Local multi-query optimization method based on predicate statutes and cost estimation

An optimization method and multi-query technology, applied in computing, special data processing applications, instruments, etc., can solve problems such as inability to accurately estimate query overhead, CPU calculation and network transmission overhead that cannot be ignored, etc., to shorten execution time, reduce The effect of the search space

Active Publication Date: 2017-02-22
ZHEJIANG UNIV
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Problems solved by technology

However, when the bottom layer adopts a distributed computing architecture (such as most big data query systems) and there are connection operations, the overh

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  • Local multi-query optimization method based on predicate statutes and cost estimation
  • Local multi-query optimization method based on predicate statutes and cost estimation
  • Local multi-query optimization method based on predicate statutes and cost estimation

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Embodiment Construction

[0068] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0069] like figure 1 As shown, the local multi-query optimization method based on predicate reduction and cost estimation is divided into three stages: preprocessing, local multi-query optimization processing and multi-query plan optimization.

[0070] (1) The main steps in the preprocessing stage include:

[0071] Step (1-1), use the existing query optimizer of the data query system to optimize each query in the input query set, find the optimal query plan respectively, and express it in the form of a query plan tree, and finally get query plan tree collection;

[0072] The query plan tree is expressed as T(V,E,D). V is a collection of all nodes in the query plan tree. Each subtask corresponds to a query plan tree node. The nodes contain some operation...

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Abstract

The invention discloses a local multi-query optimization method based on predicate statutes and cost estimation and belongs to the field of big data query optimization. The method comprises the steps that firstly, an existing optimizer of a data query system is utilized to optimize queries in a query set, and the queries are shown in the form of query trees to obtain an optimized query tree set; then, the local multi-query optimization method is combined to conduct equivalent or statute processing on same or similar sub-tasks among the queries through multiple times of iteration to generate a global multi-query plan tree; finally, the statute relation between generated global multi-query plans and the sub-tasks is combined to estimate intermediate result reuse expenditure according to a price model, whether the sub-tasks are directly executed or intermediate results are reused is judged, and the global multi-query plans are optimized. The method fully considers the balance between intermediate result utilization and query concurrence, repeated operations are decreased, and the query performance is effectively improved.

Description

technical field [0001] The invention relates to the field of big data query optimization, in particular to a local multi-query optimization method based on predicate reduction and cost estimation. Background technique [0002] Early research on query optimization and scheduling was mainly focused on single queries, but with the increase in concurrency and the continuous improvement of data query systems, concurrent query processing has become an indispensable function of modern data query systems. When concurrent queries are composed of related (involving the same or similar operations) queries, traditional single query optimization methods do not consider the correlation between queries, thus limiting the improvement of system query performance. [0003] Multi-query optimization means that when the system inputs multiple queries at the same time, it analyzes the batch of queries and combines the parts involving the same or similar operations to generate a global multi-query...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/2453
Inventor 陈岭杨谊
Owner ZHEJIANG UNIV
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