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Redshift

There are 2 sources that provide integration with Redshift

Source ModuleDocumentation

redshift

This plugin extracts the following:

  • Metadata for databases, schemas, views and tables
  • Column types associated with each table
  • Also supports PostGIS extensions
  • Table, row, and column statistics via optional SQL profiling
  • Table lineage
tip

You can also get fine-grained usage statistics for Redshift using the redshift-usage source described below.

Prerequisites

This source needs to access system tables that require extra permissions. To grant these permissions, please alter your datahub Redshift user the following way:

ALTER USER datahub_user WITH SYSLOG ACCESS UNRESTRICTED;
GRANT SELECT ON pg_catalog.svv_table_info to datahub_user;
GRANT SELECT ON pg_catalog.svl_user_info to datahub_user;
note

Giving a user unrestricted access to system tables gives the user visibility to data generated by other users. For example, STL_QUERY and STL_QUERYTEXT contain the full text of INSERT, UPDATE, and DELETE statements.

Lineage

There are multiple lineage collector implementations as Redshift does not support table lineage out of the box.

stl_scan_based

The stl_scan based collector uses Redshift's stl_insert and stl_scan system tables to discover lineage between tables. Pros:

  • Fast
  • Reliable

Cons:

  • Does not work with Spectrum/external tables because those scans do not show up in stl_scan table.
  • If a table is depending on a view then the view won't be listed as dependency. Instead the table will be connected with the view's dependencies.

sql_based

The sql_based based collector uses Redshift's stl_insert to discover all the insert queries and uses sql parsing to discover the dependecies.

Pros:

  • Works with Spectrum tables
  • Views are connected properly if a table depends on it

Cons:

  • Slow.
  • Less reliable as the query parser can fail on certain queries

mixed

Using both collector above and first applying the sql based and then the stl_scan based one.

Pros:

  • Works with Spectrum tables
  • Views are connected properly if a table depends on it
  • A bit more reliable than the sql_based one only

Cons:

  • Slow
  • May be incorrect at times as the query parser can fail on certain queries
note

The redshift stl redshift tables which are used for getting data lineage only retain approximately two to five days of log history. This means you cannot extract lineage from queries issued outside that window.

Read more...

redshift-usage

This plugin extracts usage statistics for datasets in Amazon Redshift.

Note: Usage information is computed by querying the following system tables -

  1. stl_scan
  2. svv_table_info
  3. stl_query
  4. svl_user_info

To grant access this plugin for all system tables, please alter your datahub Redshift user the following way:

ALTER USER datahub_user WITH SYSLOG ACCESS UNRESTRICTED;

This plugin has the below functionalities -

  1. For a specific dataset this plugin ingests the following statistics -
    1. top n queries.
    2. top users.
    3. usage of each column in the dataset.
  2. Aggregation of these statistics into buckets, by day or hour granularity.
note

This source only does usage statistics. To get the tables, views, and schemas in your Redshift warehouse, ingest using the redshift source described above.

note

Redshift system tables have some latency in getting data from queries. In addition, these tables only maintain logs for 2-5 days. You can find more information from the official documentation here.

Read more...

To get all metadata from Redshift you need to use two plugins redshift and redshift-usage. Both of them are described in this page. These will require 2 separate recipes. We understand this is not ideal and we plan to make this easier in the future.

Module redshift

Certified

Important Capabilities

CapabilityStatusNotes
Data ProfilingOptionally enabled via configuration
Dataset UsageNot provided by this module, use bigquery-usage for that.
DescriptionsEnabled by default
Detect Deleted EntitiesEnabled via stateful ingestion
DomainsSupported via the domain config field
Platform InstanceEnabled by default
Table-Level LineageOptionally enabled via configuration

This plugin extracts the following:

  • Metadata for databases, schemas, views and tables
  • Column types associated with each table
  • Also supports PostGIS extensions
  • Table, row, and column statistics via optional SQL profiling
  • Table lineage
tip

You can also get fine-grained usage statistics for Redshift using the redshift-usage source described below.

Prerequisites

This source needs to access system tables that require extra permissions. To grant these permissions, please alter your datahub Redshift user the following way:

ALTER USER datahub_user WITH SYSLOG ACCESS UNRESTRICTED;
GRANT SELECT ON pg_catalog.svv_table_info to datahub_user;
GRANT SELECT ON pg_catalog.svl_user_info to datahub_user;
note

Giving a user unrestricted access to system tables gives the user visibility to data generated by other users. For example, STL_QUERY and STL_QUERYTEXT contain the full text of INSERT, UPDATE, and DELETE statements.

Lineage

There are multiple lineage collector implementations as Redshift does not support table lineage out of the box.

stl_scan_based

The stl_scan based collector uses Redshift's stl_insert and stl_scan system tables to discover lineage between tables. Pros:

  • Fast
  • Reliable

Cons:

  • Does not work with Spectrum/external tables because those scans do not show up in stl_scan table.
  • If a table is depending on a view then the view won't be listed as dependency. Instead the table will be connected with the view's dependencies.

sql_based

The sql_based based collector uses Redshift's stl_insert to discover all the insert queries and uses sql parsing to discover the dependecies.

Pros:

  • Works with Spectrum tables
  • Views are connected properly if a table depends on it

Cons:

  • Slow.
  • Less reliable as the query parser can fail on certain queries

mixed

Using both collector above and first applying the sql based and then the stl_scan based one.

Pros:

  • Works with Spectrum tables
  • Views are connected properly if a table depends on it
  • A bit more reliable than the sql_based one only

Cons:

  • Slow
  • May be incorrect at times as the query parser can fail on certain queries
note

The redshift stl redshift tables which are used for getting data lineage only retain approximately two to five days of log history. This means you cannot extract lineage from queries issued outside that window.

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[redshift]'

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: redshift
config:
# Coordinates
host_port: example.something.us-west-2.redshift.amazonaws.com:5439
database: DemoDatabase

# Credentials
username: user
password: pass

# Options
options:
# driver_option: some-option

include_views: True # whether to include views, defaults to True
include_tables: True # whether to include views, defaults to True

sink:
# sink configs

#------------------------------------------------------------------------------
# Extra options when running Redshift behind a proxy</summary>
# This requires you to have already installed the Microsoft ODBC Driver for SQL Server.
# See https://docs.microsoft.com/en-us/sql/connect/python/pyodbc/step-1-configure-development-environment-for-pyodbc-python-development?view=sql-server-ver15
#------------------------------------------------------------------------------

source:
type: redshift
config:
host_port: my-proxy-hostname:5439

options:
connect_args:
sslmode: "prefer" # or "require" or "verify-ca"
sslrootcert: ~ # needed to unpin the AWS Redshift certificate

sink:
# sink configs

Config Details

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

View All Configuration Options
FieldRequiredTypeDescriptionDefault
envstringThe environment that all assets produced by this connector belong toPROD
platform_instance_mapDict[str,string]A holder for platform -> platform_instance mappings to generate correct dataset urns
bucket_durationenum(BucketDuration)Size of the time window to aggregate usage stats.. Allowed symbols are DAY, HOURDAY
end_timestringLatest date of usage to consider. Default: Current time in UTCNone
start_timestringEarliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
platformstringThe platform that this source connects toNone
platform_instancestringThe instance of the platform that all assets produced by this recipe belong toNone
optionsDict{}
include_viewsbooleanWhether views should be ingested.True
include_tablesbooleanWhether tables should be ingested.True
usernamestringusernameNone
passwordstringpasswordNone
host_portstringhost URLNone
databasestringdatabase (catalog)None
database_aliasstringAlias to apply to database when ingesting.None
schemestringredshift+psycopg2
sqlalchemy_uristringURI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.None
default_schemastringThe default schema to use if the sql parser fails to parse the schema with sql_based lineage collectorpublic
include_table_lineagebooleanWhether table lineage should be ingested.True
include_copy_lineagebooleanWhether lineage should be collected from copy commandsTrue
capture_lineage_query_parser_failuresbooleanWhether to capture lineage query parser errors with dataset properties for debuggingsFalse
table_lineage_modeenum(LineageMode)Which table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]stl_scan_based
s3_lineage_configS3LineageProviderConfig (see below for fields)Common config for S3 lineage generation
s3_lineage_config.path_specs❓ (required if s3_lineage_config is set)Array of objectList of PathSpec. See below the details about PathSpecNone
stateful_ingestionSQLAlchemyStatefulIngestionConfig (see below for fields)
stateful_ingestion.enabledbooleanThe type of the ingestion state provider registered with datahub.False
stateful_ingestion.max_checkpoint_state_sizeintegerThe maximum size of the checkpoint state in bytes. Default is 16MB16777216
stateful_ingestion.state_providerDynamicTypedStateProviderConfig (see below for fields)The ingestion state provider configuration.
stateful_ingestion.state_provider.type❓ (required if stateful_ingestion.state_provider is set)stringThe type of the state provider to use. For DataHub use datahubNone
stateful_ingestion.state_provider.configGeneric dictThe 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).None
stateful_ingestion.ignore_old_statebooleanIf set to True, ignores the previous checkpoint state.False
stateful_ingestion.ignore_new_statebooleanIf set to True, ignores the current checkpoint state.False
stateful_ingestion.remove_stale_metadatabooleanSoft-deletes the entities of type in the last successful run but missing in the current run with stateful_ingestion enabled.True
stateful_ingestion.fail_safe_thresholdnumberPrevents large amount of soft deletes & the state from committing from accidental changes to the source configuration if the relative change percent in entities compared to the previous state is above the 'fail_safe_threshold'.95.0
schema_patternAllowDenyPattern (see below for fields){'allow': ['.*'], 'deny': ['information_schema'], 'ignoreCase': True}
schema_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
schema_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
schema_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
table_patternAllowDenyPattern (see below for fields)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.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
table_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
table_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
view_patternAllowDenyPattern (see below for fields)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.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
view_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
view_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profile_patternAllowDenyPattern (see below for fields)Regex patterns to filter tables for profiling during ingestion. Allowed by the table_pattern.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
profile_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
profile_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
domainDict[str, AllowDenyPattern]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.{}
domain.key.allowArray of stringList of regex patterns to include in ingestion['.*']
domain.key.denyArray of stringList of regex patterns to exclude from ingestion.[]
domain.key.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profilingGEProfilingConfig (see below for fields){'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_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, 'max_number_of_fields_to_profile': None, 'profile_if_updated_since_days': 1, 'profile_table_size_limit': 1, 'profile_table_row_limit': 50000, 'max_workers': 10, 'query_combiner_enabled': True, 'catch_exceptions': True, 'partition_profiling_enabled': True, 'bigquery_temp_table_schema': None, 'partition_datetime': None}
profiling.enabledbooleanWhether profiling should be done.False
profiling.limitintegerMax number of documents to profile. By default, profiles all documents.None
profiling.offsetintegerOffset in documents to profile. By default, uses no offset.None
profiling.report_dropped_profilesbooleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.False
profiling.turn_off_expensive_profiling_metricsbooleanWhether 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.False
profiling.profile_table_level_onlybooleanWhether to perform profiling at table-level only, or include column-level profiling as well.False
profiling.include_field_null_countbooleanWhether to profile for the number of nulls for each column.True
profiling.include_field_min_valuebooleanWhether to profile for the min value of numeric columns.True
profiling.include_field_max_valuebooleanWhether to profile for the max value of numeric columns.True
profiling.include_field_mean_valuebooleanWhether to profile for the mean value of numeric columns.True
profiling.include_field_median_valuebooleanWhether to profile for the median value of numeric columns.True
profiling.include_field_stddev_valuebooleanWhether to profile for the standard deviation of numeric columns.True
profiling.include_field_quantilesbooleanWhether to profile for the quantiles of numeric columns.False
profiling.include_field_distinct_value_frequenciesbooleanWhether to profile for distinct value frequencies.False
profiling.include_field_histogrambooleanWhether to profile for the histogram for numeric fields.False
profiling.include_field_sample_valuesbooleanWhether to profile for the sample values for all columns.True
profiling.max_number_of_fields_to_profileintegerA 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.profile_if_updated_since_daysnumberProfile 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, snowflake-beta and BigQuery.1
profiling.profile_table_size_limitintegerProfile 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-beta and BigQuery1
profiling.profile_table_row_limitintegerProfile 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-beta and BigQuery50000
profiling.max_workersintegerNumber of worker threads to use for profiling. Set to 1 to disable.10
profiling.query_combiner_enabledbooleanThis 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.catch_exceptionsbooleanTrue
profiling.partition_profiling_enabledbooleanTrue
profiling.bigquery_temp_table_schemastringOn bigquery for profiling partitioned tables needs to create temporary views. You have to define a dataset where these will be created. Views will be cleaned up after profiler runs. (Great expectation tech details about this (https://legacy.docs.greatexpectations.io/en/0.9.0/reference/integrations/bigquery.html#custom-queries-with-sql-datasource).None
profiling.partition_datetimestringFor partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.None

Code Coordinates

  • Class Name: datahub.ingestion.source.sql.redshift.RedshiftSource
  • Browse on GitHub

Module redshift-usage

Certified

Important Capabilities

CapabilityStatusNotes
Platform InstanceEnabled by default

This plugin extracts usage statistics for datasets in Amazon Redshift.

Note: Usage information is computed by querying the following system tables -

  1. stl_scan
  2. svv_table_info
  3. stl_query
  4. svl_user_info

To grant access this plugin for all system tables, please alter your datahub Redshift user the following way:

ALTER USER datahub_user WITH SYSLOG ACCESS UNRESTRICTED;

This plugin has the below functionalities -

  1. For a specific dataset this plugin ingests the following statistics -
    1. top n queries.
    2. top users.
    3. usage of each column in the dataset.
  2. Aggregation of these statistics into buckets, by day or hour granularity.
note

This source only does usage statistics. To get the tables, views, and schemas in your Redshift warehouse, ingest using the redshift source described above.

note

Redshift system tables have some latency in getting data from queries. In addition, these tables only maintain logs for 2-5 days. You can find more information from the official documentation here.

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[redshift-usage]'

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: redshift-usage
config:
# Coordinates
host_port: db_host:port
database: dev
email_domain: acryl.io

# Credentials
username: username
password: "password"

sink:
# sink configs

Config Details

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

View All Configuration Options
FieldRequiredTypeDescriptionDefault
envstringThe environment that all assets produced by this connector belong toPROD
bucket_durationenum(BucketDuration)Size of the time window to aggregate usage stats.. Allowed symbols are DAY, HOURDAY
end_timestringLatest date of usage to consider. Default: Current time in UTCNone
start_timestringEarliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
top_n_queriesintegerNumber of top queries to save to each table.10
include_operational_statsbooleanWhether to display operational stats.True
include_read_operational_statsbooleanWhether to report read operational stats. Experimental.False
format_sql_queriesbooleanWhether to format sql queriesFalse
include_top_n_queriesbooleanWhether to ingest the top_n_queries.True
platform_instance_mapDict[str,string]A holder for platform -> platform_instance mappings to generate correct dataset urns
platformstringThe platform that this source connects toNone
platform_instancestringThe instance of the platform that all assets produced by this recipe belong toNone
optionsDictAny 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.{}
include_viewsbooleanWhether views should be ingested.True
include_tablesbooleanWhether tables should be ingested.True
usernamestringusernameNone
passwordstringpasswordNone
host_portstringhost URLNone
databasestringdatabase (catalog)None
database_aliasstringAlias to apply to database when ingesting.None
schemestringredshift+psycopg2
sqlalchemy_uristringURI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.None
default_schemastringThe default schema to use if the sql parser fails to parse the schema with sql_based lineage collectorpublic
include_table_lineagebooleanWhether table lineage should be ingested.True
include_copy_lineagebooleanWhether lineage should be collected from copy commandsTrue
capture_lineage_query_parser_failuresbooleanWhether to capture lineage query parser errors with dataset properties for debuggingsFalse
table_lineage_modeenum(LineageMode)Which table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]stl_scan_based
email_domainstringEmail domain of your organisation so users can be displayed on UI appropriately.None
user_email_patternAllowDenyPattern (see below for fields)regex patterns for user emails to filter in usage.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
user_email_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
user_email_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
user_email_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
s3_lineage_configS3LineageProviderConfig (see below for fields)Common config for S3 lineage generation
s3_lineage_config.path_specs❓ (required if s3_lineage_config is set)Array of objectList of PathSpec. See below the details about PathSpecNone
stateful_ingestionSQLAlchemyStatefulIngestionConfig (see below for fields)
stateful_ingestion.enabledbooleanThe type of the ingestion state provider registered with datahub.False
stateful_ingestion.max_checkpoint_state_sizeintegerThe maximum size of the checkpoint state in bytes. Default is 16MB16777216
stateful_ingestion.state_providerDynamicTypedStateProviderConfig (see below for fields)The ingestion state provider configuration.
stateful_ingestion.state_provider.type❓ (required if stateful_ingestion.state_provider is set)stringThe type of the state provider to use. For DataHub use datahubNone
stateful_ingestion.state_provider.configGeneric dictThe 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).None
stateful_ingestion.ignore_old_statebooleanIf set to True, ignores the previous checkpoint state.False
stateful_ingestion.ignore_new_statebooleanIf set to True, ignores the current checkpoint state.False
stateful_ingestion.remove_stale_metadatabooleanSoft-deletes the entities of type in the last successful run but missing in the current run with stateful_ingestion enabled.True
stateful_ingestion.fail_safe_thresholdnumberPrevents large amount of soft deletes & the state from committing from accidental changes to the source configuration if the relative change percent in entities compared to the previous state is above the 'fail_safe_threshold'.95.0
schema_patternAllowDenyPattern (see below for fields){'allow': ['.*'], 'deny': ['information_schema'], 'ignoreCase': True}
schema_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
schema_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
schema_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
table_patternAllowDenyPattern (see below for fields)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.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
table_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
table_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
view_patternAllowDenyPattern (see below for fields)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.*'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
view_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
view_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profile_patternAllowDenyPattern (see below for fields)Regex patterns to filter tables for profiling during ingestion. Allowed by the table_pattern.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
profile_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
profile_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
domainDict[str, AllowDenyPattern]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.{}
domain.key.allowArray of stringList of regex patterns to include in ingestion['.*']
domain.key.denyArray of stringList of regex patterns to exclude from ingestion.[]
domain.key.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profilingGEProfilingConfig (see below for fields){'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_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, 'max_number_of_fields_to_profile': None, 'profile_if_updated_since_days': 1, 'profile_table_size_limit': 1, 'profile_table_row_limit': 50000, 'max_workers': 10, 'query_combiner_enabled': True, 'catch_exceptions': True, 'partition_profiling_enabled': True, 'bigquery_temp_table_schema': None, 'partition_datetime': None}
profiling.enabledbooleanWhether profiling should be done.False
profiling.limitintegerMax number of documents to profile. By default, profiles all documents.None
profiling.offsetintegerOffset in documents to profile. By default, uses no offset.None
profiling.report_dropped_profilesbooleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.False
profiling.turn_off_expensive_profiling_metricsbooleanWhether 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.False
profiling.profile_table_level_onlybooleanWhether to perform profiling at table-level only, or include column-level profiling as well.False
profiling.include_field_null_countbooleanWhether to profile for the number of nulls for each column.True
profiling.include_field_min_valuebooleanWhether to profile for the min value of numeric columns.True
profiling.include_field_max_valuebooleanWhether to profile for the max value of numeric columns.True
profiling.include_field_mean_valuebooleanWhether to profile for the mean value of numeric columns.True
profiling.include_field_median_valuebooleanWhether to profile for the median value of numeric columns.True
profiling.include_field_stddev_valuebooleanWhether to profile for the standard deviation of numeric columns.True
profiling.include_field_quantilesbooleanWhether to profile for the quantiles of numeric columns.False
profiling.include_field_distinct_value_frequenciesbooleanWhether to profile for distinct value frequencies.False
profiling.include_field_histogrambooleanWhether to profile for the histogram for numeric fields.False
profiling.include_field_sample_valuesbooleanWhether to profile for the sample values for all columns.True
profiling.max_number_of_fields_to_profileintegerA 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.profile_if_updated_since_daysnumberProfile 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, snowflake-beta and BigQuery.1
profiling.profile_table_size_limitintegerProfile 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-beta and BigQuery1
profiling.profile_table_row_limitintegerProfile 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-beta and BigQuery50000
profiling.max_workersintegerNumber of worker threads to use for profiling. Set to 1 to disable.10
profiling.query_combiner_enabledbooleanThis 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.catch_exceptionsbooleanTrue
profiling.partition_profiling_enabledbooleanTrue
profiling.bigquery_temp_table_schemastringOn bigquery for profiling partitioned tables needs to create temporary views. You have to define a dataset where these will be created. Views will be cleaned up after profiler runs. (Great expectation tech details about this (https://legacy.docs.greatexpectations.io/en/0.9.0/reference/integrations/bigquery.html#custom-queries-with-sql-datasource).None
profiling.partition_datetimestringFor partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.None

Code Coordinates

  • Class Name: datahub.ingestion.source.usage.redshift_usage.RedshiftUsageSource
  • Browse on GitHub

Questions

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