ibm research © 2006 ibm corporation caching dynamic web content: designing and analyzing an aspect-...

IBM Research

© 2006 IBM Corporation

Caching Dynamic Web Content: Designing and Analyzing an Aspect-Oriented Solution

Sara Bouchenak – INRIA, France

Alan Cox – Rice University, HoustonSteven Dropsho – EPFL, LausanneSumit Mittal – IBM Research, IndiaWilly Zwaenepoel – EPFL, Lausanne


Dynamic Web Caching

2

Dynamic Web Content

Database server

Client Web server Application server

SQL req.

SQL res.

HTTP response

HTTP request

Internet

Web tier Business tier Database tier

Cache

Motivation for Caching Represents large portion of web requests

Stock quotes, bidding-buying status on auction site, best-sellers on bookstore

Generation places huge burden on application servers


Dynamic Web Caching

3

Caching Dynamic Web Content

Dynamic Content Not easy to Cache

– Ensure consistency, invalidate cached entries due to updates

• Write requests can modify entries used by read requests

– Caching logic inserted at different points in the application

• Entry and exit of requests, access to underlying database

• Correlation between requests and their database accesses

Most solutions rely on “manually” understanding complex application logic


Dynamic Web Caching

4

Our Contributions

Design a cache “AutoWebCache” that• Ensures consistency of cached documents

• Insertion of caching logic transparent to application– Make use of aspect-oriented programming

Analysis of the cache• Transparency of injecting caching logic• Improvement in response time for test-bed applications


Dynamic Web Caching

5

Dynamic Web Caching – Solution Approach

Database server


SQL req.

SQL res.

HTTP response

HTTP request

Internet

Caching

Logic

AutoWebCache

Request info

Database access

TransparencyCapture information flow

Consistency Correlation between read and write requests

Web Page

Cache

Cache inserts, invalidations

Cache Check


Dynamic Web Caching

6

Outline

Design of AutoWebCache

– Maintaining cache consistency• Determine relationship between reads and updates

– Cache Structure

Aspectizing Web Caching

– Insertion of caching logic transparently

Evaluation

– Analysis of effectiveness, transparency

Conclusion


Dynamic Web Caching

7

Maintaining Cache Consistency – Read Requests

Response to read-only requests cached

Read SQL queries recorded with cache entry

……

WebPage2URI2

WebPage1URI1

Cached web page

Index: URI (readHandlerName + readHandlerArgs)

Associated Read Queries

{ Read Query 11, Read Query 12, ….}



Dynamic Web Caching

8

Maintaining Cache Consistency – Write Requests

Result not cached

Write SQL queries recorded

Intersect write SQL queries with read queries of cached pages

Invalidate if non-zero intersection

WSWS RSRS

WSWS RSRS

No No InvalidationInvalidation

InvalidationInvalidation


Dynamic Web Caching

9

Invalidating Cache Entries

WebPage3

WebPage2URI2

WebPage1URI1

Cached web page


Associated Read Queries



URIn Write Query

{ Read Query 31, Read Query 32, ….}URI3

Remove


Dynamic Web Caching

10

Query Analysis Engine

Determines intersection between SQL queries

Three levels of granularity for intersection

– Column based

– Value based

– Extra query based

Balance precision with complexity


Dynamic Web Caching

11

a b c

5

1 10 9

8 7

SELECT T.a FROM T WHERE T.b = 8

UPDATE T SET T.c = 7 WHERE T.b = 10

UPDATE T SET T.a = 12 WHERE T.b = 10

Column Based Intersection

Ok

Invalidate

Invalidate if Column_Read = Column_Updated


Dynamic Web Caching

12

a b c

5

1 10 9

8 7



Value Based Intersection


OkInvalidate

Invalidate with column-

based

Invalidate if Rows_Read = Rows_Updated


Dynamic Web Caching

13

a b c

5

1 10 9

8 7


UPDATE T SET T.a = 3 WHERE T.c = 9

SELECT T.b FROM T WHERE T.c = 9

Extra Query Based Intersection

Invalidate with value-

based

Ok

Generate extra query to find missing values

??


Dynamic Web Caching

14

Outline



– Cache Structure



Evaluation

– Analysis of effectiveness, transparency

Conclusion


Dynamic Web Caching

15

Dynamic Web Caching – Solution Approach

Database server


SQL req.

SQL res.

HTTP response

HTTP request

Internet

Caching

Logic

AutoWebCache

Request info

Database access

TransparencyCapture information flow

Web Page

Cache

Cache inserts, invalidations

Cache Check


Dynamic Web Caching

16

Aspect-Oriented Programming (AOP)

Modularize cross-cutting concerns - Aspects

– Logging, billing, exception handling

Works on three principles

– Capture the execution points of interest – Pointcuts (1)

• Method calls, exception points, read/write accesses

– Determine what to do at these pointcuts – Advice (2)

• Encode cross-cutting logic (before/ after/ around)

– Bind Pointcuts and Advice together – Weaving (3)

• AspectJ compiler for Java


Dynamic Web Caching

17

Insertion of Caching Logic

Original web

application

Original web

application

Cache-enabled web application

version

Cache-enabled web application

version

Weaving Rules

Caching library

Caching library

Aspect Weaving (Aspect J)


Dynamic Web Caching

18

Aspectizing Read Requests

// Execute SQL queries…SQL query 1SQL query 2…

// Generate a web documentwebDoc = …

// Return the web document…

// Execute SQL queries…SQL query 1SQL query 2…

// Generate a web documentwebDoc = …

// Return the web document…

Original code of a read-only request handler String cachedDoc = Cache.get (uri,

inputInfo);if (cachedDoc != null) return cachedDoc; // Cache hit

Cache checkCapturing request entry

Cache.add(webDoc, uri, inputInfo, dependencyInfo); // Cache miss

Cache insertCapturing request exit

Capture main Collect SQL query info

Collecting dependency info

Capturing SQL queries


Dynamic Web Caching

19

Aspectizing Write Requests

// Execute SQL queries…SQL query 1SQL query 2……

// Return

// Execute SQL queries…SQL query 1SQL query 2……

// Return

Original code of a write request handler Collecting

invalidation info

Collect SQL query info

Capturing SQL queries

// Cache consistencyCache.remove(invalidationInfo);

Cache invalidation

Capturing request exit

Capture main


Dynamic Web Caching

20

Capturing Servlet’s main Method

// Pointcut for Servlets’ main method pointcut servletMainMethodExecution(...) : execution( void HttpServlet+.doGet( HttpServletRequest, HttpServletResponse)) || execution( void HttpServlet+.doPost( HttpServletRequest, HttpServletResponse));

Pointcut captures entry and exit points of web request handlers

Cache Checks and Inserts for Read Requests

Invalidations for Update Requests


Dynamic Web Caching

21

Weaving Rules for Cache Checks and Inserts

// Advice for read-only requests around(...) : servletMainMethodExecution (...) {

// Pre-processing: Cache check String cachedDoc; cachedDoc = ... call Cache.get of AutoWebCache if (cachedDoc != null) { ... return cachedDoc }

// Normal execution of the request proceed(...);

// Post-processing: Cache insert ... call Cache.add of AutoWebCache

}


Dynamic Web Caching

22

Weaving Rules for Cache Invalidations

// Advice for write requests after(...) : servletMainMethodExecution (...) {

// Cache invalidation ... call Cache.remove of AutoWebCache

}


Dynamic Web Caching

23

Weaving Rules for Collecting Consistency Information

// Pointcut for SQL query calls pointcut sqlQueryCall( ) : call(ResultSet PreparedStatement.executeQuery())

|| call(int PreparedStatement.executeUpdate());

// Advice for SQL query calls after( ) : sqlQueryCall ( ) { ... collect consistency info ...}

After each SQL query, note

Query template

Query instance values


Dynamic Web Caching

24

Transparency of AutoWebCache

Ability to Capture Information Flow

– Entry and exit points of request handlers

• e.g. doGet(), doPost() APIs for Java Servlets

– Modification to underlying data sets

• e.g. JDBC calls for SQL requests

– Multiple sources of dynamic behavior

• Currently handle dynamic behavior from SQL queries• Need standard interfaces for all sources


Dynamic Web Caching

25

Hidden State Problem…

…

Number number = getRandom ( );

Image img = getImage (number);

displayImage (img);

request execution

Request does not contain all information for response creation

Occurs when random nos., timers etc. used by application

Subsequent requests result in different responses

Duty of developer to declare such requests non-cacheable


Dynamic Web Caching

26

Use of Application Semantics

Aspect-orientedness relies on code syntax

– Cannot capture semantic concepts

In TPC-W application

– Best Seller requests allows dirty reads for 30 sec

– Conforms to specification clauses 3.1.4.1 and 6.3.3.1

Application semantics can be used to improve performance

– Best seller cache entry time-out set for 30 sec


Dynamic Web Caching

27

Outline



– Cache Structure



Evaluation

– Analysis of effectiveness

Conclusion


Dynamic Web Caching

28

Evaluation Environment

RUBiS

– Auction site based on eBay

– Browsing items, bidding, leaving comments etc.

– Large number of requests that can be satisfied quickly

TPC-W

– Models an on-line bookstore

– Listing new products, best-sellers, shopping cart etc.

– Small number of requests that are database intensive

Client Emulator

– Client browser emulator generates requests

– Average think time, session time conform to TPCW v1.8 specification

– Cache warmed for 15 min, statistics gathered over 30 min


Dynamic Web Caching

29

Response Time for RUBiS – Bidding Mix

No cache

AutoWebCache

0

20

40

60

80

100

120

140

0 200 400 600 800 1000

Number of Clients

Resp

on

se T

ime (

ms)


Dynamic Web Caching

30

Relative Benefits for different Requests in RUBiS

0

5

10

15

20

25

Searc

h Cat

Searc

h Rgn

View B

ids

View It

em

View U

ser

Per

cen

t o

f R

equ

ests

About

Me

Browse

Cat

Browse

Rgn

Buy N

ow

Put B

id

Put C

mt

Request Type

Hits Misses


Dynamic Web Caching

31

Response Time for TPC-W – Shopping Mix

1

10

100

1000

10000

50 100 150 200 250 300 350 400

Number of Clients

No cache AutoWebCache Optimization for Semantics

Resp

on

se T

ime (

ms)


Dynamic Web Caching

32

Relative Benefits for different Requests in TPC-W

0

5

10

15

20

25

adm

in re

ques

t

best

selle

rs

exec

ute

sear

ch

hom

e int

erac

tion

new p

rodu

cts

orde

r disp

lay

orde

r inq

uiry

prod

uct d

etail

sear

ch re

ques

t

Per

cen

t o

f R

equ

ests

Request Type

Hits MissesHits based on app. semantics


Dynamic Web Caching

33

Implementation of AutoWebCache

Web application Caching library AOP-based caching

Application# Java classes

Java code size

# Javaclasses

Java code size

# AspectJ files (weaving rules)

Size of AspectJ code

TPC-W 46 12K lines

13 4.6K lines 1 150 lines

RUBiS 25 5.8K lines


Dynamic Web Caching

34

Conclusion

AutoWebCache - a cache that• Ensures consistency of cached documents

– Query Analysis

• Insertion of caching logic transparent to application– Make use of aspect-oriented programming

Transparency of AutoWebCache• Well-defined, standard interfaces for information flow• Presence of hidden states• Use of application semantics

IBM Research


Questions / Comments / Suggestions !

IBM Research


Thank You!!


Dynamic Web Caching

37

SQL Query Structure

SELECT T.a FROM T WHERE T.b=10

UPDATE T SET T.c WHERE 20 < T.d < 35

Column(s) Updated

Column(s) Selected Table Concerned Predicate Condition


Dynamic Web Caching

38

Response Time for RUBiS – Bidding Mix

0

20

40

60

80

100

120

140

0 200 400 600 800 1000

Number of Clients

Res

po

nse

tim

e (m

s)

No cache

Hand-coded AC extra queryAC value basedAC column based


Dynamic Web Caching

39

Response Time for TPCW – Shopping Mix

1

10

100

1000

10000

0 50 100 150 200 250 300 350 400 450

Number of Clients

Res

po

nse

tim

e (m

s)

No cache

Hand-coded AC extra queryAC value basedAC column based


Dynamic Web Caching

40

Cache Structure in AutoWebCache

……

WebPage2URI2

WebPage1URI1

Cached web page


<instance values2a, URI7>ReadQueryTemplate2

<instance values3a, URI12>ReadQueryTemplate3

……

<instance values1a, URI1><instance values1b, URI41><instance values1c, URI57>

ReadQueryTemplate1

<value vector, URI> pairIndex: SQL String

Remove

If a Write Query invalidates ReadQueryTemplate1 with instances values1a


Dynamic Web Caching

41

Evaluation

Analysis of AutoWebCache

– Effect on performance of applications

– Relation of application semantics to cache efficiency

– Relative benefit of caching on different read-only requests

– Usefulness of AOP techniques in implementing the caching system


Dynamic Web Caching

42

Breakdown of Response Times for Requests in RUBiS

Extra time for a Miss (on top of overall response time)

Overall avg. response time

050

100150200250300350

About

Me

Browse

Cat

Browse

Rgn

Buy N

ow

Put B

id

Put C

mt

Searc

h Cat

Searc

h Rgn

View B

ids

View It

em

View U

ser

Request Type

Re

sp

on

se

Tim

e (

ms

)


Dynamic Web Caching

43

Breakdown of Response Times for Requests in TPC-W

0

50

100

150

200

250

300

350

adm

in re

ques

t

best

selle

rs

exec

ute

sear

ch

hom

e int

erac

tion

new p

rodu

cts

orde

r disp

lay

orde

r inq

uiry

prod

uct d

etail

sear

ch re

ques

t

Request Type

Res

po

nse

Tim

e (m

s)

Extra time for a Miss (on top of overall response time)

Overall avg. response time


Dynamic Web Caching

44

Key Aspect-Oriented Programming Concepts

“Join points” identify executable points in system

– Method calls, read and write accesses, invocations

“Pointcuts” allow capturing of various join points

“Advice” specifies actions to be performed at pointcuts

– Before or after the execution of a pointcut

– Encode the cross-cutting logic


Dynamic Web Caching

45

Conclusion

Dynamic Content Not easy to Cache

– Ensure consistency, invalidate cached entries as a result of updates

AutoWebCache – Query Analysis

– Caching logic inserted at different points in the application• Entry and exit of requests, access to underlying database

– Most solutions rely on understanding complex application logic

AutoWebCache – Transparent insertion of caching logic using AOP

Transparency affected by• Well-defined, standard interfaces for information flow• Presence of hidden states• Use of application semantics


Dynamic Web Caching

46

Web Caching versus Query Caching

The two are complimentary

Web caching useful when app server is bottleneck

Documents can be cached nearer to the client, distributed

Can make use of application semantics with web page caching (best seller for TPC-W)

ibm research © 2006 ibm corporation caching dynamic web content: designing and analyzing an aspect-...

Documents

requests caching logic

cache consistency

cache autowebcache

cached web page index

read queries of cached

sql queries

requests response

cache entry webpage2uri