Exasolution User Manual

Total Page:16

File Type:pdf, Size:1020Kb

Exasolution User Manual The most powerful engine for your analytics! EXASolution User Manual Version 4.2.11 Copyright © 2014 EXASOL AG. All rights reserved. The information in this publication is subject to change without notice. EXASOL SHALL NOT BE HELD LIABLE FOR TECHNICAL OR EDITORIAL ERRORS OR OMISSIONS CONTAINED HEREIN NOR FOR ACCIDENTAL OR CONSEQUENTIAL DAMAGES RES- ULTING FROM THE FURNISHING, PERFORMANCE, OR USE OF. No part of this publication may be photocopied or reproduced in any form without prior written consent from EXASOL. All named trademarks and registered trademarks are the property of their respective owners. EXASolution User Manual Table of Contents Foreword ..................................................................................................................................... ix Conventions ................................................................................................................................. xi Changes in Version 4.2 ................................................................................................................. xiii 1. What is EXASolution? ................................................................................................................. 1 2. SQL reference ............................................................................................................................ 5 2.1. Basic language elements .................................................................................................... 5 2.1.1. Comments in SQL ................................................................................................. 5 2.1.2. SQL identi®er ....................................................................................................... 5 2.1.3. Regular expressions ............................................................................................... 7 2.2. SQL statements .............................................................................................................. 11 2.2.1. De®nition of the database (DDL) ............................................................................ 12 2.2.2. Manipulation of the database (DML) ....................................................................... 33 2.2.3. Access control using SQL (DCL) ............................................................................ 53 2.2.4. Query language (DQL) ......................................................................................... 65 2.2.5. Veri®cation of the data quality ................................................................................ 73 2.2.6. Other statements .................................................................................................. 78 2.3. Data types ..................................................................................................................... 91 2.3.1. Overview of EXASolution data types ....................................................................... 92 2.3.2. Data type details .................................................................................................. 92 2.3.3. Data type aliases .................................................................................................. 95 2.3.4. Type conversion rules ........................................................................................... 96 2.3.5. Default values ..................................................................................................... 98 2.3.6. Identity columns ................................................................................................ 100 2.4. Literals ....................................................................................................................... 101 2.4.1. Numeric literals ................................................................................................. 102 2.4.2. Boolean literals .................................................................................................. 103 2.4.3. Date/Time literals ............................................................................................... 103 2.4.4. Interval literals ................................................................................................... 103 2.4.5. String literals ..................................................................................................... 105 2.4.6. NULL literal ..................................................................................................... 105 2.5. Format models ............................................................................................................. 105 2.5.1. Date/Time format models ..................................................................................... 106 2.5.2. Numeric format models ....................................................................................... 108 2.6. Operators .................................................................................................................... 110 2.6.1. Arithmetic Operators ........................................................................................... 111 2.6.2. Concatenation operator || ...................................................................................... 112 2.7. Predicates .................................................................................................................... 112 2.7.1. Introduction ...................................................................................................... 113 2.7.2. List of predicates ................................................................................................ 113 2.8. Built-in functions .......................................................................................................... 118 2.8.1. Scalar functions ................................................................................................. 119 2.8.2. Aggregate functions ............................................................................................ 122 2.8.3. Analytical functions ............................................................................................ 123 2.8.4. Alphabetical list of all functions ............................................................................ 125 2.9. Geospatial data ............................................................................................................. 228 2.9.1. Geospatial objects .............................................................................................. 229 2.9.2. Geospatial functions ........................................................................................... 230 2.10. EXAPowerlytics ........................................................................................................ 232 2.10.1. What is EXAPowerlytics? .................................................................................. 233 2.10.2. Introducing examples ........................................................................................ 234 2.10.3. Details for different languages ............................................................................. 237 2.10.4. External Scripts (eUDF) ..................................................................................... 246 2.10.5. Hadoop Integration Service ................................................................................ 249 2.11. Scripting .................................................................................................................... 251 2.11.1. Introduction ..................................................................................................... 252 iii 2.11.2. General script language ...................................................................................... 253 2.11.3. Database interaction .......................................................................................... 260 2.11.4. Libraries ......................................................................................................... 267 2.11.5. System tables ................................................................................................... 274 3. Concepts ................................................................................................................................ 275 3.1. Transaction management ............................................................................................... 275 3.1.1. Basic concept .................................................................................................... 275 3.1.2. Differences to other systems ................................................................................. 276 3.1.3. Recommendations for the user .............................................................................. 276 3.2. Rights management ....................................................................................................... 276 3.2.1. User ................................................................................................................. 277 3.2.2. Roles ............................................................................................................... 277 3.2.3. Privileges .......................................................................................................... 278 3.2.4. Access control with SQL statements ....................................................................... 278 3.2.5. Meta information on rights management ................................................................. 279 3.2.6. Rights management
Recommended publications
  • Query All Tables in a Schema
    Query All Tables In A Schema Orchidaceous and unimpressionable Thor often air-dried some iceberg imperially or amortizing knee-high. Dotier Griffin smatter blindly. Zionism Danie industrializing her interlay so opaquely that Timmie exserts very fustily. Redshift Show Tables How your List Redshift Tables FlyData. How to query uses mutexes will only queried data but what is fine and built correctly: another advantage we can easily access a string. Exception to query below queries to list of. 1 Do amount of emergency following Select Tools List Tables On the toolbar click 2 In the. How can easily access their business. SQL to Search for her VALUE data all COLUMNS of all TABLES in. This system table has the user has a string value? Search path for improving our knowledge and dedicated professional with ai model for redshift list all constraints, views using schemas. Sqlite_temp_schema works without loop. True if you might cause all. Optional message bit after finishing an awesome blog. Easy way are a list all objects have logs all databases do you can be logged in lowercase, fully managed automatically by default description form. How do not running sap, and sizes of all object privileges granted, i understood you all redshift of how about data professional with sqlite? Any questions or. The following research will bowl the T-SQL needed to change every rule change the WHERE clause define the schema you need and replace. Lists all of schema name is there you can be specified on other roles held by email and systems still safe even following command? This data scientist, thanx for schemas that you learn from sysindexes as sqlite.
    [Show full text]
  • A Platform for Networked Business Analytics BUSINESS INTELLIGENCE
    BROCHURE A platform for networked business analytics BUSINESS INTELLIGENCE Infor® Birst's unique networked business analytics technology enables centralized and decentralized teams to work collaboratively by unifying Leveraging Birst with existing IT-managed enterprise data with user-owned data. Birst automates the enterprise BI platforms process of preparing data and adds an adaptive user experience for business users that works across any device. Birst networked business analytics technology also enables customers to This white paper will explain: leverage and extend their investment in ■ Birst’s primary design principles existing legacy business intelligence (BI) solutions. With the ability to directly connect ■ How Infor Birst® provides a complete unified data and analytics platform to Oracle Business Intelligence Enterprise ■ The key elements of Birst’s cloud architecture Edition (OBIEE) semantic layer, via ODBC, ■ An overview of Birst security and reliability. Birst can map the existing logical schema directly into Birst’s logical model, enabling Birst to join this Enterprise Data Tier with other data in the analytics fabric. Birst can also map to existing Business Objects Universes via web services and Microsoft Analysis Services Cubes and Hyperion Essbase cubes via MDX and extend those schemas, enabling true self-service for all users in the enterprise. 61% of Birst’s surveyed reference customers use Birst as their only analytics and BI standard.1 infor.com Contents Agile, governed analytics Birst high-performance in the era of
    [Show full text]
  • Exasol User Manual Version 6.0.8
    Exasol User Manual Version 6.0.8 Empowering analytics. Experience the world´s fastest, most intelligent, in-memory analytics database. Copyright © 2018 Exasol AG. All rights reserved. The information in this publication is subject to change without notice. EXASOL SHALL NOT BE HELD LIABLE FOR TECHNICAL OR EDITORIAL ERRORS OR OMISSIONS CONTAINED HEREIN NOR FOR ACCIDENTAL OR CONSEQUENTIAL DAMAGES RES- ULTING FROM THE FURNISHING, PERFORMANCE, OR USE OF. No part of this publication may be photocopied or reproduced in any form without prior written consent from Exasol. All named trademarks and registered trademarks are the property of their respective owners. Exasol User Manual Table of Contents Foreword ..................................................................................................................................... ix Conventions ................................................................................................................................. xi Changes in Version 6.0 ................................................................................................................. xiii 1. What is Exasol? .......................................................................................................................... 1 2. SQL reference ............................................................................................................................ 5 2.1. Basic language elements .................................................................................................... 5 2.1.1. Comments
    [Show full text]
  • SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N
    SQL Server Column Store Indexes Per-Åke Larson, Cipri Clinciu, Eric N. Hanson, Artem Oks, Susan L. Price, Srikumar Rangarajan, Aleksandras Surna, Qingqing Zhou Microsoft {palarson, ciprianc, ehans, artemoks, susanpr, srikumar, asurna, qizhou}@microsoft.com ABSTRACT SQL Server column store indexes are “pure” column stores, not a The SQL Server 11 release (code named “Denali”) introduces a hybrid, because they store all data for different columns on new data warehouse query acceleration feature based on a new separate pages. This improves I/O scan performance and makes index type called a column store index. The new index type more efficient use of memory. SQL Server is the first major combined with new query operators processing batches of rows database product to support a pure column store index. Others greatly improves data warehouse query performance: in some have claimed that it is impossible to fully incorporate pure column cases by hundreds of times and routinely a tenfold speedup for a store technology into an established database product with a broad broad range of decision support queries. Column store indexes are market. We’re happy to prove them wrong! fully integrated with the rest of the system, including query To improve performance of typical data warehousing queries, all a processing and optimization. This paper gives an overview of the user needs to do is build a column store index on the fact tables in design and implementation of column store indexes including the data warehouse. It may also be beneficial to build column enhancements to query processing and query optimization to take store indexes on extremely large dimension tables (say more than full advantage of the new indexes.
    [Show full text]
  • Replication at the Speed of Change – a Fast, Scalable Replication Solution for Near Real-Time HTAP Processing
    Replication at the Speed of Change – a Fast, Scalable Replication Solution for Near Real-Time HTAP Processing Dennis Butterstein Daniel Martin Jia Zhong Lingyun Wang Knut Stolze Felix Beier IBM Silicon Valley Lab IBM Research & Development GmbH 555 Bailey Ave Schonaicher¨ Strasse 220 San Jose, CA 95141, United States 71032 Boblingen,¨ Germany [email protected] [email protected] [email protected] fdanmartin,stolze,[email protected] ABSTRACT engine was first replaced with Netezza (IBM PureData Sys- 2 The IBM Db2 Analytics Accelerator (IDAA) is a state- tem for Analytics ). Netezza's design is to always use table of-the art hybrid database system that seamlessly extends scans on all of its disks in parallel, leveraging FPGAs to ap- the strong transactional capabilities of Db2 for z/OS with ply decompression, projection and filtering operations before the very fast column-store processing in Db2 Warehouse. the data hits the main processors of the cluster nodes. The The Accelerator maintains a copy of the data from Db2 for row-major organized tables were hash-distributed across all z/OS in its backend database. Data can be synchronized nodes (and disks) based on selected columns (to facilitate at a single point in time with a granularity of a table, one co-located joins) or in a random fashion. The engine itself or more of its partitions, or incrementally as rows changed is optimized for table scans; besides Zone Maps there are no using replication technology. other structures (e. g., indices) that optimize the processing IBM Change Data Capture (CDC) has been employed as of predicates.
    [Show full text]
  • A Peek Under the Hood
    White paper Technical A Peek under the hood White paper Technical Contents A peek under the hood 01 02 03 Introduction 3 Being really fast 5 Providing a Great User Experience 12 Massively Parallel Processing MPP 5 Self-Optimization 13 Large-Scale In-Memory Advanced Analytics 04 Architecture 9 and Data Science 14 Supporting Business Integration Filters, Joins and Sorting 10 and Day-to-Day Operation 16 Query Optimizer and Interfaces and Tool Query Cache 11 Integration 17 Data Ingestion and Data Integration 18 The Virtual Schema Framework for Data Virtualization & 05 Hybrid Clouds 20 Summary 13 Fail Safety, Dual Data Center Operation and Backup/Restore 24 SQL Preprocessor 25 01 White paper Technical 3 Introduction Exasol was founded in founders recognized that database designed specifically Nuremberg, Germany, in the year new opportunities were made for analytics. Exasol holds 2000 – a time when two trends possible by these trends. With performance records in the in hardware were starting to RAM falling in cost and rising in TPC-H online transaction emerge: capacity and cluster computing processing benchmark from being merely a commodity, it the Transaction Processing Major improvements in was now conceivable to apply Performance Council (TPC) for processing power were no the principles and architectures decision-support databases, longer coming from ever of high-performance computing outperforming competitors by increasing clock speeds of to database design. In the years orders of magnitudes and scaling central processing units (CPUs), that followed, the company up to hundreds of terabytes of but instead from parallel and exclusively focused on delivering data. distributed systems.
    [Show full text]
  • EXASOL AG Our History – Inventing World’S Fastest In-Memory Database
    The Most Powerful In-memory Analytic Database Introduction @ Sphinx IT in Vienna 25.11.2016 © 2016 EXASOL AG Our history – inventing world’s fastest in-memory database 2008 2012 Record in Inclusion in Gartner’s 2000 TPC-H Benchmark „Magic Quadrant for Company foundation („Oracle dethroned“) Data Management Systems“ 90ies 2006 2010 2014 early Research Success Pilot Customer Karstadt- Most Successful Vendor of Successful global expansion, (University Erlangen- Quelle uses EXASolution in analytical database systems in 400+ customers across 12 countries Nürnberg) Production Germany (BARC) 100TB TPC-H benchmark © 2016 EXASOL AG Great recognition in the market 2016 © 2016 EXASOL AG What Gartner says about EXASOL “EXASOL is a prime example of what Gartner considers to be the future of DBMS” Source: Gartner © 2016 EXASOL AG Why would you be looking for a new Database Performance/ Pricing Issues with New Requests for Existing DWH agile or predictive Analytics Changing Plattforms or Regulatory Issues growing DataSources © 2016 EXASOL AG King: leading interactive entertainment company . Analyzes customer behavior . Optimizes game revenues . Lots and lots of data . 100 million daily active users . 1 billion game plays per day . 10 billion events per day EXASOL database: 200TB © 2016 EXASOL AG Zalando: Rising star of e-commerce . Online fashion retailer with 14m+ customers across Europe . 150,000 products available online . EXASOL complements DWH to enable fast analytics and reporting . Database optimizes stock availability, returns process and targeted marketing EXASOL database: 15TB © 2016 EXASOL AG Adidas: CRM . Several projects in different regions (Europe, USA, Russia) . Agile BI -> flexible reporting functionality for quick projects . BW on HANA: “Cruise liner” .
    [Show full text]
  • IBM Cognos Analytics Version 11.1 : Administration and Security Guide Chapter 1
    IBM Cognos Analytics Version 11.1 Administration and Security Guide IBM © Product Information This document applies to IBM Cognos Analytics version 11.1.0 and may also apply to subsequent releases. Copyright Licensed Materials - Property of IBM © Copyright IBM Corp. 2005, 2021. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. IBM, the IBM logo and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at " Copyright and trademark information " at www.ibm.com/legal/copytrade.shtml. The following terms are trademarks or registered trademarks of other companies: • Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. • Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. • Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both. • UNIX is a registered trademark of The Open Group in the United States and other countries. • Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. Microsoft product screen shot(s) used with permission from Microsoft. © Copyright International Business Machines Corporation . US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp.
    [Show full text]
  • Columnar Storage in SQL Server 2012
    Columnar Storage in SQL Server 2012 Per-Ake Larson Eric N. Hanson Susan L. Price [email protected] [email protected] [email protected] Abstract SQL Server 2012 introduces a new index type called a column store index and new query operators that efficiently process batches of rows at a time. These two features together greatly improve the performance of typical data warehouse queries, in some cases by two orders of magnitude. This paper outlines the design of column store indexes and batch-mode processing and summarizes the key benefits this technology provides to customers. It also highlights some early customer experiences and feedback and briefly discusses future enhancements for column store indexes. 1 Introduction SQL Server is a general-purpose database system that traditionally stores data in row format. To improve performance on data warehousing queries, SQL Server 2012 adds columnar storage and efficient batch-at-a- time processing to the system. Columnar storage is exposed as a new index type: a column store index. In other words, in SQL Server 2012 an index can be stored either row-wise in a B-tree or column-wise in a column store index. SQL Server column store indexes are “pure” column stores, not a hybrid, because different columns are stored on entirely separate pages. This improves I/O performance and makes more efficient use of memory. Column store indexes are fully integrated into the system. To improve performance of typical data warehous- ing queries, all a user needs to do is build a column store index on the fact tables in the data warehouse.
    [Show full text]
  • Fastest Analytics Database
    Exasol at a Glance Opening Introducing Exasol Three ways to use Exasol for acceleration Summary Performance limitations Performance is king for many mission-critical analytics workloads such as fraud and risk analysis, compliance reporting, and real-time customer analytics. Delayed time to analytics output can lead to catastrophic outcomes including increased risk exposure, steep fines, and customer churn. As more businesses become data-driven, more people than ever before need access to analytics. But current data warehouses seldom scale well with increased use, nor do they perform well enough to support those time-sensitive analytics workloads due to their suboptimal architecture. As a result, the analytics team must constantly tune Opening performance, a time-consuming and costly effort. Lack of support for modern analytics use cases Despite nearly four decades of data warehouse advancements, the majority of enterprises still struggle to achieve tangible The current business climate has many unforeseen questions, and organizations are ROI for their analytics investments. In fact, a number of recent increasingly turning to their data to find the best possible answers, fast. Those changing studies have shown that over 80% are not satisfied with the business dynamics drive the need for new types of analytics use cases such as ad hoc results of their data warehousing initiatives. and real-time analysis. Given the complex, time-sensitive nature of these modern analytics workloads, analytics teams must put other projects on hold to support them, resulting in even more manual performance tuning and reconfiguration. What is preventing data warehouses from delivering on their promise? Difficulty operationalizing data science workloads Despite the hype and investment going into machine learning, 85% of data science projects fail.
    [Show full text]
  • An Analytics Database Company to Watch
    Vendor Profile Exasol: An Analyt ics Dat abase Company t o Wat ch Philip Carnelley Erica Spinoni David Wells IDC OPINION Exasol is a Germany-headquartered analytics database management software company founded in 2000 in Nuremberg, with offices across Europe and the U.S. and recently growing rapidly on both sides of the Atlantic. With 20 years of experience in distributed database technology and a significant customer base, Exasol is becoming an influential player in the in-memory analytics database market. IN THIS VENDOR PROFILE This IDC Vendor Profile analyzes the success and the market positioning of Exasol, a Germany- headquartered analytics database management software company. From its foundation, Exasol planned to build database management software designed to take advantage of the increasing affordability of both parallel processing hardware platforms and RAM. SITUATION OVERVIEW Company Overview Exasol is an analytics database management software company founded 20 years ago in Nuremberg, Germany. In recent years, it has transformed from a small start-up to a company with an international footprint, principally in Europe and the U.S. In the nine months to November 2019, Exasol's recurring revenue grew over 45%, and IDC estimates it exceeded $23 million in 2019, with around a fifth of that coming from the U.S. Exasol's customer base consists mainly of large commercial enterprises with complex analytical needs from industries such as retail, financial services, media and publishing, and ecommerce — its entire range of customers is quite broad. The company has offices in both Europe and the U.S., providing support for its 165+ customers and over 500 installations.
    [Show full text]
  • Bring Your Language (And Libraries) to Your Data
    Bring Your Language (and libraries) to Your Data Stefan Mandl, Oleksandr Kozachuk, Jens Graupmann © 2016 EXASOL AG What is EXASOL? . a column store, massively parallel processing (MPP), in-memory analytic database . modern software designed for analytics . runs on standard x86 hardware . ACID compliance . uses standard SQL language (with optional extensions) . suitable for any scale of data & any number of users . mature, proven & very cost effective . quick to implement & easy to operate The World’s Fastest Analytic Database © 2017 EXASOL AG We are the benchmark leader (TPC-H) On 1TB data - an order of magnitude faster than the nearest rival Sept ´14 5,246,338 Dec ´14 MS SQL Server 588,831 Sep ´14 Actian Vector 585,319 Apr ´14 MS SQL Server 519,976 Jun ´14 Actian Vector 485,242 Sep ´14 MS SQL Server 390,590 Feb ´14 Oracle 326,454 Aug ´14 MS SQL Server 304,361 Nov ´13 Sybase IQ 258,474 Source: www.tpc.org As of Oct 13, 2015 QphH@1000 GB 1,000,000 2,000,000 3,000,000 4.000,000 Queries per hour Full report can be viewed here: http://www.tpc.org/tpch/results/tpch_perf_results.asp © 2017 EXASOL AG History of innovation: MPP, in-memory, performance Record in Inclusion in Gartner’s TPC-H Benchmark “Magic Quadrant for 100+ customers across EXASOL is founded (“Oracle dethroned“) DW DBMS Systems“ 16 countries/5 continents 2000 2008 2012 2015 2010 90ies 2006 Most successful vendor 2014 Early Research Pilot Customer of analytic database Successful global expansion, Success (University Karstadt-Quelle uses systems in Germany 100TB TPC-H benchmark Erlangen-Nürnberg) EXASOL in production (BARC) © 2017 EXASOL AG 100+ customers in 16 countries/5 continents and counting… © 2017 EXASOL AG CCV: Analytics that redefines the customer experience .
    [Show full text]