
- Explicit is better than implicit9
- Clear and easy and powerfull8
- Ease of development8
- Unlimited power8
- Import antigravity7
- It's lean and fun to code6
- Print "life is short, use python"6
- Python has great libraries for data processing5
- Fast coding and good for competitions5
- There should be one-- and preferably only one --obvious5
- High Documented language5
- I love snakes5
- Although practicality beats purity5
- Flat is better than nested5
- Great for tooling5
- Readability counts4
- Rapid Prototyping4
- Web scraping3
- Plotting3
- Multiple Inheritence3
- Complex is better than complicated3
- Beautiful is better than ugly3
- Now is better than never3
- Lists, tuples, dictionaries3
- Socially engaged community3
- Great for analytics3
- CG industry needs3
- Generators2
- Simple and easy to learn2
- Import this2
- No cruft2
- Easy to learn and use2
- List comprehensions2
- Pip install everything2
- Special cases aren't special enough to break the rules2
- If the implementation is hard to explain, it's a bad id2
- If the implementation is easy to explain, it may be a g2
- Easy to setup and run smooth2
- Many types of collections2
- Flexible and easy1
- Powerful language for AI1
- Shitty1
- It is Very easy , simple and will you be love programmi1
- Batteries included1
- Can understand easily who are new to programming1
- Should START with this but not STICK with This1
- A-to-Z1
- Only one way to do it1
- Because of Netflix1
- Better outcome1
- Good for hacking1
- Powerful0
- Still divided between python 2 and python 351
- Performance impact28
- Poor syntax for anonymous functions26
- GIL21
- Package management is a mess19
- Too imperative-oriented14
- Hard to understand12
- Dynamic typing12
- Very slow11
- Not everything is expression8
- Indentations matter a lot7
- Explicit self parameter in methods7
- Incredibly slow7
- Requires C functions for dynamic modules6
- Poor DSL capabilities6
- No anonymous functions6
- Official documentation is unclear.5
- The "lisp style" whitespaces5
- Fake object-oriented programming5
- Hard to obfuscate5
- Threading5
- Circular import4
- The benevolent-dictator-for-life quit4
- Lack of Syntax Sugar leads to "the pyramid of doom"4
- Not suitable for autocomplete4
- Meta classes2
- Training wheels (forced indentation)1
related Python posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
Clojure
- It is a lisp118
- Persistent data structures101
- Concise syntax100
- jvm-based language90
- Concurrency89
- Interactive repl82
- Code is data76
- Lazy data structures61
- Open source61
- Macros56
- Functional49
- Simplistic22
- Immutable by default22
- Excellent collections19
- Fast-growing community18
- Multiple host languages14
- Practical Lisp14
- Simple (not easy!)14
- Addictive9
- Community9
- Because it's really fun to use9
- Web friendly8
- Rapid development8
- It creates Reusable code8
- Minimalist7
- Java interop6
- Programmable programming language5
- Regained interest in programming4
- Compiles to JavaScript3
- EDN3
- Share a lot of code with clojurescript/use on frontend2
- Cryptic stacktraces9
- Need to wrap basically every java lib4
- Toxic community4
- Good code heavily relies on local conventions3
- Slow application startup2
- Tonns of abandonware2
- Usable only with REPL1
- Hiring issues1
- Bad documented libs1
- Macros are overused by devs1
- Tricky profiling1
- IDE with high learning curve1
- Configuration bolierplate1
- Conservative community1
- Have no good and fast fmt0
related Clojure posts
Stitch is run entirely on AWS. All of our transactional databases are run with Amazon RDS, and we rely on Amazon S3 for data persistence in various stages of our pipeline. Our product integrates with Amazon Redshift as a data destination, and we also use Redshift as an internal data warehouse (powered by Stitch, of course).
The majority of our services run on stateless Amazon EC2 instances that are managed by AWS OpsWorks. We recently introduced Kubernetes into our infrastructure to run the scheduled jobs that execute Singer code to extract data from various sources. Although we tend to be wary of shiny new toys, Kubernetes has proven to be a good fit for this problem, and its stability, strong community and helpful tooling have made it easy for us to incorporate into our operations.
While we continue to be happy with Clojure for our internal services, we felt that its relatively narrow adoption could impede Singer's growth. We chose Python both because it is well suited to the task, and it seems to have reached critical mass among data engineers. All that being said, the Singer spec is language agnostic, and integrations and libraries have been developed in JavaScript, Go, and Clojure.
I adopted Clojure and ClojureScript because:
- it's 1 language, multiple platforms.
- Simple syntax.
- Designed to avoid unwanted side effects and bugs.
- Immutable data-structures.
- Compact code, very expressive.
- Source code is data.
- It has super-flexible macro.
- Has metadata.
- Interoperability with JavaScript, Java and C#.
Java
- Great libraries594
- Widely used444
- Excellent tooling400
- Huge amount of documentation available390
- Large pool of developers available333
- Open source205
- Excellent performance201
- Great development155
- Vast array of 3rd party libraries149
- Used for android148
- Compiled Language60
- Used for Web51
- Managed memory46
- High Performance45
- Native threads44
- Statically typed43
- Easy to read35
- Great Community33
- Reliable platform29
- Sturdy garbage collection24
- JVM compatibility24
- Cross Platform Enterprise Integration22
- Universal platform20
- Good amount of APIs20
- Great Support18
- Great ecosystem14
- Backward compatible11
- Lots of boilerplate11
- Everywhere10
- Excellent SDK - JDK9
- Cross-platform7
- It's Java7
- Static typing7
- Portability6
- Long term language6
- Better than Ruby6
- Mature language thus stable systems6
- Vast Collections Library5
- Clojure5
- Used for Android development5
- Most developers favorite4
- Old tech4
- Testable3
- History3
- Stable platform, which many new languages depend on3
- Great Structure3
- Javadoc3
- Best martial for design3
- Type Safe2
- Faster than python2
- Verbosity33
- NullpointerException27
- Nightmare to Write16
- Overcomplexity is praised in community culture16
- Boiler plate code12
- Classpath hell prior to Java 98
- No REPL6
- No property4
- Code are too long3
- Non-intuitive generic implementation2
- There is not optional parameter2
- Floating-point errors2
- Java's too statically, stronglly, and strictly typed1
- Returning Wildcard Types1
- Terrbible compared to Python/Batch Perormence1
related Java 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
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.
Golang
- High-performance537
- Simple, minimal syntax391
- Fun to write358
- Easy concurrency support via goroutines299
- Fast compilation times271
- Goroutines193
- Statically linked binaries that are simple to deploy179
- Simple compile build/run procedures150
- Backed by google136
- Great community134
- Garbage collection built-in52
- Built-in Testing45
- Excellent tools - gofmt, godoc etc43
- Elegant and concise like Python, fast like C39
- Awesome to Develop37
- Used for Docker26
- Flexible interface system25
- Deploy as executable24
- Great concurrency pattern24
- Open-source Integration20
- Fun to write and so many feature out of the box17
- Easy to read17
- Go is God16
- Its Simple and Heavy duty14
- Powerful and simple14
- Easy to deploy14
- Best language for concurrency13
- Concurrency12
- Rich standard library11
- Safe GOTOs11
- Clean code, high performance10
- Easy setup10
- Simplicity, Concurrency, Performance9
- High performance9
- Hassle free deployment8
- Single binary avoids library dependency issues8
- Simple, powerful, and great performance7
- Cross compiling7
- Used by Giants of the industry7
- Gofmt6
- Garbage Collection6
- Very sophisticated syntax5
- Excellent tooling5
- WYSIWYG5
- Keep it simple and stupid4
- Widely used4
- Kubernetes written on Go4
- No generics2
- Operator goto1
- You waste time in plumbing code catching errors41
- Verbose25
- Packages and their path dependencies are braindead23
- Dependency management when working on multiple projects15
- Google's documentations aren't beginer friendly15
- Automatic garbage collection overheads10
- Uncommon syntax8
- Type system is lacking (no generics, etc)6
- Collection framework is lacking (list, set, map)3
- Best programming language2
related Golang posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.
We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)
We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.
Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.
#FrameworksFullStack #Languages
- Open-source60
- Fast and Flexible48
- Great for distributed SQL like applications8
- One platform for every big data problem8
- Easy to install and to use6
- Works well for most Datascience usecases3
- In memory Computation2
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- Speed3
related Apache Spark posts
The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.
Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).
At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.
For more info:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :
Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:
https://eng.uber.com/marmaray-hadoop-ingestion-open-source/
(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )
- Purely-functional programming89
- Statically typed66
- Type-safe59
- Open source39
- Great community38
- Built-in concurrency30
- Composable29
- Built-in parallelism29
- Referentially transparent23
- Generics19
- Intellectual satisfaction14
- Type inference14
- If it compiles, it's correct11
- Flexible7
- Monads7
- Proposition testing with QuickCheck4
- Great type system4
- Purely-functional Programming3
- One of the most powerful languages *(see blub paradox)*3
- Highly expressive, type-safe, fast development time2
- Reliable2
- Kind system2
- Pattern matching and completeness checking2
- Better type-safe than sorry2
- Type classes2
- Great maintainability of the code2
- Fun2
- Best in class thinking tool2
- Orthogonality0
- Predictable0
- Error messages can be very confusing8
- Too much distraction in language extensions8
- Libraries have poor documentation4
- No best practices3
- No good ABI3
- Sometimes performance is unpredictable2
- Poor packaging for apps written in it for Linux distros2
- Slow compilation1
related Haskell posts
Why I am using Haskell in my free time?
I have 3 reasons for it. I am looking for:
Fun.
Improve functional programming skill.
Improve problem-solving skill.
Laziness and mathematical abstractions behind Haskell makes it a wonderful language.
It is Pure functional, it helps me to write better Scala code.
Highly expressive language gives elegant ways to solve coding puzzle.
- Java platform43
- Much more productive than java32
- Concise and readable28
- Very little code needed for complex tasks27
- Dynamic language21
- Nice dynamic syntax for the jvm12
- Very fast9
- Can work with JSON as an object6
- Easy to setup6
- Supports closures (lambdas)5
- Literal Collections5
- Developer Friendly2
- Optional static typing2
- Syntactic sugar2
- Groovy Code can be slower than Java Code3
- Objects cause stateful/heap mess1
related Groovy posts
Some may wonder why did we choose Grails ? Really good question :) We spent quite some time to evaluate what framework to go with and the battle was between Play Scala and Grails ( Groovy ). We have enough experience with both and, to be honest, I absolutely in love with Scala; however, the tipping point for us was the potential speed of development. Grails allows much faster development pace than Play , and as of right now this is the most important parameter. We might convert later though. Also, worth mentioning, by default Grails comes with Gradle as a build tool, so why change?
Presently, a web-based ERP is developed in Groovy on Grails. Now the ERP is getting revamped with more functionalities. Is it advisable to continue with the same software and framework or try something new especially Node.js over ExpressJS?