How to merge csv files based on unique columns in java

I have 3 different very large files and my requirement is to merge them together in one single csv file. All these 3 files have different number of columns but they contain some unique column between each other.

Example
file 1:

StudentId   StudentName   ClassId
1           Ajay           6
2           Vinay          8
3           Geeta          6
4           Sameer         7

file 2:

ClassId       ClassColor
6           Blue
7           Grey
8           White

file 3:

HouseId    HouseName    StudentId
1          Knights       4
2          Spartans      1
3          Samurai       2
4          Trojans       3

As you can see file1 and file2 has classId in common And file1 and file2 has studentId in common.
Now based on this information I want have result like this by joining files based on common key.

StudentId     StudentName    ClassId     ClassColor    HouseId    HouseName
1             Ajay           6           Blue          2          Spartans
2             Vinay          8           White         3          Samurai
3             Geeta          6           Blue          4          Trojans
4             Sameer         7           Grey          1          Knights

This scenario is completely new to me, I did some research but didn't find solution for it in java.

At this stage I am able to join two files with below implementation:

Class to hold csv file data:

import java.util.LinkedHashMap;
import java.util.Map;
public class CsvVo {
private Map<String, String> keyVal;
public CsvVo(String id) {
keyVal = new LinkedHashMap<>();// you may also use HashMap if you don't need to keep order
}
public Map<String, String> getKeyVal() {
return keyVal;
}
public void setKeyVal(Map<String, String> keyVal) {
this.keyVal = keyVal;
}
public void put(String key, String val) {
keyVal.put(key, val);
}
public String get(String key) {
return keyVal.get(key);
}
}

CSV parser to read files:

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Set;
public class CsvParser {
public static List<CsvVo> getRecodrsFromACsv(File file, List<String> keys) throws IOException {
BufferedReader br = new BufferedReader(new FileReader(file));
List<CsvVo> records = new ArrayList<>();
boolean isHeader = true;
String line = null;
while ((line = br.readLine()) != null) {
if (isHeader) {// first line is header
isHeader = false;
continue;
}
CsvVo record = new CsvVo(file.getName());
String[] lineSplit = line.split(",");
for (int i = 0; i < lineSplit.length; i++) {
record.put(keys.get(i), lineSplit[i]);
}
records.add(record);
}
br.close();
return records;
}
public static List<String> getHeadersFromACsv(File file) throws IOException {
BufferedReader br = new BufferedReader(new FileReader(file));
List<String> headers = null;
String line = null;
while ((line = br.readLine()) != null) {
String[] lineSplit = line.split(",");
headers = new ArrayList<>(Arrays.asList(lineSplit));
break;
}
br.close();
return headers;
}
public static void writeToCsv(final File file, final Set<String> headers, final List<CsvVo> records)
throws IOException {
FileWriter csvWriter = new FileWriter(file);
// write headers
String sep = "";
String[] headersArr = headers.toArray(new String[headers.size()]);
for (String header : headersArr) {
csvWriter.append(sep);
csvWriter.append(header);
sep = ",";
}
csvWriter.append("\n");
// write records at each line
for (CsvVo record : records) {
sep = "";
for (int i = 0; i < headersArr.length; i++) {
csvWriter.append(sep);
csvWriter.append(record.get(headersArr[i]));
sep = ",";
}
csvWriter.append("\n");
}
csvWriter.flush();
csvWriter.close();
}
}

Main Class to test the functionality:

import java.io.File;
import java.io.IOException;
import java.util.*;
public class CsvApplication {
public static void main(String[] args) throws IOException {
File csv1 = new File("/Users/guru/Desktop/Standard.csv");
File csv2 = new File("/Users/guru/Desktop/Match.csv");
List<String> csv1Headers = CsvParser.getHeadersFromACsv(csv1);
//csv1Headers.forEach(h -> System.out.print(h + " "));
//System.out.println();
List<String> csv2Headers = CsvParser.getHeadersFromACsv(csv2);
//csv2Headers.forEach(h -> System.out.print(h + " "));
//System.out.println();
List<String> allCsvHeaders = new ArrayList<>();
allCsvHeaders.addAll(csv1Headers);
allCsvHeaders.addAll(csv2Headers);
//allCsvHeaders.forEach(h -> System.out.print(h + " "));
//System.out.println();
Set<String> uniqueHeaders = new HashSet<>(allCsvHeaders);
//uniqueHeaders.forEach(h -> System.out.print(h + " "));
//System.out.println();
List<CsvVo> csv1Records = CsvParser.getRecodrsFromACsv(csv1, csv1Headers);
List<CsvVo> csv2Records = CsvParser.getRecodrsFromACsv(csv2, csv2Headers);
List<CsvVo> allCsvRecords = new ArrayList<>();
String key = "StudentID";
getUniqueRecordsForKey(key, csv1Records, csv2Records,allCsvRecords);
// allCsvRecords.addAll(csv1Records);
//allCsvRecords.addAll(csv2Records);
CsvParser.writeToCsv(new File("/Users/guru/Desktop/Output.csv"), uniqueHeaders, allCsvRecords);
}
private static void getUniqueRecordsForKey(String key, List<CsvVo> csv1Records, List<CsvVo> csv2Records, List<CsvVo> allCsvRecords) {
for (CsvVo record1: csv1Records){
for(CsvVo record2: csv2Records){
if( !record1.getKeyVal().get(key).isEmpty() && record1.getKeyVal().get(key).equals(record2.getKeyVal().get(key))){
HashMap<String ,String> mergedMap = new LinkedHashMap();
CsvVo mergedRecord = new CsvVo(record2.getKeyVal().get(key));
mergeRecords(mergedMap, record1,record2);
mergedRecord.setKeyVal(mergedMap);
csv2Records.remove(record2);
allCsvRecords.add(mergedRecord);
break;
}
}
}
}
private static void mergeRecords(HashMap mergedMap,CsvVo record1, CsvVo record2 ){
mergedMap.putAll(record1.getKeyVal());
mergedMap.putAll(record2.getKeyVal());
}
}

Here we are loading entire data of files to lists and then comparing data based on the column we want to join. In this case i have taken StudentID.

Not sure if this is good approach for big files.