@ Carnegie Mellon Databases 1 Invalidation Clues for Database Scalability Services Amit Manjhi* 1,...

1 @Carnegie MellonDatabases

Invalidation Clues for Database Invalidation Clues for Database Scalability ServicesScalability ServicesInvalidation Clues for Database Invalidation Clues for Database Scalability ServicesScalability Services

Amit Manjhi*1, Phillip B. Gibbonsz, Anastassia Ailamaki*, Charles Garrod*, Bruce M. Maggs*y, Todd C. Mowry*z, Christopher Olston©*, Anthony Tomasic*, Haifeng Yux

* Carnegie Mellon University 1 Buxfer, Inc.z Intel Research Pittsburgh y Akamai Technologies© Yahoo! Research x National University of Singapore


Typical Architecture of Dynamic Typical Architecture of Dynamic Web ApplicationsWeb Applications

Home server

Web Server

App Server

DB

Users Request

Response

Execute code

Access DB

Internet

Dynamic Web applications need to provision for variable and unpredictable load


Content Delivery NetworksContent Delivery Networks

Users

• Scales central web server• Works well for static content

CDN nodes

Internet


CDN Application ServicesCDN Application Services

Users

CDN nodes

Database server is still a bottleneck

Internet


Database Scalability Service Database Scalability Service (DBSS) Architecture(DBSS) Architecture

Users

User queries answered from DB cache

Internet

How to guarantee privacy of data?


Privacy concerns dictate that:Privacy concerns dictate that:

UsersInternet

Home server maintains master copy and handles updates directly

DBSS is provided encrypted data• Cache base tables: does not work• Cache query results – invalidate on

updates


A Simple ExampleA Simple Example

Empty

Home server database

Q:SELECT id FROM comments WHERE story=“Wintel” AND rating>0

DBSS nodeQ

Q:id=11,15

U

Empty

Q

Nothing is encrypted

Results are encrypted

No Invalidations

Q:

Q:

U

Invalidate

More encryption can lead to more invalidations

comments (id, rating, story)

Result

Result

U:UPDATE comments SET rating=2 WHERE id=15

Q: id=11,15

11 1 Wintel

15 1 Wintel

11 1 Wintel

15 1 Wintel

11 1 Wintel

15 2 Wintel

11 1 Wintel

15 2 Wintel


Privacy-Scalability Space for Query Privacy-Scalability Space for Query Result CachingResult Caching

Sca

labi

lity

Privacy

(Maximum privacy, read-only scalability)

No encryption

Encrypt everything

Encrypt data not useful for invalidation (Our prior work, SIGMOD 2006)

Want solutions in this space Want solutions in this space

No Prior

Full


Our Approach: Invalidation CluesOur Approach: Invalidation Clues

Home serverDBSS

Database

Query

Update

Emptyquery clue

ResultQuery

query clue

ResultQueryResultQuery

Updateupdate clue

Invalidations (query clue, update clue)

Invalidation clues offer a more general, flexible framework

• Limit unnecessary invalidation• Limit revealed information

Limit home server overhead


UPDATE comments SET rating=? WHERE id=?

Example Bulletin-Board Example Bulletin-Board ApplicationApplication

Invalidation clues enable more precise invalidations than the “No” encryption scenario

1. Extra invalidation in no encryption scenario: results with rating_param<2 and no id=5 in result

2.Example clue: :

• story of comment being updated (update clue)

2 5

SELECT id FROM comments WHERE story=? AND rating>?


Privacy-Scalability Space for Query Privacy-Scalability Space for Query Result CachingResult Caching

Sca

labi

lity

Privacy

(Maximum privacy, read-only scalability)

No encryption

Encrypt everything

Encrypt data not useful for invalidation (Our prior work, SIGMOD 2006)

Want solutions in this space Want solutions in this space

No Prior

Full

Database

(Code-analysis privacy, maximum scalability)

clues offer fine-grained tradeoff


OutlineOutline

Introduction to invalidation clues framework Improving scalability in the clues framework Improving privacy in the clues framework Evaluation results Related work and summary


Improving Scalability in the Improving Scalability in the Clues FrameworkClues Framework

Fewer invalidations More scalability

What is the “most precise” invalidation that can be done?

As a first cut,

Database Inspection Strategy: Invalidate as if

using the database

Extra data (database clues) can either be attached to query results (query result clue) or updates (update clue)


Database Clues and BeyondDatabase Clues and Beyond



Query Clue: Story of ALL comments

Auxiliary viewid story

Update Clue: Story of the comment being updated

On-the-fly1. Consistency2. Privacy

Still better: Opportunistic Strategy – use database clues only when benefit exceeds overhead


OutlineOutline



Attack Model of the DBSSAttack Model of the DBSS

UsersInternet

2. DBSS can pose as a user – chosen-plaintext attack

1. DBSS learns from query clues, update clues, and invalidations – ciphertext-only attack


Results on Improving PrivacyResults on Improving Privacy

Invalidation decision involves equality on id and story; order comparison on rating

Needless invalidations can improve privacy



Key idea

Paper has details on improving privacy for equality and order comparisons

Extreme: If all query results are always invalidated, DBSS can’t distinguish between any two query results


OutlineOutline



Benchmark ApplicationsBenchmark Applications

Auction (RUBiS, from Rice)

Bulletin board (RUBBoS, from Rice)

Bookstore (TPC-W, from UW-Madison)


Evaluation MethodologyEvaluation Methodology

Home serverCDN and DBSSUsers

5 ms 100 ms

Scalability: max # concurrent users with acceptable response times


0

300

600

900

Auction Bboard Bookstore

No clues Clues (no DB clues)

Clues (incl. DB clues) Opportunistic

Sca

labi

lity

(num

ber

of

conc

urre

nt u

sers

sup

port

ed)

Benchmark Applications

0

1. Clues help2. Opportunistic has the best scalability


Related WorkRelated Work

Outsource database: [Hacigumus+ 2002], [Hacigumus+ 2002], [Agrawal+ 2004]

Outsource database scalability: DBCache [Luo+ 2002, Altinel+ 2003], DBProxy [Amiri+ 2003], NEC cache portal [Li+ 2003], MTCache [Larson+ 2004], [Manjhi+ 2006]


Related WorkRelated Work

View invalidation strategies: [Levy and Sagiv 1993], [Candan+ 2002], [Choi and Luo 2004]

Privacy: [Agrawal+ 2004], [Hore+ 2004], [Manjhi+ 2006]


SummarySummary

Invalidation clues: general framework for limiting Unnecessary invalidation Revealed information Home server overhead

Fine-grained tradeoff between privacy and scalability Database clues

Update clues better than query clues Opportunistic use of database clues best scalability

Evaluation on three application benchmarks


Back-up slides….Back-up slides….

@ Carnegie Mellon Databases 1 Invalidation Clues for Database Scalability Services Amit Manjhi* 1,...

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