1. P2P Databases

2. Overview 0. Data objects, pointers (URLs), and attributes 1. Freeform versus structured attribute data 2. Centralized indices for attribute data and pointers (ex: Napster) 3. Query by flooding (ex: Gnutella) 4. DHTs (ex: Chord) 5. Problems with DHTs 6. Keyword queries in DHTs (Magnolia) 7. Popularity queries 8. Demo of system 9. (if time) Data transmission - Overlay vs DHT Multicast - Bittorrent / Splitstream 10. (if time) P2P file systems and versioning (precursor to undo/redo logging from later in the course)

3. P2P Today napster gnutella morpheus kazaa bearshare seti@home folding@home ebay limewire icq fiorana mojo nation jxta united devices open cola uddi process tree can chord ocean store farsite pastry tapestry ? grove netmeeting freenet popular power aim jabber bittorrent edonkey

4. Object representation and storage Attributes : Name, Artist, Album, Genre Objects Pointer to object

5. P2P vs. Distributed DBMS Transactions Distributed Query Optimization Interoperation of heterogeneous data sources Reliability/failure of nodes Complex features do not scale Traditional DDBMS Issues:

6. P2P vs. Distributed DBMS Example application: file-sharing Simple data model and query language –No complex query optimization –Easy interoperation No guarantee on quality of results –Individual site availability unimportant Local updates –No transactions –Network partitions OK Simple Amenable to large-scale network of PCs

7. Example: file sharing Challenge #1: Performance –Asking everyone is expensive! –If I am smart, I only need to ask one peer –How can I be smart? ? ? ? ? File X?

8. Search in P2P System can control: –Connections made by users/topology –Data placement –Query type Tight control: “Structured” –Efficient, comprehensive Loose control: “Unstructured” –Inefficient, not comprehensive, simple, expressive –Used in real life Both are useful to study

9. Centralized Napster model Benefits: –Efficient search –Limited bandwidth usage –No per-node state Drawbacks: –Central point of failure –Limited scale BobAlice JaneJudy

10. http://www.snocap.com/

11. Unstructured – Query Flooding = forward query = processed query = query source = found result = forward response

12. Problems with unstructured Inefficient –Query messages are flooded –Even if routing is intelligent, worst case load is still O(n), where n is # nodes in system Not comprehensive –If I do not get a result for my query, is it because none exists? (Of course, many optimizations are possible…) Structured systems address these problems

13. Distributed Hash Table (DHTs) Model: –Key/Object pair, the key is hashed to get an ID –Example: Objects are files The key is the content of the file The ID is the hash of the file contents Single operation: Lookup(ID) –Input: integer ID –Output: the object with the corresponding ID

14. Identifiers IDs are m-bit integers Nodes are also assigned IDs –Commonly assigned by hashing a node’s IP address, although many problems with this An object is stored on the node with the smallest ID greater than the object’s ID –This node is called the successor of the object’s ID –IDs are arranged on a circle, so 0 > 2 m -1

15. Data Placement 0 1 2 3 4 5 6 7 m = 3 Nodes: 0 1 3 Data: 1 2 6 1 2 2 6 6

16. Connections 0 1 2 3 4 5 6 7 “Finger pointers” Distance 2 0 2 1 …. 2 m-1

17. Query Lookup(objectID) –objectID is typically the ID of the object you are looking for, but not necessarily Approach: –Find the predecessor of the object I.e. the node with the largest ID that is smaller than the object ID –Return the successor of the predecessor

18. Query Example Say node 0 wants to find the object with ID = 7 For simplicity, we will assume a node exists at every ID in the space

19. Query Example 0 1 3 4 5 6 7 2 Node 0: Lookup(7) Node 0: FindPred (7)

20. Query Example 0 1 3 4 5 6 7 2 Node 4: FindPred(7)

21. Query Example 0 1 3 4 5 6 7 2 Node 6: FindPred(7) Node 6 is predecessor Return successor node 7

22. Query characteristics With high probability, a query can be answered by contacting O(log N) nodes –N total nodes in the network  Efficient! Also notice: if an object with the ID exists in the network, it will be found  Comprehensive! State is also O(log N) in size

23. Query characteristics Note that finger pointers are not required for correct operation –Only successor pointers are needed –But then cost of query increases O(N) in worst case

24. Advantages of Structured? Scalability/Efficiency –load grows with O(log N) Comprehensiveness

25. Disadvantages? (cont) Availability of Data –If a node dies suddenly, what happens to the data it was storing? –MUST replicate data across multiple nodes Query Language –How can we express keyword queries efficiently? –Many useful applications require different languages

26. Magnolia Current approach: Hash each keyword separately and store pointers at h(keyword) Seven Innovation Myths h(some) h(innovation) h(myths) 1100100101 “Seven Innovation Myths” 1100100101 h(title) “Innovation”

27. Resulting Distribution

28. Prefix hashing …………. m’ m bits Innovation h P (innovation)h P = m’ bit hash function Partitions network into ~ n/2 m’ separate sibling groups n = nodes, m’  partitioning factor For m’=12, n= 1 million, ~ 256 nodes will share same prefix Assumption: h is uniformly distributed 100 Prefix Hashing

29. 100 Innovation Balanced over the sibling group Sibling group ID=100 Balancing All siblings in a group share the same prefix

30. Random Sibling Insert Keyword h P  SiblingGroup ID Locate a sibling node via SIFT Lookup Keyword O(1) Group Broadcast or Multicast Replies

31. Advantages Good Balancing Properties

32. Advantages Low Traffic Load on nodes for popular queries Quick Lookup Popularity Ranking of Objects Distributed Replication for resilience

33. Implementing Magnolia Developed on top of a chord clone written in Python –If you’re going to write a peer-to-peer app, why not leverage existing modules and libraries? Challenge: How do we implement group- based stores and queries without requiring additional network maintenance?

34. Chord’s Finger Table A chord node maintains a finger table of M IP’s pointing to nodes ahead of it in the ring. –A pointer at index i is the successor of node id + (2^i-1). This lets us reach any node in the network in O(log M) hops We use the M’ most significant bits in a node’s id to indicate it’s group. We want to reach any group in O(log M’) hops. –Do we need another table? –Nope. The last M’ entries in our finger table provide this.

35. Talking to Siblings How do we propagate queries through the group? Naïve solution: send to our predecessor and successor. A better solution: We can send a query throughout the group by treating the sibling group as a tree.

36. Sibling Tree 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 1 8 2 3 15 4 5 6 7 9 1011 12 13 14 0+1 1+1 2+1 0+2^3 8+2^21+2^2 2+2^1 5+2^19+2^112+2^1 14+2^0 5+19+1 8+1 12+1 N/N’ = 16; M/M’ = 4 Every edge can be found in the finger table!

37. Sibling Tree Problems Problems: –Not every possible node will exist –Not every node will have results to report –The query maker needs to know when the search is done But we’re okay! –Nodes can determine if a child sub-tree is dead –Even if a child node in our sibling table is of a higher ID than expected its sub-tree contains all existing descendents of the expected id we can predict when a child is in a sibling our ancestor’s tree

38. Bigger Problems What if a pointer in our finger table fails? –We either have to find the successor to it’s id or fail to query the sub-tree What if the lowest ID node isn’t the root of our tree? –Some of our edges won’t be in our finger table

39. Popularity queries

40. Yulania, Demo

41. BitTorrent

42. SplitStream