Question-1:
Imagine you are given a real file system, how will you search files? DFS or BFS?
Answer:
core idea: DFS
Reason: if depth of directory is not too deeper, which is suitable to use DFS, comparing with BFS.
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.nio.file.attribute.BasicFileAttributes;
import java.security.MessageDigest;
import java.math.BigInteger;
class Directory {
List<Directory> subDirectories;
List<File> files;
}
public static String makeHashQuick(File file) {
try {
FileInputStream fileInput = new FileInputStream(file);
byte[] data = new byte[(int) file.length()];
fileInput.read(data);
fileInput.close();
MessageDigest md = MessageDigest.getInstance("MD5");
String fileHash = new BigInteger(1, md.digest(data)).toString(16);
return fileHash;
} catch (IOException e) {
throw new RuntimeException("can't read file: " + file.getAbsolutePath(), e);
}
}
public static void findDuplicatedFilesByMD5FromFile(Map<String, List<String>> lists, File file) {
String fileHash = makeHashQuick(file)
List<String> list = lists.get(fileHash);
if (list==null) {
list = new LinkedList<String>();
lists.put(fileHash, list);
}
list.add(file.getAbsolutePath());
}
public static void findDuplicatedFilesByMD5(Map<String, List<String>> lists, Directory dir) {
for (File file: dir.files) {
findDuplicatedFilesByMD5FromFile(lists, file);
}
for (Directory curDir: dir.subDirectories) {
findDuplicatedFilesByMD5(lists, curDir);
}
}
public static List<List<String>> storeDuplicateFiles(Directory dir) {
List<List<String>> ret = new ArrayList<List<String>>();
Map<String, List<String>> listsByMD5 = new HashMap<String, List<String>>();
findDuplicatedFilesByMD5(listsByMD5, dir);
for (List<String> list: listsByMD5) {
if (list.size()>1) {
ret.add(list);
}
}
return ret;
}
Question-2:
If the file content is very large (GB level), how will you modify your solution?
Answer:
core idea: make use of meta data, like file size before really reading large content.
Two steps:
- DFS to map each size to a set of paths that have that size: Map<Integer, Set>
- For each size, if there are more than 2 files there, compute hashCode of every file by MD5, if any files with the same size have the same hash, then they are identical files: Map<String, Set>, mapping each hash to the Set of filepaths+filenames. This hash id's are very very big, so we use the Java library BigInteger.
public static void findDuplicatedFilesBySizeFromFile(Map<Integer, List<String>> lists, File file) {
try {
Path filePath = Paths.get(file.getAbsolutePath());
BasicFileAttributes attr = Files.readAttributes(filePath, BasicFileAttributes.class);
int size = attr.size();
List<String> list = lists.get(size);
if (list==null) {
list = new LinkedList<String>();
lists.put(size, list);
}
list.add(file.getAbsolutePath());
} catch (IOException e) {
throw new RuntimeException("can't read file attributes: " + file.getAbsolutePath(), e);
}
}
public static void findDuplicatedFilesBySize(Map<Integer, List<String>> lists, Directory dir) {
for (File file: dir.files) {
findDuplicatedFilesBySizeFromFile(lists, file);
}
for (Directory curDir: dir.subDirectories) {
findDuplicatedFilesBySize(lists, curDir);
}
}
public static List<List<String>> storeDuplicateFiles(Directory dir) {
List<List<String>> ret = new ArrayList<List<String>>();
Map<Integer, List<String>> listsBySize = new HashMap<Integer, List<String>>();
findDuplicatedFilesBySize(listsBySize, dir);
Map<String, List<String>> listsByMD5 = new HashMap<String, List<String>>)();
for (List<String> list: listsBySize) {
if (list.size()>1) {
for (String fileName: list) {
findDuplicatedFilesByMD5FromFile(listsByMD5, new File(fileName));
}
}
}
for (List<String> list: listsByMD5) {
if (list.size()>1) {
ret.add(list);
}
}
return ret;
}
To optimize Step-2. In GFS, it stores large file in multiple "chunks" (one chunk is 64KB). we have meta data, including the file size, file name and index of different chunks along with each chunk's checkSum(the xor for the content). For step-2, we just compare each file's checkSum.
Disadvantage: there might be flase positive duplicates, because two different files might share the same checkSum.
Question-3:
If you can only read the file by 1kb each time, how will you modify your solution?
Answer:
- makeHashQuick Function is quick but memory hungry, might likely to run with java -Xmx2G or the likely to increase heap space if RAM avaliable.
- we might need to play with the size defined by "buffSize" to make memory efficient.
import java.io.RandomAccessFile;
public static String makeHashLean(File infile) {
RandomAccessFile file = new RandomAccessFile(infile, "r");
int buffSize = 1024;
byte[] buffer = new byte[buffSize];
// calculate the hash of the whole file
long offset = file.length();
long read = 0;
int unitsize;
while(read<offset) {
unitsize = (int) (((offset-read)>=buffSize)?buffSize:(offset-read));
file.read(buffer, 0, unitsize);
md.update(buffer, 0, unitsize);
read += unitsize;
}
file.close();
String hash = new BigInteger(1, md.digest()).toString(16);
return hash;
}
Question-4:
What is the time complexity of your modified solution? What is the most time-consuming part and memory consuming part of it? How to optimize?
Answer:
- hashing part is the most time-consuming and memory consuming.
- optimize as above mentioned, but also introduce false positive issue.
Question-5:
How to make sure the duplicated files you find are not false positive?
Answer:
Question-2-Answer-1 will avoid it.