DOCSLIB.ORG

What Opportunities Should the Database Community Seize to Maximize Its Impact?

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
What Opportunities Should the Database Community Seize to Maximize Its Impact? SIGMOD Panel SIGMOD ’20, June 14–19, 2020, Portland, OR, USA The Next 5 Years: What Opportunities Should the Database Community Seize to Maximize its Impact? Magda Balazinska Surajit Chaudhuri University of Washington, Seattle Microsoft Research Anastasia Ailamaki Juliana Freire EPFL NYU Sailesh Krishnamurthy Michael Stonebraker Google MIT Panel Overview Some of the research challenges from the report that we The database research community has been will discuss in the panel include: spectacularly successful in impacting the industry and academia since the invention of the relational model. • Data to Insight: How can we leverage our Examples of innovation in the last decade include expertise in data cleaning and data integration columnar storage for data analytic platforms, cloud data for accelerating discovery of insights? How can services, HTAP systems, and a new generation of data we make it easy for data scientists to use both wrangling systems. Nonetheless, critical self-assessment query systems and machine learning inferences by the community and identifying key opportunities for in a seamless way? the future is essential if we are to continue the tradition • Data Governance: How do we support tracking of impactful research. of provenance across data stores efficiently and In the Fall of 2018, following a long tradition that dates at scale? What breakthroughs are needed to back to 1988 [1], and five years after the last such support data privacy in data platforms? What meeting [2], a group of approximately thirty database are the unique challenges in ethical data researchers gathered at the University of Washington, science? Seattle for two days to discuss the opportunities we have • Cloud Database Services: How does the as a community for impactful research. A report from architecture of database services adapt to and that meeting is now available [3]. The discussions in the take full advantage of disaggregation of Seattle meeting focused not only on technical challenges resources in the cloud? How do we architect and opportunities but also on topics related to how we hybrid clouds that span on-premise data organize ourselves as a community. systems and cloud data services? What should This SIGMOD panel will provide a forum for the broader be the next generation “serverless” cloud data database community to review and debate the findings service architecture? from the Seattle Report on Database Research [3] as well as to identify other considerations that need to be taken • Novel Scenarios for Database Systems: What are into account. the unique challenges of data lakes that go beyond traditional SQL data warehouse and Permission to make digital or hard copies of part or all of this work for today’s big data architectures? What are the personal or classroom use is granted without fee provided that copies implications of edge computing? are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights • Implications of Hardware Trends: How will the for third-party components of this work must be honored. For all other era of heterogeneous computation and modern uses, contact the Owner/Author. hardware impact the architecture of our SIGMOD ’20, June 14–19, 2020, Portland, OR, USA. © 2020 Copyright is held by the owner/author(s). engines? ACM ISBN 978-1-4503-6735-6/20/06. DOI: https://doi.org/10.1145/3318464.3394837 411 SIGMOD Panel SIGMOD ’20, June 14–19, 2020, Portland, OR, USA On the community front, we will discuss the changes different avenues to have impact, including collaboration that may be needed in our conferences and the with the industry. reviewing process to encourage early stage sharing of In summary, we hope that the Seattle Report on potentially impactful ideas. In the era of cloud services, Database Research [3] and the discussion in the panel another important question is understanding how the with the broader community will help database academia and the industry can collaborate as database researchers and industry participants identify key services from the public clouds continue to gain opportunities that we should seize as the community for popularity. Last but not the least, we will discuss our an impactful future. role in data science education. Moderators: Role of Panel Members The moderators will open the panel with a summary of Magdalena Balazinska is Professor and Director of the the Seattle Report on Database Research and will frame Paul G. Allen School of Computer Science & Engineering some of the open questions (e.g., as discussed earlier in at the University of Washington. Magdalena's research this document) for the panel members. interests are in the field of database management Anastasia Ailamaki will wear two hats in the panel. In systems. Her current research focuses on data addition to her perspectives on challenges in systems management for data science, big data systems, cloud research, as the 2019 SIGMOD Program Committee computing, and image and video analytics. She also Chair, she can provide her view from the ground on served as Co-Editor-in-Chief for Volume 13 of the “what is working” and “what is broken” in our peer Proceedings of the Very Large Data Bases Endowment review process. (PVLDB) journal and as PC co-chair for the Juliana Freire has wide ranging experience in data corresponding, prestigious VLDB'20 conference. science and is deeply involved on many innovative data- Magdalena holds a Ph.D. from the Massachusetts driven applications. Thus, her thoughts on how best to Institute of Technology (2006). Magdalena received the influence data science will be valuable. As the chair of inaugural VLDB Women in Database Research Award the SIGMOD executive, she can comment on the (2016) for her work on scalable distributed data systems. evolution of database conferences. She also received an ACM SIGMOD Test-of-Time Award Sailesh Krishnamurthy brings a wealth of expertise in (2017) for her work on fault-tolerant distributed stream cloud systems and will address the key challenges in processing and a 10-year most influential paper award evolution of cloud data services. He will be the voice of (2010) from her earlier work on reengineering software the “industry” and will also reflect on how best the clones. Magdalena received an NSF CAREER Award academia can partner with cloud providers to have impact. (2009), the UW CSE ACM Teaching Award (2013), the Jean Loup Baer Career Development Professorship in Mike Stonebraker will reflect on how to accelerate Computer Science and Engineering (2014-2017), two innovation and systems research. He will also share his Google Research Awards (2011 and 2018), an HP Labs view of our role in Data to Insights pipeline. His wide breadth of experience across research as well as the Research Innovation Award (2009 and 2010), a Rogel start-up ecosystem enables him to provide a unique Faculty Support Award (2006), a Microsoft Research perspective. Graduate Fellowship (2003-2005), and multiple best- paper (and "best of") awards. Expected outcome Surajit Chaudhuri is a Distinguished Scientist at Microsoft Research and leads the Data Management, We expect the panel to serve as a forum to update the Exploration and Mining group. His current areas of broader community on the Seattle Report on Database interest are data analytics for big data platforms, self- Research [3] and seek their criticism and feedback. The manageability, and cloud database services. Working technical part of discussions will also result in a frank with his colleagues in Microsoft Research, he helped assessment of topics where we are “polishing the round incorporate the Index Tuning Wizard (and subsequently ball” and where there are big opportunities. the Database Engine Tuning Advisor) and Data Cleaning We also hope to identify concrete action items that the technology in Microsoft SQL Server. Surajit is an ACM SIGMOD and VLDB Executive can take to improve the Fellow, a recipient of the ACM SIGMOD Edgar F. Codd state of the community. Last but not the least, we want Innovations Award, ACM SIGMOD Contributions to provide the academia with a better understanding of Award, a VLDB 10-year Best Paper Award, and an IEEE 412 SIGMOD Panel SIGMOD ’20, June 14–19, 2020, Portland, OR, USA Data Engineering Influential Paper Award. Surajit compatible with MySQL. Prior to AWS, Sailesh was at received his Ph.D. from Stanford University. Cisco Systems via the acquisition of Truviso, a real-time streaming data analytics software company that he co- founded to commercialize his prior academic research. Panelists: Michael Stonebraker has been a pioneer of data base Anastasia Ailamaki is a Professor of Computer and research and technology for more than a quarter of a Communication Sciences at EPFL as well as the CEO and century. He was the main architect of the INGRES co-founder of RAW Labs SA, a Swiss company that relational DBMS, and the object-relational DBMS, develops enables digital transformation for enterprises POSTGRES. These prototypes were developed at the through real-time analysis of heterogeneous big data. University of California at Berkeley where Stonebraker Previously, she was on the faculty of the Computer was a Professor of Computer Science for twenty-five Science Department at CMU, where she held the years. More recently at M.I.T. he was a co-architect of Finmeccanica endowed chair. She has received the 2019 the Aurora/Borealis stream processing engine, the C- ACM SIGMOD Edgar F. Codd Innovations Award, the Store column-oriented DBMS, the H-Store transaction 2019 EDBT Test of Time award, the 2018 Nemitsas Prize processing engine, the SciDB array DBMS, and the Data in Computer Science, an ERC Consolidator Award Tamer data curation system. Presently he serves as Chief (2013), the European Young Investigator Award from the Technology Officer of Paradigm4 and Tamr, Inc.
Load more

Recommended publications
  • Oracle Vs. Nosql Vs. Newsql Comparing Database Technology
    Oracle vs. NoSQL vs. NewSQL Comparing Database Technology John Ryan Senior Solution Architect, Snowflake Computing Table of Contents The World has Changed . 1 What’s Changed? . 2 What’s the Problem? . .. 3 Performance vs. Availability and Durability . 3 Consistecy vs. Availability . 4 Flexibility vs . Scalability . 5 ACID vs. Eventual Consistency . 6 The OLTP Database Reimagined . 7 Achieving the Impossible! . .. 8 NewSQL Database Technology . 9 VoltDB . 10 MemSQL . 11 Which Applications Need NewSQL Technology? . 12 Conclusion . 13 About the Author . 13 ii The World has Changed The world has changed massively in the past 20 years. Back in the year 2000, a few million users connected to the web using a 56k modem attached to a PC, and Amazon only sold books. Now billions of people are using to their smartphone or tablet 24x7 to buy just about everything, and they’re interacting with Facebook, Twitter and Instagram. The pace has been unstoppable . Expectations have also changed. If a web page doesn’t refresh within seconds we’re quickly frustrated, and go elsewhere. If a web site is down, we fear it’s the end of civilisation as we know it. If a major site is down, it makes global headlines. Instant gratification takes too long! — Ladawn Clare-Panton Aside: If you’re not a seasoned Database Architect, you may want to start with my previous articles on Scalability and Database Architecture. Oracle vs. NoSQL vs. NewSQL eBook 1 What’s Changed? The above leads to a few observations: • Scalability — With potentially explosive traffic growth, IT systems need to quickly grow to meet exponential numbers of transactions • High Availability — IT systems must run 24x7, and be resilient to failure.
    [Show full text]
  • The Declarative Imperative Experiences and Conjectures in Distributed Logic
    The Declarative Imperative Experiences and Conjectures in Distributed Logic Joseph M. Hellerstein University of California, Berkeley [email protected] ABSTRACT The juxtaposition of these trends presents stark alternatives. The rise of multicore processors and cloud computing is putting Will the forecasts of doom and gloom materialize in a storm enormous pressure on the software community to find solu- that drowns out progress in computing? Or is this the long- tions to the difficulty of parallel and distributed programming. delayed catharsis that will wash away today’s thicket of im- At the same time, there is more—and more varied—interest in perative languages, preparing the ground for a more fertile data-centric programming languages than at any time in com- declarative future? And what role might the database com- puting history, in part because these languages parallelize nat- munity play in shaping this future, having sowed the seeds of urally. This juxtaposition raises the possibility that the theory Datalog over the last quarter century? of declarative database query languages can provide a foun- Before addressing these issues directly, a few more words dation for the next generation of parallel and distributed pro- about both crisis and opportunity are in order. gramming languages. 1.1 Urgency: Parallelism In this paper I reflect on my group’s experience over seven years using Datalog extensions to build networking protocols I would be panicked if I were in industry. and distributed systems. Based on that experience, I present — John Hennessy, President, Stanford University [35] a number of theoretical conjectures that may both interest the database community, and clarify important practical issues in The need for parallelism is visible at micro and macro scales.
    [Show full text]
  • The Best Nurturers in Computer Science Research
    The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities.
    [Show full text]
  • SQL Vs Nosql
    SQL vs NoSQL By: Mohammed-Ali Khan Email: [email protected] Date: October 21, 2012 YORK UNIVERSITY Agenda ● History of DBMS – why relational is most popular? ● Types of DBMS – brief overview ● Main characteristics of RDBMS ● Main characteristics of NoSQL DBMS ● SQL vs NoSQL ● DB overview – Cassandra ● Criticism of NoSQL ● Some Predictions / Conclusions History of DBMS – why relational is most popular? ● 19th century: US Government needed reports from large datasets to conduct nation-wide census. ● 1890: Herman Hollerith creates the first automatic processing equipment which allowed American census to be conducted. ● 1911: IBM founded. ● 1960s: Efforts to standardize the database technology – US DoD: Conference on Data Systems Language (Codasyl). History of DBMS – why relational is most popular? ● 1968: IBM introduced IMS DBMS (a hierarchical DBMS). ● IBM employee Edgar Codd not satisfied, quoted as saying: – “Taking the old line view, that the burden of finding information should be placed on users...” ● Edgar Codd publishes a paper “A relational Model of Data for large shared Data Banks” – Key points: Independence of data from hardware/storage implementation – High level non-procedural language for data access – Pointers/keys (primary/secondary) History of DBMS – why relational is most popular? ● 1970s: Two database projects launched based on relational model – Ingres (Department of Defence Initiative) – System R (IBM) ● Ingres had its own query language called QUEL whereas System R used SQL. ● Larry Ellison, who worked at IBM, read publications of the System R group and eventually founded ORACLE. History of DBMS – why relational is most popular? ● 1980: IBM introduced SQL for the mainframe market. ● In a nutshell, American Government's requirements had a strong role in development of the relational DBMS.
    [Show full text]
  • DATA BASE RESEARCH at BERKELEY Michael Stonebraker
    DATA BASE RESEARCH AT BERKELEY Michael Stonebraker Department of Electrical Engineering and Computer Science University of California, Berkeley 1. INTRODUCTION the performance of the prototype, finishing the imple- Data base research at Berkeley can be decom- mentation of promised function, and on integrating ter- posed into four categories. First there is the POSTGRES tiary memory into the POSTGRES environment in a project led by Michael Stonebraker, which is building a more flexible way. next-generation DBMS with novel object management, General POSTGRES information can be obtained knowledge management and time travel capabilities. from the following references. Second, Larry Rowe leads the PICASSO project which [MOSH90] Mosher, C. (ed.), "The POSTGRES Ref- is constructing an advanced application development erence Manual, Version 2," University of tool kit for use with POSTGRES and other DBMSs. California, Electronics Research Labora- Third, there is the XPRS project (eXtended Postgres on tory, Technical Report UCB/ERL Raid and Sprite) which is led by four Berkeley faculty, M90-60, Berkeley, Ca., August 1990. Randy Katz, John Ousterhout, Dave Patterson and Michael Stonebraker. XPRS is exploring high perfor- [STON86] Stonebraker M. and Rowe, L., "The mance I/O systems and their efficient utilization by oper- Design of POSTGRES," Proc. 1986 ating systems and data managers. The last project is one ACM-SIGMOD Conference on Manage- aimed at improving the reliability of DBMS software by ment of Data, Washington, D.C., June dealing more effectively with software errors that is also 1986. led by Michael Stonebraker. [STON90] Stonebraker, M. et. al., "The Implementa- In this short paper we summarize work on POST- tion of POSTGRES," IEEE Transactions GRES, PICASSO, and software reliability and report on on Knowledge and Data Engineering, the XPRS work that is relevant to data management.
    [Show full text]
  • Arxiv:2106.11534V1 [Cs.DL] 22 Jun 2021 2 Nanjing University of Science and Technology, Nanjing, China 3 University of Southampton, Southampton, U.K
    Noname manuscript No. (will be inserted by the editor) Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact Yinyu Jin1 · Sha Yuan1∗ · Zhou Shao2, 4 · Wendy Hall3 · Jie Tang4 Received: date / Accepted: date Abstract The Turing Award is recognized as the most influential and presti- gious award in the field of computer science(CS). With the rise of the science of science (SciSci), a large amount of bibliographic data has been analyzed in an attempt to understand the hidden mechanism of scientific evolution. These include the analysis of the Nobel Prize, including physics, chemistry, medicine, etc. In this article, we extract and analyze the data of 72 Turing Award lau- reates from the complete bibliographic data, fill the gap in the lack of Turing Award analysis, and discover the development characteristics of computer sci- ence as an independent discipline. First, we show most Turing Award laureates have long-term and high-quality educational backgrounds, and more than 61% of them have a degree in mathematics, which indicates that mathematics has played a significant role in the development of computer science. Secondly, the data shows that not all scholars have high productivity and high h-index; that is, the number of publications and h-index is not the leading indicator for evaluating the Turing Award. Third, the average age of awardees has increased from 40 to around 70 in recent years. This may be because new breakthroughs take longer, and some new technologies need time to prove their influence. Besides, we have also found that in the past ten years, international collabo- ration has experienced explosive growth, showing a new paradigm in the form of collaboration.
    [Show full text]
  • Looking Back at Postgres
    Looking Back at Postgres Joseph M. Hellerstein [email protected] ABSTRACT etc.""efficient spatial searching" "complex integrity constraints" and This is a recollection of the UC Berkeley Postgres project, which "design hierarchies and multiple representations" of the same phys- was led by Mike Stonebraker from the mid-1980’s to the mid-1990’s. ical constructions [SRG83]. Based on motivations such as these, the The article was solicited for Stonebraker’s Turing Award book[Bro19], group started work on indexing (including Guttman’s influential as one of many personal/historical recollections. As a result it fo- R-trees for spatial indexing [Gut84], and on adding Abstract Data cuses on Stonebraker’s design ideas and leadership. But Stonebraker Types (ADTs) to a relational database system. ADTs were a pop- was never a coder, and he stayed out of the way of his development ular new programming language construct at the time, pioneered team. The Postgres codebase was the work of a team of brilliant stu- by subsequent Turing Award winner Barbara Liskov and explored dents and the occasional university “staff programmers” who had in database application programming by Stonebraker’s new collab- little more experience (and only slightly more compensation) than orator, Larry Rowe. In a paper in SIGMOD Record in 1983 [OFS83], the students. I was lucky to join that team as a student during the Stonebraker and students James Ong and Dennis Fogg describe an latter years of the project. I got helpful input on this writeup from exploration of this idea as an extension to Ingres called ADT-Ingres, some of the more senior students on the project, but any errors or which included many of the representational ideas that were ex- omissions are mine.
    [Show full text]
  • Operating Systems and Middleware: Supporting Controlled Interaction
    Operating Systems and Middleware: Supporting Controlled Interaction Max Hailperin Gustavus Adolphus College Revised Edition 1.1 July 27, 2011 Copyright c 2011 by Max Hailperin. This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Bibliography [1] Atul Adya, Barbara Liskov, and Patrick E. O’Neil. Generalized iso- lation level definitions. In Proceedings of the 16th International Con- ference on Data Engineering, pages 67–78. IEEE Computer Society, 2000. [2] Alfred V. Aho, Peter J. Denning, and Jeffrey D. Ullman. Principles of optimal page replacement. Journal of the ACM, 18(1):80–93, 1971. [3] AMD. AMD64 Architecture Programmer’s Manual Volume 2: System Programming, 3.09 edition, September 2003. Publication 24593. [4] Dave Anderson. You don’t know jack about disks. Queue, 1(4):20–30, 2003. [5] Dave Anderson, Jim Dykes, and Erik Riedel. More than an interface— SCSI vs. ATA. In Proceedings of the 2nd Annual Conference on File and Storage Technology (FAST). USENIX, March 2003. [6] Ross Anderson. Security Engineering: A Guide to Building Depend- able Distributed Systems. Wiley, 2nd edition, 2008. [7] Apple Computer, Inc. Kernel Programming, 2003. Inside Mac OS X. [8] Apple Computer, Inc. HFS Plus volume format. Technical Note TN1150, Apple Computer, Inc., March 2004. [9] Ozalp Babaoglu and William Joy. Converting a swap-based system to do paging in an architecture lacking page-referenced bits.
    [Show full text]
  • THE DESIGN of POSTGRES Abstract 1. INTRODUCTION 1
    THE DESIGN OF POSTGRES Michael Stonebraker and Lawrence A. Rowe Department of Electrical Engineering and Computer Sciences University of California Berkeley, CA 94720 Abstract This paper presents the preliminary design of a new database management system, called POSTGRES, that is the successor to the INGRES relational database system. The main design goals of the new system are to: 1) provide better support for complex objects, 2) provide user extendibility for data types, operators and access methods, 3) provide facilities for active databases (i.e., alerters and triggers) and inferencing includ- ing forward- and backward-chaining, 4) simplify the DBMS code for crash recovery, 5) produce a design that can take advantage of optical disks, workstations composed of multiple tightly-coupled processors, and custom designed VLSI chips, and 6) make as few changes as possible (preferably none) to the relational model. The paper describes the query language, programming langauge interface, system architecture, query processing strategy, and storage system for the new system. 1. INTRODUCTION The INGRES relational database management system (DBMS) was implemented during 1975-1977 at the Univerisity of California. Since 1978 various prototype extensions have been made to support distributed databases [STON83a], ordered relations [STON83b], abstract data types [STON83c], and QUEL as a data type [STON84a]. In addition, we proposed but never pro- totyped a new application program interface [STON84b]. The University of California version of INGRES has been ``hacked up enough'' to make the inclusion of substantial new function extremely dif®cult. Another problem with continuing to extend the existing system is that many of our proposed ideas would be dif®cult to integrate into that system because of earlier design decisions.
    [Show full text]
  • Readings in Database Systems, 5Th Edition (2015)
    Readings in Database Systems Fifth Edition edited by Peter Bailis Joseph M. Hellerstein Michael Stonebraker Readings in Database Systems Fifth Edition (2015) edited by Peter Bailis, Joseph M. Hellerstein, and Michael Stonebraker Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://www.redbook.io/ Contents Preface 3 Background Introduced by Michael Stonebraker 4 Traditional RDBMS Systems Introduced by Michael Stonebraker 6 Techniques Everyone Should Know Introduced by Peter Bailis 8 New DBMS Architectures Introduced by Michael Stonebraker 12 Large-Scale Dataflow Engines Introduced by Peter Bailis 14 Weak Isolation and Distribution Introduced by Peter Bailis 18 Query Optimization Introduced by Joe Hellerstein 22 Interactive Analytics Introduced by Joe Hellerstein 25 Languages Introduced by Joe Hellerstein 29 Web Data Introduced by Peter Bailis 33 A Biased Take on a Moving Target: Complex Analytics by Michael Stonebraker 35 A Biased Take on a Moving Target: Data Integration by Michael Stonebraker 40 List of All Readings 44 References 46 2 Readings in Database Systems, 5th Edition (2015) Introduction In the ten years since the previous edition of Read- Third, each selection is a primary source. There are ings in Database Systems, the field of data management good surveys on many of the topics in this collection, has exploded. Database and data-intensive systems to- which we reference in commentaries. However, read- day operate over unprecedented volumes of data, fueled ing primary sources provides historical context, gives in large part by the rise of “Big Data” and massive de- the reader exposure to the thinking that shaped influen- creases in the cost of storage and computation.
    [Show full text]
  • BIBTEX Meets Relational Databases Dedicated to the Memory of Edgar Frank “Ted” Codd (1923–2003) and James Nicholas “Jim” Gray (1944–2007) Nelson H
    252 TUGboat, Volume 30 (2009), No. 2 BIBTEX meets relational databases Dedicated to the memory of Edgar Frank “Ted” Codd (1923–2003) and James Nicholas “Jim” Gray (1944–2007) Nelson H. F. Beebe Abstract After giving some background and comments on the BIBTEX bibliographic database system, we discuss the problem of search- ing large collections of such data. We briefly describe how relational databases are structured and queried. Portable new programs, bibtosql and bibsql, are introduced and their use is illustrated. The first handles the conversion of BIBTEX data to input for three free, popular, and portable, database systems. The second provides a uniform and simple user interface for issuing search queries to any of the supported backend databases. We finish with a discussion of the contributions of the two late computer scientists to whom this article is dedicated. Contents 1 Introduction 252 2 An overview of BIBTEX 253 3 BIBTEX style-file language 253 4 BIBTEX grammar and software tools 254 5 The search problem 254 6 Relational databases 255 7 Structured Query Language (SQL) 256 8 BIBTEX database tables 256 9 BIBTEX/SQL software tools 258 10 Using SQL with bibsql 259 11 The impact of Ted Codd and Jim Gray 268 12 Conclusion 268 1 Introduction Oren Patashnik’s BIBTEX has been in wide use in the TEX and LATEX communities now for more than two decades af- ter it was introduced in the LATEX User’s Guide and Reference Manual (Lamport 1985, Appendix B). The first mention of BIBTEX in that book is at the bottom of page 72, where we find With LATEX, you can either produce the list of sources yourself or else use a separate program called BIBTEX to generate it from information contained in a bibliographic database.
    [Show full text]
  • In-Network Support for Transaction Triaging
    In-Network Support for Transaction Triaging Theo Jepsen Alberto Lerner Fernando Pedone Stanford University University of Fribourg Università della Svizzera italiana USA Switzerland Switzerland Robert Soulé Philippe Cudré-Mauroux Yale University University of Fribourg USA Switzerland ABSTRACT 0,#3#%/"*'&,$/4' &,#/3$:*'&,$/4' !"#$%&' ()*+$,-$, We introduce Transaction Triaging, a set of techniques that ma- ./,&#&#0%' nipulate streams of transaction requests and responses while they 1 2,03,/44/5"$ travel to and from a database server. Compared to normal transac- *6$&70,8 tion streams, the triaged ones execute faster once they reach the 1 &,/%'/9&#0%*&,#/3#%3*"03#9 database. The triaging algorithms do not interfere with the transac- tion execution nor require adherence to any particular concurrency &,/%'/9&#0% control method, making them easy to port across database systems. :/&/5/'$*./,&#&#0%' &;%*&<.$' Transaction Triaging leverages recent programmable network- ing hardware that can perform computations on in-fight data. We Figure 1: Transaction Triaging uses a programmable net- evaluate our techniques on an in-memory database system using work to rearrange transaction streams mid-fight. Triaged an actual programmable hardware network switch. Our experimen- streams incur less networking overhead and can be executed tal results show that triaging brings enough performance gains to with fewer server resources. compensate for almost all networking overheads. In high-overhead network stacks such as UDP/IP, we see throughput improvements one random core running the database, and not necessarily the from 2.05× to 7.95×. In an RDMA stack, the gains range from 1.08× core that should process that particular transaction. Moving the to 1.90× without introducing signifcant latency.
    [Show full text]