#java#cache#caffine#data#draft#fetch#graphql#immer#immutable#immutable_collections#immutable_datastructures#java#jdbc#kotlin#orm#orm_framework#orm_library#orms#redis#redis_cache
Jimmer is a powerful and advanced ORM (Object-Relational Mapping) framework for Java and Kotlin that lets you easily read and write complex data structures without needing to predefine their shapes. It supports dynamic multi-table queries, automatic SQL optimization, and efficient saving of incomplete or nested objects. Jimmer also generates type-safe DTOs (Data Transfer Objects) for complex queries and updates, avoids common problems like "N+1" queries, and offers strong caching and GraphQL support. This means you can build complex business logic faster and with less hassle, improving both development speed and code quality. It works well with modern IDEs and supports both Java and Kotlin seamlessly.
https://github.com/babyfish-ct/jimmer
https://realpython.com/blog/python/caching-in-django-with-redis/
Caching in #Django With #Redis
Application performance is vital to the success of your product. In an environment where users expect website response times of less than a second, the consequences of a slow application can be measured in dollars and cents. Even if you are not selling anything, fast page loads improve the experience of visiting your site.
Everything that happens on the server between the moment it receives a request to the moment it returns a response increases the amount of time it takes to load a page. As a general rule of thumb, the more processing you can eliminate on the server, the faster your application will perform. Caching data after it has been processed and then serving it from the #cache the next time it is requested is one way to relieve stress on the server. In this tutorial, we will explore some of the factors that bog down your application, and we will demonstrate how to implement caching with Redis to counteract their effects.
http://www.bogotobogo.com/python/python_redis_with_python.php
Redis with Python
In order to use #Redis with Python, we will need a Python Redis #client.
In following sections, we will demonstrate the use of redis-py, a Redis Python Client.
redis-py requires a running Redis #server. See Redis Install for installation.
https://pypi.python.org/pypi/python-memcached
This software is a 100% Python interface to the #memcached#memory#cache daemon. It is the #client side software which allows storing values in one or more, possibly remote, memcached servers. Search google for memcached for more information.
http://www.csestack.org/python-libraries-for-data-science/
As per the DIKW Pyramid Model, #Data_Science job revolves around finding the information, knowledge from Raw Data. And it can be bundled into the stack of 4 entities:
source of #data
manage and store data
analyze the data
display analyzed output (#visualization, statistics)
https://alysivji.github.io/flask-part1-generating-html-pages-with-mongoengine-jinja2.html
Generating HTML Pages from #MongoDB with #MongoEngine and #Jinja2 (Flask Part 1)
Summary
Overview of MongoDB
Discussion of Object-Relational Mapping (#ORM)
Use MongoEngine to get items out of MongoDB
Render #HTML pages using Jinja2
Interact with #REST API to send emails with #Requests