testcontainers / testcontainers/testcontainers-python

New Container: Couchbase

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🚀 enhancement
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Python
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描述

What is the new container you'd like to have?

I would like to contribute a Couchbase container. More documentation here.

Couchbase Server is a powerful NoSQL database designed for high performance and scalability. It provides fast, low-latency data access with built-in replication and high availability. It also supports SQL-like queries through N1QL, making it easy to work with for developers familiar with relational databases.

I am proposing a dedicated Couchbase TestContainer for integration/system testing when using Couchbase as a NoSQL database.

Why not just use a generic container for this?

A dedicated custom Couchbase container is beneficial because it provides an abstraction layer when using it together with couchbase-python-client library. The container also allows for the simple and easy setup and interactions with the Couchbase database server via the couchbase-python-client.

Additionally, the container requires some initialization steps before it is ready to serve requests, such as setting up authentication credentials and creating a default bucket, scope, and collection. There are also several ports that need to be configured. Having these steps handled automatically in a dedicated Couchbase TestContainer class simplifies the process and ensures that the test container is ready to use quickly, with most of the necessary setup pre-configured. This results in a smoother integration and testing experience.

Other references:

The Java version of Testcontainers already supports Couchbase and is widely used within the community. Given its popularity, it would be beneficial to have similar functionality available for Python as well. Below are references to the Couchbase Testcontainers in Java:

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调研方向

首先查看 Java Couchbase Testcontainers 参考资料和 Couchbase 的 Docker 入门文档。定义 Python 容器的初始化、凭据、Bucket/Scope/Collection 设置、端口以及与 couchbase-python-client 的交互。完成的标准是:专用的 Testcontainers 类已准备好用于集成测试和系统测试,并自动处理这些设置步骤。

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评估

技术栈
docker, python
领域
database, testing
Issue 类型
功能
难度
4/5
预计耗时
3-5 天
活跃度
冷清
描述清晰度
基本清楚
新手友好度
48/100

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