Open Source Business Intelligence and Data Warehousing Seth Grimes Alta Plana Corporation +1 301-270-0795
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Monetdb/X100: Hyper-Pipelining Query Execution
MonetDB/X100: Hyper-Pipelining Query Execution Peter Boncz, Marcin Zukowski, Niels Nes CWI Kruislaan 413 Amsterdam, The Netherlands fP.Boncz,M.Zukowski,[email protected] Abstract One would expect that query-intensive database workloads such as decision support, OLAP, data- Database systems tend to achieve only mining, but also multimedia retrieval, all of which re- low IPC (instructions-per-cycle) efficiency on quire many independent calculations, should provide modern CPUs in compute-intensive applica- modern CPUs the opportunity to get near optimal IPC tion areas like decision support, OLAP and (instructions-per-cycle) efficiencies. multimedia retrieval. This paper starts with However, research has shown that database systems an in-depth investigation to the reason why tend to achieve low IPC efficiency on modern CPUs in this happens, focusing on the TPC-H bench- these application areas [6, 3]. We question whether mark. Our analysis of various relational sys- it should really be that way. Going beyond the (im- tems and MonetDB leads us to a new set of portant) topic of cache-conscious query processing, we guidelines for designing a query processor. investigate in detail how relational database systems The second part of the paper describes the interact with modern super-scalar CPUs in query- architecture of our new X100 query engine intensive workloads, in particular the TPC-H decision for the MonetDB system that follows these support benchmark. guidelines. On the surface, it resembles a The main conclusion we draw from this investiga- classical Volcano-style engine, but the cru- tion is that the architecture employed by most DBMSs cial difference to base all execution on the inhibits compilers from using their most performance- concept of vector processing makes it highly critical optimization techniques, resulting in low CPU CPU efficient. -
Sql Plus Download Mac
Sql plus download mac LINK TO DOWNLOAD oracle sql plus free download - Insight Developer for Oracle, SQLite Database, Orac, and many more programs. SQL*Plus Instant Client can be installed in two ways: Download the packages from the Oracle Technology Network (OTN). Copy the same files that are in the packages from an Oracle Database 10 g Client Administrator installation. Both the SQL*Plus and OCI packages must be from the same Oracle Database version, for example, SQL*PLus on Mac. December 1st, Goto comments Leave a comment. I would think installing SQL*Plus on the Mac would be point, click download, point click, install bam it works. Nah. It did install mostly straight forward on my old Mac. Got a new Mac and no dice. Jul 30, · Download the (free) Docker Community Edition for Mac (unless you’ve already got it installed on your system). This will enable you to run SQL Server from within a Docker container. To download, visit the Docker CE for Mac download page and click Get Docker. Find "Macintosh OSX" and download both the Basic and SQL*Plus packages from the Instant Client downloads page. Move the contents of both into a single convenient folder (I used /Applications/Application_folders/instantclient). Add the location to your PATH variable, and also set DYLD_LIBRARY_PATH to point to it; for example. That could affect running SQL*Plus from a shell script, for example. There are workarounds for the 11g instant client. The installation notes at the bottom of the download page have changed since I last did this, and it now says to hard link the library files to the user's ~/lib directory to avoid that issue. -
Squirrel SQL Client
Pagina: 1 di 22 Versione: 01.09 Data agg.: 08/02/2021 RM Squirrel SQL client guida rapida Autori: Marco Riva Revisori: g:\installazioni\squirrel sql client 4.1.0\squirrel sql client - guida rapida.docx Squirrel SQL client Pagina: 2 di 22 guida rapida Versione: 01.09 Data agg.: 08/02/2021 RM Sommario 1. Introduzione ........................................ 3 1.1. Fast startup .......................................... 3 2. Installazione ........................................ 4 2.1. Requisiti ............................................... 4 2.2. Download ............................................. 4 2.3. Installazione .......................................... 4 2.4. Aggiornamento ...................................... 4 3. Overview ............................................ 5 4. Configurazione ..................................... 5 4.1. Configurazione driver per DB2 for i ........... 5 4.2. Configurazione alias ............................... 7 5. Utilizzo .............................................. 12 5.1. Connessione ad un database .................. 12 5.2. Maggiori informazioni sull’area object ...... 14 5.2.1. Filtro e ricerca oggetti ..................... 14 5.2.2. Scheda content .............................. 15 5.2.3. Esportazione contenuti.................... 15 5.2.4. Modifica diretta in tabella ................ 16 5.3. Editor SQL .......................................... 17 5.3.1. Scorciatoie da tastiera .................... 17 5.3.2. Regole sintassi .............................. 17 5.3.3. Barra dei pulsanti .......................... -
Hannes Mühleisen
Large-Scale Statistics with MonetDB and R Hannes Mühleisen DAMDID 2015, 2015-10-13 About Me • Postdoc at CWI Database architectures since 2012 • Amsterdam is nice. We have open positions. • Special interest in data management for statistical analysis • Various research & software projects in this space 2 Outline • Column Store / MonetDB Introduction • Connecting R and MonetDB • Advanced Topics • “R as a Query Language” • “Capturing The Laws of Data Nature” 3 Column Stores / MonetDB Introduction 4 Postgres, Oracle, DB2, etc.: Conceptional class speed flux NX 1 3 Constitution 1 8 Galaxy 1 3 Defiant 1 6 Intrepid 1 1 Physical (on Disk) NX 1 3 Constitution 1 8 Galaxy 1 3 Defiant 1 6 Intrepid 1 1 5 Column Store: class speed flux Compression! NX 1 3 Constitution 1 8 Galaxy 1 3 Defiant 1 6 Intrepid 1 1 NX Constitution Galaxy Defiant Intrepid 1 1 1 1 1 3 8 3 6 1 6 What is MonetDB? • Strict columnar architecture OLAP RDBMS (SQL) • Started by Martin Kersten and Peter Boncz ~1994 • Free & Open Open source, active development ongoing • www.monetdb.org Peter A. Boncz, Martin L. Kersten, and Stefan Manegold. 2008. Breaking the memory wall in MonetDB. Communications of the ACM 51, 12 (December 2008), 77-85. DOI=10.1145/1409360.1409380 7 MonetDB today • Expanded C code • MAL “DB assembly” & optimisers • SQL to MAL compiler • Memory-Mapped files • Automatic indexing 8 Some MAL • Optimisers run on MAL code • Efficient Column-at-a-time implementations EXPLAIN SELECT * FROM mtcars; | X_2 := sql.mvc(); | | X_3:bat[:oid,:oid] := sql.tid(X_2,"sys","mtcars"); | | X_6:bat[:oid,:dbl] -
LDBC Introduction and Status Update
8th Technical User Community meeting - LDBC Peter Boncz [email protected] ldbcouncil.org LDBC Organization (non-profit) “sponsors” + non-profit members (FORTH, STI2) & personal members + Task Forces, volunteers developing benchmarks + TUC: Technical User Community (8 workshops, ~40 graph and RDF user case studies, 18 vendor presentations) What does a benchmark consist of? • Four main elements: – data & schema: defines the structure of the data – workloads: defines the set of operations to perform – performance metrics: used to measure (quantitatively) the performance of the systems – execution & auditing rules: defined to assure that the results from different executions of the benchmark are valid and comparable • Software as Open Source (GitHub) – data generator, query drivers, validation tools, ... Audience • For developers facing graph processing tasks – recognizable scenario to compare merits of different products and technologies • For vendors of graph database technology – checklist of features and performance characteristics • For researchers, both industrial and academic – challenges in multiple choke-point areas such as graph query optimization and (distributed) graph analysis SPB scope • The scenario involves a media/ publisher organization that maintains semantic metadata about its Journalistic assets (articles, photos, videos, papers, books, etc), also called Creative Works • The Semantic Publishing Benchmark simulates: – Consumption of RDF metadata (Creative Works) – Updates of RDF metadata, related to Annotations • Aims to be -
The Database Architectures Research Group at CWI
The Database Architectures Research Group at CWI Martin Kersten Stefan Manegold Sjoerd Mullender CWI Amsterdam, The Netherlands fi[email protected] http://www.cwi.nl/research-groups/Database-Architectures/ 1. INTRODUCTION power provided by the early MonetDB implementa- The Database research group at CWI was estab- tions. In the years following, many technical inno- lished in 1985. It has steadily grown from two PhD vations were paired with strong industrial maturing students to a group of 17 people ultimo 2011. The of the software base. Data Distilleries became a sub- group is supported by a scientific programmer and sidiary of SPSS in 2003, which in turn was acquired a system engineer to keep our machines running. by IBM in 2009. In this short note, we look back at our past and Moving MonetDB Version 4 into the open-source highlight the multitude of topics being addressed. domain required a large number of extensions to the code base. It became of the utmost importance to support a mature implementation of the SQL- 2. THE MONETDB ANTHOLOGY 03 standard, and the bulk of application program- The workhorse and focal point for our research is ming interfaces (PHP, JDBC, Python, Perl, ODBC, MonetDB, an open-source columnar database sys- RoR). The result of this activity was the first official tem. Its development goes back as far as the early open-source release in 2004. A very strong XQuery eighties when our first relational kernel, called Troll, front-end was developed with partners and released was shipped as an open-source product. It spread to in 2005 [1]. -
Micro Adaptivity in Vectorwise
Micro Adaptivity in Vectorwise Bogdan Raducanuˇ Peter Boncz Actian CWI [email protected] [email protected] ∗ Marcin Zukowski˙ Snowflake Computing marcin.zukowski@snowflakecomputing.com ABSTRACT re-arrange the shape of a query plan in reaction to its execu- Performance of query processing functions in a DBMS can tion so far, thereby enabling the system to correct mistakes be affected by many factors, including the hardware plat- in the cost model, or to e.g. adapt to changing selectivities, form, data distributions, predicate parameters, compilation join hit ratios or tuple arrival rates. Micro Adaptivity, intro- method, algorithmic variations and the interactions between duced here, is an orthogonal approach, taking adaptivity to these. Given that there are often different function imple- the micro level by making the low-level functions of a query mentations possible, there is a latent performance diversity executor more performance-robust. which represents both a threat to performance robustness Micro Adaptivity was conceived inside the Vectorwise sys- if ignored (as is usual now) and an opportunity to increase tem, a high performance analytical RDBMS developed by the performance if one would be able to use the best per- Actian Corp. [18]. The raw execution power of Vectorwise forming implementation in each situation. Micro Adaptivity, stems from its vectorized query executor, in which each op- proposed here, is a framework that keeps many alternative erator performs actions on vectors-at-a-time, rather than a function implementations (“flavors”) in a system. It uses a single tuple-at-a-time; where each vector is an array of (e.g. -
When and Why to Put What Data Where the Usage of ODS and Data Mart Platforms in a Well Architected Data Warehouse
Data Warehousing When and Why to Put What Data Where The usage of ODS and data mart platforms in a well architected data warehouse By: Rob Armstrong Teradata Corporation When and Why to Put What Data Where Table of Contents Executive Summary 2 Executive Summary Operational Data Stores 3 Companies are trying to overcome two seemingly conflicting Data Warehouse Core 3 challenges. The first is how to integrate more historical Data Marts 4 Conclusion 5 and cross-functional analytic processes into their front-line About the Author 5 operations to take the most relevant actions against the most profitable customers. The second challenge is how to integrate more timely interactions and transactions into the back-end analytics so that strategy can be measured and fine tuned at an ever increasing pace. In order to balance these two objectives, companies are looking to push data to the process by incorpo- rating operational data stores and data marts into their data management architectures. Unfortunately, this constant pushing of data results in more data inconsistency, more data latency, and a more fractured understanding of the business Author’s note: This paper is a companion as a whole. Understanding the correct usage of the layers helps of the “Optimizing the Data Warehouse Layer” white paper. While this paper to alleviate some of the problems. However, the real solution describes the differences and functions of the ODS and data mart layers, the other is to integrate the data once and drive the process to the data. paper discusses how to best optimize your Having said that, and understanding the larger challenge of core data warehouse repository layer to minimize data movement, minimize data centralization will keep some on the distributed path, there are management costs, and increase reusability some guidelines to understanding how and when to use the and end-user accessibility. -
Selecting a Data Warehouse Architecture David Septoff, SAS, Rockville, MD Kevin Simmons, SAS, Rockville, MD
Selecting a Data Warehouse Architecture David Septoff, SAS, Rockville, MD Kevin Simmons, SAS, Rockville, MD Abstract Data Warehouse projects have provided end-users with after implementation begins. Waiting until after extraordinary returns on investment. Maintaining those implementation begins usually increases the amount of extraordinary returns requires an architecture that is rework. Most developers will admit that it is more time robust, flexible and scalable. Careful thought must be consuming and difficult to do rework than to do the job given to the selection of the data warehouse and right the first time. For this reason, there is little doubt decision-support architecture. A mistake in this area about the value of a carefully architected data warehouse often leads to the deployment of an application that and decision-support environment. There has been, cannot provide long-term support for the data and however, a great deal of debate surrounding the "best" analytical needs of the end-user community. architecture to deploy. The selection of the "best" architecture will be driven by such factors as: available Selecting the most appropriate architecture requires technology, business requirements, commitment to the examination of a number of factors. Some of those development effort, skill level of the developers, and the factors are rooted in technical constraints, while others availability of resources. are rooted in how end-users are organized. There are also political issues that must be noted during selection There are several architectures, which can be supported of the architecture. The first step in selecting an via the SAS ® System as well as products from other architecture is to understand your decision-support vendors. -
Presto: the Definitive Guide
Presto The Definitive Guide SQL at Any Scale, on Any Storage, in Any Environment Compliments of Matt Fuller, Manfred Moser & Martin Traverso Virtual Book Tour Starburst presents Presto: The Definitive Guide Register Now! Starburst is hosting a virtual book tour series where attendees will: Meet the authors: • Meet the authors from the comfort of your own home Matt Fuller • Meet the Presto creators and participate in an Ask Me Anything (AMA) session with the book Manfred Moser authors + Presto creators • Meet special guest speakers from Martin your favorite podcasts who will Traverso moderate the AMA Register here to save your spot. Praise for Presto: The Definitive Guide This book provides a great introduction to Presto and teaches you everything you need to know to start your successful usage of Presto. —Dain Sundstrom and David Phillips, Creators of the Presto Projects and Founders of the Presto Software Foundation Presto plays a key role in enabling analysis at Pinterest. This book covers the Presto essentials, from use cases through how to run Presto at massive scale. —Ashish Kumar Singh, Tech Lead, Bigdata Query Processing Platform, Pinterest Presto has set the bar in both community-building and technical excellence for lightning- fast analytical processing on stored data in modern cloud architectures. This book is a must-read for companies looking to modernize their analytics stack. —Jay Kreps, Cocreator of Apache Kafka, Cofounder and CEO of Confluent Presto has saved us all—both in academia and industry—countless hours of work, allowing us all to avoid having to write code to manage distributed query processing. -
Hmi + Clds = New Sdsc Center
New Technologies for Data Management Chaitan Baru 2013 Summer Institute: Discover Big Data, August 5-9, San Diego, California SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA, SAN DIEGO 2 2 Why new technologies? • Big Data Characteristics: Volume, Velocity, Variety • Began as a Volume problem ▫ E.g. Web crawls … ▫ 1’sPB-100’sPB in a single cluster • Velocity became an issue ▫ E.g. Clickstreams at Facebook ▫ 100,000 concurrent clients ▫ 6 billion messages/day • Variety is now important ▫ Integrate, fuse data from multiple sources ▫ Synoptic view; solve complex problems 2013 Summer Institute: Discover Big Data, August 5-9, San Diego, California SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA, SAN DIEGO 3 3 Varying opinions on Big Data • “It’s all about velocity. The others issues are old problems.” • “Variety is the important characteristic.” • “Big Data is nothing new.” 2013 Summer Institute: Discover Big Data, August 5-9, San Diego, California SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA, SAN DIEGO 4 4 Expanding Enterprise Systems: From OLTP to OLAP to “Catchall” • OLTP: OnLine Transaction Processing ▫ E.g., point-of-sale terminals, e-commerce transactions, etc. ▫ High throughout; high rate of “single shot” read/write operations ▫ Large number of concurrent clients ▫ Robust, durable processing 2013 Summer Institute: Discover Big Data, August 5-9, San Diego, California SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA, SAN DIEGO 5 5 Expanding Enterprise Systems: OLAP • OLAP: Decision Support Systems requiring complex query processing ▫ Fast response times for complex SQL queries ▫ Queries over historical data ▫ Fewer number of concurrent clients ▫ Data aggregated from multiple systems Multiple business systems, e.g. -
Big Data Solutions and Applications
P. Bellini, M. Di Claudio, P. Nesi, N. Rauch Slides from: M. Di Claudio, N. Rauch Big Data Solutions and applications Slide del corso: Sistemi Collaborativi e di Protezione Corso di Laurea Magistrale in Ingegneria 2013/2014 http://www.disit.dinfo.unifi.it Distributed Data Intelligence and Technology Lab 1 Related to the following chapter in the book: • P. Bellini, M. Di Claudio, P. Nesi, N. Rauch, "Tassonomy and Review of Big Data Solutions Navigation", in "Big Data Computing", Ed. Rajendra Akerkar, Western Norway Research Institute, Norway, Chapman and Hall/CRC press, ISBN 978‐1‐ 46‐657837‐1, eBook: 978‐1‐ 46‐657838‐8, july 2013, in press. http://www.tmrfindia.org/b igdata.html 2 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 3 Index 6. Access/Data Rendering aspects 7. Data Analysis and Mining/Ingestion Aspects 8. Comparison and Analysis of Architectural Features • Data Management • Data Access and Visual Rendering • Mining/Ingestion • Data Analysis • Architecture 4 Part 1 The Big Data Problem. 5 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 6 What is Big Data? Variety Volume Value 5V’s of Big Data Velocity Variability 7 5V’s of Big Data (1/2) • Volume: Companies amassing terabytes/petabytes of information, and they always look for faster, more efficient, and lower‐ cost solutions for data management.