The performance optimization technique of the CSV extension framework in the Java library
CSV (COMMA-SEPARATD VALUES) is a commonly used data storage format that is usually used to exchange data between different applications.The Java class library provides many CSV extension frameworks to provide faster and efficient CSV file processing functions.This article will introduce the skills of optimizing the CSV extension framework in the Java library and give the relevant Java code example.
1. Use high -performance CSV parser/writinger
1. Apache Commons CSV
Apache Commons CSV is a popular CSV processing library, which provides a fast and easy -to -use CSV parser and writer.The following is a sample code that uses Apache Commons CSV to analyze the CSV file:
import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVParser;
import org.apache.commons.csv.CSVRecord;
import java.io.FileReader;
import java.io.IOException;
public class CSVReaderExample {
public static void main(String[] args) {
try (CSVParser parser = new CSVParser(new FileReader("data.csv"), CSVFormat.DEFAULT)) {
for (CSVRecord record : parser) {
String name = record.get(0);
int age = Integer.parseInt(record.get(1));
System.out.println("Name: " + name + ", Age: " + age);
}
} catch (IOException e) {
e.printStackTrace();
}
}
}
2. OpenCSV
OpenCSV is another popular CSV processing library, which provides a fast and flexible CSV parser and writer.The following is an example code using OpenCSV to analyze the CSV file:
import com.opencsv.CSVReader;
import java.io.FileReader;
import java.io.IOException;
public class CSVReaderExample {
public static void main(String[] args) {
try (CSVReader reader = new CSVReader(new FileReader("data.csv"))) {
String[] record;
while ((record = reader.readNext()) != null) {
String name = record[0];
int age = Integer.parseInt(record[1]);
System.out.println("Name: " + name + ", Age: " + age);
}
} catch (IOException e) {
e.printStackTrace();
}
}
}
2. Use the appropriate CSV configuration option
1. Select the correct separator
CSV files can use different separators, common ones, seminars, and expressive symbols.By default, most CSV parsers use comma as a separators, but problems may occur when processing data containing commas.Therefore, choosing the appropriate separators according to the actual situation can improve performance.The following is a sample code that uses Apache Commons CSV to customize separators:
import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVParser;
import org.apache.commons.csv.CSVRecord;
import java.io.FileReader;
import java.io.IOException;
public class CSVReaderExample {
public static void main(String[] args) {
try (CSVParser parser = new CSVParser(new FileReader("data.csv"), CSVFormat.newFormat(';'))) {
for (CSVRecord record : parser) {
String name = record.get(0);
int age = Integer.parseInt(record.get(1));
System.out.println("Name: " + name + ", Age: " + age);
}
} catch (IOException e) {
e.printStackTrace();
}
}
}
2. Skip the header
The CSV file usually contains the header, which describes the meaning of each column.If you do not need to process the header data, you can save the analysis time by setting the header option.The following is an example code using OpenCSV to skip the head head:
import com.opencsv.CSVReader;
import java.io.FileReader;
import java.io.IOException;
public class CSVReaderExample {
public static void main(String[] args) {
try (CSVReader reader = new CSVReaderBuilder(new FileReader("data.csv")).withSkipLines(1).build()) {
String[] record;
while ((record = reader.readNext()) != null) {
String name = record[0];
int age = Integer.parseInt(record[1]);
System.out.println("Name: " + name + ", Age: " + age);
}
} catch (IOException e) {
e.printStackTrace();
}
}
}
3. Use appropriate memory management skills
1. Use streaming processing
When processing large CSV files, loading the entire file into memory may cause memory overflow.In order to avoid this situation, only one line of data can be used at a time.The following is a sample code for processing CSV files with Apache Commons CSV streamlined:
import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVParser;
import org.apache.commons.csv.CSVRecord;
import java.io.FileReader;
import java.io.IOException;
public class CSVReaderExample {
public static void main(String[] args) {
try (CSVParser parser = new CSVParser(new FileReader("data.csv"), CSVFormat.DEFAULT)) {
parser.forEach(record -> {
String name = record.get(0);
int age = Integer.parseInt(record.get(1));
System.out.println("Name: " + name + ", Age: " + age);
});
} catch (IOException e) {
e.printStackTrace();
}
}
}
2. Use memory mapping file
Memory mapping file technology can map the file part or all content to the memory, avoiding repeated file reading and writing operations, and improved the processing speed.The following is a sample code for processing the CSV file with the Java NiO memory mapping file:
import java.io.IOException;
import java.io.RandomAccessFile;
import java.nio.MappedByteBuffer;
import java.nio.channels.FileChannel;
import java.nio.charset.StandardCharsets;
public class CSVReaderExample {
public static void main(String[] args) {
try (FileChannel channel = new RandomAccessFile("data.csv", "r").getChannel()) {
MappedByteBuffer buffer = channel.map(FileChannel.MapMode.READ_ONLY, 0, channel.size());
StringBuilder sb = new StringBuilder();
while (buffer.hasRemaining()) {
char c = (char) buffer.get();
if (c == '
') {
processLine(sb.toString());
sb.setLength(0);
} else {
sb.append(c);
}
}
processLine(sb.toString());
} catch (IOException e) {
e.printStackTrace();
}
}
private static void processLine(String line) {
String[] parts = line.split(",");
String name = parts[0];
int age = Integer.parseInt(parts[1]);
System.out.println("Name: " + name + ", Age: " + age);
}
}
In summary, by using high -performance CSV parser/writing, suitable CSV configuration options and appropriate memory management skills, the performance of the CSV expansion framework in the Java class library can be optimized.These techniques can improve the speed and efficiency of CSV file processing, making data exchange faster and reliable.
