What is Apache Spark? A Complete Guide

What is Apache Spark?

Apache Spark is a popular open-source big data framework for processing big datasets and is specifically developed to build data pipelines for machine learning applications. Unlike Hadoop MapReduce, Spark does not have its own file storage system and is designed to be used with distributed file systems like HDFS. However, Spark can also be run on a single node (single computer) in standalone mode with a non-distributed dataset.

Spark uses the RAM of each cluster node in unison, harnessing the power of multiple computers. Spark applications execute analyses up to 100 times faster than MapReduce because Spark caches data and intermediate tables in RAM rather than writing them to disk. However, as datasets become larger, the advantage of using RAM decreases and can disappear altogether.

Now that we’ve got an idea of what Apache Spark is, let’s understand how it works.

How Apache Spark works

The Spark driver is the entry point of a Spark application and is used to create a Spark session. The driver program communicates with the cluster manager to create resilient distributed datasets (RDDs). To create an RDD, the data is divided up and distributed across worker nodes in a cluster. Copies of the RDD across the nodes ensure that RDDs are fault-tolerant, so information is recoverable in the event of a failure. We can perform two types of operations on RDDs:

1. Transformations manipulate RDDs on the cluster.
2. Actions return a computation back to the main driver program.

The cluster manager determines the resources that the Spark application requires and assigns a specific number of worker nodes as executors to handle the processing of RDDs. Spark can be run on top of three different cluster managers, including Hadoop’s YARN and Apache Mesos.

Next, let’s have a look at the different types of modules in Spark.

Apache Spark modules and components

The driver program is the core of the Spark application, but there are also modules that have been developed to enhance the utility of Spark. These modules include:

• Spark SQL: An API that converts SQL queries and actions into Spark tasks to be distributed by the cluster manager. This allows for the integration of existing SQL pipelines without the redevelopment of code and subsequent testing required for quality control.
• Spark Streaming: A solution for processing live data streams that creates a discretized stream (Dstream) of RDD batches.
• MLlib and ML: Machine learning modules for designing pipelines used for feature engineering and algorithm training. ML is the DataFrame-based improvement on the original MLlib module.
• GraphX: A robust graphing solution for Spark. More than just visualizing data, this API converts RDDs to resilient distributed property graphs (RDPGs) which utilize vertex and edge properties for relational data analysis.

With Spark modules covered, let’s compare Apache Spark with another popular open-source big data framework, Hadoop MapReduce.

Apache Spark vs. Hadoop MapReduce

Here are the differences between Apache Spark and Hadoop MapReduce:

In summary, Apache Spark is more flexible and faster than Hadoop MapReduce for most modern big data applications.

Now, let’s go through the advantages and disadvantages of using Spark.

Spark advantages and disadvantages

Apache Spark offers several advantages, including:

• Speed: In-memory processing makes Spark 10-100x faster than Hadoop MapReduce.
• Unified platform: One tool for batch, streaming, SQL, ML, and graph workloads.
• Ease of use: Offers high-level APIs and libraries.
• Scalability: Can scale to thousands of nodes.

However, Spark has some disadvantages as well:

• Expensive hardware requirements: Spark provides a solution for more time-efficient analyses of large, distributed datasets, but the analyses are much less cost-effective.
• Real-time processing is not possible: Spark Streaming offers near real-time data processing, but true real-time data analysis is not supported.
• Manual optimization is required: The benefits and power of Spark must be optimized by the developer, which requires an advanced understanding of the program and backend, creating a technical hurdle for developers.

Next, let’s discuss the real-world use cases of Spark.

Spark use cases

The use cases of Spark include:

• ETL pipelines: Extract, transform, and load required data from multiple sources.
• Real-time analytics: Fraud detection, log analysis, and monitoring.
• Machine learning: Customer segmentation, predictive analytics.
• Recommendation systems: Personalized content in media and e-commerce.
• Graph processing: Social network analysis and relationship mapping.

These use cases prove that Apache Spark is already a popular tool across various industries.

Conclusion

In this guide, we discussed what Apache Spark is and how it works. We covered its modules, advantages and disadvantages, and use cases. We also compared it to Hadoop MapReduce and found out that it is far superior to Hadoop MapReduce in various aspects.

Apache Spark is a game-changer in big data processing. Its speed, scalability, and versatility make it the go-to framework for modern data engineering and analytics. Whether you’re building an ETL pipeline, performing real-time analytics, or training machine learning models, Spark provides the tools and performance to get the job done efficiently.

If you want to expand your knowledge of big data, check out the Introduction to Big Data with PySpark course on Codecademy.

Frequently asked questions

1. What is RDD in Spark?

RDD (Resilient Distributed Dataset) is the fundamental data structure in Spark. It’s an unchangeable distributed collection of objects that can be processed in parallel. RDDs support fault tolerance via lineage and allow transformations (e.g., map, filter) and actions (e.g., collect, count).

2. What is the block size in Spark?

Spark doesn’t use a fixed block size like Hadoop HDFS (which defaults to 128MB). Instead, Spark divides RDDs into partitions, and the size of each partition depends on the input data source and cluster configuration (e.g., number of cores, data locality).

3. What is the principle of Spark?

The core principle of Apache Spark is distributed, in-memory computation. It processes data across multiple nodes in a cluster to ensure speed and fault tolerance while minimizing disk I/O.

4. Is Spark used for ETL?

Yes. Spark is extensively used for ETL (Extract, Transform, Load) operations due to its ability to handle massive data volumes quickly and efficiently. Spark SQL, DataFrames, and PySpark are commonly used for building ETL pipelines.

5. Is Spark on AWS?

Yes. Apache Spark is available on AWS through services like Amazon EMR (Elastic MapReduce), AWS Glue, and Amazon SageMaker. These services allow users to run Spark jobs without managing infrastructure.