DOCSLIB.ORG

IBM Informix Dynamic Server Getting Started Guide

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
IBM Informix Dynamic Server Getting Started Guide IBM Informix Version 11.1 IBM Informix Dynamic Server Getting Started Guide G229-6372-00 IBM Informix Version 11.1 IBM Informix Dynamic Server Getting Started Guide G229-6372-00 Note: Before using this information and the product it supports, read the information in “Notices” on page C-1. This document contains proprietary information of IBM. It is provided under a license agreement and is protected by copyright law. The information contained in this publication does not include any product warranties, and any statements provided in this publication should not be interpreted as such. When you send information to IBM, you grant IBM a nonexclusive right to use or distribute the information in any way it believes appropriate without incurring any obligation to you. © Copyright International Business Machines Corporation 1996, 2007. All rights reserved. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Introduction . vii In This Introduction . vii About This Publication . vii Types of Users . vii Software Dependencies . viii Assumptions About Your Locale . viii Demonstration Database . viii New Features in Version 11.10 . .ix Documentation Conventions . .ix Typographical Conventions . .ix Feature, Product, and Platform Markup . .x Syntax Diagrams . .x Example Code Conventions . xiii Additional Documentation . xiv IBM Informix Information Center . xiv Installation Guides . xiv Online Notes . xiv Informix Error Messages . xvi Publications . xvi Online Help . xvi Accessibility . xvii Compliance with Industry Standards . xvii IBM Welcomes Your Comments . xvii Chapter 1. Introducing Dynamic Server and Client Products . 1-1 In This Chapter . 1-1 IBM Informix Dynamic Server . 1-1 IBM Informix Dynamic Server Edition Comparison . 1-2 Installation and Migration . 1-2 Products Bundled with the Database Server . 1-2 BladeManager . 1-2 DataBlade API . 1-2 DataBlade Developers Kit . 1-3 IBM Informix Spatial DataBlade module . 1-3 Server Studio . 1-3 IBM Informix Client SDK Products . 1-3 Related IBM Informix Products . 1-5 IBM Informix Server Administrator (ISA) . 1-5 OpenAdmin Tool for IDS . 1-6 IBM Informix MaxConnect (UNIX) . 1-6 IBM Office Connect . 1-6 IBM Informix Data Director for Web . 1-6 DataBlade Modules . 1-6 Related IBM Data Server Products . 1-7 Other Related IBM Products . 1-7 Global Language Support . 1-9 Chapter 2. Using New Features in Dynamic Server . 2-1 In This Chapter . 2-4 New Features in Version 11.10 of IBM Informix Dynamic Server . 2-5 Multiple Remote Standalone Secondary Servers . 2-6 Multiple Shared Disk Secondary Servers . 2-6 Backup and Restore to Directories with ontape . 2-6 Continuous Logical Log Restore . 2-6 Encrypted Communications for HDR . 2-7 © Copyright IBM Corp. 1996, 2007 iii Improved Parallelism during Backup and Restore . 2-7 Automatic Ordering of dbspaces during Backup and Restore . 2-7 RTO Policy to Manage Server Restart . 2-7 Non-blocking Checkpoints . 2-7 Performance Improvements for Enterprise Replication . 2-8 ON-Bar Performance Report . 2-8 Transform Data during Backup and Restore . 2-8 Improved Performance for Cooked Files with Direct I/O on UNIX . 2-8 Improved Performance of Online Index Creation . 2-9 SQL Administration API . 2-9 Schedule Administrative Tasks . 2-9 Monitor and Analyze Recent SQL Statements . 2-10 Dynamically Change Enterprise Replication Configuration Parameters and Environment Variables . 2-10 Dynamically Rename Enterprise Replication Columns, Tables, and Databases . 2-10 Truncate Replicated Tables . 2-10 Improved Statistics Maintenance . 2-10 Installation Improvements on Windows Platforms . 2-11 Session Configuration Routines . 2-11 Multiple Users for Administration Mode . 2-11 PHP-based OpenAdmin Tool for IDS . 2-12 Named Parameters in a JDBC CallableStatement . 2-12 Index Binary Data Types . 2-12 Trigger Enhancements . 2-12 Derived tables in the FROM Clause of Queries . 2-13 Index Self-Join Query Plans . 2-13 Optimizer Directives in ANSI-Compliant Joined Queries . 2-14 Deployment Wizard . 2-14 Enhanced Concurrency with Committed Read Isolation . 2-14 Enhanced Data Types and UDR Support in Cross-Server Distributed Operations . 2-15 XML Publishing . 2-15 Index Hierarchical Data . 2-15 Basic Text Search . 2-16 Improved Concurrency with Private Memory Caches for Virtual Processors . 2-16 Web Feature Service for Geospatial Data . 2-16 Support for Data Server Clients with DRDA . 2-16 Statement Labels, GOTO, and LOOP Statements in the SPL language . 2-17 New SQL Functions . 2-17 Automatic Re-Compilation of Prepared Statements . 2-18 Label-Based Access Control . 2-18 New Features in Version 10.00 of IBM Informix Dynamic Server . 2-19 New Features in Version 10.00.xC4 . 2-19 New Features in Version 10.00.xC3 . 2-20 New Features in Version 10.00.xC1 . 2-23 New Features in Version 9.4 . 2-33 Security Enhancement . 2-34 Database Server Usability Enhancements . 2-34 Performance Enhancements . 2-36 Enterprise Replication Enhancements . 2-37 Extensibility Enhancements . 2-39 SQL Enhancements . 2-40 GLS Enhancements . 2-44 Reliability, Availability, and Supportability Features . 2-44 DataBlade API Enhancements . 2-45 High-Performance Loader Enhancements . 2-46 Backup and Restore Enhancements . 2-46 Installation Enhancements . 2-46 Changed or New URLs . ..
Load more

Recommended publications
  • Socrates: the New SQL Server in the Cloud
    Socrates: The New SQL Server in the Cloud Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, Vijendra Purohit, Hugh Qu, Chaitanya Sreenivas Ravella, Krystyna Reisteter, Sheetal Shrotri, Dixin Tang, Vikram Wakade Microsoft Azure & Microsoft Research ABSTRACT 1 INTRODUCTION The database-as-a-service paradigm in the cloud (DBaaS) The cloud is here to stay. Most start-ups are cloud-native. is becoming increasingly popular. Organizations adopt this Furthermore, many large enterprises are moving their data paradigm because they expect higher security, higher avail- and workloads into the cloud. The main reasons to move ability, and lower and more flexible cost with high perfor- into the cloud are security, time-to-market, and a more flexi- mance. It has become clear, however, that these expectations ble “pay-as-you-go” cost model which avoids overpaying for cannot be met in the cloud with the traditional, monolithic under-utilized machines. While all these reasons are com- database architecture. This paper presents a novel DBaaS pelling, the expectation is that a database runs in the cloud architecture, called Socrates. Socrates has been implemented at least as well as (if not better) than on premise. Specifically, in Microsoft SQL Server and is available in Azure as SQL DB customers expect a “database-as-a-service” to be highly avail- Hyperscale. This paper describes the key ideas and features able (e.g., 99.999% availability), support large databases (e.g., of Socrates, and it compares the performance of Socrates a 100TB OLTP database), and be highly performant.
    [Show full text]
  • Nosql – Factors Supporting the Adoption of Non-Relational Databases
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Trepo - Institutional Repository of Tampere University NoSQL – Factors Supporting the Adoption of Non-Relational Databases Olli Sutinen University of Tampere Department of Computer Sciences Computer Science M.Sc. thesis Supervisor: Marko Junkkari December 2010 University of Tampere Department of Computer Sciences Computer science Olli Sutinen: NoSQL – Factors Supporting the Adoption of Non-Relational Databases M.Sc. thesis November 2010 Relational databases have been around for decades and are still in use for general data storage needs. The web has created usage patterns for data storage and querying where current implementations of relational databases fit poorly. NoSQL is an umbrella term for various new data stores which emerged virtually simultaneously at the time when relational databases were the de facto standard for data storage. It is claimed that the new data stores address the changed needs better than the relational databases. The simple reason behind this phenomenon is the cost. If the systems are too slow or can't handle the load, the users will go to a competing site, and continue spending their time there watching 'wrong' advertisements. On the other hand, scaling relational databases is hard. It can be done and commercial RDBMS vendors have such systems available but it is out of reach of a startup because of the price tag included. This study reveals the reasons why many companies have found existing data storage solutions inadequate and developed new data stores. Keywords: databases, database scalability, data models, nosql Contents 1.
    [Show full text]
  • Oracle Nosql Database and DMS for Enhancing Iot Environments
    How Oracle NoSQL Database and Oracle Database Mobile Server Enhance an Internet of Things Data Management Solution ORACLE WHITE P A P E R | JANUARY 2017 Table of Contents Introduction 1 IoT Explained and Data Volumes 1 Business Value 1 Critical Components of an High Performance IoT Environment 2 Basic Architecture 2 Edge Filtering 2 Oracle NoSQL Database Benefits 3 Oracle NoSQL Database Architecture 3 Edge Filtering and the Database Mobile Server 4 Oracle Database Mobile Server Benefits 4 Lambda Architecture 5 Existing Oracle IoT Cloud Service 6 Oracle Technologies 6 Summary 7 HOW ORACLE NOSQL DATABASE AND ORACLE DATABASE MOBILE SERVER ENHANCE AN INTERNET OF THINGS DATA MANAGEMENT SOLUTION Introduction The Internet of Things (IoT) has become an important technology that can facilitate both personal and organizational understanding of the devices that matter. As smart devices become more ubiquitous, architectures need to be put in place that effectively and efficiently deal with the wide range of devices and environments that will exist. This includes scenarios where data is streaming over a network as well as periods where data is produced but the devices are off-line. An IoT strategy can open up new businesses and increase the understanding of existing, intelligent products and services. The business benefits of understanding the massive amounts of data flowing into analytics systems will be impressive. IoT is bridging the gap between IT (Information Technology) and OT (operations technology) to derive insights towards controlling the costs for various industries such as manufacturing, retail, logistics etc. For all those organizations that are implementing IoT strategies, data management will be at the centre of this digital transformation and a key differentiator would be the ability to derive meaningful business insights from gathered data against organizations that will merely gather data.
    [Show full text]
  • Linear Scalability of Distributed Applications
    Linear Scalability of Distributed Applications THÈSE NO 5278 (2012) PRÉSENTÉE LE 3 FÉVRIER 2012 À LA FACULTÉ INFORMATIQUE ET COMMUNICATIONS LABORATOIRE DE SYSTÈMES D'INFORMATION RÉPARTIS PROGRAMME DOCTORAL EN INFORMATIQUE, COMMUNICATIONS ET INFORMATION ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE POUR L'OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES PAR Nicolas Bonvin acceptée sur proposition du jury: Prof. B. Faltings, président du jury Prof. K. Aberer, directeur de thèse Prof. A. Ailamaki, rapporteur Prof. D. Kossmann, rapporteur Prof. C. Pu, rapporteur Suisse 2012 R´esum´e L’explosion des applications sociales, du commerce electronique´ ou de la recherche sur Internet a cre´e´ le besoin de nouvelles technologies et de systemes` adaptes´ au traitement et a` la gestion efficace d’un nombre considerable´ de donnees´ et d’utilisateurs. Ces applications doivent fonc- tionner sans interruption tous les jours de l’annee,´ et doivent donc etreˆ capables de survivre a` de subites et brutales montees´ en charge, ainsi qu’a` toutes sortes de defaillances´ logicielles, materielles,´ humaines et or- ganisationnelles. L’augmentation (ou la diminution) elastique´ et extensible des ressources affectees´ a` une application distribuee,´ tout en satisfaisant des exigences de disponibilite´ et de performance, est essentielle pour la viabilite´ com- merciale mais presente´ de grands defis´ pour les infrastructures actuelles. En effet, le Cloud Computing permet de fournir des ressources a` la de- mande: il devient desormais´ aise´ de demarrer´ des dizaines de serveurs en parallele` (ressources de calcul) ou de stocker un tres` grand nombre de donnees´ (ressources de stockage), memeˆ pour une duree´ tres` limitee,´ en payant uniquement pour les ressources consommees.´ Toutefois, ces infrastructures complexes constituees´ de ressources het´ erog´ enes` a` faible coutˆ sont sujettes aux pannes.
    [Show full text]
  • Comparison of Nosql and SQL Databases in the Cloud
    Hammes, Medero, et al. NoSQL and SQL Databases Comparison of NoSQL and SQL Databases in the Cloud Dayne Hammes Hiram Medero Georgia Southern University Georgia Southern University [email protected] [email protected] Harrison Mitchell Georgia Southern University [email protected] ABSTRACT In this paper, we examine the design, execution, and subsequent performance between traditional Relational Database Management Systems (RDBMS) and modern NoSQL Database Systems, when implemented on a cloud server. While significant groundwork has been laid for this comparison, we will present two distinct scenarios for real-world comparison: one containing highly structured data, and one containing unstructured data. In addition to concerns of design, we will test our approaches to each model side-by-side in the same cloud environment. Keywords NoSQL, RDBMS, SQL, Cloud, Big Data, MongoDB, PostgreSQL, Elasticity, Structured Data, Unstructured Data INTRODUCTION The rise of cloud computing in the business consciousness has opened an ongoing debate regarding the pros and cons of using traditional Relational Database Management Systems or document and key-value based NoSQL (Not- Only SQL) implementations. Cloud computing was first utilized in the 1950’s, but only after the dot-com bubble burst of the early 2000’s did it grow into the standard we know today. Amazon’s Elastic Compute Cloud (EC2) on the AWS platform was released in 2006 and took advantage of low-cost servers and high-bandwidth networks first offered by Grid and Utility computing methods.[1] The merits of reliability, scalability and accessibility of data quickly led the public to see the values of using Infrastructure-as-a-Service over dedicated or shared hosting solutions, and several copycat companies have sprung up in recent years as a result.
    [Show full text]
  • Is Distributed Database Evaluation Cloud-Ready?
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ZENODO Is Distributed Database Evaluation Cloud-ready? Daniel Seybold and J¨org Domaschka Ulm University, Institute of Information Resource Management, Ulm, Germany fdaniel.seybold, [email protected] Abstract. The database landscape has significantly evolved over the last decade as cloud computing enables to run distributed databases on virtually unlimited cloud resources. Hence, the already non-trivial task of selecting and deploying a distributed database system becomes more challenging. Database evaluation frameworks aim at easing this task by guiding the database selection and deployment decision. The evaluation of databases has evolved as well by moving the evaluation focus from per- formance to distribution aspects such as scalability and elasticity. This paper presents a cloud-centric analysis of distributed database evalua- tion frameworks based on evaluation tiers and framework requirements. It analysis eight well adopted evaluation frameworks. The results point out that the evaluation tiers performance, scalability, elasticity and con- sistency are well supported, in contrast to resource selection and avail- ability. Further, the analysed frameworks do not support cloud-centric requirements but support classic evaluation requirements. Keywords: NoSQL, distributed database, database evaluation, cloud 1 Introduction Relational database management systems (RDBMS) have been the common choice for persisting data for many decades. Yet, the database landscape has changed over the last decade and a plethora of new database management sys- tems (DBMS) have evolved, namely NoSQL [20] and NewSQL [15]. These are promising persistence solutions not only for Web applications, but also for new domains such as \BigData" and \IoT".
    [Show full text]
  • Technical Comparison of Oracle Database Vs. SQL Server 2000: Focus on Performance
    Technical Comparison of Oracle Database vs. SQL Server 2000: Focus on Performance An Oracle White Paper May 2005 Technical Comparison of Oracle Database vs. SQL Server 2000: Focus on Performance Introduction........................................................................................... 3 Platform availability ............................................................................. 4 Concurrency Model .............................................................................. 5 Multi-Version Read Consistency...................................................... 5 Non-Escalating Row-Level Locking ................................................ 7 Impact of locking on packaged applications..................................... 8 Indexing capabilities............................................................................. 8 Bitmap Indexes & Bitmap Join Indexes ........................................... 8 Partitioning............................................................................................ 9 Oracle ’s Partitioning Options .......................................................... 9 SQL Server 2000’s Partitioning Options........................................ 10 Performance benefits of partitioning .............................................. 11 Global indexes ............................................................................ 11 Partition Pruning......................................................................... 11 Partition-Wise Joins...................................................................
    [Show full text]
  • Quantitative Analysis of Scalable Nosql Databases
    QUANTITATIVE ANALYSIS OF SCALABLE NOSQL DATABASES by SURYA NARAYANAN SWAMINATHAN Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN COMPUTER SCIENCE THE UNIVERSITY OF TEXAS AT ARLINGTON December 2015 Copyright c by SURYA NARAYANAN SWAMINATHAN 2015 All Rights Reserved To my professor Dr. Ramez Elmasri ACKNOWLEDGEMENTS I express my sincere gratitude to my advisor Dr. Ramez Elmasri. I would also like to thank Dr. Leonidas Fegaras for his support. Special thanks to Professor David Levine for taking a special interest in my research beyond the call of duty. I am grateful to Professor Ronald Cross for his support, motivation and guid- ance. I would also like to thank my research mates Mr. Mohammadhani Fouladgar, Mr. Upa Gupta, Mr. Neelabh Pant and Mr. Vivek Sharma for their support and camaraderie. Finally, I extend my gratitude towards everyone in Computer Science depart- ment for the memorable experiences I had during my masters November 20, 2015 iv ABSTRACT QUANTITATIVE ANALYSIS OF SCALABLE NOSQL DATABASES SURYA NARAYANAN SWAMINATHAN, M.S. The University of Texas at Arlington, 2015 Supervising Professor: Ramez Elmasri NoSQL databases are rapidly becoming the customary data platform for big data applications. These databases are emerging as a gateway for more alternative approaches outside traditional relational databases and are characterized by efficient horizontal scalability, schema-less approach to data modeling, high performance data access, and limited querying capabilities. The lack of transactional semantics among NoSQL databases has made the application determine the choice of a particular con- sistency model.
    [Show full text]
  • Choosing the Right Nosql Database for the Job: a Quality Attribute Evaluation
    Lourenço et al. Journal of Big Data (2015) 2:18 DOI 10.1186/s40537-015-0025-0 RESEARCH Open Access Choosing the right NoSQL database for the job: a quality attribute evaluation João Ricardo Lourenço1* , Bruno Cabral1, Paulo Carreiro2, Marco Vieira1 and Jorge Bernardino1,3 *Correspondence: [email protected] Abstract 1CISUC, Department of Informatics For over forty years, relational databases have been the leading model for data storage, Engineering, University of Coimbra, Pólo II – Pinhal de Marrocos, retrieval and management. However, due to increasing needs for scalability and 3030-290 Coimbra, Portugal performance, alternative systems have emerged, namely NoSQL technology. The rising Full list of author information is interest in NoSQL technology, as well as the growth in the number of use case available at the end of the article scenarios, over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies. While most research work mostly focuses on performance evaluation using standard benchmarks, it is important to notice that the architecture of real world systems is not only driven by performance requirements, but has to comprehensively include many other quality attribute requirements. Software quality attributes form the basis from which software engineers and architects develop software and make design decisions. Yet, there has been no quality attribute focused survey or classification of NoSQL databases where databases are compared with regards to their suitability for quality attributes common on the design of enterprise systems. To fill this gap, and aid software engineers and architects, in this article, we survey and create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use case scenarios from the software engineer point of view and the quality attributes that each of them is most suited to.
    [Show full text]
  • Survey on Nosql Database
    Survey on NoSQL Database Jing Han, Haihong E, Guan Le PCN&CAD Center Jian Du Beijing University of Posts and Telecommunications Super Instruments Corporation Beijing, 100876, China Beijing, 100876, China babyblue11 01 [email protected], [email protected] [email protected], [email protected] Abstract have emerged, which made database technology more demands, mainly in the following aspects [1][2]: With the development of the Internet and cloud • High concurrent of reading and writing with low computing, there need databases to be able to store latency and process big data effectively, demand for high­ Database were demand to meet the needs of high performance when reading and writing, so the concurrent of reading and writing with low latency, at traditional relational database is facing many new the same time, in order to greatly enhance customer challenges. Especially in large scale and high­ satisfaction, database were demand to help applications concurrency applications, such as search engines and reacting quickly enough. SNS, using the relational database to store and query • Efficient big data storage and access requirements dynamic user data has appeared to be inadequate. In Large applications, such as SNS and search engines, this case, NoSQL database created This paper need database to meet the efficient data storage (PB describes the background, basic characteristics, data level) and can respond to the needs of millions of model of NoSQL. In addition, this paper classifies traffic. NoSQL databases according to the CAP theorem. • High scalability and high availability Finally, the mainstream NoSQL databases are With the increasing number of concurrent requests separately described in detail, and extract some and data, the database needs to be able to support easy properties to help enterprises to choose NoSQL.
    [Show full text]
  • Wintercorp White Paper
    W I N T E R C ORPORATION PRACTICES AT THE FRONTIERS OF DATABASE SCALABILITY 2006 TopTen Program Members’ Report a b a s e s D a t e s t r g L a s d ’ r l o W e h t f o y e v r u S e h T REASERCH REPORT www.wintercorp.com PRACTICES AT THE FRONTIERS OF DATABASE SCALABILITY 2006 TopTen Program Members’ Report Kathy Auerbach September 2006 411 WAVERLEY OAKS ROAD, SUITE 327 WALTHAM, MA 02452 781-642-0300 ©2006 WinterCorp, Waltham, MA. All rights reserved. WinterCorp and TopTen are trademarks of Winter Corporation. All other marks are the property of the owners of those marks. Duplication only as authorized in writing by WinterCorp. Practices at the Frontiers of Database Scalability 2006: TopTen Program Members’ Report 5 A WINTERCORP RESEARCH REPORT Highlights 1 This report presents key findings from the 2005 WinterCorp TopTen™ Program, the world’s only independent survey of the largest and most heavily used databases in operation today. The report identifies the products, platforms and architectures used by 175 terabyte-scale database practitioners who span the globe and represent all major industries. Unfiltered by vendors and validated by WinterCorp’s staff, the survey results provide visibility into today’s leading database solutions and best practices—enabling executive owners and sponsors of large-scale data management systems to make informed, fact-based technology decisions. • The largest data warehouse in the 2005 program contained 100.4 TB of data, a growth of 243% in two years. The average size of data warehouses grew 82% to 9.5 TB.
    [Show full text]
  • An Elastic, Scalable, and Self Managing Transactional Database for the Cloud
    UCSB Computer Science Technical Report 2010-04. ElasTraS: An Elastic, Scalable, and Self Managing Transactional Database for the Cloud Sudipto Das Shashank Agarwal Divyakant Agrawal Amr El Abbadi Department of Computer Science University of California, Santa Barbara, CA, USA {sudipto, shashank, agrawal, amr}@cs.ucsb.edu ABSTRACT resources and infinite scalability. As a result, there has been a Cloud computing has emerged as a pervasive platform for deploy- widespread migration of IT services from enterprise computing in- ing scalable and highly available Internet applications. To facili- frastructures (i.e., networked cluster of expensive servers) to cloud tate the migration of data-driven applications to the cloud: elas- infrastructures (i.e., large data-centers with hundreds of thousands ticity, scalability, fault-tolerance, and self-manageability (hence- of commodity servers). Since one of the primary uses of the cloud forth referred to as cloud features) are fundamental requirements is for hosting a wide range of web applications, scalable data man- agement systems that drive these applications are a crucial technol- for database management systems (DBMS) driving such applica- 1 tions. Even though extremely successful in the traditional enter- ogy component in the cloud. Relational databases (RDBMS) have prise setting – the high cost of commercial relational database soft- been extremely successful in the enterprise setting for more than ware, and the lack of the desired cloud features in the open source two decades, but data management in the cloud requires DBMS’s to be elastic, scalable, fault-tolerant, self managing and self-recovering counterparts – relational databases (RDBMS) are not a competi- 2 tive choice for cloud-bound applications.
    [Show full text]