--- title: Plugins keywords: fastai sidebar: home_sidebar nb_path: "nbs/plugin.pluginbase.ipynb" ---
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Creating a plugin

The memri pod uses a plugin system to add features to the backend memri backend. Plugins can import your data (importers), change your data (indexers), or call other serivces. Users can define their own plugins to add new behaviour to their memri app. Let's use the following plugin as an example of how we can start plugins.

class MyItem(Item):
    properties = Item.properties + ["name", "age"]
    edges = Item.edges
    def __init__(self, name=None, age=None, **kwargs):
        super().__init__(**kwargs)
        self.name = name
        self.age = age

class MyPlugin(PluginBase):
    """"""
    properties = PluginBase.properties
    edges= PluginBase.edges

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def run(self, run, client):
        print("running")
        client.create(MyItem("some person", 20))

    def add_to_schema(self, client):
        client.add_to_schema(MyItem("my name", 10))

Memri plugins need to define at least 2 methods: .run() and .add_to_schema(). .run() defines the logic of the plugin. .add_to_schema() defines the schema for the plugin in the pod. Note that currently, add_to_schema requires all item to have all properties defined that are used in the plugin. In the future, we might replace add_to_schema, to be done automatically, based on a declarative schema defined in the plugin.

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Running your plugin using the CLI

Plugins can be started using the pymemri run_plugin or run_plugin_from_pod CLI. With run_plugin the plugin is invoked directly by spawning a new python process, while run_plugin_from_pod requests the pod to spawn a new process, docker container, or kubernetes container, which in calls run_plugin (for more info see run_plugin_from_pod. When using run_plugin, you can either pass your run arguments as parameters, or set them as environment variables. If both are set, the CLI will use the passed arguments.

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run_plugin[source]

run_plugin(pod_full_address:Param object at 0x7f80e367c370>=None, plugin_run_id:Param object at 0x7f80e367c3d0>=None, database_key:Param object at 0x7f80e367c400>=None, owner_key:Param object at 0x7f80e367c430>=None, container:Param object at 0x7f80e367c460>=None)

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To start a plugin on your local machine, you can use the CLI. This will create a client for you, and run the code defined in <myplugin>.run()

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client = PodClient()
run = PluginRun(containerImage="pymemri", pluginModule="pymemri.plugin.pluginbase",
                pluginName="MyPlugin", config="", state="not started")
assert client.add_to_schema(PluginRun("", "", "", "", "")) and client.create(run)
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!run_plugin --pod_full_address=$DEFAULT_POD_ADDRESS --owner_key=$client.owner_key \
            --database_key=$client.database_key --container="pymemri" --plugin_run_id=$run.id
Used arguments passed to `run_plugin()` (ignoring environment)
pod_full_address=http://localhost:3030
plugin_run_id=1bf06Bfb7B63f114FcfB8DBC67eA49Be
owner_key=3961574559904730128514448689870083724159258570216156665995117538
auth_json=None

running
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{% include note.html content='The data that is created here should be in the pod in order for this to work' %}

Run from pod

In production, we start plugins by making an API call to the pod, which in turn creates an environment for the plugin and starts it using docker containers, kubernetes containers or a shell script. We can start this process using the run_plugin_from_pod CLI. Note that when using docker, provided container name should be "installed" within the Pod environemnt (e.g. docker build -t pymemri . for this repo) in order to start it.

running a plugin

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run_plugin_from_pod[source]

run_plugin_from_pod(pod_full_address:"The pod full address"=None, database_key:"Database key of the pod"=None, owner_key:"Owner key of the pod"=None, container:"Pod container to run frod"=None, plugin_module:"Plugin module"=None, plugin_name:"Plugin class name"=None, settings_file:"Plugin settings (json)"=None)

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client = PodClient()
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!run_plugin_from_pod --pod_full_address=$DEFAULT_POD_ADDRESS --owner_key=$client.owner_key \
                     --database_key=$client.database_key --container="pymemri" \
                     --plugin_module="pymemri.plugin.pluginbase" --plugin_name="MyPlugin"
pod_full_address=http://localhost:3030
owner_key=4566065835753341432440539540364977361089552669232085362436171995

calling the `create` api on http://localhost:3030 to make your Pod start a plugin with id cc01FaEbAf5DC88ADc27e408a790b8D6.
*Check the pod log/console for debug output.*
Created PluginRun: cc01FaEbAf5DC88ADc27e408a790b8D6
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