Concurrent OLAP-Oriented Database Query Processing Method
First Claim
1. A concurrent on-line analytical processing (OLAP)-oriented database query processing method, for performing, on the basis of predicate vector-based memory OLAP star-join optimization, concurrent OLAP query processing based on a batch query predicate vector bit operation, wherein:
- the predicate vector-based memory OLAP star-join optimization comprises the following steps;
loading a dimension table in a memory;
vectorizing a predicate operation result in a query;
completing a bitmap filtering operation for a multi-table star-join through a bit operation on predicate vectors of multiple query-related dimension tables, so as to select a fact table tuple satisfying a condition; and
mapping a dimension table primary key to a memory offset address of a memory column-stored dimensional attribute so as to implement direct access to a dimension table group-by attribute value; and
the concurrent OLAP query processing based on the batch query predicate vector bit operation comprises the following steps;
grouping concurrent queries in a designated time window, and performing the queries in a batch processing manner;
using a multi-bit predicate vector to store a predicate operation result of a concurrent query group, wherein each bit of each data item of the predicate vector corresponds to a designated query predicate operation result flag bit;
performing a bit operation in a unit of the multi-bit predicate vector when a star-join bitmap filtering operation is performed, wherein a position of 1 in an operation result represents a query number satisfying predicate conditions in all dimension tables; and
invoking a HASH aggregate processing thread corresponding to 1 in bits of a predicate vector result to complete iterative aggregate calculation on a current fact table tuple.
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Abstract
A concurrent on-line analytical processing (OLAP)-oriented database query processing method is described, for performing, on the basis of predicate vector-based memory OLAP star-join optimization, concurrent OLAP query processing based on a batch query predicate vector bit operation. The concurrent query processing optimization technology is implemented for I/O performance and parallel OLAP processing performance in a database management system, and setting of concurrent OLAP processing load in an optimized way catering to the I/O performance is supported, thereby improving predictable processing performance oriented to diversified OLAP queries and implementing concurrent query star-join bitmap filtering processing based on predicate vector arrays.
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Citations
10 Claims
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1. A concurrent on-line analytical processing (OLAP)-oriented database query processing method, for performing, on the basis of predicate vector-based memory OLAP star-join optimization, concurrent OLAP query processing based on a batch query predicate vector bit operation, wherein:
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the predicate vector-based memory OLAP star-join optimization comprises the following steps;
loading a dimension table in a memory;
vectorizing a predicate operation result in a query;
completing a bitmap filtering operation for a multi-table star-join through a bit operation on predicate vectors of multiple query-related dimension tables, so as to select a fact table tuple satisfying a condition; and
mapping a dimension table primary key to a memory offset address of a memory column-stored dimensional attribute so as to implement direct access to a dimension table group-by attribute value; andthe concurrent OLAP query processing based on the batch query predicate vector bit operation comprises the following steps;
grouping concurrent queries in a designated time window, and performing the queries in a batch processing manner;
using a multi-bit predicate vector to store a predicate operation result of a concurrent query group, wherein each bit of each data item of the predicate vector corresponds to a designated query predicate operation result flag bit;
performing a bit operation in a unit of the multi-bit predicate vector when a star-join bitmap filtering operation is performed, wherein a position of 1 in an operation result represents a query number satisfying predicate conditions in all dimension tables; and
invoking a HASH aggregate processing thread corresponding to 1 in bits of a predicate vector result to complete iterative aggregate calculation on a current fact table tuple. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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Specification