Python Intefere targets if running too fast
I have a number of threads that are designed to be executed in parallel, as threads are often designed to be executed: D
If I don't slow them down, they tend to puppet towards each other, gaining space for each other. So when I type the expression, I see what looks like "echo" in some streams (not an encoding term, but a literal echo).
When I slow them down, no puppet show happens.
Here's an example of what I get on the terminal when I run these bogus accounts:
Meikitotokiku83: day_2, Wed Jun 10 03:32:53 2015, nine
Robinia6424: day_2, Wed Jun 10 03:32:53 2015, four
Meikitotokiku83: day_7, Wed Jun 10 03:32:53 2015, fifteen
Meikitotokiku83: day_7, Wed Jun 10 03:32:53 2015, thirteen
Mekushishifu643: day_7, Wed Jun 10 03:32:53 2015, two
Meikitotokiku83: day_7, Wed Jun 10 03:32:53 2015, eleven
Meikitotokiku83: day_2, Wed Jun 10 03:32:53 2015, six
Mekushishifu643: day_2, Wed Jun 10 03:32:53 2015, three
**Meikitotokiku83: day_7, Wed Jun 10 03:32:53 2015, ten
Meikitotokiku83: day_7, Wed Jun 10 16:33:03 2015, ten
Meikitotokiku83: day_7, Wed Jun 10 16:33:03 2015, ten
Meikitotokiku83: day_7, Wed Jun 10 16:33:03 2015, ten**
Meikitotokiku83: day_2, Wed Jun 10 03:32:53 2015, fourteen
Meikitotokiku83: day_7, Wed Jun 10 03:32:53 2015, sixteen
DaDaFurstig6304: day_7, Wed Jun 10 03:32:53 2015, five
DoraDiggle5529: day_7, Wed Jun 10 03:32:53 2015, one
Meikitotokiku83: day_2, Wed Jun 10 03:32:53 2015, seven
Notice the echo that occurs in the ten characters.
I really wish I had to post my code, because most Python programmers will probably find out what I don't see in this threading issue, without my 300 line classes being hosted here.
The classes are pretty big, so I won't be posting them here. I suspect this is a common problem that I just don't recognize, like a CPU issue?
What is the common cause of this and how can you get around it without using time delays?
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Are these streams accessible to shared data - that is, to the same variable or variables? If so, and if the data is not locked / synchronized, then it is prone to thread race conditions . This is true in any parallel programming environment, not just Python. Check out this good overview of Python sync .
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This is the standard thread behavior. Do not use streams that share output. Python threads are atomic, which means that if you give them an isolated environment, they won't be a problem for you. If you have access to dynamically modify shared data or write to a shared object or buffer, then you have problems like this.
The problem is the misuse of the stream. Write to a separate buffer inside each thread, and when the threads are done, combine the results.
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