Remember these heuristics make a lot of sense for the server

It was also the fastest-growing programming language of the previous decade. Python’s rise in popularity has a lot to do with the emergence of big data in the 2010s as well as developments in machine learning and artificial intelligence. Businesses urgently required a language for quick development with low barriers of entry that could help manage large-scale data and scientific computing tasks. Python was well-suited to all these challenges.
Rust is still trying to catch up with the machine learning community, and so I believe Swift and Julia are the languages that will dethrone Python and eventually rule data science. Let’s see why odds are against Python.
Worse, unlike compiled code, Python’s interpreter analyzes every line of code at execution time. This leads to an overhead that causes a significantly slower performance when compared to other languages.

Despite its clean syntax, the exposure Python got in its first decade wasn’t encouraging, and it didn’t really find inroads into the developer’s workspace. Perl was the first choice scripting language and Java had established itself as the go-to in the object-oriented programming arena. Of course, any language takes time to mature and only gets adopted when it’s better suited to a task than the existing tools.
This realization led to a larger number of developers incorporating Python into their applications. The emergence of Django eventually led to the doom of Perl, and Python started gaining more momentum. Still, it wasn’t even close in popularity to Java and JavaScript, both of which were newer than Python.
Besides having those factors in its favor, Python was an interpreted language with dynamic typing support. More importantly, it had the backing of Google, who’d invested in Python for Tensorflow, which led to its emergence as the preferred language for data analysis, visualization, and machine learning.
For Python, that time first arrived during the early 2000s when people started realizing it has an easier learning curve than Perl and offers interoperability with other languages.
/usr/bin/time -v ./primes-defaults -n 1 -l 100
23:57:47.286 [main] INFO i.m.context.env.DefaultEnvironment — Established active environments: [oraclecloud, cloud, cli]
[53, 59, 61, 67, 71, 73]
Command being timed: “./primes-defaults -n 1 -l 100”
User time (seconds): 0.01
System time (seconds): 0.01
Percent of CPU this job got: 113%
Elapsed (wall clock) time (h:mm:ss or m:ss): 0:00.02
Average shared text size (kbytes): 0
Average unshared data size (kbytes): 0
Average stack size (kbytes): 0
Average total size (kbytes): 0
Maximum resident set size (kbytes): 49980
Average resident set size (kbytes): 0
Major (requiring I/O) page faults: 0
Minor (reclaiming a frame) page faults: 1226
Voluntary context switches: 232
Involuntary context switches: 2
Swaps: 0
File system inputs: 0
File system outputs: 0
Socket messages sent: 0
Socket messages received: 0
Signals delivered: 0
Page size (bytes): 4096
Exit status: 0

Yet, despite the growing demand for machine learning and AI at the turn of this decade, Python won’t stay around for long. Like every programming language, it has its own set of weaknesses. Those weaknesses make it vulnerable to replacement by languages more suited to the common tasks businesses ask of them. Despite the presence of R, the emergence of newer languages such as Swift, Julia, and Rust actually poses a bigger threat to the current king of data science.Julia allows you to avoid some of these problems. Despite being dynamically typed, it has a just-in-time compiler. The JIT compiler either generates the machine code right before it’s executed or uses previously stored, cached compilations, which makes it as performant as statically typed languages. More importantly, it has a key feature known as multiple dispatch that is like function overloading of OOPs, albeit at runtime. The power of multiple dispatch lies in its ability to handle different argument types without the need to create separate function names or nested if statements. This helps in writing compact code, which is a big win in numeric computations since unlike Python, you can easily scale solutions to deal with all types of arguments.This is much-much better of course, but since we’re not configuring the memory limits, it’ll still use the heuristics and grow memory much more aggressively than you might expect if we increase the workload.

All good things come at a cost, and Python’s dynamically typed nature is no exception. It hampers developers, especially when running the code in production. Dynamic typing that makes it easy to write code quickly without defining types increases the risk of running into runtime issues, especially when the codebase size increases. Bugs that a compiler would easily figure out could go unidentified in Python, causing hindrances in production and ultimately slowing the development process in large-scale applications.

Command being timed: “./primes-defaults -n 100000 -l 100”
User time (seconds): 0.88
System time (seconds): 0.63
Percent of CPU this job got: 72%
Elapsed (wall clock) time (h:mm:ss or m:ss): 0:02.07
Average shared text size (kbytes): 0
Average unshared data size (kbytes): 0
Average stack size (kbytes): 0
Average total size (kbytes): 0
Maximum resident set size (kbytes): 309956
Average resident set size (kbytes): 0
Major (requiring I/O) page faults: 0
Minor (reclaiming a frame) page faults: 65110
Voluntary context switches: 199
Involuntary context switches: 5
Swaps: 0
File system inputs: 0
File system outputs: 0
Socket messages sent: 0
Socket messages received: 0
Signals delivered: 0
Page size (bytes): 4096
Exit status: 0

Now a much better way to run this application is as a native executable generated by GraalVM Native Image. Fortunately, building a native executable of the Micronaut application is very straightforward as there’s a Gradle plugin that helps with the configuration and execution of the native-image utility.

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