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OpenMetadata/openmetadata-docs/content/connectors/database/athena/cli.md
Mayur Singal 69729ac8eb
Fix #10320: Add Athena Usage & Lineage (#10879)
2023-04-03 11:21:55 +00:00

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---
title: Run Athena Connector using the CLI
slug: /connectors/database/athena/cli
---
# Run Athena using the metadata CLI
<Table>
| Stage | Metadata |Query Usage | Data Profiler | Data Quality | Lineage | DBT | Supported Versions |
|:------:|:------:|:-----------:|:-------------:|:------------:|:-------:|:---:|:------------------:|
| PROD | ✅ | ✅ (1.0 release onwards) | ✅ | ✅ | ✅ (1.0 release onwards) | ✅ | -- |
</Table>
<Table>
| Lineage | Table-level | Column-level |
|:------:|:-----------:|:-------------:|
| ✅ (1.0 release onwards) | ✅ | ✅ |
</Table>
In this section, we provide guides and references to use the Athena connector.
Configure and schedule Athena metadata and profiler workflows from the OpenMetadata UI:
- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
- [Query Usage](#query-usage)
- [Data Profiler](#data-profiler)
- [dbt Integration](#dbt-integration)
- [Lineage](#lineage)
## Requirements
<InlineCallout color="violet-70" icon="description" bold="OpenMetadata 0.12 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 Athena ingestion, you will need to install:
```bash
pip3 install "openmetadata-ingestion[athena]"
```
## 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/athenaConnection.json)
you can find the structure to create a connection to Athena.
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 Athena:
```yaml
source:
type: athena
serviceName: local_athena
serviceConnection:
config:
type: Athena
awsConfig:
awsAccessKeyId: KEY
awsSecretAccessKey: SECRET
awsRegion: us-east-2
# endPointURL: https://athena.us-east-2.amazonaws.com/
# awsSessionToken: TOKEN
s3StagingDir: s3 directory for datasource
workgroup: workgroup name
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:
# - table1
# - table2
# excludes:
# - table3
# - table4
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
- **awsAccessKeyId**: Enter your secure access key ID for your Athena connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.
- **awsSecretAccessKey**: Enter the Secret Access Key (the passcode key pair to the key ID from above).
- **awsRegion**: Enter the location of the amazon cluster that your data and account are associated with.
- **awsSessionToken**: The AWS session token is an optional parameter. If you want, enter the details of your temporary session token.
- **endPointURL**: Your Athena connector will automatically determine the AWS Athena endpoint URL based on the region. You may override this behavior by entering a value to the endpoint URL.
- **s3StagingDir**: The S3 staging directory is an optional parameter. Enter a staging dirrectory to override the default staging directory for AWS Athena.
- **workgroup**: The Athena workgroup is an optional parameter. If you wish to have your Athena connection related to an existing AWS workgroup add your workgroup name here.
- **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.
- **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"`
#### 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.
## Query Usage
To ingest the Query Usage, the `serviceConnection` configuration will remain the same.
However, the `sourceConfig` is now modeled after this JSON Schema.
### 1. Define the YAML Config
This is a sample config for BigQuery Usage:
```yaml
source:
type: athena-usage
serviceName: <service name>
serviceConnection:
config:
type: Athena
awsConfig:
awsAccessKeyId: KEY
awsSecretAccessKey: SECRET
awsRegion: us-east-2
# endPointURL: https://athena.us-east-2.amazonaws.com/
# awsSessionToken: TOKEN
s3StagingDir: s3 directory for datasource
workgroup: workgroup name
sourceConfig:
config:
# Number of days to look back
queryLogDuration: 7
# This is a directory that will be DELETED after the usage runs
stageFileLocation: <path to store the stage file>
# resultLimit: 1000
# If instead of getting the query logs from the database we want to pass a file with the queries
# queryLogFilePath: path-to-file
processor:
type: query-parser
config: {}
stage:
type: table-usage
config:
filename: /tmp/athena_usage
bulkSink:
type: metadata-usage
config:
filename: /tmp/athena_usage
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
```
#### Source Configuration - Service Connection
You can find all the definitions and types for the `serviceConnection` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/bigQueryConnection.json).
They are the same as metadata ingestion.
#### 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/databaseServiceQueryUsagePipeline.json).
- `queryLogDuration`: Configuration to tune how far we want to look back in query logs to process usage data.
- `resultLimit`: Configuration to set the limit for query logs
#### Processor, Stage and Bulk Sink
To specify where the staging files will be located.
Note that the location is a directory that will be cleaned at the end of the ingestion.
#### Workflow Configuration
The same as the metadata ingestion.
### 2. Run with the CLI
For the usage workflow creation, the Airflow file will look the same as for the metadata ingestion. Updating the YAML configuration will be enough.
## Data Profiler
The Data Profiler workflow will be using the `orm-profiler` processor.
While the `serviceConnection` will still be the same to reach the source system, the `sourceConfig` will be
updated from previous configurations.
### 1. Define the YAML Config
This is a sample config for the profiler:
```yaml
source:
type: athena
serviceName: <service name>
serviceConnection:
config:
type: Athena
awsConfig:
awsAccessKeyId: KEY
awsSecretAccessKey: SECRET
awsRegion: us-east-2
# endPointURL: https://athena.us-east-2.amazonaws.com/
# awsSessionToken: TOKEN
s3StagingDir: s3 directory for datasource
workgroup: workgroup name
sourceConfig:
config:
type: Profiler
# generateSampleData: true
# profileSample: 85
# threadCount: 5 (default)
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
# tableFilterPattern:
# includes:
# - table1
# - table2
# excludes:
# - table3
# - table4
processor:
type: orm-profiler
config: {} # Remove braces if adding properties
# tableConfig:
# - fullyQualifiedName: <table fqn>
# profileSample: <number between 0 and 99> # default will be 100 if omitted
# profileQuery: <query to use for sampling data for the profiler>
# columnConfig:
# excludeColumns:
# - <column name>
# includeColumns:
# - columnName: <column name>
# - metrics:
# - MEAN
# - MEDIAN
# - ...
# partitionConfig:
# enablePartitioning: <set to true to use partitioning>
# partitionColumnName: <partition column name. Must be a timestamp or datetime/date field type>
# partitionInterval: <partition interval>
# partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>
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
- You can find all the definitions and types for the `serviceConnection` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/athenaConnection.json).
- The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceProfilerPipeline.json).
Note that the filter patterns support regex as includes or excludes. E.g.,
```yaml
tableFilterPattern:
includes:
- *users$
```
#### Processor
Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI:
```yaml
processor:
type: orm-profiler
config:
tableConfig:
- fullyQualifiedName: <table fqn>
profileSample: <number between 0 and 99>
partitionConfig:
partitionField: <field to use as a partition field>
partitionQueryDuration: <for date/datetime partitioning based set the offset from today>
partitionValues: <values to uses as a predicate for the query>
profileQuery: <query to use for sampling data for the profiler>
columnConfig:
excludeColumns:
- <column name>
includeColumns:
- columnName: <column name>
- metrics:
- MEAN
- MEDIAN
- ...
```
`tableConfig` allows you to set up some configuration at the table level.
All the properties are optional. `metrics` should be one of the metrics listed [here](https://docs.open-metadata.org/connectors/ingestion/workflows/profiler/metrics)
#### Workflow Configuration
The same as the metadata ingestion.
### 2. Run with the CLI
After saving the YAML config, we will run the command the same way we did for the metadata ingestion:
```bash
metadata profile -c <path-to-yaml>
```
Note how instead of running `ingest`, we are using the `profile` command to select the Profiler workflow.
## dbt Integration
You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).
## Lineage
You can learn more about how to ingest lineage [here](/connectors/ingestion/workflows/lineage).
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