Reader small image

You're reading from  Machine Learning with Spark. - Second Edition

Product typeBook
Published inApr 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781785889936
Edition2nd Edition
Languages
Right arrow
Authors (2):
Rajdeep Dua
Rajdeep Dua
author image
Rajdeep Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.
Read more about Rajdeep Dua

Manpreet Singh Ghotra
Manpreet Singh Ghotra
author image
Manpreet Singh Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.
Read more about Manpreet Singh Ghotra

View More author details
Right arrow

SchemaRDD

SchemaRDD is a combination of RDD and schema information. It also offers many rich and easy-to-use APIs (that is, the DataSet API). SchemaRDD is not used with 2.0 and is internally used by DataFrame and Dataset APIs.

A schema is used to describe how structured data is logically organized. After obtaining the schema information, the SQL engine is able to provide the structured query capability for the corresponding data. The DataSet API is a replacement for Spark SQL parser's functions. It is an API to achieve the original program logic tree. Subsequent processing steps reuse Spark SQL's core logic. We can safely consider DataSet API's processing functions as completely equivalent to that of SQL queries.

SchemaRDD is an RDD subclass. When a program calls the DataSet API, a new SchemaRDD object is created, and a logic plan attribute of the new object is created by adding a new logic operation node on the original logic plan tree. Operations of the DataSet API (like RDD) are of two types--Transformation and Action.

APIs related to the relational operations are attributed to the Transformation type.

Operations associated with data output sources are of Action type. Like RDD, a Spark job is triggered and delivered for cluster execution, only when an Action type operation is called.

Previous PageNext Page
You have been reading a chapter from
Machine Learning with Spark. - Second Edition
Published in: Apr 2017Publisher: PacktISBN-13: 9781785889936
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
undefined
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime

Authors (2)

author image
Rajdeep Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.
Read more about Rajdeep Dua

author image
Manpreet Singh Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.
Read more about Manpreet Singh Ghotra