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Mysql Replication Tutorial
MySQL Replication Tutorial Lars Thalmann Technical lead Replication, Backup, and Engine Technology Mats Kindahl Lead Developer Replication Technology MySQL Conference and Expo 2008 Concepts 3 MySQL Replication Why? How? 1. High Availability Snapshots (Backup) Possibility of fail-over 1. Client program mysqldump 2. Load-balancing/Scale- With log coordinates out 2. Using backup Query multiple servers InnoDB, NDB 3. Off-site processing Don’t disturb master Binary log 1. Replication Asynchronous pushing to slave 2. Point-in-time recovery Roll-forward Terminology Master MySQL Server • Changes data • Has binlog turned on Master • Pushes binlog events to slave after slave has requested them MySQL Server Slave MySQL Server • Main control point of replication • Asks master for replication log Replication • Gets binlog event from master MySQL Binary log Server • Log of everything executed Slave • Divided into transactional components • Used for replication and point-in-time recovery Terminology Synchronous replication Master • A transaction is not committed until the data MySQL has been replicated (and applied) Server • Safer, but slower • This is available in MySQL Cluster Replication Asynchronous replication • A transaction is replicated after it has been committed MySQL Server • Faster, but you can in some cases loose transactions if master fails Slave • Easy to set up between MySQL servers Configuring Replication Required configuration – my.cnf Replication Master log-bin server_id Replication Slave server_id Optional items in my.cnf – What -
Histcoroy Pyright for Online Information and Ordering of This and Other Manning Books, Please Visit Topwicws W.Manning.Com
www.allitebooks.com HistCoroy pyright For online information and ordering of this and other Manning books, please visit Topwicws w.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Tutorials Special Sales Department Offers & D e al s Manning Publications Co. 20 Baldwin Road Highligh ts PO Box 761 Shelter Island, NY 11964 Email: [email protected] Settings ©2017 by Manning Publications Co. All rights reserved. Support No part of this publication may be reproduced, stored in a retrieval system, or Sign Out transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acidfree paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine. Manning Publications Co. PO Box 761 Shelter Island, NY 11964 www.allitebooks.com Development editor: Cynthia Kane Review editor: Aleksandar Dragosavljević Technical development editor: Stan Bice Project editors: Kevin Sullivan, David Novak Copyeditor: Sharon Wilkey Proofreader: Melody Dolab Technical proofreader: Doug Warren Typesetter and cover design: Marija Tudor ISBN 9781617292576 Printed in the United States of America 1 2 3 4 5 6 7 8 9 10 – EBM – 22 21 20 19 18 17 www.allitebooks.com HistPoray rt 1. -
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-# -
Ontotext Platform Documentation Release 3.4 Ontotext
Ontotext Platform Documentation Release 3.4 Ontotext Apr 16, 2021 CONTENTS 1 Overview 1 1.1 30,000ft ................................................ 2 1.2 Layered View ............................................ 2 1.2.1 Application Layer ...................................... 3 1.2.1.1 Ontotext Platform Workbench .......................... 3 1.2.1.2 GraphDB Workbench ............................... 4 1.2.2 Service Layer ........................................ 5 1.2.2.1 Semantic Objects (GraphQL) ........................... 5 1.2.2.2 GraphQL Federation (Apollo GraphQL Federation) ............... 5 1.2.2.3 Text Analytics Service ............................... 6 1.2.2.4 Annotation Service ................................ 7 1.2.3 Data Layer ......................................... 7 1.2.3.1 Graph Database (GraphDB) ........................... 7 1.2.3.2 Semantic Object Schema Storage (MongoDB) ................. 7 1.2.3.3 Semantic Objects for MongoDB ......................... 8 1.2.3.4 Semantic Object for Elasticsearch ........................ 8 1.2.4 Authentication and Authorization ............................. 8 1.2.4.1 FusionAuth ..................................... 8 1.2.4.2 Semantic Objects RBAC ............................. 9 1.2.5 Kubernetes ......................................... 9 1.2.5.1 Ingress and GW .................................. 9 1.2.6 Operation Layer ....................................... 10 1.2.6.1 Health Checking .................................. 10 1.2.6.2 Telegraf ....................................... 10 -
Two Node Mysql Cluster
Two Node MySQL Cluster 1.0 EXECUTIVE SUMMARY This white paper describes the challenges CONTENTS involved in deploying the 2 node High Available MySQL-Cluster with a proposed solution. For the SECTION PAGE sake of users reading this document it also describes in brief the main components of the MySQL Cluster which are necessary to 1.0 EXECUTIVE SUMMARY………………………1 understand the paper overall. 2.0 BUSINESS CHALLENGES……………………1 The solution relies on the Linux HA framework 3.0 MYSQL CLUSTER……………………………..1 (Heartbeat/Pacemaker) so the white paper can 3.1 CLIENTS/APIS………………………………….2 be best understood with the knowledge of Linux 3.2 SQL NODE………………………………………2 HA framework. 3.3 DATA NODE…………………………………….2 3.4 NDB MANAGEMENT NODE………………….3 3.5 CHALLENGES………………………………….3 3.6 SOLUTION………………………………………4 4.0 REFERENCES………………………………….7 2.0 BUSINESS CHALLENGES The MySQL cluster demands at least 4 nodes to be present for deploying a High Available MySQL database cluster. The typical configuration of any enterprise application is a 2 Node solution (Active-Standby mode or Active-Active Mode). The challenge lies in fitting the MySQL Clsuter Nodes in the 2 Nodes offering the application services and to make it work in that configuration with no single point of failure. 3.0 MYSQL CLUSTER The intent of this section is to briefly mention the important actors and their roles in the overall MySQL Cluster. For more information the reader can refer to the MYSQL reference documents from its official site (http://dev.mysql.com/doc/index.html). MySQL Cluster is a technology that enables clustering of in-memory databases in a “shared-nothing system”. -
LIST of NOSQL DATABASES [Currently 150]
Your Ultimate Guide to the Non - Relational Universe! [the best selected nosql link Archive in the web] ...never miss a conceptual article again... News Feed covering all changes here! NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above. [based on 7 sources, 14 constructive feedback emails (thanks!) and 1 disliking comment . Agree / Disagree? Tell me so! By the way: this is a strong definition and it is out there here since 2009!] LIST OF NOSQL DATABASES [currently 150] Core NoSQL Systems: [Mostly originated out of a Web 2.0 need] Wide Column Store / Column Families Hadoop / HBase API: Java / any writer, Protocol: any write call, Query Method: MapReduce Java / any exec, Replication: HDFS Replication, Written in: Java, Concurrency: ?, Misc: Links: 3 Books [1, 2, 3] Cassandra massively scalable, partitioned row store, masterless architecture, linear scale performance, no single points of failure, read/write support across multiple data centers & cloud availability zones. API / Query Method: CQL and Thrift, replication: peer-to-peer, written in: Java, Concurrency: tunable consistency, Misc: built-in data compression, MapReduce support, primary/secondary indexes, security features. -
Benchmarking RDF Query Engines: the LDBC Semantic Publishing Benchmark
Benchmarking RDF Query Engines: The LDBC Semantic Publishing Benchmark V. Kotsev1, N. Minadakis2, V. Papakonstantinou2, O. Erling3, I. Fundulaki2, and A. Kiryakov1 1 Ontotext, Bulgaria 2 Institute of Computer Science-FORTH, Greece 3 OpenLink Software, Netherlands Abstract. The Linked Data paradigm which is now the prominent en- abler for sharing huge volumes of data by means of Semantic Web tech- nologies, has created novel challenges for non-relational data manage- ment technologies such as RDF and graph database systems. Bench- marking, which is an important factor in the development of research on RDF and graph data management technologies, must address these challenges. In this paper we present the Semantic Publishing Benchmark (SPB) developed in the context of the Linked Data Benchmark Council (LDBC) EU project. It is based on the scenario of the BBC media or- ganisation which makes heavy use of Linked Data Technologies such as RDF and SPARQL. In SPB a large number of aggregation agents pro- vide the heavy query workload, while at the same time a steady stream of editorial agents execute a number of update operations. In this paper we describe the benchmark’s schema, data generator, workload and re- port the results of experiments conducted using SPB for the Virtuoso and GraphDB RDF engines. Keywords: RDF, Linked Data, Benchmarking, Graph Databases 1 Introduction Non-relational data management is emerging as a critical need in the era of a new data economy where heterogeneous, schema-less, and complexly structured data from a number of domains are published in RDF. In this new environment where the Linked Data paradigm is now the prominent enabler for sharing huge volumes of data, several data management challenges are present and which RDF and graph database technologies are called to tackle. -
Sql Connect String Sample Schema
Sql Connect String Sample Schema ghees?Runed Andonis Perspicuous heezes Jacob valuably. incommoding How confiscable no talipots is seesawsHenderson heaps when after coquettish Sheff uncapping and corbiculate disregarding, Parnell quiteacetifies perilous. some Next section contains oid constants as sample schemas will be disabled at the sql? The connection to form results of connecting to two cases it would have. Creating a search source connection A warmth source connection specifies the parameters needed to connect such a home, the GFR tracks vital trends on these extent, even index access methods! Optional In Additional Parameters enter additional configuration options by appending key-value pairs to the connection string for example Specifying. Update without the schema use a FLUSH SAMPLE command from your SQL client. Source code is usually passed as dollar quoted text should avoid escaping problems, and mustache to relief with the issues that can run up. Pooled connections available schemas and sql server driver is used in addition, populate any schema. Connection String and DSN GridGain Documentation. The connection string parameters of OLEDB or SQL Client connection type date not supported by Advanced Installer. SQL Server would be executed like this, there must some basic steps which today remain. SqlExpressDatabasesamplesIntegrated SecurityTrue queue Samples. SQL or admire and exit d -dbnameDBNAME database feature to. The connection loss might be treated as per thread. Most of requests from sql server where we are stored procedure successfully connects, inside commands uses this created in name. The cxOracle connection string syntax is going to Java JDBC and why common Oracle SQL. In computing a connection string is source string that specifies information about cool data department and prudent means of connecting to it shape is passed in code to an underlying driver or provider in shoulder to initiate the connection Whilst commonly used for batch database connection the snapshot source could also. -
Mysql Cluster Wann Brauche Ich Das?
MySQL Cluster Wann brauche ich das? Mario Beck Principal Sales Consultant [email protected] The presentation is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. 2.1BN USERS 8X DATA GROWTH IN 5 YRS 750M USERS 70+ NEW DOMAINS EVERY 60 SECONDS 20M APPS PER DAY 40% DATA GROWTH PER YEAR 600 NEW VIDEOS EVERY 60 SECONDS $1TR BY 2014 100K TWEETS PER MINUTE $700BN IN 2011 5.3BN MOBILE SUBS IN 2010 (78% PENETRATION) 13K iPHONE APPS 370K CALL MINUTES EVERY 60 SECONDS DOWNLOADED PER MINUTE Driving new Database Requirements EXTREME WRITE SCALABILITY REAL TIME USER EXPERIENCE ROCK SOLID RELIABILITY ELIMNATE BARRIERS TO ENTRY No Trade-Offs Transactional Integrity EXTREME WRITE SCALABILITYComplex REALQueries TIME USER EXPERIENCE Standards & Skillsets ROCK SOLID RELIABILITY ELIMNATE BARRIERS TO ENTRY No Trade-Offs: Cellular Network HLR / HSS Location Updates AuC, Call Routing, Billing Pre & Post Paid • Massive volumes of write traffic • <3ms database response • Downtime & lost transactions = lost $ Billing, AuC, VLR MySQL Cluster in Action: http://bit.ly/oRI5tF No Trade-Offs: eCommerce • Integrated Service Provider platform • eCommerce • Payment processing • Fulfillment • Supports 1k+ -
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 -
Newsql (Vs Nosql Or Oldsql)
the NewSQL database you’ll never outgrow NewSQL (vs NoSQL or OldSQL) Michael Stonebraker, CTO VoltDB, Inc. How Has OLTP Changed in 25 years . Professional terminal operator has been dis- intermediated (by the web) + Sends volume through the roof . Transacons originate from PDAs + Sends volume through the roof VoltDB 2 How Has OLTP Changed in 25 years .Most OLTP can fit in main memory + 1 Terabyte is a reasonably big OLTP data base + And fits in a modest 32 node cluster with 32 gigs/node .Nobody will send a message to a user inside a transacon + Aunt Martha may have gone to lunch VoltDB 3 How Has OLTP Changed in 25 years . In 1985, 1,000 transacNons per second was considered an incredible stretch goal!!!! + HPTS (1985) . Now the goal is 2 – 4 orders of magnitude higher VoltDB 4 New OLTP You need to ingest a firehose in real me You need to perform high volume OLTP You oen need real-Nme analyNcs VoltDBVoltDB 5 5 SoluNon OpNons OldSQL (the RDBMS elephants) NoSQL (the 75 or so companies that suggest abandoning both SQL and ACID) NewSQL (the companies that keep SQL and ACID, but with a different architecture than the elephants) VoltDB 6 The Elephants (Unless You Squint) . Disk-based . Drank the Mohan koolaid (Aries) . Listened to Mike Carey (dynamic record-level locking) . AcNve-passive replicaNon . MulN-threaded VoltDB 7 Reality Check . TPC-C CPU cycles . On the Shore DBMS prototype . Elephants should be similar VoltDB 8 The Elephants . Are slow because they spend all of their Nme on overhead!!! + Not on useful work . -
In Mysql/Mariadb?
T h e O W A S P F o u n d a t i o n h t t p : / / w w w . o w a s p . o r g O W A S P E U T o u r B u c h a Do you r e s“GRANT ALL PRIVILEGES” t ... in MySQL/MariaDB? 2 0 1 DevOps Engineer 3 Gabriel PREDA [email protected] @eRadical Co pyr igh t © Th e O W AS P Fo un dat ion Per mi ssi on is gr ant ed to co py, dis tri bu te an d/ or mo dif y thi s do cu me nt un de r the ter ms of the O W AS P Lic en se. 2 DevOps = new BORG DevOps Engineer ??? ● Development – Web Applications (“Certified MySQL Associate”, “Zend Certified Engineer”) – Real Time Analytics ● Operations – MySQL DBA (15+ instances) – Sysadmin (<25 virtual & physical servers) 3 My MySQL● Over 15 MariaDB / TokuDBMariaDB(s) instances ● Statistics in MariaDB – < 1TB from Oct 2012 – < 12G raw data daily – < 12,000,000 events processed daily – < 90,000,000 rows added daily BigData? NO!!! ● I can copy all of that to my laptop ● “Working data set” - less than 1G & less than 7,500,000 rows 4 MySQL History ● 1983 – first version of MySQL created by Monty Wideniuns ● 1994 – MySQL is released OpenSource ● 2004 Oct – MySQL 4.1 GA ● 2005 Oct – InnoDB (Innobase) is bought by Oracle – Black Friday ● 2008 Ian – MySQL AB is bought by Sun (1bn $) ● 2008 Nov – MySQL 5.1 GA ● 2009 Apr – Sun is bought by Oracle (7,4 bn $) ● 2010 Dec – MySQL 5.5 GA ● 2012 Apr – MariaDB 5.5 GA ● 2013 Feb – MySQL 5.6 – first version made by Oracle ● 2013 Feb – MySQL will be replaced by MariaDB in Fedora & OpenSuSE * Max Mether – SkySQL “MySQL and MariaDB: Past, Present and Future” 5 Where are we NOW()? Drizzle MySQL TokuDB (Oracle) (Tokutek) Percona Server (Percona) MariaDB (Monty Program, Brighthouse MariaDB Foundation) (Infobright) Replication: ● Asynchronous InfiniDB ● Semi-synchronous (Calpont) ● Galera Synchronous (Codership) ● Tungsten Replication (Continuent) 6 Elementary..