Scaling Web Databases Webinar 2

8/3/2019 Scaling Web Databases Webinar 2

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Scaling Web Databases: MySQL ClusterAuto-Partitioning, SQL & NoSQL Interfaces, Schema Flexibility

Mat KeepProduct Management, MySQL Cluster

[email protected]

Andrew MorganProduct Management, MySQL Cluster

[email protected]


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The Reality of Being Successful on the Web

Scale fast

.writes as well as reads, linearly

.on commodity hardware

.without downtime

Choose the right tool for the job

.integrating multiple lightweight services.multiple data access & data integrity requirements

.SQL & NoSQL, when and where

Rapidly iterate

.evolve the app and the database Stay upstay on-line


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Session Agenda

Best Practices in Scaling Web Services withMySQL Cluster

Scaling Reads & Writes with Auto-Sharding

On-Line Scaling with Commodity Hardware

Choosing the Right Interface(s): NoSQL and SQL

Rapidly Evolving Web Services

On-Line Scale-Out & Schema Updates

Staying Up, Staying On-Line Case Studies

Resources to Get Started


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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 berelied upon in making purchasing decisions.

The development, release, and timing of any

features or functionality described for Oracles

products remains at the sole discretion of Oracle.


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MySQL Cluster Overview

ACID Compliant Relational DatabaseSQL & NoSQL interfaces

Write-Scalable & Real-TimeDistributed, auto-partitioning (sharding), multi-master

99.999% AvailabilityShared-nothing, integrated clustering & sub-second recovery, local & geographicreplication, on-line operations

Low TCOOpen-source, management & monitoring tools, scale-out on commodity hardware


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MySQL Cluster Architecture

Data Nodes

Node Group 1

F1

F3

F3

F1

Node

1

Node2

Node Group 2

F2

F4

F4

F2

Node3

Node4

Application Nodes

ClusterMgmt

ClusterMgmt

LDAPREST


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MySQL Cluster - Extreme Resilience

Data Nodes

Node Group 1

F1

F3

F3

F1

Node

1

Node2

Node Group 2

F2

F4

F4

F2

Node3

Node4

Application Nodes

ClusterMgmt

ClusterMgmt

LDAPREST


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MySQL Cluster Users & ApplicationsHA, Transactional Services: Web & Telecoms

http://www.mysql.com/customers/cluster/

Web

User profile management Session stores

eCommerce

On-Line Gaming

Application Servers

Telecoms

Subscriber Databases (HLR/HSS)

Service Delivery Platforms

VoIP, IPTV & VoD

Mobile Content Delivery

On-Line app stores and portals

IP Management

Payment Gateways


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Auto-Sharding


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Scale-Out Reads & Writes onCommodity Hardware

NDB API Performance 4.33 MQueries per second!

8 Intel servers, dual-6-core [email protected] GHz, 24GB RAM

2 Data Nodes per server flexAsync benchmark

16 parallel threads, each issuing 256simultaneous transactions

Read / Write 100 byte attribute

Interim results from 2 days testing

watch this space:mikaelronstrom.blogspot.com


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Out of the Box Scalability: Auto-Sharding

Partitioning happens automatically & transparent to the application A little knowledge of how it works though can massively increase application performance

Transparency maintained during failover, upgrades and scale-out

No need for application-layer sharding logic

No need to limit application to single-shard transactions (though can helpefficiency)

http://www.mysql.com/why-mysql/white-papers/mysql_wp_cluster_perfomance.php


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Data Node 1

Data Node 2

Data Node 3

Data Node 4A fragment is a copy of a partition (aka fragment replica)

Number of fragments = # of partitions * # of replicas

Table T1

P2

P3

P4

Px Partition

4 Partitions * 2 Replicas = 8 Fragments

P1

Automatic Data Partitioning


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Data Node 1

Data Node 2

F1

Primary Fragment

Secondary Fragment (fragment replica)

Data Node 3

Data Node 4Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1



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Data Node 1

Data Node 2

F1

Primary Fragment

F1

Data Node 3

Data Node 4Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1

Primary Fragment

F3 F1

Data Node 3

Data Node 4Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2

Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2

F2

Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2

F4 F2


Fx

Fx

Table T1

P2

P3

P4

Px Partition

P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2 F4

F4 F2

Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F3

Primary Fragment

F1

Data Node 3

Data Node 4

F2 F4

F4 F2

Node Group 1

Fx

Fx

Table T1

P2

P3

P4

Px Partition


P1

F1

F3




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2 F4

F4 F2

Node Group 1

Node Group 2Fx

Fx

- Node groups are created automatically

- # of groups = # of data nodes / # ofreplicas

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2 F4

F4 F2

Node Group 1

Node Group 2Fx

Fx

As long as one data node in eachnode group is running we have a

complete copy of the data

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2 F4

F4 F2

Node Group 1

Node Group 2Fx

Fx

As long as one data node in eachnode group is running we have a

complete copy of the data

Table T1

P2

P3

P4

Px Partition


P1




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Data Node 1

Data Node 2

F1 F3

Primary Fragment

F3 F1

Data Node 3

Data Node 4

F2 F4

F4 F2

Node Group 1

Node Group 2Fx

Fx

As long as one data node in eachnode group

is running we have a complete copy ofthe data

Table T1

P2

P3

P4

Px Partition


P1




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Table T1 Data Node 1

Data Node 2

F1 F3

Primary Fragment

P2

P3

P4

Px Partition

F3 F1

Data Node 3

Data Node 4

F2 F4

F4 F2

Node Group 1

Node Group 2


Fx

Fx

- No complete copy of thedata

- Cluster shutdownsautomatically

P1




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General Design Considerations

Overall design goalMinimize network roundtrips for your

most important requests!

MySQL Cluster is designed for Short transactions

Many parallel transactions

Utilize Simple access patterns to fetch data

Use efficient scans and batching interfaces Analyze what your most typical use cases are

optimize for those


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Best Practice : Primary Keys

To avoid problems with Cluster 2 Cluster replication

Recovery

Application behavior (KEY NOT FOUND.. etc)

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 haveid BIGINT AUTO_INCREMENT PRIMARY KEY

Even if you don't need it for you applications


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Best Practice: Distribution Aware Apps

Partition selected using hash onPartition Key

Primary Key by default

User can override in table definition

MySQL Server (or NDB API) willattempt to send transaction to thecorrect data node

If all data for the transaction are inthe same partition, less messaging -> faster

Aim to have all rows for high-running queries in same partition

Partition Key

Primary Key

town country population

Maidenhead UK 78000

Paris France 2193031

Boston UK 58124

Boston USA 617594

SELECT SUM(population) FROM towns

WHERE country=UK;

Partition Key

Primary Key

town country population

Maidenhead UK 78000

Paris France 2193031

Boston UK 58124

Boston USA 617594

SELECT SUM(population) FROM towns

WHERE town=Boston;


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Best Practice: Distribution Aware Multiple Tables

Extend partition awareness overmultiple tables

Same rule aim to have all data for

instance of high runningtransactions in the same partition

ALTER TABLE service_ids

PARTITION BY KEY(sub_id);

Partition Key

Primary Key

service sub_id svc_id

twitter 19724 76325732

twitter 84539 67324782

facebook 19724 83753984

facebook 73642 87324793

Partition Key

Primary Key

sub_id age gender

19724 25 male

84539 43 female

19724 16 female

74574 21 female


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Scaling Distributed JoinsAdaptive Query Localization

Complex joins traditionally slower in

MySQL Cluster

Complex = lots of levels and interimresults in JOIN

JOIN was implemented in the MySQLServer:

Nested Loop join

When data is needed, it must be fetched

over the network from the Data Nodes;row by row

This causes latency and consumesresources

Can now push the execution down intothe data nodes, greatly reducing the

network trips 25x-40x performance gain in

customer PoC!

mysqld

Data Nodes

mysqld

Data Nodes

AQL

http://www.mysql.com/news-and-events/on-demand-webinars/display-od-583.html

The existence, content and timing of future releases described here is included

for information only and may be changed at Oracles discretion. May 26, 2011

7.2DM


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Testing ofAdaptive Query Localization has yielded over 20xhigher performanceon complex queries within our application,enabling Docudesk to expand our use of MySQL Cluster into abroader range of highly dynamic web services.

Casey BrownManager, Development & DBA Services, Docudesk

Early Adopter Speaks!


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Data Node 1 Data Node 2

Need more throughput?

Scale Out


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Scale Out


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Scale Out

Oops, need to increase capacity as well!


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Scale Out



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Scaling Across Data CentersGeographic Replication with Multi-Master Replication

Cluster 1

Synchronousreplication

Cluster 2

InnoDB

Asynchronousreplication

Synchronous replication withina Cluster node group for HA

Bi-Direction asynchronousreplication to remote Cluster forgeographic redundancy

Master-slave or multi-master Automated conflict detection

and resolution

Asynchronous replication tonon-Cluster databases for

specialised activities such asreport generation

Mix and match replication types

InnoDBInnoDB


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SQL & NoSQL Interfaces


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PerformanceI FlexibilityI Simplification SQL and NoSQL Access Methods to tables

SQL: complex queries, rich ecosystem of apps & expertise Simple Key/Value interfaces bypassing SQL layer for blazing fast reads &

writes Real-time interfaces for micro-second latency Developers free to work in their preferred environment


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MySQL Cluster: SQL & NoSQL Combined

NoSQL Multiple ways to bypass SQL, and maximize performance: NDB API. C++ for highest performance, lowest latency Cluster/J for optimized access in Java NEW! Memcached. Use all your existing clients/applications

Mix & Match!

Same data accessedsimultaneously throughSQL & NoSQL interfaces


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Which to Choose ?


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NoSQL With NDB APIBest possible performance

MySQL Cluster Data Nodes

Applications with embedded NDB APILibrary

Clients Application embeds the NDB API C++

interface library

NDB API make intelligent decision (wherepossible) about which data node to sendqueries to With a little planning in the schema design,

achieve linear scalability

Used by all of the other application nodes(MySQL, LDAP, ClusterJ,)

Best possible performance but requires >development skill

Favourite API for real-time networkapplications

Foundation for all interfaces

7 2DM


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NoSQL with memcached

Memcached is a distributed memory

based hash-key/value store with nopersistence to disk

NoSQL, simple API, popular withdevelopers

MySQL Cluster already provides scalable,in-memory performance with NoSQL(hashed) access as well as persistence

Provide the Memcached API but map to NDBAPI calls

Writes-in-place, so no need to invalidate

cache Simplifies architecture as caching &

database integrated into 1 tier

Access data from existing relational tables

7.2DM

Memcached protocol

NoSQL with Memcached 7 2DM


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NoSQL with MemcachedPre-GA version available from labs.mysql.com

Flexible: Deployment options

Multiple Clusters

Simultaneous SQL Access

Can still cache in Memcached server Flat key-value store or map to multiple

tables/columns

Simple:set maidenhead 0 0 3

SL6

STORED

get maidenhead

VALUE maidenhead 0 3

SL6

END

7.2DM


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Supporting Rapidly Evolving Services


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On-Line Operations

Scale the cluster (add data nodes)

Repartition tables

Recover failed nodes

Upgrade / patch servers & OS

Upgrade / patch MySQL Cluster

Back-Up

Evolve the schema on-line, in real-time


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Online Add Node (1) add node group

Node Group New Node Group

JapanTanizakiJunichiro4

GermanyGoetheJohann3

USAHemingwayErnest2

FranceCamusAlbert1

countrylnamefnameauthid (PK)



USAHemingwayErnest2

FranceCamusAlbert1


Application

http://www.mysql.com/why-mysql/white-papers/mysql_wp_cluster7_architecture.php

Copyright 2011 Oracle Corporation 47


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No extra space needed on existing nodes!JapanTanizakiJunichiro4

GermanyGoetheJohan3

USAHemingwayErnest2

FranceCamusAlbert1




USAHemingwayErnest2

FranceCamusAlbert1



USAHemingwayErnest2




Online Add Node (2) copy data



USAHemingwayErnest2

FranceCamusAlbert1



Application

O


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GermanyGoetheJohan3

USAHemingwayErnest2

FranceCamusAlbert1



USAHemingwayErnest2




USAHemingwayErnest2

FranceCamusAlbert1



Online Add Node (3) switch distribution


Application


O li Add N d ( ) d l


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Node Group 1 Node Group 2


USAHemingwayErnest2



FranceCamusAlbert1


Node Group 2


GermanyGoetheJohan3

USAHemingwayErnest2

FranceCamusAlbert1


Dynamic scaling of arunning Cluster no

interruption to service


Online Add Node (4) - delete rows

Application


R l Ti O Li S h Ch


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Real-Time, On-Line Schema Changes

Fully online transactionresponse times unchanged

Add and remove indexes,add new columns and tables

No temporary table creation

No recreation of data ordeletion required

Faster and better performingtable maintenanceoperations

Less memory and diskrequirements

CREATE OFFLINE INDEX b ON t1(b);

Query OK, 1356 rows affected (2.20 sec)

DROP OFFLINE INDEX b ON t1;


CREATE ONLINE INDEX b ON t1(b);


DROP ONLINE INDEX b ON t1;


ALTER ONLINE TABLE t1 ADD COLUMN d INT;



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Case Studies

Sh t C Pl tf


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53

Since deploying MySQL Cluster as our eCommerce database, we have had

continuous uptime with linear scalability enabling us to exceed our uptime requirements

Sean Collier, CIO & COO, Shopatron Inc

Shopatron: eCommerce Platform

Applications Ecommerce back-end, user authentication,

order data & fulfilment, payment data &inventory tracking. Supports severalthousand 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

http://www.mysql.com/why-mysql/case-studies/mysql_cs_shopatron.php


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COMPANY OVERVIEW

UK-based retail and wholesale ISP &Hosting Services

2010 awards for best home broadbandand customer service

Acquired by BT in 2007

CHALLENGES / OPPORTUNITIES

Enter market for wholesale services,demanding more stringent SLAs

Re-architect AAA systems for dataintegrity & continuous availability tosupport billing sytems

Consolidate data to for ease of reportingand operating efficiency

Fast time to market

SOLUTIONS

MySQL Cluster

MySQL Server with InnoDB

CUSTOMER PERSPECTIVESince 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, exceedingwholesale SLAs

2x faster time to market for new services

Agility and scale by separating database

from applications Improved management & infrastructure

efficiency through database consolidation

S


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Summary

MySQL Cluster

Web-Scale Performance with Carrier-Grade Availability SQL, JSON/REST, memcached protocol and native Java

access

No Compromise Scale-Out, Real Time Performance, 99.999% Uptime

Proven Deployed across telecoms networks

Powering mission-critical web and internet services

G tti St t d


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Getting Started

Learn More GA Release

Evaluate MySQL Cluster 7.2 Quick Start Guides

Architecture &New FeaturesGuidewww.mysql.com/cluster/

Download Today

http://dev.mysql.com/do

wnloads/cluster/

http://labs.mysql.com(memcached)

Linux, Solaris,

Windowshttp://tinyurl.com/5wkl4dy

Session Management Webinar: http://tinyurl.com/3dmtf9r
http://dev.mysql.com/downloads/cluster/http://dev.mysql.com/downloads/cluster/http://labs.mysql.com/http://labs.mysql.com/http://dev.mysql.com/downloads/cluster/http://dev.mysql.com/downloads/cluster/


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NDB API E ample


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NDB API Example

NdbTransaction *tx = ndb->startTransaction();NdbOperation *op = tx->getNdbOperation(myTable);

op->readTuple(NdbOperation::LM_CommittedRead);

op->equal("code", code);

op->getValue("name", name);

tx->execute( NdbTransaction::Commit );

ndb->closeTransaction(tx);

cout


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MySQL Cluster Connector for Java

New Domain Object Model PersistenceAPI (ClusterJ) :

Java API

High performance, low latency

Feature rich

JPA interface built upon this new Java

layer: Java Persistence API compliant

Uses ClusterJ where possible, reverts toJDBC for some operations

Higher performance than JDBC

More natural for most Java designers

Easier Cluster adoption for webapplications

Data Nodes

Network

JPA P f


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JPA Performance

Out of the Box Scalability: Data Partitioning


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Out of the Box Scalability: Data Partitioning

Data partitioned across Data Nodes

Rows are divided into partitions, based on a hash of all/part of the primarykey (you choose)

Each Data Node holds primary fragment for 1 partition Also stores secondary fragment of another partition

Records larger than 8KB stored as BLOBs


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COMPANY OVERVIEW

Division of Docudesk

Deliver Document Management SaaS

CHALLENGES / OPPORTUNITIES

Provide a single repository for customers tomanage, archive, and distribute documents

Implement scalable, fault tolerant, real timedata management back-end

PHP session state cached for in-servicepersonalization

Store document meta-data, text (asBLOBs), ACL, job queues and billing data

Data volumes growing at 2% per day

SOLUTION

MySQL Cluster deployed on EC2

USER PERSPECTIVEMySQL Cluster exceeds our requirements for low

latency, high throughput performance withcontinuous availability, in a single solution thatminimizes complexity and overall cost.

--Casey Brown, Manager of Dev & DBA Services,Docudesk

RESULTS

Successfully deployed documentmanagement solution, eliminating papertrails from legal processes

Integrate caching and database into onelayer, reducing complexity & cost

Support workload with 50:50 read/writeratio

Low latency for real-time user experienceand document time-stamping

Continuous database availability

Scaling Web Databases Webinar 2

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