PySpark vs Scala

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PySpark

169
201
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0
Scala

7.7K
5.9K
+ 1
1.5K
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PySpark vs Scala: What are the differences?

PySpark: The Python API for Spark. It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data; Scala: A pure-bred object-oriented language that runs on the JVM. 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.

PySpark can be classified as a tool in the "Data Science Tools" category, while Scala is grouped under "Languages".

Scala is an open source tool with 11.9K GitHub stars and 2.76K GitHub forks. Here's a link to Scala's open source repository on GitHub.

According to the StackShare community, Scala has a broader approval, being mentioned in 557 company stacks & 1895 developers stacks; compared to PySpark, which is listed in 8 company stacks and 6 developer stacks.

Advice on PySpark and Scala
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ScalaScalaNode.jsNode.js
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GoGo

Finding the best server-side tool for building a personal information organizer that focuses on performance, simplicity, and scalability.

performance and scalability get a prototype going fast by keeping codebase simple find hosting that is affordable and scales well (Java/Scala-based ones might not be affordable)

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Replies (1)
David Annez
Head of Engineering at loveholidays · | 4 upvotes · 137K views

I've picked Node.js here but honestly it's a toss up between that and Go around this. It really depends on your background and skillset around "get something going fast" for one of these languages. Based on not knowing that I've suggested Node because it can be easier to prototype quickly and built right is performant enough. The scaffolding provided around Node.js services (Koa, Restify, NestJS) means you can get up and running pretty easily. It's important to note that the tooling surrounding this is good also, such as tracing, metrics et al (important when you're building production ready services).

You'll get more scalability and perf from go, but balancing them out I would say that you'll get pretty far with a well built Node.JS service (our entire site with over 1.5k requests/m scales easily and holds it's own with 4 pods in production.

Without knowing the scale you are building for and the systems you are using around it it's hard to say for certain this is the right route.

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Decisions about PySpark and Scala

We needed to incorporate Big Data Framework for data stream analysis, specifically Apache Spark / Apache Storm. The three options of languages were most suitable for the job - Python, Java, Scala.

The winner was Python for the top of the class, high-performance data analysis libraries (NumPy, Pandas) written in C, quick learning curve, quick prototyping allowance, and a great connection with other future tools for machine learning as Tensorflow.

The whole code was shorter & more readable which made it easier to develop and maintain.

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Pros of PySpark
Pros of Scala
    Be the first to leave a pro
    • 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

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    Cons of PySpark
    Cons of Scala
      Be the first to leave a con
      • 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

      Sign up to add or upvote consMake informed product decisions