Postgresql Database Limits
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												  Delivering Oracle CompatibilityDELIVERING ORACLE COMPATIBILITY A White Paper by EnterpriseDB www.EnterpriseDB.com TABLE OF CONTENTS Executive Summary 4 Introducing EnterpriseDB Advanced Server 6 SQL Compatibility 6 PL/SQL Compatibility 9 Data Dictionary Views 11 Programming Flexibility and Drivers 12 Transfer Tools 12 Database Replication 13 Enterprise-Class Reliability and Scalability 13 Oracle-Like Tools 14 Conclusion 15 Downloading EnterpriseDB 15 EnterpriseDB EXECUTIVE SUMMARY Enterprises running Oracle® are generally interested in alternative databases for at least three reasons. First, these enterprises are experiencing budget constraints and need to lower their database Total Cost of Ownership (TCO). Second, they are trying to gain greater licensing flexibility to become more agile within the company and in the larger market. Finally, they are actively pursuing vendors who will provide superior technical support and a richer customer experience. And, subsequently, enterprises are looking for a solution that will complement their existing infrastructure and skills. The traditional database vendors have been unable to provide the combination of all three benefits. While Microsoft SQL ServerTM and IBM DB2TM may provide the flexibility and rich customer experience, they cannot significantly reduce TCO. Open source databases, on the other hand, can provide the TCO benefits and the flexibility. However, these open source databases either lack the enterprise-class features that today’s mission critical applications require or they are not equipped to provide the enterprise-class support required by these organizations. Finally, none of the databases mentioned above provide the database compatibility and interoperability that complements their existing applications and staff. The fear of the costs of changing databases, including costs related to application re-coding and personnel re-training, outweigh the expected savings and, therefore, these enterprises remain paralyzed and locked into Oracle.
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												  SAQE: Practical Privacy-Preserving Approximate Query Processing for Data FederationsSAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations Johes Bater Yongjoo Park Xi He Northwestern University University of Illinois (UIUC) University of Waterloo [email protected] [email protected] [email protected] Xiao Wang Jennie Rogers Northwestern University Northwestern University [email protected] [email protected] ABSTRACT 1. INTRODUCTION A private data federation enables clients to query the union of data Querying the union of multiple private data stores is challeng- from multiple data providers without revealing any extra private ing due to the need to compute over the combined datasets without information to the client or any other data providers. Unfortu- data providers disclosing their secret query inputs to anyone. Here, nately, this strong end-to-end privacy guarantee requires crypto- a client issues a query against the union of these private records graphic protocols that incur a significant performance overhead as and he or she receives the output of their query over this shared high as 1,000× compared to executing the same query in the clear. data. Presently, systems of this kind use a trusted third party to se- As a result, private data federations are impractical for common curely query the union of multiple private datastores. For example, database workloads. This gap reveals the following key challenge some large hospitals in the Chicago area offer services for a cer- in a private data federation: offering significantly fast and accurate tain percentage of the residents; if we can query the union of these query answers without compromising strong end-to-end privacy. databases, it may serve as invaluable resources for accurate diag- To address this challenge, we propose SAQE, the Secure Ap- nosis, informed immunization, timely epidemic control, and so on.
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												  Veritas: Shared Verifiable Databases and Tables in the CloudVeritas: Shared Verifiable Databases and Tables in the Cloud Lindsey Alleny, Panagiotis Antonopoulosy, Arvind Arasuy, Johannes Gehrkey, Joachim Hammery, James Huntery, Raghav Kaushiky, Donald Kossmanny, Jonathan Leey, Ravi Ramamurthyy, Srinath Settyy, Jakub Szymaszeky, Alexander van Renenz, Ramarathnam Venkatesany yMicrosoft Corporation zTechnische Universität München ABSTRACT A recent class of new technology under the umbrella term In this paper we introduce shared, verifiable database tables, a new "blockchain" [11, 13, 22, 28, 33] promises to solve this conundrum. abstraction for trusted data sharing in the cloud. For example, companies A and B could write the state of these shared tables into a blockchain infrastructure (such as Ethereum [33]). The KEYWORDS blockchain then gives them transaction properties, such as atomic- ity of updates, durability, and isolation, and the blockchain allows Blockchain, data management a third party to go through its history and verify the transactions. Both companies A and B would have to write their updates into 1 INTRODUCTION the blockchain, wait until they are committed, and also read any Our economy depends on interactions – buyers and sellers, suppli- state changes from the blockchain as a means of sharing data across ers and manufacturers, professors and students, etc. Consider the A and B. To implement permissioning, A and B can use a permis- scenario where there are two companies A and B, where B supplies sioned blockchain variant that allows transactions only from a set widgets to A. A and B are exchanging data about the latest price for of well-defined participants; e.g., Ethereum [33]. widgets and dates of when widgets were shipped.
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												  Sql Server to Aurora Postgresql Migration PlaybookMicrosoft SQL Server To Amazon Aurora with Post- greSQL Compatibility Migration Playbook 1.0 Preliminary September 2018 © 2018 Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offer- ings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without war- ranty of any kind, whether express or implied. This document does not create any warranties, rep- resentations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agree- ments, and this document is not part of, nor does it modify, any agreement between AWS and its cus- tomers. - 2 - Table of Contents Introduction 9 Tables of Feature Compatibility 12 AWS Schema and Data Migration Tools 20 AWS Schema Conversion Tool (SCT) 21 Overview 21 Migrating a Database 21 SCT Action Code Index 31 Creating Tables 32 Data Types 32 Collations 33 PIVOT and UNPIVOT 33 TOP and FETCH 34 Cursors 34 Flow Control 35 Transaction Isolation 35 Stored Procedures 36 Triggers 36 MERGE 37 Query hints and plan guides 37 Full Text Search 38 Indexes 38 Partitioning 39 Backup 40 SQL Server Mail 40 SQL Server Agent 41 Service Broker 41 XML 42 Constraints
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												  SQL Database Management PortalAPPENDIX A SQL Database Management Portal This appendix introduces you to the online SQL Database Management Portal built by Microsoft. It is a web-based application that allows you to design, manage, and monitor your SQL Database instances. Although the current version of the portal does not support all the functions of SQL Server Management Studio, the portal offers some interesting capabilities unique to SQL Database. Launching the Management Portal You can launch the SQL Database Management Portal (SDMP) from the Windows Azure Management Portal. Open a browser and navigate to https://manage.windowsazure.com, and then login with your Live ID. Once logged in, click SQL Databases on the left and click on your database from the list of available databases. This brings up the database dashboard, as seen in Figure A-1. To launch the management portal, click the Manage icon at the bottom. Figure A-1. SQL Database dashboard in Windows Azure Management Portal 257 APPENDIX A N SQL DATABASE MANAGEMENT PORTAL N Note You can also access the SDMP directly from a browser by typing https://sqldatabasename.database. windows.net, where sqldatabasename is the server name of your SQL Database. A new web page opens up that prompts you to log in to your SQL Database server. If you clicked through the Windows Azure Management Portal, the database name will be automatically filled and read-only. If you typed the URL directly in a browser, you will need to enter the database name manually. Enter a user name and password; then click Log on (Figure A-2).
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												  Modèle Bull 2009 Blanc FrCNAF PostgreSQL project Philippe BEAUDOIN, Project leader [email protected] 2010, Dec 7 CNAF - Caisse Nationale des Allocations Familiales - Key organization of the French social security system - Distributes benefits to help - Families - Poor people - 123 CAF (local organizations) all over France - 11 million families and 30 million people - 69 billion € in benefits distributed (2008) 2 ©Bull, 2010 CNAF PostgreSQL project The project … in a few clicks GCOS 8 GCOS 8 CRISTAL SDP CRISTAL SDP (Cobol) (Cobol) (Cobol) (Cobol) DBSP RFM-II RHEL-LINUX PostgreSQL Bull - NovaScale 9000 servers (mainframes) 3 ©Bull, 2010 CNAF PostgreSQL project DBSP GCOS 8 CRISTAL SDP (Cobol) (Cobol) InfiniBand link LINUX S.C. S.C. (c+SQL) (c+SQL) PostgreSQL S.C. = Surrogate Client 4 ©Bull, 2010 CNAF PostgreSQL project CNAF I.S. - CRISTAL + SDP : heart of the Information System - Same application running on Bull (GCOS 8) and IBM (z/OS+DB2) mainframes - Also J2E servers, under AIX, … and a lot of peripheral applications - 6 to 8 CRISTAL versions per year ! 5 ©Bull, 2010 CNAF PostgreSQL project Migration to PostgreSQL project plan - A lot of teams all over France (developers, testers, production,...) - 3 domains - CRISTAL application, SDP application - Infrastructure and production - National project leading CRISTAL Developmt Tests 1 CAF C. Deployment C. PRODUCTION Preparation 1 Depl S SDP Develpmt Tests 9/08 1/09 5/09 9/09 12/09 3/10 6 ©Bull, 2010 CNAF PostgreSQL project How BULL participated to the project - Assistance to project leading - Technical expertise
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												  On Snakes and Elephants Using Python Inside PostgresqlOn snakes and elephants Using Python inside PostgreSQL Jan Urba´nski [email protected] New Relic PyWaw Summit 2015, Warsaw, May 26 Jan Urba´nski (New Relic) On snakes and elephants PyWaw Summit 1 / 32 For those following at home Getting the slides $ wget http://wulczer.org/pywaw-summit.pdf Jan Urba´nski (New Relic) On snakes and elephants PyWaw Summit 2 / 32 1 Introduction Stored procedures PostgreSQL's specifics 2 The PL/Python language Implementation Examples 3 Using PL/Python Real-life applications Best practices Jan Urba´nski (New Relic) On snakes and elephants PyWaw Summit 3 / 32 Outline 1 Introduction Stored procedures PostgreSQL's specifics 2 The PL/Python language 3 Using PL/Python Jan Urba´nski (New Relic) On snakes and elephants PyWaw Summit 4 / 32 What are stored procedures I procedural code callable from SQL I used to implement operations that are not easily expressed in SQL I encapsulate business logic Jan Urba´nski (New Relic) On snakes and elephants PyWaw Summit 5 / 32 Stored procedure examples Calling stored procedures SELECT purge_user_records(142); SELECT lower(username) FROM users; CREATE TRIGGER notify_user_trig AFTER UPDATEON users EXECUTE PROCEDURE notify_user(); Jan Urba´nski (New Relic) On snakes and elephants PyWaw Summit 6 / 32 Stored procedure languages I most RDBMS have one blessed language in which stored procedures can we written I Oracle has PL/SQL I MS SQL Server has T-SQL I but Postgres is better Jan Urba´nski (New Relic) On snakes and elephants PyWaw Summit 7 / 32 Stored procedures in Postgres I a stored
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												  Ubuntu Server Guide Basic Installation Preparing to InstallUbuntu Server Guide Welcome to the Ubuntu Server Guide! This site includes information on using Ubuntu Server for the latest LTS release, Ubuntu 20.04 LTS (Focal Fossa). For an offline version as well as versions for previous releases see below. Improving the Documentation If you find any errors or have suggestions for improvements to pages, please use the link at thebottomof each topic titled: “Help improve this document in the forum.” This link will take you to the Server Discourse forum for the specific page you are viewing. There you can share your comments or let us know aboutbugs with any page. PDFs and Previous Releases Below are links to the previous Ubuntu Server release server guides as well as an offline copy of the current version of this site: Ubuntu 20.04 LTS (Focal Fossa): PDF Ubuntu 18.04 LTS (Bionic Beaver): Web and PDF Ubuntu 16.04 LTS (Xenial Xerus): Web and PDF Support There are a couple of different ways that the Ubuntu Server edition is supported: commercial support and community support. The main commercial support (and development funding) is available from Canonical, Ltd. They supply reasonably- priced support contracts on a per desktop or per-server basis. For more information see the Ubuntu Advantage page. Community support is also provided by dedicated individuals and companies that wish to make Ubuntu the best distribution possible. Support is provided through multiple mailing lists, IRC channels, forums, blogs, wikis, etc. The large amount of information available can be overwhelming, but a good search engine query can usually provide an answer to your questions.
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												  Plan Stitch: Harnessing the Best of Many PlansPlan Stitch: Harnessing the Best of Many Plans Bailu Ding Sudipto Das Wentao Wu Surajit Chaudhuri Vivek Narasayya Microsoft Research Redmond, WA 98052, USA fbadin, sudiptod, wentwu, surajitc, [email protected] ABSTRACT optimizer chooses multiple query plans for the same query, espe- Query performance regression due to the query optimizer select- cially for stored procedures and query templates. ing a bad query execution plan is a major pain point in produc- The query optimizer often makes good use of the newly-created indexes and statistics, and the resulting new query plan improves tion workloads. Commercial DBMSs today can automatically de- 1 tect and correct such query plan regressions by storing previously- in execution cost. At times, however, the latest query plan chosen executed plans and reverting to a previous plan which is still valid by the optimizer has significantly higher execution cost compared and has the least execution cost. Such reversion-based plan cor- to previously-executed plans, i.e., a plan regresses [5, 6, 31]. rection has relatively low risk of plan regression since the deci- The problem of plan regression is painful for production applica- sion is based on observed execution costs. However, this approach tions as it is hard to debug. It becomes even more challenging when ignores potentially valuable information of efficient subplans col- plans change regularly on a large scale, such as in a cloud database lected from other previously-executed plans. In this paper, we service with automatic index tuning. Given such a large-scale pro- propose a novel technique, Plan Stitch, that automatically and op- duction environment, detecting and correcting plan regressions in portunistically combines efficient subplans of previously-executed an automated manner becomes crucial.
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												  Exploring Query Re-Optimization in a Modern Database SystemRadboud University Nijmegen Institute of Computing and Information Sciences Exploring Query Re-Optimization In a Modern Database System Master Thesis Computing Science Supervisor: Prof. dr. ir. Arjen P. Author: de Vries BSc Laurens N. Kuiper Second reader: Prof. dr. ir. Djoerd Hiemstra May 2021 Abstract The standard approach to query processing in database systems is to optimize before executing. When available statistics are accurate, optimization yields the optimal plan, and execution is as quick as it can be. However, when queries become more complex, the quality of statistics degrades, which leads to sub-optimal query plans, sometimes up to several orders of magnitude worse. Improving statistics for these queries is infeasible because the cardinality estimation error propagates exponentially through the plan. This problem can be alleviated through re-optimization, which is to progressively optimize the query, executing parts of the plan at a time, improving plan quality at the cost of additional overhead. In this work, re-optimization is implemented and simulated in DuckDB, a main-memory database system designed for analytical workloads, and evaluated on the Join Order Benchmark. Total plan cost of the benchmark is reduced by up to 44% using this method, and a reduction in end-to-end query latency of up to 20% is observed using only simple re-optimization schemes, showing the potential of this approach. Acknowledgements The past year has been a challenging and exciting year. From breaking my leg to starting a job, and finally finishing my thesis. I would like to thank everyone who supported me during this period, and helped me get here.
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												  Queryguard: Privacy-Preserving Latency-Aware Query Optimization for Edge ComputingQueryGuard: Privacy-preserving Latency-aware Query Optimization for Edge Computing Runhua Xu ∗, Balaji Palanisamy y and James Joshi z School of Computing and Information University of Pittsburgh, Pittsburgh, PA USA ∗[email protected], [email protected], [email protected] Abstract—The emerging edge computing paradigm has en- adopting distributed database techniques in edge computing abled applications having low response time requirements to brings new challenges and concerns. First, in contrast to meet the quality of service needs of applications by moving the cloud data centers where cloud servers are managed through computations to the edge of the network that is geographically closer to the end-users and end-devices. Despite the low latency strict and regularized policies, edge nodes may not have the advantages provided by the edge computing model, there are same degree of regulatory and monitoring oversight. This significant privacy risks associated with the adoption of edge may lead to higher privacy risks compared to that in cloud computing services for applications dealing with sensitive data. servers. In particular, when dealing with a join query, tra- In contrast to cloud data centers where system infrastructures are ditional distributed query processing techniques sometimes managed through strict and regularized policies, edge computing nodes are scattered geographically and may not have the same ship selected or projected data to different nodes, some of degree of regulatory and monitoring oversight. This can lead which may be untrusted or semi-trusted. Thus, such techniques to higher privacy risks for the data processed and stored at may lead to greater disclosure of private information within the edge nodes, thus making them less trusted.
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												  Lesson 4: Optimize a Query Using the Sybase IQ Query PlanAuthor: Courtney Claussen – SAP Sybase IQ Technical Evangelist Contributor: Bruce McManus – Director of Customer Support at Sybase Getting Started with SAP Sybase IQ Column Store Analytics Server Lesson 4: Optimize a Query using the SAP Sybase IQ Query Plan Copyright (C) 2012 Sybase, Inc. All rights reserved. Unpublished rights reserved under U.S. copyright laws. Sybase and the Sybase logo are trademarks of Sybase, Inc. or its subsidiaries. SAP and the SAP logo are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other trademarks are the property of their respective owners. (R) indicates registration in the United States. Specifications are subject to change without notice. Table of Contents 1. Introduction ................................................................................................................1 2. SAP Sybase IQ Query Processing ............................................................................2 3. SAP Sybase IQ Query Plans .....................................................................................4 4. What a Query Plan Looks Like ................................................................................5 5. What a Query Plan Will Tell You ............................................................................7 5.1 Query Tree Nodes and Node Detail ......................................................................7 5.1.1 Root Node Details ..........................................................................................7