Sort Merge Join Research Articles

High-Parallelism and Pipelined Architecture for Accelerating Sort-Merge Join on FPGA

High-Parallelism and Pipelined Architecture for Accelerating Sort-Merge Join on FPGA

Advanced Block Nested Loop Join for Extending SSD Lifetime

Flash technology trends have shown that greater densities between flash memory cells increase read/write error rates and shorten solid-state drive (SSD) device lifetimes. This is critical for enterprise systems, causing such problems as service instability and increased total cost of ownership (TCO) because of SSD replacement. Therefore, numerous studies have focused on decreasing the amount of the DBMS writes. However, there has been no research that focused on decreasing the amount of temporary writes, which are primarily created by join processing. In DBMSs, there are two major join-processing algorithms, i.e., hybrid hash join (HHJ) and sort merge join (SMJ), proven to be the best according to DBMS workload; however, the two algorithms produce temporary writes of intermediate results. Therefore, we instead look to the block-nested loop join (BNLJ); it is well-known that the two algorithms are better than BNLJ, but BNLJ creates no intermediate result writes. It is reasonable to use BNLJ for a major join algorithm if its performance can be enhanced similar to those of HHJ and SMJ, considering BNLJ's advantage of extending SSD lifetimes. Therefore, in this paper, we propose an advanced BNLJ (ANLJ) algorithm that can match the performance of the two main join algorithms.

PRAM 기반의 조인 알고리즘 성능 비교 연구

With the advent of non-volatile memories such as Phase Change Memory (PCM or PRAM) and Magneto Resistive RAM (MRAM), active studies have been carried out on how to replace Dynamic Random-Access Memory (DRAM) with PRAM. In this paper, we study both endurance and performance issues of existing join algorithms that are based on PRAM-based computer systems and have been widely used until now: Block Nested Loop Join, Sort-Merge Join, Grace Hash Join, and Hybrid Hash Join. Our experimental results show that the existing join algorithms need to be redesigned to improve both the endurance and performance of PRAMs. To the best of our knowledge, this is the first research to scientifically study the results of the four join algorithms running on PRAM-based systems. In this work, our main contribution is the modeling and implementation of a PRAM-based simulator for a comparative study of the existing join algorithms.

An End-Around Approach for Efficient Join Query Processing

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.