422 lines
12 KiB
Markdown
Raw Normal View History

---
title: Run DynamoDB Connector using Airflow SDK
slug: /connectors/database/dynamodb/airflow
---
# Run DynamoDB using the Airflow SDK
{% multiTablesWrapper %}
| Feature | Status |
| :----------------- | :--------------------------- |
| Stage | PROD |
| Metadata | {% icon iconName="check" /%} |
| Query Usage | {% icon iconName="cross" /%} |
| Data Profiler | {% icon iconName="check" /%} |
| Data Quality | {% icon iconName="check" /%} |
| Lineage | {% icon iconName="cross" /%} |
| DBT | {% icon iconName="cross" /%} |
| Supported Versions | -- |
| Feature | Status |
| :----------- | :--------------------------- |
| Lineage | {% icon iconName="cross" /%} |
| Table-level | {% icon iconName="cross" /%} |
| Column-level | {% icon iconName="cross" /%} |
{% /multiTablesWrapper %}
In this section, we provide guides and references to use the DynamoDB connector.
Configure and schedule DynamoDB metadata workflows from the OpenMetadata UI:
- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
## Requirements
{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%}
To deploy OpenMetadata, check the Deployment guides.
{%/inlineCallout%}
To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with
custom Airflow plugins to handle the workflow deployment.
### Python Requirements
To run the DynamoDB ingestion, you will need to install:
```bash
pip3 install "openmetadata-ingestion[dynamodb]"
```
## Metadata Ingestion
All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/dynamoDBConnection.json)
you can find the structure to create a connection to DynamoDB.
In order to create and run a Metadata Ingestion workflow, we will follow
the steps to create a YAML configuration able to connect to the source,
process the Entities if needed, and reach the OpenMetadata server.
The workflow is modeled around the following
[JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)
### 1. Define the YAML Config
This is a sample config for DynamoDB:
{% codePreview %}
{% codeInfoContainer %}
#### Source Configuration - Service Connection
{% codeInfo srNumber=1 %}
**awsAccessKeyId**: Enter your secure access key ID for your DynamoDB connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**awsSecretAccessKey**: Enter the Secret Access Key (the passcode key pair to the key ID from above).
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**awsSessionToken**: The AWS session token is an optional parameter. If you want, enter the details of your temporary session token.
{% /codeInfo %}
{% codeInfo srNumber=4 %}
**awsRegion**: Enter the location of the amazon cluster that your data and account are associated with.
{% /codeInfo %}
{% codeInfo srNumber=5 %}
**endPointURL**: Your DynamoDB connector will automatically determine the AWS DynamoDB endpoint URL based on the region. You may override this behavior by entering a value to the endpoint URL.
{% /codeInfo %}
{% codeInfo srNumber=6 %}
**databaseName**: Optional name to give to the database in OpenMetadata. If left blank, we will use default as the database name.
{% /codeInfo %}
#### Source Configuration - Source Config
{% codeInfo srNumber=9 %}
The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json):
**markDeletedTables**: To flag tables as soft-deleted if they are not present anymore in the source system.
**includeTables**: true or false, to ingest table data. Default is true.
**includeViews**: true or false, to ingest views definitions.
**databaseFilterPattern**, **schemaFilterPattern**, **tableFilternPattern**: Note that the they support regex as include or exclude. E.g.,
{% /codeInfo %}
#### Sink Configuration
{% codeInfo srNumber=10 %}
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
{% /codeInfo %}
#### Workflow Configuration
{% codeInfo srNumber=11 %}
The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
{% /codeInfo %}
#### Advanced Configuration
{% codeInfo srNumber=7 %}
**Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
{% /codeInfo %}
{% codeInfo srNumber=8 %}
**Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
- In case you are using Single-Sign-On (SSO) for authentication, add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "sso_login_url"`
- In case you authenticate with SSO using an external browser popup, then add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "externalbrowser"`
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="filename.yaml" %}
```yaml
source:
type: dynamodb
serviceName: local_dynamodb
serviceConnection:
config:
type: DynamoDB
awsConfig:
```
```yaml {% srNumber=1 %}
awsAccessKeyId: aws_access_key_id
```
```yaml {% srNumber=2 %}
awsSecretAccessKey: aws_secret_access_key
```
```yaml {% srNumber=3 %}
awsSessionToken: AWS Session Token
```
```yaml {% srNumber=4 %}
awsRegion: aws region
```
```yaml {% srNumber=5 %}
endPointURL: https://dynamodb.<region_name>.amazonaws.com
```
```yaml {% srNumber=6 %}
database: custom_database_name
```
```yaml {% srNumber=7 %}
# connectionOptions:
# key: value
```
```yaml {% srNumber=8 %}
# connectionArguments:
# key: value
```
```yaml {% srNumber=9 %}
sourceConfig:
config:
type: DatabaseMetadata
markDeletedTables: true
includeTables: true
includeViews: true
# includeTags: true
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
# tableFilterPattern:
# includes:
# - users
# - type_test
# excludes:
# - table3
# - table4
```
```yaml {% srNumber=10 %}
sink:
type: metadata-rest
config: {}
```
```yaml {% srNumber=11 %}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
```
{% /codeBlock %}
{% /codePreview %}
### Workflow Configs for Security Provider
We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
## Openmetadata JWT Auth
- JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details [here](/deployment/security/enable-jwt-tokens).
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
```
- You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc [link](/deployment/security/workflow-config-auth).
### 2. Prepare the Ingestion DAG
Create a Python file in your Airflow DAGs directory with the following contents:
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=12 %}
#### Import necessary modules
The `Workflow` class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
{% /codeInfo %}
{% codeInfo srNumber=13 %}
**Default arguments for all tasks in the Airflow DAG.**
- Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.
{% /codeInfo %}
{% codeInfo srNumber=14 %}
- **config**: Specifies config for the metadata ingestion as we prepare above.
{% /codeInfo %}
{% codeInfo srNumber=15 %}
- **metadata_ingestion_workflow()**: This code defines a function `metadata_ingestion_workflow()` that loads a YAML configuration, creates a `Workflow` object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
{% /codeInfo %}
{% codeInfo srNumber=16 %}
- **DAG**: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
- For more Airflow DAGs creation details visit [here](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#declaring-a-dag).
{% /codeInfo %}
Note that from connector to connector, this recipe will always be the same.
By updating the `YAML configuration`, you will be able to extract metadata from different sources.
{% /codeInfoContainer %}
{% codeBlock fileName="filename.py" %}
```python {% srNumber=12 %}
import pathlib
import yaml
from datetime import timedelta
from airflow import DAG
from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago
try:
from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
from airflow.operators.python_operator import PythonOperator
```
```python {% srNumber=13 %}
default_args = {
"owner": "user_name",
"email": ["username@org.com"],
"email_on_failure": False,
"retries": 3,
"retry_delay": timedelta(minutes=5),
"execution_timeout": timedelta(minutes=60)
}
```
```python {% srNumber=14 %}
config = """
<your YAML configuration>
"""
```
```python {% srNumber=15 %}
def metadata_ingestion_workflow():
workflow_config = yaml.safe_load(config)
workflow = Workflow.create(workflow_config)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
```python {% srNumber=16 %}
with DAG(
"sample_data",
default_args=default_args,
description="An example DAG which runs a OpenMetadata ingestion workflow",
start_date=days_ago(1),
is_paused_upon_creation=False,
schedule_interval='*/5 * * * *',
catchup=False,
) as dag:
ingest_task = PythonOperator(
task_id="ingest_using_recipe",
python_callable=metadata_ingestion_workflow,
)
```
{% /codeBlock %}
{% /codePreview %}
## dbt Integration
{% tilesContainer %}
{% tile
icon="mediation"
title="dbt Integration"
description="Learn more about how to ingest dbt models' definitions and their lineage."
link="/connectors/ingestion/workflows/dbt" /%}
{% /tilesContainer %}
## Related
{% tilesContainer %}
{% tile
title="Ingest with the CLI"
description="Run a one-time ingestion using the metadata CLI"
link="/connectors/database/dynamodb/cli"
/ %}
{% /tilesContainer %}