golang data verification failed

With the wide application of golang in the development field, data verification has become an important part of the development process. Whether it is parameter verification of the back-end interface or verification of the front-end form, the accuracy of the data needs to be checked. However, in the actual development process, data verification failure often leads to program crashes or loopholes, bringing hidden dangers to the system. This article will discuss the reasons and solutions for golang data verification failure.

1. The importance of data verification

1. Improve data accuracy: Data verification can effectively eliminate invalid or erroneous data and ensure the accuracy and reliability of data.

2. Improve program robustness: Verifying data can avoid unnecessary errors when the program is running and reduce the risk of crashes.

3. Avoid security vulnerabilities: Data verification can prevent malicious attackers from using program vulnerabilities to steal or tamper with data, thus ensuring data security.

2. How to implement data verification

golang can implement data verification in a variety of ways, the most commonly used of which is to use a third-party library to complete data verification. The following are some commonly used data validation libraries:

1.gopkg.in/go-playground/validator.v9

2.github.com/asaskevich/govalidator

3.github.com/thedevsaddam/govalidator

These third-party libraries have their own advantages and disadvantages, and you can choose the appropriate library to use according to actual project needs.

3. Reasons for data verification failure

Data verification failure is usually caused by the following reasons:

1. Data type problem: Because golang is a static In type language, the data type is determined at compile time. If the incoming data type does not match the definition, data verification will fail.

2. Illegal data values: Data values also need to be verified during data verification. For example, for numeric types, it needs to be judged whether it is less than or equal to 0, or whether it exceeds the defined legal range, etc.

3. Missing necessary parameters: Some parameters may be required. If these necessary parameters are missing, data verification will fail.

4. The data format does not meet the requirements: In some cases, if the data format does not meet the requirements, for example, it does not meet the matching requirements of regular expressions, it will cause data verification to fail.

4. How to solve the problem of data verification failure

1. Define a rigorous data model: When defining the data model, you need to ensure that all parameters have reasonable types and default values, as well as necessary verification logic.

2. Use third-party libraries for verification: As mentioned above, you can use third-party libraries to complete data verification. These libraries have been widely used in various scenarios and can solve some common data problems. Verification problem.

3. Customized verification logic: In some scenarios, you cannot just rely on third-party libraries. You can customize the verification logic according to business needs.

4. Provide friendly error prompts: When data verification fails, a friendly error prompt should be provided so that users can clearly know the reason for the verification failure and how to repair it.

5. Summary

Data verification is a very important task in any application. It can not only improve data accuracy, but also increase the robustness and security of the program. In practice, there are many reasons for data verification failure, and it is necessary to carefully analyze the reasons and solve the problems reasonably. Through reasonable data verification work, the stability and reliability of the program can be improved and the user's data security can be protected.

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