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Complex Queries in DHT-based Peer-to-Peer Networks. Matthew Harren, Joe Hellerstein, Ryan Huebsch, Boon Thau Loo, Scott Shenker, Ion Stoica UC Berkeley, CS Division. IPTPS 3/8/02. Outline. Contrast P2P & DB systems Motivation Architecture DHT Requirements

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complex queries in dht based peer to peer networks

Complex Queries in DHT-based Peer-to-Peer Networks

Matthew Harren, Joe Hellerstein,

Ryan Huebsch, Boon Thau Loo,

Scott Shenker, Ion Stoica

UC Berkeley, CS Division

IPTPS 3/8/02

  • Contrast P2P & DB systems
  • Motivation
  • Architecture
    • DHT Requirements
    • Query Processor
  • Current Status
  • Future Research
uniting dhts and query processing









Group By




Relational Data

Uniting DHTs andQuery Processing…
p2p db
P2P + DB = ?
  • P2P Database? No!
    • ACID transactional guarantees do not scale, nor does the everyday user want ACID semantics
    • Much too heavyweight of a solution for the everyday user
  • Query Processing on P2P!
    • Both P2P and DBs do data location and movement
    • Can be naturally unified (lessons in both directions)
    • P2P brings scalability & flexibilityDB brings relational model & query facilities
p2p query processing simple example
P2P Query Processing(Simple) Example

SELECT song, size, server…

FROM album, song

WHERE album.ID = song.albumID AND = “Rubber Soul”

  • Filesharing+
  • Keyword searching is ONE canned SQL query
  • Imagine what else you could do!
p2p query processing simple example1
P2P Query Processing(Simple) Example

SELECT song, size, server…

FROM album-ngrams AN, song

WHERE AN.ID = song.albumID AND AN.ngram IN <list of search ngrams>



<# of ngrams in search>

  • Filesharing+
  • Keyword searching is ONE canned SQL query
  • Imagine what else you could do!
    • Fuzzy Searching, Resource Discovery, Enhanced DNS
what this project is and is not about
What this projectIS and IS NOT about…
  • IS NOT ABOUT: Absolute Performance
    • In most situations a centralized solution could be faster…
  • IS ABOUT: Decentralized Features
    • No administrator, anonymity, shared resources, tolerates failures, resistant to censorship…
  • IS NOTABOUT: Replacing RDBMS
    • Centralized solutions still have their place for many applications (commercial records, etc.)
  • IS ABOUT: Research synergies
    • Unifying/morphing design principles and techniques from DB and NW communities
general architecture
Based on Distributed Hash Tables (DHT) to get many good networking properties

A query processor is built on top

Note: the data is stored separately from the query engine, not a standard DB practice!

General Architecture
dht api
  • Basic API
    • publish(RID, object)
    • lookup(RID)
    • multicast(object)
  • NOTE: Applications can only fetch-by-name… a very limited query language!
dht api enhancements i
DHT – API Enhancements I
  • Basic API
    • publish(namespace, RID, object)
    • lookup(namespace, RID)
    • multicast(namespace, object)
  • Namespaces: subsets of the ID space for logical and physical data partitioning
dht api enhancements ii
DHT – API Enhancements II
  • Additions
    • lscan(namespace) – retrieve the data stored locally from a particular namespace
    • newData(namespace) – receive a callback when new data is inserted into the local store for the namespace
  • This violates the abstraction of location independence
  • Why necessary? Parallel scanning of base relation
  • Why acceptable? Access is limited to reading, applications can not control the location of data
query processor qp architecture
QP is just another application as far as the DHT is concerned… DHT objects = QP tuples

User applications can use QP to query data using a subset of SQL




Group By / Aggregate

Data can be metadata (for a file sharing type application) or entire records, mechanisms are the same

Query Processor(QP) Architecture
indexes the lifeblood of a database engine
Indexes. The lifeblood of a database engine.
  • DHT’s mapping of RID/Object is equivalent to an index
  • Additional indexes are created by adding another key/value pair with the key being the value of the indexed field(s) and value being a ‘pointer’ to the object (the RID or primary key)




Index NS








Primary Index

Secondary Index

relational algorithms
Relational Algorithms
  • Selection/Projection
  • Join Algorithms
    • Symmetric Hash
      • Use lscan on tables R & S. Republish tuples in a temporary namespace using the join attributes as the RID. Nodes in the temporary namespace perform mini-joins locally as tuples arrive and forwards results to requestor.
    • Fetch Matches
      • If there is an index on the join attribute(s) for one table (say R), use lscan for other table (say S) and then issue a lookup probing for matches in R.
    • Semi-Join like algorithms
    • Bloom-Join like algorithms
  • Group-By (Aggregation)
interesting note
Interesting note…
  • The state of the join is stored in the DHT store
    • Rehashed data is automatically re-routed to the proper node if the coordinate space adjusted
    • When a node splits (to accept a new node into the network) the data is also split, this includes previously delivered rehashed tuples
  • Allows for graceful re-organization of the network not to interfere with ongoing operations
where we are
Where we are…
  • A working real implementation of our Query Processing (currently named PIER) on top of a CAN simulator
  • Initial work studying and analyzing algorithms… nothing really ground-breaking… YET!
  • Analyzing the design space and which problems seem most interesting to pursue
where to go from here
Where to go from here?
  • Common Issues:
    • Caching – Both at DHT and QP levels
    • Using Replication – for speed and fault tolerance (both in data and computation)
    • Security
  • Database Issues:
    • Pre-computation of (intermediate) results
    • Continuous queries/alerters
    • Query optimization (Is this like network routing?)
    • More algorithms, Dist-DBMS have more tricks
    • Performance Metrics for P2P QP Systems
  • What are the new apps the system enables?