Chapter 1. Analyzing Data with Apache Spark
Hortonworks Data Platform (HDP) 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 Apache engines such as Hive, Storm, and HBase, all running simultaneously on a single data platform. 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.
Spark on YARN leverages YARN services for resource allocation, runs Spark executors in YARN containers, and 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 the Spark Thrift server run in YARN-client mode only.
HDP 2.6 supports Spark versions 1.6 and 2.0; Livy, for local and remote access to Spark through the Livy REST API; and Apache Zeppelin, for browser-based notebook access to Spark. (For more information about Zeppelin, see the Zeppelin Component Guide.)
Table 1.1. Spark and Livy Feature Support by HDP Version
| HDP Version(s) | 2.6.5 | 2.6.4 | 2.6.3 | 2.6.2 | 2.6.1 | 2.6.0 | 2.5.0, 2.5.3 | 2.4.3 | 2.4.2 | 2.4.0 | 2.3.4, 2.3.4.7, 2.3.6 | 2.3.2 | 2.2.8, 2.2.9, 2.3.0, | 2.2.4, 2.2.6 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Spark Version | 1.6.3, 2.3.0 | 1.6.3, 2.2.0 | 1.6.3, 2.2.0 | 1.6.3, 2.1.1 | 1.6.3, 2.1.1 | 1.6.3, 2.1.0 | 1.6.2 | 1.6.2 | 1.6.1 | 1.6.0 | 1.5.2 | 1.4.1 | 1.3.1 | 1.2.1 |
| Support for Livy | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 | ||||||||
| Support for Hive | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 1.2.1 | 0.13.1 | 0.13.1 | |
| Spark Core | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Spark on YARN | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Spark on YARN for Kerberos-enabled clusters | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Spark history server | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Spark MLlib | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| ML Pipeline API | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
| DataFrame API | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | |
| Optimized Row Columnar (ORC) Files | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | |
| PySpark | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | |
| SparkR | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | TP | TP | TP | TP | ||
| Spark SQL | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | TP | TP |
| Spark SQL Thrift server for JDBC, ODBC access | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | TP | |
| Spark Streaming | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | TP | TP |
| Structured Streaming | TP | TP | TP | |||||||||||
| Dynamic resource allocation | ✓* | ✓* | ✓* | ✓* | ✓* | ✓* | ✓* | ✓* | ✓* | ✓* | ✓* | TP | TP | |
| HBase connector | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | TP | TP | |||||
| GraphX | TP | TP | TP | TP | TP | TP | TP | TP | TP | TP | TP | |||
| DataSet API | TP | TP | TP | TP | TP | TP | TP | TP | TP |
* Dynamic Resource Allocation does not work with Spark Streaming.
TP: Technical Preview. Technical previews are considered under development. Do not use these features in production systems. If you have questions regarding these features, contact Support through the Hortonworks Support Portal, https://support.hortonworks.com.
The following features and associated tools are not officially supported by Hortonworks:
Spark Standalone
Spark on Mesos
Jupyter Notebook (formerly IPython)