MongoDB and mongo-express Docker Compose Setup

This repository contains a Docker Compose configuration to quickly set up a MongoDB instance and mongo-express, a web-based MongoDB admin interface.

Prerequisites

Make sure you have Docker and Docker Compose installed on your system. If not, you can install them from the official Docker website:

• Docker
• Docker Compose

Usage

To use this setup, follow these steps:

1. Clone this repository to your local machine:

   git clone https://github.com/Ylenia1211/BDM24_spark_example.git

2. Navigate to the cloned repository directory:

   cd your-repository

3. Start the services using Docker Compose:

   docker-compose up 

The services will now start in detached mode, and you can access mongo-express at http://localhost:8081 in your web browser. Use root as the username and example as the password to log in.

Configuration

The docker-compose.yml file defines two services:

• mongo: MongoDB instance with the specified root credentials.

• mongo-express: Web-based MongoDB admin interface connected to the MongoDB instance.

  https://hub.docker.com/_/mongo

Credentials

• Username: root
• Password: example

Ports

• mongo-express: Accessible at http://localhost:8081. You can change the host port in the docker-compose.yml file if needed.

Notes

• This setup is intended for development and testing purposes. Make sure to secure your MongoDB instance appropriately before deploying it in a production environment.
• Ensure that the provided credentials are strong and not used in production environments without proper security measures.

MongoDB CRUD Operations Example

Link to MongoCRUDExample.java

This Java program demonstrates basic CRUD (Create, Read, Update, Delete) operations using the MongoDB Java driver.

Make sure you have the MongoDB Java driver added to your project dependencies.

• Make sure your MongoDB instance is running and accessible.

• Customize the connection string (uri) according to your MongoDB setup.

  Spark MongoDB Connector Example

Link to SparkMongoDBExample.java This Java program demonstrates how to use the Apache Spark MongoDB Connector to interact with MongoDB data in a Spark application.

Prerequisites

Ensure you have Apache Spark installed and configured on your system. Also, make sure you have the MongoDB Java driver and the Spark MongoDB Connector added to your project dependencies.

Usage

1. Import necessary libraries:

   import org.apache.spark.api.java.JavaRDD;
   import org.apache.spark.api.java.JavaSparkContext;
   import org.apache.spark.sql.SparkSession;
   import org.bson.Document;
   import com.mongodb.spark.MongoSpark;

2. Define the main class SparkMongoDBExample and its main method.

3. Inside the main method, create a Spark session:

   SparkSession spark = SparkSession.builder()
       .appName("MongoDBSparkConnectorExample")
       .master("local[*]")  // Set the master URL
       .config("spark.mongodb.input.uri","mongodb://root:example@localhost:27017/db_spark.collection1?authSource=admin")
       .config("spark.mongodb.output.uri", "mongodb://root:example@localhost:27017/db_spark.collection1?authSource=admin")
       .config("spark.jars.packages", "org.mongodb.spark:mongo-spark-connector_2.12:10.0.0")
       .getOrCreate();

   • Customize the connection URI according to your MongoDB setup.

4. Create a Spark context:

   JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());

5. Read data from MongoDB collection:

   JavaRDD<Document> rdd = MongoSpark.load(jsc);

6. Perform operations on the data, for example, displaying the first 10 documents and counting the total number of documents:

   rdd.take(10).forEach(System.out::println);
   long count = rdd.count();
   System.out.println("Number of documents in the collection: " + count);

7. Close the Spark session and context:

   jsc.close();
   spark.stop();

Notes

• Ensure your MongoDB instance is running and accessible.
• Customize the connection URI (spark.mongodb.input.uri and spark.mongodb.output.uri) according to your MongoDB setup.
• Replace db_spark with the name of your database and collection1 with the name of your collection.
• This example assumes a local Spark setup (master("local[*]")). Adjust the master URL accordingly for a cluster setup.