Hash-merge join: a non-blocking join algorithm for producing fast and early join results

Mokbel, Mohamed F.; Lu, Ming–Wei; Aref, Walid G.

doi:10.1109/icde.2004.1320002

Cited by 71 publications

(117 citation statements)

References 18 publications

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“…This algorithm is suitable for medium-size data but does not perform well for large-size data. A related symmetric join algorithm is the Hash-Merge Join (HMJ) [18], which is based on push technology and consists of two phases, hashing and merging.…”

Section: Related Workmentioning

confidence: 99%

Skewed distributions in semi-stream joins: How much can caching help?

Naeem

Dobbie

Lutteroth

et al. 2017

Information Systems

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Semi-stream join algorithms join a fast data stream with a disk-based relation. This is important, for example, in real-time data warehousing where a stream of transactions is joined with master data before loading it into a data warehouse. In many important scenarios, the stream input has a skewed distribution, which makes certain performance optimisations possible.We propose two such optimisation techniques: 1) a caching technique for frequently used master data, and 2) a technique for selective load shedding of stream tuples. The caching technique is fine-grained, operating on a tuple-level. Furthermore, it is generic in the sense that it can be applied to different semistream join algorithms to deal with high loads. We analyze it by combining it with various well-known semi-stream joins, and show that it improves the service rate by more than 40% for typical data with skewed distributions. The load shedding technique sheds the fraction of the stream that is most expensive to join. In contrast to existing approaches, the service rate improves under load shedding. We present experimental data showing significant improvements as compared to related approaches and perform a sensitivity analysis for various internal parameters.

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Section: Related Workmentioning

confidence: 99%

Skewed distributions in semi-stream joins: How much can caching help?

Naeem

Dobbie

Lutteroth

et al. 2017

Information Systems

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“…HMJ [2] main idea is to minimize time to produce first few result and produce join result if two sources of operator already blocked. HMJ was implemented taking benefits of XJoin and Progressive Merge Join with two phases: The hashing phase and the merging phase.…”

Section: Hash Merge Joinmentioning

confidence: 99%

“…Hash join algorithms [2] are used mostly for joining large tables. The algorithm of hash join is divided in two parts:…”

Section: Hash Based Join Algorithmmentioning

confidence: 99%

An End-Around Approach for Efficient Join Query Processing

Chauhan¹,

Patel²

2014

IJCA

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This paper introduced a method for producing immediate and result in multi-join query, in homogeneous and heterogeneous environment. In recent years Adaptive or Non Blocking join algorithms have attracted a lot of attention in streaming applications, where data is provided from autonomous data sources in heterogeneous network environments. This algorithms are better as compared to traditional algorithms is that they can generates join results as early as the first input tuples are on hand hence it improves pipelining, smooth out join result production and also masking source or network delays. As response time of the queries places a very important role in adaptive join, the join algorithm like Hash Join, Sort Merge Join are become unacceptable for this environment because they require preprocessing before generating the join result. Hence, in adaptive join technique only possible algorithm is Nested loop join. In Nested Loop Join, every single record of the outer relation is compared with every single record of the inner relation. The no. of comparisons done by the nested loop join can be reduced by making improvement in Block Nested loop Join. In proposed End-Around Block Nested loop join outer and inner table's comparison is done in parallel and whenever a row in first location didn't find a match then row from first location removed and placed at rear end as like in a queue, the matched row removed from inner relation and added to result set. Whenever, New tuple arrive is then pushed into rear end and process is continuing with new incoming tuples in streaming environments.

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“…The XJoin algorithm (Urhan & Franklin, 2000) is another extension of SHJ. Hash-Merge Join (HMJ) (Mokbel, Lu, & Aref, 2004) which is also based on symmetric join algorithm, uses push technology and consists of two phases, hashing and merging.…”

Section: Related Workmentioning

confidence: 99%