DOCSLIB.ORG

Percona Server Documentation Release 8.0.18-9

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Percona Server Documentation Release 8.0.18-9 Percona LLC and/or its affiliates 2009-2019 Dec 11, 2019 CONTENTS I Introduction2 II Installation 12 III Scalability Improvements 36 IV Performance Improvements 39 V Flexibility Improvements 52 VI Reliability Improvements 77 VII Management Improvements 81 VIII Security Improvements 115 IX Diagnostics Improvements 151 X TokuDB 183 XI Percona MyRocks 294 XII Reference 352 i Percona Server Documentation, Release 8.0.18-9 Percona Server for MySQL is an enhanced drop-in replacement for MySQL. With Percona Server for MySQL, • Your queries will run faster and more consistently. • You will consolidate servers on powerful hardware. • You will delay sharding, or avoid it entirely. • You will save money on hosting fees and power. • You will spend less time tuning and administering. • You will achieve higher uptime. • You will troubleshoot without guesswork. Does this sound too good to be true? It’s not. Percona Server for MySQL offers breakthrough performance, scalability, features, and instrumentation. Its self-tuning algorithms and support for extremely high-performance hardware make it the clear choice for companies who demand the utmost performance and reliability from their database server. CONTENTS 1 Part I Introduction 2 CHAPTER ONE THE PERCONA XTRADB STORAGE ENGINE Percona XtraDB is an enhanced version of the InnoDB storage engine, designed to better scale on modern hardware. It also includes a variety of other features useful in high-performance environments. It is fully backwards compatible, and so can be used as a drop-in replacement for standard InnoDB. Percona XtraDB includes all of InnoDB ‘s robust, reliable ACID-compliant design and advanced MVCC architecture, and builds on that solid foundation with more features, more tunability, more metrics, and more scalability. In particu- lar, it is designed to scale better on many cores, to use memory more efficiently, and to be more convenient and useful. The new features are especially designed to alleviate some of InnoDB‘s limitations. We choose features and fixes based on customer requests and on our best judgment of real-world needs as a high-performance consulting company. Percona XtraDB engine will not have further binary releases, it is distributed as part of Percona Server for MySQL. 3 CHAPTER TWO LIST OF FEATURES AVAILABLE IN PERCONA SERVER FOR MYSQL RELEASES Percona Server for MySQL 5.6 Percona Server for MySQL 5.7 Percona Server for MySQL 8.0 Improved Buffer Pool Scalability Improved Buffer Pool Scalability Improved Buffer Pool Scalability Improved InnoDB I/O Scalability Improved InnoDB I/O Scalability Improved InnoDB I/O Scalability Multiple Adaptive Hash Search Multiple Adaptive Hash Search Multiple Adaptive Hash Search Partitions Partitions Partitions Atomic write support for Fusion-io Atomic write support for Fusion-io Atomic write support for Fusion-io devices devices devices Query Cache Enhancements Query Cache Enhancements Feature not implemented Improved NUMA support Improved NUMA support Feature not implemented Thread Pool Thread Pool Thread Pool Suppress Warning Messages Suppress Warning Messages Suppress Warning Messages Ability to change database for Ability to change database for Ability to change database for mysqlbinlog mysqlbinlog mysqlbinlog Fixed Size for the Read Ahead Area Fixed Size for the Read Ahead Area Fixed Size for the Read Ahead Area Improved MEMORY Storage En- Improved MEMORY Storage En- Improved MEMORY Storage En- gine gine gine Restricting the number of binlog Restricting the number of binlog Restricting the number of binlog files files files Ignoring missing tables in mysql- Ignoring missing tables in mysql- Ignoring missing tables in mysql- dump dump dump Too Many Connections Warning Too Many Connections Warning Too Many Connections Warning Handle Corrupted Tables Handle Corrupted Tables Handle Corrupted Tables Lock-Free SHOW SLAVE STA- Lock-Free SHOW SLAVE STA- Lock-Free SHOW SLAVE STA- TUS TUS TUS Expanded Fast Index Creation Expanded Fast Index Creation Expanded Fast Index Creation Percona Toolkit UDFs Percona Toolkit UDFs Percona Toolkit UDFs Support for Fake Changes Support for Fake Changes Support for Fake Changes Kill Idle Transactions Kill Idle Transactions Kill Idle Transactions XtraDB changed page tracking XtraDB changed page tracking XtraDB changed page tracking Enforcing Storage Engine Enforcing Storage Engine Replaced with upstream implemen- tation Utility user Utility user Feature not implemented Extending the secure-file-priv Extending the secure-file-priv Extending the secure-file-priv server option server option server option Expanded Program Option Modi- Expanded Program Option Modi- Feature not implemented fiers fiers PAM Authentication Plugin PAM Authentication Plugin PAM Authentication Plugin Continued on next page 4 Percona Server Documentation, Release 8.0.18-9 Table 2.1 – continued from previous page Percona Server for MySQL 5.6 Percona Server for MySQL 5.7 Percona Server for MySQL 8.0 Log Archiving for XtraDB Log Archiving for XtraDB Log Archiving for XtraDB User Statistics User Statistics User Statistics Slow Query Log Slow Query Log Slow Query Log Count InnoDB Deadlocks Count InnoDB Deadlocks Count InnoDB Deadlocks Log All Client Commands (syslog) Log All Client Commands (syslog) Log All Client Commands (syslog) Response Time Distribution Response Time Distribution Feature not implemented Show Storage Engines Show Storage Engines Show Storage Engines Show Lock Names Show Lock Names Show Lock Names Process List Process List Process List Misc. INFORMATION_SCHEMA Misc. INFORMATION_SCHEMA Misc. INFORMATION_SCHEMA Tables Tables Tables Extended Show Engine InnoDB Extended Show Engine InnoDB Extended Show Engine InnoDB Status Status Status Thread Based Profiling Thread Based Profiling Thread Based Profiling XtraDB Performance Improve- XtraDB Performance Improve- XtraDB Performance Improve- ments for I/O-Bound Highly- ments for I/O-Bound Highly- ments for I/O-Bound Highly- Concurrent Workloads Concurrent Workloads Concurrent Workloads Page cleaner thread tuning Page cleaner thread tuning Page cleaner thread tuning Statement Timeout Statement Timeout Statement Timeout Extended SELECT INTO OUT- Extended SELECT INTO OUT- Extended SELECT INTO OUT- FILE/DUMPFILE FILE/DUMPFILE FILE/DUMPFILE Per-query variable statement Per-query variable statement Per-query variable statement Extended mysqlbinlog Extended mysqlbinlog Extended mysqlbinlog Slow Query Log Rotation and Ex- Slow Query Log Rotation and Ex- Slow Query Log Rotation and Ex- piration piration piration Metrics for scalability measure- Metrics for scalability measure- Feature not implemented ment ment Audit Log Audit Log Audit Log Backup Locks Backup Locks Backup Locks CSV engine mode for standard- CSV engine mode for standard- CSV engine mode for standard- compliant quote and comma pars- compliant quote and comma pars- compliant quote and comma pars- ing ing ing Super read-only Super read-only Super read-only Other Reading • Changed in Percona Server 5.6 • Percona Server for MySQL In-Place Upgrading Guide: From 5.7 to 8.0 • What Is New in MySQL 5.5 • What Is New in MySQL 5.6 2.1. Other Reading 5 CHAPTER THREE PERCONA SERVER FOR MYSQL FEATURE COMPARISON Percona Server for MySQL is an enhanced drop-in replacement for MySQL. With Percona Server for MySQL, • Your queries will run faster and more consistently. • You will consolidate servers on powerful hardware. • You will delay sharding, or avoid it entirely. • You will save money on hosting fees and power. • You will spend less time tuning and administering. • You will achieve higher uptime. • You will troubleshoot without guesswork. We provide these benefits by significantly enhancing Percona Server for MySQL as compared to the standard MySQL database server: Features Percona Server for MySQL 8.0.13 MySQL 8.0.13 Open source Yes Yes ACID Compliance Yes Yes Multi-Version Concurrency Control Yes Yes Row-Level Locking Yes Yes Automatic Crash Recovery Yes Yes Table Partitioning Yes Yes Views Yes Yes Subqueries Yes Yes Triggers Yes Yes Stored Procedures Yes Yes Foreign Keys Yes Yes Window Functions Yes Yes Common Table Expressions Yes Yes Geospatial Features (GIS, SPRS) Yes Yes GTID Replication Yes Yes Group Replication Yes Yes MyRocks Storage Engine Yes No TokuDB Storage Engine Yes No 6 Percona Server Documentation, Release 8.0.18-9 Improvements for Developers Feature Percona Server for MySQL 8.0.13 MySQL 8.0.13 NoSQL Socket-Level Interface Yes Yes X API Support Yes Yes JSON Functions Yes Yes InnoDB Full-Text Search Improvements Yes No Extra Hash/Digest Functions Yes No Extra Diagnostic Features Feature Percona Server for MySQL 8.0.13 MySQL 8.0.13 INFORMATION_SCHEMA Tables 95 65 Global Performance and Status Counters 853 434 Optimizer Histograms Yes Yes Per-Table Performance Counters Yes No Per-Index Performance Counters Yes No Per-User Performance Counters Yes No Per-Client Performance Counters Yes No Per-Thread Performance Counters Yes No Enhanced SHOW ENGINE INNODB STATUS Yes No Temporary tables Information Yes No Extended Slow Query Logging Yes No User Statistics Yes No Performance & Scalability Enhancements Feature Percona Server for MySQL MySQL 8.0.13 8.0.13 InnoDB Resource Groups Yes Yes Configurable Page Sizes Yes Yes Contention-Aware Transaction Scheduling Yes Yes Improved Scalability by Splitting Mutexes Yes Yes Improved MEMORY Storage Engine Yes No Improved Flushing Yes No Parallel Doublewrite Buffer Yes No Configurable Fast Index Creation Yes No Per-Column Compression for VARCHAR/BLOB
Recommended publications
  • Percona Xtradb Cluster Documentation Release 8.0.18-9.3

    Percona Xtradb Cluster Documentation Release 8.0.18-9.3

    Percona XtraDB Cluster Documentation Release 8.0.18-9.3 Percona LLC and/or its affiliates 2009-2020 May 14, 2020 CONTENTS I Introduction3 II Getting Started9 III Features 33 IV PXC Security 47 V User’s Manual 61 VI How-tos 97 VII Reference 163 i ii Percona XtraDB Cluster Documentation, Release 8.0.18-9.3 Percona XtraDB Cluster is a database clustering solution for MySQL. It ensures high availability, prevents downtime and data loss, and provides linear scalability for a growing environment. Features of Percona XtraDB Cluster Feature Details Synchronous Data is written to all nodes simultaneously, or not written at all in case of a failure replication** even on a single node Multi-master replication Any node can trigger a data update. True parallel replication Multiple threads on slave performing replication on row level Automatic node You simply add a node and it automatically syncs. provisioning Data consistency No more unsynchronized nodes. PXC Strict Mode Avoids the use of experimental and unsupported features Configuration script for Percona XtraDB Cluster includes the proxysql-admin tool that automatically ProxySQL configures Percona XtraDB Cluster nodes using ProxySQL. Automatic configuration Percona XtraDB Cluster includes the pxc-encrypt-cluster-traffic of SSL encryption variable that enables automatic configuration of SSL encryption Optimized Performance Percona XtraDB Cluster performance is optimized to scale with a growing production workload Percona XtraDB Cluster 8.0 is fully compatible with MySQL Server Community Edition 8.0 and Percona Server for MySQL 8.0. See also: Overview of changes in the most recent Percona XtraDB Cluster release Important changes in Percona XtraDB Cluster 8.0 MySQL Community Edition https://www.mysql.com/products/community/ Percona Server for MySQL https://www.percona.com/doc/percona-server/LATEST/index.html How We Made Percona XtraDB Cluster Scale https://www.percona.com/blog/2017/04/19/ how-we-made-percona-xtradb-cluster-scale Data compatibility You can use data created by any MySQL variant.
  • Data Platforms Map from 451 Research

    Data Platforms Map from 451 Research

    1 2 3 4 5 6 Azure AgilData Cloudera Distribu2on HDInsight Metascale of Apache Kaa MapR Streams MapR Hortonworks Towards Teradata Listener Doopex Apache Spark Strao enterprise search Apache Solr Google Cloud Confluent/Apache Kaa Al2scale Qubole AWS IBM Azure DataTorrent/Apache Apex PipelineDB Dataproc BigInsights Apache Lucene Apache Samza EMR Data Lake IBM Analy2cs for Apache Spark Oracle Stream Explorer Teradata Cloud Databricks A Towards SRCH2 So\ware AG for Hadoop Oracle Big Data Cloud A E-discovery TIBCO StreamBase Cloudera Elas2csearch SQLStream Data Elas2c Found Apache S4 Apache Storm Rackspace Non-relaonal Oracle Big Data Appliance ObjectRocket for IBM InfoSphere Streams xPlenty Apache Hadoop HP IDOL Elas2csearch Google Azure Stream Analy2cs Data Ar2sans Apache Flink Azure Cloud EsgnDB/ zone Platforms Oracle Dataflow Endeca Server Search AWS Apache Apache IBM Ac2an Treasure Avio Kinesis LeanXcale Trafodion Splice Machine MammothDB Drill Presto Big SQL Vortex Data SciDB HPCC AsterixDB IBM InfoSphere Towards LucidWorks Starcounter SQLite Apache Teradata Map Data Explorer Firebird Apache Apache JethroData Pivotal HD/ Apache Cazena CitusDB SIEM Big Data Tajo Hive Impala Apache HAWQ Kudu Aster Loggly Ac2an Ingres Sumo Cloudera SAP Sybase ASE IBM PureData January 2016 Logic Search for Analy2cs/dashDB Logentries SAP Sybase SQL Anywhere Key: B TIBCO Splunk Maana Rela%onal zone B LogLogic EnterpriseDB SQream General purpose Postgres-XL Microso\ Ry\ X15 So\ware Oracle IBM SAP SQL Server Oracle Teradata Specialist analy2c PostgreSQL Exadata
  • Percona Xtrabackup Provides

    Percona Xtrabackup Provides

    Percona XtraBackup provides: • Fast and reliable backups • Uninterrupted transaction processing during backups • Savings on disk space and network bandwidth with better compression • Automatic backup Percona XtraBackup verification You’re only as good as the tools you have to use. When it comes to your business, the • Higher uptime due to faster software tools you employ can be the difference between success and failure. restore time Percona’s suite of MySQL and MongoDB software and toolkits are a powerhouse of performance, the backbone of the organization. As a product of the open source Bringing immediate, noticeable community, our software has been tested by fire and proven resilient. and long lasting benefits to Percona XtraBackup is a free, open source, complete online backup solution for all meet your budget and needs. versions of Percona Server for MySQL, MySQL® and MariaDB®. With over 1,800,000 downloads, Percona XtraBackup performs online non-blocking, tightly compressed, highly secure backups on transactional systems so that applications remain fully available during planned maintenance windows. Percona XtraBackup is the world’s only open-source, free MySQL hot backup software that performs non-blocking backups for InnoDB and XtraDB databases. With Percona XtraBackup, you can achieve the following benefits: • Create hot InnoDB backups without pausing your database • Make incremental backups of MySQL • Stream compressed MySQL backups to another server • Move tables between MySQL servers on-line • Create new MySQL replication slaves easily • Backup MySQL without adding load to the server Percona XtraBackup makes MySQL hot backups for all versions of Percona Server for MySQL, MySQL, and MariaDB. It performs streaming, compressed, and incremental MySQL backups.
  • Betrfs: a Right-Optimized Write-Optimized File System

    Betrfs: a Right-Optimized Write-Optimized File System

    BetrFS: A Right-Optimized Write-Optimized File System William Jannen, Jun Yuan, Yang Zhan, Amogh Akshintala, Stony Brook University; John Esmet, Tokutek Inc.; Yizheng Jiao, Ankur Mittal, Prashant Pandey, and Phaneendra Reddy, Stony Brook University; Leif Walsh, Tokutek Inc.; Michael Bender, Stony Brook University; Martin Farach-Colton, Rutgers University; Rob Johnson, Stony Brook University; Bradley C. Kuszmaul, Massachusetts Institute of Technology; Donald E. Porter, Stony Brook University https://www.usenix.org/conference/fast15/technical-sessions/presentation/jannen This paper is included in the Proceedings of the 13th USENIX Conference on File and Storage Technologies (FAST ’15). February 16–19, 2015 • Santa Clara, CA, USA ISBN 978-1-931971-201 Open access to the Proceedings of the 13th USENIX Conference on File and Storage Technologies is sponsored by USENIX BetrFS: A Right-Optimized Write-Optimized File System William Jannen, Jun Yuan, Yang Zhan, Amogh Akshintala, John Esmet∗, Yizheng Jiao, Ankur Mittal, Prashant Pandey, Phaneendra Reddy, Leif Walsh∗, Michael Bender, Martin Farach-Colton†, Rob Johnson, Bradley C. Kuszmaul‡, and Donald E. Porter Stony Brook University, ∗Tokutek Inc., †Rutgers University, and ‡Massachusetts Institute of Technology Abstract (microwrites). Examples include email delivery, creat- The Bε -tree File System, or BetrFS, (pronounced ing lock files for an editing application, making small “better eff ess”) is the first in-kernel file system to use a updates to a large file, or updating a file’s atime. The un- write-optimized index. Write optimized indexes (WOIs) derlying problem is that many standard data structures in are promising building blocks for storage systems be- the file-system designer’s toolbox optimize for one case cause of their potential to implement both microwrites at the expense of another.
  • Algorithms and Complexity (AL)

    Algorithms and Complexity (AL)

    Algorithms and Complexity (AL) Algorithms are fundamental to computer science and software engineering. The real-world performance of any software system depends on the algorithms chosen and the suitability of the various layers of implementation. Good algorithm design is therefore crucial for the performance of all software systems. Moreover, the study of algorithms provides insight into the intrinsic nature of the problem as well as possible solution techniques independent of programming language, programming paradigm, computer hardware, or any other implementation aspect. An important part of computing is the ability to select algorithms appropriate to particular purposes and to apply them, recognizing the possibility that no suitable algorithm may exist. This facility relies on understanding the range of algorithms that address an important set of well-defined problems, recognizing their strengths and weaknesses, and their suitability in particular contexts. Efficiency is a pervasive theme throughout this area. This knowledge area defines the central concepts and skills required to design, implement, and analyze algorithms for solving problems. Algorithms are essential in all advanced areas of computer science: artificial intelligence, databases, distributed computing, graphics, networking, operating systems, programming languages, security, and so on. Algorithms that have specific utility in each of these are listed in the relevant knowledge areas. Cryptography, for example, appears in the new Knowledge Area on Information Assurance and Security (IAS), while parallel and distributed algorithms appear in the Knowledge Area in Parallel and Distributed Computing (PD). As with all knowledge areas, the order of topics and their groupings do not necessarily correlate to a specific order of presentation. Different programs will teach the topics in different courses and should do so in the order they believe is most appropriate for their students.
  • Assignment of Master's Thesis

    Assignment of Master's Thesis

    CZECH TECHNICAL UNIVERSITY IN PRAGUE FACULTY OF INFORMATION TECHNOLOGY ASSIGNMENT OF MASTER’S THESIS Title: Approximate Pattern Matching In Sparse Multidimensional Arrays Using Machine Learning Based Methods Student: Bc. Anna Kučerová Supervisor: Ing. Luboš Krčál Study Programme: Informatics Study Branch: Knowledge Engineering Department: Department of Theoretical Computer Science Validity: Until the end of winter semester 2018/19 Instructions Sparse multidimensional arrays are a common data structure for effective storage, analysis, and visualization of scientific datasets. Approximate pattern matching and processing is essential in many scientific domains. Previous algorithms focused on deterministic filtering and aggregate matching using synopsis style indexing. However, little work has been done on application of heuristic based machine learning methods for these approximate array pattern matching tasks. Research current methods for multidimensional array pattern matching, discovery, and processing. Propose a method for array pattern matching and processing tasks utilizing machine learning methods, such as kernels, clustering, or PSO in conjunction with inverted indexing. Implement the proposed method and demonstrate its efficiency on both artificial and real world datasets. Compare the algorithm with deterministic solutions in terms of time and memory complexities and pattern occurrence miss rates. References Will be provided by the supervisor. doc. Ing. Jan Janoušek, Ph.D. prof. Ing. Pavel Tvrdík, CSc. Head of Department Dean Prague February 28, 2017 Czech Technical University in Prague Faculty of Information Technology Department of Knowledge Engineering Master’s thesis Approximate Pattern Matching In Sparse Multidimensional Arrays Using Machine Learning Based Methods Bc. Anna Kuˇcerov´a Supervisor: Ing. LuboˇsKrˇc´al 9th May 2017 Acknowledgements Main credit goes to my supervisor Ing.
  • UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ Optimizing Large

    UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ Optimizing Large

    UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ Colegio de Ciencias e Ingenierías Optimizing Large Databases: A Study on Index Structures Proyecto de Investigación . Ricardo Andres Leon Ruiz Ingeniería en Sistemas Trabajo de titulación presentado como requisito para la obtención del título de Ingeniero en Sistemas Quito, 22 de Diciembre de 2017 2 UNIVERSIDAD SAN FRANCISCO DE QUITO USFQ COLEGIO DE CIENCIAS E INGENIERÍAS HOJA DE CALIFICACIÓN DE TRABAJO DE TITULACIÓN Optimizing Large Databases: A Study on Index Structures Ricardo Andres Leon Ruiz Calificación: Nombre del profesor, Título académico Aldo Cassola, Ph.D. Firma del profesor Quito, Diciembre de 2017 3 Derechos de Autor Por medio del presente documento certifico que he leído todas las Políticas y Manuales de la Universidad San Francisco de Quito USFQ, incluyendo la Política de Propiedad Intelectual USFQ, y estoy de acuerdo con su contenido, por lo que los derechos de propiedad intelectual del presente trabajo quedan sujetos a lo dispuesto en esas Políticas. Asimismo, autorizo a la USFQ para que realice la digitalización y publicación de este trabajo en el repositorio virtual, de conformidad a lo dispuesto en el Art. 144 de la Ley Orgánica de Educación Superior. Firma del estudiante: Nombres y Apellidos: Ricardo Andres Leon Ruiz Código de estudiante: 110346 C. I.: 1714286265 Lugar, Fecha Quito, 22 de Diciembre de 2017 4 RESUMEN La siguiente investigación trata sobre comparar estructuras de índice para grandes bases de datos, tanto analíticamente, como experimentalmente. El estudio se encuentra dividido en dos partes principales. La primera parte se centra en índices de hash y B-trees. Ambas estructuras son estudiadas en el contexto del modelo de acceso de disco tradicional.
  • Conception Et Exploitation D'une Base De Modèles: Application Aux Data

    Conception Et Exploitation D'une Base De Modèles: Application Aux Data

    Conception et exploitation d’une base de modèles : application aux data sciences Cyrille Ponchateau To cite this version: Cyrille Ponchateau. Conception et exploitation d’une base de modèles : application aux data sciences. Autre [cs.OH]. ISAE-ENSMA Ecole Nationale Supérieure de Mécanique et d’Aérotechique - Poitiers, 2018. Français. NNT : 2018ESMA0005. tel-01939430 HAL Id: tel-01939430 https://tel.archives-ouvertes.fr/tel-01939430 Submitted on 29 Nov 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE pour l’obtention du Grade de DOCTEUR DE L'ÉCOLE NATIONALE SUPÉRIEURE DE MÉCANIQUE ET D'AÉROTECHNIQUE (Diplôme National — Arrêté du 25 mai 2016) Ecole Doctorale : Sciences et Ingénierie des Systèmes, Mathématiques, Informatique (SISMI) Secteur de Recherche : INFORMATIQUE ET APPLICATIONS Présentée par : Cyrille PONCHATEAU ******************************************************** Conception et exploitation d’une base de modèles : application aux data sciences ******************************************************** Directeur de thèse : Ladjel BELLATRECHE
  • In Mysql/Mariadb?

    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..
  • Big Data Solutions and Applications

    Big Data Solutions and Applications

    P. Bellini, M. Di Claudio, P. Nesi, N. Rauch Slides from: M. Di Claudio, N. Rauch Big Data Solutions and applications Slide del corso: Sistemi Collaborativi e di Protezione Corso di Laurea Magistrale in Ingegneria 2013/2014 http://www.disit.dinfo.unifi.it Distributed Data Intelligence and Technology Lab 1 Related to the following chapter in the book: • P. Bellini, M. Di Claudio, P. Nesi, N. Rauch, "Tassonomy and Review of Big Data Solutions Navigation", in "Big Data Computing", Ed. Rajendra Akerkar, Western Norway Research Institute, Norway, Chapman and Hall/CRC press, ISBN 978‐1‐ 46‐657837‐1, eBook: 978‐1‐ 46‐657838‐8, july 2013, in press. http://www.tmrfindia.org/b igdata.html 2 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 3 Index 6. Access/Data Rendering aspects 7. Data Analysis and Mining/Ingestion Aspects 8. Comparison and Analysis of Architectural Features • Data Management • Data Access and Visual Rendering • Mining/Ingestion • Data Analysis • Architecture 4 Part 1 The Big Data Problem. 5 Index 1. The Big Data Problem • 5V’s of Big Data • Big Data Problem • CAP Principle • Big Data Analysis Pipeline 2. Big Data Application Fields 3. Overview of Big Data Solutions 4. Data Management Aspects • NoSQL Databases • MongoDB 5. Architectural aspects • Riak 6 What is Big Data? Variety Volume Value 5V’s of Big Data Velocity Variability 7 5V’s of Big Data (1/2) • Volume: Companies amassing terabytes/petabytes of information, and they always look for faster, more efficient, and lower‐ cost solutions for data management.
  • Mariadb Viable Mysql Replacement Scale12x.Pdf

    Mariadb Viable Mysql Replacement Scale12x.Pdf

    MariaDB: Viable MySQL replacement Colin Charles, Team MariaDB, SkySQL Ab [email protected] | [email protected] http://mariadb.org/ http://bytebot.net/blog/ | @bytebot on Twitter SCALE12x, Los Angeles, California, USA 22 February 2014 Sunday, 23 February 14 whoami • Work on MariaDB at SkySQL Ab • Merged with Monty Program Ab, makers of MariaDB • Formerly MySQL AB (exit: Sun Microsystems) • Past lives include Fedora Project (FESCO), OpenOffice.org • MHA experience • since November 2011 (MHA 0.52, 0.53) • NRE work to make it run in a Solaris 10 environment... with no Internet access! • Continued deployment advice + work for data centre use • Much thanks to SkySQL for the experience Sunday, 23 February 14 MySQL? Percona Server? MariaDB? Sunday, 23 February 14 Agenda • 4 years: major server releases (5.1, 5.2, 5.3, 5.5, 5.5+TokuDB, Galera Cluster) and 10.0 series • Delving into history of previous releases • MariaDB 10.0 • Client libraries, Galera Cluster • Roadmap Sunday, 23 February 14 What isn’t covered • MariaDB Enterprise • Galera Cluster + GUI + API • mariadb.com • SkySQL • trademarks... Sunday, 23 February 14 What is MariaDB? • Community developed branch of MySQL • Feature enhanced • Fully compatible & feature complete with MySQL Sunday, 23 February 14 Backed by MariaDB Foundation • Driver of the MariaDB project • Foundation not controlled by single entity/ person; has a Board • Ensure MariaDB is compatible with MySQL, maintain mariadb.org, keep community voice • Major sponsors: SkySQL, Parallels, Booking.com, Automattic, OpenQuery, Percona,
  • Never Lose Mysql Data PERCONA XTRADB CLUSTER on AMAZON EC2

    Never Lose Mysql Data PERCONA XTRADB CLUSTER on AMAZON EC2

    Never Lose MySQL Data PERCONA XTRADB CLUSTER ON AMAZON EC2 This solution brief outlines replication and multi-master capabilities using a Percona XtraDB Cluster EC2 architecture built with Percona Server for MySQL and Percona’s enhanced Codership Galera library. Best fit for: • Amazon EC2 environments geared to maintaining uptime & high availability • Industries with high-read environments (financial or healthcare) • Companies with in-house or external dedicated database resources • Applications with read-heavy workloads Summary CLOUD ARCHITECTURE An Amazon EC2 environment running Percona XtraDB SOLUTION Cluster provides availability and data consistency. This architecture provides a strong foundation for SOLUTION COVERAGE more advanced deployments capable of surviving disaster scenarios. AVAILABILITY It does add the expense of complexity compared to DATA PROTECTION deploying and running a standard MySQL® OPERATIONAL COST environment. SIMPLICITY Percona XtraDB Cluster’s automated failover and recovery allow the database to continually service the READ PERF application transparently with ProxySQL directing WRITE PERF traffic to available nodes. Percona Monitoring and Management (PMM) provides advanced visibility into BACKUP RECOVERY TIME the environment. This document describes a proven standard Percona XtraDB Cluster EC2 architecture that is built on Percona Server for MySQL and Percona’s enhanced PROS Codership Galera library for replication and Quicker failover means higher application uptime with little to multi-master capabilities. no application downtime, with consistent data across nodes. Failover is transparent to applications and doesn’t affect Use Case application performance. Provides detailed data analytics from enhanced Percona This solution deploys Percona XtraDB Cluster on software packages. Amazon Web Services (AWS) Elastic Compute Cloud (EC2) environment. This Percona XtraDB Cluster Cloud solution lets you only pay for the infrastructure you need.