CN111090525B - Processing method for Redis to achieve high concurrency optimization of distributed lock - Google Patents
Processing method for Redis to achieve high concurrency optimization of distributed lock Download PDFInfo
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- CN111090525B CN111090525B CN201911310027.XA CN201911310027A CN111090525B CN 111090525 B CN111090525 B CN 111090525B CN 201911310027 A CN201911310027 A CN 201911310027A CN 111090525 B CN111090525 B CN 111090525B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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Abstract
The invention discloses a processing method for Redis to realize high concurrency optimization of distributed locks, which comprises the following steps of: s1: dividing an original database into a plurality of sub databases; s2: distributing the purchase demand of the customer to each sub-database; s3, each sub-database adopts Redis distributed lock to process the purchase demand of several distributed clients; and S4, when the residual number of all sub-databases can not individually satisfy the number of customer service requirements, automatically transferring the customer to another sub-database. The method is suitable for processing the database with high concurrency and ordering at the same time.
Description
Technical Field
The invention relates to the field of database processing, in particular to a processing method for Redis to realize high concurrency optimization of distributed locks.
Background
The phenomenon of stock over-selling of the database is shown in fig. 2, when the database only has 12 products, and two clients propose to buy 10 products to the database within a short time, the 12 products in the database respond to the first client first, the quantity of the 12 products in the database is greater than the requirement (10 products) of the first client and the purchase is allowed, however, the first client does not order and confirms to submit an order for purchase, the 10 products purchased by the first client are not subtracted from the database first, and the database still displays that 12 products exist at this time. The second customer then places an order for 10 products, the 12 products in the database are still larger than the second customer's (10) requirements, and the second customer also submits an order later. When the first customer orders successfully, the second customer orders successfully, but although the second customer orders successfully, 10 products purchased by the first customer are subtracted from the database, only 2 products remain in the database, and the demand database of the actual 10 products of the second customer cannot be supplied, so that the phenomenon of over-sale with low stock occurs.
As shown in fig. 3, the principle of Redis implementing distributed lock to solve stock over-selling is that when a first client purchases 10 products, the database locks the products purchased by the first client, and a second client waits for the first client to submit an order, that is, after the first client unlocks, the second client can continue to make an order. If the time for unlocking a single client is 20ms, namely the second client can continue to issue the order after 20ms after the first client issues the order, only 50 clients can issue the order in sequence within 1s, and the method is not suitable for high-concurrence simultaneous second-time rush services (many clients issue orders at the same time).
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a processing method for Redis to realize high concurrency optimization of a distributed lock, which is suitable for processing a database with high concurrency and ordering at the same time.
In order to achieve the above object, a processing method for Redis to implement high concurrency optimization of distributed locks, comprising the following steps: s1: dividing an original database into a plurality of sub databases; s2: distributing the purchase demand of the customer to each sub-database; s3, each sub database adopts Redis distributed lock to process the purchase demand of several distributed clients; and S4, automatically transferring the customer to another sub-database when the remaining number of the single sub-database can not meet the customer service requirement.
Further, S2 is assigned to each sub-database in a random manner.
Further, in S4, when the remaining number of all sub-databases cannot satisfy the customer 'S requirement alone and the sum of the remaining number of all sub-databases can satisfy the customer' S requirement, the customer is prompted to reduce the number of single purchases and then purchase again.
Further, the number of products in each sub-database in S1 is equal.
Further, when the remaining number of the single sub-database cannot satisfy the number of the customer service requirements, in S4, the sub-databases are sequentially switched to another database in a decreasing manner according to the names of the sub-databases.
Has the advantages that: the original database is divided into a plurality of sub-databases, each sub-database receives the customer purchase demand at the same time, the original database can only complete one customer purchase demand every 20ms, each sub-database in the plurality of sub-databases can complete one customer purchase demand every 20ms, and the processing speed of the order is dependent on the number of the divided sub-databases.
Drawings
The present invention will be further described and illustrated with reference to the following drawings.
FIG. 1 is a schematic diagram of the steps of an optimization of a distributed lock in the present application;
FIG. 2 is a schematic diagram illustrating a prior art stock over-selling phenomenon;
FIG. 3 is a schematic diagram illustrating the principles of Redis implementing a distributed lock to resolve stock oversell in the prior art.
Detailed Description
The technical scheme of the invention is more clearly and completely explained by the description of the preferred embodiment of the invention in combination with the attached drawings.
As shown in fig. 1, a processing method for Redis to implement high concurrency optimization of distributed locks includes the following steps:
s1: dividing an original database into a plurality of sub databases;
s2: distributing the purchase demand of the customer to each sub-database;
s3, each sub database adopts Redis distributed lock to process the purchase demand of several distributed clients;
and S4, automatically transferring the customer to another sub-database when the remaining number of the single sub-database can not meet the customer service requirement.
In step S1, the original database has a plurality of data sets, each data set is all information (brand, color, size, etc.) of a product, the plurality of data sets are divided into a plurality of data set sets according to a fixed number, each data set is a sub-database, and each sub-database is given a name.
In step S2, a random algorithm is implemented by the code, through which each customer' S purchase demand is randomly assigned to one of the plurality of sub-databases.
In step S3, after a plurality of customer requirements are randomly allocated to each sub-database, each sub-database can only process one customer requirement allocated to the sub-database every 20ms due to the Redis distributed lock, but within every 20ms, a plurality of sub-databases simultaneously process a different customer requirement, and compared with the original database processing, the processing speed of the customer is increased by a plurality of sub-databases, which is the number of sub-databases.
When the remaining number of the single sub-database of the client is not enough to satisfy the number of the customer service requirement in step S4, the client is transferred to another sub-database, and the sub-databases are sequentially forwarded according to the names of the sub-databases in step S1 until the customer requirement is satisfied.
In step S4, when the remaining number of all sub-databases alone cannot satisfy the customer requirement, but the sum of the remaining number of all sub-databases can satisfy the customer requirement, the customer is prompted to reduce the number of single purchases and then purchase again. And if the sum of the residual quantity of all the sub-databases cannot meet the customer requirement, prompting that the customer is insufficient in quantity and cannot purchase the sub-databases.
The above detailed description merely describes preferred embodiments of the present invention and does not limit the scope of the invention. Without departing from the spirit and scope of the present invention, it should be understood that various changes, substitutions and alterations can be made herein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents. The scope of the invention is defined by the claims.
Claims (5)
1. A processing method for Redis to realize high concurrency optimization of distributed locks is characterized by comprising the following steps:
s1: dividing an original database into a plurality of sub databases;
s2: distributing the purchase demand of the customer to each sub-database;
s3, each sub database adopts Redis distributed lock to process the purchase demand of several distributed clients;
and S4, when the residual number of the sub-database can not meet the customer service requirement number, automatically transferring the customer to another sub-database.
2. The method of claim 1, wherein the Redis is assigned to each sub-database in a random manner in S2.
3. The processing method of claim 1, wherein in S4, when the remaining number of all sub databases cannot individually satisfy the customer requirement and the sum of the remaining number of all sub databases can satisfy the customer requirement, the customer is prompted to reduce the number of single purchases and then purchase again.
4. The method of claim 1, wherein the number of products in each sub-database in the S1 is equal.
5. The method as claimed in claim 1, wherein when the remaining number of the single sub-database cannot satisfy the number of the customer service requirements, the step of S4 is performed by sequentially forwarding to another database in a descending manner according to the names of the sub-databases.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201911310027.XA CN111090525B (en) | 2019-12-18 | 2019-12-18 | Processing method for Redis to achieve high concurrency optimization of distributed lock |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201911310027.XA CN111090525B (en) | 2019-12-18 | 2019-12-18 | Processing method for Redis to achieve high concurrency optimization of distributed lock |
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| CN111090525A CN111090525A (en) | 2020-05-01 |
| CN111090525B true CN111090525B (en) | 2022-09-23 |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372994A (en) * | 2016-08-31 | 2017-02-01 | 北京炎黄新星网络科技有限公司 | Method and system for processing orders |
| CN106649328A (en) * | 2015-10-30 | 2017-05-10 | 宁波轩悦行电动汽车服务有限公司 | Time-sharing lease system data access layer interface establishment method |
| CN106874094A (en) * | 2017-02-17 | 2017-06-20 | 广州爱九游信息技术有限公司 | timed task processing method, device and computing device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109155021B (en) * | 2016-03-18 | 2022-04-15 | 沃尔玛阿波罗有限责任公司 | System and method for managing inventory of products purchased by a customer from a retailer |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106649328A (en) * | 2015-10-30 | 2017-05-10 | 宁波轩悦行电动汽车服务有限公司 | Time-sharing lease system data access layer interface establishment method |
| CN106372994A (en) * | 2016-08-31 | 2017-02-01 | 北京炎黄新星网络科技有限公司 | Method and system for processing orders |
| CN106874094A (en) * | 2017-02-17 | 2017-06-20 | 广州爱九游信息技术有限公司 | timed task processing method, device and computing device |
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