Spark Guide
Also available as:
loading table of contents...

Chapter 1. Introduction

Hortonworks Data Platform supports Apache Spark, a fast, large-scale data processing engine.

Deep integration of Spark with YARN allows Spark to operate as a cluster tenant alongside other engines such as Hive, Storm, and HBase, all running simultaneously on a single data platform. YARN allows flexibility: you can choose the right processing tool for the job. Instead of creating and managing a set of dedicated clusters for Spark applications, you can store data in a single location, access and analyze it with multiple processing engines, and leverage your resources. In a modern data architecture with multiple processing engines using YARN and accessing data in HDFS, Spark on YARN is the leading Spark deployment mode.

Spark Features

Spark on HDP supports the following features:

  • Spark Core

  • Spark on YARN

  • Spark on YARN on Kerberos-enabled clusters

  • Spark History Server

  • Spark MLLib

  • Support for Hive 1.2

  • ML Pipeline API

  • DataFrame API

  • Optimized Row Columnar (ORC) files

  • Spark SQL

  • Spark SQL Thrift Server

  • Spark Streaming

  • PySpark

  • Dynamic Resource Allocation

The following features and associated tools are available as technical previews:

The following features and associated tools are not officially supported by Hortonworks:

  • Spark Standalone

  • Spark on Mesos

  • Jupyter/iPython Notebook

  • Oozie Spark action is not supported, but there is a tech note available for HDP customers

Spark on YARN leverages YARN services for resource allocation, and runs Spark Executors in YARN containers. Spark on YARN supports workload management and Kerberos security features. It has two modes:

  • YARN-cluster mode, optimized for long-running production jobs.

  • YARN-client mode, best for interactive use such as prototyping, testing, and debugging. Spark Shell runs in YARN-client mode only.

Table 1.1. Spark - HDP Version Support


Table 1.2. Spark Feature Support by Version

Spark CoreYesYesYesYes
Spark on YARNYesYesYesYes
Spark on YARN, Kerberos-enabled clustersYesYesYesYes
Spark History ServerYesYesYesYes
Spark MLLibYesYesYesYes
Hive support, including collect_list UDF Hive version 0.13.1Hive version 0.13.1Hive version 1.2
ML Pipeline API  YesYes
DataFrame API TPYesYes
ORC Files TPYesYes
Spark Thrift Server TPTPYes
Spark StreamingTPTPTPYes
PySpark TPYesYes
Dynamic Resource Allocation TPTPYes*
SparkR  TPTP
GraphX   TP

   TP: Tech Preview

* Note: Dynamic Resource Allocation does not work with Spark Streaming.