Column-storage-oriented Hash joint method for indexes in barrels

Abstract

The invention relates to a column-storage-oriented Hash joint method for indexes in barrels. The column-storage-oriented Hash joint method is characterized by comprising a first step, initiating; a second step, filling data Si into proper positions of corresponding barrel joints according to values of the data; a third step, judging whether the number of elements in the barrels is larger than a tolerance value T or not, turning to a fourth step if the number of the elements is larger than the tolerance value T to build indexes in the barrels, scattering the elements into the barrel according to a common Hash algorithm if the number of elements is larger than the tolerance value T, and turning to a fifth step; the fourth step, building the indexes in the barrels; the fifth step, building an array of the indexes of the barrels; and a sixth step, realizing matched joint. The column-storage-oriented Hash joint method has the advantages that the indexes are built in the barrels, shortcomings of traditional Hash joint are overcome, matching finding time is shortened, and the efficiency of Hash joint is improved.

Description

technical field [0001] The invention relates to a column storage-oriented index hash join method in a bucket, which is suitable for a column storage database management system environment based on a binary table <row number, column value>. Background technique [0002] Join (Join) occupies a very heavy proportion in data query, especially in column storage data system. In addition to the connection operations required by user queries, in many cases, additional connections with the columns of the table are required in order to recombine the data in each column into rows. The classic join algorithms in the database include nested loop joins, merge joins, and hash joins. Among them, hash join is an efficient join algorithm, and its performance is better than traditional nested loop join and merge join in most cases. [0003] However, there are also some defects in the hash connection, mainly in two aspects. One is that it is difficult to choose an appropriate number of...

AI Technical Summary

Problems solved by technology

One is that it is difficult to choose the appropriate number of buckets, and the other is that it is difficult to choose the appropriate hash function

Due to the "massive characteristics" of data in column storage analytical applications, it is more difficult to choose the number of buckets and hash functions, and the above problems are more obvious.

First of all, if the number of buckets is too small, the number of data in each bucket may be too much, resulting in low connection efficiency, and if the number of buckets is too large, it will cause memory waste and increase management costs

Secondly, since the characteristics of the data processed by each hash connection are different, it is difficult to find a general hash function so that the data can be hashed evenly, so it is also very difficult to choose a general hash function