How does fatpacks and App::FatPacker affect performance or memory usage?

FatPacks and App::FatPacker are tools used in Perl to pack dependencies into a single file, which can potentially affect performance and memory usage in various ways. When you use these tools, they allow for faster application startup times since there are fewer files to load from the filesystem. However, they may also increase memory usage if the fatpacked binary includes unused dependencies or if the entire application is loaded into memory regardless of whether all parts are needed.

In general, the performance impact is nuanced, as it depends on factors such as the size of the packed application, the number of dependencies included, and how the application interacts with those dependencies during runtime. While fatpacking can lead to reduced disk I/O and faster loading, the memory trade-offs must be carefully considered.

Here's a simple example of using App::FatPacker to create a fatpacked application:

#!/usr/bin/env perl use strict; use warnings; use App::FatPacker; my $fatpacked_file = 'app-fatpacked.pl'; my $source_file = 'app.pl'; # Fatpack the application App::FatPacker::fatpack($source_file, $fatpacked_file); # Running the fatpacked application system("perl $fatpacked_file");

fatpacks App::FatPacker performance memory usage Perl dependencies application startup time

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?