Major Technical Advancements in Apache Hive
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E6895 Advanced Big Data Analytics Lecture 4: Data Store
E6895 Advanced Big Data Analytics Lecture 4: Data Store Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Chief Scientist, Graph Computing, IBM Watson Research Center E6895 Advanced Big Data Analytics — Lecture 4 © CY Lin, 2016 Columbia University Reference 2 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Spark SQL 3 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Spark SQL 4 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Apache Hive 5 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using Hive to Create a Table 6 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Creating, Dropping, and Altering DBs in Apache Hive 7 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Another Hive Example 8 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Hive’s operation modes 9 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using HiveQL for Spark SQL 10 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Hive Language Manual 11 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using Spark SQL — Steps and Example 12 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Query testtweet.json Get it from Learning Spark Github ==> https://github.com/databricks/learning-spark/tree/master/files 13 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University SchemaRDD 14 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Row Objects Row objects represent records inside SchemaRDDs, and are simply fixed-length arrays of fields. -
File Formats for Big Data Storage Systems
International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019 File Formats for Big Data Storage Systems Samiya Khan, Mansaf Alam analysis or visualization. Hadoop allows the user to store data Abstract: Big data is one of the most influential technologies of on its repository in several ways. Some of the commonly the modern era. However, in order to support maturity of big data available and understandable working formats include XML systems, development and sustenance of heterogeneous [9], CSV [10] and JSON [11]. environments is requires. This, in turn, requires integration of Although, JSON, XML and CSV are human-readable technologies as well as concepts. Computing and storage are the two core components of any big data system. With that said, big formats, but they are not the best way, to store data, on a data storage needs to communicate with the execution engine and Hadoop cluster. In fact, in some cases, storage of data in such other processing and visualization technologies to create a raw formats may prove to be highly inefficient. Moreover, comprehensive solution. This brings the facet of big data file parallel storage is not possible for data stored in such formats. formats into picture. This paper classifies available big data file In view of the fact that storage efficiency and parallelism are formats into five categories namely text-based, row-based, the two leading advantages of using Hadoop, the use of raw column-based, in-memory and data storage services. It also compares the advantages, shortcomings and possible use cases of file formats may just defeat the whole purpose. -
Research Article Improving I/O Efficiency in Hadoop-Based Massive Data Analysis Programs
Hindawi Scientific Programming Volume 2018, Article ID 2682085, 9 pages https://doi.org/10.1155/2018/2682085 Research Article Improving I/O Efficiency in Hadoop-Based Massive Data Analysis Programs Kyong-Ha Lee ,1 Woo Lam Kang,2 and Young-Kyoon Suh 3 1Research Data Hub Center, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea 2School of Computing, KAIST, Daejeon, Republic of Korea 3School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea Correspondence should be addressed to Young-Kyoon Suh; [email protected] Received 30 April 2018; Revised 24 October 2018; Accepted 6 November 2018; Published 2 December 2018 Academic Editor: Basilio B. Fraguela Copyright © 2018 Kyong-Ha Lee et al. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Apache Hadoop has been a popular parallel processing tool in the era of big data. While practitioners have rewritten many conventional analysis algorithms to make them customized to Hadoop, the issue of inefficient I/O in Hadoop-based programs has been repeatedly reported in the literature. In this article, we address the problem of the I/O inefficiency in Hadoop-based massive data analysis by introducing our efficient modification of Hadoop. We first incorporate a columnar data layout into the conventional Hadoop framework, without any modification of the Hadoop internals. We also provide Hadoop with indexing capability to save a huge amount of I/O while processing not only selection predicates but also star-join queries that are often used in many analysis tasks. -
Unravel Data Systems Version 4.5
UNRAVEL DATA SYSTEMS VERSION 4.5 Component name Component version name License names jQuery 1.8.2 MIT License Apache Tomcat 5.5.23 Apache License 2.0 Tachyon Project POM 0.8.2 Apache License 2.0 Apache Directory LDAP API Model 1.0.0-M20 Apache License 2.0 apache/incubator-heron 0.16.5.1 Apache License 2.0 Maven Plugin API 3.0.4 Apache License 2.0 ApacheDS Authentication Interceptor 2.0.0-M15 Apache License 2.0 Apache Directory LDAP API Extras ACI 1.0.0-M20 Apache License 2.0 Apache HttpComponents Core 4.3.3 Apache License 2.0 Spark Project Tags 2.0.0-preview Apache License 2.0 Curator Testing 3.3.0 Apache License 2.0 Apache HttpComponents Core 4.4.5 Apache License 2.0 Apache Commons Daemon 1.0.15 Apache License 2.0 classworlds 2.4 Apache License 2.0 abego TreeLayout Core 1.0.1 BSD 3-clause "New" or "Revised" License jackson-core 2.8.6 Apache License 2.0 Lucene Join 6.6.1 Apache License 2.0 Apache Commons CLI 1.3-cloudera-pre-r1439998 Apache License 2.0 hive-apache 0.5 Apache License 2.0 scala-parser-combinators 1.0.4 BSD 3-clause "New" or "Revised" License com.springsource.javax.xml.bind 2.1.7 Common Development and Distribution License 1.0 SnakeYAML 1.15 Apache License 2.0 JUnit 4.12 Common Public License 1.0 ApacheDS Protocol Kerberos 2.0.0-M12 Apache License 2.0 Apache Groovy 2.4.6 Apache License 2.0 JGraphT - Core 1.2.0 (GNU Lesser General Public License v2.1 or later AND Eclipse Public License 1.0) chill-java 0.5.0 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 OpenCensus 0.12.3 Apache License 2.0 ApacheDS Protocol -
Hive, Spark, Presto for Interactive Queries on Big Data
DEGREE PROJECT IN INFORMATION AND COMMUNICATION TECHNOLOGY, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Hive, Spark, Presto for Interactive Queries on Big Data NIKITA GUREEV KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE TRITA TRITA-EECS-EX-2018:468 www.kth.se Abstract Traditional relational database systems can not be efficiently used to analyze data with large volume and different formats, i.e. big data. Apache Hadoop is one of the first open-source tools that provides a dis- tributed data storage system and resource manager. The space of big data processing has been growing fast over the past years and many tech- nologies have been introduced in the big data ecosystem to address the problem of processing large volumes of data, and some of the early tools have become widely adopted, with Apache Hive being one of them. How- ever, with the recent advances in technology, there are other tools better suited for interactive analytics of big data, such as Apache Spark and Presto. In this thesis these technologies are examined and benchmarked in or- der to determine their performance for the task of interactive business in- telligence queries. The benchmark is representative of interactive business intelligence queries, and uses a star-shaped schema. The performance Hive Tez, Hive LLAP, Spark SQL, and Presto is examined with text, ORC, Par- quet data on different volume and concurrency. A short analysis and con- clusions are presented with the reasoning about the choice of framework and data format for a system that would run interactive queries on big data. -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo........................................................................................................................................................... -
Apache Hadoop Goes Realtime at Facebook
Apache Hadoop Goes Realtime at Facebook Dhruba Borthakur Joydeep Sen Sarma Jonathan Gray Kannan Muthukkaruppan Nicolas Spiegelberg Hairong Kuang Karthik Ranganathan Dmytro Molkov Aravind Menon Samuel Rash Rodrigo Schmidt Amitanand Aiyer Facebook {dhruba,jssarma,jgray,kannan, nicolas,hairong,kranganathan,dms, aravind.menon,rash,rodrigo, amitanand.s}@fb.com ABSTRACT 1. INTRODUCTION Facebook recently deployed Facebook Messages, its first ever Apache Hadoop [1] is a top-level Apache project that includes user-facing application built on the Apache Hadoop platform. open source implementations of a distributed file system [2] and Apache HBase is a database-like layer built on Hadoop designed MapReduce that were inspired by Googles GFS [5] and to support billions of messages per day. This paper describes the MapReduce [6] projects. The Hadoop ecosystem also includes reasons why Facebook chose Hadoop and HBase over other projects like Apache HBase [4] which is inspired by Googles systems such as Apache Cassandra and Voldemort and discusses BigTable, Apache Hive [3], a data warehouse built on top of the applications requirements for consistency, availability, Hadoop, and Apache ZooKeeper [8], a coordination service for partition tolerance, data model and scalability. We explore the distributed systems. enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the At Facebook, Hadoop has traditionally been used in conjunction system, and how this solution has significant advantages over the with Hive for storage and analysis of large data sets. Most of this sharded MySQL database scheme used in other applications at analysis occurs in offline batch jobs and the emphasis has been on Facebook and many other web-scale companies. -
Apache Ranger 0.5.0 Installation
Apache Ranger 0.5.0 Installation 1 Overview 2 Prerequisites 3 Installation Instructions 3.1 Preparing to install 3.1.1 Install Maven 3.1.2 Install git 3.1.3 Install gcc 3.1.4 Install MySQL 3.2 Build Ranger Admin from source 3.2.1 Clone the ranger source code 3.2.2 Build the source 3.2.3 Install Steps for Ranger Policy Admin on RHEL/CentOS 3.2.3.1 Lay down the build into appropriate places. 3.2.3.2 Install and configure Solr or SolrCloud 3.3 Installing the Ranger UserSync Process 3.4 Installing Apache Hadoop 3.4.1 Enabling Ranger HDFS Plugins 3.5 Installing Apache Hive(1.2.0) 3.5.1 Enabling Ranger Hive Plugin 3.5.2 Helpful info Ranger Hive Plugin / Hive Services 3.6 Installing Apache HBase (1.1.0.1) 3.6.1 Enabling Ranger HBase Plugins 3.6.2 Helpful info Ranger HBase Plugin / HBase Services 3.7 Installing Apache Knox Gateway 3.7.1 Enabling Ranger Knox Plugins 3.7.2 Helpful info Ranger Knox Plugin / Knox Services 3.7.3 Trusting Self Signed Knox Certificate 3.8 Enabling Ranger Solr Plugin 3.8.1 Install and configure Apache Solr in SolrCloud mode 3.8.2 Solr Service in Ranger Admin 3.8.3 Install and Enable Solr Plugin 3.8.4 Configuring Solr for Ranger 3.9 Installing Apache Storm (0.10.0) 3.9.1 Enabling Ranger Storm Plugins 3.9.2 Enabling Ranger Yarn Plugin 4 Installing Ranger KMS (0.5.0) 5 Enabling Audit Logging To HDFS 6 Enabling Audit Logging To SOLR 7 Configuring Kerberos Authentication 7.1 Installing and Configuring the KDC 7.2 Creating the Kerberos Database 7.3 Installing and Configuring the Kerberos Clients 7.4 Configure Storm to Work with a Secured Zookeeper Cluster 7.5 Configure Kerberos Authentication for Storm 7.6 Ranger UI setup Overview This document details the steps involved in Installing latest version of Apache Incubator Ranger independently on RHEL / Ubuntu / SUSE / Debian OS. -
International Journal of Advanced Research in Computer Science And
Volume 5, Issue 9, September 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Effective Image Analysis on Twitter Streaming using Hadoop Eco System on Amazon Web Service EC2 Gautam Goswami Irisidea Technologies Pvt. Limited, India Abstract: Twitter is becoming the most popular online micro blogging network of real time post that enables users to send and read short 140-character messages called "tweets". Registered users can read and post tweets, but unregistered users can only read them. Today's Twitter is now less focused on what are you doing but has emerged as a source for discovery, with a focus on sharing relevant information and engaging in conversation. Sharing various visual information in the form of images/photos are becoming very popular where all the follower can see what images/photos have been posted/twitted instantly. In this paper I am going to explain how effectively registered users shares/uploads images among the followers. This information/statistics would be of great value for any organization/company when they launch their new product in market. If a particular image/photo sharing is high among tweeter community, organization/company can be assured that their product is penetrating more in the market. Here I have analyzed the momentum of visual information propagation . So that followers can be aware of that something new have been lunched in market and subsequently will have the curiosity to dig more on it. In this paper, the collected twitter steaming which has been (rich amount of data in semi structure format JSON for an interval of time) referred to as big data are processed efficiently to achieve mentioned output. -
Cloudera JDBC Driver for Hive Installation and Configuration Guide
Cloudera JDBC Driver for Apache Hive Important Notice © 2010-2021 Cloudera, Inc. All rights reserved. Cloudera, the Cloudera logo, and any other product or service names or slogans contained in this document, except as otherwise disclaimed, are trademarks of Cloudera and its suppliers or licensors, and may not be copied, imitated or used, in whole or in part, without the prior written permission of Cloudera or the applicable trademark holder. Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation. All other trademarks, registered trademarks, product names and company names or logos mentioned in this document are the property of their respective owners. Reference to any products, services, processes or other information, by trade name, trademark, manufacturer, supplier or otherwise does not constitute or imply endorsement, sponsorship or recommendation thereof by us. Complying with all applicable copyright laws is the responsibility of the user. Without limiting the rights under copyright, no part of this document may be reproduced, stored in or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose, without the express written permission of Cloudera. Cloudera may have patents, patent applications, trademarks, copyrights, or other intellectual property rights covering subject matter in this document. Except as expressly provided in any written license agreement from Cloudera, the furnishing of this document does not give you any license to these patents, trademarks copyrights, or other intellectual property. The information in this document is subject to change without notice. Cloudera shall not be liable for any damages resulting from technical errors or omissions which may be present in this document, or from use of this document. -
TR-4744: Secure Hadoop Using Apache Ranger with Netapp In
Technical Report Secure Hadoop using Apache Ranger with NetApp In-Place Analytics Module Deployment Guide Karthikeyan Nagalingam, NetApp February 2019 | TR-4744 Abstract This document introduces the NetApp® In-Place Analytics Module for Apache Hadoop and Spark with Ranger. The topics covered in this report include the Ranger configuration, underlying architecture, integration with Hadoop, and benefits of Ranger with NetApp In-Place Analytics Module using Hadoop with NetApp ONTAP® data management software. TABLE OF CONTENTS 1 Introduction ........................................................................................................................................... 4 1.1 Overview .........................................................................................................................................................4 1.2 Deployment Options .......................................................................................................................................5 1.3 NetApp In-Place Analytics Module 3.0.1 Features ..........................................................................................5 2 Ranger ................................................................................................................................................... 6 2.1 Components Validated with Ranger ................................................................................................................6 3 NetApp In-Place Analytics Module Design with Ranger.................................................................. -
Customer Behavior Analysis of Web Server Logs Using Hive in Hadoop Framework Lavanya KS, Srinivasa R Dept
International Journal of Advanced Networking & Applications (IJANA) ISSN: 0975-0282 Customer behavior analysis of web server logs using Hive in Hadoop Framework Lavanya KS, Srinivasa R Dept. of Studies in CSE, RRCE College of Engineering, Bangalore, Karnataka, India Email: [email protected] , [email protected] Abstract: Web log file is a log file created and stored by a web server automatically. Analyzing such web server access logs files will provide us various insights about website usage. Due to high usage of web, the log files are growing at much faster rate with increase in size. Processing this fast growing log files using relational database technology has been a challenging task these days. Therefore to analyze such large datasets we need a parallel processing system and a reliable data storage mechanism (Hadoop). Hadoop runs the big data where a massive quantity of information is processed via cluster of commodity hardware. In this paper we present the methodology used in pre-processing of high volume web log files, studying the statics of website and learning the user behavior using the architecture of Hadoop MapReduce framework, Hadoop Distributed File System, and HiveQL query language. Keywords: big data, customer behavior analysis, hadoop, log analysis, web server logs Foundation. Hadoop enables applications to work with 1. INTRODUCTION thousands of nodes with petabytes of data. While it can be used on a single machine, its true capability lies in scaling to In today's competitive environment, manufacturers/service hundreds or thousands of computers. Tom White describes providers are keen to know whether they provide the best Hadoop is specially designed to work on large volume of service/product to customers or whether customers look data using commodity hardware in parallel.