Parallel-ForkManager, DBI. Faster than before forking, but still too slow - performance

Question

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.

Answer 1

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.

Answer 2

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.