asdf2014 opened a new issue, #16945:
URL: https://github.com/apache/druid/issues/16945

   ### Description
   
   In Apache Druid, we need to support a new feature that can check the 
interval range to avoid cases where the year is inappropriately entered.
   
   Specifically, when dealing with time data, there are instances where 
incorrect years are entered due to typos or other reasons. For example, 
entering the year as 20240 instead of 2024. These incorrect years can lead to 
significant deviations in data processing and analysis results, affecting the 
accuracy and reliability of the data.
   
   To avoid such situations, we plan to add an interval range check feature in 
Apache Druid. This feature will allow users to set a reasonable range for 
years, such as from the year 2000 to 2100. During data input and processing, 
the system will automatically check whether the year falls within this range. 
If a year outside this range is detected, the system will issue a warning or 
error message, prompting the user to make corrections.
   
   The implementation of this new feature will include the following steps:
   
   1. **Define a reasonable year range**: Users can set a reasonable year range 
through configuration files or the interface.
   2. **Data input check**: During the data input phase, the system will check 
whether the year of each data entry falls within the set range.
   3. **Data processing check**: During the data processing phase, the system 
will also perform year checks to ensure that all processing data years are 
within the reasonable range.
   4. **Error handling and notification**: If a year outside the range is 
detected, the system will log the error and issue a warning or error message to 
the user.
   
   By introducing this interval range check feature, we can effectively avoid 
data issues caused by incorrect year entries, enhancing the accuracy and 
reliability of data processing. This will provide users with higher quality 
data analysis services, ensuring that their decisions are based on accurate and 
error-free data.
   
   


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