CloudPos_WebStore

huge data store and show dotnet core api, mongodb

Layer Architecture, Dotnet8

Package: MongoDB.Driver

MONGODB:

• Download install MongoDB Campass
• MongoDB's shell or GUI tool (e.g., MongoDB Compass): CloudPos_WebStoreDb
• Open Mongo DB Shell: _MONGOSH
• use CloudPos_WebStoreDb (*Create DB)
• ( Create Table with a row will autometically create table) db.Customers.insertOne({ "CustomerId": "CUST001", "FirstName": "John", "MiddleName": "A.", ... });

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1. Data Transfer Format Use JSON: JSON is lightweight and widely supported. It is easier and faster to parse compared to formats like XML or Excel. Compress the Payload: Enable compression (e.g., Gzip) in both the external application and the API to reduce network latency and data transfer size
2. Optimize API Design Batch Processing: Design the API to accept data in batches rather than single records or the entire dataset at once. Define an appropriate batch size (e.g., 1,000 - 10,000 records per batch), based on MongoDB's document size limits and server resources.
3. MongoDB Insertion Strategy Use InsertManyAsync: MongoDB's InsertManyAsync method is optimized for bulk inserts and performs better than inserting records individually. Ensure that ordered is set to false to skip errors for individual documents without stopping the entire batch. Indexing: Pre-create necessary indexes on MongoDB collections to avoid slow writes. For example, create indexes on frequently queried fields like Phone or Email.
4. Error Handling Validate Data: Validate the incoming data at the API layer to avoid inserting invalid records into the database. Partial Success Handling: Return details of successful and failed records to the external application for transparency
5. Asynchronous Operations Use asynchronous methods to handle incoming requests and MongoDB operations without blocking threads. Ensure proper resource utilization and allow the system to handle multiple requests simultaneously.
6. API and Server Configuration Rate Limiting: Implement rate limiting to prevent the server from being overwhelmed by excessive requests. Connection Pooling: Configure MongoDB's connection pool to handle multiple concurrent operations efficiently. Timeouts: Set appropriate timeouts for requests and database operations to prevent hanging processes.
7. Scaling for Huge Data Horizontal Scaling: Use a load balancer to distribute API requests across multiple instances of the application. Sharded MongoDB Cluster: For extremely large datasets, consider using a sharded MongoDB cluster to distribute data across multiple nodes.
8. Monitoring and Optimization Monitor Performance: Use tools like MongoDB Atlas or custom monitoring solutions to track insertion performance and server health. Profiling and Optimization: Profile the API to identify bottlenecks in parsing, validation, or MongoDB operations. Background Processing: For massive datasets, offload insertion to background workers or queues (e.g., RabbitMQ, Kafka) to decouple the API from database writes.

Best Practices for External Applications Chunk Data Before Sending: Ensure the external application splits data into manageable chunks to align with the API's batch size. Retry Mechanism: Implement retries for failed API calls to handle transient issues like network failures.

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*** Get API Huge data:

1. Use Pagination
2. Implement Streaming Responses
3. Use Background Processing with an Export Link Background Process: Hangfire- Radis Package: Hangfire Hangfire.AspNetCore Hangfire.MemoryStorage Hangfire.Mongo

run with: /hangfire

http://localhost:5021/api/Customers/get-all-export-to-file aa7fa91a-4d37-4604-a3fa-4ecf9cc53163