Related
Assume I have an array of bytes which are truly random (e.g. captured from an entropy source).
byte[] myTrulyRandomBytes = MyEntropyHardwareEngine.GetBytes(8);
Now, I want to get a random double precision floating point value, but between the values of 0 and positive 1 (like the Random.NextDouble() function performs).
Simply passing an array of 8 random bytes into BitConverter.ToDouble() can yield strange results, but most importantly, the results will almost never be less than 1.
I am fine with bit-manipulation, but the formatting of floating point numbers has always been mysterious to me. I tried many combinations of bits to apply randomness to and always ended up finding the numbers were either just over 1, always VERY close to 0, or very large.
Can someone explain which bits should be made random in a double in order to make it random within the range 0 and 1?
Though working answers have been given, I'll give an other one, that looks worse but isn't:
long asLong = BitConverter.ToInt64(myTrulyRandomBytes, 0);
double number = (double)(asLong & long.MaxValue) / long.MaxValue;
The issue with casting from an ulong to double is that it's not directly supported by hardware, so it compiles to this:
vxorps xmm0,xmm0,xmm0
vcvtsi2sd xmm0,xmm0,rcx ; interpret ulong as long and convert it to double
test rcx,rcx ; add fixup if it was "negative"
jge 000000000000001D
vaddsd xmm0,xmm0,mmword ptr [00000060h]
vdivsd xmm0,xmm0,mmword ptr [00000068h]
Whereas with my suggestion it will compile more nicely:
vxorps xmm0,xmm0,xmm0
vcvtsi2sd xmm0,xmm0,rcx
vdivsd xmm0,xmm0,mmword ptr [00000060h]
Both tested with the x64 JIT in .NET 4, but this applies in general, there just isn't a nice way to convert an ulong to a double.
Don't worry about the bit of entropy being lost: there are only 262 doubles between 0.0 and 1.0 in the first place, and most of the smaller doubles cannot be chosen so the number of possible results is even less.
Note that this as well as the presented ulong examples can result in exactly 1.0 and distribute the values with slightly differing gaps between adjacent results because they don't divide by a power of two. You can change them exclude 1.0 and get a slightly more uniform spacing (but see the first plot below, there is a bunch of different gaps, but this way it is very regular) like this:
long asLong = BitConverter.ToInt64(myTrulyRandomBytes, 0);
double number = (double)(asLong & long.MaxValue) / ((double)long.MaxValue + 1);
As a really nice bonus, you can now change the division to a multiplication (powers of two usually have inverses)
long asLong = BitConverter.ToInt64(myTrulyRandomBytes, 0);
double number = (double)(asLong & long.MaxValue) * 1.08420217248550443400745280086994171142578125E-19;
Same idea for ulong, if you really want to use that.
Since you also seemed interested specifically in how to do it with double-bits trickery, I can show that too.
Because of the whole significand/exponent deal, it can't really be done in a super direct way (just reinterpreting the bits and that's it), mainly because choosing the exponent uniformly spells trouble (with a uniform exponent, the numbers are necessarily clumped preferentially near 0 since most exponents are there).
But if the exponent is fixed, it's easy to make a double that's uniform in that region. That cannot be 0 to 1 because that spans a lot of exponents, but it can be 1 to 2 and then we can subtract 1.
So first mask away the bits that won't be part of the significand:
x &= (1L << 52) - 1;
Put in the exponent (1.0 - 2.0 range, excluding 2)
x |= 0x3ff0000000000000;
Reinterpret and adjust for the offset of 1:
return BitConverter.Int64BitsToDouble(x) - 1;
Should be pretty fast, too. An unfortunate side effect is that this time it really does cost a bit of entropy, because there are only 52 but there could have been 53. This way always leaves the least significant bit zero (the implicit bit steals a bit).
There were some concerns about the distributions, which I will address now.
The approach of choosing a random (u)long and dividing it by the maximum value clearly has a uniformly chosen (u)long, and what happens after that is actually interesting. The result can justifiably be called a uniform distribution, but if you look at it as a discrete distribution (which it actually is) it looks (qualitatively) like this: (all examples for minifloats)
Ignore the "thicker" lines and wider gaps, that's just the histogram being funny. These plots used division by a power of two, so there is no spacing problem in reality, it's only plotted strangely.
Top is what happens when you use too many bits, as happens when dividing a complete (u)long by its max value. This gives the lower floats a better resolution, but lots of different (u)longs get mapped onto the same float in the higher regions. That's not necessarily a bad thing, if you "zoom out" the density is the same everywhere.
The bottom is what happens when the resolution is limited to the worst case (0.5 to 1.0 region) everywhere, which you can do by limiting the number of bits first and then doing the "scale the integer" deal. My second suggesting with the bit hacks does not achieve this, it's limited to half that resolution.
For what it's worth, NextDouble in System.Random scales a non-negative int into the 0.0 .. 1.0 range. The resolution of that is obviously a lot lower than it could be. It also uses an int that cannot be int.MaxValue and therefore scales by approximately 1/(231-1) (cannot be represented by a double, so slightly rounded), so there are actually 33 slightly different gaps between adjacent possible results, though the majority of the gaps is the same distance.
Since int.MaxValue is small compared to what can be brute-forced these days, you can easily generate all possible results of NextDouble and examine them, for example I ran this:
const double scale = 4.6566128752458E-10;
double prev = 0;
Dictionary<long, int> hist = new Dictionary<long, int>();
for (int i = 0; i < int.MaxValue; i++)
{
long bits = BitConverter.DoubleToInt64Bits(i * scale - prev);
if (!hist.ContainsKey(bits))
hist[bits] = 1;
else
hist[bits]++;
prev = i * scale;
if ((i & 0xFFFFFF) == 0)
Console.WriteLine("{0:0.00}%", 100.0 * i / int.MaxValue);
}
This is easier than you think; its all about scaling (also true when going from a 0-1 range to some other range).
Basically, if you know that you have 64 truly random bits (8 bytes) then just do this:
double zeroToOneDouble = (double)(BitConverter.ToUInt64(bytes) / (decimal)ulong.MaxValue);
The trouble with this kind of algorithm comes when your "random" bits aren't actually uniformally random. That's when you need a specialized algorithm, such as a Mersenne Twister.
I don't know wether it's the best solution for this, but it should do the job:
ulong asLong = BitConverter.ToUInt64(myTrulyRandomBytes, 0);
double number = (double)asLong / ulong.MaxValue;
All I'm doing is converting the byte array to a ulong which is then divided by it's max value, so that the result is between 0 and 1.
To make sure the long value is within the range from 0 to 1, you can apply the following mask:
long longValue = BitConverter.ToInt64(myTrulyRandomBytes, 0);
longValue &= 0x3fefffffffffffff;
The resulting value is guaranteed to lay in the range [0, 1).
Remark. The 0x3fefffffffffffff value is very-very close to 1 and will be printed as 1, but it is really a bit less than 1.
If you want to make the generated values greater, you could set a number higher bits of an exponent to 1. For instance:
longValue |= 0x03c00000000000000;
Summarizing: example on dotnetfiddle.
If you care about the quality of the random numbers generated, be very suspicious of the answers that have appeared so far.
Those answers that use Int64BitsToDouble directly will definitely have problems with NaNs and infinities. For example, 0x7ff0000000000001, a perfectly good random bit pattern, converts to NaN (and so do thousands of others).
Those that try to convert to a ulong and then scale, or convert to a double after ensuring that various bit-pattern constraints are met, won't have NaN problems, but they are very likely to have distributional problems. Representable floating point numbers are not distributed uniformly over (0, 1), so any scheme that randomly picks among all representable values will not produce values with the required uniformity.
To be safe, just use ToInt32 and use that int as a seed for Random. (To be extra safe, reject 0.) This won't be as fast as the other schemes, but it will be much safer. A lot of research and effort has gone into making RNGs good in ways that are not immediately obvious.
Simple piece of code to print the bits out for you.
for (double i = 0; i < 1.0; i+=0.05)
{
var doubleToInt64Bits = BitConverter.DoubleToInt64Bits(i);
Console.WriteLine("{0}:\t{1}", i, Convert.ToString(doubleToInt64Bits, 2));
}
0.05: 11111110101001100110011001100110011001100110011001100110011010
0.1: 11111110111001100110011001100110011001100110011001100110011010
0.15: 11111111000011001100110011001100110011001100110011001100110100
0.2: 11111111001001100110011001100110011001100110011001100110011010
0.25: 11111111010000000000000000000000000000000000000000000000000000
0.3: 11111111010011001100110011001100110011001100110011001100110011
0.35: 11111111010110011001100110011001100110011001100110011001100110
0.4: 11111111011001100110011001100110011001100110011001100110011001
0.45: 11111111011100110011001100110011001100110011001100110011001100
0.5: 11111111011111111111111111111111111111111111111111111111111111
0.55: 11111111100001100110011001100110011001100110011001100110011001
0.6: 11111111100011001100110011001100110011001100110011001100110011
0.65: 11111111100100110011001100110011001100110011001100110011001101
0.7: 11111111100110011001100110011001100110011001100110011001100111
0.75: 11111111101000000000000000000000000000000000000000000000000001
0.8: 11111111101001100110011001100110011001100110011001100110011011
0.85: 11111111101011001100110011001100110011001100110011001100110101
0.9: 11111111101100110011001100110011001100110011001100110011001111
0.95: 11111111101110011001100110011001100110011001100110011001101001
I have a list of entities, and for the purpose of analysis, an entity can be in one of three states. Of course I wish it was only two states, then I could represent that with a bool.
In most cases there will be a list of entities where the size of the list is usually 100 < n < 500.
I am working on analyzing the effects of the combinations of the entities and the states.
So if I have 1 entity, then I can have 3 combinations. If I have two entities, I can have six combinations, and so on.
Because of the amount of combinations, brute forcing this will be impractical (it needs to run on a single system). My task is to find good-but-not-necessarily-optimal solutions that could work. I don't need to test all possible permutations, I just need to find one that works. That is an implementation detail.
What I do need to do is to register the combinations possible for my current data set - this is basically to avoid duplicating the work of analyzing each combination. Every time a process arrives at a certain configuration of combinations, it needs to check if that combo is already being worked at or if it was resolved in the past.
So if I have x amount of tri-state values, what is an efficient way of storing and comparing this in memory? I realize there will be limitations here. Just trying to be as efficient as possible.
I can't think of a more effective unit of storage then two bits, where one of the four "bit states" is not used. But I don't know how to make this efficient. Do I need to make a choice on optimizing for storage size or performance?
How can something like this be modeled in C# in a way that wastes the least amount of resources and still performs relatively well when a process needs to ask "Has this particular combination of tri-state values already been tested?"?
Edit: As an example, say I have just 3 entities, and the state is represented by a simple integer, 1, 2 or 3. We would then have this list of combinations:
111
112
113
121
122
123
131
132
133
211
212
213
221
222
223
231
232
233
311
312
313
321
322
323
331
332
333
I think you can break this down as follows:
You have a set of N entities, each of which can have one of three different states.
Given one particular permutation of states for those N entities, you
want to remember that you have processed that permutation.
It therefore seems that you can treat the N entities as a base-3 number with 3 digits.
When considering one particular set of states for the N entities, you can store that as an array of N bytes where each byte can have the value 0, 1 or 2, corresponding to the three possible states.
That isn't a memory-efficient way of storing the states for one particular permutation, but that's OK because you don't need to store that array. You just need to store a single bit somewhere corresponding to that permutation.
So what you can do is to convert the byte array into a base 10 number that you can use as an index into a BitArray. You then use the BitArray to remember whether a particular permutation of states has been processed.
To convert a byte array representing a base three number to a decimal number, you can use this code:
public static int ToBase10(byte[] entityStates) // Each state can be 0, 1 or 2.
{
int result = 0;
for (int i = 0, n = 1; i < entityStates.Length; n *= 3, ++i)
result += n * entityStates[i];
return result;
}
Given that you have numEntities different entities, you can then create a BitArray like so:
int numEntities = 4;
int numPerms = (int)Math.Pow(numEntities, 3);
BitArray states = new BitArray(numPerms);
Then states can store a bit for each possible permutation of states for all the entities.
Let's suppose that you have 4 entities A, B, C and D, and you have a permutation of states (which will be 0, 1 or 2) as follows: A2 B1 C0 D1. That is, entity A has state 2, B has state 1, C has state 0 and D has state 1.
You would represent that as a boolean array like so:
byte[] permutation = { 2, 1, 0, 1 };
Then you can convert that to a base 10 number like so:
int asBase10 = ToBase10(permutation);
Then you can check if that permutation has been processed like so:
if (!bits[permAsBase10])
{
// Not processed, so process it.
process(permutation);
bits[permAsBase10] = true; // Remember that we processed it.
}
Without getting overly fancy with algorithms and data structures and assuming your tri-state values can be represented in strings and doesn't have a easily determined fix maximum amount. ie. "111", "112", etc (or even "1:1:1", "1:1:2") then a simple SortedSet may end up being fairly efficient.
As a bonus, it doesn't care about the number of values in your set.
SortedSet<string> alreadyTried = new SortedSet<string>();
if(!HasSetBeenTried("1:1:1"){
// do whatever
}
if(!HasSetBeenTried("500:212:100"){
// do whatever
}
public bool HasSetBeenTried(string set){
if(alreadyTried.Contains(set)) return false;
alreadyTried.Add(set);
return true;
}
Simple mathematic says:
3 entities in 3 states makes 27 combinations.
So you need exactly log(27)/log(2) = ~ 4.75 bits to store that information.
Because a pc only can make use of whole bits, you need to "waste" ~0.25 bits and use 5 bits per combination.
The more data you gather, the better you can pack that information, but in the end, maybe a compression algorithm could help even more.
Again: you only asked for memory efficiency, not performance.
In general you can calculate the bits you need by Math.Ceil(Math.Log( noCombinations , 2 )).
I need to generate a unique ID and was considering Guid.NewGuid to do this, which generates something of the form:
0fe66778-c4a8-4f93-9bda-366224df6f11
This is a little long for the string-type database column that it will end up residing in, so I was planning on truncating it.
The question is: Is one end of a GUID more preferable than the rest in terms of uniqueness? Should I be lopping off the start, the end, or removing parts from the middle? Or does it just not matter?
You can save space by using a base64 string instead:
var g = Guid.NewGuid();
var s = Convert.ToBase64String(g.ToByteArray());
Console.WriteLine(g);
Console.WriteLine(s);
This will save you 12 characters (8 if you weren't using the hyphens).
Keep all of it.
From the above link:
* Four bits to encode the computer number,
* 56 bits for the timestamp, and
* four bits as a uniquifier.
you can redefine the Guid to right-size it to your needs.
If the GUID were simply a random number, you could keep an arbitrary subset of the bits and suffer a certain percent chance of collision that you can calculate with the "birthday algorithm":
double numBirthdays = 365; // set to e.g. 18446744073709551616d for 64 bits
double numPeople = 23; // set to the maximum number of GUIDs you intend to store
double probability = 1; // that all birthdays are different
for (int x = 1; x < numPeople; x++)
probability *= (double)(numBirthdays - x) / numBirthdays;
Console.WriteLine("Probability that two people have the same birthday:");
Console.WriteLine((1 - probability).ToString());
However, often the probability of a collision is higher because, as a matter of fact, GUIDs are in general NOT random. According to Wikipedia's GUID article there are five types of GUIDs. The 13th digit specifies which kind of GUID you have, so it tends not to vary much, and the top two bits of the 17th digit are always fixed at 01.
For each type of GUID you'll get different degrees of randomness. Version 4 (13th digit = 4) is entirely random except for digits 13 and 17; versions 3 and 5 are effectively random, as they are cryptographic hashes; while versions 1 and 2 are mostly NOT random but certain parts are fairly random in practical cases. A "gotcha" for version 1 and 2 GUIDs is that many GUIDs could come from the same machine and in that case will have a large number of identical bits (in particular, the last 48 bits and many of the time bits will be identical). Or, if many GUIDs were created at the same time on different machines, you could have collisions between the time bits. So, good luck safely truncating that.
I had a situation where my software only supported 64 bits for unique IDs so I couldn't use GUIDs directly. Luckily all of the GUIDs were type 4, so I could get 64 bits that were random or nearly random. I had two million records to store, and the birthday algorithm indicated that the probability of a collision was 1.08420141198273 x 10^-07 for 64 bits and 0.007 (0.7%) for 48 bits. This should be assumed to be the best-case scenario, since a decrease in randomness will usually increase the probability of collision.
I suppose that in theory, more GUID types could exist in the future than are defined now, so a future-proof truncation algorithm is not possible.
I agree with Rob - Keep all of it.
But since you said you're going into a database, I thought I'd point out that just using Guid's doesn't necessarily mean that it will index well in a database. For that reason, the NHibernate developers created a Guid.Comb algorithm that's more DB friendly.
See NHibernate POID Generators revealed and documentation on the Guid Algorithms for more information.
NOTE: Guid.Comb is designed to improve performance on MsSQL
Truncating a GUID is a bad idea, please see this article for why.
You should consider generating a shorter GUID, as google reveals some solutions for. These solutions seem to involve taking a GUID and changing it to be represented in full 255 bit ascii.
I'm trying to determine the number of digits in a c# ulong number, i'm trying to do so using some math logic rather than using ToString().Length. I have not benchmarked the 2 approaches but have seen other posts about using System.Math.Floor(System.Math.Log10(number)) + 1 to determine the number of digits.
Seems to work fine until i transition from 999999999999997 to 999999999999998 at which point, it i start getting an incorrect count.
Has anyone encountered this issue before ?
I have seen similar posts with a Java emphasis # Why log(1000)/log(10) isn't the same as log10(1000)? and also a post # How to get the separate digits of an int number? which indicates how i could possibly achieve the same using the % operator but with a lot more code
Here is the code i used to simulate this
Action<ulong> displayInfo = number =>
Console.WriteLine("{0,-20} {1,-20} {2,-20} {3,-20} {4,-20}",
number,
number.ToString().Length,
System.Math.Log10(number),
System.Math.Floor(System.Math.Log10(number)),
System.Math.Floor(System.Math.Log10(number)) + 1);
Array.ForEach(new ulong[] {
9U,
99U,
999U,
9999U,
99999U,
999999U,
9999999U,
99999999U,
999999999U,
9999999999U,
99999999999U,
999999999999U,
9999999999999U,
99999999999999U,
999999999999999U,
9999999999999999U,
99999999999999999U,
999999999999999999U,
9999999999999999999U}, displayInfo);
Array.ForEach(new ulong[] {
1U,
19U,
199U,
1999U,
19999U,
199999U,
1999999U,
19999999U,
199999999U,
1999999999U,
19999999999U,
199999999999U,
1999999999999U,
19999999999999U,
199999999999999U,
1999999999999999U,
19999999999999999U,
199999999999999999U,
1999999999999999999U
}, displayInfo);
Thanks in advance
Pat
log10 is going to involve floating point conversion - hence the rounding error. The error is pretty small for a double, but is a big deal for an exact integer!
Excluding the .ToString() method and a floating point method, then yes I think you are going to have to use an iterative method but I would use an integer divide rather than a modulo.
Integer divide by 10. Is the result>0? If so iterate around. If not, stop.
The number of digits is the number of iterations required.
Eg. 5 -> 0; 1 iteration = 1 digit.
1234 -> 123 -> 12 -> 1 -> 0; 4 iterations = 4 digits.
I would use ToString().Length unless you know this is going to be called millions of times.
"premature optimization is the root of all evil" - Donald Knuth
From the documentation:
By default, a Double value contains 15
decimal digits of precision, although
a maximum of 17 digits is maintained
internally.
I suspect that you're running into precision limits. Your value of 999,999,999,999,998 probably is at the limit of precision. And since the ulong has to be converted to double before calling Math.Log10, you see this error.
Other answers have posted why this happens.
Here is an example of a fairly quick way to determine the "length" of an integer (some cases excluded). This by itself is not very interesting -- but I include it here because using this method in conjunction with Log10 can get the accuracy "perfect" for the entire range of an unsigned long without requiring a second log invocation.
// the lookup would only be generated once
// and could be a hard-coded array literal
ulong[] lookup = Enumerable.Range(0, 20)
.Select((n) => (ulong)Math.Pow(10, n)).ToArray();
ulong x = 999;
int i = 0;
for (; i < lookup.Length; i++) {
if (lookup[i] > x) {
break;
}
}
// i is length of x "in a base-10 string"
// does not work with "0" or negative numbers
This lookup-table approach can be easily converted to any base. This method should be faster than the iterative divide-by-base approach but profiling is left as an exercise to the reader. (A direct if-then branch broken into "groups" is likely quicker yet, but that's way too much repetitive typing for my tastes.)
Happy coding.
I want a number that would be unique forever, I came up with the following code,
it generates a number and adds a check digit to the end of it, I would like to know how reliable is this code?
public void GenerateUniqueNumber(out string ValidUniqueNumber) {
string GeneratedUniqueNumber = "";
// Default implementation of UNIX time of the current UTC time
TimeSpan ts = DateTime.UtcNow - new DateTime(1970, 1, 1, 0, 0, 0, 0);
string FormatedDateTime = Convert.ToInt64(ts.TotalSeconds).ToString();
string ssUniqueId = DateTime.UtcNow.ToString("fffffff");
//Add Padding to UniqueId
string FormatedUniqueId = ssUniqueId.PadLeft(7, '0');
if (FormatedDateTime.Length == 10 && FormatedUniqueId.Length == 7)
{
// Calculate checksum number using Luhn's algorithm.
int sum = 0;
bool odd = true;
string InputData = FormatedDateTime + FormatedUniqueId;
int CheckSumNumber;
for (int i = InputData.Length - 1; i >= 0; i--)
{
if (odd == true)
{
int tSum = Convert.ToInt32(InputData[i].ToString()) * 2;
if (tSum >= 10)
{
string tData = tSum.ToString();
tSum = Convert.ToInt32(tData[0].ToString()) + Convert.ToInt32(tData[1].ToString());
}
sum += tSum;
}
else
sum += Convert.ToInt32(InputData[i].ToString());
odd = !odd;
}
//CheckSumNumber = (((sum / 10) + 1) * 10) - sum;
CheckSumNumber = (((sum + 9) / 10) * 10) - sum;
// Compute Full length 18 digit UniqueNumber
GeneratedUniqueNumber = FormatedDateTime + FormatedUniqueId + Convert.ToString(CheckSumNumber);
}
else
{
// Error
GeneratedUniqueNumber = Convert.ToString(-1);
}
ValidUniqueNumber = GeneratedUniqueNumber;
}
EDIT: clarification
GUID can not be used, the number will need to be entered into a IVR system via telephone keypad.
You cannot use GUIDs, but you can create your own format of unique number similar to a GUID, that is based on the machine's MAC address (space) and the current time and date (time). This is guaranteed to be unique if the machines all have synchronised clocks.
For more information, please see here
Why don't you just use a Guid?
There are a few problems with this method:
You're basically just counting the number of milliseconds from January 1, 1970. You can get this from ts.TotalSeconds rounded to 0.0000001. All your conversion and millisecond calculation is unnecessary.
10 years is about 3×10¹¹ milliseconds. You are keeping 17 significant digits, so for the next 10 years the first 5 digits will never change and cannot be used to distinguish numbers. They are useless.
Are you generating numbers for milliseconds between 1970 and now? If not, they also cannot be used to distinguish numbers and are useless.
This is totally dependent on what machine is returning the date. Anyone who has access to this machine can generate whatever "unique" numbers they want. Is this is problem?
Anyone who sees one of these numbers can tell when it was generated. Is this a problem?
Anyone can predict what number will be generated when. Is this a problem?
1015 milliseconds is about 30000 years. After then, your algorithm will repeat numbers. Seems like a long time, but you specified "forever" and 30000 years is not "forever". Do you really mean "forever"?
If I understand your implementation correctly, it only uses the current date/time as a basis. That means that if you create two IDs simultaneously, they will not be unique.
Since you mentioned (in comments) that the IDs are stored in a DB, you can generate the IDs either using the method you mentioned or randomly and check for the existence in the DB.
If it already exists, generate a new one, otherwise you're done.
One thing though, I would make sure that checking for the existence of the ID and the actual saving of the record to the DB be done in a transaction, otherwise you run the risk of having another request create that record in between the checking for the ID and the creation of the row.
Also just checking, why wouldn't an auto-increment number generated by the database itself work? The DB would guarantee it's uniqueness (for that table anyway)
You don't say what the numbers are to be used for. Do they have some sort of value associated with them? Will it be a problem if users can figure out the scheme and guess valid ticket numbers?
If it is important for these numbers to be hard to guess, this scheme falls down; something that outputs data that looks really random would be better. You might take a monotonically increasing serial number and encrypt it using a block cipher (with a 64-bit block size); that gives you a 64-bit output or about 20 decimal digits worth, which you could take (say) the last 18 of. (If reversibility is important, i.e. given a ticket number you want to be able to recover the serial number, you need to be a bit more careful here.)
Do you need a cast-iron 100% guarantee that no ticket numbers will ever be the same? If so, you need to keep them in a database and mark them off when used. If you do that, it might be reasonable to just use a good random number generator and check for dupes every time.
Using the system time is a good start, but it gives you collisions if you need to generate two UIDs at the same time. It doesn't help that you're using the "fffffff" format: The Windows clock resolution is only 15-16 ms, so only one or two of those "f"s are doing any good.
Also, your approach tells you exactly when the ID was generated. Depending on your needs, this may be a desirable feature, or it may be a security risk.
You'll need your IDs to include other information instead of or in addition to the time. Some possible choices are:
A random number
A cyclic counter
A hash of the program name (if your need these IDs in multiple programs)
The MAC address or other identifier for the machine (If the IDs need to be unique across multiple computers)
If you want to ensure uniqueness, then store your IDs in a database so you can check for duplicates.
As "Andrew Hare" says, You can use Guid.
About your code the answer is "NO"!
because if client's computer's DateTime was wrong or change result may be couple or more!
No such thing as random anyway. Here's a suggestion.
Create your own "random" 18 digit number
Before sending it to the user, check it against existing ones in DB
If already in DB, rinse and repeat.