Scala: A Hybrid Language for Big Data

 

What is Scala?

Scala is a highly scalable general-purpose programming language combining object-oriented and functional programming aspects. It’s become increasingly important in data science, rivalling more established languages like Java and Python. One of the main drivers of Scala’s rise to prominence has been the explosive growth of Apache Spark (written in Scala), giving Scala a well-earned reputation as a powerful language for data processing, machine learning, and streaming analytics.

Powerful and General Purpose

Scala is designed to be much more concise and expressive. This does give it a steeper learning curve than Java, but for many developers, the trade-off is well worth it. Still, the Java legacy is evident in many of Scala’s attributes, from its strong OOP support, curly brace syntax, and high interoperability with Java libraries. Moreover, Scala’s source code is written to be compiled to Java bytecode and then run on the Java Virtual Machine, making it highly portable and safe. This gives Scala a wide range of potential applications. Its Java compatibility makes it well suited to Android development, and its ability to compile to Javascript means Scala can even be used to write web apps. Suppose you’re an object-oriented programmer with no interest in learning functional programming. In that case, you can still pick up Scala and take advantage of Java’s many advantages (its rich libraries and the Java Virtual Machine), all while writing less boilerplate.

Combining Functional And Object-Oriented Programming

One of Scala’s significant advantages is its support for object-oriented and functional programming. Both approaches aim to create readable, bug-free code but approach it differently. Where object-oriented programming combines data structures with the actions you want to perform on them, functional programming keeps both separate.

Each approach has its advantages. For many people, the object-oriented paradigm makes intuitive sense, and combining behaviours with the data structures they’ll interact with can make it easy to figure out what’s going on in an unfamiliar codebase. At the same time, functional programming’s preference for cleanly separated and immutable data structures and discrete behaviours often allows you to do more with less code.

What to Look for in a Scala Developer

As with any developer role, the exact skills and experience you want will depend on your project and business goals. When looking for a Scala developer, it’s essential to gauge their skills with the language and whether they can learn quickly and build resilient systems. Experience with testing and program design is invaluable. Beyond those skills, here are some specific technologies and paradigms to look for in a Scala developer:

• Object-oriented programming
• The Java Virtual Machine
• Tools of statistical analysis
• Distributed file storage systems (like HDFS)
• SQL and relational database management systems

Scala Interview Questions

Scala is a high-level language that combines the best of both worlds: object-oriented programming (OOP) and functional programming (FP). By treating functions as first-class citizens and embracing static types, Scala encourages developers to write safer code. Support for Java Virtual Machine (JVM) and JavaScript runtimes gives developers access to various libraries for enhanced programmer productivity.

1. What are the advantages of using Scala?

Scala was created to enable programmers to use OOP and FP together. It brings OOP concepts such as first-class modules, dot syntax, and first-class type classes/instances together with FP concepts such as higher-order functions and pattern matching.

Other advantages include type safety, a concise syntax, flexibility, and scalability. Built on top of the JVM, Scala is compatible and interoperable with Java. It can perform many of the same tasks as Java with fewer lines of code without sacrificing readability.

2. What is functional programming?

FP is about composing code with pure functions (functions that always return the same result from the same input). This eliminates the side effects associated with changing data or state. FP is generally characterised by:

• Declarative programming model. You express the logic of a program’s structure and elements (what you want data to do) without describing its control flow (how it’s done).
• Support for higher-order functions. These functions take in one or more other functions and return a function.
• Immutable data and state.
• Absence of side effects. The complete absence of side effects is impossible (because the software has to interact with the world). Still, functional languages either isolate side effects in a functional way (e.g., using monads in Haskell) or make usage of side effects explicit via language syntax (as in Clojure).

3. What is the difference between var, val, and def in Scala?

The var keyword lets you declare a variable, a changeable reference to a value. The val keyword enables you to declare a constant, an immutable reference to a value. The def keyword lets you declare a function or a method.

4. Explain the difference between concurrency and parallelism.

Understanding the difference between concurrency and parallelism is essential when composing multithreaded programs. Concurrency is the ability to handle many things simultaneously, such as a web server handling multiple requests. When one task starts, the program does not have to wait for it to finish before starting another task. In Scala, concurrency is handled with constructs called actors.

Parallelism is a distinct concept that is more concerned with the actual simultaneous execution of tasks. It often breaks a task into smaller subtasks that can be processed simultaneously across multiple threads and/or cores. Parallel collections, futures, and the Async library are all examples of parallelism in Scala.

5. What is a Scala future?

In Scala, a future is a placeholder for a value that may not yet exist. It makes it easier to write asynchronous, nonblocking, parallel code.

6. Explain higher-order functions.

Higher-order functions are simply functions that take other functions as parameters or return functions as results. The map, reduce, and filter functions are typical examples—they form the bread and butter of modern-day data analytics.

7. Describe your experience working with Spark.

Written in Scala, Spark is a popular unified data analytics engine for large-scale data processing. This question is meant to be open-ended to give candidates a chance to show you how familiar they are with Spark. It’s generally a good sign if they mention RDDs (resilient distributed datasets) or lazy evaluation or if they have experience applying Spark to common big data projects such as:

8. Describe your experience working with Akka.

Akka is a library for creating fault-tolerant, concurrent, and distributed applications on the JVM inspired by the Reactive Manifesto. It uses actor-based concurrency to insulate developers from the details of dealing with low-level threads and locks. This open-ended question should give you insights into whether candidates have experience applying Akka to common big data projects like those listed above.

9. Explain how pattern matching works in Scala.

Many languages, such as Java, use conditionals such as if/else or switch statements to check a series of possible conditions and take a different action for each condition based on the outcome—in other words, matching patterns. Pattern matching is a mechanism for checking a value against a pattern.

Example of matching on case classes in Scala:

abstract class Devicecase class Phone(model: String) extends Device { def screen = "Turning screen off"}case class Computer(model: String) extends Device { def screenSaverOn = "Turning screen saver on..."} def goIdle(device: Device) = device match { case p: Phone => p.screenOff case c: Computer => c.screenSaverOn}

You can even use pattern matching with containers and container operations:

val list = List("a", "b", "c")val optional = list.adoption optional match { case Some(s) => s.toUpperCase case None => "EMPTY"} list match { case first :: _ => s"first element is $first" case _ => "list is empty"}

Scala makes it syntactically simple to compose blocks of cases that let you pattern-match tuples, arrays, lists, classes, expressions, and more. Better still, pattern matching makes it easy to decompose object hierarchies, allowing you access an object's parameters and process them on a case-by-case basis.

10. What is a monad?

A monad is an FP design pattern that manages complexity through composition. If you come from an object-oriented background, it’s helpful to think of a monad as a type amplifier (such as Nullable in C#) that follows a strict set of laws and supports certain operations (“unit” and bind”) that allow it to compose together functions which can operate on amplified types.

In Scala, this most often takes the form of data structures that use the higher-order methods map and flatMap. To qualify as a monad, a type must satisfy these three laws:

1. Associativity

(m flatMap f) flatMap g == m flatMap (x => f(x) flatMap g)

2. Left Unit

unit (x) flatMap f == f(x)

3. Right Unit

m flatMap unit == m

Monads are an advanced topic best understood through category theory. The interviewee should be able to explain common examples of monads in Scala, such as list, set, option, and generator.