StreamGuard is a powerful data management NodeJS Application designed to efficiently handle high-velocity data streams and reduce the load on MongoDB. It leverages Kafka to streamline the processing of real-time data, making it ideal for scenarios such as live GPS tracking and other applications requiring rapid data handling.

• Real-Time Data Processing: Handles fast-moving data streams efficiently.
• Reduced MongoDB Overload: Uses Kafka to alleviate pressure on MongoDB.
• Bulk Insertion: Performs bulk data operations to optimize performance.

Clone the repository and install the dependencies:

git clone https://github.com/codeterrayt/StreamGuard.git
cd StreamGuard
npm install

1. Start the Required Services:

   • MongoDB:

     docker run -p 27017:27017 mongo

   • Zookeeper:

     docker run -p 2181:2181 zookeeper

   • Kafka:

     docker run -p 9092:9092 -e KAFKA_ZOOKEEPER_CONNECT=<IPv4-Address>:2181 -e KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://<IPv4-Address>:9092 -e KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1 confluentinc/cp-kafka

2. Run the Server and Consumer Applications:

   node server.js
   node consumer-app.js

• MAX_DATA_LENGTH_BUFFER: Defines the maximum number of data entries to buffer before performing a bulk insertion. Set this to 10 to trigger bulk operations after accumulating 10 data entries.

  Example configuration in .env file:

  MAX_DATA_LENGTH_BUFFER=100

• Server: Manages data flow and interacts with Kafka.
• Consumer: Processes incoming data and performs bulk insertions based on the configured buffer length.

To test the data ingestion and processing, you can use the provided test script:

1. Run the Test Script:

   node test/test.js

   This script sends 3 requests per second with incrementing latitude and longitude values to simulate data streaming.

Feel free to contribute by submitting issues or pull requests. For any questions or feedback, open an issue on the GitHub repository.