Join queries

Due to the specific method of DHTs for data distribution, i.e. using hash functions for choosing the location where the data should be stored, the execution of join queries over these systems is quite challenging. In [HHLT+_{03], the authors addressed the problem of join query processing in}

PIER. A key is constructed from the “namespace” and the “resource ID”. There is a namespace for each relation and the resource ID is the primary key for base tuples in that relation. Queries are multicast to all peers in the two namespaces (relations) to be joined. Two algorithms are used for processing equi-join queries. The first algorithm is a version of the symmetric hash join algorithm [WA91]. Each peer in the two namespaces finds the relevant tuples and hashes them to a new query namespace. The resource ID in the new namespace is the concatenation of join attributes. The second algorithm, called “fetch matches”, assumes that one of the relations is already hashed on the join attributes. Each peer in the second namespace finds tuples matching the query and retrieves the corresponding tuples from the first relation. The authors leverage two other techniques, namely the symmetric semi-join rewrite and the Bloom filter rewrite, to reduce the high bandwidth overheads of the symmetric hash join. For an overlay of 10,000 peers, they evaluated the performance of their algorithms through simulation. The results show good per- formance of the proposed algorithms. However, for the cases where the join relations have a large number of tuples, this solution is not efficient, especially in terms of communication cost.

To avoid multicasting the query to large numbers of peers, the authors in [TP03] propose to allocate a limited number of special powerful peers, called range guards, for the task of join query processing. They divide the domain of the join attributes, and each partition is dedicated to a range guard. Join queries are sent only to range guards, where the query is executed. Effi- cient selection of range guards and performance evaluation of their proposal were left as future work. However, the disadvantage of this solution is that the existence of the required special peers is not evident in many P2P networks.

5.2.3 Multi-Attribute queries

There has been some work for supporting multi-attribute queries on top of DHTs [BAHS+_06].

Multi-Attribute Addressable Network (MAAN) [CFCS03] is a P2P system that addresses both multi-attribute and range queries in DHTs. It is built on top of Chord and is the first system that supports both query types in DHTs. MAAN executes multi-attribute range queries in O(log n +

n ∗ smin) routing hops, where n is the number of peers of the DHT and smin is the minimum range

selectivity across all attributes. The range selectivity is defined to be the ratio of the query range to the entire attribute domain range. MAAN makes the assumption that a locality preserving hash function would ensure load balance. The authors acknowledge that there is a selectivity breakpoint at which full flooding becomes more efficient than their technique. Another draw- back of MAAN is that it requires a fixed global schema which is known in advance to all peers. In RDFPeers [CF04] the authors extend this solution to allow heterogeneity in peers schemas. Each peer contains RDF based data items described as triples <subject, predicate, object>. The triples are hashed onto MAAN peers. The experimental results show improvement in load bal- ance, but no test for skewed query loads was done.

6 Conclusion

In this paper, we discussed schema based query processing in P2P systems. First, we introduced the three main kinds of P2P networks: unstructured, structured and super-peer. We briefly de- scribed each of these P2P networks, and compared them based on the main requirements for data management: autonomy, query expressiveness, efficiency, quality of service, fault-tolerance and security. Each kind of P2P network provides partial support for these requirements, i.e. there is no P2P network that supports all of the requirements. For example structured networks,

i.e. DHTs, have low query expressiveness, super-peer networks are not fault-tolerant, and un- structured networks are usually inefficient.

Second, we discussed schema mapping in P2P systems. Due to their specific characteristics,

e.g. the dynamic and autonomous nature of peers, P2P systems cannot rely on centralized global schemas which have been proposed for distributed database systems. We presented the tech- niques used in P2P systems. Although there has been significant progress, there are still many limitations for P2P schema mapping. In particular, these techniques typically need some kind of human intervention and the quality of the mappings is not very high.

Third, we presented the techniques for routing queries to relevant peers. We first described the algorithms of query routing in unstructured systems. The main concern in unstructured sys- tems is how to route the query to obtain high quality answers while minimizing the communica- tion cost. Usually, the algorithms where peers maintain some kind of statistics outperform the others. However, for highly dynamic systems, these algorithms may incur a high communication overhead without significant gains in answers' quality. We also discussed the problem of query routing in structured systems, particularly in DHTs. We presented the main routing geometries which are used in DHTs. We analyzed the routing properties of these geometries and compared them from the point of view of these properties.

Finally, we described the techniques for processing complex queries in P2P systems, in par- ticular in DHTs. We focused on top-k queries, join queries, range queries, and multi-attribute queries.

Although significant progress has been made in P2P schema-based query processing, there are still many open issues. In particular, there are some limitations with the solutions proposed for schema mapping and query processing in DHTs.

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