chore: Add publishing section to python plugin template

README.md

@@ -1,67 +1,75 @@
 # Python Source Plugin Template
+
 This repo contains everything you need to get started with building a new plugin.
 To get started all you need to do is change a few names, define some tables, and write an API Client to populate the tables.
 
 [![Mastering CloudQuery: How to build a Source Plugin in Python](https://i.ytimg.com/vi/TSbGHz5Z09M/maxresdefault.jpg)](https://youtu.be/TSbGHz5Z09M "Mastering CloudQuery: How to build a Source Plugin in Python")
 
 ## Key files & classes
- - plugin/tables/items.py
-    - Items - A boilerplate table definition
-    - ItemResolver - A boilerplate table resolver
- - plugin/example/client.py
-     - ExampleClient - A boilerplate API Client
- - plugin/client/client.py
-    - Spec - Defines the CloudQuery Config
-    - Client (uses: ExampleClient) - Wraps your API Client
- - plugin/plugin.py
-    - ExamplePlugin - The plugin registration / how CloudQuery knows what tables your plugin exposes.
-
 
+- [plugin/tables/items.py](plugin/tables/items.py)
+  - `Items` - A boilerplate table definition
+  - `ItemResolver` - A boilerplate table resolver
+- [plugin/example/client.py](plugin/example/client.py)
+  - `ExampleClient` - A boilerplate API Client
+- [plugin/client/client.py]
+  - `Spec` - Defines the CloudQuery Config
+  - `Client` (uses: `ExampleClient`) - Wraps your API Client
+- [plugin/plugin.py](plugin/plugin.py)
+  - `ExamplePlugin` - The plugin registration / how CloudQuery knows what tables your plugin exposes.
 
 ## Getting started
 
 ### Defining your tables
+
 The first thing you need to do is identify the tables you want to create with your plugin.
 Conventionally, CloudQuery plugins have a direct relationship between tables and API responses.
 
 For example:
-   If you had an API endpoint https://api.example.com/items/{num} and for each value of `num` it provided an object
-   ```json
-   {
-      "num": {{num}},
-      "date": "2023-10-12", 
-      "title": "A simple example"
-   }
-   ```
-   Then you would design the table class as
-   ```python
-   class Items(Table):
-       def __init__(self) -> None:
-           super().__init__(
-               name="item",
-               title="Item",
-               columns=[
-                   Column("num", pa.uint64(), primary_key=True),
-                   Column("date", pa.date64()),
-                   Column("title", pa.string()),
-               ],
-           )
-       ...
-   ```
+If you had an API endpoint https://api.example.com/items/{num} and for each value of `num` it provided an object
+
+```json
+{
+   "num": {{num}},
+   "date": "2023-10-12",
+   "title": "A simple example"
+}
+```
+
+Then you would design the table class as
+
+```python
+class Items(Table):
+    def __init__(self) -> None:
+        super().__init__(
+            name="item",
+            title="Item",
+            columns=[
+                Column("num", pa.uint64(), primary_key=True),
+                Column("date", pa.date64()),
+                Column("title", pa.string()),
+            ],
+        )
+    ...
+```
 
 Creating one table for each endpoint that you want to capture.
 
 ### API Client
+
 Next you'll need to define how the tables are retrieved, it's recommended to implement this as a generator, as per the example in `plugin/example/client.py`.
 
 ### Spec
+
 Having written your API Client you will have, identified the authentication and/or operational variables needed.
 Adding these to the CloudQuery config spec can be done by editing the `Spec` `dataclass` using standard python, and adding validation where needed.
 
 ### Plugin
-Finally, you need to edit the `plugin.py` file to set the plugin name and version, and add the `Tables` to the `get_tables` function. 
+
+Finally, you need to edit the `plugin.py` file to set the plugin name and version, and add the `Tables` to the `get_tables` function.
 
 ### Test run
+
 To test your plugin you can run it locally.
 
 To automatically manage your virtual environment and install the dependencies listed in the `pyproject.toml` you can use `poetry`.
@@ -75,6 +83,13 @@ Then to run the plugin `poetry run main serve`, which will launch the plugin man
 With that running you can adjust the `TestConfig.yaml` to match your plugin and run `cloudquery sync`.
 This should result in the creation of a sqlite database `db.sqlite` where you can validate your tables are as expected.
 
+### Publishing
+
+1. Update the plugin metadata in [plugin/plugin.py](plugin/plugin.py#L13) to match your team and plugin name.
+2. Run `python main.py package -m "Initial release" "v0.0.1" .`. `-m` specifies changelog and `v0.0.1` is the version.
+3. Run `cloudquery plugin publish -f` to publish the plugin to the CloudQuery registry.
+
+> More about publishing plugins [here](https://docs.cloudquery.io/docs/developers/publishing-an-addon-to-the-hub)
 
 ## Links
 

plugin/plugin.py

@@ -11,7 +11,7 @@
 from plugin.client import Client, Spec
 
 PLUGIN_NAME = "example"
-PLUGIN_VERSION = "1.0.0"  # {x-release-please-version}
+PLUGIN_VERSION = "0.0.1"
 TEAM_NAME = "cloudquery"
 PLUGIN_KIND = "source"