K. Bratbergsengen. Hashing methods and relational algebra operations. In Proc. of the 10th VLDB Conference, pages 323--333, Singapore, August 1984.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....groups in a single pass. But one pass aggregation requires data only to be grouped and not sorted Our approach allows us to infer that the result of the sort merge join will always be grouped on c custkey, making the sorting step (or a hash based grouping step, as might be used in other systems [1]) unnecessary. When applied to the query plan of Figure 5a, our refinement algorithm would detect the unneeded Sort operation and produce the plan of Figure 5c. In Section 4.2, we give a detailed explanation of how we are able to infer that the result of the sort merge join is guaranteed to be ....

Kjell Bratbergsengen. Hashing methods and relational algebra operations. In Umeshwar Dayal, Gunter Schlageter, and Lim Huat Seng, editors, Tenth International Conference on Very Large Data Bases, August 27-31, 1984.

....Methodology In this section, we describe the experimental methodology used to illustrate the benefits of Flux mechanisms. Throughout this paper, we use a hash based, windowed group by aggregate operator as an example. It is similar to a traditional hash based group by aggregate operator [3] except that it maintains a history of the most recently processed tuples for each group. In the CQ context, this operator takes a stream, splits it into multiple logical streams (one per group) and computes a statistic over the recent history of the extracted streams. Such an operator can be ....

K. Bratbergsengen. Hashing Methods and Relational Algebra Operations. In VLDB, 1984.

....Methodology In this section, we describe the experimental methodology used to illustrate the benefits of Flux mechanisms. Throughout this paper, we use a hash based, windowed group by aggregate operator as an example. It is similar to a traditional hash based group by aggregate operator [3] except that it maintains a history of the most recently processed tuples for each group. In the CQ context, this operator takes a stream, splits it into multiple logical streams (one per group) and computes a statistic over the recent history of the extracted streams. Such an operator can be ....

K. Bratbergsengen. Hashing Methods and Relational Algebra Operations. In VLDB, 1984.

....Finally, our results present a strong case for storing sets in the nested internal form, since PSJ and even signature nested loops outperform the rewritten queries over the unnested external rep resentation. 1. 1 Related Work Joins have been studied extensively in relational [MK76] [Bra84], DKO 84] DNS91] and spatial domains [LR96] PD96] Pointer joins for effi ciently traversing path expressions in object oriented databases has also been studied extensively [DLM93] SC90] However, there is very little previous work on set containment joins. The only reported work of which ....

K. Bratbergsengen. Hashing methods and re- lational algebra operations. In Proceedings of International Conference on Very Large Databases (VLDB), pages 323-333, 1984.

....Finally, our re sults present a strong case for storing sets in the nested internal form, since PSJ and even signature nested loops outperform the rewritten queries over the unnested external representation. 1. 1 Related Work Joins have been studied extensively in relational [MK76] [Bra84], DKO 84] DNS91] and spatial domains [LR96] PD96] Pointer joins for efiiciently traversing path expressions in object oriented databases has also been studied extensively [DLM93] SC90] However, there is very little previous work on set containment joins. The only reported work of which we ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proceedings of International Conference on Very Large Databases (VLDB), pages 323-333, 1984.

....P2P users are impatient, they do not expect perfect answers, and they often ask broad queries even when they are only interested in a few results. Next, we we focus on the example of joins; grouping and other unary hashing operators are quite analogous to joins, with only some subtle di#erences [2]. Our basic join algorithm on two relations R and S is based on the pipelined or symmetric hash join [16] using the DHT infrastructure to route and store tuples. The algorithm begins with the query node initializing a unique temporary DHT namespace, T joinID . We assume that data is iterating ....

Bratbergsengen, K. Hashing Methods and Relational Algebra Operations. In Proc. of the International Conferrence on Very Large Data Bases (VLDB) (1984), pp. 323--333.

....queries that require string valued output of the color attribute. Since such encoding tables are typically small, e.g. in the order of a few kilobytes, efficient hash based algorithms can be used for the join that outperform both naive methods like nested loops join or the sort based merge join [5, 7, 26]. In the long run, we hope that such encodings can be administered automatically very much like indices in today s database management systems, and that they can be recommended by automated physical database design and tuning software where appropriate. In query processing, compression can be ....

K. Bratbergsengen, Hashing Methods and Relational Algebra Operations, Proc. Int'l. Conf. on Very Large Data Bases, Singapore, August 1984, 323.

....traditional application of semi joins. The main reason for using semijoins in distributed database systems is to reduce inter site communication. Projected attributes are sent from one server to another. After performing semi join reduction the reduced table is sent to the first server [AHY83, Bra84] But all these approaches are beneficial for traditional distributed systems only in some cases [BG81] Queries containing only functional joins (non expansive joins 1 ) produce only very small benefits or no benefits at all in traditional symmetric systems by applying inter site semi joins. ....

.... A = 100, B = C = 100.000, A #B = C # A =10.000 Figure 4 shows the measured results. Another case when additional semi joins are profitable is their use as prerecordered filters to expedite subsequent join operations (e.g. A # B) # B) A similar technique was studied in [Bra84] 3 # client # client # 1 # 1 A 1 # ## # B 1 # ## # C 1 # ## # D 2 # ## # E 2 traditional plan # client # 1 # 1 A 1 # ## # B 1 # ## # C 1 # ## # #2 #2 A 2 # ## # D 2 # ## # E 2 new plan Figure 3: Exploiting Machine Resources # # B# ## # A# ## # ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proc. of the Conf. on Very Large Data Bases (VLDB), pages 323--333, Singapore, Singapore, 1984.

....Without Join Partner: For those o # O that definitely do not have a join partner in C we need not execute the inner loop at all. We will compute a separate bitmap, called used, to identify those objects. This kind of bitmap has also been proposed to speed up traditional hash join operations [Bra84] 2. Objects Without Collisions: For those o # O that are definitely not inserted into more than one partition (i.e. objects that won t drop into a false partition) we can exit the inner loop as soon as they are inserted into some partition C i . Again, we maintain a separate bitmap, ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proc. of the Conf. on Very Large Data Bases (VLDB), pages 323--333, Singapore, Singapore, 1984.

....of the method on x 0 , since it is guaranteed that no more x 0 s will appear. This is illustrated in Figure 2. 4 Hybrid Cache The third technique we consider is unary hybrid hashing. Unary hybrid hashing has been used in the past to perform grouping for aggregation or duplicate elimination[Bra84] but to our knowledge this paper represents the first application of the technique to the problem of caching. Unary hybrid hashing is based on the hybrid hash join algorithm [DKO 84] We introduce some minor modifications to unary hybrid hashing, and since we are applying it to the problem ....

Kjell Bratbergsengen. Hashing Methods and Relational Algebra Operations. In Proc. 10th International Conference on Very Large Data Bases, pages 323--333, Singapore, August 1984.

....implementation of Heraclitus[Alg,C] was to provide strong evidence that deltas can be incorporated into a relational DBMS with nominal loss of efficiency. Conceptually, it is straightforward to extend our hash based implementation to form variations of the Grace [KTMo83] and Hybrid hash join [Bra84, DKO 84, DG85, Sha86] techniques, so that thrashing behavior is eliminated for deltas that are considerably larger than the main memory buffer. The design, implementation, and evaluation of these algorithms as compared to sort merge has been studied extensively [SD89, DGS 90] We expect ....

K. Bratbergsengen. Hashing Methods and Relational Algebra Operations. In Proceedings of the Conference on Very Large Data Bases, 1984.

....The term ad hoc join is used to describe the process of taking two relations and forming their join without the benefit of any pre computed specialized data structures such as indexes. A number of techniques have been developed to perform joins in this setting [Blasgen and Eswaran, 1977, Bratbergsengen, 1984, DeWitt et al. 1984, Kim, 1980, Mishra and Eich, 1992, Shapiro, 1986] Decision support systems are characterized by complex ad hoc join queries over large data sets. Relational systems relying on conventional data structures and ad hoc join methods fail to deliver the needed response time for ....

Bratbergsengen, B. (1984). Hashing methods and relational algebra operations. In Proceedings of the VLDB Conference, pages 323--333.

....for hash join: If initially tuples are already fully or partially sorted, sort based join techniques may very well outperform hash join since for sorted data, sort based join execution reduces to one linear scan of each relations. Hash join can generally expect to outperform sort merge join [7, 14, 17, 19, 23, 26]. However, mostly their performances differ by percentages rather than factors [54] In [54] Graefe et al. presented a quite detailed comparison between sort based join and hash join. Their conclusion is that these two classes of join techniques are to a large extent complimentary to each other. ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proc. of the Tenth Int'l Conf. on Very Large Data Bases, pp. 323-333, Singapore, August 1984.

....sort merge join could outperform hash join. 7 Some Speed Up Techniques for Join Executions This section discusses some general techniques that can be used along with other join techniques to speed up join executions. Bit vector filtering: Join execution can be improved by using bit vectors [7, 16, 21, 25, 38] for small or medium sized joins. Before performing join, an vector of n bits is initialized and each bit is set to 0. During the first phase (sorting phase for sort merge join and hashing phase for hash join) each join attribute value in R is hashed and the result is used to set bits in the bit ....

Bratbergsengen, Kjell. Hashing Methods and Relational Algebra Operations. Proc. of the 1984 Very Large Database Conf., 1984.

....either on nested loop join or merge join [BE76] Both these schemes are expensive since nested loop join performs a lot of disk I Os and merge join requires sorting of data prior to the join. Hence hash join was proposed as a better alternative and has since been enhanced to improve performance [Bra84, DKO 84, NKT88, KNT89, Sha86] Query processing schemes in commercial products currently use both merge join or hash join. Note that if the data is stored in a sorted order or if an index (like B tree) exists on the datasets, then data need not be sorted prior to merge join. Similarly hash ....

....as necessary, it does not require much memory and is also independent of the input sizes. However sort is an expensive operation. Hash indices were already being used for quick access to data [SWKH76, FNPS79] This sparked interest in hash join schemes as an alternative to merge join schemes [Bra84, DKO 84] The basic principle is to build an in memory hash table for the smaller of the join inputs and probe this table for items in the large input. To overcome the memory size limitation while handling large inputs (table size greater than the size of the memory available) the hash join ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proceedings of the 10th Conference on Very Large Databases, Morgan Kaufman pubs. (Los Altos CA), Singapore, August 1984.

....Client HypRa Application Figure 1: The HypRa database client server architecture. The server is drawn as a simplified 3D hypercube. In the shown configuration a communication co processor is connected to each node. 4. Implicit load distribution through hash based data distribution ( 10] 16] [11], 19] 5. Internal use of a nested transaction mechanism to ensure consistency and correctness in the internal resource management ( 28] 26] HypRa fits into the server role in a typical client server architecture (Figure 1) The multiprocessor is interconnected in a hypercube topology. In ....

....transaction processing capacity depends on the query execution strategy and the level of intra transaction parallelism. The main scaling issues are intra transaction parallelism in relational algebra and sorting algorithms. HypRa uses the hash based methods for relational algebra operations ([11], 19] 15] These are shown to scale almost perfectly linearly with number of nodes ( 12] 20] HypRa employs the parallel sorting algorithm described in [6] and [7] This shows the same linear scalability as the relational algebra operations. 4.4 Scaling availability Data availability is ....

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Kjell Bratbergsengen. Hashing Methods and Relational Algebra Operations. In Proceedings of the 10th International Conference on Very Large Databases, pages 323--333, 1984.

....distribution is therefore also the basis for the transaction workload distribution among nodes. By providing powerful nodes, we are also able to handle uneven transaction workloads between nodes. The distribution of query workload over nodes is part of the query and sorting algorithms used ( 12] [7], 17] 8] 5] 18] 11] 6 Dynamic Reconfiguration of Data as a Mechanism for Repairing Faults In addition to masking single node failures, and providing gradual data unavailability in the presence of double failures, the HypRa DBMS performs automatic corrective online repair of single ....

Kjell Bratbergsengen. Hashing Methods and Relational Algebra Operations. In Proceedings of the 10th International Conference on Very Large Databases, pages 323--333, 1984.

....the nested loop, sort merge and hash join algorithms. Merrett [51] argued that the sort merge algorithm gives the best implementation of the natural join based on a theory of clustering relations. This conclusion has since been disputed by many, including Kitsuregawa et al. 36] Bratbergsengen [8], and DeWitt et al. 15] They showed that variations of the hash join algorithm perform better than the sort merge algorithm when the initial data is unsorted. They also showed that, for small numbers of records, the nested loop algorithm performs better than the sort merge algorithm. Some are ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proceedings of the Tenth International Conference on Very Large Data Bases, pages 323--333, Singapore, August 1984.

....Without Join Partner : For those o # O that definitely do not have a join partner in C we need not execute the inner loop at all. We will compute a separate bitmap, called used, to identify those objects. This kind of bitmap has also been proposed to speed up traditional hash join operations [Bra84]. 2. Objects Without Collisions : For those o # O that are definitely not inserted into more than one partition (i.e. objects that won t drop into a false partition) we can exit the inner loop as soon as they are inserted into some partition C i . Again, we maintain a separate bitmap, ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proc. of the Conf. on Very Large Data Bases (VLDB), pages 323--333, Singapore, Singapore, 1984.

....path level are not considered. Firstly, the evaluation gives no instruction on how indexes of relations can be shared across iterations to speed up the query processing. For performing a large join efficiently, it is desirable to make use of certain indexes, such as hash tables in hash based join [Bra] or sorted lists in merge sort join [BE] Since current relations in any two consecutive iterations differ only by an increment computed in one iteration, the join index in each iteration can be obtained from the join index used in the last iteration by reflecting only the increment. Secondly, at ....

K. Bratbergsengen, "Hashing methods and relational algebra operations," in proceedings of International Conference on Very Large Data Bases, pp. 323-333, 1984

....Without Join Partner: For those o 2 O that definitely do not have a join partner in C we need not execute the inner loop at all. We will compute a separate bitmap, called used, to identify those objects. This kind of bitmap has also been proposed to speed up traditional hash join operations [Bra84] 2. Objects Without Collisions: For those o 2 O that are definitely not inserted into more than one partition (i.e. objects that won t drop into a false partition) we can exit the inner loop as soon as they are inserted into some partition C i . Again, we maintain a separate bitmap, ....

....the same performance as generalized hash teams. 7 Related Work The use of bitmaps is becoming increasingly popular to support decision support queries. In the database context, bitmaps have been used to speed up the execution of joins in distributed [Bab79, VG84] as well as centralized systems [Bra84] In these proposals so called Bloom filters [Blo70] are used to filter out tuples without join partners. HM97] use bitmap signatures for processing joins involving predicates on nested sets. Also, bitmap indexing is a well known concept; see, e.g. the early work on signature files [CS89] or ....

K. Bratbergsengen. Hashing methods and relational algebra operations. In Proc. of the Conf. on Very Large Data Bases (VLDB), pages 323--333, Singapore, Singapore, 1984.

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K. Bratbergsengen. Hashing methods and relational algebra operations. In Proc. of the 10th VLDB Conference, pages 323--333, Singapore, August 1984.

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K. Bratbergsengen, Hashing Methods and Relational Algebra Operations, Proc. 10th VLDB, Aug. 1984, pp. 323-332

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Bratbergsengen, Kjell, "Hashing Methods and Relational Algebra Operations", Proceedings of the 1984 Very Large Database Conference, August, 1984.

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Bratbergsengen, Kjell, "Hashing Methods and Relational Algebra Operations", Proceedings of the 1984 Very Large Database Conference, August, 1984.

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