Spark Guide
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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 on YARN

  • Spark Core

  • Spark SQL

  • Spark SQL Thrift Server (JDBC/ODBC)

  • Spark MLLib

  • Spark Streaming

  • Spark History Server

  • DataFrame API

  • Optimized Row Columnar (ORC) files

  • ML Pipeline API

  • Support for Hive 1.2.1

  • PySpark

  • Dynamic Resource Allocation

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

  • SparkR

  • HBase connector

  • GraphX

  • Apache Zeppelin

  • DataSet API

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

  • Spark Standalone

  • Spark on Mesos

  • Jupyter/iPython Notebook


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 and Spark Thrift Server both run in YARN-client mode only.

Table 1.1. Spark - HDP Version Support


Table 1.2. Spark Feature Support by Version

Spark CoreYesYesYesYesYesYesYes
Spark on YARNYesYesYesYesYesYesYes
Spark on YARN, Kerberos-enabled clustersYesYesYesYesYesYesYes
Spark History ServerYesYesYesYesYesYesYes
Spark MLLibYesYesYesYesYesYesYes
Hive 13 (or later) support, including collect_list UDF Hive version 0.13.1Hive version 0.13.1Hive version 1.2.1Hive version 1.2.1Hive version 1.2.1Hive version 1.2.1
ML Pipeline API  YesYesYesYesYes
DataFrame API TPYesYesYesYesYes
ORC Files TPYesYesYesYesYes
PySpark TPYesYesYesYesYes
Spark Thrift Server (JDBC/ODBC) TPTPYesYesYesYes
Spark StreamingTPTPTPYesYesYesYes
Dynamic Resource Allocation TPTPYes*Yes*Yes*Yes*
Spark HBase connector     TPTP

   TP: Tech Preview

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