cyanide VS mongo-go-driver

No, it stands for "Binary JSON".

BSON specification

I recommend looking at https://ubjson.org and https://bsonspec.org , this will answer most of your questions.

or https://bsonspec.org/

BJSON Binary JSON, with a Swift Library here and should be one for your other end

Starting this tutorial we specified that data in MongoDB is stored in collections. We also specified that in MongoDB we use syntax similar to JSON. That syntax is called "Binary JSON" or BSON. BSON is similar to JSON; but it's more like an encoded serialization of JSON. We can find useful information in the BSON website.

> I'm saying that when you decode an Avro document, the result that comes out (presuming you don't tell the Avro decoder anything special about custom types your runtime supports and how it should map them) is a JSON document.

Semantic point: it's not a "document".

There are tools which will decode Avro and output the data in JSON (typically using the JSON encoding of Avro: https://avro.apache.org/docs/current/spec.html#json_encoding), but the ADT that is created is by no means a JSON document. The ADT that is created has more complex semantics than JSON; JSON is not the canonical representation.

> By which I don't mean JSON-encoded text, but rather an in-memory ADT that has the exact set of types that exist in JSON, no more and no less.

Except Avro has data types that are not the exact set of types that exist in JSON. The first clue on this might be that the Avro spec includes mappings that list how primitive Avro types are mapped to JSON types.

> Or, to put that another way, Avro is a way to encode JSON-typed data, just as "JSON text", or https://bsonspec.org/, is a way to encode JSON-typed data

BSON, by design, was meant to be a more efficient way to encode JSON data, so yes, it is a way to encode JSON-typed data. Avro, however, was not defined as a way to encode JSON data. It was defined as a way to encode data (with a degree of specialization for the case of Hadoop sequence files, where you are generally storing a large number of small records in one file).

A simple counter example: Avro has a "float" type, which is a 32-bit IEEE 754 floating point number. Neither JSON nor BSON have that type.

Technically, JSON doesn't really have types, it has values, but even if you pretend that JavaScript's types are JSON's types, there's nothing "canonical" about JavaScript's types for Avro.

Yes, you can represent JSON data in Avro, and Avro in JSON, much as you can represent data in two different serialization formats. Avro's data model is very much defined independently of JSON's data model (as you'd expect).

For me, this exploration started when I was attempting to improve handling of Unicode surrogate pair values in the MongoDB Go driver's Extended JSON unmarshaler. The Extended JSON format is an extension to the standard JSON format that adds type information and allows deterministic conversion to and from BSON.

MongoDB, a document-oriented NoSQL database, will be our data powerhouse. We'll utilize the mongo-driver library to seamlessly connect our Golang application to MongoDB. This section will cover essential database interactions, including creating collections, storing metadata, and efficiently querying for image-related data. Understanding these fundamentals is crucial for building a robust image storage and retrieval system.

mongodb.org/mongo-driver: The MongoDB supported driver for Go.

Learning how to integrate no-sql databases with applications is becoming a must-know skill for all developers out there. Golang in particular provides the MongoDB Go Driver for easier and efficient connection with the mongo database.

IDE: use whatever make you productive. I personally use vscode. VCS: git, as golang communities use github heavily as base for many libraries. AFAIK Linter: use staticcheck for linting as it looks like mostly used linting tool in go, supported by many also. In Vscode it will be recommended once you install go plugin. Libraries/Framework: actually the standard libraries already included many things you need, decent enough for your day-to-day development cycles(e.g. `net/http`). But here are things for extra: - Struct fields validator: validator - Http server lib: chi router , httprouter , fasthttp (for non standard http implementations, but fast) - Web Framework: echo , gin , fiber , beego , etc - Http client lib: most already covered by stdlib(net/http), so you rarely need extra lib for this, but if you really need some are: resty - CLI: cobra - Config: godotenv , viper - DB Drivers: sqlx , postgre , sqlite , mysql - nosql: redis , mongodb , elasticsearch - ORM: gorm , entgo , sqlc(codegen) - JS Transpiler: gopherjs - GUI: fyne - grpc: grpc - logging: zerolog - test: testify , gomock , dockertest - and many others you can find here

PostgeSQL as event store database Kafka as messages broker gRPC Go implementation of gRPC Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus MongoDB MongoDB database Elasticsearch Elasticsearch client for Go. Echo web framework Kibana Kibana is data visualization dashboard software for Elasticsearch Migrate for migrations

mongodb/mongo-go-driver 6.6k Stars, Used by -

In this project we have microservice working with EventStoreDB using oficial go client, for [projections (https://zimarev.com/blog/event-sourcing/projections/) used MongoDB and Elasticsearch for search, and communicate by gRPC and REST. Did not implement here any interesting business logic and didn't cover tests, because don't have enough time, the events list is very simple: create a new order, update shopping cart, pay, submit, cancel, change the delivery address, complete order, and of course in real-world better use more concrete and meaningfully events, but the target here is to show the idea and how it works. Event Sourcing can be implemented in different ways, used here EventStoreDB, but we can do it with PostgreSQL and Kafka for example. After trying both approaches, found EventStoreDB is a better solution because all required features are implemented out of the box, it is optimized and really very good engineers developing it.

For that same reason I decided to create a simple REST API using my favorite Go framework, Fiber and this time I decided to use the MongoDB Driver.

Mgo has been unmaintained for years. Use the official https://github.com/mongodb/mongo-go-driver