What is Apache Spark and how does it work?

In com­par­i­son to its pre­de­ces­sors such as Hadoop or com­peti­tors like PySpark, Apache Spark excels thanks to its im­pres­sive­ly quick per­for­mance. This is one of the most important aspects when querying, pro­cess­ing and analyzing large amounts of data. As a big-data and in-memory analytics framework Spark offers many benefits for data analysis, machine learning, data streaming and SQL.

What is Apache Spark?

Apache Spark, the data analysis framework from Berkeley is one of the most popular big-data platforms worldwide and is a “top-level project” for the Apache Software Foun­da­tion. The analytics engine is used to process large amounts of data and analyze data at the same time in dis­trib­uted computer clusters. Spark was developed to meet the demands of big data in regard to computing speeds, ex­pand­abil­i­ty and scal­a­bil­i­ty.

It has in­te­grat­ed modules which are ben­e­fi­cial for cloud computing, machine learning, AI ap­pli­ca­tions as well as streaming and graphical data. Due to its power and scal­a­bil­i­ty the engine is used by large companies such as Netflix, Yahoo and eBay.

What makes Apache Spark special?

Apache Spark is a much quicker and more powerful engine than Apache Hadoop or Apache Hive. It processes tasks 100-times quicker compared to Hadoop if the pro­cess­ing is carried out in the memory and ten-times faster if using the hard drive. This means that Spark gives companies improved per­for­mance which at the same time reduces costs.

One of the most in­ter­est­ing things about Spark is its flex­i­bil­i­ty. The engine can be run not only as a stand­alone option, but also in Hadoop clusters run by YARN. It also allows de­vel­op­ers to write ap­pli­ca­tions for Spark in different pro­gram­ming languages. It’s not only SQL which can be used, but also Python, Scala, R or Java.

There are other char­ac­ter­is­tics which make Spark special, for example, it doesn’t need to use the Hadoop file system and it can also be run on other data platforms such as AWS S3, Apache Cassandra or HBase. Fur­ther­more, when spec­i­fy­ing the data source, it processes both batch processes, which is the case with Hadoop, as well as stream data and different workloads with almost identical code. With an in­ter­ac­tive query process, you can dis­trib­ute and process current and historic real time data as well as run mul­ti­lay­er analysis on the hard drive and memory.

How does Spark work?

The way Spark works is based on the hi­er­ar­chi­cal, primary-secondary principal (pre­vi­ous­ly known as the master-slave model. To do this the Spark driver serves as a master node managed by the cluster manager. This in turn manages the slave nodes and forwards data analysis to the client. The dis­tri­b­u­tion and mon­i­tor­ing of the ex­e­cu­tions and queries is carried out by Spark­Con­text, created by the Spark driver. It co­op­er­ates with the cluster managers on how they offer Spark, Yarn, Hadoo or Ku­ber­netes. This in turn creates resilient dis­trib­uted datasets (RDDs).

Spark sets what resources are used to query or save data or where queried data should be sent. By **dy­nam­i­cal­ly pro­cess­ing the engine data directly in the memory of server clusters it reduces latency and offers very fast per­for­mance. In addition, parallel workflows are used together with the use of virtual as well as physical memory.

Apache Spark also processes data from different data storages. Among these you’ll find the Hadoop Dis­trib­uted File System (HDFS) and re­la­tion­al data storages such as Hive or NoSQL databases. On top of this there is the per­for­mance-in­creas­ing in-memory or hard-disk pro­cess­ing. Which one depends on the size of the cor­re­spond­ing datasets.

RDDs as a dis­trib­uted, error-proof dataset

Resilient dis­trib­uted datasets are important in Apache Spark to process struc­tured or un­struc­tured data. These are error-tolerant data ag­gre­ga­tions, which Spark dis­trib­utes using clus­ter­ing on server clusters and processes them at the same time or moves them to data storage. It’s also possible to forward them to other analysis models. In RDDs, datasets are separated into logical par­ti­tions which are opened, newly created or processed as well as cal­cu­lat­ed with trans­for­ma­tions and actions.

DataFrames und Datasets

Other data types processed by Spark are known as DataFrames and Datasets. DataFrames are APIs set up as data tables in rows and columns. On the other hand, Datasets are an extension to DataFrames for an object-oriented user interface for pro­gram­ming. By far, DataFrames play a key role in par­tic­u­lar when used with the Machine Learning Library (MLlib) as an API with a unique structure across pro­gram­ming languages.

Which language does Spark use?

Spark was developed using Scala, which is also the primary language for the Spark Core engine. In addition, Spark also has con­nec­tors to Java and Python. Python offers many benefits for effective data analysis in par­tic­u­lar for data science and data en­gi­neer­ing with Spark in con­nec­tion with other pro­gram­ming languages. Spark also supports high-level in­ter­faces for the data science language R, which is used for large datasets and machine learning.

When is Spark used?

Spark is suitable for many different in­dus­tries thanks to its varied library and data storage, the many pro­gram­ming languages which are com­pat­i­ble with APIs as well as the effective in-memory pro­cess­ing. If you need to process, query or calculate large and com­pli­cat­ed amounts of data, thanks to its speed, scal­a­bil­i­ty and flex­i­bil­i­ty, Spark is a great solution for busi­ness­es, es­pe­cial­ly when it comes to big data. Spark is par­tic­u­lar­ly popular in online marketing and e-commerce busi­ness­es as well as financial companies to evaluate financial data or for in­vest­ment models as well as sim­u­la­tions, ar­ti­fi­cial in­tel­li­gence and trend fore­cast­ing.

Spark is primarily used for the following reasons:

• The pro­cess­ing, in­te­gra­tion and col­lec­tion of datasets from different sources and ap­pli­ca­tions
• The in­ter­ac­tive querying and analysis of big data
• The eval­u­a­tion of data streams in real time
• Machine learning and ar­ti­fi­cial in­tel­li­gence
• Large ETL processes

Important com­po­nents and libraries in the Spark ar­chi­tec­ture

Among the most important elements of the Spark ar­chi­tec­ture include:

Spark Core

Spark Core is the basis of the entire Spark system and makes the core Spark features available as well as managing the task dis­tri­b­u­tion, data ab­strac­tion, use planning and the input and output processes. Spark Core uses RDDs dis­trib­uted across multiple server clusters and computers as its data structure. It’s also the basis for Spark SQL, libraries, Spark Streaming and all other important in­di­vid­ual com­po­nents.

Spark SQL

Spark SQL is a par­tic­u­lar­ly well used library, which can be used with RRDs as SQL queries. For this, Spark SQL generates temporary DataFrame tables. You can use Spark SQL to access various data sources, work with struc­tured data as well as use data queries via SQL and other DataFrame APIs. What’s more, Spark SQL allows you to connect to the HiveQL database language to access a managed data warehouse using Hive.

Spark Streaming

This high-level API function allows you to use highly scalable, error-proof data streaming functions and con­tin­u­al­ly process or create data streams in real time. Spark generates in­di­vid­ual packages for data actions from these streams. You can also employ trained machine learning modules in the data streams.

MLIB Machine Learning Library

This scalable Spark library has machine learning code to use expanded, sta­tis­ti­cal processes in server clusters or to develop analysis ap­pli­ca­tions. They include common learning al­go­rithms such as clus­ter­ing, re­gres­sion, clas­si­fi­ca­tion and rec­om­men­da­tion, workflow services, model eval­u­a­tions, linear dis­trib­uted sta­tis­tics and algebra or feature trans­for­ma­tions. You can use MLlib to ef­fec­tive­ly scale and simplify machine learning.

GraphX

The Spark API GraphX works to calculate graphs and combines ETL, in­ter­ac­tive graph pro­cess­ing and ex­plo­rative analysis.

How did Apache Spark come about?

Apache Spark was developed in 2009 at the Uni­ver­si­ty of Cal­i­for­nia, Berkeley as part of the AMPlabs framework. Since 2010, it’s been available for free under an open-source license. Further de­vel­op­ment and op­ti­miza­tion of Spark started in 2013 by the Apache Software Foun­da­tion. The pop­u­lar­i­ty and potential of the big data framework ensured that Spark was named as a “top level project” by AFS in February 2014. In May 2014, Spark version 1.0 was published. Currently (as of April 2023) Spark is running version 3.3.2.

The aim of Spark was to ac­cel­er­ate queries and tasks in Hadoop systems. With the Spark Core basis, it allows task transfer, entry and output func­tion­al­i­ties as well as in-memory pro­cess­ing which far in a way out­per­form the common Hadoop framework MapReduce through its dis­trib­uted functions.

What are the pros of Apache Spark?

To quickly query and process large data amounts Spark offers the following benefits:

• Speed: Workloads can be processed and executed up to 100-times faster compared to Hadoop’s MapReduce. Other per­for­mance benefits come from support for batch and stream data pro­cess­ing, directed cyclical graphs, a physical execution engine as well as query op­ti­miza­tion.
• Scal­a­bil­i­ty: With in-memory pro­cess­ing of data dis­trib­uted on clusters, Spark offers flexible, needs-based resource scal­a­bil­i­ty.
• Uni­for­mi­ty: Spark works as a complete big-data framework which combines different features and libraries in one ap­pli­ca­tion. Among these include SQL queries, DataFrames, Spark Streaming, MLlib for machine learning and Graph X for graph pro­cess­ing. This also includes a con­nec­tion to HiveQL.
• User friend­li­ness: Thanks to the user-friendly API in­ter­faces to different data sources as well as over 80 common operators to develop ap­pli­ca­tions, Spark connects multiple ap­pli­ca­tion options in one framework. It’s par­tic­u­lar­ly useful when using Scala, Python, R or SQL shells to write services.
• Open-source framework: With its open-source design, Spark offers an active, global community made up of experts who con­tin­u­ous­ly develop Spark, close security gaps and quickly push im­prove­ments.
• Increase in ef­fi­cien­cy and cost re­duc­tions: Since you don’t need physical high-end server struc­tures to use Spark, when it comes to big-data analysis, the platform is a cost-reducing and powerful feature. This is es­pe­cial­ly true for computer-intensive machine learning al­go­rithms and complex parallel data processes.

What are the cons of Apache Spark?

Despite all of its strengths, Spark also has some dis­ad­van­tages. One of those is the fact that Spark doesn’t have an in­te­grat­ed storage engine and therefore relies on many dis­trib­uted com­po­nents. Fur­ther­more, due to the in-memory pro­cess­ing, you need a lot of RAM which can cause a lack of resources affecting per­for­mance. What’s more if you use Spark, it takes a long time to get used to it, to un­der­stand the back­ground processes when in­stalling your own Apache web server or another cloud structure using Spark.