ANTLR Hello World! - Arithmetic Expression Parser

Ever wondered how all these programming languages understand what you write? This article reveals the truth: Language Parsing. It is often referred to as parsing, syntax analysis, or syntactic analysis. Regardless of the term, it is the process of analyzing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar. The following diagram depicts the language parsing process:

As you can see, the Language Parser (which is part of the compiler) takes an input (which is the source code), validates it against the Language Grammar and produces an Abstract Syntax Tree (commonly known as AST which is representing the source code in a tree structure).

ANTLR (ANother Tool for Language Recognition) is a tool to define such grammar and to build a parser automatically using that grammar. It also provides two high-level design patterns to analyze the AST: Visitor and Listener. ANTLR is being used by several languages and frameworks including Ballerina, Siddhi, and Presto SQL. This article introduces ANTLR to you using a hello world application to evaluate basic mathematical expressions as a string.

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Javalin: A Tiny but Mighty Framework

Two years ago, I wrote an article Microservices in a minute using the open source framework MSF4J. Today I came across another framework Javalin: another lightweight framework to develop lightweight web applications with less or no effort. We already have plenty of web frameworks including the shining star Spring. What makes Javalin different is its simplicity. In addition, it can be used as a microservice framework or a tiny web framework to serve a web application with static files. In Javalin developers' words:

Javalin’s main goals are simplicity, a great developer experience, and first-class interoperability between Kotlin and Java.

Comparing Javalin with Spring is like comparing a shaving blade with a Wenger 16999 Swiss Army Knife Giant, but it does what it is supposed to do. If you want to quickly add a REST endpoint for a quick demo or if you just need a simple web framework without any additional gimmicks like Dependency Injection or Object Relational Mapping, consider Javalin. It is easy to learn and lighter to run.

In this article, you will see how to use Javalin as a web framework to serve a contact-us page and how to build a CRUD micro-service using Javalin.

Requirements:

• Java Development Kit 1.8 or the latest
• Apache Maven 3.x
• IntelliJ Idea or any other IDEs

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Serve TensorFlow Models in Java

TensorFlow is a famous machine learning framework from Google and a must to know asset for machine learning engineers. Even though Python is recommended to build TensorFlow models, Google offers Java API to use TensorFlow in Java. Still, Python is the easiest language to build TensorFlow models, even for Java developers (learn Python, my friend). However, enterprise applications developed in Java may require the artificial intelligence offered by a trained TensorFlow model. In this article, you will learn how to load and use a simple TensorFlow model exported from Python.

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Spark 06: Broadcast Variables

If you read the Spark 04: Key-Value RDD and Average Movie Ratings article, you might wonder what to do with popular movie IDs printed at the end. A data analyst cannot ask his/her users to manually check those IDs in a CSV file to find the movie name. In this article, you will learn how to map those movie IDs to movie names using Apache Spark's variable broadcasting.

Suppose you want to share a read-only data that can fit into memory with every worker in your Spark cluster, broadcast that data. The broadcasted variable will be distributed only once and cached in every worker node so that it can be reused any number of times. More about broadcasting will be covered later in this article after the code example.

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Spark 05: List Action Movies with Spark flatMap

Welcome to the fifth article in the series of Apache Spark tutorials. In this article, you will learn the application of flatMap transform operation. After the introduction to flatMap operation, a sample Spark application is developed to list all action movies from the MovieLens dataset.

In the previous articles, we have used the map transform operation which transforms an entity into another entity where the transformation is one-to-one. For example, suppose you have a String RDD named lines, applying lines.map(x => x.toUpperCase) operation creates a new String RDD with the same number of records but with uppercase string literals as shown below:

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