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Generate two random numbers between 1 to 10 using specific string

I would like to implement a logic based on a provided string I have to generate two random numbers between 1 to 10.
I have a string like Johnsen using it I have to generate two numbers like 1 and 3 and next time with the same string it should give the same numbers for the same string.
Need help to develop this algorithm or logic.

Java has random number generator support via the java.util.Random class. This class 'works' by having a seed value, then giving you some random data based on this seed value, which then updates the seed value to something new.
This pragmatically means:
2 instances of j.u.Random with the same seed value will produce the same sequence of values if you invoke the same sequence of calls on it to give you random data.
But, seed values are of type long - 64 bits worth of data.
Thus, to do what you want, you need to write an algorithm that turns any String into a long.
Given that long, you simply make an instance of j.u.Random with that long as seed, using the new Random(seedValue) constructor.
So that just leaves: How do I turn a string into a long?
Easy way
The simplest answer is to invoke hashCode() on them. But, note, hashcodes only have 32 bits of info (they are int, not long), so this doesn't cover all possible seed values. This is unlikely to matter unless you're doing this for crypto purposes. If you ARE, then you need to stop what you are doing and do a lot more research, because it is extremely easy to mess up and have working code that seems to test fine, but which is easy to hack. You don't want that. For starters, you'd want SecureRandom instead, but that's just the tip of the iceberg.
Harder way
Hashing algorithms exist that turn arbitrary data into fixed size hash representations. The hashCode algorithm of string [A] only makes 32-bits worth of hash, and [B] is not cryptographically secure: If you task me to make a string that hashes to a provided value I can trivially do so; a cryptographically secure hash has the property that I can't just cook you up a string that hashes to a desired value.
You can search the web for hashing strings or byte arrays (you can turn a string into one with str.getBytes(StandardCharsets.UTF_8)).
You can 'collapse' a byte array containing a hash into a long also quite easily - just take any 8 bytes in that hash and use them to construct a long. "Turn 8 bytes into a long" also has tons of tutorials if you search the web for it.
I assume the easy way is good enough for this exercise, however.
Thus:
String key = ...;
Random rnd = new Random(key.hashCode());
int number1 = rnd.nextInt(10) + 1;
int number2 = rnd.nextInt(10) + 1;
System.out.println("First number: " + number1);
System.out.println("Second number: " + number2);

You could get the hashcode of the string, then use that to seed a random number generator. Use the RNG to get numbers in the range 1 - 10.

Java program to generate a unique and random six alpha numeric code

I need to generate a reservation code of 6 alpha numeric characters, that is random and unique in java.
Tried using UUID.randomuuid().toString(), However the id is too long and the requirement demands that it should only be 6 characters.
What approaches are possible to achieve this?
Just to clarify, (Since this question is getting marked as duplicate).
The other solutions I've found are simply generating random characters, which is not enough in this case. I need to reasonably ensure that a random code is not generated again.

Consider using the hashids library to generate salted hashes of integers (your database ids or other random integers which is probably better).
http://hashids.org/java/
Hashids hashids = new Hashids("this is my salt",6);
String id = hashids.encode(1, 2, 3);
long[] numbers = hashids.decode(id);

You have 36 characters in the alphanumeric character set (0-9 digits + a-z letters). With 6 places you achieve 366 = 2.176.782.336 different options, that is slightly larger than 231.
Therefore you can use Unix time to create a unique ID. However, you must assure that no ID generated within the same second.
If you cannot guarantee that, you end up with a (synchronized) counter within your class. Also, if you want to survive a JVM restart, you should save the current value (e.g. to a database, file, etc. whatever options you have).

Despite its name, UUIDs are not unique. It's simply extremely unlikely to get a 128 bit collision. With 6 (less than 32 bit) it's very likely that you get a collision if you just hash stuff or generate a random string.
If the uniqueness constraint is necessary then you need to
generate a random 6 character string
Check if you generated that string before by querying your database
If you generated it before, go back to 1
Another way would be to use a pseadorandom permutation (PRP) of size 32 bit. Block ciphers are modeled as PRP functions, but there aren't many that support 32 bit block sizes. Some are Speck by the NSA and the Hasty Pudding Cipher.
With a PRP you could for example take an already unique value like your database primary key and encrypt it with the block cipher. If the input is not bigger than 32 bit then the output will still be unique.
Then you would run Base62 (or at least Base 41) over the output and remove the padding characters to get a 6 character output.

if you do a substring that value may not be unique
for more info please see following similar link
Generating 8-character only UUIDs

Lets say your corpus is the collection of alpha numberic letters. a-zA-Z0-9.
char[] corpus = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789".toCharArray();
We can use SecureRandom to generate a seed, which will ask the OS for entropy, depending on the os. The trick here is to keep a uniform distribution, each byte has 255 values, but we only need around 62 so I will propose rejection sampling.
int generated = 0;
int desired=6;
char[] result= new char[desired];
while(generated<desired){
byte[] ran = SecureRandom.getSeed(desired);
for(byte b: ran){
if(b>=0&&b<corpus.length){
result[generated] = corpus[b];
generated+=1;
if(generated==desired) break;
}
}
}
Improvements could include, smarter wrapping of generated values.
When can we expect a repeat? Lets stick with the corpus of 62 and assume that the distribution is completely random. In that case we have the birthday problem. That gives us N = 62^6 possiblities. We want to find n where the chance of a repeat around 10%.
p(r)= 1 - N!/(N^n (N-n)!)
And using the approximation given in the wikipedia page.
n = sqrt(-ln(0.9)2N)
Which gives us about 109000 numbers for 10% chance. For a 0.1% chance it woul take about 10000 numbers.

you can trying to make substring out of your generated UUID.
String uuid = UUID.randomUUID().toString();
System.out.println("uuid = " + uuid.substring(0,5);

generate secure random bits

public static void main(String[] args)
{
Random ranGen = new SecureRandom();
ranGen.setSeed(0);
int randomNumber = ranGen.nextInt(2);
System.out.print(randomNumber);
}
Is the above code a good way to either produce a truly random and secure/unbiased 0 or 1 ?

No, it isn't (a good way to either produce a truly random and secure/unbiased 0 or 1).
new SecureRandom() is OK, but setting the seed directly after isn't. Oracle's implementation of "SHA1PRNG" - which is normally the default - will replace the initial seed by the one given if setSeed() is called before any entropy is retrieved from the random number generator.
If it is not seeded the SecureRandom implementation will be seeded using an entropy source of the operating system. This should be relatively safe. Often the operating system is the only part that has access to real hardware devices so it is better positioned to retrieve entropy than the JVM. You can call setSeed(), but you should only call it after a call to nextBytes(), requesting at least one byte. Afterwards you can add some seed to the pool.
Finally, calling nextInt(2) is OK, but not very efficient. It will request 32 bits, and then toss away the 31 bits it doesn't require. Requesting random bytes and extracting the bits from the array will be more efficient (if efficiency is required, don't over-optimize). Calling nextBoolean() will probably just toss away 7 bits, so it is at least 4 times more efficient and much more concise.

Consistent random numbers across versions and platforms

I need/want to get random (well, not entirely) numbers to use for password generation.
What I do: Currently I am generating them with SecureRandom.
I am obtaining the object with
SecureRandom sec = SecureRandom.getInstance("SHA1PRNG", "SUN");
and then seeding it like this
sec.setSeed(seed);
Target: A (preferably fast) way to create random numbers, which are cryptographically at least a safe as the SHA1PRNG SecureRandom implementation. These need to be the same on different versions of the JRE and Android.
EDIT: The seed is generated from user input.
Problem: With SecureRandom.getInstance("SHA1PRNG", "SUN"); it fails like this:
java.security.NoSuchProviderException: SUN. Omitting , "SUN" produces random numbers, but those are different than the default (JRE 7) numbers.
Question: How can I achieve my Target?
You don't want it to be predictable: I want, because I need the predictability so that the same preconditions result in the same output. If they are not the same, its impossible hard to do what the user expects from the application.
EDIT: By predictable I mean that, when knowing a single byte (or a hundred) you should not be able to predict the next, but when you know the seed, you should be able to predict the first (and all others). Maybe another word is reproducible.
If anyone knows of a more intuitive way, please tell me!

I ended up isolating the Sha1Prng from the sun sources which guarantees reproducibility on all versions of Java and android. I needed to drop some important methods to ensure compatibility with android, as android does not have access to nio classes...

I recommend using UUID.randomUUID(), then splitting it into longs using getLeastSignificantBits() and getMostSignificantBits()

If you want predictable, they aren't random. That breaks your "Target" requirement of being "safe" and devolves into a simple shared secret between two servers.
You can get something that looks sort of random but is predicatable by using the characteristics of prime integers where you build a set of integers by starting with I (some specific integer) and add the first prime number and then modulo by the 2nd prime number. (In truth the first and second numbers only have to be relatively prime--meaning they have no common prime factors--not counting 1, in case you call that a factor.
If you repeat the process of adding and doing the modulo, you will get a set of numbers that you can repeatably reproduce and they are ordered in the sense that taking any member of the set, adding the first prime and doing the modulo by the 2nd prime, you will always get the same result.
Finally, if the 1st prime number is large relative to the second one, the sequence is not easily predictable by humans and seems sort of random.
For example, 1st prime = 7, 2nd prime = 5 (Note that this shows how it works but is not useful in real life)
Start with 2. Add 7 to get 9. Modulo 5 to get 4.
4 plus 7 = 11. Modulo 5 = 1.
Sequence is 2, 4, 1, 3, 0 and then it repeats.
Now for real life generation of numbers that seem random. The relatively prime numbers are 91193 and 65536. (I chose the 2nd one because it is a power of 2 so all modulo-ed values can fit in 16 bits.)
int first = 91193;
int modByLogicalAnd = 0xFFFF;
int nonRandomNumber = 2345; // Use something else
for (int i = 0; i < 1000 ; ++i) {
nonRandomNumber += first;
nonRandomNumber &= modByLogicalAnd;
// print it here
}
Each iteration generates 2 bytes of sort of random numbers. You can pack several of them into a buffer if you need larger random "strings".
And they are repeatable. Your user can pick the starting point and you can use any prime you want (or, in fact, any number without 2 as a factor).
BTW - Using a power of 2 as the 2nd number makes it more predictable.

Ignoring RNGs that use some physical input (random clock bits, electrical noise, etc) all software RNGs are predicable, given the same starting conditions. They are, after all, (hopefully) deterministic computer programs.
There are some algorithms that intentionally include the physical input (by, eg, sampling the computer clock occasionally) in attempt to prevent predictability, but those are (to my knowledge) the exception.
So any "conventional" RNG, given the same seed and implemented to the same specification, should produce the same sequence of "random" numbers. (This is why a computer RNG is more properly called a "pseudo-random number generator".)
The fact that an RNG can be seeded with a previously-used seed and reproduce a "known" sequence of numbers does not make the RNG any less secure than one where your are somehow prevented from seeding it (though it may be less secure than the fancy algorithms that reseed themselves at intervals). And the ability to do this -- to reproduce the same sequence again and again is not only extraordinarily useful in testing, it has some "real life" applications in encryption and other security applications. (In fact, an encryption algorithm is, in essence, simply a reproducible random number generator.)

Why are initial random numbers similar when using similar seeds?

I discovered something strange with the generation of random numbers using Java's Random class.
Basically, if you create multiple Random objects using close seeds (for example between 1 and 1000) the first value generated by each generator will be almost the same, but the next values looks fine (i didn't search further).
Here are the two first generated doubles with seeds from 0 to 9 :
0 0.730967787376657 0.24053641567148587
1 0.7308781907032909 0.41008081149220166
2 0.7311469360199058 0.9014476240300544
3 0.731057369148862 0.07099203475193139
4 0.7306094602878371 0.9187140138555101
5 0.730519863614471 0.08825840967622589
6 0.7307886238322471 0.5796252073129174
7 0.7306990420600421 0.7491696031336331
8 0.7302511331990172 0.5968915822372118
9 0.7301615514268123 0.7664359929590888
And from 991 to 1000 :
991 0.7142160704801332 0.9453385235522973
992 0.7109015598097105 0.21848118381994108
993 0.7108119780375055 0.38802559454181795
994 0.7110807233541204 0.8793923921785096
995 0.7109911564830766 0.048936787999225295
996 0.7105432327208906 0.896658767102804
997 0.7104536509486856 0.0662031629235198
998 0.7107223962653005 0.5575699754613725
999 0.7106328293942568 0.7271143712820883
1000 0.7101849056320707 0.574836350385667
And here is a figure showing the first value generated with seeds from 0 to 100,000.
First random double generated based on the seed :
I searched for information about this, but I didn't see anything referring to this precise problem. I know that there is many issues with LCGs algorithms, but I didn't know about this one, and I was wondering if this was a known issue.
And also, do you know if this problem only for the first value (or first few values), or if it is more general and using close seeds should be avoided?
Thanks.

You'd be best served by downloading and reading the Random source, as well as some papers on pseudo-random generators, but here are some of the relevant parts of the source. To begin with, there are three constant parameters that control the algorithm:
private final static long multiplier = 0x5DEECE66DL;
private final static long addend = 0xBL;
private final static long mask = (1L << 48) - 1;
The multiplier works out to approximately 2^34 and change, the mask 2^48 - 1, and the addend is pretty close to 0 for this analysis.
When you create a Random with a seed, the constructor calls setSeed:
synchronized public void setSeed(long seed) {
seed = (seed ^ multiplier) & mask;
this.seed.set(seed);
haveNextNextGaussian = false;
}
You're providing a seed pretty close to zero, so initial seed value that gets set is dominated by multiplier when the two are OR'ed together. In all your test cases with seeds close to zero, the seed that is used internally is roughly 2^34; but it's easy to see that even if you provided very large seed numbers, similar user-provided seeds will yield similar internal seeds.
The final piece is the next(int) method, which actually generates a random integer of the requested length based on the current seed, and then updates the seed:
protected int next(int bits) {
long oldseed, nextseed;
AtomicLong seed = this.seed;
do {
oldseed = seed.get();
nextseed = (oldseed * multiplier + addend) & mask;
} while (!seed.compareAndSet(oldseed, nextseed));
return (int)(nextseed >>> (48 - bits));
}
This is called a 'linear congruential' pseudo-random generator, meaning that it generates each successive seed by multiplying the current seed by a constant multiplier and then adding a constant addend (and then masking to take the lower 48 bits, in this case). The quality of the generator is determined by the choice of multiplier and addend, but the ouput from all such generators can be easily predicted based on the current input and has a set period before it repeats itself (hence the recommendation not to use them in sensitive applications).
The reason you're seeing similar initial output from nextDouble given similar seeds is that, because the computation of the next integer only involves a multiplication and addition, the magnitude of the next integer is not much affected by differences in the lower bits. Calculation of the next double involves computing a large integer based on the seed and dividing it by another (constant) large integer, and the magnitude of the result is mostly affected by the magnitude of the integer.
Repeated calculations of the next seed will magnify the differences in the lower bits of the seed because of the repeated multiplication by the constant multiplier, and because the 48-bit mask throws out the highest bits each time, until eventually you see what looks like an even spread.

I wouldn't have called this an "issue".
And also, do you know if this problem only for the first value (or first few values), or if it is more general and using close seeds should be avoided?
Correlation patterns between successive numbers is a common problem with non-crypto PRNGs, and this is just one manifestation. The correlation (strictly auto-correlation) is inherent in the mathematics underlying the algorithm(s). If you want to understand that, you should probably start by reading the relevant part of Knuth's Art of Computer Programming Chapter 3.
If you need non-predictability you should use a (true) random seed for Random ... or let the system pick a "pretty random" one for you; e.g. using the no-args constructor. Or better still, use a real random number source or a crypto-quality PRNG instead of Random.
For the record:
The javadoc (Java 7) does not specify how Random() seeds itself.
The implementation of Random() on Java 7 for Linux, is seeded from the nanosecond clock, XORed with a 'uniquifier' sequence. The 'uniquifier' sequence is LCG which uses different multiplier, and whose state is static. This is intended to avoid auto-correlation of the seeds ...

This is a fairly typical behaviour for pseudo-random seeds - they aren't required to provide completely different random sequences, they only provide a guarantee that you can get the same sequence again if you use the same seed.
The behaviour happens because of the mathematical form of the PRNG - the Java one uses a linear congruential generator, so you are just seeing the results running the seed through one round of the linear congruential generator. This isn't enough to completely mix up all the bit patterns, hence you see similar results for similar seeds.
Your best strategy is probably just to use very different seeds - one option would be to obtain these by hashing the seed values that you are currently using.

By making random seeds (for instance, using some mathematical functions on System.currentTimeMillis() or System.nanoTime() for seed generation) you can get better random result. Also can look at here for more information