gentle into to datagrid technology and customer use cases

XTP using WebSphere eXtreme Scale

@billynewport

IBM Distinguished Engineer

© IBM 2010, @billynewport

Agenda

• Gentle introduction to DataGrids

• General usage Patterns

• What they are good for and bad for

• Customer use cases

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Traditional Cache Operation

App

App

App

App

EIS

A

A

A

A

App

Inva

lida

tion

cha

tter

Inva

lida

tion

cha

tter

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WXS based Cache Operation

App

App

App

App

EIS

A

B

D

C

C’

D’

A

A

AppA

B’

A’Cache is

4x larger!

Cache is

5x larger!

Cache cluster can be co-located with the application or run

in it’s own tier.

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© IBM 2010, @billynewport 5

First, what’s a cache?● A database cache? A page fragment cache? A service Cache?

TOO SPECIFIC! • A cache is a tool for reducing application path length

• OR the distance data has to travel before it gets to the customer/ data sink

DBORMapLogicData

Service

Web Channel

Mobile Channel

Akamai


Customer DiversityData Grids are not just for investment banks!

• Retailers

• Insurance Companies

• Health care

• Retail Banking

• Hotel

• Travel Agencies

• Investment Banking

• Telco

• Government

• Utilities

• Trucking Companies

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Market Drivers

1. “The Performance of Web Applications: Customers Are Won or Lost in One Second,” Bojan Simic, Aberdeen Group, November 2008.


Market Drivers

• The competition is only a click away in today's web-facing world.

• Response times are critical to giving customers a good experience and generating revenue.

• Customer sessions are becoming more critical.

• The cost of attracting new customers to your web site for enrollment is significant.

• Losing the data that they have entered will likely create a negative impression and result much higher abandonment rates


Market Drivers

• Customers are looking to control the growth of their enterprise systems.– A caching tier in front of it can allow more growth

without expanding the existing enterprise systems.

• Customers are looking at building front ends more independent from the back ends.

• Public and private cloud like systems will need elastic state management solutions such as IBM WebSphere eXtreme Scale.

• More resilient front ends decoupled from back ends.


IBM WebSphere eXtreme Scale• Proven mature product:

– Third major release of product with V7.0– Public References– Private References– Used at some of the largest web

sites/companies in the world

• Lightweight runtime footprint (15MB jar)

• Integrates with all versions of WebSphere and almost any Java-based application container or Java Virtual Machine

• Proven multi-data center capabilities

• Proven low-latency access to data


Common set of patterns

• Side Cache– Hibernate/ OpenJPA L2 cache

– Dynacache (ND 6.1/ ND 7.0)• Page fragment cache• Web services cache• Command cache

– SOA State store

– SOA Result cache

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AppAppAppApp


Common set of patterns• Inline backend cache

– Loaders used to integrate with an existing data service

– Read through cache– Write through cache

• System of Record Data Store– Cache is used as the system

of record– Write behind technology

pushes changes asynchronously to the backend.

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AppAppAppApp

Loader


Common set of patterns• HTTP Session Store

– Application uses standard HTTPSession APIs– Can be shared between applications sharing same

domain name– Grid runs inside web cluster JVMs using affinity

• Application Session– Application uses WXS APIs to manipulate the shared

session– Works even when web apps use different domain

names

• Multi data center support for sessions

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Common set of patterns

• XTP (eXtreme Transaction Processing)– Lowest possible latency– Application code runs in grid itself

• Think Grid + Stored Procedures

– Write behind• Databases relegated to durable log/search and

reports

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CRUD Evolves with KV or NoSQL stores

• Traditionally, we think of the big four operations:– (C)reate– (R)ead– (U)pdate– (D)elete

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• This changes now to become:– (P)ut– (R)etrieve by key– (D)elete– ** (Q)uery (this is different than search)


Schema becomes more flexible

• Traditionally, the data store understands the data schema and enforces constraints.

• KV or NoSQL separates the schema management from the storage.– Schema is more like a JSON value– Developers more in control– More flexible to evolve than SQL oriented approach– Remember, it’s about retrieval, not query/search!

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SQL Benefits

• Relationships through joins

• Easy indexing

• No consistency issues, one copy/system of record

• No need to partition data model.


SQL means domain centric

• Think about the data, find the nouns

• Nouns become tables

• identify attributes/keys

• normalize the tables to Nth normal form…


Domain centric

• Use SQL to ask any question

• Use indexes to speed up SQL queries.

• Think Data Model first, worry about questions/access patterns later.


Question Centric

• NoSQL seems to start with the questions rather than the data.

• Once we know the questions then we can layout the data using some partitioned model.

• We can now scale it out and all is good

What could you do if scale wasn’t an issue?


Question Centric

Ask a different question maybe?


Table scans aka Map/Reduce…

• Multi-machine table scans won’t work for anything online.

– Google doesn’t map/reduce for every google search!

• Offline complex queries can be done using Map/Reduce

• You need to write code for most complex searches!


SOME USE CASES

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Client Usage: One of the largest retail US banks

Retail Banking & Investments

35x reducedresponsetimes

22 Milliononline banking users

Next-generation Online Banking – Before: 700ms to login with 2 backend calls – After: 20ms to login with profile cache access– $6M/year cost savings in MIPs reduction – 700k logins per hour across 3 data centers– 8Gb of data transfer per hour between DC’s– 60 million page views/day– 10 million logins per day– 3 active data centers

$500kreducedcosts per month

20x reduction in “lost sessions”


Session Persistence Results

Sample chart that shows response gains with Session & without Session persistence changes.


Market Drivers

1. “The Performance of Web Applications: Customers Are Won or Lost in One Second,” Bojan Simic, Aberdeen Group, November 2008.

2 seconds


Session Persistence Results

•Data represents the number of Session Recovery events logged each day•Key dates:

• 5/30, Session Recovery turned on & available for 60% of customers• Rollouts on 6/13 (to 80%) and 6/20 (to 100%)• Very high volume day on 6/15


Client Usage: Telco Mobile Application

Load profile for cell phone

3ms Response time

160 million customers46k tps

Mobile phone profile/feature checking– Before: DBMS uses to read profile. Mobile

applications and web sites generate too much load.

– After: 3.1ms response time. 46k profile look ups per second.

– Data distributed amongst several data centers using the grid.

Linear Scaling for more throughput


Client Usage: eCommerce Retailer

Catalog page cache

1k page views/sec

Faster startup

JVM restarts are nondisruptive

Cache consistency

One of the largest ecommerce sites– Before: Existing WebSphere Commerce Server

customer. – Per JVM catalog cache lead to lots of memory

wastage and cache warming problems on cluster start as well as JVM restarts

– After: – Moving to a shared cache using WXS, speeds

cluster start time by 60%– customer response times see no impact from JVM

restarts. – Memory is dramatically reduced. – Catalog consistency is better (time based

promotions)– Less CPU used


Entertainment 10x reducedresponsetimes

7 Billion requests per day

Fantasy Sports Web Infrastructure– Before: 60ms response time against database– After: WXS improved to 6ms response time – 450k concurrent users– 80k requests per second up to 1M in 2011– 6 weeks from concept to production

Client Usage: Social Media Website

OracleORMWXSApp ORMWXS

ORMWXS


Client Usage: Investment Bank

Investment Banking

4x increase in revenue

12 Million orders per day

Next-generation Order Management System – Before: Oracle RAC based architecture unable to

scale to necessary demands – After: 300K transactions / day 12M / day– Revenue up 4X and growing … “all because of

WebSphere eXtreme Scale”– Response time drops to 2.5ms– Moving to “22 x 7” operations (more than 9AM -

4PM)

40x number of transactions supported


Client Usage: Insurance Company (SOA Result Cache)

Insurance Policies

reducedresponsetimes

125,000 policies concurrently processed

Reduce Database Processing – Before: Changes to policies required time consuming

and costly access to multiple databases (e.g. drivers, accident history, previous claims, etc.)

– After: Construct policy once, from multiple data sources, for fast access (in-memory) and fault tolerance (using replication)

– Policies can be processed for up to 1 hour +– 100 requests / second – 1 MB of XML per policy, ¼ terabyte stored in cache

reducedcosts per month

better availability


Client Usage: Travel Web Site

Holiday Inventory


2,500,000Package combinations

Improve response times– Before: Database cached inventory from SAP

system– After: Cache allows faster searches and reductions

in load on SAP system

reducedcosts per month (SAP)

better availability


Client Usage: Investment Bank Global Risk Management


250,000 40msCounterparties SLA

Handle peaks– Before: Database held risk information information,

batch daily exposure calculation– After: Data held in memory, Monte Carlo exposure

calculations, multi-master global replication.– Multiple Global Data centers (US, Asia, Europe),

executing trades concurrently against counterparty risk limits.

– Local grids in each data center allow fast responses versus centralized repository

– Multi-master asynchronous replication between data centers provides consistency.

Handles peaks better

Authorize trades

Higher profits



Stock price service


200,000Prices updates/sec

Handle dramatic volume increases

– Provides latest and 20 minute old prices for each stock

– Before: Z/390 application using VSAM kept latest and 20 minute old price for each stock

– After: WXS Grid maintains the same data.– 200k stocks– 200k price update/sec– 20k price lookups/day


better availability



Algorithmic Trading


144 cores2m TPS @ 800uS

Handle dramatic volume increases

– Grid is system of record for algo trading application which runs embedded

– Before: Home grown infrastructure– After: 144 cores hosting application. 800uS response

time 90% of time. 4 WXS transactions for each response. Approx 2m tps at peak possible.

800uS24/7


Client Usage: Insurance company – SOA Result Cache

Mainframe service calls


25% reduction

Reduce mainframe spend

– Before: All service calls went to mainframe– After: 25% of calls are serviced using results from

prior invocation within 24 hours.

– 25% reduction in backend transaction costs using a cache to intercept calls on an ESB.


CICSORMWXSApp ORMWXS WXS

FilterESB


Client Usage: Trucking company

HTTP Sessions in DB/390


HTTP Sessions were stored in DB/390

– Before: All session persistence used DB/390– After: All session persistence provided by

WebSphere eXtreme Scale.

– Significant reduction in application response time– Reduction in MIPS cost on 390.



Client Usage: Telco LDAP caching

Cache LDAP Directory


Credential or data is stored in an LDAP directory

– Before: Slow access to data in LDAP (84ms), Complex striped LDAP server arrangement.

– After: Sub millisecond access to same data stored in WXS.

– Significant reduction in application response time– Smaller LDAP infrastructure.



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So how do I get started?

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Two New Service Offerings1. IBM Project Discovery Workshop for WebSphere eXtreme Scale2. IBM QuickStart for WebSphere eXtreme Scale ibm.com/developerworks/websphere/services/contacts.html

WebSphere Education ibm.com/software/websphere/education/

YouTube IBM eXtreme Scale channel youtube.com/ibmextremescale

DeveloperWorks Blog for XTP Community ibm.com/developerworks/mydeveloperworks/blogs/xtp/?lang=en_us

Follow us on Twitter twitter.com/WebSphereXTP

WebSphere eXtreme Scale V7 Solution Architecture Redbook redbooks.ibm.com/redpieces/abstracts/redp4602.html

Download Replay and Presentation ibm.com/developerworks/websphere/services/buzztalk.html

Contact your IBM Representative

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Getting Started


Learn More About Dynamic Application Infrastructure!

Application Foundationibm.com/appfoundation

Intelligent Managementibm.com/intellmgmt

Extreme Transaction Processingibm.com/xtp

ibm.com/appinfrastructure


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