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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 p2p@db.cs.berkeley.edu 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

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  1. Complex Queries in DHT-based Peer-to-Peer Networks Matthew Harren, Joe Hellerstein, Ryan Huebsch, Boon Thau Loo, Scott Shenker, Ion Stoica p2p@db.cs.berkeley.edu UC Berkeley, CS Division IPTPS 3/8/02

  2. Outline • Contrast P2P & DB systems • Motivation • Architecture • DHT Requirements • Query Processor • Current Status • Future Research

  3. DHT Query Processor CAN Chord Predicates SQL Tapestry Group By Joins Pastry Aggregation Relational Data Uniting DHTs andQuery Processing…

  4. P2P & DB Systems P2P DB

  5. 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

  6. P2P Query Processing(Simple) Example SELECT song, size, server… FROM album, song WHERE album.ID = song.albumID AND album.name = “Rubber Soul” • Filesharing+ • Keyword searching is ONE canned SQL query • Imagine what else you could do!

  7. 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> GROUP BY AN.ID HAVING COUNT(AN.ngram) >= <# of ngrams in search> • Filesharing+ • Keyword searching is ONE canned SQL query • Imagine what else you could do! • Fuzzy Searching, Resource Discovery, Enhanced DNS

  8. 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

  9. 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

  10. DHT – API • Basic API • publish(RID, object) • lookup(RID) • multicast(object) • NOTE: Applications can only fetch-by-name… a very limited query language!

  11. 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

  12. 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

  13. 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 Select Project Joins Group By / Aggregate Data can be metadata (for a file sharing type application) or entire records, mechanisms are the same Query Processor(QP) Architecture

  14. 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) Secondary PKey Key Index NS Data Ptr DHT DHT Primary PKey Data Primary Index Secondary Index

  15. 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)

  16. 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

  17. 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

  18. 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?

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