The Design and Implementation of Modern Column-Oriented Database Systems
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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. -
Data Warehouse Fundamentals for Storage Professionals – What You Need to Know EMC Proven Professional Knowledge Sharing 2011
Data Warehouse Fundamentals for Storage Professionals – What You Need To Know EMC Proven Professional Knowledge Sharing 2011 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents Introduction ................................................................................................................................ 3 Data Warehouse Background .................................................................................................... 4 What Is a Data Warehouse? ................................................................................................... 4 Data Mart Defined .................................................................................................................. 8 Schemas and Data Models ..................................................................................................... 9 Data Warehouse Design – Top Down or Bottom Up? ............................................................10 Extract, Transformation and Loading (ETL) ...........................................................................11 Why You Build a Data Warehouse: Business Intelligence .....................................................13 Technology to the Rescue?.......................................................................................................19 RASP - Reliability, Availability, Scalability and Performance ..................................................20 Data Warehouse Backups .....................................................................................................26 -
Voodoo - a Vector Algebra for Portable Database Performance on Modern Hardware
Voodoo - A Vector Algebra for Portable Database Performance on Modern Hardware Holger Pirk Oscar Moll Matei Zaharia Sam Madden MIT CSAIL MIT CSAIL MIT CSAIL MIT CSAIL [email protected] [email protected] [email protected] [email protected] ABSTRACT Single Thread Branch Multithread Branch GPU Branch In-memory databases require careful tuning and many engi- Single Thread No Branch Multithread No Branch GPU No Branch neering tricks to achieve good performance. Such database performance engineering is hard: a plethora of data and 10 hardware-dependent optimization techniques form a design space that is difficult to navigate for a skilled engineer { even more so for a query compiler. To facilitate performance- 1 oriented design exploration and query plan compilation, we present Voodoo, a declarative intermediate algebra that ab- stracts the detailed architectural properties of the hard- ware, such as multi- or many-core architectures, caches and Absolute time in s 0.1 SIMD registers, without losing the ability to generate highly tuned code. Because it consists of a collection of declarative, vector-oriented operations, Voodoo is easier to reason about 1 5 10 50 100 and tune than low-level C and related hardware-focused ex- Selectivity tensions (Intrinsics, OpenCL, CUDA, etc.). This enables Figure 1: Performance of branch-free selections based on our Voodoo compiler to produce (OpenCL) code that rivals cursor arithmetics [28] (a.k.a. predication) over a branching and even outperforms the fastest state-of-the-art in memory implementation (using if statements) databases for both GPUs and CPUs. In addition, Voodoo makes it possible to express techniques as diverse as cache- conscious processing, predication and vectorization (again PeR [18], Legobase [14] and TupleWare [9], have arisen. -
A Survey on Parallel Database Systems from a Storage Perspective: Rows Versus Columns
A Survey on Parallel Database Systems from a Storage Perspective: Rows versus Columns Carlos Ordonez1 ? and Ladjel Bellatreche2 1 University of Houston, USA 2 LIAS/ISAE-ENSMA, France Abstract. Big data requirements have revolutionized database technol- ogy, bringing many innovative and revamped DBMSs to process transac- tional (OLTP) or demanding query workloads (cubes, exploration, pre- processing). Parallel and main memory processing have become impor- tant features to exploit new hardware and cope with data volume. With such landscape in mind, we present a survey comparing modern row and columnar DBMSs, contrasting their ability to write data (storage mecha- nisms, transaction processing, batch loading, enforcing ACID) and their ability to read data (query processing, physical operators, sequential vs parallel). We provide a unifying view of alternative storage mecha- nisms, database algorithms and query optimizations used across diverse DBMSs. We contrast the architecture and processing of a parallel DBMS with an HPC system. We cover the full spectrum of subsystems going from storage to query processing. We consider parallel processing and the impact of much larger RAM, which brings back main-memory databases. We then discuss important parallel aspects including speedup, sequential bottlenecks, data redistribution, high speed networks, main memory pro- cessing with larger RAM and fault-tolerance at query processing time. We outline an agenda for future research. 1 Introduction Parallel processing is central in big data due to large data volume and the need to process data faster. Parallel DBMSs [15, 13] and the Hadoop eco-system [30] are currently two competing technologies to analyze big data, both based on au- tomatic data-based parallelism on a shared-nothing architecture. -
Form Follows Function Model-Driven Engineering for Clinical Trials
Form Follows Function Model-Driven Engineering for Clinical Trials Jim Davies1, Jeremy Gibbons1, Radu Calinescu2, Charles Crichton1, Steve Harris1, and Andrew Tsui1 1 Department of Computer Science, University of Oxford Wolfson Building, Parks Road, Oxford OX1 3QD, UK http://www.cs.ox.ac.uk/firstname.lastname/ 2 Computer Science Research Group, Aston University Aston Triangle, Birmingham B4 7ET, UK http://www-users.aston.ac.uk/~calinerc/ Abstract. We argue that, for certain constrained domains, elaborate model transformation technologies|implemented from scratch in general- purpose programming languages|are unnecessary for model-driven en- gineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of soft- ware tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial pro- tocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automati- cally from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conduct- ing the trial instantiates such models in reporting observations|again by completing a form to create a conformant XML document, represent- ing the data gathered during that observation. Simple standard form management tools are all that is needed. -
Data Analysis Expressions (DAX) in Powerpivot for Excel 2010
Data Analysis Expressions (DAX) In PowerPivot for Excel 2010 A. Table of Contents B. Executive Summary ............................................................................................................................... 3 C. Background ........................................................................................................................................... 4 1. PowerPivot ...............................................................................................................................................4 2. PowerPivot for Excel ................................................................................................................................5 3. Samples – Contoso Database ...................................................................................................................8 D. Data Analysis Expressions (DAX) – The Basics ...................................................................................... 9 1. DAX Goals .................................................................................................................................................9 2. DAX Calculations - Calculated Columns and Measures ...........................................................................9 3. DAX Syntax ............................................................................................................................................ 13 4. DAX uses PowerPivot data types ......................................................................................................... -
(BI) Using MS Excel Powerpivot
2018 ASCUE Proceedings Developing an Introductory Class in Business Intelligence (BI) Using MS Excel Powerpivot Dr. Sam Hijazi Trevor Curtis Texas Lutheran University 1000 West Court Street Seguin, Texas 78130 [email protected] Abstract Asking questions about your data is a constant application of all business organizations. To facilitate decision making and improve business performance, a business intelligence application must be an in- tegral part of everyday management practices. Microsoft Excel added PowerPivot and PowerPivot offi- cially to facilitate this process with minimum cost, knowing that many business people are already fa- miliar with MS Excel. This paper will design an introductory class to business intelligence (BI) using Excel PowerPivot. If an educator decides to adopt this paper for teaching an introductory BI class, students should have previ- ous familiarity with Excel’s functions and formulas. This paper will focus on four significant phases all students need to complete in a three-credit class. First, students must understand the process of achiev- ing small database normalization and how to bring these tables to Excel or develop them directly within Excel PowerPivot. This paper will walk the reader through these steps to complete the task of creating the normalization, along with the linking and bringing the tables and their relationships to excel. Sec- ond, an introduction to Data Analysis Expression (DAX) will be discussed. Introduction It is not that difficult to realize the increase in the amount of data we have generated in the recent memory of our existence as a human race. To realize that more than 90% of the world’s data has been amassed in the past two years alone (Vidas M.) is to realize the need to manage such volume. -
Rdbmss Why Use an RDBMS
RDBMSs • Relational Database Management Systems • A way of saving and accessing data on persistent (disk) storage. 51 - RDBMS CSC309 1 Why Use an RDBMS • Data Safety – data is immune to program crashes • Concurrent Access – atomic updates via transactions • Fault Tolerance – replicated dbs for instant failover on machine/disk crashes • Data Integrity – aids to keep data meaningful •Scalability – can handle small/large quantities of data in a uniform manner •Reporting – easy to write SQL programs to generate arbitrary reports 51 - RDBMS CSC309 2 1 Relational Model • First published by E.F. Codd in 1970 • A relational database consists of a collection of tables • A table consists of rows and columns • each row represents a record • each column represents an attribute of the records contained in the table 51 - RDBMS CSC309 3 RDBMS Technology • Client/Server Databases – Oracle, Sybase, MySQL, SQLServer • Personal Databases – Access • Embedded Databases –Pointbase 51 - RDBMS CSC309 4 2 Client/Server Databases client client client processes tcp/ip connections Server disk i/o server process 51 - RDBMS CSC309 5 Inside the Client Process client API application code tcp/ip db library connection to server 51 - RDBMS CSC309 6 3 Pointbase client API application code Pointbase lib. local file system 51 - RDBMS CSC309 7 Microsoft Access Access app Microsoft JET SQL DLL local file system 51 - RDBMS CSC309 8 4 APIs to RDBMSs • All are very similar • A collection of routines designed to – produce and send to the db engine an SQL statement • an original -
30” Built-In Refrigerator Column (Right-Hand Door Swing)
30” BUILT-IN REFRIGERATOR COLUMN (RIGHT-HAND DOOR SWING) Signature Features JBRFR30IGX OVER 250 TRINITY COOLING REMOTE ACCESS CONFIGURATIONS Revel in the trinity of food Open door. Power outages. Freezer or refrigerator. Left- preservation: three zones, three Filter changes. Connect to WiFi hand or right-hand swing. One, precision sensors, calibrated for real-time notifications and two, three columns in a row. every second. Fortified by an control your columns from Four different widths. With 12 exposed air tower, you can anywhere. Tap and swipe on one-unit options, 75 two-unit conquer humidity levels and both iOS and Android devices. combinations and over 250 three- customize the temperature of <sup>1</sup><br><sup>1 unit combinations, configuration each zone to your deepest Appliance must be set to remote is where bespoke begins. desires. enable. WiFi & app required. Features subject to change. For ECLIPTIC LIGHTING DARING OBSIDIAN details and privacy statement, INTERIOR visit jennair.com/connect.</sup> Live beyond the shadow. Abandoning the dark spots cast Inspired by the beauty of SOLID GLASS AND METAL by dated puck lighting, over 650 volcanic glass, this industry- LEDs frame and fill your exclusive dark finish erupts with All-metal bin and shelf frames. column, coming alive at a touch reflective, high-contrast style. Thick, naturally odor-resistant of the controls. Each zone Reject the unfeeling, lifeless solid glass. A forcefield at the awakens and pulses as you mold vessel of harsh steel. Let fine edge that prevents spillovers. To your column to your desires. foods and beverages emerge hell with cheap plastic in luxury from the interior of your door bins, shelves and liners. -
HOME OFFICE SOLUTIONS Hettich Ideas Book Table of Contents
HOME OFFICE SOLUTIONS Hettich Ideas Book Table of Contents Eight Elements of Home Office Design 11 Home Office Furniture Ideas 15 - 57 Drawer Systems & Hinges 58 - 59 Folding & Sliding Door Systems 60 - 61 Further Products 62 - 63 www.hettich.com 3 How will we work in the future? This is an exciting question what we are working on intensively. The fact is that not only megatrends, but also extraordinary events such as a pandemic are changing the world and influencing us in all areas of life. In the long term, the way we live, act and furnish ourselves will change. The megatrend Work Evolution is being felt much more intensively and quickly. www.hettich.com 5 Work Evolution Goodbye performance society. Artificial intelligence based on innovative machines will relieve us of a lot of work in the future and even do better than we do. But what do we do then? That’s a good question, because it puts us right in the middle of a fundamental change in the world of work. The creative economy is on the advance and with it the potential development of each individual. Instead of a meritocracy, the focus is shifting to an orientation towards the strengths and abilities of the individual. New fields of work require a new, flexible working environment and the work-life balance is becoming more important. www.hettich.com 7 Visualizing a Scenario Imagine, your office chair is your couch and your commute is the length of your hallway. Your snack drawer is your entire pantry. Do you think it’s a dream? No! Since work-from-home is very a reality these days due to the pandemic crisis 2020. -
IBM DB2 for I Indexing Methods and Strategies Learn How to Use DB2 Indexes to Boost Performance
IBM DB2 for i indexing methods and strategies Learn how to use DB2 indexes to boost performance Michael Cain Kent Milligan DB2 for i Center of Excellence IBM Systems and Technology Group Lab Services and Training July 2011 © Copyright IBM Corporation, 2011 Table of contents Abstract........................................................................................................................................1 Introduction .................................................................................................................................1 The basics....................................................................................................................................3 Database index introduction .................................................................................................................... 3 DB2 for i indexing technology .................................................................................................................. 4 Radix indexes.................................................................................................................... 4 Encoded vector indexes .................................................................................................... 6 Bitmap indexes - the limitations ....................................................................................................... 6 EVI structure details......................................................................................................................... 7 EVI runtime usage........................................................................................................................... -
Jsfa [email protected]
John Sergio Fisher & Associates Inc. 5567 Reseda Blvd. Suite 209 Los Angeles, CA 91356 818.344.3045 Fax 818.344.0338 jsfa [email protected] July 18, 2019 Peter Tauscher, Senior Civil Engineer City of Newport Beach – Public Works Department Newport Beach Library Lecture Hall Building Project 100 Civic Center Drive Newport Beach, CA 92660 Dear Mr. Tauscher, John Sergio Fisher & Associates, Inc. (JSFA) is most honored to submit a proposal for the Newport Beach Library Lecture Hall Building Project. We are architects, planners/ urban designers, interior designers, theatre consultants and acoustical consultants with offices in Los Angeles and San Francisco. We’ve been in business 42 years involved primarily with cultural facilities for educational and civic institutions with a great majority of that work being for performance facilities. We are experts in seating arrangements whether they be for lecture halls, theatres/ concert halls or recital halls. We have won many design awards including 48 AIA design excellence awards and our work has been published regionally, nationally and abroad. We use a participatory programming and design process involving the city and the stakeholders. We pride ourselves in delivering award- winning, green building designs on time and on budget. Our current staff is 18 and our principals are involved with every project. Thank you for inviting us and for your consideration. Sincerely, John Sergio Fisher & Associates, Inc. John Fisher, AIA President 818.344.3045 Fax: 818.344.0338 Architecture Planning/Urban Design Interiors Arts/Entertainment Facilities Theatre Consulting Acoustical Consulting Los Angeles San Francisco jsfa Table of Contents Tab 1 1.