What is Apache Spark? Architecture, Use Cases, and Benefits

What is Apache Spark? Architecture, Use Cases, and Benefits

Wojciech Gębiś - November 17, 2022

Apache Spark is a powerful, open-source processing engine for big data analytics that has been gaining popularity in recent years. In this article, we’ll take a closer look at what Apache Spark is and how it can be used to benefit your business.

Modern Big Data Architecture

Big data is more than just a buzzword- it’s a reality for businesses of all sizes. To take advantage of big data, you need a modern big data infrastructure.

A modern extensive data ecosystem includes hardware, software, and services that work together to process and analyze large volumes of data. The goal is to enable businesses to make better decisions faster and improve their bottom line.

Several components are essential to a flourishing big data ecosystem:

  • Data Variety: Different data types from multiple sources are ingested and outputted (structured, unstructured, semi-structured).
  • Velocity: Fast ingest and processing of data in real-time.
  • Volume: Scalable storage and processing of large amounts of data.
  • Cheap raw storage: Ability to store data affordably in its original form.
  • Flexible processing: Ability to run various processing engines on the same data.
  • Support for streaming analytics: Streaming analytics refers to providing low latency to process real-time data streams in near-real-time.
  • Support for modern applications: Ability to power new types of applications that require fast, flexible data processing like BI tools, machine learning systems, log analysis, and more.

What is Stream Processing?

Before we get into Apache Spark, it’s essential to understand stream processing. Stream processing is a type of data processing that deals with continuous, real-time data streams.

Data streaming differs from batch processing, which deals with discrete data sets processed in batches. Batch processing can be thought of as dealing with “data at rest,” while stream processing deals with “data in motion.”

Continuous stream processing - stream processing tools run operations on streaming data to enable real time analytics

Continuous stream processing - stream processing tools run operations on streaming data to enable real time analytics
Stream processing has several benefits over batch processing:

  • Lower latency: Since stream processors deal with data in near-real-time, the overall latency is lower and offers the opportunity for multiple specific use cases that need in-motion checks.
  • Flexibility: Stream process transaction data is generally more flexible than batch, as a wider variety of end applications, data types, and formats can easily be handled. It can also accommodate changes to the data sources (e.g., adding a new sensor to an IoT application).
  • Less expensive: Since stream processors can handle a continuous data flow, the overall cost is lower (lack of a need to store data before processing it).

Stream Processing Tools

Now that we’ve covered the basics of big data and stream processing, let’s take a closer look at stream processing frameworks.

Several stream processing tools are available, each with its own strengths and weaknesses. Some of the most popular stream processing tools include Apache Storm, Apache Samza, Apache Flink, and Apache Spark - the framework we want to focus on in this article.

Enter Apache Spark Project

Apache Spark is an open-source data processing tool from the Apache Software Foundation designed to improve data-intensive applications’ performance. It does this by providing a more efficient way to process data, which can be used to speed up the execution of data-intensive tasks. It was designed to replace MapReduce and improve upon its shortcomings, such as slow batch processing times and lack of support for interactive and real-time data analysis. This tool uses in-memory caching and optimized query execution to provide fast analytic queries against data of any size.

In addition, Apache Spark also provides several other features that make it an attractive option for data-intensive applications, such as its ability to scale up to large data sets and its support for multiple programming languages (high-level APIs in Java, Scala, Python, and R). As a result, Apache Spark has become a popular choice for data-intensive applications and is likely to continue to be so in the future.

It is the only data processing framework that combines data and artificial intelligence. Users can apply it to execute huge-scale data transformations and analyses, followed by state-of-the-art machine learning algorithms and graph processing applications.

Apache Spark Architecture and Key Components

Apache Spark is a powerful tool for big data analytics. At its core is a distributed execution engine that supports various workloads, including batch processing, streaming, and machine learning.

Spark’s architecture is based on the concept of Resilient Distributed Datasets (RDDs), which are immutable collections of data that can be divided across a cluster of machines. RDDs are used to store data in memory, providing both performance and fault tolerance. Spark also features a Directed Acyclic Graph (DAG) scheduler that determines the order in which RDDs are computed. This allows for the efficient execution of complex pipelines, including multiple stages of shuffling and aggregation. By understanding the critical components of Spark’s architecture, developers can unleash the power of this tool to build scalable, high-performance applications.

To process big data, you need a platform that is designed for scalability and performance. Apache Spark is built on top of the Hadoop Distributed File System (HDFS), a scalable, reliable, distributed file system that can store large amounts of data. It can also use other standard data stores like Amazon Redshift, Amazon S3, Cassandra, etc. Spark on Hadoop leverages YARN (Yet Another Resource Negotiator) as a resource manager to share a common cluster and dataset as other Hadoop engines, guaranteeing uniform levels of service and response.

Spark architecture with HDFS, YARN, and MapReduce

Spark architecture with HDFS, YARN, and MapReduce

Spark uses a master/slave architecture with a driver program for spark context that runs on a master node and executes user-defined functions on data stored in HDFS. The driver program then sends tasks to the cluster manager that executes spark jobs and executor processes, which run on worker nodes, to process the data.

Spark architecture and cluster manager

Spark architecture and cluster manager

The advantage of this architecture is that it can process data in parallel, which makes it much faster than traditional big data processing platforms.

Apache Spark Ecosystem

Spark is not just a data processing tool but an ecosystem that contains many different tools and libraries. The most important ones are the following:

Apache Spark Ecosystem - Spark Core API and dedicated tools, Spark SQL, Spark Streaming API, MLlib Machine Learning library, and GraphX the distributed graph processing framework

Apache Spark Ecosystem - Spark Core API and dedicated tools, Spark SQL, Spark Streaming API, MLlib Machine Learning library, and GraphX the distributed graph processing framework

Spark Core

Spark Core is the heart of the Spark platform. It contains the basic functionality of Spark, including distributed data processing, task scheduling and dispatching, memory management, fault recovery, and interaction with storage systems.

Spark SQL

This module allows for structured data processing. It contains a relational query processor that supports SQL and HiveQL.

Spark Streaming and Structured Streaming

These modules allow Spark to process streaming data. Spark Streaming can process live data streams, while Structured Streaming can handle stream processing with a higher level of abstraction with even lower latency.

GraphX

This is Spark’s graph computation library that enables the analysis of scalable, graph-structured data.

MLlib

This is Spark’s machine learning library. It contains many common machine learning algorithms that can be applied to large data sets.

Key Use Cases for Spark

Generally, Spark is the best solution when time is of the essence. Apache Spark can be used for a wide variety of data processing workloads, including:

  • Real-time processing and insight: Spark can also be used to process data close to real-time. For example, you could use Spark Streaming to read live tweets and perform sentiment analysis on them.
  • Machine learning: You can use Spark MLlib to train machine learning models on large data sets and then deploy those models in your applications. It has prebuilt machine learning algorithms for tasks like regression, classification, clustering, collaborative filtering, and pattern mining. For example, you could use Spark MLlib to build a model that predicts customer churn based on their activity data.
  • Graph processing: You can use Spark GraphX to process graph-structured data, such as social networks or road networks. For example, you could use GraphX to instantly find the shortest path between two nodes in a graph.

Advantages of Using Apache Spark

Apache Spark is a powerful open-source analytics engine that has become increasingly popular in recent years. There are many reasons for Spark’s popularity, but some of the most important benefits include its speed, ease of use, and ability to handle large data sets.

We can specify many advantages of using Apache Spark, including the following:

  • Flexibility: Apache Spark can be used for batch processing, streaming, interactive analytics, iterative graph computation, machine learning, and SQL queries. All these processes can be seamlessly combined in one application.
  • Processing speed: Apache Spark is much faster than MapReduce for most workloads as it uses RAM instead of reading and writing intermediate data to disk storage. Thanks to its in-memory computing capabilities, Spark can run up to 100x faster than Hadoop MapReduce. It is capable of processing data much faster than similar engines, making it ideal for applications where data needs to be processed quickly.
  • Developer friendly: Spark is much easier to use than other engines, which makes it accessible to a broader range of users. Apache Spark Core has a simple API and wide language support, making it easy to learn and use.
  • Support for big data processing: Spark can handle huge data sets, which is another major advantage.

Limitations of Apache Spark

There are also some limitations and disadvantages to using Apache Spark, including the following:

  • Complexity: While the API is simple, the underlying architecture is complex. This complexity can make it challenging to debug applications and tune performance.
  • Costly infrastructure: Apache Spark uses RAM for its in-memory computations for real-time data processing.
  • Close-to-real-time: Apache Spark is not designed for true real-time processing as it processes data in micro-batches, with a maximum latency of around 100 milliseconds. You need to turn to other frameworks like Apache Flink for real-time processing.

Apache Spark as Part of the Big Data Infrastructure Stack

Apache Spark is often used as part of a larger big data infrastructure stack, which might include the following components (most from Apache Software Foundation):

  • Data ingestion: This is the process of loading data into the system. It can be done manually or automatically using tools like Apache Kafka, Apache NiFi, Apache Flume, or Apache Storm.
  • Data storage: Data needs to be stored somewhere before processing. This is usually done using a distributed file system like Apache Hadoop HDFS, Apache Hive, Apache Kudu, Apache Kylin, Apache HBase, or Amazon S3.
  • Data processing: This is where spark streaming comes in. Once the data is ingested and stored, it can then be processed using Spark. For batch processing, you may want to use Apache Hadoop, and for real-time stream processing Apache Flink. You can also turn to Kafka Streams, Samza, Hive, Storm, or Apex.
  • Data analytics: After the data has been processed, it can be further analyzed to extract insights (most data processing tools already have some functionalities to support this part). You can use Spark SQL to work with structured data. Data analytics can also be performed using tools like Apache Impala, Hive, or Zeppelin.

As you can see, Apache Spark is just one piece of the puzzle when it comes to big data processing. To build a complete big data infrastructure, you need to use a variety of different tools and technologies for data ingestion, storage, analytics, and visualization.

Big data architecture based on Kafka, Hadoop, Spark and other frameworks and DBs

Big data architecture based on Kafka, Hadoop, Spark and other frameworks and DBs

Who is Using Apache Spark Project?

Apache Spark is an open-source project that is supported by a wide range of companies and organizations. Some of the biggest users of the Spark platform include Amazon, Uber, Shopify, Netflix, eBay, and Slack.

Apache Spark as a Fully-Managed Service

You can implement Spark on your own or use it as a fully-managed service. Fully-managed services are an alternative approach to getting started with Spark without worrying about the underlying infrastructure.

If you seek a managed solution, then Apache Spark can be found as part of Amazon EMR, Google Cloud Dataproc, and Microsoft Azure HDInsight. Although they may be less flexible in some cases, these comprehensive managed services offer Apache Spark clusters, streaming support, integrated web-based notebook development, and optimized cloud I/O performance over a standard Apache Spark distribution.

Conclusion

So there you have it – a quick introduction to Apache Spark, common use cases, and its many benefits. Like most of the stream processing frameworks on the market, it can be used together with other tools to create a more robust bid data processing architecture.

Overall, Apache Spark offers several significant benefits, making it one of the most popular analytics engines available today. Its speed, ease of use, and ability to handle large data sets make it an appealing option for a wide range of applications.

Apache Spark is worth considering if you’re looking for a powerful big data processing engine that can handle all your workloads (and more). And if you need help getting started, don’t hesitate to contact our team of experts. We’d be happy to walk you through the basics and help get your Spark implementation up and running in no time!

References

Apache Spark

Apache Spark GitHub page

About the author

Wojciech Gębiś

Wojciech Gębiś

Project Lead & DevOps Engineer

Linkedin profile Twitter Github profile

Wojciech is a seasoned engineer with experience in development and management. He has worked on many projects and in different industries, making him very knowledgeable about what it takes to succeed in the workplace by applying Agile methodologies. Wojciech has deep knowledge about DevOps principles and Machine Learning. His practices guarantee that you can reliably build and operate a scalable AI solution.
You can find Wojciech working on open source projects or reading up on new technologies that he may want to explore more deeply.

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