Apache Spark Components

The Spark project consists of various types of components that are closely integrated. Spark is at its core a computational engine capable of scheduling, distributing, and monitoring multiple apps.

The main components of Spark are:

Apache Spark Components
  • Spark Core
  • Spark SQL
  • Spark Streaming
  • Mlib Machine Learning
  • GraphX graph Processing

Spark core

  • Spark Core is the heart of Spark, which is built on all other functionalities.
  • It includes the components for job scheduling, fault recovery, communicating with storage, and memory management systems.

Spark SQL

  • On top of Spark Core, the Spark SQL is developed. It supports structured data.
  • Spark SQL(Structured Query Language) allows querying data from SQL as well as Apache Hive of SQL, which is called HQL (Hive Query Language).
  • It supports connections between JDBC and ODBC that create a relationship between Java objects and existing databases, data warehouses, and business intelligence tools.
  • It also supports complex data sources such as Hive tables, Parquet, and JSON.

SQL Streaming

  • Spark Streaming is a component of Spark that supports scalable and fault-tolerant streaming data processing.
  • It uses the quick scheduling capabilities of Spark Core to conduct streaming analytics.
  • It accepts mini-batch data and executes RDD transformations on that data.
  • Its architecture ensures that the applications written for streaming data can be reused with little modification to analyze batches of historical data.
  • Log files generated by web servers can be considered as an example of a data stream in real-time.


  • MLlib is a type of machine library, which includes a variety of machine learning algorithms.
  • It comprises checking of associations and theories, classification and regression, clustering, and study of main components.
  • The disk-based implementations have been used nine times by the Apache mahout to make it faster.


  • GraphX is a library used to manipulate graphs and perform parallel graph computing.
  • It facilitates the development of a directed graph with arbitrary properties attached to each vertex and edge.
  • To control the graph, it supports various key operators, such as subgraph, merges Vertices, and aggregate Messages.