The best practice of Super CSV Dozer Extension framework
The best practice of Super CSV Dozer Extension framework
introduce:
Super CSV Dozer Extension is a powerful CSV processing framework. It combines the functions of Super CSV and Dozer to provide data mapping when processing CSV files.This article will introduce how to use the Super CSV Dozer Extension framework to achieve the best practice.
1. Introduce dependencies
First of all, you need to introduce the dependencies of Super CSV Dozer Extension in the pom.xml file of the project so that the function of the framework can be used.Introduce the following dependencies:
<dependency>
<groupId>net.sf.supercsv</groupId>
<artifactId>super-csv-dozer</artifactId>
<version>2.4.0</version>
</dependency>
2. Create CSV file mapping entity
In order to use the Super CSV Dozer Extension framework, you need to create a physical class for mapping the CSV file.This physical class should contain the corresponding attributes corresponding to the columns in the CSV file.For example, if the CSV file has two columns of "name" and "Age", then your physical class should have the properties of "name" and "Age".
public class Person {
private String name;
private int age;
// omit the getter and setter method
}
3. Create a CSV file parser
Next, you need to create a CSV file parser to read data from the CSV file and map it into the physical class.The following is a simple example:
public class CsvParser {
public static List<Person> parseCsvFile(String filePath) throws Exception {
List<Person> persons = new ArrayList<>();
ICsvDozerBeanReader beanReader = null;
try {
Reader reader = new FileReader(filePath);
beanReader = new CsvDozerBeanReader(reader, CsvPreference.STANDARD_PREFERENCE);
final String[] header = beanReader.getHeader(true);
final CellProcessor[] processors = getProcessors();
Person person;
while ((person = beanReader.read(Person.class, header, processors)) != null) {
persons.add(person);
}
} finally {
if (beanReader != null) {
beanReader.close();
}
}
return persons;
}
private static CellProcessor[] getProcessors() {
// Define the processor
final CellProcessor[] processors = new CellProcessor[] {
new notnull (), // name non -empty
New Parseint () // Age Analysis is an integer
};
return processors;
}
}
4. Analyze the CSV file and map it to the physical class
Now, you can use the PARSECSVFile method in the CSVPARSER class to analyze the CSV file and map it into the physical class.The following is an example:
public class Main {
public static void main(String[] args) {
String filePath = "path/to/csv/file.csv";
try {
List<Person> persons = CsvParser.parseCsvFile(filePath);
for (Person person : persons) {
System.out.println(person.getName() + ", " + person.getAge());
}
} catch (Exception e) {
e.printStackTrace();
}
}
}
In this way, you successfully use the Super CSV Dozer Extension framework to analyze and mappore the data into the physical class from the CSV file.
Summarize:
The Super CSV Dozer Extension framework can easily process CSV files and implement data mapping.In this article, we introduce the best practice of how to use the Super CSV Dozer Extension framework.By introducing dependencies, creating a CSV file mapping entity, writing a CSV file parser, and using a parser to map the data to the physical class. You can quickly and efficiently process the data in the CSV file.I hope this article will help you use the Super CSV Dozer Extension framework.
