yujunjun/OpenMetadata
1
0
Fork 0
mirror of https://github.com/open-metadata/OpenMetadata.git synced 2025-11-03 20:19:31 +00:00
Code Issues Packages Projects Releases Wiki Activity
OpenMetadata/openmetadata-docs/content/connectors/database/deltalake/cli.md
Onkar Ravgan 14fa96958f
Added dbt workflow docs (#9493) 
* Added dbt workflow docs

* added dbt small case

* Fixed review comments
2022-12-22 13:11:18 +00:00

357 lines
11 KiB
Markdown
Raw Blame History

---
title: Run DeltaLake Connector using the CLI
slug: /connectors/database/deltalake/cli
---
# Run Deltalake using the metadata CLI
In this section, we provide guides and references to use the Deltalake connector.
Configure and schedule Deltalake metadata and profiler workflows from the OpenMetadata UI:
- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
- [dbt Integration](#dbt-integration)
## Requirements
<InlineCallout color="violet-70" icon="description" bold="OpenMetadata 0.12.1 or later" href="/deployment">
To deploy OpenMetadata, check the <a href="/deployment">Deployment</a> 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 Deltalake ingestion, you will need to install:
```bash
pip3 install "openmetadata-ingestion[deltalake]"
```
## 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/deltaLakeConnection.json)
you can find the structure to create a connection to Deltalake.
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 Deltalake:
```yaml
source:
type: deltalake
serviceName: "<service name>"
serviceConnection:
config:
type: DeltaLake
metastoreConnection:
# Pick only of the three
metastoreHostPort: "<metastore host port>"
# metastoreDb: jdbc:mysql://localhost:3306/demo_hive
# metastoreFilePath: "<path_to_metastore>/metastore_db"
appName: MyApp
sourceConfig:
config:
markDeletedTables: true
includeTables: true
includeViews: true
# includeTags: true
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
# tableFilterPattern:
# includes:
# - table1
# - table2
# excludes:
# - table3
# - table4
# For dbt, choose one of Cloud, Local, HTTP, S3 or GCS configurations
# dbtConfigSource:
# # For cloud
# dbtCloudAuthToken: token
# dbtCloudAccountId: ID
# # For Local
# dbtCatalogFilePath: path-to-catalog.json
# dbtManifestFilePath: path-to-manifest.json
# # For HTTP
# dbtCatalogHttpPath: http://path-to-catalog.json
# dbtManifestHttpPath: http://path-to-manifest.json
# # For S3
# dbtSecurityConfig: # These are modeled after all AWS credentials
# awsAccessKeyId: KEY
# awsSecretAccessKey: SECRET
# awsRegion: us-east-2
# dbtPrefixConfig:
# dbtBucketName: bucket
# dbtObjectPrefix: "dbt/"
# # For GCS
# dbtSecurityConfig: # These are modeled after all GCS credentials
# type: My Type
# projectId: project ID
# privateKeyId: us-east-2
# privateKey: |
# -----BEGIN PRIVATE KEY-----
# Super secret key
# -----END PRIVATE KEY-----
# clientEmail: client@mail.com
# clientId: 1234
# authUri: https://accounts.google.com/o/oauth2/auth (default)
# tokenUri: https://oauth2.googleapis.com/token (default)
# authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
# clientX509CertUrl: https://cert.url (URI)
# dbtPrefixConfig:
# dbtBucketName: bucket
# dbtObjectPrefix: "dbt/"
sink:
type: metadata-rest
config: {}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: "<OpenMetadata host and port>"
authProvider: "<OpenMetadata auth provider>"
```
#### Source Configuration - Service Connection
- **Metastore Host Port**: Enter the Host & Port of Hive Metastore Service to configure the Spark Session. Either
of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
- **Metastore File Path**: Enter the file path to local Metastore in case Spark cluster is running locally. Either
of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
- **Metastore DB**: The JDBC connection to the underlying Hive metastore DB. Either
of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
- **appName (Optional)**: Enter the app name of spark session.
- **Connection Arguments (Optional)**: Key-Value pairs that will be used to pass extra `config` elements to the Spark
Session builder.
We are internally running with `pyspark` 3.X and `delta-lake` 2.0.0. This means that we need to consider Spark
configuration options for 3.X.
##### Metastore Host Port
When connecting to an External Metastore passing the parameter `Metastore Host Port`, we will be preparing a Spark Session with the configuration
```
.config("hive.metastore.uris", "thrift://{connection.metastoreHostPort}")
```
Then, we will be using the `catalog` functions from the Spark Session to pick up the metadata exposed by the Hive Metastore.
##### Metastore File Path
If instead we use a local file path that contains the metastore information (e.g., for local testing with the default `metastore_db` directory), we will set
```
.config("spark.driver.extraJavaOptions", "-Dderby.system.home={connection.metastoreFilePath}")
```
To update the `Derby` information. More information about this in a great [SO thread](https://stackoverflow.com/questions/38377188/how-to-get-rid-of-derby-log-metastore-db-from-spark-shell).
- You can find all supported configurations [here](https://spark.apache.org/docs/latest/configuration.html)
- If you need further information regarding the Hive metastore, you can find
it [here](https://spark.apache.org/docs/3.0.0-preview/sql-data-sources-hive-tables.html), and in The Internals of
Spark SQL [book](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-hive-metastore.html).
#### Source Configuration - Source Config
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.,
```yaml
tableFilterPattern:
includes:
- users
- type_test
```
#### Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
#### Workflow Configuration
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:
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: '{bot_jwt_token}'
```
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).
You can find the different implementation of the ingestion below.
<Collapse title="Configure SSO in the Ingestion Workflows">
### Openmetadata JWT Auth
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: openmetadata
securityConfig:
jwtToken: '{bot_jwt_token}'
```
### Auth0 SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: auth0
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### Azure SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: azure
securityConfig:
clientSecret: '{your_client_secret}'
authority: '{your_authority_url}'
clientId: '{your_client_id}'
scopes:
- your_scopes
```
### Custom OIDC SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### Google SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: google
securityConfig:
secretKey: '{path-to-json-creds}'
```
### Okta SSO
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: http://localhost:8585/api
authProvider: okta
securityConfig:
clientId: "{CLIENT_ID - SPA APP}"
orgURL: "{ISSUER_URL}/v1/token"
privateKey: "{public/private keypair}"
email: "{email}"
scopes:
- token
```
### Amazon Cognito SSO
The ingestion can be configured by [Enabling JWT Tokens](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens)
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: auth0
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### OneLogin SSO
Which uses Custom OIDC for the ingestion
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
### KeyCloak SSO
Which uses Custom OIDC for the ingestion
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: 'http://localhost:8585/api'
authProvider: custom-oidc
securityConfig:
clientId: '{your_client_id}'
secretKey: '{your_client_secret}'
domain: '{your_domain}'
```
</Collapse>
### 2. Run with the CLI
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
```bash
metadata ingest -c <path-to-yaml>
```
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.
## dbt Integration
You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).
Reference in New Issue View Git Blame Copy Permalink