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SQL (Structured Query Language) execution optimization method of distributed database in multi-region deployment scene

An optimization method and database technology, applied in the field of distributed database optimization, can solve the problems of high cross-region network latency and easy performance.

Pending Publication Date: 2022-07-29
上海沄熹科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, in the multi-region deployment scenario of a distributed relational database, the network latency across regions is generally high, and performance problems are prone to occur in some read-write scenarios.

Method used

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  • SQL (Structured Query Language) execution optimization method of distributed database in multi-region deployment scene

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Embodiment Construction

[0026] Range: The division range of the data of the distributed database in the kv storage mode.

[0027] kv: key-value, key-value pair, represents a row of records in the storage.

[0028] Raft: Consensus algorithm that ensures synchronization and consistency of multiple copies.

[0029] replica: Data replica.

[0030] Shuffle: Redistribution of data.

[0031] CBO: SQL Cost-Based Optimization.

[0032] RBO: SQL rule-based optimization.

[0033] union: relational operation, and union.

[0034] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0035] The invention provides a method for optimizing SQL execution of a distributed database in a multi-region deployment scenario. According to the distributed database in the kv storage mode, a ...

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Abstract

The invention discloses an SQL (Structured Query Language) execution optimization method of a distributed database in a multi-region deployment scene, and relates to the field of distributed database optimization. The method comprises the following steps: configuring a Range copy strategy of a distributed database table according to a distributed database in a kv storage mode, and deploying Range copy distribution of the distributed database table in a multi-region scene according to the Range copy strategy: each region comprises at least one database node, and deploying a Range copy of the distributed database table according to the Range copy strategy by each database node; according to the copy distribution of the distributed database table in the multi-region scene, converting the distributed database table into a distributed database node containing the Range copy of the distributed database table to carry out related operation when corresponding operation under region is executed on the distributed database table.

Description

technical field [0001] The invention discloses a method, which relates to the field of distributed database optimization, in particular to an SQL execution optimization method of a distributed database in a multi-region deployment scenario. Background technique [0002] Distributed relational database, designed for scalability, strong consistency and high reliability. The scalability is shown in: the distributed database adopts a completely decentralized architecture, the status of each node in the cluster is completely equal, the server side provides a standard SQL interface externally, and the access node converts the SQL plan into the corresponding KV operation, and when necessary When the operation is sent to other nodes for processing. [0003] However, in a multi-region deployment scenario of a distributed relational database, the network latency across regions is generally high, and performance problems are prone to occur in some read-write scenarios. SUMMARY OF TH...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/27G06F16/28G06F16/2453G06F16/22
CPCG06F16/27G06F16/284G06F16/24532G06F16/2282G06F16/24539
Inventor 徐佳庆柴毅牟冠学
Owner 上海沄熹科技有限公司
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