The Flyweight design pattern is a structural design pattern that focuses on optimizing memory usage by sharing data between multiple objects. It is useful when you need to create a large number of similar objects that share common properties.
The main idea behind the Flyweight pattern is to separate the intrinsic (shared) and extrinsic (unique) parts of an object. The intrinsic part is immutable and can be shared among multiple objects, while the extrinsic part varies and is stored externally.
The builder pattern can be used for constructing complex objects with many configurable options.
The benefit of the Builder Pattern is that it allows for the creation of complex objects step by step, providing a clear and flexible way to configure and construct objects with varying attributes and options.
class Pizza:
def __init__(self):
This is a simplified example to demonstrate the Factory Pattern concept. In practice, you may have more complex creation logic and configurations in the factory, but the core idea remains the same: creating objects based on specific criteria or configurations.
class DataProcessor:
def process(self):
raise NotImplementedError()
As for the problem and the suggestion presented in the my previous gist Voting System Schema Model Analysis, this is the first version for the new Voting System.
This is a self contained Django application with all the logic and behavior encapsulated in the models layer.
For simple election based on the rules
This is an overview of the relationship between the main entities of the annotation process of the system and a analysis of the voting system used in WildePod - wildlife research project https://wildepod.org.
The analysis provided here was initially motivated by the need to establish a common understanding of the data flow within the annotation system and the voting system of WildePod.
Furthermore, the analysis aims to provide an understanding of the customized voting system used in WildePod and compare it to a traditional voting system.
It also seeks to identify specific improvements to the system in order to address bottlenecks in the annotation process and improve access to the internal data of the system.
PL/pgSQL is a loadable procedural language for the Postgres database system.
Extract Transform Load
We can create an entire ETL process in PL/pgSQL to handle data stored in our databases. It is the fastest and most reliable way to do this. It's fast because we don't have to move data from the DB engine, and considering that all related tables are indexed, we only have to write and run the code in the engine to retrieve the information. Even during the transformation and loading process, the engine handles pointers and indices very efficiently.
Since we are working with data inside the database, we can perform operations with large datasets without any issues regarding data transfer (network) and memory usage. Instead of making atomic transactions, we can perform large set operations with large datasets. To do this, we will use SQL Functions.
I am a highly committed and skilled professional with a strong track record of solving complex software engineering problems efficiently.
I like to complete simpler tasks fast, and focus on delivering high-quality solutions and attention to detail ensures that I am always able to meet project deadlines and exceed expectations.
My ability to tackle challenging problems and deliver results quickly makes me an invaluable asset to any team.
Google Cloud provides a vast array of managed services for computing, databases, NoSQL, and more. While these services offer numerous benefits, it's essential to weigh the tradeoff between cost and advantages.
Managed services are often the better choice as they ensure data security and eliminate the need for additional configuration and maintenance.
But using Google Compute Engine (GCE) to run cronjobs can provide a more cost-effective solution than Google App Engine (GAE) and enable more powerful hardware to process complex tasks in a unmanaged service.
To set up cronjobs on GCE, a well-defined process can be followed, which is documented below.
Just a readme to remember how to install docker-postgres with SSL enabled
This is the original post https://stackoverflow.com/a/55072885.
Some people share the information without the source. If you have any question, go there, it is well explainde.