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BigQuery

Ingesting metadata from Bigquery requires using the bigquery module.

Module bigquery​

Certified

Important Capabilities​

CapabilityStatusNotes
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​

  1. Create a custom role for datahub as per BigQuery docs.
  2. Follow the sections below to grant permissions to this role on this project and other projects.
Basic Requirements (needed for metadata ingestion)​
  1. 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.getDataGet data from the read session. This only needs for the extractor project where the service account belongs                      
  1. Grant the following permissions to the Service Account on every project where you would like to extract metadata from
info

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.getIamPolicyRead 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 extractionroles/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 extractionroles/logging.privateLogViewer
logging.privateLogEntries.listFetch log entries for lineage/usage data. Not required if use_exported_bigquery_audit_metadata is enabled.Lineage Extraction/Usage extractionroles/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​

  1. Setup a ServiceAccount as per BigQuery docs and assign the previously created role to this service account.
  2. 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"
}

  1. 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​

Note that a . is used to denote nested fields in the YAML recipe.

View All Configuration Options
Field [Required]TypeDescriptionDefaultNotes
bigquery_audit_metadata_datasets [✅]array(string)None
bucket_duration [✅]EnumSize of the time window to aggregate usage stats.DAY
capture_dataset_label_as_tag [✅]booleanCapture BigQuery dataset labels as DataHub tagNone
capture_table_label_as_tag [✅]booleanCapture BigQuery table labels as DataHub tagNone
column_limit [✅]integerMaximum 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.300
convert_urns_to_lowercase [✅]booleanConvert urns to lowercase.None
debug_include_full_payloads [✅]booleanInclude full payload into events. It is only for debugging and internal use.None
enable_legacy_sharded_table_support [✅]booleanUse the legacy sharded table urn suffix added.True
end_time [✅]string(date-time)Latest date of usage to consider. Default: Current time in UTCNone
extra_client_options [✅]objectAdditional options to pass to google.cloud.logging_v2.client.Client.None
extract_lineage_from_catalog [✅]booleanThis 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-lineageNone
include_external_url [✅]booleanWhether to populate BigQuery Console url to Datasets/TablesTrue
include_table_lineage [✅]booleanOption to enable/disable lineage generation. Is enabled by default.True
include_table_location_lineage [✅]booleanIf the source supports it, include table lineage to the underlying storage location.True
include_tables [✅]booleanWhether tables should be ingested.True
include_usage_statistics [✅]booleanGenerate usage statisticTrue
include_views [✅]booleanWhether views should be ingested.True
incremental_lineage [✅]booleanWhen enabled, emits lineage as incremental to existing lineage already in DataHub. When disabled, re-states lineage on each run.True
lineage_parse_view_ddl [✅]booleanSql parse view ddl to get lineage.True
lineage_sql_parser_use_raw_names [✅]booleanThis parameter ignores the lowercase pattern stipulated in the SQLParser. NOTE: Ignored if lineage_use_sql_parser is False.None
lineage_use_sql_parser [✅]booleanExperimental. Use sql parser to resolve view/table lineage. If there is a view being referenced then bigquery sends both the view as well as underlying tablein the references. There is no distinction between direct/base objects accessed. So doing sql parsing to ensure we only use direct objects accessed for lineage.None
log_page_size [✅]integerThe number of log item will be queried per page for lineage collection1000
match_fully_qualified_names [✅]booleanWhether dataset_pattern is matched against fully qualified dataset name <project_id>.<dataset_name>.None
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.900.0
number_of_datasets_process_in_batch_if_profiling_enabled [✅]integerNumber 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.200
options [✅]objectAny options specified here will be passed to SQLAlchemy's create_engine as kwargs. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine for details.None
platform_instance [✅]stringThe instance of the platform that all assets produced by this recipe belong toNone
project_id [✅]string[deprecated] Use project_id_pattern or project_ids instead.None
project_ids [✅]array(string)None
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.None
rate_limit [✅]booleanShould we rate limit requests made to API.None
requests_per_min [✅]integerUsed to control number of API calls made per min. Only used when rate_limit is set to True.60
scheme [✅]stringbigquery
sharded_table_pattern [✅]stringThe 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,((.+)[_$])?(\d{8})$
sql_parser_use_external_process [✅]booleanWhen 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.None
start_time [✅]string(date-time)Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
store_last_lineage_extraction_timestamp [✅]booleanEnable checking last lineage extraction date in store.None
store_last_profiling_timestamps [✅]booleanEnable storing last profile timestamp in store.None
store_last_usage_extraction_timestamp [✅]booleanEnable checking last usage timestamp in store.True
temp_table_dataset_prefix [✅]stringIf 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._
upstream_lineage_in_report [✅]booleanUseful for debugging lineage information. Set to True to see the raw lineage created internally.None
use_date_sharded_audit_log_tables [✅]booleanWhether to read date sharded tables or time partitioned tables when extracting usage from exported audit logs.None
use_exported_bigquery_audit_metadata [✅]booleanWhen configured, use BigQueryAuditMetadata in bigquery_audit_metadata_datasets to compute lineage information.None
env [✅]stringThe environment that all assets produced by this connector belong toPROD
credential [✅]BigQueryCredentialBigQuery credential informationsNone
credential.auth_provider_x509_cert_url [❓ (required if credential is set)]stringAuth provider x509 certificate urlhttps://www.googleapis.com/oauth2/v1/certs
credential.auth_uri [❓ (required if credential is set)]stringAuthentication urihttps://accounts.google.com/o/oauth2/auth
credential.client_email [❓ (required if credential is set)]stringClient emailNone
credential.client_id [❓ (required if credential is set)]stringClient IdNone
credential.client_x509_cert_url [❓ (required if credential is set)]stringIf not set it will be default to https://www.googleapis.com/robot/v1/metadata/x509/client_emailNone
credential.private_key [❓ (required if credential is set)]stringPrivate key in a form of '-----BEGIN PRIVATE KEY-----\nprivate-key\n-----END PRIVATE KEY-----\n'None
credential.private_key_id [❓ (required if credential is set)]stringPrivate key idNone
credential.project_id [❓ (required if credential is set)]stringProject id to set the credentialsNone
credential.token_uri [❓ (required if credential is set)]stringToken urihttps://oauth2.googleapis.com/token
credential.type [❓ (required if credential is set)]stringAuthentication typeservice_account
dataset_pattern [✅]AllowDenyPatternRegex 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'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
dataset_pattern.allow [❓ (required if dataset_pattern is set)]array(string)None
dataset_pattern.deny [❓ (required if dataset_pattern is set)]array(string)None
dataset_pattern.ignoreCase [❓ (required if dataset_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
domain [✅]map(str,AllowDenyPattern)A class to store allow deny regexesNone
domain.key.allow [❓ (required if domain is set)]array(string)None
domain.key.deny [❓ (required if domain is set)]array(string)None
domain.key.ignoreCase [❓ (required if domain is set)]booleanWhether to ignore case sensitivity during pattern matching.True
profile_pattern [✅]AllowDenyPatternRegex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allow [❓ (required if profile_pattern is set)]array(string)None
profile_pattern.deny [❓ (required if profile_pattern is set)]array(string)None
profile_pattern.ignoreCase [❓ (required if profile_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
project_id_pattern [✅]AllowDenyPatternRegex patterns for project_id to filter in ingestion.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
project_id_pattern.allow [❓ (required if project_id_pattern is set)]array(string)None
project_id_pattern.deny [❓ (required if project_id_pattern is set)]array(string)None
project_id_pattern.ignoreCase [❓ (required if project_id_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
schema_pattern [✅]AllowDenyPatternRegex 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'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
schema_pattern.allow [❓ (required if schema_pattern is set)]array(string)None
schema_pattern.deny [❓ (required if schema_pattern is set)]array(string)None
schema_pattern.ignoreCase [❓ (required if schema_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
table_pattern [✅]AllowDenyPatternRegex 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.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow [❓ (required if table_pattern is set)]array(string)None
table_pattern.deny [❓ (required if table_pattern is set)]array(string)None
table_pattern.ignoreCase [❓ (required if table_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
usage [✅]BigQueryUsageConfigUsage related configs{'bucket_duration': 'DAY', 'end_time': '2023-04-04T19:29:40.741731+00:00', 'start_time': '2023-04-03T00:00:00+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, 'query_log_delay': None, 'max_query_duration': 900.0}
usage.bucket_duration [❓ (required if usage is set)]EnumSize of the time window to aggregate usage stats.DAY
usage.end_time [❓ (required if usage is set)]string(date-time)Latest date of usage to consider. Default: Current time in UTCNone
usage.format_sql_queries [❓ (required if usage is set)]booleanWhether to format sql queriesNone
usage.include_operational_stats [❓ (required if usage is set)]booleanWhether to display operational stats.True
usage.include_read_operational_stats [❓ (required if usage is set)]booleanWhether to report read operational stats. Experimental.None
usage.include_top_n_queries [❓ (required if usage is set)]booleanWhether to ingest the top_n_queries.True
usage.max_query_duration [❓ (required if usage is set)]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.900.0
usage.query_log_delay [❓ (required if usage is set)]integerTo account for the possibility that the query event arrives after the read event in the audit logs, we wait for at least query_log_delay additional events to be processed before attempting to resolve BigQuery job information from the logs. If query_log_delay is None, it gets treated as an unlimited delay, which prioritizes correctness at the expense of memory usage.None
usage.start_time [❓ (required if usage is set)]string(date-time)Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
usage.top_n_queries [❓ (required if usage is set)]integerNumber of top queries to save to each table.10
usage.user_email_pattern [❓ (required if usage is set)]AllowDenyPatternregex patterns for user emails to filter in usage.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
usage.user_email_pattern.allow [❓ (required if user_email_pattern is set)]array(string)None
usage.user_email_pattern.deny [❓ (required if user_email_pattern is set)]array(string)None
usage.user_email_pattern.ignoreCase [❓ (required if user_email_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
view_pattern [✅]AllowDenyPatternRegex 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.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allow [❓ (required if view_pattern is set)]array(string)None
view_pattern.deny [❓ (required if view_pattern is set)]array(string)None
view_pattern.ignoreCase [❓ (required if view_pattern is set)]booleanWhether to ignore case sensitivity during pattern matching.True
profiling [✅]GEProfilingConfig{'enabled': False, 'limit': None, 'offset': None, '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': None, 'profile_if_updated_since_days': None, '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': None}
profiling.catch_exceptions [❓ (required if profiling is set)]booleanTrue
profiling.enabled [❓ (required if profiling is set)]booleanWhether profiling should be done.None
profiling.field_sample_values_limit [❓ (required if profiling is set)]integerUpper limit for number of sample values to collect for all columns.20
profiling.include_field_distinct_count [❓ (required if profiling is set)]booleanWhether to profile for the number of distinct values for each column.True
profiling.include_field_distinct_value_frequencies [❓ (required if profiling is set)]booleanWhether to profile for distinct value frequencies.None
profiling.include_field_histogram [❓ (required if profiling is set)]booleanWhether to profile for the histogram for numeric fields.None
profiling.include_field_max_value [❓ (required if profiling is set)]booleanWhether to profile for the max value of numeric columns.True
profiling.include_field_mean_value [❓ (required if profiling is set)]booleanWhether to profile for the mean value of numeric columns.True
profiling.include_field_median_value [❓ (required if profiling is set)]booleanWhether to profile for the median value of numeric columns.True
profiling.include_field_min_value [❓ (required if profiling is set)]booleanWhether to profile for the min value of numeric columns.True
profiling.include_field_null_count [❓ (required if profiling is set)]booleanWhether to profile for the number of nulls for each column.True
profiling.include_field_quantiles [❓ (required if profiling is set)]booleanWhether to profile for the quantiles of numeric columns.None
profiling.include_field_sample_values [❓ (required if profiling is set)]booleanWhether to profile for the sample values for all columns.True
profiling.include_field_stddev_value [❓ (required if profiling is set)]booleanWhether to profile for the standard deviation of numeric columns.True
profiling.limit [❓ (required if profiling is set)]integerMax number of documents to profile. By default, profiles all documents.None
profiling.max_number_of_fields_to_profile [❓ (required if profiling is set)]integerA 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.None
profiling.max_workers [❓ (required if profiling is set)]integerNumber of worker threads to use for profiling. Set to 1 to disable.20
profiling.offset [❓ (required if profiling is set)]integerOffset in documents to profile. By default, uses no offset.None
profiling.partition_datetime [❓ (required if profiling is set)]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.None
profiling.partition_profiling_enabled [❓ (required if profiling is set)]booleanTrue
profiling.profile_if_updated_since_days [❓ (required if profiling is set)]numberProfile 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.None
profiling.profile_table_level_only [❓ (required if profiling is set)]booleanWhether to perform profiling at table-level only, or include column-level profiling as well.None
profiling.profile_table_row_count_estimate_only [❓ (required if profiling is set)]booleanUse an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.None
profiling.profile_table_row_limit [❓ (required if profiling is set)]integerProfile 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 BigQuery5000000
profiling.profile_table_size_limit [❓ (required if profiling is set)]integerProfile 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 BigQuery5
profiling.query_combiner_enabled [❓ (required if profiling is set)]booleanThis 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.True
profiling.report_dropped_profiles [❓ (required if profiling is set)]booleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.None
profiling.turn_off_expensive_profiling_metrics [❓ (required if profiling is set)]booleanWhether 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.None
stateful_ingestion [✅]StatefulStaleMetadataRemovalConfigBase specialized config for Stateful Ingestion with stale metadata removal capability.None
stateful_ingestion.enabled [❓ (required if stateful_ingestion is set)]booleanThe type of the ingestion state provider registered with datahub.None
stateful_ingestion.ignore_new_state [❓ (required if stateful_ingestion is set)]booleanIf set to True, ignores the current checkpoint state.None
stateful_ingestion.ignore_old_state [❓ (required if stateful_ingestion is set)]booleanIf set to True, ignores the previous checkpoint state.None
stateful_ingestion.remove_stale_metadata [❓ (required if stateful_ingestion is set)]booleanSoft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.True

Code Coordinates​

  • Class Name: datahub.ingestion.source.bigquery_v2.bigquery.BigqueryV2Source
  • Browse on GitHub

Questions​

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