oracle openworld

A Presentation to Oracle OpenWorld

Blistering Web Applications with Oracle TimesTen In Memory Option

2

Intro & Welcome

Bill Binko, Perot Systems Consulting (Tampa)Perot Consulting Performance Practice

–Tuning & Optimizing Enterprise Applications–Custom Development of High-Volume

ApplicationsContact: [email protected]

3

Overview

Very High Volume Web Applications– Delivery & Data Collection

Leverage TimesTen (IMDB) and Cache Connect Based on several engagements (past & present)

– Online Content Delivery, Smart Grid, Smart CDN Management Fundamental:

– Find high-volume functionality (usually not hard)– Re-implement using TimesTen IMDB & Simpler Architecture

Results:– Highly scalable (horizontally)– Deliveries (finding constrained Content): 1,000 req/sec/node– Data Collection (simple recording & aggregation) : 75,000 req/sec/node

4

Java EE Tutorial #1 – What’s wrong with this picture?

Web Container

JSP Servlet

JST

LJava S

erver Faces

StA

XJava P

ersistenceJT

AC

onnectorsJM

SM

anagement

WS

MetaD

ataW

eb Services

JAC

CJA

XR

JAX

-WS

JAX

-RP

C

SAAJ

Java Mail

JAF

J2SE

Database

5

Java EE Tutorial #2

Web Container

JSP Servlet

JST

L

Java Server F

aces

StA

X

Java Persistence

JTA

Connectors

JMS

Managem

ent

WS

MetaD

ata

Web S

ervices

JAC

C

JAX

R

JAX

-WS

JAX

-RP

C

SAAJ

Java Mail

JAF

J2SE

Database

EJB Container

EJB

StA

X

Java Persistence

JTA

Connectors

JMS

Managem

ent

WS

MetaD

ata

Web S

ervices

JAC

C

JAX

R

JAX

-WS

JAX

-RP

C

SAAJ

J2SE

Java Mail

JAF

6

A More Reasonable App Structure

Application Server

Presentation

Application Logic

App S

rv. Services

Business Logic

Data Access

7

Traditional Application Stack

Flavor somewhat irrelevant– Java EE, .NET, LAMP, even Rails

Most still have relational DB as foundation Many Framework Options (Even just for Java!)

– Presentation: Struts, JSF, JSP+Tag Libs– App Logic: Struts, Spring, Seam, etc.– Business Logic: Internal & External

External: EJB, SOA, ESB, etc.– Data Access: Java Persistence, ORMs, JDBC, DAO

Works well for “General” Applications– Many Benefits (Technical & Business)– Many Problems (Debugging, Performance, Don’t get me started)

Important: Every Layer Adds Overhead!

Application Server

Presentation

Application Logic

App S

rv. Services

Business Logic

Data Access

8

What is a “General” Application?

“Normal” Distribution of Effort/Results

Follows “Pareto” 80/20 Rules– Bugs, Traffic, Load, Time, etc.

Consistent “Type” of activities– CRUD Applications– Similar Actions

No major Outliers– Resource Consumption

“What Manager Expect”– (Not as common as we’d like)

“Trivial Many”

“Vital Few”

Effort Result

80%

20%

20%

80%

9

High-Volume Applications

Fundamental Split in Functionality– Configuration/Analysis (Low Volume)

– Delivery/Collection (High Volume)

No longer 80/20! More like 100/1 (or 1000/1!) Two Main Categories

– Content Delivery– Data Collection– Combination

Normal Architecture– L.V. Benefits– H.V. Fails (Spectacularly)

“Trivial Many”

“Vital Few”

Features(URLs, Methods, etc)

Resources(Time, Traffic, Hits)

>99%

>99% Delivery/

Measurement

ConfigurationAnalysis

10

Category 1: High-Volume Delivery Applications

Delivery vs. Web App vs. Hosting? – Many rapid requests from separate clients– Mostly Stateless/Sessionless– Dynamic Content: not just hosting files/images

Examples– Online Ads– Personalized Feeds (RSS/Atom/etc.)– APIs (esp. REST)

Pseudo-Delivery– Ajax (Auto-Complete, Status Check)– Smart CDN Management

11

High-Volume Delivery Application Solution

The Trick is to Split the App– Low Volume: Configuration– High Volume: Delivery

Session, Workflow, Human Interfaces are SLOWHandle Delivery SeparatelyTimesTen (Oracle IMDB) Helps Tremendously

12

Delivery Solution - Remember the RDBMS!

Relational Database – Oracle RAC

Application Server

Presentation

Application Logic

App S

rv. Services

Business Logic

Data Access

Table

Table

Table

Table

Table

Table

Table

Table Table

Table

ODBC/JDBC/OCI

13

Delivery Solution - One option: just split the app


Low Volume Server

Presentation

Application Logic

App S

rv. Services

Business Logic

Data Access

Table

Table

Table

Table

Table

Table

Table

Table Table

Table

ODBC/JDBC/OCI

Required For Delivery

High Volume Server(Delivery Node)

Request Management

Direct Data Access

ODBC/JDBC/OCI

14

Delivery Solution – Add TimesTen (IMDB Option)


Low Volume Server

Presentation

Application Logic

App S

rv. Services

Business Logic

Data Access

Table

Table

Table

Table

Table

Table

Table

Table Table

Table

ODBC/JDBC/OCI


High Volume Server

Request Management

Direct Data Access

TimesTen IMDB

Table

Table

Table

Table Table

Read Only Cache Groups

Cache Connect

Shared Memory O/JDBC

15

TimesTen Overview

JDB

C

Driv

erO

DB

C

Driv

er

Sha

red

Mem

ory

TimesTen – In-Memory RDBMS

Cache Group

Table

Table

Table

Table

Table

Table

Table

Table Table

Table

Oracle RAC

Cache Connect

Disk

Log

Shi

p

Exp

oses

16

High Volume Delivery Server Design

High Volume Delivery Node

TimesTen/IMDB

Ap

ache

Web

Se

rver

AJP

Java EE App Server

Custom Apache C ModuleS

erv

let

JDB

C

Tim

esT

en

Driv

er

Sh

ared

Me

mo

ry

Ca

che

Gro

ups

Ca

che

Co

nnec

t

RAC

Del

iver

y R

equi

red

Dat

a

HTTPAJP Direct

Direct

Low Volume Server

Pre

sen

tatio

n

Ap

plic

atio

n L

og

ic

App Srv. Services

Bu

sin

ess

Lo

gic

Da

ta A

cce

ssHTTP JDBC

17

Nothing Magic about WebLogic / Java

High Volume Delivery Node

TimesTen/IMDB

Apa

che

Web

Ser

ver

PH

P M

odul

e

PHP Environment

Custom Apache C ModuleP

age

Logi

c

MD

B2

OD

BC

Driv

er

Sha

red

Mem

ory

Cac

he G

roup

s

Cac

he C

onne

ct

RAC

De

live

ry R

eq

uire

d D

ata

HTTPAJP Direct

Direct

Low Volume Server

Pre

sent

atio

n

App

licat

ion

Log

ic

App Srv. Services

Bus

ines

s L

ogic

Dat

a A

cces

s

HTTP JDBC

18

Scales Well Horizontally

Edg

e S

erve

r(A

pach

e/F

5/IB

M)

HT

TP

HV Delivery Node

Oracle RAC

HV Delivery Node

Low Volume Server

Ora

cle/

OC

I

HV Delivery Node

HV Delivery Node

HV Delivery Node

TimesTen/IMDB

Apa

che

Web

Ser

ver

AJP

Java EE App Server

Custom Apache C Module

Ser

vlet

JDB

C

Tim

esT

enD

river

Sha

red

Mem

ory

Cac

he G

roup

s

Cac

he C

onne

ctAJP Direct

Direct

Low Volume Server

Pre

sen

tatio

n

Ap

plic

atio

n L

og

ic

App Srv. Services

Bu

sin

ess

Lo

gic

Da

ta A

cce

ss

19

Category 2: Data Collection And Analysis

Not about Intelligent Delivery About Rapid Data Collection Simple “Requests” are just data submission Attributes:

– High Volume– Low Latency– Many Concurrent Clients

Examples– Analytics (JavaScript/Ajax/Beacon Images)– Sensor Data– (Content) Delivery Tracking

20

High-Volume Data Collection Solution

The Trick is STILL to Split the App– Low Volume: Analysis– High Volume: Data Collection

Analysis uses normal BI/DW techniques– Don’t change this

Data Collection is critical Pre-Aggregate on the High-Volume Server

– Data is still data, and you CAN’T keep it all– (unless you’re Google/Amazon/etc.)

21

Data Collection & Analysis

Dimensional DW or Relational for ETL later

LV Analysis Server

Presentation

Application Logic

Ap

p Srv. S

ervices

Business Logic

Data Access

Table

Table

Table

FactTable

Table

Table

Table

Table Table

Table

ODBC/JDBC/OCI


HV Data Collection Server

Request Management

Direct Data Access

TimesTen IMDB

Table

Table

Table

Table Table

AWT/SWT Group

Shared Memory O/JDBC

AggregatedData

Table

Table

Local TT Tables

Aggregator

Aggregates

22

Data Collector HV Design

High Volume Data Collection Node

TimesTen/IMDB

Apa

che

Web

Ser

ver

Background Process


Sha

red

Mem

ory

Cac

he G

roup

s

Cac

he C

onne

ct

RAC

Agg

rega

ted

Dat

a

HTTPDirect

Direct

Very Little Logic

Primarily Image/Code Responses

Just Record & Respond

Background Aggregator Moves to SWT

Cac

he M

odul

es

23

Combining Delivery & Data Collection

One final option (our original client’s needs)Use Data Collection to track DeliveriesReuse the High Volume Server for both Delivery

& Data Collection“Round Trip” feedback Loop

24

Combination Approach – Both Delivery & Data Collection in one Node


TimesTen/IMDB

Ap

ach

e W

eb

Se

rve

rBackground Process


Sh

are

d M

emor

y

Ca

che

Gro

ups

Ca

che

Con

nect

RAC

HTTP

Direct

Direct

Java EEDelivery Servlet Direct

Low Volume Server

Pre

sen

tatio

n

Ap

plic

atio

n L

ogi

c

App Srv. Services

Bu

sin

ess

Lo

gic

Da

ta A

cce

ssHTTP JDBC

AJP

Mod

ule AJP

Exp

ose

s

25

Combination – Data Flows


TimesTen/IMDB

Apa

che

Web

Ser

ver

Background Process


Sha

red

Me

mor

y

Ca

che

Gro

ups

Ca

che

Con

nect

RAC

HTTP

Direct

Direct

Java EEDelivery Servlet Direct

Low Volume Server

Pre

sen

tatio

n

Ap

plic

atio

n L

og

ic

App Srv. Services

Bu

sin

ess

Lo

gic

Da

ta A

cce

ssHTTP JDBC

AJP

Mod

ule AJP

Exp

ose

s

26

Combination – Full Picture

Delivery/Data Collection Node

Background Processor

Operational RAC Instance

Delivery Servlet

Feedback Script

Data Collection Module

Delivery Required Data (R/O)

Aggregated Data (SWT)

Raw Data Coll.(Local TimesTen)

Operational DB

Aggregated Data ETL Scripts

DW RAC Instance

ETL Scripts

Staging Tables

DW Schema(Dimensional)

J2EEApache TimesTen OracleColors:

Read

Record

Use

27

Questions?

Questions about Architecture?

28

Other Issues: Connection Management

Internet

F5

LTM

340

0(L

ayer

7 S

witc

h)

`

Publisher Web Server

SSL – Direct Backbone Connection

WAP

Cable

DSL

Dial-Up

Gig

aBit

Eth

erne

t

HV Delivery Node

HV Delivery Node

HV Delivery Node

HV Delivery Node

HV Delivery Node

TimesTen/IMDB

Apa

che

Web

Ser

ver

AJP

Java EE App Server


Ser

vlet

JDB

C

Tim

esT

enD

river

Sha

red

Mem

ory

Cac

he G

roup

s

Cac

he C

onne

ctAJP Direct

Direct

29

Other Issues: Hardware

Low Volume – Standard HardwareRAC Instance – Standard HardwareHigh Volume – Not Standard

– CPU Intensive– Memory Hogs– Saturates Network I/O– Odd Disk Usage (Sequential Writes)

TimesTen Group great at helping find write hardware!

30

Questions?

Questions?

Contact:– Bill Binko– Perot Systems (Tampa)– [email protected]

A Presentation to Oracle OpenWorld

Blistering Web Applications with Oracle TimesTen In Memory Option

oracle openworld

Documents