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128 lines
6.0 KiB
Markdown
128 lines
6.0 KiB
Markdown
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### Prerequisites
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#### Create a datahub profile in GCP
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1. Create a custom role for datahub as per [BigQuery docs](https://cloud.google.com/iam/docs/creating-custom-roles#creating_a_custom_role)
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2. Grant the following permissions to this role:
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```
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# basic requirements
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# This needs to list datasets
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bigquery.datasets.get
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bigquery.datasets.getIamPolicy
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# Needs to submit queries.
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bigquery.jobs.create
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# Needs to check submitet queries status
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bigquery.jobs.list
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# Needs for lineage generation and usage to see all the queries were run on a project
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bigquery.jobs.listAll
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# Needs to list/get metadata about Bigquery Routines
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bigquery.routines.get
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bigquery.routines.list
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# Needs to list/get metadata about Bigquery Tables
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bigquery.tables.get
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bigquery.tables.list
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# Needs to get resutlset of queries
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bigquery.readsessions.create
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bigquery.readsessions.getData
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# Needs to get project names/metadata
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resourcemanager.projects.get
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# needed if profiling enabled
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# profiler needs to access data to do the profiling
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bigquery.tables.getData
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# It needs to create temporary tables to profile partitioned/sharded tables and that is why it needs
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# table create/delete privilege.
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# Use profiling.bigquery_temp_table_schema to restrict to one specific dataset the create/delete permission
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bigquery.tables.create
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bigquery.tables.delete
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# needed for lineage generation via GCP logging
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logging.logEntries.list
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logging.privateLogEntries.list
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```
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#### Create a service account
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1. Setup a ServiceAccount as per [BigQuery docs](https://cloud.google.com/iam/docs/creating-managing-service-accounts#iam-service-accounts-create-console)
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and assign the previously created role to this service account.
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2. Download a service account JSON keyfile.
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Example credential file:
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```json
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{
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"type": "service_account",
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"project_id": "project-id-1234567",
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"private_key_id": "d0121d0000882411234e11166c6aaa23ed5d74e0",
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"private_key": "-----BEGIN PRIVATE KEY-----\nMIIyourkey\n-----END PRIVATE KEY-----",
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"client_email": "test@suppproject-id-1234567.iam.gserviceaccount.com",
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"client_id": "113545814931671546333",
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"auth_uri": "https://accounts.google.com/o/oauth2/auth",
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"token_uri": "https://oauth2.googleapis.com/token",
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"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
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"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/test%suppproject-id-1234567.iam.gserviceaccount.com"
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}
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```
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3. To provide credentials to the source, you can either:
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Set an environment variable:
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$ export GOOGLE_APPLICATION_CREDENTIALS="/path/to/keyfile.json"
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*or*
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Set credential config in your source based on the credential json file. For example:
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```yml
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credential:
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project_id: project-id-1234567
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private_key_id: "d0121d0000882411234e11166c6aaa23ed5d74e0"
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private_key: "-----BEGIN PRIVATE KEY-----\nMIIyourkey\n-----END PRIVATE KEY-----\n"
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client_email: "test@suppproject-id-1234567.iam.gserviceaccount.com"
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client_id: "123456678890"
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```
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### Lineage Computation Details
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When `use_exported_bigquery_audit_metadata` is set to `true`, lineage information will be computed using exported bigquery logs. On how to setup exported bigquery audit logs, refer to the following [docs](https://cloud.google.com/bigquery/docs/reference/auditlogs#defining_a_bigquery_log_sink_using_gcloud) on BigQuery audit logs. Note that only protoPayloads with "type.googleapis.com/google.cloud.audit.BigQueryAuditMetadata" are supported by the current ingestion version. The `bigquery_audit_metadata_datasets` parameter will be used only if `use_exported_bigquery_audit_metadat` is set to `true`.
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Note: the `bigquery_audit_metadata_datasets` parameter receives a list of datasets, in the format $PROJECT.$DATASET. This way queries from a multiple number of projects can be used to compute lineage information.
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Note: Since bigquery source also supports dataset level lineage, the auth client will require additional permissions to be able to access the google audit logs. Refer the permissions section in bigquery-usage section below which also accesses the audit logs.
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### Profiling Details
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Profiling can profile normal/partitioned and sharded tables as well but due to performance reasons, we only profile the latest partition for Partitioned tables and the latest shard for sharded tables.
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If limit/offset parameter is set or partitioning partitioned or sharded table Great Expectation (the profiling framework we use) needs to create temporary
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views. By default, these views are created in the schema where the profiled table is but you can control to create all these
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tables into a predefined schema by setting `profiling.bigquery_temp_table_schema` property.
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Temporary tables are removed after profiling.
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```yaml
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profiling:
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enabled: true
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bigquery_temp_table_schema: my-project-id.my-schema-where-views-can-be-created
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```
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:::note
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Due to performance reasons, we only profile the latest partition for Partitioned tables and the latest shard for sharded tables.
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You can set partition explicitly with `partition.partition_datetime` property if you want. (partition will be applied to all partitioned tables)
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:::
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### Working with multi-project GCP setups
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Sometimes you may have multiple GCP project with one only giving you view access rights and other project where you have view/modify rights.
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The GCP roles with which this setup has been tested are as follows
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- Storage Project
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- BigQuery Data Viewer
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- BigQuery Metadata Viewer
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- Logs Viewer
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- Private Logs Viewer
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- Compute Project
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- BigQuery Admin
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- BigQuery Data Editor
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- BigQuery Job User
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If you are using `use_exported_bigquery_audit_metadata = True` then make sure you prefix the datasets in `bigquery_audit_metadata_datasets` with storage project id.
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### Caveats
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- For Materialized views lineage is dependent on logs being retained. If your GCP logging is retained for 30 days (default) and 30 days have passed since the creation of the materialized view we won't be able to get lineage for them.
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