Performance Benchmark Postgresql / Mongodb Performance Benchmark Postgresql / Mongodb
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Big Data Velocity in Plain English
Big Data Velocity in Plain English John Ryan Data Warehouse Solution Architect Table of Contents The Requirement . 1 What’s the Problem? . .. 2 Components Needed . 3 Data Capture . 3 Transformation . 3 Storage and Analytics . 4 The Traditional Solution . 6 The NewSQL Based Solution . 7 NewSQL Advantage . 9 Thank You. 10 About the Author . 10 ii The Requirement The assumed requirement is the ability to capture, transform and analyse data at potentially massive velocity in real time. This involves capturing data from millions of customers or electronic sensors, and transforming and storing the results for real time analysis on dashboards. The solution must minimise latency — the delay between a real world event and it’s impact upon a dashboard, to under a second. Typical applications include: • Monitoring Machine Sensors: Using embedded sensors in industrial machines or vehicles — typically referred to as The Internet of Things (IoT) . For example Progressive Insurance use real time speed and vehicle braking data to help classify accident risk and deliver appropriate discounts. Similar technology is used by logistics giant FedEx which uses SenseAware technology to provide near real-time parcel tracking. • Fraud Detection: To assess the risk of credit card fraud prior to authorising or declining the transaction. This can be based upon a simple report of a lost or stolen card, or more likely, an analysis of aggregate spending behaviour, aligned with machine learning techniques. • Clickstream Analysis: Producing real time analysis of user web site clicks to dynamically deliver pages, recommended products or services, or deliver individually targeted advertising. Big Data: Velocity in Plain English eBook 1 What’s the Problem? The primary challenge for real time systems architects is the potentially massive throughput required which could exceed a million transactions per second. -
Niklas Appelmann
Niklas Appelmann Full-Stack Web Developer [email protected] https://niklasappelmann.de +49 151 6469 0684 Nationality German Education Bachelor of Science Computer Science Certificates Company Data Protection Officer IHK / Betrieblicher Datenschutzbeauftragter IHK (June 2018) Languages English (fluent) German (native) Skills Project Management Agile, Scrum, Kanban Frameworks and Tools AWS, Docker, Selenium, Puppeteer, MongoDB Programming Languages / Frameworks React, Meteor, Node.js, JavaScript, Python, Clojure, ClojureScript, CoffeeScript, TypeScript Relevant 3.5 years Experience Project examples since 01/2020 Fullstack Development, Freelance Various engagements in web development. • Automation of lead generation and business processes using Python, Selenium and Puppeteer. • Frontend development using ClojureScript and Reagent • Backend development using Node.js, Clojure and MongoDB • DevOps and Backend development at the government hackathon #wirvsvirus (2020) • AWS consulting (S3, Lambda, EC2, ECS, Route53, SQS, SES, Lightsail, DynamoDB, CloudFront) • Serverless fullstack architecture using AWS Team members: Other freelancers and remote contributors Technologies / Python, Selenium, Puppeteer, ClojureScript, Clojure, reagent, MongoDB, Tools Node.js, Meteor 03/2018 - Fullstack-Developer, blink.it GmbH & Co. KG 12/2019 Development of a progressive Meteor web application in a fast paced startup environment. • Built frontend functionality with React (JavaScript, CoffeeScript), Blaze, HTML5, SCSS/CSS • Built backend functionality with Node.js, Python and MongoDB • Worked on performance optimiziation on a large scale multi-tenant reactive application • Developed tools for data migration and automation of internal processes with Python • Developed an internal solution to migrate Pipedrive leads to Hubspot for a large set of leads and past deal flows Team members: 10 Technologies / React, AWS, JavaScript, CoffeeScript, Python, MongoDB, Node.js, Blaze, Tools HTML5, SCSS/CSS, Selenium, SCORM 10/2016 - Software Engineer (working student), blink.it GmbH & Co. -
PS Non-Standard Database Systems Overview
PS Non-Standard Database Systems Overview Daniel Kocher March 2019 is document gives a brief overview on three categories of non-standard database systems (DBS) in the context of this class. For each category, we provide you with a motivation, a discussion about the key properties, a list of commonly used implementations (proprietary and open-source/freely-available), and recommendation(s) for your project. For your convenience, we also recap some relevant terms (e.g., ACID, OLTP, ...). Terminology OLTP Online transaction processing. OLTP systems are highly optimized to process (1) short queries (operating only on a small portion of the database) and (2) small transac- tions (inserting/updating few tuples per relation). Typically, OLTP includes insertions, updates, and deletions. e main focus of OLTP systems is on low response time and high throughput (in terms of transactions per second). Databases in OLTP systems are highly normalized [5]. OLAP Online analytical processing. OLAP systems analyze complex data (usually from a data warehouse). Typically, queries in an OLAP system may run for a long time (as opposed to queries in OLTP systems) and the number of transactions is usually low. Fur- thermore, typical OLAP queries are read-only and touch a large portion of the database (e.g., scan or join large tables). e main focus of OLAP systems is on low response time. Databases in OLAP systems are oen de-normalized intentionally [5]. ACID Traditional relational database systems guarantee ACID properties: – Atomicity All or no operation(s) of a transaction are reected in the database. – Consistency Isolated execution of a transaction preserves consistency of the database. -
Performance Analysis of Blockchain Platforms
UNLV Theses, Dissertations, Professional Papers, and Capstones August 2018 Performance Analysis of Blockchain Platforms Pradip Singh Maharjan Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Computer Sciences Commons Repository Citation Maharjan, Pradip Singh, "Performance Analysis of Blockchain Platforms" (2018). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3367. http://dx.doi.org/10.34917/14139888 This Thesis is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. PERFORMANCE ANALYSIS OF BLOCKCHAIN PLATFORMS By Pradip S. Maharjan Bachelor of Computer Engineering Tribhuvan University Institute of Engineering, Pulchowk Campus, Nepal 2012 A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science Department of Computer Science Howard R. Hughes College of Engineering The Graduate College University of Nevada, Las Vegas August 2018 c Pradip S. Maharjan, 2018 All Rights Reserved Thesis Approval The Graduate College The University of Nevada, Las Vegas May 4, 2018 This thesis prepared by Pradip S. -
E-Commerce Application Using MERN Stack
Quang Nhat Mai E-commerce Application using MERN stack Metropolia University of Applied Sciences Bachelor of Engineering Information Technology Bachelor’s Thesis 30 August 2018 Abstract Author Quang Nhat Mai Title E-commerce Application using MERN stack Number of Pages 36 pages + x appendices Date 30 August 2020 Degree Bachelor of Engineering Degree Programme Information Technology Professional Major Software Engineering Instructors Janne Salonen This thesis is about studying the basic components of MERN Stack technology such as: MongoDB, ExpressJS framework, ReactJS library, and NodeJS platform. Discussing the basic functions of an e-commerce web application such as sign up, sign in, showing dash- boards, displaying store categories and products Using MERN Stack technology in conjunc- tion with Braintree to build a web application to search for product stores and payment gate- way. Develop administrative functions for the website such as: user management, store management, statistics, and reports. Since then, this topic is declared to research and create an online product search website so that small grocery stores and groceries can post and manage their products right on the website. website system and branding. At the same time, the store can link to its own website (if any). For customers, they can quickly search for urgent products from their nearby stores. Based on the search results, customers can directly contact the store owner to discuss more about the product they are looking for. Keywords Contents List of Abbreviations 1 Introduction -
Fat-Free Framework 1.4.1 Quick Reference Akismet Auth Data
Akismet Data An API wrapper that you can use during input validation to Input data handlers and validators. determine if a blog comment, trackback, or pingback contains spam. This plug-in requires a key from akismet.com. input( string fields, mixed handler, [ string tags ],[ integer filter ], [ array options ] ); check( string text, string author, string email, string url ); Assign handler to HTML form fields for validation and Submit content (usually a blog comment) to akismet.com. manipulation. handler may be an anonymous or named function, Returns TRUE if the content is determined as spam. a single or daisy-chained string of named functions similar to the route() method. This command passes two arguments to handler ham( string text, string author, string email, string url ); function: value and field name. HTML and PHP tags are stripped Report content as a false positive. If the argument tags is not specified. PHP validation/sanitize filters, filter flags, and options may be passed as additional spam( string text, string author, string email, string url ); Quick Reference Quick Reference arguments. See the PHP filter_var() function for more details on Report content that was not marked as spam. filter types. verify( string key ); scrub( mixed value, [ string tags ] ); Secure approval from akismet.com to use the public API for Remove all HTML tags to protect against XSS/SQL injection spam checking. A valid key is required to use the API. Returns attacks. tags , if specified, will be preserved. If value is an array, TRUE if authentication succeeds. 1.4.1 1.4.1 HTML tags in all string elements will be scrubbed. -
Evaluating and Comparing Oracle Database Appliance Performance Updated for Oracle Database Appliance X8-2-HA
Evaluating and Comparing Oracle Database Appliance Performance Updated for Oracle Database Appliance X8-2-HA ORACLE WHITE PAPER | JANUARY 2020 DISCLAIMER The performance results in this paper are intended to give an estimate of the overall performance of the Oracle Database Appliance X8-2-HA system. The results are not a benchmark, and cannot be used to characterize the relative performance of Oracle Database Appliance systems from one generation to another, as the workload characteristics and other environmental factors including firmware, OS, and database patches, which includes Spectre/Meltdown fixes, will vary over time. For an accurate comparison to another platform, you should run the same tests, with the same OS (if applicable) and database versions, patch levels, etc. Do not rely on older tests or benchmark runs, as changes made to the underlying platform in the interim may substantially impact results. EVALUATING AND COMPARING ORACLE DATABASE APPLIANCE PERFORMANCE Table of Contents Introduction and Executive Summary 1 Audience 2 Objective 2 Oracle Database Appliance Deployment Architecture 3 Oracle Database Appliance Configuration Templates 3 What is Swingbench? 4 Swingbench Download and Setup 4 Configuring Oracle Database Appliance for Testing 5 Configuring Oracle Database Appliance System for Testing 5 Benchmark Setup 5 Database Setup 6 Schema Setup 6 Workload Setup and Execution 10 Benchmark Results 11 Workload Performance 11 Database and Operating System Statistics 12 Average CPU Busy 12 REDO Writes 13 Transaction Rate 13 Important Considerations for Performing the Benchmark 14 Conclusion 15 Appendix A Swingbench configuration files 16 Appendix B The loadgen.pl file 19 Appendix C The generate_awr.sh script 24 References 25 EVALUATING AND COMPARING ORACLE DATABASE APPLIANCE PERFORMANCE Introduction and Executive Summary Oracle Database Appliance X8-2-HA is a highly available Oracle database system. -
60-539: Emerging Non-Traditional Database Systems (Data Warehousing and Mining)
60-539: Emerging non-traditional database systems (Data Warehousing and Mining): sdb1 warehouse sdb2 sdb3 Dr. C.I. Ezeife School of Computer Science, University of Windsor, Canada. Email: [email protected] 60-539 Dr. C.I. Ezeife @c 2021 1 PART I (DATABASE MANAGEMENT SYSTEM OVERVIEW) DBMS (PART 1) OVERVIEW Components of a DBMS DBMS Data model Data Definition and Manipulation Language File Organization Techniques Query Optimization and Evaluation Facility Database Design and Tuning Transaction Processing Concurrency Control Database Security and Integrity Issues 60-539 Dr. C.I. Ezeife © 2021 2 DBMS OVERVIEW(What are?) What is a database? : It is a collection of data, typically describing the activities of one or more related organizations, e.g., a University, an airline reservation or a banking database. What is a DBMS?: A DBMS is a set of software for creating, querying, managing and keeping databases. Examples of DBMS’s are DB2, Informix, Sybase, Oracle, MYSQL, Microsoft Access (relational). Alternative to Databases: Storing all data for university, airline and banking information in separate files and writing separate program for each data file. What is a Data Warehouse?:A subject-oriented, historical, non- volatile database integrating a number of data sources. What is Data Mining?:Is data analysis for finding interesting trends or patterns in large dataset to guide decisions about future activities. 60-539 Dr. C.I. Ezeife © 2021 3 DBMS OVERVIEW(Evolution of Information Technology) 1960s and earlier: Primitive file processing: data are collected in files and manipulated with programs in Cobol and other languages. Disadv.: any change in storage structure of data requires changing the program (i.e., no logical or physical data independence). -
A Survey of Ledger Technology-Based Databases
future internet Article A Survey of Ledger Technology-Based Databases Dénes László Fekete 1 and Attila Kiss 1,2,* 1 Department of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary; [email protected] 2 Department of Informatics, J. Selye University, 945 01 Komárno, Slovakia * Correspondence: [email protected] Abstract: The spread of crypto-currencies globally has led to blockchain technology receiving greater attention in recent times. This paper focuses more broadly on the uses of ledger databases as a traditional database manager. Ledger databases will be examined within the parameters of two categories. The first of these are Centralized Ledger Databases (CLD)-based Centralised Ledger Technology (CLT), of which LedgerDB will be discussed. The second of these are Permissioned Blockchain Technology-based Decentralised Ledger Technology (DLT) where Hyperledger Fabric, FalconDB, BlockchainDB, ChainifyDB, BigchainDB, and Blockchain Relational Database will be examined. The strengths and weaknesses of the reviewed technologies will be discussed, alongside a comparison of the mentioned technologies. Keywords: ledger technology; permissioned blockchain; centralised ledger database 1. Introduction Citation: Fekete, D.L.; Kiss, A. A In recent years, we have been witness to the rise of multiple new ledger technologies. Survey of Ledger Technology-Based Since Satosi Nakamoto wrote the Bitcoin white paper [1] in 2009, many different systems Databases. Future Internet 2021, 13, that endeavour to provide decentralization and trustless collaboration have been developed 197. https://doi.org/10.3390/ based on blockchain technology. Of late, a great amount of research and academia has been fi13080197 focused on the further development and reformation of the basis of blockchain in an attempt to solve the issues that continue to arise [2–4]. -
JSON As an XML Alternative
JSON The Fat-Free Alternative to XML { “Lecture”: 27, “Course”: “CSC375”, “Days”: ”TTh", “Instructor”: “Haidar Harmanani” } JSON as an XML Alternative • JSON is a light-weight alternative to XML for data- interchange • JSON = JavaScript Object Notation – It’s really language independent – most programming languages can easily read it and instantiate objects or some other data structure • Defined in RFC 4627 • Started gaining tracking ~2006 and now widely used • http://json.org/ has more information JSON as an XML Alternative • What is JSON? – JSON is language independent – JSON is "self-describing" and easy to understand – *JSON uses JavaScript syntax for describing data objects, but JSON is still language and platform independent. JSON parsers and JSON libraries exists for many different programming languages. • JSON -Evaluates to JavaScript Objects – The JSON text format is syntactically identical to the code for creating JavaScript objects. – Because of this similarity, instead of using a parser, a JavaScript program can use the built-in eval() function and execute JSON data to produce native JavaScript objects. Example {"firstName": "John", l This is a JSON object "lastName" : "Smith", "age" : 25, with five key-value pairs "address" : l Objects are wrapped by {"streetAdr” : "21 2nd Street", curly braces "city" : "New York", "state" : "NY", l There are no object IDs ”zip" : "10021"}, l Keys are strings "phoneNumber": l Values are numbers, [{"type" : "home", "number": "212 555-1234"}, strings, objects or {"type" : "fax", arrays "number” : "646 555-4567"}] l Aarrays are wrapped by } square brackets The BNF is simple When to use JSON? • SOAP is a protocol specification for exchanging structured information in the implementation of Web Services. -
CSE 344 Final Examination Name
CSE 344 Final Examination Monday, December 10, 2018, 2:30-4:20 Name: Question Points Score 1 30 2 20 3 20 4 30 5 20 6 35 7 45 Total: 200 • This exam is CLOSED book and CLOSED devices. • You are allowed TWO, HAND-WRITTEN letter-size sheets with notes (both sides). • You have 110 minutes; • Answer the easy questions before you spend too much time on the more difficult ones. • Good luck! 1 CSE 344 Final Dec 10, 2018, 2:30-4:20 1 Relational Data Model 1. (30 points) You are now a Climate Scientist, and run simulations to predict future changes in the climate. Your job is to improve the simulator in order to improve the climate predictions. Every time you have a new version of the simulator, you run it, and it will generate a dataset where, for each year, it records the predicted temperature at each location of your simulated area. You analyze the results, improve the simulator, and repeat. As a good scientist, you keep the results of all simulations in a relation with the following schema: Sim(version, year, loc, temp) The data types of Version and year are integers; temp is a real number; and loc is a text. (a) (5 points) Write a SQL CREATE TABLE statement to create the Sim relation. Identify the key in Sim, and create the key in your SQL statement. Choose float and for the real number type and text for the text type. Page 2 CSE 344 Final Dec 10, 2018, 2:30-4:20 Sim(version, year, loc, temp) (b) (5 points) Simulation version 13 is wrong, due to a bug in the simulator. -
Extending ACID Semantics to the File System
Extending ACID Semantics to the File System CHARLES P. WRIGHT IBM T. J. Watson Research Center RICHARD SPILLANE, GOPALAN SIVATHANU, and EREZ ZADOK Stony Brook University An organization’s data is often its most valuable asset, but today’s file systems provide few facilities to ensure its safety. Databases, on the other hand, have long provided transactions. Transactions are useful because they provide atomicity, consistency, isolation, and durability (ACID). Many applications could make use of these semantics, but databases have a wide variety of non-standard interfaces. For example, applications like mail servers currently perform elaborate error handling to ensure atomicity and consistency, because it is easier than using a DBMS. A transaction-oriented programming model eliminates complex error-handling code, because failed operations can simply be aborted without side effects. We have designed a file system that exports ACID transactions to user-level applications, while preserving the ubiquitous and convenient POSIX interface. In our prototype ACID file system, called Amino, updated applications can protect arbitrary sequences of system calls within a transaction. Unmodified applications operate without any changes, but each system call is transaction protected. We also built a recoverable memory library with support for nested transactions to allow applications to keep their in-memory data structures consistent with the file system. Our performance evaluation shows that ACID semantics can be added to applications with acceptable overheads.