Query time prediction method for time sequence database

A query time and prediction method technology, applied in the computer field, can solve the problem of slow query time prediction speed, etc., and achieve the effect of considerable response speed

Active Publication Date: 2022-03-22
北京诺司时空科技有限公司 +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose a query time prediction method for time series databases in view of the problem of slow query time prediction speed in the prior art

Method used

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  • Query time prediction method for time sequence database
  • Query time prediction method for time sequence database
  • Query time prediction method for time sequence database

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Experimental program
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specific Embodiment approach 1

[0028] Specific implementation mode one: refer to image 3 Specifically illustrate this embodiment, a kind of time-series database-oriented query time prediction method described in this embodiment includes the following steps:

[0029] Step 1: Read time series data;

[0030] Step 2: Write the time series data into CnosDB, and CnosDB uses CnoSQL query statements to query and retrieve the time series data, and record the query time;

[0031] Step 3: Encode the query statement into vectorized data;

[0032] Step 4: Extract data distribution features from the vectorized data;

[0033] Step 5: Use PCA to reduce the dimensionality of the data distribution characteristics;

[0034] Step 6: Use vectorized data and dimensionally reduced data distribution features as input, query time as output, and train the gradient boosting regression tree model;

[0035] Step 7: Use the trained gradient boosting regression tree model to predict query time.

specific Embodiment approach 2

[0036] Embodiment 2: This embodiment is a further description of Embodiment 1. The difference between this embodiment and Embodiment 1 is that the following steps are included before the third step: rewriting CnoSQL into standard SQL.

specific Embodiment approach 3

[0037] Embodiment 3: This embodiment is a further description of Embodiment 2. The difference between this embodiment and Embodiment 2 is that the encoding in Step 3 includes join graph encoding and column information encoding, join graph encoding and column information The result of the encoding is concatenated as the encoding of the entire query.

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Abstract

The invention discloses a time sequence database-oriented query time prediction method, relates to the technical field of computers, and aims to solve the problem of low query time prediction speed in the prior art. 2, the time series data are written into a CnosDB, the CnosDB uses a CnoSQL query statement to conduct query retrieval on the time series data, and query time is recorded; 3, encoding the query statement into vectorized data; 4, extracting data distribution characteristics of the vectorized data; 5, performing dimension reduction on the data distribution characteristics by using PCA; step 6, using the vectorized data and the dimension-reduced data distribution characteristics as input, using query time as output, and training a gradient lifting regression tree model; and 7, performing query time prediction by using the trained gradient lifting regression tree model. In the aspect of prediction time, the model can give a prediction result within dozens of milliseconds in the experiment, and the response speed is very considerable.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a time series database-oriented query time prediction method. Background technique [0002] Query time prediction is the technical basis of many hot issues in the database field, such as access control, query optimization, and query scheduling. For example, in database optimization, the main goals of optimization are query response time and space utilization. Therefore, query execution time will be used as an important feedback indicator to indicate the quality of the optimization results. However, in actual use, if the query load is physically executed to obtain the real execution time, it will bring an unacceptable cost to the optimization process, because the load often needs to be executed repeatedly for hundreds or thousands of rounds. [0003] At present, the relevant research on the direction of query time prediction is relatively mature. In general, there are currentl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2455G06F16/2458G06F16/28G06K9/62
CPCG06F16/2474G06F16/284G06F16/2456G06F18/24323
Inventor 王宏志李同欣张凯欣郑博梁栋叶天生燕钰丁小欧
Owner 北京诺司时空科技有限公司
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