2013Unpublished venueRequires access

DAP join: Produce massive and immediate result in multi join query using flushing

Pranjal Bansal, Rakesh Rathi, Vinesh Jain

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Abstract

This paper introduced a method for producing massive and immediate result in multi-join query (DAP Join, for short). Mostly previous research are based on single join operator while DAP join is based on multiple join operators. DAP join distinguishes itself from all previous techniques by concluding benefits of Hash Merge join, Rate Based Progressive Join and State Spilling and its own new approach. DAP join employs a new flushing technique to move N amount of data from memory to disk when memory allotment is exhausted and move N/2 amount of data when data arrival rate is slow in online environment. DAP join moves data to disk which is least useful and use Symmetric Hash Join in in-memory join makes it efficient in order to maximize overall throughput and produce early result.

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What this paper is about

This paper introduced a method for producing massive and immediate result in multi-join query (DAP Join, for short). Mostly previous research are based on single join operator while DAP join is based on multiple join operators. DAP join distinguishes itself from all previous techniques by concluding benefits of Hash Merge join, Rate Based Progressive Join and State Spilling and its own new approach. DAP join employs a new flushing technique to move N amount of data from memory to disk when memory allotment is exhausted and move N/2 amount of data when data arrival rate is slow in online environment. DAP join moves data to disk which is least useful and use Symmetric Hash Join in in-memory join makes it efficient in order to maximize overall throughput and produce early result.

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Available abstract

This paper introduced a method for producing massive and immediate result in multi-join query (DAP Join, for short). Mostly previous research are based on single join operator while DAP join is based on multiple join operators. DAP join distinguishes itself from all previous techniques by concluding benefits of Hash Merge join, Rate Based Progressive Join and State Spilling and its own new approach. DAP join employs a new flushing technique to move N amount of data from memory to disk when memory allotment is exhausted and move N/2 amount of data when data arrival rate is slow in online environment. DAP join moves data to disk which is least useful and use Symmetric Hash Join in in-memory join makes it efficient in order to maximize overall throughput and produce early result.

Key concepts: Join (topology), Hash join, Sort-merge join, Computer science, Merge (version control), Hash function, Database, Parallel computing

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