Spark

What is Spark?

Apache Spark is a powerful open source processing engine built around speed, ease of use, and sophisticated analytics. It was originally developed at UC Berkeley in 2009. Databricks was founded by the creators of Spark in 2013.

Since its release, Spark has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Yahoo, Baidu, and Tencent, have eagerly deployed Spark at massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. It has quickly become the largest open source community in big data, with over 500 contributors from 200+ organizations. To help Spark achieve this growth, Databricks continues to contribute broadly throughout the project, both with roadmap development and with community evangelism.

What is Spark used for?

Spark is a general-purpose engine used for many types of data processing. Spark comes packaged with support for ETL, interactive queries (SQL), advanced analytics (e.g. machine learning) and streaming over large datasets. For loading and storing data, Spark integrates with many storage systems (e.g. HDFS, Cassandra, HBase, S3). Spark is also pluggable, with dozens of third party libraries and storage integrations.
Additionally, Spark supports a variety of popular development languages including Java, Python and Scala.

“At Databricks, we’re working hard to make Spark easier to use and run than ever, through our efforts on both the Spark codebase and support materials around it. All of our work on Spark is open source and goes directly to Apache.”
— Matei Zaharia, VP, Apache Spark, Founder & CTO, Databricks

What are the benefits of Spark?

Spark was initially designed for interactive queries and iterative algorithms, as these were two major use cases not well served by batch frameworks like MapReduce. Consequently Spark excels in scenarios that require fast performance, such as iterative processing, interactive querying, large-scale batch computations, streaming, and graph computations.

Developers and enterprises typically deploy Spark because of its inherent benefits:

Simple

Easy-to-use APIs for operating on large datasets. This includes a collection over 100 operators for transforming data and familiar data frame APIs for manipulating semi-structured data.

Fast

Engineered from the bottom-up for performance, running 100x faster than Hadoop by exploiting in memory computing and other optimizations. Spark is also fast when data is stored on disk, and currently holds the world record for large-scale on-disk sorting.

Unified Engine

Packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning and graph processing. These standard libraries increase developer productivity and can be seamlessly combined to create complex workflows.

Broadly Compatible

Built-in support for many data sources, such as HDFS, RDBMS, S3, Cassandra, and MongoDB.


Spark Ecosystem

 

Spark_Ecosystem_Chart1

General Execution: Spark Core

Spark Core is the underlying general execution engine for the Spark platform that all other functionality is built on top of. It provides in-memory computing capabilities to deliver speed, a generalized execution model to support a wide variety of applications, and Java, Scala, and Python APIs for ease of development.

Structured Data: Spark SQL

Many data scientists, analysts, and general business intelligence users rely on interactive SQL queries for exploring data. Spark SQL is an engine for Hive data that enables unmodified Hadoop Hive queries to run up to 100x faster on existing deployments and data. It also provides powerful integration with the rest of the Spark ecosystem (e.g., integrating SQL query processing with machine learning).

Streaming Analytics: Spark Streaming

Many applications need the ability to process and analyze not only batch data, but also streams of new data in real-time. Running on top of Spark, Spark Streaming enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics. It readily integrates with a wide variety of popular data sources, including HDFS, Flume, Kafka, and Twitter.

Machine Learning: MLlib

Machine learning has quickly emerged as a critical piece in mining Big Data for actionable insights. Built on top of Spark, MLlib is a scalable machine learning library that delivers both high-quality algorithms (e.g., multiple iterations to increase accuracy) and blazing speed (up to 100x faster than MapReduce). The library is usable in Java, Scala, and Python as part of Spark applications, so that you can include it in complete workflows.

Graph Computation: GraphX

GraphX is a graph computation engine built on top of Spark that enables users to interactively build, transform and reason about graph structured data at scale. It comes complete with a library of common algorithms.

Other Projects

One of the benefits of Spark’s vibrant open-source community is continued innovation that helps extend Spark’s capabilities, many of which originated in UC Berkeley’s AMPLab. Here is a sampling of some on-going projects in the community (that are still in alpha):

BlinkDB: An approximate query engine for interactive SQL queries in Shark that allows users to trade-off query accuracy for response time. This enables interactive queries over massive data by using data samples and presenting results annotated with meaningful error bars.

 

SparkR: A package for the R statistical language that enables R-users to leverage Spark functionality interactively from within the R shell.


Get Spark

The 100% open source Apache Spark project can be downloaded from Apache.
The site also contains installation instructions, video tutorials, and documentation to get you started.

Download Spark