Skip to main content

The End Is near: January 19, 2038 3:14:07 GMT

 



  • Year 2038 problem

The Year 2038 problem is an issue for computing and data storage situations in which time values are stored or calculated as a signed 32-bit integer, and the number is interpreted as the number of seconds since 00:00:00 UTC on 1 January 1970 (the epoch). Systems working on 32-bit cannot encode times after 03:14:07 UTC on 19 January 2038, analogous to the Y2K problem , in which 2-digit values representing the number of years since 1900 could not encode the year 2000 or later. Most 32-bit Unix like systems store and manipulate time in this Unix time format, so the year 2038 problem is sometimes referred to as the Unix Millennium Bug by association.

  • What is Unix time?

The Unix epoch time is the number of seconds that have elapsed since January 1, 1970 (midnight UTC/GMT), not counting leap seconds . Literally speaking the epoch is Unix time 0 (midnight 1/1/1970), but ‘epoch’ is often used as a synonym for ‘Unix time’. Many Unix systems store epoch dates as a signed 32-bit integer, which might cause problems on January 19, 2038 (known as the Y2038).

  • Data structures with time problems
  • Many data structures in use today have 32-bit time representations embedded into their structure. A full list of these data structures is virtually impossible to derive but there are well-known data structures that have the Unix time problem:
  • file systems (many file systems use only 32 bits to represent times in inodes).
  • binary file formats (that use 32-bit time fields).
  • databases (that have 32-bit time fields).
  • database query languages, like SQL that have UNIX_TIMESTAMP() like commands.
  • Solutions:

Most operating systems designed to run on 64-bit hardware already use signed 64-bit time_t integers. Using a signed 64-bit value introduces a new wraparound date that is over twenty times greater than the estimated age of universe.

(age of universe: approximately 292 billion years from now, at 15:30:08 UTC on Sunday, 4 December 292,277,026,596.)

for more information refer:Wikipedia

Comments

Popular posts from this blog

Need of Open Source ?

You have already got what is  Open Source  from previous blogs. The simple reason is that why to pay if we get is free. Lets take an example you spend lots of money to buy an windows genuine some of the people don’t buy the licence copy of it but they buy an creak (DOS) version of windows but they have some problem in it. Some what same not a completely different OS are freely available in market then why should we pay for it ? You get high quality of software and hardware also in open source. They are very powerful and smooth running no lags get while working.They also give full support to solve your problem.Think an example of google you search on google and you get the result of what you search if google say that I want money for every search results then ?you will pay for it ? That the need of  open source .

What is Dynamic Programming ?

  What actually dynamic programming is ? In computer science or mathematics,  dynamic programming  is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions. Also known as  dynamic   optimization . Dynamic programming is similar to divide and conquer only difference is that dynamic programming is used when there is overlapping subproblem property and in divide and conquer there is no overlapping subproblem property. example, fibonacci series. When to use dynamic programming ? Following two attributes suggests that a problem can be solved using  dynamic programming  : optimal substructure overlapping sub-problems. Ways to of using dynamic programming : Top-Down :  Firstly, Start solving the given problem by breaking it down. If you see that the problem has been solved already, then just return the saved answer. If it has not been solved, solve it and save the answer. Th