Top 3 Ways to Make Sausage with MongoDB & Java

I’ve been working with Java a ton this year, more so than the previous years, so I decided to put together the top three Java libraries for accessing MongoDB that I’ve been using. That top 3 list shapes up like this.

  1. MongoDB Java Driver: This is the official Java driver provided by MongoDB. It allows Java applications to connect to MongoDB and work with data. The driver supports synchronous and asynchronous interaction with MongoDB and provides a rich set of features for database operations.
    • Key Methods:
      • MongoClients.create(): To create a new client connection to the database.
      • MongoDatabase.getCollection(): To access a collection from the database.
      • MongoCollection.find(): To find documents within a collection.
  2. Morphia: Morphia is an Object-Document Mapper (ODM) for MongoDB and Java. It provides a higher-level, object-oriented API to interact with MongoDB, and maps Java objects to MongoDB documents.
    • Key Methods:
      • Datastore.createQuery(): To create a query for the type of entity you want to retrieve.
      • Datastore.save(): To save an entity to the database.
      • Query.asList(): To execute a query and get the results as a list.
  3. Spring Data MongoDB: Part of the larger Spring Data project, Spring Data MongoDB provides integration with MongoDB to work with the data as easily as if it were a relational database. It’s a popular choice for Spring-based applications.
    • Key Methods:
      • MongoRepository.findAll(): To find all documents in a collection.
      • MongoRepository.save(): To save a given entity.
      • MongoRepository.findById(): To find a document by its ID.

These libraries offer comprehensive methods for connecting to and working with MongoDB from Java applications. They are widely used in the Java community and are supported by a large number of developers and organizations. Let’s dive deeper with each, and also more specifically talk about some of their respective query methods.

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Fruit and Snakes: Frequent Mutative Mongo User Database with Python

Recently I had a singular mission to build a GraphQL API against a Mongo database where the idea is, one could query the underlying collections, documents, and fields with the assumption that users would be adding or possibly removing said collections, documents, and fields as they needed.

That sounds somewhat straight forward enough, but before even getting started with the GraphQL API I really needed some type of environment that would mimic this process. That is what this article is about, creating a test bed for this criteria.

The Mongo Database & Environment

First thing I did was setup a new Python environment using virtualenv. I wrote about that a bit in the past if you want to dig into that deeper, the post is available here.

virtualenv fruit_schema_watcher

Next up I created a git repo with git init then added a README.md, LICENSE (MIT), and .gitignore file. The next obvious thing was the need for a Mongo database! I went to cracking on a docker-compose file, which formed up to look like this.

version: '3.1'  
  
services:  
  mongo:  
    image: mongo:latest  
    container_name: mongodb_container  
    ports:  
      - "27017:27017"  
    environment:  
      MONGO_INITDB_ROOT_USERNAME: root  
      MONGO_INITDB_ROOT_PASSWORD: examplepass  
    volumes:  
      - mongo-data:/data/db  
  
volumes:  
  mongo-data:

With that server running, I went ahead and created a database called test manually. I’d just do all the work from here on out with that particular database.

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How you write time elapsed stamps in Python?

A common practice in any coding is to get a time stamp for the start and stop of a process, and probably calc the elapsed time since it started (ya know, because that means we don’t have to mentally calc it ourselves the zillion times the code will run!). The following is usually an example of how I do this particular activity:

start_time = datetime.datetime.now()
print(f"Process started at: {start_time.strftime('%Y-%m-%d %H:%M:%S')}")

# ...do the process and all here that is being timed...

end_time = datetime.datetime.now()
print(f"Process ended at: {end_time.strftime('%Y-%m-%d %H:%M:%S')}")
time_elapsed = end_time - start_time
print(f"Time elapsed: {time_elapsed}")

Now, my question is, how do YOU do this in Python? Are there other tricks, cleaner ways to do it?

Chat GPT-4 SQL Refactoring for PostgreSQL

Just recently I wrote up a post about a multi-tenant database for music collectors. That SQL was the first draft I put together to cover some of the basic data points and relations the database would need to have to provide the multi-tenant capabilities. However, there are a number of refactorings that I’d like done before I get started developing against this particular database. For this, I could just go through the refactorings with something like DataGrip or one of the other IDEs or other tools that I’ve used. But instead, for this exercise I decided why not give Chat GPT-4 algo a shot?

This is my experience refactoring SQL with Chat GPT-4.

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Building a Multi-Tenant Music Collector’s Database

Ok, I’ve gotten deep into collecting music again, after just doing the *streaming* thing for a decade plus. In this deep dive back into music and playing, composing, collecting, reactions, reviews, live shows, and related worlds coming together I’ve found it might be interesting to put together a multi-tenant database to start collecting all my effort’s collateral data together. This, is the beginning of that journey.

I’ve got some tunes cranked as I’m all set to dive deep into this, let’s get it done!

Music has always been a significant part of my life. From the melodies that accompany my daily routines to the anthems of my most memorable moments, it’s been a constant. As my collection grew, I realized I needed a better way to organize it. That’s when I stumbled upon the concept of multi-tenancy databases and decided to give it a shot with PostgreSQL. Here’s my experience.

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