Apache Spark is a distributed processing solution for big data workloads that is open-source. It combines in-memory caching and rapid query execution for quick analytic queries against any size of data. It includes development APIs in Java, Scala, Python, and R and allows code reuse across a variety of workloads, including batch processing, interactive queries, real-time analytics, machine learning, and graph processing.
FINRA, Yelp, Zillow, DataXu, Urban Institute, and CrowdStrike are among the companies that use Spark. With 365,000 meetup members in 2017, Apache Spark is one of the most popular big data distributed processing frameworks.
History of Apache Spark
In 2009, Apache Spark was born as a research project at UC Berkeley’s AMPLab, a collaboration of students, researchers, and professors focused on data-intensive application domains. The goal was to design a new framework that was optimized for quick iterative processing, such as machine learning and interactive data analysis while having Hadoop MapReduce’s scalability and fault tolerance.
Spark was open-sourced under a BSD license after the initial article, “Spark: Cluster Computing with Working Sets” was released in June 2010. Spark was accepted into the Apache Software Foundation’s (ASF) incubation programme in June 2013, and it became an Apache Top-Level Project in February 2014. Apache Spark may be operated standalone, or on Apache Mesos or Apache Hadoop.
Apache Spark is now one of the most popular projects in the Hadoop ecosystem, with many companies using it in conjunction with Apache Hadoop to process humongous amounts of data. Spark’s had a 5x increase in just two years. Since 2009, it has benefited from the contributions of over 1,000 developers from over 200 organizations.
What is Apache Spark? How Does it Function?
Hadoop MapReduce is a programming approach that uses a parallel, distributed algorithm to process large data sets. Developers don’t have to worry about job distribution or fault tolerance when writing massively parallelized operators. The sequential multi-step process required to run a job, however, is difficult for MapReduce.
MapReduce gets data from the cluster, performs operations, and publishes the results back to HDFS at the end of each phase. Due to the latency of disc I/O, MapReduce jobs are slower since each step involves a disc read and write.
By performing in-memory processing, lowering the number of steps in a job, and reusing data across several concurrent operations, Spark is able to solve the constraints of MapReduce. With Apache Spark, data is read into memory in a single step, operations are executed, and the results are written back, resulting in significantly faster execution.
Spark, additionally, reuses data by employing an in-memory cache to substantially accelerate machine learning algorithms that call the same function on the same dataset several times.
The construction of DataFrames – an abstraction over the Resilient Distributed Dataset (RDD), which is a collection of objects cached in memory and reused in many Spark operations – allows for data reuse. This reduces latency substantially, making Spark several times faster than MapReduce, especially when performing machine learning and interactive analytics.
What are the Advantages of Using Apache Spark?
There are numerous perks of using Apache Spark. Three of the best ones are:
1. Fast
Spark can conduct quick analytic queries against any size of data because of in-memory caching and efficient query execution.
2. Developer-friendly
Apache Spark comes with native support for Java, Scala, R, and Python, providing you with a wide range of languages to choose from when developing your applications. These APIs make it simple for your developers by hiding the complexities of distributed processing behind simple, high-level operations, resulting in a significant reduction in the amount of code required.
3. Capable to Handle Various Workloads
Interactive searches, real-time data analytics, machine learning, and graph processing are just a few of the workloads that Apache Spark can handle. Multiple workloads can be effortlessly combined in a single application.
Components of Apache Spark
The Spark framework includes the following components:
- The platform’s foundation is Spark Core.
- It handles interactive queries with Spark SQL.
- For dealing with real-time analytics, it has Spark Streaming.
- MLlib is the scalable machine learning library for Apache Spark.
- It uses Spark GraphX for graph processing.
1) Spark Core
The platform’s foundation is Spark Core. Memory management, fault recovery, scheduling, distributing, and monitoring jobs, as well as communicating with storage systems, are all responsibilities of this component. An application programming interface (API) for Java, Scala, Python, and R is available to access Spark Core. The complexity of distributed processing is hidden behind simple, high-level operators in these APIs.
2) Spark SQL
Apache Spark is 100 times faster than MapReduce at providing low-latency, interactive queries. While growing to thousands of nodes, it incorporates a cost-based optimizer, columnar storage, and code generation. The Spark Packages ecosystem also includes Amazon Redshift, Amazon S3, Couchbase, Cassandra, MongoDB, Elasticsearch, and many other popular data stores.
3) Spark Streaming
Spark Streaming is a real-time streaming analytics solution that takes advantage of Spark Core’s quick scheduling capabilities. It ingests data in mini-batches and performs analytics on that data using the same batch analytics application code.
Developer productivity increases as a result of the ability to utilize the same code for batch processing and real-time streaming applications. Data from Twitter, Kafka, Flume, HDFS, and ZeroMQ, as well as many other Apache Spark Packages, is supported by Spark Streaming.
4) Spark MLlib
Apache Spark includes MLlib, a library of techniques for large-scale machine learning. Data scientists may train machine Learning models with R or Python on any Hadoop data source, save them with MLlib, and import them into a Java or Scala-based pipeline.
Spark was created to enable machine learning by allowing for fast, interactive computation that operates in memory. Classification, regression, clustering, collaborative filtering, and pattern mining are among the algorithms available.
5) Spark GraphX
Spark GraphX is a Spark-based distributed graph processing framework. GraphX enables users to interactively build and alter a graph data structure at scale via ETL, exploratory analysis, and iterative graph computation. It has a highly versatile API as well as a number of distributed graph algorithms.
Apache Spark vs. Apache Hadoop
There are many fundamental differences between Apache Spark and Apache Hadoop. Despite this, many organizations have found both big data frameworks to be complementary, therefore, combining them to tackle larger business problems.
Hadoop is an open-source system that includes the Hadoop Distributed File System (HDFS) for storage, YARN for managing computing resources shared by several applications, and an implementation of the MapReduce programming paradigm as an execution engine. Different execution engines, like Spark, Tez, and Presto, are used in a typical Hadoop deployment.
On the other hand, Spark is a free and open-source platform for interactive queries, machine learning, and real-time workloads. It doesn’t have its own storage system. Hence, it uses HDFS or other popular data stores like Amazon Redshift, Amazon S3, Couchbase, and Cassandra to execute analytics. Apache Spark on Hadoop makes use of YARN to share a shared cluster and dataset with other Hadoop engines. This ensures a continuous service and better response times.
Conclusion
Apache Spark is currently one of the best tools to deal with big data processing needs. Thanks to its thoughtful architecture, it is able to solve many real-time data processing needs with less effort and more efficiency. Paired with Apache Hadoop or Mesos, it offers a powerful system to deal with big data processing.
What do you like best about Spark? Prefer some of the big data processing framework? Jot down in the comments section below!
Aditya is a seasoned JavaScript developer with extensive experience in MEAN stack development. He also has solid knowledge of various programming tools and technologies, including .NET and Java. His hobbies include reading comics, playing games, and camping.