Dataclay: a Distributed Data Store for Effective Inter-Player Data Sharing
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Beyond Relational Databases
EXPERT ANALYSIS BY MARCOS ALBE, SUPPORT ENGINEER, PERCONA Beyond Relational Databases: A Focus on Redis, MongoDB, and ClickHouse Many of us use and love relational databases… until we try and use them for purposes which aren’t their strong point. Queues, caches, catalogs, unstructured data, counters, and many other use cases, can be solved with relational databases, but are better served by alternative options. In this expert analysis, we examine the goals, pros and cons, and the good and bad use cases of the most popular alternatives on the market, and look into some modern open source implementations. Beyond Relational Databases Developers frequently choose the backend store for the applications they produce. Amidst dozens of options, buzzwords, industry preferences, and vendor offers, it’s not always easy to make the right choice… Even with a map! !# O# d# "# a# `# @R*7-# @94FA6)6 =F(*I-76#A4+)74/*2(:# ( JA$:+49>)# &-)6+16F-# (M#@E61>-#W6e6# &6EH#;)7-6<+# &6EH# J(7)(:X(78+# !"#$%&'( S-76I6)6#'4+)-:-7# A((E-N# ##@E61>-#;E678# ;)762(# .01.%2%+'.('.$%,3( @E61>-#;(F7# D((9F-#=F(*I## =(:c*-:)U@E61>-#W6e6# @F2+16F-# G*/(F-# @Q;# $%&## @R*7-## A6)6S(77-:)U@E61>-#@E-N# K4E-F4:-A%# A6)6E7(1# %49$:+49>)+# @E61>-#'*1-:-# @E61>-#;6<R6# L&H# A6)6#'68-# $%&#@:6F521+#M(7#@E61>-#;E678# .761F-#;)7-6<#LNEF(7-7# S-76I6)6#=F(*I# A6)6/7418+# @ !"#$%&'( ;H=JO# ;(\X67-#@D# M(7#J6I((E# .761F-#%49#A6)6#=F(*I# @ )*&+',"-.%/( S$%=.#;)7-6<%6+-# =F(*I-76# LF6+21+-671># ;G';)7-6<# LF6+21#[(*:I# @E61>-#;"# @E61>-#;)(7<# H618+E61-# *&'+,"#$%&'$#( .761F-#%49#A6)6#@EEF46:1-# -
Reflex: Remote Flash at the Performance of Local Flash
ReFlex: Remote Flash at the Performance of Local Flash Ana Klimovic Heiner Litz Christos Kozyrakis Flash Memory Summit 2017 Santa Clara, CA Stanford MAST1 ! Flash in Datacenters § NVMe Flash • 1,000x higher throughput than disk (1MIOPS) • 100x lower latency than disk (50-70usec) § But Flash is often underutilized due to imbalanced resource requirements Example Datacenter Flash Use-Case Applica(on Tier Datastore Tier Datastore Service get(k) Key-Value Store So9ware put(k,val) App App Tier Tier App Clients Servers TCP/IP NIC CPU RAM Hardware Flash 3 Imbalanced Resource Utilization Sample utilization of Facebook servers hosting a Flash-based key-value store over 6 months 4 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar. Imbalanced Resource Utilization Sample utilization of Facebook servers hosting a Flash-based key-value store over 6 months 5 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar. Imbalanced Resource Utilization u"lizaon Sample utilization of Facebook servers hosting a Flash-based key-value store over 6 months 6 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar. Imbalanced Resource Utilization u"lizaon Flash capacity and bandwidth are underutilized for long periods of time 7 [EuroSys’16] Flash storage disaggregation. Ana Klimovic, Christos Kozyrakis, Eno Thereska, Binu John, Sanjeev Kumar Solution: Remote Access to Storage § Improve resource utilization by sharing NVMe Flash between remote tenants § There are 3 main concerns: 1. Performance overhead for remote access 2. Interference on shared Flash 3. Flexibility of Flash usage 8 Issue 1: Performance Overhead 4kB random read 1000 Local Flash iSCSI (1 core) 800 libaio+libevent (1core) 600 4x throughput drop 400 200 p95 read latency(us) 2× latency increase 0 0 50 100 150 200 250 300 IOPS (Thousands) • Traditional network/storage protocols and Linux I/O libraries (e.g. -
Multi-Resolution Storage and Search in Sensor Networks Deepak Ganesan University of Massachusetts Amherst
University of Massachusetts Amherst ScholarWorks@UMass Amherst Computer Science Department Faculty Publication Computer Science Series 2003 Multi-resolution Storage and Search in Sensor Networks Deepak Ganesan University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/cs_faculty_pubs Part of the Computer Sciences Commons Recommended Citation Ganesan, Deepak, "Multi-resolution Storage and Search in Sensor Networks" (2003). Computer Science Department Faculty Publication Series. 79. Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/79 This Article is brought to you for free and open access by the Computer Science at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Computer Science Department Faculty Publication Series by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Multi-resolution Storage and Search in Sensor Networks Deepak Ganesan Department of Computer Science, University of Massachusetts, Amherst, MA 01003 Ben Greenstein, Deborah Estrin Department of Computer Science, University of California, Los Angeles, CA 90095, John Heidemann University of Southern California/Information Sciences Institute, Marina Del Rey, CA 90292 and Ramesh Govindan Department of Computer Science, University of Southern California, Los Angeles, CA 90089 Wireless sensor networks enable dense sensing of the environment, offering unprecedented op- portunities for observing the physical world. This paper addresses two key challenges in wireless sensor networks: in-network storage and distributed search. The need for these techniques arises from the inability to provide persistent, centralized storage and querying in many sensor networks. Centralized storage requires multi-hop transmission of sensor data to internet gateways which can quickly drain battery-operated nodes. -
Database Solutions on AWS
Database Solutions on AWS Leveraging ISV AWS Marketplace Solutions November 2016 Database Solutions on AWS Nov 2016 Table of Contents Introduction......................................................................................................................................3 Operational Data Stores and Real Time Data Synchronization...........................................................5 Data Warehousing............................................................................................................................7 Data Lakes and Analytics Environments............................................................................................8 Application and Reporting Data Stores..............................................................................................9 Conclusion......................................................................................................................................10 Page 2 of 10 Database Solutions on AWS Nov 2016 Introduction Amazon Web Services has a number of database solutions for developers. An important choice that developers make is whether or not they are looking for a managed database or if they would prefer to operate their own database. In terms of managed databases, you can run managed relational databases like Amazon RDS which offers a choice of MySQL, Oracle, SQL Server, PostgreSQL, Amazon Aurora, or MariaDB database engines, scale compute and storage, Multi-AZ availability, and Read Replicas. You can also run managed NoSQL databases like Amazon DynamoDB -
Oracle Nosql Database EE Data Sheet
Oracle NoSQL Database 21.1 Enterprise Edition (EE) Oracle NoSQL Database is a multi-model, multi-region, multi-cloud, active-active KEY BUSINESS BENEFITS database, designed to provide a highly-available, scalable, performant, flexible, High throughput and reliable data management solution to meet today’s most demanding Bounded latency workloads. It can be deployed in on-premise data centers and cloud. It is well- Linear scalability suited for high volume and velocity workloads, like Internet of Things, 360- High availability degree customer view, online contextual advertising, fraud detection, mobile Fast and easy deployment application, user personalization, and online gaming. Developers can use a single Smart topology management application interface to quickly build applications that run in on-premise and Online elastic configuration cloud environments. Multi-region data replication Enterprise grade software Applications send network requests against an Oracle NoSQL data store to and support perform database operations. With multi-region tables, data can be globally distributed and automatically replicated in real-time across different regions. Data can be modeled as fixed-schema tables, documents, key-value pairs, and large objects. Different data models interoperate with each other through a single programming interface. Oracle NoSQL Database is a sharded, shared-nothing system which distributes data uniformly across multiple shards in a NoSQL database cluster, based on the hashed value of the primary keys. An Oracle NoSQL Database data store is a collection of storage nodes, each of which hosts one or more replication nodes. Data is automatically populated across these replication nodes by internal replication mechanisms to ensure high availability and rapid failover in the event of a storage node failure. -
Object Databases As Data Stores for High Energy Physics
OBJECT DATABASES AS DATA STORES FOR HIGH ENERGY PHYSICS Dirk Düllmann CERN IT/ASD & RD45, Geneva, Switzerland Abstract Starting from 2005, the LHC experiments will generate an unprecedented amount of data. Some 100 Peta-Bytes of event, calibration and analysis data will be stored and have to be analysed in a world-wide distributed environment. At CERN the RD45 project has been set-up in 1995 to investigate different approaches to solve the data storage problems at LHC. The focus of RD45 soon moved to the use of Object Database Management Systems (ODBMS) as a central component. This paper gives an overview of the main advantages of ODBMS systems for HEP data stores. Several prototype and production applications will be discussed and a summary of the current use of ODBMS based systems in HEP will be presented. The second part will concentrate on physics data analysis based on an ODBMS. 1 INTRODUCTION 1.1 Data Management at LHC The new experiments at the Large Hadron Collider (LHC) at CERN will gather an unprecedented amount of data. Starting from 2005 each of the four LHC experiments ALICE, ATLAS, CMS and LHCb will measure of the order of 1 Peta Byte (1015 Bytes) per year. All together the experiments will store and repeatedly analyse some 100 PB of data during their lifetimes. Such an enormous task can only be accomplished by large international collaborations. Thousands of physicists from hundreds of institutes world-wide will participate. This also implies that nearly any available hardware platform will be used resulting in a truly heterogeneous and distributed system. -
Database Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
Network-Compute Co-Design for Distributed In-Memory Computing” Advisors: Prof
Network–Compute Co-Design for Distributed In-Memory Computing Alexandros Daglis Ph.D. Thesis July 6, 2018 Thesis Committee Prof. Babak Falsafi, Prof. Edouard Bugnion, thesis directors Prof. Paolo Ienne, jury president Dr. Paolo Faraboschi, external examiner Prof. James Larus, internal examiner Prof. Gurindar Sohi, external examiner Communication and Information Sciences École Polytechnique Fédérale de Lausanne Lausanne, Switzerland T¨c paideÐac êfh tὰς μὲn ûÐzac εἶnai πικράς, tὸn δὲ καρπὸn γλυκύν. — Aristotèlhc The roots of education are bitter, but the fruit is sweet. — Aristotle To my family Acknowledgements My PhD journey has undoubtedly been the most challenging and, at the same time, most rewarding so far in my life. A journey that transformed me into a better person and taught me the true value of perseverance, team work, empathy, and patience. I would not have been able to reach the finish line if it weren’t for the wonderful people around me; people who were always there to magnify the joy of the best moments; people who would always support and help me push through the hardest times. To all of these people, I owe my deepest gratitude. It is therefore apposite to start this thesis by thanking them. First and foremost, I am grateful to my advisors, Babak and Ed. Babak has been a constant source of stimulation to keep pushing myself out of my comfort zone, a practice instrumental to my success. He taught me the importance of asking the right questions and taking a step back to look at the big picture. Babak’s attention to detail and quest for perfection confirms that the "the path of virtues goes through toils". -
Succinct: Enabling Queries on Compressed Data
Succinct: Enabling Queries on Compressed Data Rachit Agarwal, Anurag Khandelwal, and Ion Stoica, University of California, Berkeley https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/agarwal This paper is included in the Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’15). May 4–6, 2015 • Oakland, CA, USA ISBN 978-1-931971-218 Open Access to the Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’15) is sponsored by USENIX Succinct: Enabling Queries on Compressed Data Rachit Agarwal Anurag Khandelwal Ion Stoica UC Berkeley UC Berkeley UC Berkeley Abstract that indexes can be as much as 8× larger than the in- Succinct is a data store that enables efficient queries di- put data size. Traditional compression techniques can rectly on a compressed representation of the input data. reduce the memory footprint but suffer from degraded Succinct uses a compression technique that allows ran- throughput since data needs to be decompressed even dom access into the input, thus enabling efficient stor- for simple queries. Thus, existing data stores either re- age and retrieval of data. In addition, Succinct natively sort to using complex memory management techniques supports a wide range of queries including count and for identifying and caching “hot” data [5, 6, 26, 35] or search of arbitrary strings, range and wildcard queries. simply executing queries off-disk or off-SSD [25].In What differentiates Succinct from previous techniques either case, latency and throughput advantages of in- is that Succinct supports these queries without storing dexes drop compared to in-memory query execution. -
Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory
Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory Shivaram Venkataraman†∗, Niraj Tolia‡, Parthasarathy Ranganathan†, and Roy H. Campbell∗ †HP Labs, Palo Alto, ‡Maginatics, and ∗University of Illinois, Urbana-Champaign Abstract ther disk or flash. Not only are they byte-addressable and low-latency like DRAM but, they are also non-volatile. The predicted shift to non-volatile, byte-addressable Projected cost [19] and power-efficiency characteris- memory (e.g., Phase Change Memory and Memristor), tics of Non-Volatile Byte-addressable Memory (NVBM) the growth of “big data”, and the subsequent emergence lead us to believe that it can replace both disk and mem- of frameworks such as memcached and NoSQL systems ory in data stores (e.g., memcached, database systems, require us to rethink the design of data stores. To de- NoSQL systems, etc.) but not through legacy inter- rive the maximum performance from these new mem- faces (e.g., block interfaces or file systems). First, the ory technologies, this paper proposes the use of single- overhead of PCI accesses or system calls will dominate level data stores. For these systems, where no distinc- NVBM’s sub-microsecond access latencies. More im- tion is made between a volatile and a persistent copy of portantly, these interfaces impose a two-level logical sep- data, we present Consistent and Durable Data Structures aration of data, differentiating between in-memory and (CDDSs) that, on current hardware, allows programmers on-disk copies. Traditional data stores have to both up- to safely exploit the low-latency and non-volatile as- date the in-memory data and, for durability, sync the data pects of new memory technologies. -
(ASE 18 US 8,370.224 B2 Page 2
USOO8370224B2 (12) United States Patent (10) Patent No.: US 8,370.224 B2 Grewal (45) Date of Patent: Feb. 5, 2013 (54) GRAPHICAL INTERFACE FOR DISPLAY OF 6,574,779 B2 6/2003 Allen et al. ASSETS IN AN ASSET MANAGEMENT 6,581,045 B1 6/2003 Watson 6,631,552 B2 10/2003 Yamaguchi SYSTEM 6,650,346 B1 1 1/2003 Jaeger et al. 6,691,115 B2 2/2004 Mosher, Jr. et al. (75) Inventor: Ardaman Singh Grewal, Milwaukee, 6,735,752 B2 5, 2004 Manoo WI (US) 6,738,958 B2 5, 2004 Manoo 6,763,377 B1 7/2004 Belknap et al. 6,775,576 B2 8/2004 Spriggs et al. (73) Assignee: Rockwell Automation Technologies, 6,782,399 B2 8/2004 Mosher, Jr. Inc., Mayfield Heights, OH (US) 6,789,214 B1 9/2004 De Bonis-Hamelin et al. 6,806,813 B1 10/2004 Cheng et al. (*) Notice: Subject to any disclaimer, the term of this 6,847,982 B2 1/2005 Parker et al. patent is extended or adjusted under 35 6,889,096 B2 * 5/2005 Spriggs et al. .................. 7OO/17 U.S.C. 154(b) by 247 days. (Continued) (21) Appl. No.: 11/535,549 FOREIGN PATENT DOCUMENTS GB 2414568 A 11, 2005 (22) Filed: Sep. 27, 2006 WO 2006/081024 A2 8, 2006 (65) Prior Publication Data OTHER PUBLICATIONS US 2008/OO77512 A1 Mar. 27, 2008 “Gibson Petroleum Selects Couay to Add Location Intelligence to Fleet Management System.” Canada NewsWire Nov. 29, 2001 (51) Int. Cl. ProQuest Newsstand, ProQuest. -
An Evaluation of Key-Value Stores in Scientific Applications
AN EVALUATION OF KEY-VALUE STORES IN SCIENTIFIC APPLICATIONS A Thesis Presented to the Faculty of the Department of Computer Science University of Houston In Partial Fulfillment of the Requirements for the Degree Master of Science By Sonia Shirwadkar May 2017 AN EVALUATION OF KEY-VALUE STORES IN SCIENTIFIC APPLICATIONS Sonia Shirwadkar APPROVED: Dr. Edgar Gabriel, Chairman Dept. of Computer Science, University of Houston Dr. Weidong Shi Dept. of Computer Science, University of Houston Dr. Dan Price Honors College, University of Houston Dean, College of Natural Sciences and Mathematics ii Acknowledgments \No one who achieves success does so without the help of others. The wise acknowledge this help with gratitude." - Alfred North Whitehead Although, I have a long way to go before I am wise, I would like to take this opportunity to express my deepest gratitude to all the people who have helped me in this journey. First and foremost, I would like to thank Dr. Gabriel for being a great advisor. I appreciate the time, effort and ideas that you have invested to make my graduate experience productive and stimulating. The joy and enthusiasm you have for research was contagious and motivational for me, even during tough times. You have been an inspiring teacher and mentor and I would like to thank you for the patience, kindness and humor that you have shown. Thank you for guiding me at every step and for the incredible understanding you showed when I came to you with my questions. It has indeed been a privilege working with you. I would like to thank Dr.