The Magic of Apache Spark in Java

If you're using Java for big data, it might not be obvious to you that Apache Spark is a viable option. At first glance, a lot of the resources available on Spark talk about using it with Scala, but in reality, a number of integrations exist that allow developers to supercharge their applications using the magic of Apache Spark in Java.  

For beginners new to the concept of Apache Spark, it's a lightning-fast technology capable of handling massive amounts of data while efficiently utilizing resources.

Using Spark, both data engineers and developers can process petabytes of data and transform it into meaningful analytics, whether you choose Java, Scala, Python, or R.

We'll look at the magic of Apache Spark in Java, specifically, to help you understand how it works and why you should use it.

What is Apache Spark?

Apache Spark is an open-source unified analytics engine for large-scale data processing, always a good choice if you are looking forward having a Node.

Spark is widely renowned for its capabilities and processing speed, but part of what makes it so capable is that it was developed by a team of experts at the University of Berkeley, and the technology was eventually donated to the Apache ecosystem so they could maintain it.

Since Spark's creation in 2009, it has been adopted by major companies to help support big data processing, including the likes of NASA, Apple, and eBay.

It's so popular that you can even find Apache Spark as part of a managed service through Google Cloud Dataproc, Microsoft Azure HDInsight, or Amazon EMR.

Spark architecture is very simple and utilizes a Master node and Slave nodes to process data. Using a Spark API, it can be extended to handle machine learning, real-time data, and other advanced functions.

Even for projects that use Spark Core as a standalone solution, its power as a cluster computing program is ideal for a variety of data science applications.  

Why use Apache Spark in Java?

Some of the reasons why Spark has been so well-received over the years is that it's built equally for data scientists as it is for developers, and it supports stream processing, machine learning, and other advancing technologies. Best of all, Apache Spark's capabilities by no means slow it down.

According to IBM, Apache Spark is up to 100x faster compared to similar technologies-like Hadoop. The primary reason why Apache Spark is so much faster is that it defaults to using memory over disk space to process data.

Even if you choose to use disk space, you'll still see about 10x faster-processing thanks to the efficiency of Apache Spark.

If you're interested in running Apache Spark in Java, you'll be glad to learn that the setup is very simple with minimal tech requirements.

Out-of-the-box, you simply need the Apache Spark framework and a Java Virtual Machine (JVM). Install these two things on any machine, and you can then group those machines using a cluster manager, which is about as simple as big data tech can get.

Choosing the right Spark libraries

Spark Core is easy enough to get up and running, but it may not be enough for some projects. Fortunately, there are several libraries that can support various features to extend Apache Spark in Java.  

Spark SQL

For developers familiar with SQL, Spark SQL will make them feel right at home. This API provides an interface that makes processing simple with the use of SQL queries. However, it should be noted this API is intended for structured data only.  

The biggest benefit of using this API is time savings. Instead of forcing developers to get used to Spark Core, they can use this API to start writing Spark jobs without experiencing a significant learning curve if they are most comfortable with querying in a Relational Database Management System (RDBMS).

GraphX

For graphs and related computations, GraphX provides a platform for handling them within Spark. Aside from creating graphs, GraphX allows developers to join, transform, and alter graphs to better analyze and compare data.  

GraphX even comes pre-equipped with a handful of algorithms, allowing any user (data scientist or not) to quickly visualize connected components, shortest paths, label propagation, and more.

Spark Streaming

The extensibility of Spark is surely one of its top advantages. Using Spark Streaming, a developer can quickly turn Spark Core into an advanced big data platform capable of handling streaming or "real-time" data.

Some of the data you could process using Spark Streaming include log files, online purchases, or even Twitter feeds. For better performance, you can integrate Spark with other tools, like Kafka, to improve speed and avoid errors.

Spark MLlib

The SPark MLlib, or Machine Learning Library, allows for developers to turn Spark Core into a program capable of modeling, classification, and handling other machine learning tasks.

Due to the processing speed of Spark, you can train a machine learning algorithm even faster than some other solutions. Spark also offers advantages over platforms like MapReduce because it leverages iterations instead of taking a single-pass approach.

If you aren't sure which libraries you need or how to implement them without harming performance consider reaching out professional resources.

Best practices for using Apache Spark in Java

The magic of Apache Spark in Java cannot be realized unless a developer follows the best practices for utilizing this powerful technology.

While Spark Core can be set up with relative ease, finding the right libraries to support a project's requirements and ensuring that integrations are handled properly can prove difficult.

Ultimately, while Apache Spark is renowned for its processing speed and advanced capabilities, it's only as powerful as the environment it is running in.

What's more, Apache Spark in Java can be hindered by improper setup or failing to follow best practices, like using memory over disk when possible.

If you are new to Apache Spark in Java, remember the following:

• The Spark pipeline means one job is performed at a time, with each one writing to a file for another job to pick up. To boost performance, use a serialized and optimized format instead of CVS or textfile.
• Use DataFrame when possible as it stores and manages data more efficiently, thus speeding up processing compared to a Resilient Distributed Dataset (RDD).
• Try not to use user-defined functions (UDFs) within Apache Spark because they cannot be optimized and, therefore, can greatly dampen performance.
• Shuffling allows you to redistribute data within Spark, but it is resource-intensive. Avoid it when possible.
• Follow clean coding practices so that you don't have to use the debug or logging functions, which greatly increase the size of workloads.

For developers that are already using Apache Spark, getting familiar with the best practices may help them overcome some key challenges and performance issues they have been facing.

For those new to the technology, getting off on the right foot can save hours of time spent improving the system later on, and it will allow for all of the benefits of using Apache Spark in Java to shine through. With that said, where do you begin?

Make the most of Apache Spark

Whether you're considering using Apache Spark in Java to handle machine learning, real-time streaming data, complex graph computations, or a combination of these advanced functions, you'll find that this technology is fully capable.

The question is, do you have the time and expertise to make a Spark cluster work the best it can? If you're unsure about the flat map, arrays, dependencies, HDFS, aggregating, or spark config settings, it might be time to call in an expert.

We've helped countless teams implement and improve Spark applications through cleaner coding, optimizing architecture, and teaching key best practices. If you need help with your next project, our team is standing by to help.

Reach us out and let our code quality experts handle the process of using Apache Spark in Java for your next big data app.