# CLI Ingestion

Batch ingestion involves extracting metadata from a source system in bulk. Typically, this happens on a predefined schedule using the [Metadata Ingestion](../docs/components.md#ingestion-framework) framework.
The metadata that is extracted includes point-in-time instances of dataset, chart, dashboard, pipeline, user, group, usage, and task metadata.

## Installing DataHub CLI

:::note Required Python Version
Installing DataHub CLI requires Python 3.6+.
:::

Run the following commands in your terminal:

```
python3 -m pip install --upgrade pip wheel setuptools
python3 -m pip install --upgrade acryl-datahub
python3 -m datahub version
```

Your command line should return the proper version of DataHub upon executing these commands successfully.

Check out the [CLI Installation Guide](../docs/cli.md#installation) for more installation options and troubleshooting tips.

## Installing Connector Plugins

Our CLI follows a plugin architecture. You must install connectors for different data sources individually.
For a list of all supported data sources, see [the open source docs](../docs/cli.md#sources).
Once you've found the connectors you care about, simply install them using `pip install`.
For example, to install the `mysql` connector, you can run

```shell
pip install --upgrade 'acryl-datahub[mysql]'
```

Check out the [alternative installation options](../docs/cli.md#alternate-installation-options) for more reference.

## Configuring a Recipe

Create a [Recipe](recipe_overview.md) yaml file that defines the source and sink for metadata, as shown below.

```yaml
# example-recipe.yml

# MySQL source configuration
source:
  type: mysql
  config:
    username: root
    password: password
    host_port: localhost:3306

# Recipe sink configuration.
sink:
  type: "datahub-rest"
  config:
    server: "https://<your domain name>.acryl.io/gms"
    token: <Your API key>
```

The **source** configuration block defines where to extract metadata from. This can be an OLTP database system, a data warehouse, or something as simple as a file. Each source has custom configuration depending on what is required to access metadata from the source. To see configurations required for each supported source, refer to the [Sources](source_overview.md) documentation.

The **sink** configuration block defines where to push metadata into. Each sink type requires specific configurations, the details of which are detailed in the [Sinks](sink_overview.md) documentation.

To configure your instance of DataHub as the destination for ingestion, set the "server" field of your recipe to point to your DataHub Cloud instance's domain suffixed by the path `/gms`, as shown below.
A complete example of a DataHub recipe file, which reads from MySQL and writes into a DataHub instance:

For more information and examples on configuring recipes, please refer to [Recipes](recipe_overview.md).

### Using Recipes with Authentication

In DataHub Cloud deployments, only the `datahub-rest` sink is supported, which simply means that metadata will be pushed to the REST endpoints exposed by your DataHub instance. The required configurations for this sink are

1. **server**: the location of the REST API exposed by your instance of DataHub
2. **token**: a unique API key used to authenticate requests to your instance's REST API

The token can be retrieved by logging in as admin. You can go to Settings page and generate a Personal Access Token with your desired expiration date.

<p align="center">
  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/saas/home-(1).png"/>
</p>

<p align="center">
  <img width="70%"  src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/saas/settings.png"/>
</p>

:::info Secure Your API Key
Please keep Your API key secure & avoid sharing it.
If you are on DataHub Cloud and your key is compromised for any reason, please reach out to the DataHub team at support@acryl.io.
:::

## Ingesting Metadata

The final step requires invoking the DataHub CLI to ingest metadata based on your recipe configuration file.
To do so, simply run `datahub ingest` with a pointer to your YAML recipe file:

```shell
datahub ingest -c <path/to/recipe.yml>
```

## Scheduling Ingestion

Ingestion can either be run in an ad-hoc manner by a system administrator or scheduled for repeated executions. Most commonly, ingestion will be run on a daily cadence.
To schedule your ingestion job, we recommend using a job schedule like [Apache Airflow](https://airflow.apache.org/). In cases of simpler deployments, a CRON job scheduled on an always-up machine can also work.
Note that each source system will require a separate recipe file. This allows you to schedule ingestion from different sources independently or together.
Learn more about scheduling ingestion in the [Scheduling Ingestion Guide](/metadata-ingestion/schedule_docs/intro.md).

## Reference

Please refer the following pages for advanced guids on CLI ingestion.

- [Reference for `datahub ingest` command](../docs/cli.md#ingest)
- [UI Ingestion Guide](../docs/ui-ingestion.md)

:::tip Compatibility

DataHub server uses a 3 digit versioning scheme, while the CLI uses a 4 digit scheme. For example, if you're using DataHub server version 0.10.0, you should use CLI version 0.10.0.x, where x is a patch version.
We do this because we do CLI releases at a much higher frequency than server releases, usually every few days vs twice a month.

For ingestion sources, any breaking changes will be highlighted in the [release notes](../docs/how/updating-datahub.md). When fields are deprecated or otherwise changed, we will try to maintain backwards compatibility for two server releases, which is about 4-6 weeks. The CLI will also print warnings whenever deprecated options are used.
:::