Faster: a Concurrent Key-Value Store with In-Place Updates
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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, -
Modeling and Analyzing Latency in the Memcached System
Modeling and Analyzing Latency in the Memcached system Wenxue Cheng1, Fengyuan Ren1, Wanchun Jiang2, Tong Zhang1 1Tsinghua National Laboratory for Information Science and Technology, Beijing, China 1Department of Computer Science and Technology, Tsinghua University, Beijing, China 2School of Information Science and Engineering, Central South University, Changsha, China March 27, 2017 abstract Memcached is a widely used in-memory caching solution in large-scale searching scenarios. The most pivotal performance metric in Memcached is latency, which is affected by various factors including the workload pattern, the service rate, the unbalanced load distribution and the cache miss ratio. To quantitate the impact of each factor on latency, we establish a theoretical model for the Memcached system. Specially, we formulate the unbalanced load distribution among Memcached servers by a set of probabilities, capture the burst and concurrent key arrivals at Memcached servers in form of batching blocks, and add a cache miss processing stage. Based on this model, algebraic derivations are conducted to estimate latency in Memcached. The latency estimation is validated by intensive experiments. Moreover, we obtain a quantitative understanding of how much improvement of latency performance can be achieved by optimizing each factor and provide several useful recommendations to optimal latency in Memcached. Keywords Memcached, Latency, Modeling, Quantitative Analysis 1 Introduction Memcached [1] has been adopted in many large-scale websites, including Facebook, LiveJournal, Wikipedia, Flickr, Twitter and Youtube. In Memcached, a web request will generate hundreds of Memcached keys that will be further processed in the memory of parallel Memcached servers. With this parallel in-memory processing method, Memcached can extensively speed up and scale up searching applications [2]. -
Release 2.5.5 Ask Solem Contributors
Celery Documentation Release 2.5.5 Ask Solem Contributors February 04, 2014 Contents i ii Celery Documentation, Release 2.5.5 Contents: Contents 1 Celery Documentation, Release 2.5.5 2 Contents CHAPTER 1 Getting Started Release 2.5 Date February 04, 2014 1.1 Introduction Version 2.5.5 Web http://celeryproject.org/ Download http://pypi.python.org/pypi/celery/ Source http://github.com/celery/celery/ Keywords task queue, job queue, asynchronous, rabbitmq, amqp, redis, python, webhooks, queue, dis- tributed – • Synopsis • Overview • Example • Features • Documentation • Installation – Bundles – Downloading and installing from source – Using the development version 1.1.1 Synopsis Celery is an open source asynchronous task queue/job queue based on distributed message passing. Focused on real- time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes using multiprocessing, Eventlet or gevent. Tasks can execute asynchronously (in the background) or synchronously (wait until ready). Celery is used in production systems to process millions of tasks every hour. 3 Celery Documentation, Release 2.5.5 Celery is written in Python, but the protocol can be implemented in any language. It can also operate with other languages using webhooks. There’s also RCelery for the Ruby programming language, and a PHP client. The recommended message broker is RabbitMQ, but support for Redis, MongoDB, Beanstalk, Amazon SQS, CouchDB and databases (using SQLAlchemy or the Django ORM) is also available. Celery is easy to integrate with web frameworks, some of which even have integration packages: Django django-celery Pyramid pyramid_celery Pylons celery-pylons Flask flask-celery web2py web2py-celery 1.1.2 Overview This is a high level overview of the architecture. -
Online Visualization of Geospatial Stream Data Using the Worldwide Telescope
Online Visualization of Geospatial Stream Data using the WorldWide Telescope Mohamed Ali#, Badrish Chandramouli*, Jonathan Fay*, Curtis Wong*, Steven Drucker*, Balan Sethu Raman# #Microsoft SQL Server, One Microsoft Way, Redmond WA 98052 {mali, sethur}@microsoft.com *Microsoft Research, One Microsoft Way, Redmond WA 98052 {badrishc, jfay, curtis.wong, sdrucker}@microsoft.com ABSTRACT execution query pipeline. StreamInsight has been used in the past This demo presents the ongoing effort to meld the stream query to process geospatial data, e.g., in traffic monitoring [4, 5]. processing capabilities of Microsoft StreamInsight with the On the other hand, the WorldWide Telescope (WWT) [9] enables visualization capabilities of the WorldWide Telescope. This effort a computer to function as a virtual telescope, bringing together provides visualization opportunities to manage, analyze, and imagery from the various ground and space-based telescopes in process real-time information that is of spatio-temporal nature. the world. WWT enables a wide range of user experiences, from The demo scenario is based on detecting, tracking and predicting narrated guided tours from astronomers and educators featuring interesting patterns over historical logs and real-time feeds of interesting places in the sky, to the ability to explore the features earthquake data. of planets. One interesting feature of WWT is its use as a 3-D model of earth, enabling its use as an earth viewer. 1. INTRODUCTION Real-time stream data acquisition has been widely used in This demo presents the ongoing work at Microsoft Research and numerous applications such as network monitoring, Microsoft SQL Server groups to meld the value of stream query telecommunications data management, security, manufacturing, processing of StreamInsight with the unique visualization and sensor networks. -
An End-To-End Application Performance Monitoring Tool
Applications Manager - an end-to-end application performance monitoring tool For enterprises to stay ahead of the technology curve in today's competitive IT environment, it is important for their business critical applications to perform smoothly. Applications Manager - ManageEngine's on-premise application performance monitoring solution offers real-time application monitoring capabilities that offer deep visibility into not only the performance of various server and infrastructure components but also other technologies used in the IT ecosystem such as databases, cloud services, virtual systems, application servers, web server/services, ERP systems, big data, middleware and messaging components, etc. Highlights of Applications Manager Wide range of supported apps Monitor 150+ application types and get deep visibility into all the components of your application environment. Ensure optimal performance of all your applications by visualizing real-time data via comprehensive dashboards and reports. Easy installation and setup Get Applications Manager running in under two minutes. Applications Manager is an agentless monitoring tool and it requires no complex installation procedures. Automatic discovery and dependency mapping Automatically discover all applications and servers in your network and easily categorize them based on their type (apps, servers, databases, VMs, etc.). Get comprehensive insight into your business infrastructure, drill down to IT application relationships, map them effortlessly and understand how applications interact with each other with the help of these dependency maps. Deep dive performance metrics Track key performance metrics of your applications like response times, requests per minute, lock details, session details, CPU and disk utilization, error states, etc. Measure the efficiency of your applications by visualizing the data at regular periodic intervals. -
Improving High Contention OLTP Performance Via Transaction
Improving High Contention OLTP Performance via Transaction Scheduling Guna Prasaad, Alvin Cheung, Dan Suciu University of Washington {guna, akcheung, suciu}@cs.washington.edu ABSTRACT Validation-based protocols [5, 26] are known to be well- Research in transaction processing has made significant suited for workloads with low data contention or conflicts, i.e., progress in improving the performance of multi-core in- when data items are being accessed by transactions concur- memory transactional systems. However, the focus has mainly rently, with at least one access being a write. Since conflicting been on low-contention workloads. Modern transactional accesses are rare, it is unlikely that the value of a data item systems perform poorly on workloads with transactions ac- is updated by another transaction during its execution, and cessing a few highly contended data items. We observe that hence validation mostly succeeds. On the other hand, for most transactional workloads, including those with high workloads where data contention is high, locking-based con- contention, can be divided into clusters of data conflict-free currency control protocols are generally preferred as they transactions and a small set of residuals. pessimistically block other transactions that require access In this paper, we introduce a new concurrency control to the same data item instead of incurring the overhead of protocol called Strife that leverages the above observation. repeatedly aborting and restarting the transaction like in Strife executes transactions in batches, where each batch is OCC. When the workload is known to be partitionable, parti- partitioned into clusters of conflict-free transactions and a tioned concurrency control [12] is preferred as it eliminates small set of residual transactions. -
A High-Performance, Distributed Main Memory Transaction Processing System
H-Store: A High-Performance, Distributed Main Memory Transaction Processing System Robert Kallman Evan P. C. Jones John Hugg Hideaki Kimura Samuel Madden Vertica Inc. Jonathan Natkins Michael Stonebraker [email protected] Andrew Pavlo Yang Zhang Alexander Rasin Massachusetts Institute of Stanley Zdonik Technology Daniel J. Abadi Brown University Yale University {evanj, madden, {rkallman, hkimura, stonebraker, [email protected] jnatkins, pavlo, alexr, yang}@csail.mit.edu sbz}@cs.brown.edu ABSTRACT sponsibilities across multiple shared-nothing machines. Al- Our previous work has shown that architectural and appli- though some RDBMS platforms now provide support for cation shifts have resulted in modern OLTP databases in- this paradigm in their execution framework, research shows creasingly falling short of optimal performance [10]. In par- that building a new OLTP system that is optimized from ticular, the availability of multiple-cores, the abundance of its inception for a distributed environment is advantageous main memory, the lack of user stalls, and the dominant use over retrofitting an existing RDBMS [2]. of stored procedures are factors that portend a clean-slate Using a disk-oriented RDBMS is another key bottleneck redesign of RDBMSs. This previous work showed that such in OLTP databases. All but the very largest OLTP applica- a redesign has the potential to outperform legacy OLTP tions are able to fit their entire data set into the memory of databases by a significant factor. These results, however, a modern shared-nothing cluster of server machines. There- were obtained using a bare-bones prototype that was devel- fore, disk-oriented storage and indexing structures are un- oped just to demonstrate the potential of such a system. -
The Fourth Paradigm
ABOUT THE FOURTH PARADIGM This book presents the first broad look at the rapidly emerging field of data- THE FOUR intensive science, with the goal of influencing the worldwide scientific and com- puting research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud- computing technologies. This collection of essays expands on the vision of pio- T neering computer scientist Jim Gray for a new, fourth paradigm of discovery based H PARADIGM on data-intensive science and offers insights into how it can be fully realized. “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science.” —Bill GaTES “I often tell people working in eScience that they aren’t in this field because they are visionaries or super-intelligent—it’s because they care about science The and they are alive now. It is about technology changing the world, and science taking advantage of it, to do more and do better.” —RhyS FRANCIS, AUSTRALIAN eRESEARCH INFRASTRUCTURE COUNCIL F OURTH “One of the greatest challenges for 21st-century science is how we respond to this new era of data-intensive -
WEB2PY Enterprise Web Framework (2Nd Edition)
WEB2PY Enterprise Web Framework / 2nd Ed. Massimo Di Pierro Copyright ©2009 by Massimo Di Pierro. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. Requests to the Copyright owner for permission should be addressed to: Massimo Di Pierro School of Computing DePaul University 243 S Wabash Ave Chicago, IL 60604 (USA) Email: [email protected] Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created ore extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data: WEB2PY: Enterprise Web Framework Printed in the United States of America. -
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 ............................................................... -
High Performance with Distributed Caching
High Performance with Distributed Caching Key Requirements For Choosing The Right Solution High Performance with Distributed Caching: Key Requirements for Choosing the Right Solution Table of Contents Executive summary 3 Companies are choosing Couchbase for their caching layer, and much more 3 Memory-first 4 Persistence 4 Elastic scalability 4 Replication 5 More than caching 5 About this guide 5 Memcached and Oracle Coherence – two popular caching solutions 6 Oracle Coherence 6 Memcached 6 Why cache? Better performance, lower costs 6 Common caching use cases 7 Key requirements for an effective distributed caching solution 8 Problems with Oracle Coherence: cost, complexity, capabilities 8 Memcached: A simple, powerful open source cache 10 Lack of enterprise support, built-in management, and advanced features 10 Couchbase Server as a high-performance distributed cache 10 General-purpose NoSQL database with Memcached roots 10 Meets key requirements for distributed caching 11 Develop with agility 11 Perform at any scale 11 Manage with ease 12 Benchmarks: Couchbase performance under caching workloads 12 Simple migration from Oracle Coherence or Memcached to Couchbase 13 Drop-in replacement for Memcached: No code changes required 14 Migrating from Oracle Coherence to Couchbase Server 14 Beyond caching: Simplify IT infrastructure, reduce costs with Couchbase 14 About Couchbase 14 Caching has become Executive Summary a de facto technology to boost application For many web, mobile, and Internet of Things (IoT) applications that run in clustered performance as well or cloud environments, distributed caching is a key requirement, for reasons of both as reduce costs. performance and cost. By caching frequently accessed data in memory – rather than making round trips to the backend database – applications can deliver highly responsive experiences that today’s users expect. -
WWW 2013 22Nd International World Wide Web Conference
WWW 2013 22nd International World Wide Web Conference General Chairs: Daniel Schwabe (PUC-Rio – Brazil) Virgílio Almeida (UFMG – Brazil) Hartmut Glaser (CGI.br – Brazil) Research Track: Ricardo Baeza-Yates (Yahoo! Labs – Spain & Chile) Sue Moon (KAIST – South Korea) Practice and Experience Track: Alejandro Jaimes (Yahoo! Labs – Spain) Haixun Wang (MSR – China) Developers Track: Denny Vrandečić (Wikimedia – Germany) Marcus Fontoura (Google – USA) Demos Track: Bernadette F. Lóscio (UFPE – Brazil) Irwin King (CUHK – Hong Kong) W3C Track: Marie-Claire Forgue (W3C Training, USA) Workshops Track: Alberto Laender (UFMG – Brazil) Les Carr (U. of Southampton – UK) Posters Track: Erik Wilde (EMC – USA) Fernanda Lima (UNB – Brazil) Tutorials Track: Bebo White (SLAC – USA) Maria Luiza M. Campos (UFRJ – Brazil) Industry Track: Marden S. Neubert (UOL – Brazil) Proceedings and Metadata Chair: Altigran Soares da Silva (UFAM - Brazil) Local Arrangements Committee: Chair – Hartmut Glaser Executive Secretary – Vagner Diniz PCO Liaison – Adriana Góes, Caroline D’Avo, and Renato Costa Conference Organization Assistant – Selma Morais International Relations – Caroline Burle Technology Liaison – Reinaldo Ferraz UX Designer / Web Developer – Yasodara Córdova, Ariadne Mello Internet infrastructure - Marcelo Gardini, Felipe Agnelli Barbosa Administration– Ana Paula Conte, Maria de Lourdes Carvalho, Beatriz Iossi, Carla Christiny de Mello Legal Issues – Kelli Angelini Press Relations and Social Network – Everton T. Rodrigues, S2Publicom and EntreNós PCO – SKL Eventos