Develop Reference

How to generate 1000 unique email-ids using java

My requirement is to generate 1000 unique email-ids in Java. I have already generated random Text and using for loop I'm limiting the number of email-ids to be generated. Problem is when I execute 10 email-ids are generated but all are same.
Below is the code and output:
public static void main() {
first fr = new first();
String n = fr.genText()+"#mail.com";
for (int i = 0; i<=9; i++) {
System.out.println(n);
}
}
public String genText() {
String randomText = "abcdefghijklmnopqrstuvwxyz";
int length = 4;
String temp = RandomStringUtils.random(length, randomText);
return temp;
}
and output is:
myqo#mail.com
myqo#mail.com
...
myqo#mail.com
When I execute the same above program I get another set of mail-ids. Example: instead of 'myqo' it will be 'bfta'. But my requirement is to generate different unique ids.
For Example:
myqo#mail.com
bfta#mail.com
kjuy#mail.com

Put your String initialization in the for statement:
for (int i = 0; i<=9; i++) {
String n = fr.genText()+"#mail.com";
System.out.println(n);
}
I would like to rewrite your method a little bit:
public String generateEmail(String domain, int length) {
return RandomStringUtils.random(length, "abcdefghijklmnopqrstuvwxyz") + "#" + domain;
}
And it would be possible to call like:
generateEmail("gmail.com", 4);
As I understood, you want to generate unique 1000 emails, then you would be able to do this in a convenient way by Stream API:
Stream.generate(() -> generateEmail("gmail.com", 4))
.limit(1000)
.collect(Collectors.toSet())
But the problem still exists. I purposely collected a Stream<String> to a Set<String> (which removes duplicates) to find out its size(). As you may see, the size is not always equals 1000
999
1000
997
that means your algorithm returns duplicated values even for such small range.
Therefore, you'd better research already written email generators for Java or improve your own (for example, by adding numbers, some special characters that, in turn, will generate a plenty of exceptions).

If you are planning to use MockNeat, the feature for implementing email strings is already implemented.
Example 1:
String corpEmail = mock.emails().domain("startup.io").val();
// Possible Output: tiptoplunge#startup.io
Example 2:
String domsEmail = mock.emails().domains("abc.com", "corp.org").val();
// Possible Output: funjulius#corp.org
Note: mock is the default "mocking" object.

To guarantee uniqueness you could use a counter as part of the email address:
myqo0000#mail.com
bfta0001#mail.com
kjuy0002#mail.com
If you want to stick to letters only then convert the counter to base 26 representation using 'a' to 'z' as the digits.

JVisualVM HeapDump OQL rendering array inside an Object

I am trying to write a query such as this:
select {r: referrers(f), count:count(referrers(f))}
from com.a.b.myClass f
However, the output doesn't show the actual objects:
{
count = 3.0,
r = [object Object]
}
Removing the Javascript Object notation once again shows referrers normally, but they are no longer compartmentalized. Is there a way to format it inside the Object notation?

So I see that you asked this question a year ago, so I don't know if you still need the answer, but since I was searching around for something similar, I can answer this. The problem is that referrers(f) returns an enumeration and so it doesn't really translate well when you try to put it into your hashmap. I was doing a similar type of analysis where I was trying to find unique char arrays (count the unique combinations of char arrays up to the first 50 characters). What I came up with was this:
var counts = {};
filter(
map(
unique(
map(
filter(heap.objects('char[]'), "it.length > 50"), // filter out strings less than 50 chars in length
function(charArray) { // chop the string at 50 chars and then count the unique combos
var subs = charArray.toString().substr(0,50);
if (! counts[subs]) {
counts[subs] = 1;
} else {
counts[subs] = counts[subs] + 1;
}
return subs;
}
) // map
) // unique
, function(subs) { // map the strings into an array that has the string and the counts of that string
return { string: subs, count: counts[subs] };
}) // map
, "it.count > 5000"); // filter out strings that have counts < 5000
This essentially shows how to take an enumeration (heap.objects('char[]') in this case) and filter it and map it so that you can compute statistics on it. Hope this helps someone.

Tips optimizing Java code

So, I've written a spellchecker in Java and things work as they should. The only problem is that if I use a word where the max allowed distance of edits is too large (like say, 9) then my code runs out of memory. I've profiled my code and dumped the heap into a file, but I don't know how to use it to optimize my code.
Can anyone offer any help? I'm more than willing to put up the file/use any other approach that people might have.
-Edit-
Many people asked for more details in the comments. I figured that other people would find them useful, and they might get buried in the comments. Here they are:
I'm using a Trie to store the words themselves.
In order to improve time efficiency, I don't compute the Levenshtein Distance upfront, but I calculate it as I go. What I mean by this is that I keep only two rows of the LD table in memory. Since a Trie is a prefix tree, it means that every time I recurse down a node, the previous letters of the word (and therefore the distance for those words) remains the same. Therefore, I only calculate the distance with that new letter included, with the previous row remaining unchanged.
The suggestions that I generate are stored in a HashMap. The rows of the LD table are stored in ArrayLists.
Here's the code of the function in the Trie that leads to the problem. Building the Trie is pretty straight forward, and I haven't included the code for the same here.
/*
* #param letter: the letter that is currently being looked at in the trie
* word: the word that we are trying to find matches for
* previousRow: the previous row of the Levenshtein Distance table
* suggestions: all the suggestions for the given word
* maxd: max distance a word can be from th query and still be returned as suggestion
* suggestion: the current suggestion being constructed
*/
public void get(char letter, ArrayList<Character> word, ArrayList<Integer> previousRow, HashSet<String> suggestions, int maxd, String suggestion){
// the new row of the trie that is to be computed.
ArrayList<Integer> currentRow = new ArrayList<Integer>(word.size()+1);
currentRow.add(previousRow.get(0)+1);
int insert = 0;
int delete = 0;
int swap = 0;
int d = 0;
for(int i=1;i<word.size()+1;i++){
delete = currentRow.get(i-1)+1;
insert = previousRow.get(i)+1;
if(word.get(i-1)==letter)
swap = previousRow.get(i-1);
else
swap = previousRow.get(i-1)+1;
d = Math.min(delete, Math.min(insert, swap));
currentRow.add(d);
}
// if this node represents a word and the distance so far is <= maxd, then add this word as a suggestion
if(isWord==true && d<=maxd){
suggestions.add(suggestion);
}
// if any of the entries in the current row are <=maxd, it means we can still find possible solutions.
// recursively search all the branches of the trie
for(int i=0;i<currentRow.size();i++){
if(currentRow.get(i)<=maxd){
for(int j=0;j<26;j++){
if(children[j]!=null){
children[j].get((char)(j+97), word, currentRow, suggestions, maxd, suggestion+String.valueOf((char)(j+97)));
}
}
break;
}
}
}

Here's some code I quickly crafted showing one way to generate the candidates and to then "rank" them.
The trick is: you never "test" a non-valid candidate.
To me your: "I run out of memory when I've got an edit distance of 9" screams "combinatorial explosion".
Of course to dodge a combinatorial explosion you don't do thing like trying to generate yourself all words that are at a distance from '9' from your misspelled work. You start from the misspelled word and generate (quite a lot) of possible candidates, but you refrain from creating too many candidates, for then you'd run into trouble.
(also note that it doesn't make much sense to compute up to a Levenhstein Edit Distance of 9, because technically any word less than 10 letters can be transformed into any other word less than 10 letters in max 9 transformations)
Here's why you simply cannot test all words up to a distance of 9 without either having an OutOfMemory error or simply a program never terminating:
generating all the LED up to 1 for the word "ptmizing", by only adding one letter (from a to z) generates already 9*26 variations (i.e. 324 variations) [there are 9 positions where you can insert one out of 26 letters)
generating all the LED up to 2, by only adding one letter to what we know have generates already 10*26*324 variations (60 840)
generating all the LED up to 3 gives: 17 400 240 variations
And that is only by considering the case where we add one, add two or add three letters (we're not counting deletion, swaps, etc.). And that is on a misspelled word that is only nine characters long. On "real" words, it explodes even faster.
Sure, you could get "smart" and generate this in a way not to have too many dupes etc. but the point stays: it's a combinatorial explosion that explodes fastly.
Anyway... Here's an example. I'm simply passing the dictionary of valid words (containing only four words in this case) to the corresponding method to keep this short.
You'll obviously want to replace the call to the LED with your own LED implementation.
The double-metaphone is just an example: in a real spellchecker words that do "sound alike"
despite further LED should be considered as "more correct" and hence often suggest first. For example "optimizing" and "aupteemising" are quite far from a LED point of view, but using the double-metaphone you should get "optimizing" as one of the first suggestion.
(disclaimer: following was cranked in a few minutes, it doesn't take into account uppercase, non-english words, etc.: it's not a real spell-checker, just an example)
#Test
public void spellCheck() {
final String src = "misspeled";
final Set<String> validWords = new HashSet<String>();
validWords.add("boing");
validWords.add("Yahoo!");
validWords.add("misspelled");
validWords.add("stackoverflow");
final List<String> candidates = findNonSortedCandidates( src, validWords );
final SortedMap<Integer,String> res = computeLevenhsteinEditDistanceForEveryCandidate(candidates, src);
for ( final Map.Entry<Integer,String> entry : res.entrySet() ) {
System.out.println( entry.getValue() + " # LED: " + entry.getKey() );
}
}
private SortedMap<Integer, String> computeLevenhsteinEditDistanceForEveryCandidate(
final List<String> candidates,
final String mispelledWord
) {
final SortedMap<Integer, String> res = new TreeMap<Integer, String>();
for ( final String candidate : candidates ) {
res.put( dynamicProgrammingLED(candidate, mispelledWord), candidate );
}
return res;
}
private int dynamicProgrammingLED( final String candidate, final String misspelledWord ) {
return Levenhstein.getLevenshteinDistance(candidate,misspelledWord);
}
Here you generate all possible candidates using several methods. I've only implemented one such method (and quickly so it may be bogus but that's not the point ; )
private List<String> findNonSortedCandidates( final String src, final Set<String> validWords ) {
final List<String> res = new ArrayList<String>();
res.addAll( allCombinationAddingOneLetter(src, validWords) );
// res.addAll( allCombinationRemovingOneLetter(src) );
// res.addAll( allCombinationInvertingLetters(src) );
return res;
}
private List<String> allCombinationAddingOneLetter( final String src, final Set<String> validWords ) {
final List<String> res = new ArrayList<String>();
for (char c = 'a'; c < 'z'; c++) {
for (int i = 0; i < src.length(); i++) {
final String candidate = src.substring(0, i) + c + src.substring(i, src.length());
if ( validWords.contains(candidate) ) {
res.add(candidate); // only adding candidates we know are valid words
}
}
if ( validWords.contains(src+c) ) {
res.add( src + c );
}
}
return res;
}

One thing you could try is, increase the Java's heap size, in order to overcome "out of memory error".
Following article will help you in order to understand how to increase heap size in Java
http://viralpatel.net/blogs/2009/01/jvm-java-increase-heap-size-setting-heap-size-jvm-heap.html
But I think the better approach to address your problem is, find out a better algorithm than the current algorithm

Well without more Information on the topic there is not much the community could do for you... You can start with the following:
Look at what your Profiler says (after it has run a little while): Does anything pile up? Are there a lot of Objects - this should normally give you a hint on what is wrong with your code.
Publish your saved dump somewhere and link it in your question, so someone else could take a look at it.
Tell us which profiler you are using, then somebody can give you hints on where to look for valuable information.
After you have narrowed down your problem to a specific part of your Code, and you cannot figure out why there are so many objects of $FOO in your memory, post a snippet of the relevant part.

Java string comparison

I am comparing substrings in two large text files. Very simple, tokenizing into two token containers, comparing with 2 for loops. Performance is disastrous! Does anybody have an advice or idea how to improve performance?
for (int s = 0; s < txtA.TokenContainer.size(); s++) {
String strTxtA = txtA.getSubStr(s);
strLengthA = txtA.getNumToken(s);
if (strLengthA >= dp.getMinStrLength()) {
int tokenFileB = 1;
for (int t = 0; t < txtB.TokenContainer.size(); t++) {
String strTxtB = txtB.getSubStr(t);
strLengthB = txtB.getNumToken(t);
if (strTxtA.equalsIgnoreCase(strTxtB)) {
try {
subStrTemp = new SubStrTemp(
txtA.ID, txtB.ID, tokenFileA, tokenFileB,
(tokenFileA + strLengthA - 1),
(tokenFileB + strLengthB - 1));
if (subStrContainer.contains(subStrTemp) == false) {
subStrContainer.addElement(subStrTemp);
}
} catch (Exception ex) {
logger.error("error");
}
}
tokenFileB += strLengthB;
}
tokenFileA += strLengthA;
}
}
Generally my code reading two large Strings with Java Tokonizer into containers A and B. And then trying to compare substrings.Possision of Substrgs which are existing in both strings to store into a Vector. But performance is awful, also don't really know how to solve it with HashMap.

Your main problem is that you go through all txtB for each token in txtA.
You should store informations on token from txtA (in a HashMap for instance) and then in a second loop (but not a nested one) you compare the strings with the existing one in the Map.
On the same topic :
term frequency using java program
How to count words in java

You are doing a join with nested loops? Yes, that is O(n^2). What about doing a hash join instead? That is, create a map from (lowercased) strText to t and do lookups with this map rather than iterating over the token container?

Put the tokens of fileA into a trie data structure. Then when tokenising fileB you can check quite quickly if these tokens are in the trie. A few code comments would help.

A said, this is an issue of complexity and you're algorithm runs in O(n^2) instead of O(n) using hash.
For second order improvements try to call less to functions, for example you can get the size once
sizeB = txtB.TokenContainer.size();
Depeneds on the size, you may call the container once to get an array of strings to save the getStr....
Roni

sample java code for approximate string matching or boyer-moore extended for approximate string matching

I need to find 1.mismatch(incorrectly played notes), 2.insertion(additional played), & 3.deletion (missed notes), in a music piece (e.g. note pitches [string values] stored in a table) against a reference music piece.
This is either possible through exact string matching algorithms or dynamic programming/ approximate string matching algos. However I realised that approximate string matching is more appropriate for my problem due to identifying mismatch, insertion, deletion of notes. Or an extended version of Boyer-moore to support approx. string matching.
Is there any link for sample java code I can try out approximate string matching? I find complex explanations and equations - but I hope I could do well with some sample code and simple explanations. Or can I find any sample java code on boyer-moore extended for approx. string matching? I understand the boyer-moore concept, but having troubles with adjusting it to support approx. string matching (i.e. to support mismatch, insertion, deletion).
Also what is the most efficient approx. string matching algorithm (like boyer-moore in exact string matching algo)?
Greatly appreciate any insight/ suggestions.
Many thanks in advance

You could start with the Wikipedia page on approximate string matching.
The problem is that this is a complex field, and simply looking at / copying some example code probably won't help you understand what is going on.
EDIT - besides, I don't see how Boyer-Moore would adapt to approximate string matching.

Here is the C# Boyer-More code, which can be tweeked to BMH or approximate matching.
Dictionary<char, int> ShiftSizeTable = new Dictionary<char, int>();
//Calculate Shifit/Skip count for each element in pattern text. So that we can skip that many no of Characters in given text while searching.
public void PreProcessBMSBadMatchTable(char[] patternCharacters)
{
ShiftSizeTable.Clear();
int totalCharacters = patternCharacters.Length;
for (int lpIndex = 0; lpIndex < totalCharacters; lpIndex++)
{
//Calculate the shift size for each character in the string or char array.
int ShiftSize = Math.Max(1, (totalCharacters - 1) - lpIndex);
//If the charater is already exists in the ShiftSize table then replace it else add it to ShiftSize table.
if (ShiftSizeTable.ContainsKey(patternCharacters[lpIndex]))
{
ShiftSizeTable.Remove(patternCharacters[lpIndex]);
}
ShiftSizeTable.Add(patternCharacters[lpIndex], ShiftSize);
}
}
//Use the PreProcessed Shift/Skip table to find the pattern Characters in text and skip the bad Characters in the text.
public int BoyerMooreSearch1UsingDictionary(char[] textCharacters, char[] patternCharacters)
{
PreProcessBMSBadMatchTable(patternCharacters);
int SkipLength;
int patternCharactersLenght = patternCharacters.Length;
int textCharactersLenght = textCharacters.Length;
// Step2. Use Loop through each character in source text use ShiftArrayTable to skip the elements.
for (int lpTextIndex = 0; lpTextIndex <= (textCharactersLenght - patternCharactersLenght); lpTextIndex += SkipLength)
{
SkipLength = 0;
for (int lpPatIndex = patternCharactersLenght - 1; lpPatIndex >= 0; lpPatIndex--)
{
if (patternCharacters[lpPatIndex] != textCharacters[lpTextIndex + lpPatIndex])
{
SkipLength = Math.Max(1, lpPatIndex - ShiftSizeTable[patternCharacters[lpPatIndex]]);
break;
}
}
if (SkipLength == 0)
{
return lpTextIndex; // Found
}
}
return -1; // Not found
}