Redis in Action
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Redis and Memcached
Redis and Memcached Speaker: Vladimir Zivkovic, Manager, IT June, 2019 Problem Scenario • Web Site users wanting to access data extremely quickly (< 200ms) • Data being shared between different layers of the stack • Cache a web page sessions • Research and test feasibility of using Redis as a solution for storing and retrieving data quickly • Load data into Redis to test ETL feasibility and Performance • Goal - get sub-second response for API calls for retrieving data !2 Why Redis • In-memory key-value store, with persistence • Open source • Written in C • It can handle up to 2^32 keys, and was tested in practice to handle at least 250 million of keys per instance.” - http://redis.io/topics/faq • Most popular key-value store - http://db-engines.com/en/ranking !3 History • REmote DIctionary Server • Released in 2009 • Built in order to scale a website: http://lloogg.com/ • The web application of lloogg was an ajax app to show the site traffic in real time. Needed a DB handling fast writes, and fast ”get latest N items” operation. !4 Redis Data types • Strings • Bitmaps • Lists • Hyperlogs • Sets • Geospatial Indexes • Sorted Sets • Hashes !5 Redis protocol • redis[“key”] = “value” • Values can be strings, lists or sets • Push and pop elements (atomic) • Fetch arbitrary set and array elements • Sorting • Data is written to disk asynchronously !6 Memory Footprint • An empty instance uses ~ 3MB of memory. • For 1 Million small Keys => String Value pairs use ~ 85MB of memory. • 1 Million Keys => Hash value, representing an object with 5 fields, -
CSC 242: Artificial Intelligence
CSC 242: Artificial Intelligence Course website: www.cs.rochester.edu/u/kyros/courses/csc242 Instructor: Kyros Kutulakos Office: 623 CSB Extension: x5-5860 Email: [email protected] Hours: TR 3:30-4:30 (or by appointment) TA: Joel Tetreault Email: [email protected] Hours: M 1:00-2:00 Recitations: TBA Textbooks: Russell & Norvig, Artificial Intelligence Wilensky, Common LISPcraft Another very good LISP textbook: Winston & Horn, LISP (Addison-Wesley) Common Lisp • LISP is one of the most common “AI programming languages” • LISP (=LISt Processing) is a language whose main power is in manipulating lists of symbols: (a b c (d e f (g h))) arithmetic operations are also included (sqrt (+ (* 3 3) (* 4 4))) • Lisp is a general-purpose, interpreter-based language • All computation consists of expression evaluations: lisp-prompt>(sqrt (+ (* 3 3) (* 4 4))) lisp-prompt> 25 • Since data are list structures and programs are list structures, we can manipulate programs just like data • Lisp is the second-oldest high-level programming language (after Fortran) Getting Started • Command-line invocation unix-prompt>cl system responds with loading & initialization messages followed by a Lisp prompt USER(1): whenever you have balanced parentheses & hit return, the value of the expression (or an error message) are returned USER(1): (+ 1 2) 3 saying “hello” USER(2): ‘hello HELLO USER(3): “Hello” “Hello” • Exiting Lisp: USER(2):(exit) unix-prompt> Getting Started (cont.) • Reading a file f that is in the same directory from which you are running Lisp: -
Redis - a Flexible Key/Value Datastore an Introduction
Redis - a Flexible Key/Value Datastore An Introduction Alexandre Dulaunoy AIMS 2011 MapReduce and Network Forensic • MapReduce is an old concept in computer science ◦ The map stage to perform isolated computation on independent problems ◦ The reduce stage to combine the computation results • Network forensic computations can easily be expressed in map and reduce steps: ◦ parsing, filtering, counting, sorting, aggregating, anonymizing, shuffling... 2 of 16 Concurrent Network Forensic Processing • To allow concurrent processing, a non-blocking data store is required • To allow flexibility, a schema-free data store is required • To allow fast processing, you need to scale horizontally and to know the cost of querying the data store • To allow streaming processing, write/cost versus read/cost should be equivalent 3 of 16 Redis: a key-value/tuple store • Redis is key store written in C with an extended set of data types like lists, sets, ranked sets, hashes, queues • Redis is usually in memory with persistence achieved by regularly saving on disk • Redis API is simple (telnet-like) and supported by a multitude of programming languages • http://www.redis.io/ 4 of 16 Redis: installation • Download Redis 2.2.9 (stable version) • tar xvfz redis-2.2.9.tar.gz • cd redis-2.2.9 • make 5 of 16 Keys • Keys are free text values (up to 231 bytes) - newline not allowed • Short keys are usually better (to save memory) • Naming convention are used like keys separated by colon 6 of 16 Value and data types • binary-safe strings • lists of binary-safe strings • sets of binary-safe strings • hashes (dictionary-like) • pubsub channels 7 of 16 Running redis and talking to redis.. -
Performance at Scale with Amazon Elasticache
Performance at Scale with Amazon ElastiCache July 2019 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 ElastiCache Overview ......................................................................................................... 2 Alternatives to ElastiCache ................................................................................................. 2 Memcached vs. Redis ......................................................................................................... 3 ElastiCache for Memcached ............................................................................................... 5 Architecture with ElastiCache for Memcached ............................................................... -
Scaling Uber with Node.Js Amos Barreto @Amos Barreto
Scaling Uber with Node.js Amos Barreto @amos_barreto Uber is everyone’s Private driver. REQUEST! RIDE! RATE! Tap to select location Sit back and relax, tell your Help us maintain a quality service" driver your destination by rating your experience YOUR DRIVERS 4 Your Drivers UBER QUALIFIED RIDER RATED LICENSED & INSURED Uber only partners with drivers Tell us what you think. Your From insurance to background who have a keen eye for feedback helps us work with checks, every driver meets or customer service and a drivers to constantly improve beats local regulations. passion for the trade. the Uber experience. 19 LOGISTICS 4 #OMGUBERICECREAM 22 UberChopper #OMGUBERCHOPPER 22 #UBERVALENTINES 22 #ICANHASUBERKITTENS 22 Trip State Machine (Simplified) Request Dispatch Accept Arrive End Begin 6 Trip State Machine (Extended) Expire / Request Dispatch (1) Reject Dispatch (2) Accept Arrive End Begin 6 OUR STORY 4 Version 1 • PHP dispatch PHP • Outsourced to remote contractors in Midwest • Half the code in spanish Cron • Flat file " • Lifetime: 6-9 months 6 33 “I read an article on HackerNews about a new framework called Node.js” """"Jason Roberts" Tradeoffs • Learning curve • Database drivers " " • Scalability • Documentation" " " • Performance • Monitoring" " " • Library ecosystem • Production operations" Version 2 • Lifetime: 9 months " Node.js • Developed in house " • Node.js application • Prototyped on 0.2 • Launched in production with 0.4 " • MongoDB datastore “I really don’t see dispatch changing much in the next three years” 33 Expect the -
Redis-Lua Documentation Release 2.0.8
redis-lua Documentation Release 2.0.8 Julien Kauffmann October 12, 2016 Contents 1 Quick start 3 1.1 Step-by-step analysis...........................................3 2 What’s the magic at play here ?5 3 One step further 7 4 What happens when I make a mistake ?9 5 What’s next ? 11 6 Table of contents 13 6.1 Basic usage................................................ 13 6.2 Advanced usage............................................. 14 6.3 API.................................................... 16 7 Indices and tables 19 i ii redis-lua Documentation, Release 2.0.8 redis-lua is a pure-Python library that eases usage of LUA scripts with Redis. It provides script loading and parsing abilities as well as testing primitives. Contents 1 redis-lua Documentation, Release 2.0.8 2 Contents CHAPTER 1 Quick start A code sample is worth a thousand words: from redis_lua import load_script # Loads the 'create_foo.lua' in the 'lua' directory. script= load_script(name='create_foo', path='lua/') # Run the script with the specified arguments. foo= script.get_runner(client=redis_client)( members={'john','susan','bob'}, size=5, ) 1.1 Step-by-step analysis Let’s go through the code sample step by step. First we have: from redis_lua import load_script We import the only function we need. Nothing too specific here. The next lines are: # Loads the 'create_foo.lua' in the 'lua' directory. script= load_script(name='create_foo', path='lua/') These lines looks for a file named create_foo.lua in the lua directory, relative to the current working directory. This example actually considers that using the current directory is correct. In a production code, you likely want to make sure to use a more reliable or absolute path. -
VSI's Open Source Strategy
VSI's Open Source Strategy Plans and schemes for Open Source so9ware on OpenVMS Bre% Cameron / Camiel Vanderhoeven April 2016 AGENDA • Programming languages • Cloud • Integraon technologies • UNIX compability • Databases • Analy;cs • Web • Add-ons • Libraries/u;li;es • Other consideraons • SoDware development • Summary/conclusions tools • Quesons Programming languages • Scrip;ng languages – Lua – Perl (probably in reasonable shape) – Tcl – Python – Ruby – PHP – JavaScript (Node.js and friends) – Also need to consider tools and packages commonly used with these languages • Interpreted languages – Scala (JVM) – Clojure (JVM) – Erlang (poten;ally a good fit with OpenVMS; can get good support from ESL) – All the above are seeing increased adop;on 3 Programming languages • Compiled languages – Go (seeing rapid adop;on) – Rust (relavely new) – Apple Swi • Prerequisites (not all are required in all cases) – LLVM backend – Tweaks to OpenVMS C and C++ compilers – Support for latest language standards (C++) – Support for some GNU C/C++ extensions – Updates to OpenVMS C RTL and threads library 4 Programming languages 1. JavaScript 2. Java 3. PHP 4. Python 5. C# 6. C++ 7. Ruby 8. CSS 9. C 10. Objective-C 11. Perl 12. Shell 13. R 14. Scala 15. Go 16. Haskell 17. Matlab 18. Swift 19. Clojure 20. Groovy 21. Visual Basic 5 See h%p://redmonk.com/sogrady/2015/07/01/language-rankings-6-15/ Programming languages Growing programming languages, June 2015 Steve O’Grady published another edi;on of his great popularity study on programming languages: RedMonk Programming Language Rankings: June 2015. As usual, it is a very valuable piece. There are many take-away from this research. -
Using Redis As a Cache Backend in Magento
Using Redis as a Cache Backend in Magento Written by: Alexey Samorukov Aleksandr Lozhechnik Kirill Morozov Table of Contents PROBLEMS WITH THE TWOLEVELS CACHE BACKEND 3 CONFIRMING THE ISSUE 5 SOLVING THE ISSUE USING THE REDIS CACHE BACKEND 7 TESTING AND BENCHMARKING THE SOLUTION 9 More Research into the TwoLevels Backend Issue 10 Magento Enterprise 1.13 and Memcached 11 Magento Enterprise Edition 1.13 with Redis and the PHP Redis Extension 11 Potential Issues with Redis as a Cache Backend 12 IMPLEMENTING AND TESTING REDIS IN A PRODUCTION ENVIRONMENT 13 Product Test Configurations 14 Redis Configuration 14 Magento Configuration 15 Production Test Results 15 Production Test Issue: Continuously Growing Cache Size 15 Root Cause and Conclusion 15 Work-around 16 Tag Clean-up Procedure and Script 16 CHANGES IN MAGENTO ENTERPRISE EDITION 1.13 AND COMMUNITY EDITION 1.8 18 ADDITIONAL LINKS 20 3 Problems with the TwoLevels Cache Backend Problems with the TwoLevels Cache Backend 4 Problems with the TwoLevels Cache Backend The most common cache storage recommendation for Magento users running multiple web nodes is to implement the TwoLevels backend – that is, to use Memcache together with the database. However, this configuration often causes the following issues: 1. First, the core_cache_tag table constantly grows. On average, each web site has about 5 million records. If a system has multiple web sites and web stores with large catalogs, it can easily grow to 15 million records in less than a day. Insertion into core_cache_tag leads to issues with the MySQL server, including performance degradation. In this context, a tag is an identifier that classifies different types of Magento cache objects. -
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New York Science Journal 2015;8(7) http://www.sciencepub.net/newyork A Comparison of API of Key-Value and Document-Based Data-Stores with Python Programming Ehsan Azizi Khadem1, Emad Fereshteh Nezhad2 1. MSc of Computer Engineering, Department of Computer Engineering, Lorestan University, Iran 2. MSc of Computer Engineering, Communications Regulatory Authority of I.R of Iran Abstract: Today, NoSql data-stores are widely used in databases. NoSql, is based on NoRel (Not Only Relational) concept in database designing. Relational databases are most important technology for storing data during 50 years. But nowadays, because of the explosive growth of data and generating complex type of it like Web2, RDBMS are not suitable and enough for data storing. NoSql has different data model and API to access the data, comparing relational method. NoSql data-stores can be classified into four categories: key-value, document-based, extensible record or column-based and graph-based. Many researches are analyzed and compared data models of these categories but there are few researches on API of them. In this paper, we implement python API to compare performance of key-value and document-based stores and then analyze the results. [Ehsan Azizi Khadem, Emad Fereshteh Nezhad. A Comparison of API of Key-Value and Document-Based Data-Stores with Python Programming. N Y Sci J 2015;8(7):42-48]. (ISSN: 1554-0200). http://www.sciencepub.net/newyork. 7 Keywords: NoSql, DataBase, Python, Key-value, Document-based 1. Introduction - Document-Based: In this model, data stored Databases re very important part of any in document format. -
Technical Update & Roadmap
18/05/2018 VMS Software Inc. (VSI) Technical Update & Roadmap May 2018 Brett Cameron Agenda ▸ What we've done to date ▸ Product roadmap ▸ TCP/IP update ▸ Upcoming new products ▸ Support roadmap ▸ Storage ▸ x86 update, roadmap, and licensing ▸ x86 servers ▸ ISV programme ▸ Other stuff 1 18/05/2018 WhatDivider we’ve with done to date OpenVMS releases to date 3 OpenVMS I64 releases: Plus … Two OpenVMS Alpha Japanese version • V8.4-1H1 – Bolton releases: • DECforms V4.2 • – June 2015 V8.4-2L1 – February • DCPS V2.8 2017 • V8.4-2 - Maynard • FMS V2.6 • Standard OpenVMS – March 2016 • DECwindows Motif V1.7E release (Hudson) • V8.4-2L1 – Hudson • V8.4-2L2 - April 2017 – August 2016 • Performance build, EV6/EV7 (Felton) 2 18/05/2018 Products introduced from 2015 to date Technical achievements to date 65 Layered Product Releases 12 Open Source Releases OpenVMS Releases 4 VSI OpenVMS Statistics 515 VSI Defect Repairs 179 New Features Since V7.3-2 0 100 200 300 400 500 600 3 18/05/2018 Defect repairs 515 Total Defect Repairs 343 Source - Internal BZ VSI - Defect Repairs 118 Source - External BZ 54 Source - Quix 0 100 200 300 400 500 600 Testing hours Test Hours Per VSI OpenVMS Version TKSBRY IA64 V8.4-2L1 Test Hours V8.4-2 V8.4-1H1 0 20000 40000 60000 80000 100000 120000 140000 4 18/05/2018 ProductDivider roadmap with OpenVMS Integrity operating environment Released Planned BOE Components: • NOTARY V1.0 BOE Components: • • V8.4-2L1 operating system OpenSSL V1.02n • CSWS additional modules • • ANT V1.7-1B Perl V5.20-2A • PHP additional modules • -
Development Process Regime Change
platform Development Process Regime Change. Platform.sh for Drupal and Symfony projects. Introduction This document explains how Platform.sh can positively impact your Drupal-centric project development process. Operational data from digital agencies and systems integrators deploying Platform.sh to deliver projects is showing savings of up to 25% in developer & systems administration effort. Delays currently experienced by development teams associated with the allocation of physical resources and provisioning of environments are largely eliminated. Platform.sh is a general-purpose hosting platform for development, and can be used without limitation for a range of scenarios. From development through testing and into production, any permitted member of the team can replicate the entire technology stack end-to-end at the touch of a button. This eliminates many of the common causes of errors that often hamper code promotion through upstream environments, changing the way developers code and test even the smallest of features. As a result, Continuous Integration (CI) processes are hugely improved and Continuous Delivery (CD) becomes an achievable reality for many organizations, enhancing their ability to respond to even the smallest of changes in the marketplace. Environments are completely self-contained and can be easily branched, which makes testing, evaluation, and onboarding fast and safe. DEVELOPMENT ENVIRONMENTS PRODUCTION ENVIRONMENTS DEV 1 DEV 2 DEV n PRODUCTION A PRODUCTION B PHP/NginxPHP/Nginx PHP/Nginx PHP/Nginx PHP/Nginx PHP/Nginx -
Lists in Lisp and Scheme
Lists in Lisp and Scheme • Lists are Lisp’s fundamental data structures • However, it is not the only data structure Lists in Lisp – There are arrays, characters, strings, etc. – Common Lisp has moved on from being merely a LISt Processor and Scheme • However, to understand Lisp and Scheme you must understand lists – common funcAons on them – how to build other useful data structures with them In the beginning was the cons (or pair) Pairs nil • What cons really does is combines two objects into a a two-part object called a cons in Lisp and a pair in • Lists in Lisp and Scheme are defined as Scheme • Conceptually, a cons is a pair of pointers -- the first is pairs the car, and the second is the cdr • Any non empty list can be considered as a • Conses provide a convenient representaAon for pairs pair of the first element and the rest of of any type • The two halves of a cons can point to any kind of the list object, including conses • We use one half of the cons to point to • This is the mechanism for building lists the first element of the list, and the other • (pair? ‘(1 2) ) => #t to point to the rest of the list (which is a nil either another cons or nil) 1 Box nota:on Z What sort of list is this? null Where null = ‘( ) null a a d A one element list (a) null null b c a b c > (set! z (list ‘a (list ‘b ‘c) ‘d)) (car (cdr z)) A list of 3 elements (a b c) (A (B C) D) ?? Pair? Equality • Each Ame you call cons, Scheme allocates a new memory with room for two pointers • The funcAon pair? returns true if its • If we call cons twice with the