International Standard Iso/Iec 9075-2
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Support Aggregate Analytic Window Function Over Large Data by Spilling
Support Aggregate Analytic Window Function over Large Data by Spilling Xing Shi and Chao Wang Guangdong University of Technology, Guangzhou, Guangdong 510006, China North China University of Technology, Beijing 100144, China Abstract. Analytic function, also called window function, is to query the aggregation of data over a sliding window. For example, a simple query over the online stock platform is to return the average price of a stock of the last three days. These functions are commonly used features in SQL databases. They are supported in most of the commercial databases. With the increasing usage of cloud data infra and machine learning technology, the frequency of queries with analytic window functions rises. Some analytic functions only require const space in memory to store the state, such as SUM, AVG, while others require linear space, such as MIN, MAX. When the window is extremely large, the memory space to store the state may be too large. In this case, we need to spill the state to disk, which is a heavy operation. In this paper, we proposed an algorithm to manipulate the state data in the disk to reduce the disk I/O to make spill available and efficiency. We analyze the complexity of the algorithm with different data distribution. 1. Introducion In this paper, we develop novel spill techniques for analytic window function in SQL databases. And discuss different various types of aggregate queries, e.g., COUNT, AVG, SUM, MAX, MIN, etc., over a relational table. Aggregate analytic function, also called aggregate window function, is to query the aggregation of data over a sliding window. -
A First Attempt to Computing Generic Set Partitions: Delegation to an SQL Query Engine Frédéric Dumonceaux, Guillaume Raschia, Marc Gelgon
A First Attempt to Computing Generic Set Partitions: Delegation to an SQL Query Engine Frédéric Dumonceaux, Guillaume Raschia, Marc Gelgon To cite this version: Frédéric Dumonceaux, Guillaume Raschia, Marc Gelgon. A First Attempt to Computing Generic Set Partitions: Delegation to an SQL Query Engine. DEXA’2014 (Database and Expert System Applications), Sep 2014, Munich, Germany. pp.433-440. hal-00993260 HAL Id: hal-00993260 https://hal.archives-ouvertes.fr/hal-00993260 Submitted on 28 Oct 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A First Attempt to Computing Generic Set Partitions: Delegation to an SQL Query Engine Fr´ed´ericDumonceaux, Guillaume Raschia, and Marc Gelgon LINA (UMR CNRS 6241), Universit´ede Nantes, France [email protected] Abstract. Partitions are a very common and useful way of organiz- ing data, in data engineering and data mining. However, partitions cur- rently lack efficient and generic data management functionalities. This paper proposes advances in the understanding of this problem, as well as elements for solving it. We formulate the task as efficient process- ing, evaluating and optimizing queries over set partitions, in the setting of relational databases. -
Look out the Window Functions and Free Your SQL
Concepts Syntax Other Look Out The Window Functions and free your SQL Gianni Ciolli 2ndQuadrant Italia PostgreSQL Conference Europe 2011 October 18-21, Amsterdam Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Outline 1 Concepts Aggregates Different aggregations Partitions Window frames 2 Syntax Frames from 9.0 Frames in 8.4 3 Other A larger example Question time Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Aggregates Aggregates 1 Example of an aggregate Problem 1 How many rows there are in table a? Solution SELECT count(*) FROM a; • Here count is an aggregate function (SQL keyword AGGREGATE). Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Aggregates Aggregates 2 Functions and Aggregates • FUNCTIONs: • input: one row • output: either one row or a set of rows: • AGGREGATEs: • input: a set of rows • output: one row Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Different aggregations Different aggregations 1 Without window functions, and with them GROUP BY col1, . , coln window functions any supported only PostgreSQL PostgreSQL version version 8.4+ compute aggregates compute aggregates via by creating groups partitions and window frames output is one row output is one row for each group for each input row Look Out The Window Functions Gianni Ciolli Concepts Syntax Other Different aggregations Different aggregations 2 Without window functions, and with them GROUP BY col1, . , coln window functions only one way of aggregating different rows in the same for each group -
Lecture 3: Advanced SQL
Advanced SQL Lecture 3: Advanced SQL 1 / 64 Advanced SQL Relational Language Relational Language • User only needs to specify the answer that they want, not how to compute it. • The DBMS is responsible for efficient evaluation of the query. I Query optimizer: re-orders operations and generates query plan 2 / 64 Advanced SQL Relational Language SQL History • Originally “SEQUEL" from IBM’s System R prototype. I Structured English Query Language I Adopted by Oracle in the 1970s. I IBM releases DB2 in 1983. I ANSI Standard in 1986. ISO in 1987 I Structured Query Language 3 / 64 Advanced SQL Relational Language SQL History • Current standard is SQL:2016 I SQL:2016 −! JSON, Polymorphic tables I SQL:2011 −! Temporal DBs, Pipelined DML I SQL:2008 −! TRUNCATE, Fancy sorting I SQL:2003 −! XML, windows, sequences, auto-gen IDs. I SQL:1999 −! Regex, triggers, OO • Most DBMSs at least support SQL-92 • Comparison of different SQL implementations 4 / 64 Advanced SQL Relational Language Relational Language • Data Manipulation Language (DML) • Data Definition Language (DDL) • Data Control Language (DCL) • Also includes: I View definition I Integrity & Referential Constraints I Transactions • Important: SQL is based on bag semantics (duplicates) not set semantics (no duplicates). 5 / 64 Advanced SQL Relational Language Today’s Agenda • Aggregations + Group By • String / Date / Time Operations • Output Control + Redirection • Nested Queries • Join • Common Table Expressions • Window Functions 6 / 64 Advanced SQL Relational Language Example Database SQL Fiddle: Link sid name login age gpa sid cid grade 1 Maria maria@cs 19 3.8 1 1 B students 2 Rahul rahul@cs 22 3.5 enrolled 1 2 A 3 Shiyi shiyi@cs 26 3.7 2 3 B 4 Peter peter@ece 35 3.8 4 2 C cid name 1 Computer Architecture courses 2 Machine Learning 3 Database Systems 4 Programming Languages 7 / 64 Advanced SQL Aggregates Aggregates • Functions that return a single value from a bag of tuples: I AVG(col)−! Return the average col value. -
Chapter 11 Querying
Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 Blind folio: 273 CHAPTER 11 Querying 273 11-ch11.indd 273 9/5/11 4:23:56 PM Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 Oracle TIGHT / Oracle Database 11g & MySQL 5.6 Developer Handbook / Michael McLaughlin / 885-8 274 Oracle Database 11g & MySQL 5.6 Developer Handbook Chapter 11: Querying 275 he SQL SELECT statement lets you query data from the database. In many of the previous chapters, you’ve seen examples of queries. Queries support several different types of subqueries, such as nested queries that run independently or T correlated nested queries. Correlated nested queries run with a dependency on the outer or containing query. This chapter shows you how to work with column returns from queries and how to join tables into multiple table result sets. Result sets are like tables because they’re two-dimensional data sets. The data sets can be a subset of one table or a set of values from two or more tables. The SELECT list determines what’s returned from a query into a result set. The SELECT list is the set of columns and expressions returned by a SELECT statement. The SELECT list defines the record structure of the result set, which is the result set’s first dimension. The number of rows returned from the query defines the elements of a record structure list, which is the result set’s second dimension. You filter single tables to get subsets of a table, and you join tables into a larger result set to get a superset of any one table by returning a result set of the join between two or more tables. -
Sql Server to Aurora Postgresql Migration Playbook
Microsoft 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 -
Firebird 3 Windowing Functions
Firebird 3 Windowing Functions Firebird 3 Windowing Functions Author: Philippe Makowski IBPhoenix Email: pmakowski@ibphoenix Licence: Public Documentation License Date: 2011-11-22 Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions What are Windowing Functions? • Similar to classical aggregates but does more! • Provides access to set of rows from the current row • Introduced SQL:2003 and more detail in SQL:2008 • Supported by PostgreSQL, Oracle, SQL Server, Sybase and DB2 • Used in OLAP mainly but also useful in OLTP • Analysis and reporting by rankings, cumulative aggregates Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions Windowed Table Functions • Windowed table function • operates on a window of a table • returns a value for every row in that window • the value is calculated by taking into consideration values from the set of rows in that window • 8 new windowed table functions • In addition, old aggregate functions can also be used as windowed table functions • Allows calculation of moving and cumulative aggregate values. Philippe Makowski - IBPhoenix - 2011-11-22 Firebird 3 Windowing Functions A Window • Represents set of rows that is used to compute additionnal attributes • Based on three main concepts • partition • specified by PARTITION BY clause in OVER() • Allows to subdivide the table, much like GROUP BY clause • Without a PARTITION BY clause, the whole table is in a single partition • order • defines an order with a partition • may contain multiple order items • Each item includes -
Using the Set Operators Questions
UUSSIINNGG TTHHEE SSEETT OOPPEERRAATTOORRSS QQUUEESSTTIIOONNSS http://www.tutorialspoint.com/sql_certificate/using_the_set_operators_questions.htm Copyright © tutorialspoint.com 1.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: A. Set operators are used to combine the results of two ormore SELECT statements.Valid set operators in Oracle 11g are UNION, UNION ALL, INTERSECT, and MINUS. When used with two SELECT statements, the UNION set operator returns the results of both queries.However,if there are any duplicates, they are removed, and the duplicated record is listed only once.To include duplicates in the results,use the UNION ALL set operator.INTERSECT lists only records that are returned by both queries; the MINUS set operator removes the second query's results from the output if they are also found in the first query's results. INTERSECT and MINUS set operations produce unduplicated results. 2.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: B. UNION ALL Returns the combined rows from two queries without sorting or removing duplicates. sql_certificate 3.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: C. INTERSECT Returns only the rows that occur in both queries' result sets, sorting them and removing duplicates. 4.Which SET operator does the following figure indicate? A. UNION B. UNION ALL C. INTERSECT D. MINUS Answer: D. MINUS Returns only the rows in the first result set that do not appear in the second result set, sorting them and removing duplicates. -
SQL Version Analysis
Rory McGann SQL Version Analysis Structured Query Language, or SQL, is a powerful tool for interacting with and utilizing databases through the use of relational algebra and calculus, allowing for efficient and effective manipulation and analysis of data within databases. There have been many revisions of SQL, some minor and others major, since its standardization by ANSI in 1986, and in this paper I will discuss several of the changes that led to improved usefulness of the language. In 1970, Dr. E. F. Codd published a paper in the Association of Computer Machinery titled A Relational Model of Data for Large shared Data Banks, which detailed a model for Relational database Management systems (RDBMS) [1]. In order to make use of this model, a language was needed to manage the data stored in these RDBMSs. In the early 1970’s SQL was developed by Donald Chamberlin and Raymond Boyce at IBM, accomplishing this goal. In 1986 SQL was standardized by the American National Standards Institute as SQL-86 and also by The International Organization for Standardization in 1987. The structure of SQL-86 was largely similar to SQL as we know it today with functionality being implemented though Data Manipulation Language (DML), which defines verbs such as select, insert into, update, and delete that are used to query or change the contents of a database. SQL-86 defined two ways to process a DML, direct processing where actual SQL commands are used, and embedded SQL where SQL statements are embedded within programs written in other languages. SQL-86 supported Cobol, Fortran, Pascal and PL/1. -
“A Relational Model of Data for Large Shared Data Banks”
“A RELATIONAL MODEL OF DATA FOR LARGE SHARED DATA BANKS” Through the internet, I find more information about Edgar F. Codd. He is a mathematician and computer scientist who laid the theoretical foundation for relational databases--the standard method by which information is organized in and retrieved from computers. In 1981, he received the A. M. Turing Award, the highest honor in the computer science field for his fundamental and continuing contributions to the theory and practice of database management systems. This paper is concerned with the application of elementary relation theory to systems which provide shared access to large banks of formatted data. It is divided into two sections. In section 1, a relational model of data is proposed as a basis for protecting users of formatted data systems from the potentially disruptive changes in data representation caused by growth in the data bank and changes in traffic. A normal form for the time-varying collection of relationships is introduced. In Section 2, certain operations on relations are discussed and applied to the problems of redundancy and consistency in the user's model. Relational model provides a means of describing data with its natural structure only--that is, without superimposing any additional structure for machine representation purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence between programs on the one hand and machine representation and organization of data on the other. A further advantage of the relational view is that it forms a sound basis for treating derivability, redundancy, and consistency of relations. -
Relational Algebra and SQL Relational Query Languages
Relational Algebra and SQL Chapter 5 1 Relational Query Languages • Languages for describing queries on a relational database • Structured Query Language (SQL) – Predominant application-level query language – Declarative • Relational Algebra – Intermediate language used within DBMS – Procedural 2 1 What is an Algebra? · A language based on operators and a domain of values · Operators map values taken from the domain into other domain values · Hence, an expression involving operators and arguments produces a value in the domain · When the domain is a set of all relations (and the operators are as described later), we get the relational algebra · We refer to the expression as a query and the value produced as the query result 3 Relational Algebra · Domain: set of relations · Basic operators: select, project, union, set difference, Cartesian product · Derived operators: set intersection, division, join · Procedural: Relational expression specifies query by describing an algorithm (the sequence in which operators are applied) for determining the result of an expression 4 2 The Role of Relational Algebra in a DBMS 5 Select Operator • Produce table containing subset of rows of argument table satisfying condition σ condition (relation) • Example: σ Person Hobby=‘stamps’(Person) Id Name Address Hobby Id Name Address Hobby 1123 John 123 Main stamps 1123 John 123 Main stamps 1123 John 123 Main coins 9876 Bart 5 Pine St stamps 5556 Mary 7 Lake Dr hiking 9876 Bart 5 Pine St stamps 6 3 Selection Condition • Operators: <, ≤, ≥, >, =, ≠ • Simple selection -
Best Practices Managing XML Data
® IBM® DB2® for Linux®, UNIX®, and Windows® Best Practices Managing XML Data Matthias Nicola IBM Silicon Valley Lab Susanne Englert IBM Silicon Valley Lab Last updated: January 2011 Managing XML Data Page 2 Executive summary ............................................................................................. 4 Why XML .............................................................................................................. 5 Pros and cons of XML and relational data ................................................. 5 XML solutions to relational data model problems.................................... 6 Benefits of DB2 pureXML over alternative storage options .................... 8 Best practices for DB2 pureXML: Overview .................................................. 10 Sample scenario: derivative trades in FpML format............................... 11 Sample data and tables................................................................................ 11 Choosing the right storage options for XML data......................................... 16 Selecting table space type and page size for XML data.......................... 16 Different table spaces and page size for XML and relational data ....... 16 Inlining and compression of XML data .................................................... 17 Guidelines for adding XML data to a DB2 database .................................... 20 Inserting XML documents with high performance ................................ 20 Splitting large XML documents into smaller pieces ..............................