I have to run a file.rb that make a micro-task (Insert a qwuery into a database) every second.
I have used a for loop (1..10^9) but I got a CPU usage exceed alert! So what's the best way to not waste all CPU?
The simplest way to run forever is just to loop
loop do
run_db_insert
sleep 1s
end
If it's important that you maintain a 1 Hz rate, note that the DB insert takes some amount of time, so the one-second sleep means each cycle takes dbtime+1 second and you will steadily fall behind. If the DB interaction is reliably less than a second, you can modify the sleep to adjust for the next one-second interval.
loop do
run_db_insert
sleep(Time.now.to_f.ceil - Time.now.to_f)
end
Simple loop with sleep command should do the job.
while true
# do stuff here
sleep 1 # wait one second
end
Related
What is the function of millis() in this code snippet?
if (millis() > timer) {
timer = millis() + 5000;
ether.browseUrl(PSTR("/demo/"), "aphorisms.php", website, response_callback);
}
The "timer" variable bumps the current cumulative time, as measured by millis(), and sets a value of 5 seconds greater. This snippet will reside in a larger loop, and whenever the time since the last iteration exceeds 5 seconds, will execute the subsequent statement and bump the timer again. Else, the snippet simply passes thru.
If you wanted to do something every 5 seconds, or whatever interval you choose, this is a simple way to do that. Of course, that interval may be elongated, depending on other code in the loop.
I have a very simple task on updating database.
my $pm = new Parallel::ForkManager(15);
for my $line (#lines){
my $pid = $pm->start and next;
my $dbh2 = $dbh->clone();
my $sth2 = $dbh2->prepare("update db1 set field1=? where field2 =?");
my ($field1, $field2) = very_slow_subroutine();
$sth2->execute($field1,$field2);
$pm->finish;
}
$pm->wait_all_children;
I could just use $dbh2->do, but I doubt it a reason for a slowness.
What interesting, is that it seems it very fast starts these 15 processes (or whatever I specify) , but right after that slows drastically, still noticeable faster than without forking, but I would expect more...
Edit:
The very_slow_subroutine is sub which get an answer from a web service. The service can answer from fraction of second to several seconds on time out. I have to ask dozen thousands times... the reason I would like to make a fork.
And if this is matters -- I am on Linux.
Parallel::ForkManager doesn't magically make things faster, it just lets you do run your code multiple times and at the same time. In order to get the benefit out of it, you have to design your code for parallelism.
Think of it this way. It takes you 10 minutes to get to the store, shop, load your car, come back, and unload it. You need to get 5 loads. You alone can do it in 50 minutes. That is working in serial. 10 minutes * 5 trips one after the other = 50 minutes.
Let's say you get four friends to help. You all start off for the store at the same time. There's still 5 trips, and they still take 10 minutes, but because you did it in parallel the total time is only 10 minutes.
But it will never take less than 10 minutes, no matter how many trips you have to make or how many friends you get to help. That is why the process starts up fast, everybody gets into their cars and drives off to the store, but then nothing happens for a while because it still takes 10 minutes for everyone to do their job.
Same thing here. Your loop body takes X time to run. If you iterate through it Y times, it will take X * Y real world human time to run. If you run it in parallel Y times, ideally it will take just X time to run. Each parallel worker must still execute the full body of the loop taking X time.
In order to speed things up further, you have to break up the big bottleneck of very_slow_subroutine and make that work in parallel. Your SQL is so simple that is where you should focus your efforts at optimization and parallelism.
Let's say the store is really close, it's only a 1 minute drive (this is your SQL UPDATE), but shopping, loading and unloading takes 9 minutes (this is very_slow_subroutine). What if instead you have 5 cars and 15 friends. You load 3 people into each car. Driving to and from the store will take the same time, but now three people are working together to do the shopping, loading and unloading taking only 4 minutes. Now each trip takes 5 minutes instead of 10.
This represents redesigning very_slow_subroutine to do its work in parallel. If it's just a big loop, you can put more workers on that loop. If it's a series of slow operations, you will have to redesign it to take advantage of parallel execution.
If you use too many workers you can clog up the system, it depends on what the bottleneck is. If it's CPU bound and you have 2 CPU cores, you're probably see performance gains up to 3 to 5 workers ((cores * 2)+1 is a good rule of thumb) and after that performance will drop off as the CPU spends more time switching between processes than doing work. If the bottleneck is IO, or an external service as is often the case with database and network calls, you can see great efficiencies throwing many workers at the problem. While one process is waiting around for a disk or network operation, the others can be using your CPU.
Whether parallelism can help depends on where your bottleneck is. If your CPU with 4 cores is the bottleneck, forking 4 processes might cause things to complete in about 1/4th the under the best case scenario, but spawning 15 processes is not going to improve things much more.
If, more likely, your bottleneck is in I/O, starting 15 processes that compete for the same I/O is not going to help much, although in cases where you have tons of memory to use as file cache, some improvement might be possible.
To explore the limits on your system, consider the following program:
#!/usr/bin/env perl
use strict;
use warnings;
use Parallel::ForkManager;
run(#ARGV);
sub run {
my $count = #_ ? $_[0] : 2;
my $pm = Parallel::ForkManager->new($count);
for (1 .. 20) {
$pm->start and next;
sleep 1;
$pm->finish;
}
$pm->wait_all_children;
}
My ancient laptop has a single CPU with 2 cores. Let's see what I get:
TimeThis : Command Line : perl sleeper.pl 1
TimeThis : Elapsed Time : 00:00:20.735
TimeThis : Command Line : perl sleeper.pl 2
TimeThis : Elapsed Time : 00:00:06.578
TimeThis : Command Line : perl sleeper.pl 4
TimeThis : Elapsed Time : 00:00:04.578
TimeThis : Command Line : perl sleeper.pl 8
TimeThis : Elapsed Time : 00:00:03.546
TimeThis : Command Line : perl sleeper.pl 16
TimeThis : Elapsed Time : 00:00:02.562
TimeThis : Command Line : perl sleeper.pl 20
TimeThis : Elapsed Time : 00:00:02.563
So, running with max 20 processes gives me a total run time over 2.5 seconds for sleeping one second 20 times.
On the other hand, with just one process, sleeping one second 20 times took just over 20 seconds. That is a huge improvement, but it also indicates a management overhead of more than 150% when you have 20 processes each sleeping for one second.
This is in the nature of parallel programming. There are a lot of formal treatments out there on what you can expect, but Amdahl's Law is required reading.
I'm writing a very simple program to calculate the factorial of a number.
Here it is:
import time
def factorial1(n):
fattoriale = 1
while (n > 0):
fattoriale = fattoriale * n
n = n - 1
return fattoriale
start_time = time.clock()
factorial1(v)
print float(time.clock() - start_time), "seconds"
The strange point (for me) are the results in term of execution time (on a value):
1° run: 0.000301 seconds
2° run: 0.000430 seconds
3° run: 0.000278 seconds
Why do you think it's so variable?
Does it has something to do with the float type approximation?
Thanks, Gianluca
On Unix based systems time.clock returns the CPU time, not the wall-clock time.
Your program is deterministic (even the print is) and on an ideal system should always run in the same amount of time. I believe that in your tests your program was interrupted and some interrupt handler was executed or the scheduler paused your process and gave the CPU to some other process. When your process is allowed to run again the CPU cache might have been filled by the other process, so the processor needs to load your code from memory into the cache again. This takes a small amount of time - which you see in your test.
For a good quantization of how fast your program is you should consider not calling factorial1 only once but thousands of times (or with greater input values). When your program runs for multiple seconds, then scheduling effects have less (relative) impact than in your test where you only tested for less than a millisecond.
It probably has a lot to do with sharing of resources. If your program runs as a separate process, it might have to contend for other processes running on your computer at the same time which are using resources like CPU and RAM. These resources are used by other processes as well so 'acquire' them in terms of concurrent terms will take variable times especially if there are some high-priority processes running parallel to it and other things like interupts may have higher priority.
As for your idea, from what I know, the approximation process should not take variable times as it runs a deterministic algorithm. However the approximation process again may have to contend for the resources.
I'm making a script with ruby that must render frames at 24 frames per second, but I need to wait 1/24th of a second between sending the commands. What is the best way to sleep for less than a second?
sleep(1.0/24.0)
As to your follow up question if that's the best way: No, you could get not-so-smooth framerates because the rendering of each frame might not take the same amount of time.
You could try one of these solutions:
Use a timer which fires 24 times a second with the drawing code.
Create as many frames as possible, create the motion based on the time passed, not per frame.
Pass a float to sleep, like:
sleep 0.1
I have a very hard problem:
I have round about 20-50 objects, which I MUST (that is given for the problem, please don't spend time in thinking around it) put througt a logic EVERY SECOND.
The logic itself need round about 200-600 milliseconds (90% it is 200ms - 10% it is 600ms).
I try to find any solution how I can make is smaller, but there isn't. I must get an object from DB, I must have a lot of if-else and I must actual it. - Even if I reduce it to 50ms or smaller, to veriable rate of the object up to 50 will break my neck with the 1 second timer, because 50 x 50mx =2,5 second. So a tick needs longer then the tickrate should be.
So, my only, not very smart I think, idea is to open for every object an own thread and lead a mainthread for handling. So the mainthread opens x other thread. So only this opening must take unter 1 second. After it logic is used, the thread can kill itself and we all are happy, aren't we?
By given the last answers, I will explain my problem:
I try to build an auctioneer site. So I have up to 50 auctions running at the same moment - nothing special. So I need to: every single second look to the auctionlist, see if the time is 00:00:01 and if it is, bid automaticly (it's a feature, that user can create).
So: get 50 objects in a list, iterate through, check if a automatic bid is need, do it.
With 50 objects and the processing time you've given on average you are doing 12 seconds worth of processing every second. Assuming you have 4 cores, you can get this down to an execution time of 4 seconds via threading. Every second. This means that you're going to start off behind and slip further behind as time goes on.
I know you said you tried to think of a way to make it more efficient, but couldn't, but I fear you're going to have to. The problem as stated now is computationally intractable. You're either going to have to process the objects in a rotating window (so each object gets hit once every 4th cycle or so), or you need to make your processing run faster.
First: Profile, if you haven't already. Figure out what section of your code are taking time, etc. I'd go after that database - how long is the I/O of the objects from the database taking? Can you cache that I/O? (If you're manipulating the same 50 objects, don't load them every second.)
Let's address your threads idea: If you want multiple threads, don't create and destroy them every second. Create your X threads, and leave them be -- creating & destroying them are going to be expensive operations. You might find that less threads will work better - such as 1 or 2 per core, as you might be able to reduce time doing context switches.
To expand on Jonathan Leffler's comment on the question, as the OP requested: (This answer is a wiki)
Say you have these three things being auctioned, ending at the times indicated:
10 Apples - ends at 1:05:00 PM
20 Blueberries - ends at 2:00:00 PM
15 Pears - ends at 3:50:00 PM
If the current time is 1:00:00 PM, then sleep for 4 minutes, 58 seconds (since the closest item ends in 5 minutes). We use the 2 seconds then for processing - adjust that threshold as needed. Once we're done with the apples, we'll sleep for (2 PM - now() - 2s), for the blueberries.
Note that when we wake up at 1:04:58 PM to process the apples auction, we do not touch the blueberries or the pears -- we know that they're still way out in the future, so we don't care.