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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.
  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 redshift-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
Field [Required]TypeDescriptionDefaultNotes
host_port [✅]stringhost URLNone
bucket_durationEnumSize of the time window to aggregate usage stats.DAY
capture_lineage_query_parser_failuresbooleanWhether to capture lineage query parser errors with dataset properties for debuggingsNone
databasestringdatabase (catalog)None
database_aliasstringAlias to apply to database when ingesting.None
default_schemastringThe default schema to use if the sql parser fails to parse the schema with sql_based lineage collectorpublic
end_timestring(date-time)Latest date of usage to consider. Default: Current time in UTCNone
include_copy_lineagebooleanWhether lineage should be collected from copy commandsTrue
include_table_lineagebooleanWhether table lineage should be ingested.True
include_table_location_lineagebooleanIf the source supports it, include table lineage to the underlying storage location.True
include_tablesbooleanWhether tables should be ingested.True
include_unload_lineagebooleanWhether lineage should be collected from unload commandsTrue
include_view_lineagebooleanInclude table lineage for viewsNone
include_viewsbooleanWhether views should be ingested.True
optionsobjectAny 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
passwordstring(password)passwordNone
platform_instancestringThe instance of the platform that all assets produced by this recipe belong toNone
platform_instance_mapmap(str,string)None
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
start_timestring(date-time)Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
table_lineage_modeEnumWhich table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]stl_scan_based
usernamestringusernameNone
envstringThe environment that all assets produced by this connector belong toPROD
domainmap(str,AllowDenyPattern)A class to store allow deny regexesNone
domain.key.allowarray(string)None
domain.key.denyarray(string)None
domain.key.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profile_patternAllowDenyPatternRegex 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.allowarray(string)None
profile_pattern.denyarray(string)None
profile_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
s3_lineage_configS3LineageProviderConfigCommon config for S3 lineage generationNone
s3_lineage_config.path_specsarray(object)None
s3_lineage_config.path_specs.include [❓ (required if path_specs is set)]stringPath to table (s3 or local file system). Name variable {table} is used to mark the folder with dataset. In absence of {table}, file level dataset will be created. Check below examples for more details.None
s3_lineage_config.path_specs.default_extensionstringFor files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.None
s3_lineage_config.path_specs.enable_compressionbooleanEnable or disable processing compressed files. Currently .gz and .bz files are supported.True
s3_lineage_config.path_specs.excludearray(string)None
s3_lineage_config.path_specs.file_typesarray(string)None
s3_lineage_config.path_specs.sample_filesbooleanNot listing all the files but only taking a handful amount of sample file to infer the schema. File count and file size calculation will be disabled. This can affect performance significantly if enabledTrue
s3_lineage_config.path_specs.table_namestringDisplay name of the dataset.Combination of named variables from include path and stringsNone
schema_patternAllowDenyPattern{'allow': ['.*'], 'deny': ['information_schema'], 'ignoreCase': True}
schema_pattern.allowarray(string)None
schema_pattern.denyarray(string)None
schema_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
table_patternAllowDenyPatternRegex 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(string)None
table_pattern.denyarray(string)None
table_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
view_patternAllowDenyPatternRegex 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(string)None
view_pattern.denyarray(string)None
view_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profilingGEProfilingConfig{'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_exceptionsbooleanTrue
profiling.enabledbooleanWhether profiling should be done.None
profiling.field_sample_values_limitintegerUpper limit for number of sample values to collect for all columns.20
profiling.include_field_distinct_countbooleanWhether to profile for the number of distinct values for each column.True
profiling.include_field_distinct_value_frequenciesbooleanWhether to profile for distinct value frequencies.None
profiling.include_field_histogrambooleanWhether to profile for the histogram for numeric fields.None
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_min_valuebooleanWhether to profile for the min value of numeric columns.True
profiling.include_field_null_countbooleanWhether to profile for the number of nulls for each column.True
profiling.include_field_quantilesbooleanWhether to profile for the quantiles of numeric columns.None
profiling.include_field_sample_valuesbooleanWhether to profile for the sample values for all columns.True
profiling.include_field_stddev_valuebooleanWhether to profile for the standard deviation of numeric columns.True
profiling.limitintegerMax number of documents to profile. By default, profiles all documents.None
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.max_workersintegerNumber of worker threads to use for profiling. Set to 1 to disable.20
profiling.offsetintegerOffset in documents to profile. By default, uses no offset.None
profiling.partition_datetimestring(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_enabledbooleanTrue
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 and BigQuery.None
profiling.profile_table_level_onlybooleanWhether to perform profiling at table-level only, or include column-level profiling as well.None
profiling.profile_table_row_count_estimate_onlybooleanUse an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.None
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 and BigQuery5000000
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 and BigQuery5
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.report_dropped_profilesbooleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.None
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.None
stateful_ingestionStatefulStaleMetadataRemovalConfigBase specialized config for Stateful Ingestion with stale metadata removal capability.None
stateful_ingestion.enabledbooleanThe type of the ingestion state provider registered with datahub.None
stateful_ingestion.ignore_new_statebooleanIf set to True, ignores the current checkpoint state.None
stateful_ingestion.ignore_old_statebooleanIf set to True, ignores the previous checkpoint state.None
stateful_ingestion.remove_stale_metadatabooleanSoft-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.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.
  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
Field [Required]TypeDescriptionDefaultNotes
email_domain [✅]stringEmail domain of your organisation so users can be displayed on UI appropriately.None
host_port [✅]stringhost URLNone
bucket_durationEnumSize of the time window to aggregate usage stats.DAY
capture_lineage_query_parser_failuresbooleanWhether to capture lineage query parser errors with dataset properties for debuggingsNone
databasestringdatabase (catalog)None
database_aliasstringAlias to apply to database when ingesting.None
default_schemastringThe default schema to use if the sql parser fails to parse the schema with sql_based lineage collectorpublic
end_timestring(date-time)Latest date of usage to consider. Default: Current time in UTCNone
format_sql_queriesbooleanWhether to format sql queriesNone
include_copy_lineagebooleanWhether lineage should be collected from copy commandsTrue
include_operational_statsbooleanWhether to display operational stats.True
include_read_operational_statsbooleanWhether to report read operational stats. Experimental.None
include_table_lineagebooleanWhether table lineage should be ingested.True
include_table_location_lineagebooleanIf the source supports it, include table lineage to the underlying storage location.True
include_tablesbooleanWhether tables should be ingested.True
include_top_n_queriesbooleanWhether to ingest the top_n_queries.True
include_unload_lineagebooleanWhether lineage should be collected from unload commandsTrue
include_view_lineagebooleanInclude table lineage for viewsNone
include_viewsbooleanWhether views should be ingested.True
optionsobjectAny 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
passwordstring(password)passwordNone
platform_instancestringThe instance of the platform that all assets produced by this recipe belong toNone
platform_instance_mapmap(str,string)None
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
start_timestring(date-time)Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
table_lineage_modeEnumWhich table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]stl_scan_based
top_n_queriesintegerNumber of top queries to save to each table.10
usernamestringusernameNone
envstringThe environment that all assets produced by this connector belong toPROD
domainmap(str,AllowDenyPattern)A class to store allow deny regexesNone
domain.key.allowarray(string)None
domain.key.denyarray(string)None
domain.key.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profile_patternAllowDenyPatternRegex 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.allowarray(string)None
profile_pattern.denyarray(string)None
profile_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
s3_lineage_configS3LineageProviderConfigCommon config for S3 lineage generationNone
s3_lineage_config.path_specsarray(object)None
s3_lineage_config.path_specs.include [❓ (required if path_specs is set)]stringPath to table (s3 or local file system). Name variable {table} is used to mark the folder with dataset. In absence of {table}, file level dataset will be created. Check below examples for more details.None
s3_lineage_config.path_specs.default_extensionstringFor files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.None
s3_lineage_config.path_specs.enable_compressionbooleanEnable or disable processing compressed files. Currently .gz and .bz files are supported.True
s3_lineage_config.path_specs.excludearray(string)None
s3_lineage_config.path_specs.file_typesarray(string)None
s3_lineage_config.path_specs.sample_filesbooleanNot listing all the files but only taking a handful amount of sample file to infer the schema. File count and file size calculation will be disabled. This can affect performance significantly if enabledTrue
s3_lineage_config.path_specs.table_namestringDisplay name of the dataset.Combination of named variables from include path and stringsNone
schema_patternAllowDenyPattern{'allow': ['.*'], 'deny': ['information_schema'], 'ignoreCase': True}
schema_pattern.allowarray(string)None
schema_pattern.denyarray(string)None
schema_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
table_patternAllowDenyPatternRegex 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(string)None
table_pattern.denyarray(string)None
table_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
user_email_patternAllowDenyPatternregex patterns for user emails to filter in usage.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
user_email_pattern.allowarray(string)None
user_email_pattern.denyarray(string)None
user_email_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
view_patternAllowDenyPatternRegex 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(string)None
view_pattern.denyarray(string)None
view_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profilingGEProfilingConfig{'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_exceptionsbooleanTrue
profiling.enabledbooleanWhether profiling should be done.None
profiling.field_sample_values_limitintegerUpper limit for number of sample values to collect for all columns.20
profiling.include_field_distinct_countbooleanWhether to profile for the number of distinct values for each column.True
profiling.include_field_distinct_value_frequenciesbooleanWhether to profile for distinct value frequencies.None
profiling.include_field_histogrambooleanWhether to profile for the histogram for numeric fields.None
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_min_valuebooleanWhether to profile for the min value of numeric columns.True
profiling.include_field_null_countbooleanWhether to profile for the number of nulls for each column.True
profiling.include_field_quantilesbooleanWhether to profile for the quantiles of numeric columns.None
profiling.include_field_sample_valuesbooleanWhether to profile for the sample values for all columns.True
profiling.include_field_stddev_valuebooleanWhether to profile for the standard deviation of numeric columns.True
profiling.limitintegerMax number of documents to profile. By default, profiles all documents.None
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.max_workersintegerNumber of worker threads to use for profiling. Set to 1 to disable.20
profiling.offsetintegerOffset in documents to profile. By default, uses no offset.None
profiling.partition_datetimestring(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_enabledbooleanTrue
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 and BigQuery.None
profiling.profile_table_level_onlybooleanWhether to perform profiling at table-level only, or include column-level profiling as well.None
profiling.profile_table_row_count_estimate_onlybooleanUse an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.None
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 and BigQuery5000000
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 and BigQuery5
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.report_dropped_profilesbooleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.None
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.None
stateful_ingestionStatefulStaleMetadataRemovalConfigBase specialized config for Stateful Ingestion with stale metadata removal capability.None
stateful_ingestion.enabledbooleanThe type of the ingestion state provider registered with datahub.None
stateful_ingestion.ignore_new_statebooleanIf set to True, ignores the current checkpoint state.None
stateful_ingestion.ignore_old_statebooleanIf set to True, ignores the previous checkpoint state.None
stateful_ingestion.remove_stale_metadatabooleanSoft-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.usage.redshift_usage.RedshiftUsageSource
  • Browse on GitHub

Questions

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