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PeerDB: A P2P-based System for Distributed Data Sharing. Wee Siong Ng, Beng Chin Ooi, Kian-Lee Tan, Aoying Zhou. Shawn Jeffery CS294-4 Peer-to-Peer Systems 11/05/03. Overview. A P2P “database” system Allows content-based search No global schema Utilizes mobile agents

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peerdb a p2p based system for distributed data sharing

PeerDB: A P2P-based System for Distributed Data Sharing

Wee Siong Ng, Beng Chin Ooi, Kian-Lee Tan, Aoying Zhou

Shawn Jeffery

CS294-4 Peer-to-Peer Systems

11/05/03

overview
Overview
  • A P2P “database” system
    • Allows content-based search
  • No global schema
  • Utilizes mobile agents
    • Provides flexibility and extensibility
  • Dynamically adjusts topology

Shawn Jeffery PeerDB

bestpeer
BestPeer
  • Generic P2P platform
  • Mobile Agents
    • Carry code and data
    • Collect stats
    • Security issues?
  • Dynamic Reconfiguration
    • How does this compare to Gia?
  • Location Independent Global Names Lookup (LIGLO) Servers
    • Small number
    • Provides a global identity for peers and peer status
    • Why not use a DHT/KBR/DOLR?

Shawn Jeffery PeerDB

bestpeer security
BestPeer Security
  • Private and sharable data
    • Agents only able to access sharable data
    • Does this adequately restrict the power of mobile agents?
  • Communications on the wire also encrypted
  • What’s missing?

Shawn Jeffery PeerDB

architecture
Architecture

Sharable Data

Local Data

Shawn Jeffery PeerDB

Database

schema mediation
Schema “Mediation”
  • Problems with supporting SQL queries:
    • No global schema information
    • Different nodes could name the same table/attribute differently (“len”, “length”)
  • Solution: User supplies metadata for each relation name and attribute
    • Users expected to do a lot
  • Formula based on matching relation keywords and attribute keywords to determine if a query matches a table
  • What about other schema mediation work (such as Piazza)?

Shawn Jeffery PeerDB

local query processing phase i
Local Query Processing – Phase I
  • “Master Agent” coordinates the entire affair
  • Check Local Dictionary for matching relations
    • Use the relation matching strategy even for the local DB
  • Create “Relation Matching Agents” and flood to all neighbors
  • Wait for responses
    • Display results to user as they arrive

Shawn Jeffery PeerDB

local query processing phase ii
Local Query Processing – Phase II
  • User selects the relations he/she wants
    • Create a “Data Retrieval Agent”
    • Rewrite query in terms of new relations
  • If local, submit SQL to local db
  • Contact remote nodes directly to access the data
    • Creates remote join plans locally - optimization?

Shawn Jeffery PeerDB

remote query processing
Remote Query Processing
  • Phase I: Find relations
    • Relation Matching Agents flood with TTL
    • Check Export Dictionary for a match
      • Return matches directly
  • Phase II: Get data
    • Data Retrieval Agent submits SQL to DBMS
    • Return data to the requesting node directly
    • Run further data processing before returning
      • Again, security issues

Shawn Jeffery PeerDB

statistics
Statistics
  • Master Agents monitor stats in the network
  • Keywords for some relations returned during Phase I
    • Update metadata
  • Number of objects returned for selected relations
    • Can be used for topology change decisions
  • Use most recently returned results as metric to determine who to connect with
    • Frequent updates – might need to change neighbors after each result returned

Shawn Jeffery PeerDB

caches
Caches
  • Cache all query results locally
  • Soft state
  • LRU replacement
  • Users choose which copy they want
    • Only provided with peer id and an indication of which is the source
    • What about timestamp, etc?
    • Again, user heavily involved

Shawn Jeffery PeerDB

relation matching performance
Relation Matching Performance
  • Significant tradeoff between precision and recall
  • Which is more important?
  • Is their approach acceptable?

Shawn Jeffery PeerDB

experimental methodology
Experimental Methodology
  • Compare P2P Model vs Client/Server model
    • CS returns via the search path (?)
  • Compare static vs reconfigurable networks
  • Compare agent vs message based approach
  • 32 Nodes
    • Is this enough?

Shawn Jeffery PeerDB

evaluation scenarios metrics
Evaluation Scenarios (Metrics?)
  • Fixed set of nodes
    • Easily test P2P protocols, Reconfiguration strategies
  • Latency
  • Quality and Quantity
  • What else is important?

Shawn Jeffery PeerDB

performance
Performance
  • As you increase the amount of storage on each node, latency decrease
    • Due to caching
  • In general, reconfiguration performs better
  • Response times O(1 Minute)
    • Is this acceptable?
  • Agent based shown to be better
    • What if agent produces more data than it processes?

Shawn Jeffery PeerDB

discussion a p2p dbms
Discussion: A P2P DBMS?
  • PeerDB represents a tiny step towards a P2P DB (also PIER, Piazza)
    • What does it do right?
    • What else is needed?
    • Is it ideal to have a P2P DB?
    • Is it feasible?

Shawn Jeffery PeerDB

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