python-memoization | powerful caching library for Python , with TTL support | Caching library

by lonelyenvoy Python Version: v0.4.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | python-memoization Summary

python-memoization is a Python library typically used in Server, Caching applications. python-memoization has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install python-memoization' or download it from GitHub, PyPI.

A powerful caching library for Python, with TTL support and multiple algorithm options.

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Quality

Security

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Support

python-memoization has a low active ecosystem.

It has 191 star(s) with 14 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 11 open issues and 10 have been closed. On average issues are closed in 225 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of python-memoization is v0.4.0

Quality

python-memoization has 0 bugs and 0 code smells.

Security

python-memoization has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

python-memoization code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

python-memoization is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

python-memoization releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 1884 lines of code, 186 functions and 31 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed python-memoization and discovered the below as its top functions. This is intended to give you an instant insight into python-memoization implemented functionality, and help decide if they suit your requirements.

Get a cache wrapper
Returns the number of members
Return the root node
Access the cache tree
Creates a pseudo - member
Return the high bit of a value
Return True if the value is a power of 2
Decompose a flag
Validate all cached algorithms
Return cache toolkit
Expect an attribute to be a callable
Log error message
Test test
Quick factorial
Construct a singleton member
Generate the next value based on count
Get package data
Get the long description

Get all kandi verified functions for this library.

python-memoization Key Features

No Key Features are available at this moment for python-memoization.

python-memoization Examples and Code Snippets

No Code Snippets are available at this moment for python-memoization.

Community Discussions

Trending Discussions on python-memoization

why is this memoized Euler14 implementation so much slower in Raku than Python?

QUESTION

why is this memoized Euler14 implementation so much slower in Raku than Python?

Asked 2020-Nov-16 at 14:09

I was recently playing with problem 14 of the Euler project: which number in the range 1..1_000_000 produces the longest Collatz sequence?

I'm aware of the issue of having to memoize to get reasonable times, and the following piece of Python code returns an answer relatively quickly using that technique (memoize to a dict):

...

ANSWER

Answered 2020-Nov-16 at 03:56

I think the majority of the extra time is because Raku has type checks, and they aren't getting removed by the runtime type specializer. Or if they are getting removed it is after a significant amount of time.

Generally the way to optimize Raku code is first to run it with the profiler:

Source https://stackoverflow.com/questions/64851261

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install python-memoization

You can install using 'pip install python-memoization' or download it from GitHub, PyPI.
You can use python-memoization like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.