BigQuery
Ingesting metadata from BigQuery requires using the bigquery module.
Important Capabilities
Capability | Status | Notes |
---|---|---|
Asset Containers | ✅ | Enabled by default |
Data Profiling | ✅ | Optionally enabled via configuration |
Descriptions | ✅ | Enabled by default |
Detect Deleted Entities | ✅ | Optionally enabled via stateful_ingestion.remove_stale_metadata |
Domains | ✅ | Supported via the domain config field |
Platform Instance | ❌ | Not supported since BigQuery project ids are globally unique |
Schema Metadata | ✅ | Enabled by default |
Table-Level Lineage | ✅ | Optionally enabled via configuration |
Prerequisites
To understand how BigQuery ingestion needs to be set up, first familiarize yourself with the concepts in the diagram below:
There are two important concepts to understand and identify:
- Extractor Project: This is the project associated with a service-account, whose credentials you will be configuring in the connector. The connector uses this service-account to run jobs (including queries) within the project.
- Bigquery Projects are the projects from which table metadata, lineage, usage, and profiling data need to be collected. By default, the extractor project is included in the list of projects that DataHub collects metadata from, but you can control that by passing in a specific list of project ids that you want to collect metadata from. Read the configuration section below to understand how to limit the list of projects that DataHub extracts metadata from.
Create a datahub profile in GCP
- Create a custom role for datahub as per BigQuery docs.
- Follow the sections below to grant permissions to this role on this project and other projects.
Basic Requirements (needed for metadata ingestion)
- Identify your Extractor Project where the service account will run queries to extract metadata.
permission | Description | Capability |
---|---|---|
bigquery.jobs.create | Run jobs (e.g. queries) within the project. This only needs for the extractor project where the service account belongs | |
bigquery.jobs.list | Manage the queries that the service account has sent. This only needs for the extractor project where the service account belongs | |
bigquery.readsessions.create | Create a session for streaming large results. This only needs for the extractor project where the service account belongs | |
bigquery.readsessions.getData | Get data from the read session. This only needs for the extractor project where the service account belongs |
- Grant the following permissions to the Service Account on every project where you would like to extract metadata from
If you have multiple projects in your BigQuery setup, the role should be granted these permissions in each of the projects.
permission | Description | Capability | Default GCP role which contains this permission |
---|---|---|---|
bigquery.datasets.get | Retrieve metadata about a dataset. | Table Metadata Extraction | roles/bigquery.metadataViewer |
bigquery.datasets.getIamPolicy | Read a dataset's IAM permissions. | Table Metadata Extraction | roles/bigquery.metadataViewer |
bigquery.tables.list | List BigQuery tables. | Table Metadata Extraction | roles/bigquery.metadataViewer |
bigquery.tables.get | Retrieve metadata for a table. | Table Metadata Extraction | roles/bigquery.metadataViewer |
bigquery.routines.get | Get Routines. Needs to retrieve metadata for a table from system table. | Table Metadata Extraction | roles/bigquery.metadataViewer |
bigquery.routines.list | List Routines. Needs to retrieve metadata for a table from system table | Table Metadata Extraction | roles/bigquery.metadataViewer |
resourcemanager.projects.get | Retrieve project names and metadata. | Table Metadata Extraction | roles/bigquery.metadataViewer |
bigquery.jobs.listAll | List all jobs (queries) submitted by any user. Needs for Lineage extraction. | Lineage Extraction/Usage extraction | roles/bigquery.resourceViewer |
logging.logEntries.list | Fetch log entries for lineage/usage data. Not required if use_exported_bigquery_audit_metadata is enabled. | Lineage Extraction/Usage extraction | roles/logging.privateLogViewer |
logging.privateLogEntries.list | Fetch log entries for lineage/usage data. Not required if use_exported_bigquery_audit_metadata is enabled. | Lineage Extraction/Usage extraction | roles/logging.privateLogViewer |
bigquery.tables.getData | Access table data to extract storage size, last updated at, data profiles etc. | Profiling |
Create a service account in the Extractor Project
- Setup a ServiceAccount as per BigQuery docs and assign the previously created role to this service account.
- Download a service account JSON keyfile. Example credential file:
{
"type": "service_account",
"project_id": "project-id-1234567",
"private_key_id": "d0121d0000882411234e11166c6aaa23ed5d74e0",
"private_key": "-----BEGIN PRIVATE KEY-----\nMIIyourkey\n-----END PRIVATE KEY-----",
"client_email": "test@suppproject-id-1234567.iam.gserviceaccount.com",
"client_id": "113545814931671546333",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/test%suppproject-id-1234567.iam.gserviceaccount.com"
}
To provide credentials to the source, you can either:
Set an environment variable:
$ export GOOGLE_APPLICATION_CREDENTIALS="/path/to/keyfile.json"
or
Set credential config in your source based on the credential json file. For example:
credential:
project_id: project-id-1234567
private_key_id: "d0121d0000882411234e11166c6aaa23ed5d74e0"
private_key: "-----BEGIN PRIVATE KEY-----\nMIIyourkey\n-----END PRIVATE KEY-----\n"
client_email: "test@suppproject-id-1234567.iam.gserviceaccount.com"
client_id: "123456678890"
Lineage Computation Details
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 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
.
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.
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.
Profiling Details
For performance reasons, we only profile the latest partition for partitioned tables and the latest shard for sharded tables.
You can set partition explicitly with partition.partition_datetime
property if you want, though note that partition config will be applied to all partitioned tables.
Caveats
- 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.
CLI based Ingestion
Install the Plugin
pip install 'acryl-datahub[bigquery]'
Starter Recipe
Check out the following recipe to get started with ingestion! See below for full configuration options.
For general pointers on writing and running a recipe, see our main recipe guide.
source:
type: bigquery
config:
# `schema_pattern` for BQ Datasets
schema_pattern:
allow:
- finance_bq_dataset
table_pattern:
deny:
# The exact name of the table is revenue_table_name
# The reason we have this `.*` at the beginning is because the current implmenetation of table_pattern is testing
# project_id.dataset_name.table_name
# We will improve this in the future
- .*revenue_table_name
include_table_lineage: true
include_usage_statistics: true
profiling:
enabled: true
profile_table_level_only: true
sink:
# sink configs
Config Details
- Options
- Schema
Note that a .
is used to denote nested fields in the YAML recipe.
Field | Description |
---|---|
bigquery_audit_metadata_datasets array(string) | |
bucket_duration Enum | Size of the time window to aggregate usage stats. Default: DAY |
capture_dataset_label_as_tag boolean | Capture BigQuery dataset labels as DataHub tag Default: False |
capture_table_label_as_tag boolean | Capture BigQuery table labels as DataHub tag Default: False |
column_limit integer | Maximum number of columns to process in a table. This is a low level config property which should be touched with care. This restriction is needed because excessively wide tables can result in failure to ingest the schema. Default: 300 |
convert_urns_to_lowercase boolean | Convert urns to lowercase. Default: False |
debug_include_full_payloads boolean | Include full payload into events. It is only for debugging and internal use. Default: False |
enable_legacy_sharded_table_support boolean | Use the legacy sharded table urn suffix added. Default: True |
end_time string(date-time) | Latest date of usage to consider. Default: Current time in UTC |
extra_client_options object | Additional options to pass to google.cloud.logging_v2.client.Client. Default: {} |
extract_lineage_from_catalog boolean | This flag enables the data lineage extraction from Data Lineage API exposed by Google Data Catalog. NOTE: This extractor can't build views lineage. It's recommended to enable the view's DDL parsing. Read the docs to have more information about: https://cloud.google.com/data-catalog/docs/concepts/about-data-lineage Default: False |
include_external_url boolean | Whether to populate BigQuery Console url to Datasets/Tables Default: True |
include_table_lineage boolean | Option to enable/disable lineage generation. Is enabled by default. Default: True |
include_table_location_lineage boolean | If the source supports it, include table lineage to the underlying storage location. Default: True |
include_tables boolean | Whether tables should be ingested. Default: True |
include_usage_statistics boolean | Generate usage statistic Default: True |
include_views boolean | Whether views should be ingested. Default: True |
incremental_lineage boolean | When enabled, emits lineage as incremental to existing lineage already in DataHub. When disabled, re-states lineage on each run. Default: True |
lineage_parse_view_ddl boolean | Sql parse view ddl to get lineage. Default: True |
lineage_sql_parser_use_raw_names boolean | This parameter ignores the lowercase pattern stipulated in the SQLParser. NOTE: Ignored if lineage_use_sql_parser is False. Default: False |
lineage_use_sql_parser boolean | Use sql parser to resolve view/table lineage. Only invoked on tables with both upstream tables and views. Used to distinguish between direct/base objects accessed, to only emit upstream lineage for directly accessed objects. Default: True |
log_page_size integer | The number of log item will be queried per page for lineage collection Default: 1000 |
match_fully_qualified_names boolean | Whether dataset_pattern is matched against fully qualified dataset name <project_id>.<dataset_name> . Default: False |
max_query_duration number(time-delta) | Correction to pad start_time and end_time with. For handling the case where the read happens within our time range but the query completion event is delayed and happens after the configured end time. Default: 900.0 |
number_of_datasets_process_in_batch_if_profiling_enabled integer | Number of partitioned table queried in batch when getting metadata. This is a low level config property which should be touched with care. This restriction is needed because we query partitions system view which throws error if we try to touch too many tables. Default: 200 |
options object | Any options specified here will be passed to SQLAlchemy.create_engine as kwargs. |
platform_instance string | The instance of the platform that all assets produced by this recipe belong to |
project_id string | [deprecated] Use project_id_pattern or project_ids instead. |
project_ids array(string) | |
project_on_behalf string | [Advanced] The BigQuery project in which queries are executed. Will be passed when creating a job. If not passed, falls back to the project associated with the service account. |
rate_limit boolean | Should we rate limit requests made to API. Default: False |
requests_per_min integer | Used to control number of API calls made per min. Only used when rate_limit is set to True . Default: 60 |
scheme string | Default: bigquery |
sharded_table_pattern string | The regex pattern to match sharded tables and group as one table. This is a very low level config parameter, only change if you know what you are doing, Default: ((.+)[_$])?(\d{8})$ |
sql_parser_use_external_process boolean | When enabled, sql parser will run in isolated in a separate process. This can affect processing time but can protect from sql parser's mem leak. Default: False |
start_time string(date-time) | Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration ) |
store_last_lineage_extraction_timestamp boolean | Enable checking last lineage extraction date in store. Default: False |
store_last_profiling_timestamps boolean | Enable storing last profile timestamp in store. Default: False |
store_last_usage_extraction_timestamp boolean | Enable checking last usage timestamp in store. Default: True |
temp_table_dataset_prefix string | If you are creating temp tables in a dataset with a particular prefix you can use this config to set the prefix for the dataset. This is to support workflows from before bigquery's introduction of temp tables. By default we use _ because of datasets that begin with an underscore are hidden by default https://cloud.google.com/bigquery/docs/datasets#dataset-naming. Default: _ |
upstream_lineage_in_report boolean | Useful for debugging lineage information. Set to True to see the raw lineage created internally. Default: False |
use_date_sharded_audit_log_tables boolean | Whether to read date sharded tables or time partitioned tables when extracting usage from exported audit logs. Default: False |
use_exported_bigquery_audit_metadata boolean | When configured, use BigQueryAuditMetadata in bigquery_audit_metadata_datasets to compute lineage information. Default: False |
env string | The environment that all assets produced by this connector belong to Default: PROD |
credential BigQueryCredential | BigQuery credential informations |
credential.client_email ❓ string | Client email |
credential.client_id ❓ string | Client Id |
credential.private_key ❓ string | Private key in a form of '-----BEGIN PRIVATE KEY-----\nprivate-key\n-----END PRIVATE KEY-----\n' |
credential.private_key_id ❓ string | Private key id |
credential.project_id ❓ string | Project id to set the credentials |
credential.auth_provider_x509_cert_url string | Auth provider x509 certificate url |
credential.auth_uri string | Authentication uri |
credential.client_x509_cert_url string | If not set it will be default to https://www.googleapis.com/robot/v1/metadata/x509/client_email |
credential.token_uri string | Token uri Default: https://oauth2.googleapis.com/token |
credential.type string | Authentication type Default: service_account |
dataset_pattern AllowDenyPattern | Regex patterns for dataset to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics' Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True} |
dataset_pattern.allow array(string) | |
dataset_pattern.deny array(string) | |
dataset_pattern.ignoreCase boolean | Whether to ignore case sensitivity during pattern matching. Default: True |
domain map(str,AllowDenyPattern) | A class to store allow deny regexes |
domain. key .allowarray(string) | |
domain. key .denyarray(string) | |
domain. key .ignoreCaseboolean | Whether to ignore case sensitivity during pattern matching. Default: True |
profile_pattern AllowDenyPattern | Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered. Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True} |
profile_pattern.allow array(string) | |
profile_pattern.deny array(string) | |
profile_pattern.ignoreCase boolean | Whether to ignore case sensitivity during pattern matching. Default: True |
project_id_pattern AllowDenyPattern | Regex patterns for project_id to filter in ingestion. Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True} |
project_id_pattern.allow array(string) | |
project_id_pattern.deny array(string) | |
project_id_pattern.ignoreCase boolean | Whether to ignore case sensitivity during pattern matching. Default: True |
schema_pattern AllowDenyPattern | Regex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics' Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True} |
schema_pattern.allow array(string) | |
schema_pattern.deny array(string) | |
schema_pattern.ignoreCase boolean | Whether to ignore case sensitivity during pattern matching. Default: True |
table_pattern AllowDenyPattern | Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*' Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True} |
table_pattern.allow array(string) | |
table_pattern.deny array(string) | |
table_pattern.ignoreCase boolean | Whether to ignore case sensitivity during pattern matching. Default: True |
usage BigQueryUsageConfig | Usage related configs Default: {'bucket_duration': 'DAY', 'end_time': '2023-05-24... |
usage.apply_view_usage_to_tables boolean | Whether to apply view's usage to its base tables. If set to False, uses sql parser and applies usage to views / tables mentioned in the query. If set to True, usage is applied to base tables only. Default: False |
usage.bucket_duration Enum | Size of the time window to aggregate usage stats. Default: DAY |
usage.end_time string(date-time) | Latest date of usage to consider. Default: Current time in UTC |
usage.format_sql_queries boolean | Whether to format sql queries Default: False |
usage.include_operational_stats boolean | Whether to display operational stats. Default: True |
usage.include_read_operational_stats boolean | Whether to report read operational stats. Experimental. Default: False |
usage.include_top_n_queries boolean | Whether to ingest the top_n_queries. Default: True |
usage.max_query_duration number(time-delta) | Correction to pad start_time and end_time with. For handling the case where the read happens within our time range but the query completion event is delayed and happens after the configured end time. Default: 900.0 |
usage.start_time string(date-time) | Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration ) |
usage.top_n_queries integer | Number of top queries to save to each table. Default: 10 |
usage.user_email_pattern AllowDenyPattern | regex patterns for user emails to filter in usage. Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True} |
usage.user_email_pattern.allow array(string) | |
usage.user_email_pattern.deny array(string) | |
usage.user_email_pattern.ignoreCase boolean | Whether to ignore case sensitivity during pattern matching. Default: True |
view_pattern AllowDenyPattern | Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*' Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True} |
view_pattern.allow array(string) | |
view_pattern.deny array(string) | |
view_pattern.ignoreCase boolean | Whether to ignore case sensitivity during pattern matching. Default: True |
profiling GEProfilingConfig | Default: {'enabled': False, 'limit': None, 'offset': None, ... |
profiling.catch_exceptions boolean | Default: True |
profiling.enabled boolean | Whether profiling should be done. Default: False |
profiling.field_sample_values_limit integer | Upper limit for number of sample values to collect for all columns. Default: 20 |
profiling.include_field_distinct_count boolean | Whether to profile for the number of distinct values for each column. Default: True |
profiling.include_field_distinct_value_frequencies boolean | Whether to profile for distinct value frequencies. Default: False |
profiling.include_field_histogram boolean | Whether to profile for the histogram for numeric fields. Default: False |
profiling.include_field_max_value boolean | Whether to profile for the max value of numeric columns. Default: True |
profiling.include_field_mean_value boolean | Whether to profile for the mean value of numeric columns. Default: True |
profiling.include_field_median_value boolean | Whether to profile for the median value of numeric columns. Default: True |
profiling.include_field_min_value boolean | Whether to profile for the min value of numeric columns. Default: True |
profiling.include_field_null_count boolean | Whether to profile for the number of nulls for each column. Default: True |
profiling.include_field_quantiles boolean | Whether to profile for the quantiles of numeric columns. Default: False |
profiling.include_field_sample_values boolean | Whether to profile for the sample values for all columns. Default: True |
profiling.include_field_stddev_value boolean | Whether to profile for the standard deviation of numeric columns. Default: True |
profiling.limit integer | Max number of documents to profile. By default, profiles all documents. |
profiling.max_number_of_fields_to_profile integer | A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up. |
profiling.max_workers integer | Number of worker threads to use for profiling. Set to 1 to disable. Default: 20 |
profiling.offset integer | Offset in documents to profile. By default, uses no offset. |
profiling.partition_datetime string(date-time) | For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this. |
profiling.partition_profiling_enabled boolean | Default: True |
profiling.profile_if_updated_since_days number | Profile table only if it has been updated since these many number of days. If set to null , no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery . |
profiling.profile_table_level_only boolean | Whether to perform profiling at table-level only, or include column-level profiling as well. Default: False |
profiling.profile_table_row_count_estimate_only boolean | Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres. Default: False |
profiling.profile_table_row_limit integer | Profile tables only if their row count is less then specified count. If set to null , no limit on the row count of tables to profile. Supported only in snowflake and BigQuery Default: 5000000 |
profiling.profile_table_size_limit integer | Profile tables only if their size is less then specified GBs. If set to null , no limit on the size of tables to profile. Supported only in snowflake and BigQuery Default: 5 |
profiling.query_combiner_enabled boolean | This feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible. Default: True |
profiling.report_dropped_profiles boolean | Whether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes. Default: False |
profiling.turn_off_expensive_profiling_metrics boolean | Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10. Default: False |
stateful_ingestion StatefulStaleMetadataRemovalConfig | Base specialized config for Stateful Ingestion with stale metadata removal capability. |
stateful_ingestion.enabled boolean | The type of the ingestion state provider registered with datahub. Default: False |
stateful_ingestion.ignore_new_state boolean | If set to True, ignores the current checkpoint state. Default: False |
stateful_ingestion.ignore_old_state boolean | If set to True, ignores the previous checkpoint state. Default: False |
stateful_ingestion.remove_stale_metadata boolean | Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled. Default: True |
The JSONSchema for this configuration is inlined below.
{
"title": "BigQueryV2Config",
"description": "Base configuration class for stateful ingestion for source configs to inherit from.",
"type": "object",
"properties": {
"store_last_profiling_timestamps": {
"title": "Store Last Profiling Timestamps",
"description": "Enable storing last profile timestamp in store.",
"default": false,
"type": "boolean"
},
"incremental_lineage": {
"title": "Incremental Lineage",
"description": "When enabled, emits lineage as incremental to existing lineage already in DataHub. When disabled, re-states lineage on each run.",
"default": true,
"type": "boolean"
},
"sql_parser_use_external_process": {
"title": "Sql Parser Use External Process",
"description": "When enabled, sql parser will run in isolated in a separate process. This can affect processing time but can protect from sql parser's mem leak.",
"default": false,
"type": "boolean"
},
"store_last_lineage_extraction_timestamp": {
"title": "Store Last Lineage Extraction Timestamp",
"description": "Enable checking last lineage extraction date in store.",
"default": false,
"type": "boolean"
},
"bucket_duration": {
"description": "Size of the time window to aggregate usage stats.",
"default": "DAY",
"allOf": [
{
"$ref": "#/definitions/BucketDuration"
}
]
},
"end_time": {
"title": "End Time",
"description": "Latest date of usage to consider. Default: Current time in UTC",
"type": "string",
"format": "date-time"
},
"start_time": {
"title": "Start Time",
"description": "Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on `bucket_duration`)",
"type": "string",
"format": "date-time"
},
"store_last_usage_extraction_timestamp": {
"title": "Store Last Usage Extraction Timestamp",
"description": "Enable checking last usage timestamp in store.",
"default": true,
"type": "boolean"
},
"env": {
"title": "Env",
"description": "The environment that all assets produced by this connector belong to",
"default": "PROD",
"type": "string"
},
"platform_instance": {
"title": "Platform Instance",
"description": "The instance of the platform that all assets produced by this recipe belong to",
"type": "string"
},
"stateful_ingestion": {
"$ref": "#/definitions/StatefulStaleMetadataRemovalConfig"
},
"options": {
"title": "Options",
"description": "Any options specified here will be passed to [SQLAlchemy.create_engine](https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine) as kwargs.",
"type": "object"
},
"schema_pattern": {
"title": "Schema Pattern",
"description": "Regex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"table_pattern": {
"title": "Table Pattern",
"description": "Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"view_pattern": {
"title": "View Pattern",
"description": "Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"profile_pattern": {
"title": "Profile Pattern",
"description": "Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the `table_pattern` will be considered.",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"domain": {
"title": "Domain",
"description": "Attach domains to databases, schemas or tables during ingestion using regex patterns. Domain key can be a guid like *urn:li:domain:ec428203-ce86-4db3-985d-5a8ee6df32ba* or a string like \"Marketing\".) If you provide strings, then datahub will attempt to resolve this name to a guid, and will error out if this fails. There can be multiple domain keys specified.",
"default": {},
"type": "object",
"additionalProperties": {
"$ref": "#/definitions/AllowDenyPattern"
}
},
"include_views": {
"title": "Include Views",
"description": "Whether views should be ingested.",
"default": true,
"type": "boolean"
},
"include_tables": {
"title": "Include Tables",
"description": "Whether tables should be ingested.",
"default": true,
"type": "boolean"
},
"include_table_location_lineage": {
"title": "Include Table Location Lineage",
"description": "If the source supports it, include table lineage to the underlying storage location.",
"default": true,
"type": "boolean"
},
"profiling": {
"title": "Profiling",
"default": {
"enabled": false,
"limit": null,
"offset": null,
"report_dropped_profiles": false,
"turn_off_expensive_profiling_metrics": false,
"profile_table_level_only": false,
"include_field_null_count": true,
"include_field_distinct_count": true,
"include_field_min_value": true,
"include_field_max_value": true,
"include_field_mean_value": true,
"include_field_median_value": true,
"include_field_stddev_value": true,
"include_field_quantiles": false,
"include_field_distinct_value_frequencies": false,
"include_field_histogram": false,
"include_field_sample_values": true,
"field_sample_values_limit": 20,
"max_number_of_fields_to_profile": null,
"profile_if_updated_since_days": null,
"profile_table_size_limit": 5,
"profile_table_row_limit": 5000000,
"profile_table_row_count_estimate_only": false,
"max_workers": 20,
"query_combiner_enabled": true,
"catch_exceptions": true,
"partition_profiling_enabled": true,
"partition_datetime": null
},
"allOf": [
{
"$ref": "#/definitions/GEProfilingConfig"
}
]
},
"rate_limit": {
"title": "Rate Limit",
"description": "Should we rate limit requests made to API.",
"default": false,
"type": "boolean"
},
"requests_per_min": {
"title": "Requests Per Min",
"description": "Used to control number of API calls made per min. Only used when `rate_limit` is set to `True`.",
"default": 60,
"type": "integer"
},
"temp_table_dataset_prefix": {
"title": "Temp Table Dataset Prefix",
"description": "If you are creating temp tables in a dataset with a particular prefix you can use this config to set the prefix for the dataset. This is to support workflows from before bigquery's introduction of temp tables. By default we use `_` because of datasets that begin with an underscore are hidden by default https://cloud.google.com/bigquery/docs/datasets#dataset-naming.",
"default": "_",
"type": "string"
},
"sharded_table_pattern": {
"title": "Sharded Table Pattern",
"description": "The regex pattern to match sharded tables and group as one table. This is a very low level config parameter, only change if you know what you are doing, ",
"default": "((.+)[_$])?(\\d{8})$",
"deprecated": true,
"type": "string"
},
"project_id_pattern": {
"title": "Project Id Pattern",
"description": "Regex patterns for project_id to filter in ingestion.",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"usage": {
"title": "Usage",
"description": "Usage related configs",
"default": {
"bucket_duration": "DAY",
"end_time": "2023-05-24T18:13:01.042832+00:00",
"start_time": "2023-05-23T00:00:00+00:00",
"top_n_queries": 10,
"user_email_pattern": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"include_operational_stats": true,
"include_read_operational_stats": false,
"format_sql_queries": false,
"include_top_n_queries": true,
"max_query_duration": 900.0,
"apply_view_usage_to_tables": false
},
"allOf": [
{
"$ref": "#/definitions/BigQueryUsageConfig"
}
]
},
"include_usage_statistics": {
"title": "Include Usage Statistics",
"description": "Generate usage statistic",
"default": true,
"type": "boolean"
},
"capture_table_label_as_tag": {
"title": "Capture Table Label As Tag",
"description": "Capture BigQuery table labels as DataHub tag",
"default": false,
"type": "boolean"
},
"capture_dataset_label_as_tag": {
"title": "Capture Dataset Label As Tag",
"description": "Capture BigQuery dataset labels as DataHub tag",
"default": false,
"type": "boolean"
},
"dataset_pattern": {
"title": "Dataset Pattern",
"description": "Regex patterns for dataset to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"match_fully_qualified_names": {
"title": "Match Fully Qualified Names",
"description": "Whether `dataset_pattern` is matched against fully qualified dataset name `<project_id>.<dataset_name>`.",
"default": false,
"type": "boolean"
},
"include_external_url": {
"title": "Include External Url",
"description": "Whether to populate BigQuery Console url to Datasets/Tables",
"default": true,
"type": "boolean"
},
"debug_include_full_payloads": {
"title": "Debug Include Full Payloads",
"description": "Include full payload into events. It is only for debugging and internal use.",
"default": false,
"type": "boolean"
},
"number_of_datasets_process_in_batch_if_profiling_enabled": {
"title": "Number Of Datasets Process In Batch If Profiling Enabled",
"description": "Number of partitioned table queried in batch when getting metadata. This is a low level config property which should be touched with care. This restriction is needed because we query partitions system view which throws error if we try to touch too many tables.",
"default": 200,
"type": "integer"
},
"column_limit": {
"title": "Column Limit",
"description": "Maximum number of columns to process in a table. This is a low level config property which should be touched with care. This restriction is needed because excessively wide tables can result in failure to ingest the schema.",
"default": 300,
"type": "integer"
},
"project_id": {
"title": "Project Id",
"description": "[deprecated] Use project_id_pattern or project_ids instead.",
"type": "string"
},
"project_ids": {
"title": "Project Ids",
"description": "Ingests specified project_ids. Use this property if you want to specify what projects to ingest or don't want to give project resourcemanager.projects.list to your service account. Overrides `project_id_pattern`.",
"type": "array",
"items": {
"type": "string"
}
},
"project_on_behalf": {
"title": "Project On Behalf",
"description": "[Advanced] The BigQuery project in which queries are executed. Will be passed when creating a job. If not passed, falls back to the project associated with the service account.",
"type": "string"
},
"lineage_use_sql_parser": {
"title": "Lineage Use Sql Parser",
"description": "Use sql parser to resolve view/table lineage. Only invoked on tables with both upstream tables and views. Used to distinguish between direct/base objects accessed, to only emit upstream lineage for directly accessed objects.",
"default": true,
"type": "boolean"
},
"lineage_parse_view_ddl": {
"title": "Lineage Parse View Ddl",
"description": "Sql parse view ddl to get lineage.",
"default": true,
"type": "boolean"
},
"lineage_sql_parser_use_raw_names": {
"title": "Lineage Sql Parser Use Raw Names",
"description": "This parameter ignores the lowercase pattern stipulated in the SQLParser. NOTE: Ignored if lineage_use_sql_parser is False.",
"default": false,
"type": "boolean"
},
"extract_lineage_from_catalog": {
"title": "Extract Lineage From Catalog",
"description": "This flag enables the data lineage extraction from Data Lineage API exposed by Google Data Catalog. NOTE: This extractor can't build views lineage. It's recommended to enable the view's DDL parsing. Read the docs to have more information about: https://cloud.google.com/data-catalog/docs/concepts/about-data-lineage",
"default": false,
"type": "boolean"
},
"convert_urns_to_lowercase": {
"title": "Convert Urns To Lowercase",
"description": "Convert urns to lowercase.",
"default": false,
"type": "boolean"
},
"enable_legacy_sharded_table_support": {
"title": "Enable Legacy Sharded Table Support",
"description": "Use the legacy sharded table urn suffix added.",
"default": true,
"type": "boolean"
},
"scheme": {
"title": "Scheme",
"default": "bigquery",
"type": "string"
},
"log_page_size": {
"title": "Log Page Size",
"description": "The number of log item will be queried per page for lineage collection",
"default": 1000,
"exclusiveMinimum": 0,
"type": "integer"
},
"credential": {
"title": "Credential",
"description": "BigQuery credential informations",
"allOf": [
{
"$ref": "#/definitions/BigQueryCredential"
}
]
},
"extra_client_options": {
"title": "Extra Client Options",
"description": "Additional options to pass to google.cloud.logging_v2.client.Client.",
"default": {},
"type": "object"
},
"include_table_lineage": {
"title": "Include Table Lineage",
"description": "Option to enable/disable lineage generation. Is enabled by default.",
"default": true,
"type": "boolean"
},
"max_query_duration": {
"title": "Max Query Duration",
"description": "Correction to pad start_time and end_time with. For handling the case where the read happens within our time range but the query completion event is delayed and happens after the configured end time.",
"default": 900.0,
"type": "number",
"format": "time-delta"
},
"bigquery_audit_metadata_datasets": {
"title": "Bigquery Audit Metadata Datasets",
"description": "A list of datasets that contain a table named cloudaudit_googleapis_com_data_access which contain BigQuery audit logs, specifically, those containing BigQueryAuditMetadata. It is recommended that the project of the dataset is also specified, for example, projectA.datasetB.",
"type": "array",
"items": {
"type": "string"
}
},
"use_exported_bigquery_audit_metadata": {
"title": "Use Exported Bigquery Audit Metadata",
"description": "When configured, use BigQueryAuditMetadata in bigquery_audit_metadata_datasets to compute lineage information.",
"default": false,
"type": "boolean"
},
"use_date_sharded_audit_log_tables": {
"title": "Use Date Sharded Audit Log Tables",
"description": "Whether to read date sharded tables or time partitioned tables when extracting usage from exported audit logs.",
"default": false,
"type": "boolean"
},
"upstream_lineage_in_report": {
"title": "Upstream Lineage In Report",
"description": "Useful for debugging lineage information. Set to True to see the raw lineage created internally.",
"default": false,
"type": "boolean"
}
},
"additionalProperties": false,
"definitions": {
"BucketDuration": {
"title": "BucketDuration",
"description": "An enumeration.",
"enum": [
"DAY",
"HOUR"
],
"type": "string"
},
"DynamicTypedStateProviderConfig": {
"title": "DynamicTypedStateProviderConfig",
"type": "object",
"properties": {
"type": {
"title": "Type",
"description": "The type of the state provider to use. For DataHub use `datahub`",
"type": "string"
},
"config": {
"title": "Config",
"description": "The configuration required for initializing the state provider. Default: The datahub_api config if set at pipeline level. Otherwise, the default DatahubClientConfig. See the defaults (https://github.com/datahub-project/datahub/blob/master/metadata-ingestion/src/datahub/ingestion/graph/client.py#L19)."
}
},
"required": [
"type"
],
"additionalProperties": false
},
"StatefulStaleMetadataRemovalConfig": {
"title": "StatefulStaleMetadataRemovalConfig",
"description": "Base specialized config for Stateful Ingestion with stale metadata removal capability.",
"type": "object",
"properties": {
"enabled": {
"title": "Enabled",
"description": "The type of the ingestion state provider registered with datahub.",
"default": false,
"type": "boolean"
},
"ignore_old_state": {
"title": "Ignore Old State",
"description": "If set to True, ignores the previous checkpoint state.",
"default": false,
"type": "boolean"
},
"ignore_new_state": {
"title": "Ignore New State",
"description": "If set to True, ignores the current checkpoint state.",
"default": false,
"type": "boolean"
},
"remove_stale_metadata": {
"title": "Remove Stale Metadata",
"description": "Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.",
"default": true,
"type": "boolean"
}
},
"additionalProperties": false
},
"AllowDenyPattern": {
"title": "AllowDenyPattern",
"description": "A class to store allow deny regexes",
"type": "object",
"properties": {
"allow": {
"title": "Allow",
"description": "List of regex patterns to include in ingestion",
"default": [
".*"
],
"type": "array",
"items": {
"type": "string"
}
},
"deny": {
"title": "Deny",
"description": "List of regex patterns to exclude from ingestion.",
"default": [],
"type": "array",
"items": {
"type": "string"
}
},
"ignoreCase": {
"title": "Ignorecase",
"description": "Whether to ignore case sensitivity during pattern matching.",
"default": true,
"type": "boolean"
}
},
"additionalProperties": false
},
"GEProfilingConfig": {
"title": "GEProfilingConfig",
"type": "object",
"properties": {
"enabled": {
"title": "Enabled",
"description": "Whether profiling should be done.",
"default": false,
"type": "boolean"
},
"limit": {
"title": "Limit",
"description": "Max number of documents to profile. By default, profiles all documents.",
"type": "integer"
},
"offset": {
"title": "Offset",
"description": "Offset in documents to profile. By default, uses no offset.",
"type": "integer"
},
"report_dropped_profiles": {
"title": "Report Dropped Profiles",
"description": "Whether to report datasets or dataset columns which were not profiled. Set to `True` for debugging purposes.",
"default": false,
"type": "boolean"
},
"turn_off_expensive_profiling_metrics": {
"title": "Turn Off Expensive Profiling Metrics",
"description": "Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.",
"default": false,
"type": "boolean"
},
"profile_table_level_only": {
"title": "Profile Table Level Only",
"description": "Whether to perform profiling at table-level only, or include column-level profiling as well.",
"default": false,
"type": "boolean"
},
"include_field_null_count": {
"title": "Include Field Null Count",
"description": "Whether to profile for the number of nulls for each column.",
"default": true,
"type": "boolean"
},
"include_field_distinct_count": {
"title": "Include Field Distinct Count",
"description": "Whether to profile for the number of distinct values for each column.",
"default": true,
"type": "boolean"
},
"include_field_min_value": {
"title": "Include Field Min Value",
"description": "Whether to profile for the min value of numeric columns.",
"default": true,
"type": "boolean"
},
"include_field_max_value": {
"title": "Include Field Max Value",
"description": "Whether to profile for the max value of numeric columns.",
"default": true,
"type": "boolean"
},
"include_field_mean_value": {
"title": "Include Field Mean Value",
"description": "Whether to profile for the mean value of numeric columns.",
"default": true,
"type": "boolean"
},
"include_field_median_value": {
"title": "Include Field Median Value",
"description": "Whether to profile for the median value of numeric columns.",
"default": true,
"type": "boolean"
},
"include_field_stddev_value": {
"title": "Include Field Stddev Value",
"description": "Whether to profile for the standard deviation of numeric columns.",
"default": true,
"type": "boolean"
},
"include_field_quantiles": {
"title": "Include Field Quantiles",
"description": "Whether to profile for the quantiles of numeric columns.",
"default": false,
"type": "boolean"
},
"include_field_distinct_value_frequencies": {
"title": "Include Field Distinct Value Frequencies",
"description": "Whether to profile for distinct value frequencies.",
"default": false,
"type": "boolean"
},
"include_field_histogram": {
"title": "Include Field Histogram",
"description": "Whether to profile for the histogram for numeric fields.",
"default": false,
"type": "boolean"
},
"include_field_sample_values": {
"title": "Include Field Sample Values",
"description": "Whether to profile for the sample values for all columns.",
"default": true,
"type": "boolean"
},
"field_sample_values_limit": {
"title": "Field Sample Values Limit",
"description": "Upper limit for number of sample values to collect for all columns.",
"default": 20,
"type": "integer"
},
"max_number_of_fields_to_profile": {
"title": "Max Number Of Fields To Profile",
"description": "A positive integer that specifies the maximum number of columns to profile for any table. `None` implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.",
"exclusiveMinimum": 0,
"type": "integer"
},
"profile_if_updated_since_days": {
"title": "Profile If Updated Since Days",
"description": "Profile table only if it has been updated since these many number of days. If set to `null`, no constraint of last modified time for tables to profile. Supported only in `snowflake` and `BigQuery`.",
"exclusiveMinimum": 0,
"type": "number"
},
"profile_table_size_limit": {
"title": "Profile Table Size Limit",
"description": "Profile tables only if their size is less then specified GBs. If set to `null`, no limit on the size of tables to profile. Supported only in `snowflake` and `BigQuery`",
"default": 5,
"type": "integer"
},
"profile_table_row_limit": {
"title": "Profile Table Row Limit",
"description": "Profile tables only if their row count is less then specified count. If set to `null`, no limit on the row count of tables to profile. Supported only in `snowflake` and `BigQuery`",
"default": 5000000,
"type": "integer"
},
"profile_table_row_count_estimate_only": {
"title": "Profile Table Row Count Estimate Only",
"description": "Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres. ",
"default": false,
"type": "boolean"
},
"max_workers": {
"title": "Max Workers",
"description": "Number of worker threads to use for profiling. Set to 1 to disable.",
"default": 20,
"type": "integer"
},
"query_combiner_enabled": {
"title": "Query Combiner Enabled",
"description": "*This feature is still experimental and can be disabled if it causes issues.* Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.",
"default": true,
"type": "boolean"
},
"catch_exceptions": {
"title": "Catch Exceptions",
"default": true,
"type": "boolean"
},
"partition_profiling_enabled": {
"title": "Partition Profiling Enabled",
"default": true,
"type": "boolean"
},
"partition_datetime": {
"title": "Partition Datetime",
"description": "For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.",
"type": "string",
"format": "date-time"
}
},
"additionalProperties": false
},
"BigQueryUsageConfig": {
"title": "BigQueryUsageConfig",
"type": "object",
"properties": {
"bucket_duration": {
"description": "Size of the time window to aggregate usage stats.",
"default": "DAY",
"allOf": [
{
"$ref": "#/definitions/BucketDuration"
}
]
},
"end_time": {
"title": "End Time",
"description": "Latest date of usage to consider. Default: Current time in UTC",
"type": "string",
"format": "date-time"
},
"start_time": {
"title": "Start Time",
"description": "Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on `bucket_duration`)",
"type": "string",
"format": "date-time"
},
"top_n_queries": {
"title": "Top N Queries",
"description": "Number of top queries to save to each table.",
"default": 10,
"exclusiveMinimum": 0,
"type": "integer"
},
"user_email_pattern": {
"title": "User Email Pattern",
"description": "regex patterns for user emails to filter in usage.",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"include_operational_stats": {
"title": "Include Operational Stats",
"description": "Whether to display operational stats.",
"default": true,
"type": "boolean"
},
"include_read_operational_stats": {
"title": "Include Read Operational Stats",
"description": "Whether to report read operational stats. Experimental.",
"default": false,
"type": "boolean"
},
"format_sql_queries": {
"title": "Format Sql Queries",
"description": "Whether to format sql queries",
"default": false,
"type": "boolean"
},
"include_top_n_queries": {
"title": "Include Top N Queries",
"description": "Whether to ingest the top_n_queries.",
"default": true,
"type": "boolean"
},
"max_query_duration": {
"title": "Max Query Duration",
"description": "Correction to pad start_time and end_time with. For handling the case where the read happens within our time range but the query completion event is delayed and happens after the configured end time.",
"default": 900.0,
"type": "number",
"format": "time-delta"
},
"apply_view_usage_to_tables": {
"title": "Apply View Usage To Tables",
"description": "Whether to apply view's usage to its base tables. If set to False, uses sql parser and applies usage to views / tables mentioned in the query. If set to True, usage is applied to base tables only.",
"default": false,
"type": "boolean"
}
},
"additionalProperties": false
},
"BigQueryCredential": {
"title": "BigQueryCredential",
"type": "object",
"properties": {
"project_id": {
"title": "Project Id",
"description": "Project id to set the credentials",
"type": "string"
},
"private_key_id": {
"title": "Private Key Id",
"description": "Private key id",
"type": "string"
},
"private_key": {
"title": "Private Key",
"description": "Private key in a form of '-----BEGIN PRIVATE KEY-----\\nprivate-key\\n-----END PRIVATE KEY-----\\n'",
"type": "string"
},
"client_email": {
"title": "Client Email",
"description": "Client email",
"type": "string"
},
"client_id": {
"title": "Client Id",
"description": "Client Id",
"type": "string"
},
"auth_uri": {
"title": "Auth Uri",
"description": "Authentication uri",
"default": "https://accounts.google.com/o/oauth2/auth",
"type": "string"
},
"token_uri": {
"title": "Token Uri",
"description": "Token uri",
"default": "https://oauth2.googleapis.com/token",
"type": "string"
},
"auth_provider_x509_cert_url": {
"title": "Auth Provider X509 Cert Url",
"description": "Auth provider x509 certificate url",
"default": "https://www.googleapis.com/oauth2/v1/certs",
"type": "string"
},
"type": {
"title": "Type",
"description": "Authentication type",
"default": "service_account",
"type": "string"
},
"client_x509_cert_url": {
"title": "Client X509 Cert Url",
"description": "If not set it will be default to https://www.googleapis.com/robot/v1/metadata/x509/client_email",
"type": "string"
}
},
"required": [
"project_id",
"private_key_id",
"private_key",
"client_email",
"client_id"
],
"additionalProperties": false
}
}
}
Code Coordinates
- Class Name:
datahub.ingestion.source.bigquery_v2.bigquery.BigqueryV2Source
- Browse on GitHub
Questions
If you've got any questions on configuring ingestion for BigQuery, feel free to ping us on our Slack.