Detailed explanation of the technical principles of the CSV verification device framework in the Java class library
The CSV verification device framework in the Java class library is a tool for verifying the content of the CSV (comma division value) file.CSV files are usually used to store and transmit information containing structured data.The verification device framework can help developers check the correctness of the data in the CSV file, and process data that is invalid or does not meet the requirements according to the set rules.
The technical principles of the CSV verification device framework are as follows:
1. CSV file analysis: The CSV verification device first reads the content of the CSV file and parses it as a data object.You can use the CSV library in Java, such as OpenCSV or Super CSV to achieve parsing function.
2. Data verification rule definition: Definitions need to define verification rules to determine the effectiveness of each field in the CSV file.You can use annotations or configuration files to define rules.Here are some common verification rules for examples:
-Filling field: Make sure the field is not empty.
-Data type: Verify whether the field is a specified data type, such as integer, floating point or date, etc.
-Data range: The values of the verification field are within the specified range.
-Forman specification: Verify whether the field meets the specified format, such as mailbox address or phone number.
3. Data verification process: Once the verification rules are defined, the CSV verification device framework will read the contents of the CSV file row and verify the fields of each line.If the value of the field does not meet the rules, there will be corresponding error messages or warnings.
4. Error processing: The verification device framework can provide a variety of ways to process verification errors.You can choose to record the error message into the log file, or to notify the caller by the method of abnormally throwing it.
Below is a simple Java code example, demonstrate how to use Apache Commons CSV and Bean Validation (JSR-303) to implement CSV verification:
import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVParser;
import org.apache.commons.csv.CSVRecord;
import javax.validation.ConstraintViolation;
import javax.validation.Validation;
import javax.validation.Validator;
import javax.validation.ValidatorFactory;
import java.io.FileReader;
import java.io.IOException;
import java.util.List;
import java.util.Set;
public class CsvValidator {
public static void main(String[] args) throws IOException {
ValidatorFactory factory = Validation.buildDefaultValidatorFactory();
Validator validator = factory.getValidator();
CSVParser parser = CSVFormat.DEFAULT.withHeader().parse(new FileReader("example.csv"));
List<CSVRecord> records = parser.getRecords();
for (CSVRecord record : records) {
CsvData data = new CsvData(record.get("name"), Integer.parseInt(record.get("age")));
Set<ConstraintViolation<CsvData>> violations = validator.validate(data);
if (violations.isEmpty()) {
// Data Verification Pass
} else {
// Process verification error
for (ConstraintViolation<CsvData> violation : violations) {
System.out.println(violation.getMessage());
}
}
}
}
// Data object class
public static class CsvData {
@NotBlank(message = "Name is required")
private String name;
@Min(value = 18, message = "Age must be greater than or equal to 18")
private int age;
public CsvData(String name, int age) {
this.name = name;
this.age = age;
}
// Getters and Setters
}
}
In the above example, we use the Apache Commons CSV library to analyze the CSV file and define and execute the verification rules through the Bean Validation framework.Through @Notblank and @min, we can define the must -filling and minimum requirements of fields.
The use of the CSV verification device framework can greatly simplify the verification process of the CSV file and provide a scalable way to define and execute the verification rules.This can help developers handle data in CSV files more effectively and ensure the correctness of the data.
