datahub/docs/advanced/writing-mcps.md

# Saving MCPs to a File

## What is an MCP?

A `MetadataChangeProposal` (MCP) represents an atomic unit of change in the DataHub Metadata Graph. Each MCP carries a single aspect in its payload and is used to propose changes to DataHub's metadata. 

- Represents a single aspect change
- Used for proposing metadata changes to DataHub
- Serves as the basic building block for metadata ingestion

For more information, please see guides on [DataHub Metadata Events](../what/mxe.md) and [MCPs](mcp-mcl.md). 

## Why Write MCPs as Files?

MCPs in JSON file format are particularly valuable because they represent the lowest and most granular form of events in DataHub. There are two main use cases for using previously saved MCP files:

### Testing

MCPs allow you to easily ingest metadata. You can:

- Use it for entity ingestion by running a simple command, without a dependency on a ingestion connector:
    
    ```bash
    datahub ingest mcps <file_name>.json
    ```
    
- Create reproducible test cases for metadata ingestion
- Write and run tests when contributing to DataHub (see DataHub Testing Guide for more details)

### Debugging

MCPs are valuable for debugging because they let you:

- Examine entities in your DataHub instance at a granular level
- Export existing entities to MCP files for analysis
- Verify entity structures and relationships before ingestion

For example, if you want to understand the structure of entities in your DataHub instance, you can emit them as MCP files and examine their contents in detail.

## Saving MCPs to a file

### Exporting rom DataHub Instance

You can export MCPs directly from your DataHub instance using a recipe file. This is useful when you want to:

- Examine existing entities in your DataHub instance
- Create test cases based on real data
- Debug entity relationships

First, create a recipe file (e.g., `export_mcps.yaml`):

```yaml
source:
  type: datahub
  config:
    # Add your DataHub connection configuration here
    server: "http://localhost:8080"
    token: "your-access-token"  # If authentication is required

sink:
  type: "file"
  config:
    filename: "mcps.json"
```

Run the ingestion:

```python
datahub ingest -c export_mcps.yaml
```

This will write all the entities from your DataHub instance to `mcps.json` in MCP format.

### Creating MCPs with Python SDK

You can use the `write_metadata_file` helper to generate MCPs programmatically:

```python
from datahub.ingestion.sink.file import write_metadata_file
from pathlib import Path
from datahub.metadata.schema_classes import DatasetPropertiesClass
from datahub.emitter.mcp import MetadataChangeProposalWrapper

records = [
    MetadataChangeProposalWrapper(
        entityType="dataset",
        entityUrn="urn:li:dataset:(urn:li:dataPlatform:hive,example_dataset,PROD)",
        changeType="UPSERT",
        aspectName="datasetProperties",
        aspect=DatasetPropertiesClass(
            description="Example dataset description",
            customProperties={"encoding": "utf-8"}
        ))

]
write_metadata_file(
    file=Path("mcps.json"),
    records=records,
)
```

Edit `records` to create the event and entities for your needs.

Run the Python script to generate your defined MCPs and save them to a file:

```bash
python <file_name>.py
```

For example, the above script will generate an MCP file with a single dataset entity.

```json
[
{
    "entityType": "dataset",
    "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:hive,example_dataset,PROD)",
    "changeType": "UPSERT",
    "aspectName": "datasetProperties",
    "aspect": {
        "json": {
            "customProperties": {
                "encoding": "utf-8"
            },
            "description": "Example dataset description",
            "tags": []
        }
    }
}
]
```