MySQL Cluster Scaling to Billion Database Queries with MySQL Cluster. Bernd Ocklin MySQL Cluster Engineering

Transcription

1 MySQL Cluster Scaling to Billion Database Queries with MySQL Cluster Bernd Ocklin MySQL Cluster Engineering

2 Servicing the Most Performance-Intensive Workloads

3 Program Agenda Databases Are Exciting Again!!! Overview of MySQL Cluster MySQL Cluster - What s New How is it used? <Insert Picture Here>

4 A converging world... Information Messaging MultiMedia Banking Social Networking Gaming

5 2.1BN USERS 8X DATA GROWTH IN 5 YRS 40% DATA GROWTH PER YEAR 850M USERS 70+ NEW DOMAINS EVERY 60 SECONDS 20M APPS PER DAY 1 TR VIDEO PLAYBACKS 250m TWEETS PER DAY $1TR BY 2014 $700BN IN BN MOBILE SUBS IN BILLION ios & ANDROID APPS DOWNLOADED PER WEEK 370K CALL MINUTES EVERY 60 SECONDS

6 Driving new Database Requirements EXTREME WRITE SCALABILITY ROCK SOLID RELIABILITY REAL TIME USER EXPERIENCE RAPID SERVICE INNOVATION

7 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:

8 No Trade-Offs Transactional Integrity EXTREME WRITE SCALABILITY TIME USER EXPERIENCE Complex REAL Queries Standards & Skillsets ROCK SOLID RELIABILITY ELIMNATE BARRIERS TO ENTRY

9 MySQL Cluster Users & Applications Extreme Scalability, Availability and Affordability Web High volume OLTP ecommerce User Profile Management Session Management & Caching Content Management On-Line Gaming Telecoms Subscriber Databases (HLR / HSS) Service Delivery Platforms VAS: VoIP, IPTV & VoD Mobile Content Delivery Mobile Payments LTE Access

10 Key Benefits Scaling Reads & Writes % Availability Real-Time Responsiveness SQL & NoSQL APIs Low TCO, Open platform Auto-sharding + Multi-master Transactional, ACID-compliant relational database Shared-nothing design, no Single Point of Failure On-Line operations: Scale, Upgrade Schema, etc. High-load, real-time performance Predictable low latency, bounded access times Complex, relational queries + Key/Value Access MySQL, Memcached, C++, Java, JPA, HTTP / REST GPL & Commercial editions Commodity hardware, management & monitoring tools

11 Basic architectures 2-tier Data access Application Logic Front-End Data Indexes

12 Basic architectures 3-tier Front-End Application Logic Data access (e.g. SQL engine) Data Indexes SQL, JDBC, ADO,...

13 Basic architectures 4-tier Front-End Application Logic Data access (e.g. SQL engine) Data Indexes

14 All services share the same data view native ClusterJ REST/JSON LDAP memcached NDB API MySQL Cluster Data Nodes SQL, JDBC, ADO,...

15 C++ example NdbOperation *op = trx >getndboperation(mytable); op >inserttuple(); op >equal("key", i); op >setvalue("value", &value); trx >execute( NdbTransaction::Commit );

16 Java example Character newcharacter = session.newinstance(character.class); newcharacter.setname( Yoda ); newcharacter.setattributes( Force ); Session.persist(newCharacter);

17 SQL example (requires MySQL Server) Mysql> INSERT INTO Charaters (Name, Attributes) VALUES ( Yoda, Force );

18 High performance and Scalability Cluster is Distributed Event Driven Asynchronous Parallel Non-locking

19 Your friends / Your enemies Disks (life-saver) CPU cache RAM Many cores Disks (slow fsync) Network latency Heap allocation NUMA Context switching

20 Use your friends Disks (your job saver) Log your data to disk (asynchrounsly) CPU cache Align to to it RAM Preallocate! Many cores Distribute to cores (have a model that supports this)

21 Avoid your enemies Disks Reduce fsyncs no swapping Network latency Reduce network round trips Slow heap allocation Pre-allocate all memory, avoid using it NUMA Disable it Context switching Lock to cores Get network interrupts out of your way

22 MySQL Cluster A distributed hash table 17 Yoda 143 Albert 12 Bernd 42 Ernest md5() % <no of nodes> MySQL Cluster Data Nodes 17 Yoda 12 Bernd 143 Ernest 143 Albert

23 Best Practice : Primary Keys ALWAYS DEFINE A PRIMARY KEY ON THE TABLE! A hidden PRIMARY KEY is added if no PK is specified. BUT.... NOT recommended The hidden primary key is for example not replicated (between Clusters)!! There are problems in this area, so avoid the problems! So always, at least have id BIGINT AUTO_INCREMENT PRIMARY KEY Even if you don't need it for you applications

24 Auto-Sharding (distribution) Application [ {id: 12, name: Bernd}, {id: 143, name: Albert}, {id: 42, name: Ernest},, {id: 17, name: Yoda}] {id: 17, } {id: 143, } {id: 42, } MySQL Cluster Data Nodes {id: 12, }

25 Auto-Sharding (distribution) Application knows the data location Application find({id: 12}) {id: 12, name: Bernd} MySQL Cluster Data Nodes

26 Auto-Sharding Transparent to the application and data access layer No need for application-layer sharding logic build into the API & kernel Partitioning based on hashing all or part of the primary key Each node stores primary fragment for 1 partition and back-up fragment for another Transparency maintained during failover, upgrades and scale-out No need to limit application to single-shard transactions

27 Adding High Availability Introducing Node Groups Application [ {id: 12, name: Bernd}, {id: 143, name: Albert}, {id: 42, name: Ernest},, {id: 17, name: Yoda}] {id: 17, } {id: 143, } {id: 42, } MySQL Cluster Data Nodes {id: 12, }

28 Adding High Availability - Synchronous Replication Application [ {id: 12, name: Bernd}, {id: 143, name: Albert}, {id: 42, name: Ernest},, {id: 17, name: Yoda}] {id: 17, } {id: 143, } {id: 42, } MySQL Cluster Data Nodes {id: 12, }

29 Adding High Availability Synchronous Replication Yoda Ernest Bernd Albert Bernd Albert Yoda Ernest

30 Handling Scheduled Maintenance On-Line Operations Scale the cluster (add & remove nodes on-line) Repartition tables Upgrade / patch servers & OS Upgrade / patch MySQL Cluster Back-Up Evolve the schema on-line, in real-time

31 Adding disk durability Memory In-memory tables {id: 17, } Data kept in memory but complemented by logging to disk. Disk Logging to disk is decoupled from transaction writing. Disk based tables Data kept on disk but cached in memory.

32 Shared Nothing SQL, JDBC, ADO,... No shared components. Cheap commodity hardware. Proper SAN acceptable but expensive.

33 Adding High Availability Extreme resilience Application Service continuing MySQL Cluster Data Nodes

34 Event driven & asynchronous find(17, {}); {id: 17, name: Albert} {id: 17} TC LQH ACC {name: Albert} TUP

35 Doing things in parallel find(17, {}); find([12, 17], {}); MySQL Cluster Data Nodes

36 Doing things in parallel find(17, {}); TC TC TC TC TC {id: 17, name: Albert} x16 {id: 17} LQH LQH LQH ACC LQH LQH x16 {name: Albert} TUP

37 Doing things in parallel Primary key reads can be directed to the correct shard on the API application level No waste of resources by doing same operation on all Each data node can handle up to 16 operations in parallel One data node can fully utilize up to 51 physical CPU cores

38 Scaleable & Elastic Increasing capacity in seconds Application Data Nodes

39 Basic Deployments 1 Data Node Front-End Application Logic Data Access Data Indexes SQL, JDBC, ADO,...

40 Basic Deployments Start scaling with 2 Data Nodes Front-End Application Logic Data Access SQL, JDBC, ADO,... Shared Data View Automated Load Balancing Data Indexes

41 Basic Deployments Continue Scaling Front-End Application Logic Data access (e.g. SQL engine) Data Indexes SQL, JDBC, ADO,... SQL, JDBC, ADO,... OS, App or Hardware Load Balancing Automated Load Balancing

42 Basic Deployments Scale on 3 levels Front-End Application Logic Data access (e.g. SQL engine) Data Indexes SQL, JDBC, ADO,... SQL, JDBC, ADO,...

43 Typical Deployments 2 Server SQL, JDBC, ADO,... Application Web Server MySQL Server Data Nodes Server 1 Server 2

44 Typical Deployments 4 Server SQL, JDBC, ADO,... Server 3 Application Server 4 Web Server MySQL Server Data Nodes Server 1 Server 2

45 Typical Deployments Geo Replication SQL, JDBC, ADO,... Server 3 Server 4 Server 3 Server 4 Server 1 Server 2 Server 1 Server 2

46 MySQL Cluster 7.2 GA

47 Million / minute READS node 4 node 8 node Million / minute UPDATE x 6-core processors 2.93GHz x5670 processors (24 threads per total) 48GB RAM Linux Infiniband networking flexasynch benchmark C++ NoSQL API (NDB API) x Commodity Intel Servers 4 node 8 node

48 Adaptive Query Localization Scaling Distributed Joins 70x More Performance Perform Complex Queries across Shards mysqld A Q L Data Nodes JOINs pushed down to data nodes Executed in parallel Returns single result set to MySQL Opens Up New Use-Cases Real-time analytics Recommendations engines Analyze click-streams mysqld Data Nodes DON T COMPROMISE FUNCTIONALITY TO SCALE-OUT!!

49 MySQL Cluster 7.2 AQL Test Query Web-Based Content Management System MySQL Server Data Node1 Copyright 2011 Oracle Corporation Data Node2 49

50 Web-Based CMS 70x More Performance seconds 1.26 seconds Must Analyze tables for best results mysql> ANALYZE TABLE <tab-name>;

51 Memcached Key-Value API Persistent, Scalable, HA Back-End to memcached No application changes: reuses standard memcached clients & libraries Consolidate Caching & Database Tiers Eliminate cache invalidation Simpler re-use of data across services Improved service levels New NoSQL Access Flexible Deployment Schema or Schema-less storage

52 Schema-Free apps Rapid application evolution New types of data constantly added No time to get schema extended Missing skills to extend schema Initially roll out to just a few users Constantly adding to live system Copyright 2011 Oracle Corporation 52

53 Cluster & Memcached Schema-Free key Application view SQL view value <town:maidenhead,sl6> key value <town:maidenhead,sl6> generic table

54 Cluster & Memcached Configured Schema key Application view value <town:maidenhead,sl6> prefix SQL view key value <town:maidenhead,sl6> Config tables map.zip

55 SQL & NoSQL Clients Native memcached HTTP/REST JDBC / ODBC PHP / PERL Python / Ruby NDB API Data Nodes Mix & Match SQL: Complex, relational queries Memcached: Key-Value web services Java: Enterprise Apps NDB API: Real-time services

56 MySQL 5.5 Server Integration Configure storage engine per-table Choose the right tool for the job InnoDB: Foreign Keys, XA Transactions, Large Rows MySQL Cluster: HA, High Write Rates, RealTime Reduces Complexity, Simplifies DevOps Take advantage of MySQL 5.5 3x higher performance Improved partitioning, diagnostics, availability, etc.

57 Cross Data Center Scalability

58 Multi-Site Clustering Split data nodes across data centers Node Group 1 Data Node 1 Synchronous Synchronous Replication Replication Data Node 2 Node Group 2 Data Node 3 Data Node 4 Synchronous replication and auto-failover between sites Improved heartbeating to handle network partitions Extends HA Options Active/Active with no need for conflict handling

59 Active/Active Geographic Replication Replicating complete clusters across data centers Geographic Replication DR & data locality No passive resources Simplified Active / Active Replication Eliminates requirement for application & schema changes Transaction-level rollback

60 Ease of Use

61 Simplified Provisioning & Maintenance User Privilege Consolidation The existence, content and timing of future releases described here is included for information only and may be changed at Oracles discretion. October 3rd, 2011

62 MySQL Cluster Manager Reducing TCO and creating a more agile, highly available database environment Automated Management Monitoring & Recovery Copyright 2011 Oracle Corporation High Availability Operation 62

63 How Does MySQL Cluster Manager Help? Example: Initiating upgrade from MySQL Cluster 7.0 to 7.2 Before MySQL Cluster Manager 1 x preliminary check of cluster state 8 x ssh commands per server 8 x per-process stop commands 4 x scp of configuration files (2 x mgmd & 2 x mysqld) 8 x per-process start commands 8 x checks for started and re-joined processes 8 x process completion verifications 1 x verify completion of the whole cluster. Excludes manual editing of each configuration file. Total: 46 commands 2.5 hours of attended operation Copyright 2011 Oracle Corporation With MySQL Cluster Manager upgrade cluster --package=7.1 mycluster; Total: 1 Command Unattended Operation Results Reduces the overhead and complexity of managing database clusters Reduces the risk of downtime resulting from administrator error Automates best practices in database cluster management 63

64 Bootstrap single host Cluster 1. Download MCM from edelivery.oracle.com: Package including Cluster 1. Unzip 2. Run agent, define, create & start Cluster! $> bin\mcmd bootstrap MySQL Cluster Manager started Connect to MySQL Cluster Manager by running "D:\Andrew\Documents\MySQL\mcm\bin\mcm" -a NOVA:1862 Configuring default cluster 'mycluster'... Starting default cluster 'mycluster'... Cluster 'mycluster' started successfully ndb_mgmd NOVA:1186 ndbd NOVA ndbd NOVA mysqld NOVA:3306 mysqld NOVA:3307 ndbapi * Connect to the database by running "D:\Andrew\Documents\MySQL\mcm\cluster\bin\mysql" -h NOVA -P u root Connect to Cluster & start using database To bootstrap with Cluster 7.2 replace contents of mcm/cluster directory Copyright 2011 Oracle Corporation 64

65 MySQL Enterprise Monitor 2.3 Copyright 2011 Oracle Corporation 65

66 Evaluate MySQL Cluster CGE 30-Day Trial Navigate to and step through (selecting MySQL Database as the Product Pack) Select MySQL Cluster Manager

67 Operational Best Practices Copyright 2011 Oracle Corporation 67

68 When to Consider MySQL Cluster What are the consequences of downtime or failing to meet performance requirements? How much effort and $ is spent in developing and managing HA in your applications? Are you considering sharding your database to scale write performance? How does that impact your application and developers? Do your services need to be real-time? Will your services have unpredictable scalability demands, especially for writes? Do you want the flexibility to manage your data with more than just SQL?

69 Where would I not Use MySQL Cluster? Hot data sets >3TB Replicate cold data to InnoDB Long running transactions Large rows, without using BLOBs Foreign Keys Can use triggers to emulate: Full table scans Savepoints Geo-Spatial indexes InnoDB storage engine would be the right choice MySQL Cluster Evaluation Guide

70 MySQL Cluster in Action Web Reference Architectures Session Management ecommerce Content Management Memcache / Application Servers MySQL Servers MySQL Servers Node Group 1 Data Refinery Node Group 2 Node Group 1 MySQL Master Node Group 2 F2 F3 F4 Node 3 F1 F4 Node 3 F2 F3 Node 3 Node 3 F1 F2 F3 F4 MySQL Cluster Data Nodes 4 x Data Nodes: 6k page hits per second Each page hit generating 8 12 database operations Node 4 F1 F4 Node 4 F2 F3 Node 4 Node 4 F1 Slave N Slave 6 Slave 7 Slave 8 Slave 9 Slave 10 Slave 1 Slave 2 Slave 3 Slave 4 MySQL Cluster Data Nodes Analytics MySQL Master Slave 1 Slave 2 Slave 3 Slave 5 XOR Distributed Storage Whitepaper:

71 World wide use

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74 Case Studies

75 COMPANY OVERVIEW Leading provider of communications platforms, solutions & services 15.2bn Revenues (2009), 77k employees across 130 countries CHALLENGES / OPPORTUNITIES Converged services driving migration to next generation HLR / HSS systems New IMS platforms for Unified Communications Reduce cost per subscriber and accelerate time to value SOLUTIONS MySQL Cluster Carrier Grade Edition MySQL Support & Consulting Services CUSTOMER PERSPECTIVE MySQL Cluster won the performance test handsdown, and it fitted our needs perfectly. We evaluated shared-disk clustered databases, but the cost would have been at least 10x more. -- François Leygues, Systems Manager RESULTS Scale out on standard ATCA hardware to support 60m+ subscribers on a single platform Low latency, high throughput with %+ availability Enabled customers to reduce cost per subscriber and improve margins Delivered data management solution at 10x less cost than alternatives

76 Shopatron: ecommerce Platform Applications Ecommerce back-end, user authentication, order data & fulfilment, payment data & inventory tracking. Supports several thousand queries per second Key business benefits Scale quickly and at low cost to meet demand Self-healing architecture, reducing TCO Why MySQL? Low cost scalability High read and write throughput Extreme availability Since deploying MySQL Cluster as our ecommerce database, we have had continuous uptime with linear scalability enabling us to exceed our most stringent SLAs Sean Collier, CIO & COO, Shopatron Inc

77 COMPANY OVERVIEW Pyro provide comms technology solutions in Core Network, OSS/BSS & VAS Deployed in 120+ networks worldwide Cell C, one of the largest mobile operators in South Africa 560 roaming partners in 186 countries CHALLENGES / OPPORTUNITIES FIFA 2010 world cup opens up network services to millions of mobile subscribers International roaming SDP to support up to 7m roaming subscribers per day Offer local pricing with home network functionality Minimize cost and time to market SOLUTIONS MySQL Cluster 7.1 & Services CUSTOMER PERSPECTIVE MySQL Cluster 7.1 gave us the perfect combination of extreme levels of transaction throughput, low latency & carrier-grade availability. We also reduced TCO by being able to scale out on commodity server blades and eliminate costly shared storage -- Phani Naik, Head of Technology at Pyro Group RESULTS Supported subscriber and traffic volumes Delivered continuous availability Implemented in 25% of the time of typical SDP solutions Choice in deployment platforms to eliminate vendor lock-in (migrated from Microsoft)

78 COMPANY OVERVIEW Leading telecoms provider across Europe and Asia. Largest Nordic provider 184m subscribers (Q2, 2010) CHALLENGES / OPPORTUNITIES Extend OSS & BSS platforms for new mobile services and evolution to LTE OSS: IP Management & AAA BSS: Subscriber Data Management & Customer Support SOLUTIONS MySQL Cluster MySQL Support Services CUSTOMER PERSPECTIVE Telenor has been using MySQL for fixed IP management since 2003 and are extremely satisfied with its speed, availability and flexibility. Now we also support mobile and LTE IP management with our solution. Telenor has found MySQL Cluster to be the best performing database in the world for our applications. - Peter Eriksson, Manager, Network Provisioning RESULTS Launch new services with no downtime, due to on-line operations of MySQL Cluster Consolidated database supports Subscriber Data Management initiatives MySQL Cluster selected due to % availability, real time performance and linear scalability on commodity hardware

79 COMPANY OVERVIEW UK-based retail and wholesale ISP & Hosting Services 2010 awards for best home broadband and customer service Acquired by BT in 2007 CHALLENGES / OPPORTUNITIES Enter market for wholesale services, demanding more stringent SLAs Re-architect AAA systems for data integrity & continuous availability to support billing sytems Consolidate data to for ease of reporting and operating efficiency Fast time to market SOLUTIONS MySQL Cluster MySQL Server with InnoDB CUSTOMER PERSPECTIVE Since deploying our latest AAA platform, the MySQL environment has delivered continuous uptime, enabling us to exceed our most stringent SLAs -- Geoff Mitchell Network Engineer RESULTS Continuous system availability, exceeding wholesale SLAs 2x faster time to market for new services Agility and scale by separating database from applications Improved management & infrastructure efficiency through database consolidation

80 COMPANY OVERVIEW Division of Docudesk Deliver Document Management SaaS CHALLENGES / OPPORTUNITIES Provide a single repository for customers to manage, archive, and distribute documents Implement scalable, fault tolerant, real time data management back-end PHP session state cached for in-service personalization Store document meta-data, text (as BLOBs), ACL, job queues and billing data Data volumes growing at 2% per day SOLUTION MySQL Cluster deployed on EC2 USER PERSPECTIVE MySQL Cluster exceeds our requirements for low latency, high throughput performance with continuous availability, in a single solution that minimizes complexity and overall cost. -- Casey Brown, Manager of Dev & DBA Services, Docudesk RESULTS Successfully deployed document management solution, eliminating paper trails from legal processes Integrate caching and database into one layer, reducing complexity & cost Support workload with 50:50 read/write ratio Low latency for real-time user experience and document time-stamping Continuous database availability

81 Getting Started Learn More Scaling Web Databases Guide Evaluate MySQL Cluster 7.2 Download Today downloads/cluster/ Copyright 2011 Oracle Corporation Bootstrap a Cluster! Download, No Obligation e.com/ 81

82 Summary Scale Web Services with Carrier-Grade Availability Don t Trade Functionality for Scale Try it out Today! Copyright 2011 Oracle Corporation 82

83

84 Tech Slide Back-up

85 Multi-threaded Data Node Extensions Scaling out on commodity hardware is the standard way to increase performance Application Nodes Node 3 Node 1 Node 4 Node 2 Node Group 1 Node Group 2 Add more data nodes and API nodes as required MySQL Cluster 7.2 increases the ability to also scale-up each data node Increases maximum number of utilised threads from 8 to 59 Can deliver ax single thread performance with bx cores

86 Multi-threaded Data Node Extensions Threads (post GA!): Application Nodes recv tc send ldm main rep io recv: <= 8 Receive threads tc: <= 24 Transaction Coordinator threads ldm: <= 16 Local Query Handler threads send: <= 8 Send threads main: 1 Main thread rep: 1 Replication thread io: 1 I/O thread Engineering guidelines provided to find the best configuration: ZXZX

87 Multi-threaded Data Node Extensions ThreadConfig :=<entry> [,<entry> ] + entry :=<type>={ [<param> ]+ } param := count = N cpubind = L cpuset = L type := ldm main recv rep maint send tc io Note that extra send, recv & tc threads will be part of postga maintenance release. Example: ThreadConfig=ldm={count=2,cpubind=1,2}, ldm={count=2,cpuset=6-9}, main={cpubind=12},rep={cpubind=11}

88 NoSQL with Memcached Flexible: Deployment options Multiple Clusters Simultaneous SQL Access Can still cache in Memcached server Flat key-value store or map to multiple tables/columns set maidenhead SL6 STORED get maidenhead VALUE maidenhead 0 3 SL6 END

89 Multi-Site Clustering changes to STONITH algorithm When heartbeat not received, all data nodes will be asked to ping all other data nodes Each node establishes its list of suspect data nodes from whom they don t receive a ping response within ConnectCheckIntervalDelay msecs If second period of ConnectCheckIntervalDelay passes without a ping response then each data node will send a Fail report to all data nodes naming its suspected node(s) On receipt of a Fail message from a suspect node, the receiving node will consider the originating node as failed rather than the requested target Leaves each side of the temporarily partitioned network with a viable set of data nodes and arbitration is used to select the surviving side if there is no longer a clear majority

90 Multi-Site Clustering WAN engineering recommendations based on user experience (Obviously) the longer the latency between sites, the higher the impact to performance Target latency should be <= 10 ms; 20 ms acceptable Test with 1000 byte packet, under load Bandwidth requirements dependent on traffic but aim for 1 Gbps+ (100 Mbps for low traffic Cluster) Simplest WAN topology possible (fewer points of failure/failover latency) Typical WAN failover times should be short enough not to trigger STONITH in Cluster

91 Active-Active Replication - Detecting Conflicts

92 Active-Active Replication - Detecting Conflicts

93 Active-Active Replication - Detecting Conflicts

94 Active-Active Replication - Detecting Conflicts

95 Active-Active Replication - Detecting Conflicts

96 Geographic Replication what s changed in conflict resolution Reflecting GCI (Global Checkpoint Index) removes requirement for applications to maintain timestamp field in each potentially conflicting table One of the two masters acts as the primary and monitors all received replication events from the secondary (including its own reflected GCI ) to establish when changes not applied in same order on primary and secondary Clusters Primary will then overwrite all conflicting transactions (or optionally just the conflicting rows) on the secondary as well as subsequent transactions influenced by the conflict To use, set the function in mysql.ndb_replication to NDB$EPOCH() or NDB$EPOCH_TRANS() Overview & worked example: Gory details:

97 How to Push Privilege Data into Data Nodes mysql> SOURCE /usr/local/mysql/share/mysql/ndb_dist_priv.sql; mysql> CALL mysql.mysql_cluster_move_privileges(); mysql> SHOW CREATE TABLE mysql.user\g *************************** 1. row *************************** Table: usercreate Table: CREATE TABLE `user` ( `Host` char(60) COLLATE utf8_bin NOT NULL DEFAULT '', ) ENGINE=ndbcluster DEFAULT CHARSET=utf8 COLLATE=utf8_bin COMMENT='Users and global privileges Fully worked example: (

98 General Back-up Slides

99 Scale the Cluster Capacity & Performance Application Nodes Data Node 1 Cluster Mgmt Data Node2 F1 F2 F2 F1 Data Nodes

100 Scale the Cluster Capacity & Performance Application Nodes Node Group 1 Node 3 Node 1 Cluster Mgmt Node Group 2 F1 F3 Node 4 Node 2 F3 F1 Data Nodes F2 F4 F4 F2 Cluster Mgmt

101 Node Failure Detection & Self-Healing Recovery

102 On-Line Scaling & Maintenance 1. New node group added 2. Data is re-partitioned 3. Redundant data is deleted 4. Distribution is switched to share load with new node group Can also update schema online Upgrade hardware & software with no downtime Perform back-ups on-line

103 Only MySQL Can.. blend the agility & innovation of the web..with the trust & capability of the network.

104 No Trade-Offs: ecommerce Integrated Service Provider platform ecommerce Payment processing Fulfillment Supports 1k+ manufacturers & 18k retail partners Requirements Scaling, On-Demand HA: failures & on-line upgrades High batch & real time loads Low TCO: capex and opex

105 No Trade-Offs: Flight Control US Navy aircraft carriers Consolidated flight operations management system Maintenance records Fuel loads Weather conditions Flight deck plans Requirements No Single Points of Failure Complete redundancy Small footprint, harsh environment 4 x MySQL Cluster nodes, Linux and Windows MySQL User Conference Session:

106 Creating & running your first Cluster - the manual way (without MCM) Up & running in minutes using Quick Start guides from Versions for Linux, Windows & Solaris Copyright 2011 Oracle Corporation 106

107 ACID Compliant Relational Database SQL & NoSQL interfaces Write-Scalable & Real-Time Distributed, multi-master, auto-sharding, optimized in-memory structures & indices % Availability Shared-nothing, integrated clustering & sub-second recovery, local & geographic replication, on-line operations Low Barriers to Entry Open-source, elastic, multiple APIs, management tools, commodity hardware