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exploring-rust-ecosystem
A set of examples that demonstrate how to create a backend application using Rust ecosystem.
The project is implemented with MongoDB Rust driver and redis-rs crate.
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InfluxDB
Purpose built for real-time analytics at any scale. InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards.
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The project is implemented with MongoDB Rust driver and redis-rs crate.
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The project is implemented with MongoDB Rust driver and redis-rs crate.
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This guide will show you how to create a Rust web backend that uses MongoDB, a document-based database, to store data and Redis for caching, rate limiting, and notifications. For visibility purposes, created demo application will also be a web application that exposes REST API. Finally, the following architecture will be obtained:
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rust-embed
Rust Macro which loads files into the rust binary at compile time during release and loads the file from the fs during dev.
But then it turned out that the images can’t be read from the database due to the lack of GridFS support in MongoDB Rust Driver (open ticket). So for simplicity purposes, I decided to use rust_embed crate which allows including images in the application binary at compile time (at the development time they are loaded from a file system). (It is also possible to store images separately from the application; images folder should be mounted as a volume in the Docker Compose service definition)
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Redis
Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.
This guide will show you how to create a Rust web backend that uses MongoDB, a document-based database, to store data and Redis for caching, rate limiting, and notifications. For visibility purposes, created demo application will also be a web application that exposes REST API. Finally, the following architecture will be obtained:
