Skip to main content

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
bucket_duration [✅]EnumSize of the time window to aggregate usage stats.DAY
capture_lineage_query_parser_failures [✅]booleanWhether to capture lineage query parser errors with dataset properties for debuggingsNone
database [✅]stringdatabase (catalog). If set to Null, all databases will be considered for ingestion.None
database_alias [✅]string[Deprecated] Alias to apply to database when ingesting.None
default_schema [✅]stringThe default schema to use if the sql parser fails to parse the schema with sql_based lineage collectorpublic
end_time [✅]string(date-time)Latest date of usage to consider. Default: Current time in UTCNone
host_port [✅]stringhost URLNone
include_copy_lineage [✅]booleanWhether lineage should be collected from copy commandsTrue
include_table_lineage [✅]booleanWhether table lineage should be ingested.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_unload_lineage [✅]booleanWhether lineage should be collected from unload commandsTrue
include_view_lineage [✅]booleanInclude table lineage for viewsNone
include_views [✅]booleanWhether views should be ingested.True
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
password [✅]string(password)passwordNone
platform_instance [✅]stringThe instance of the platform that all assets produced by this recipe belong toNone
platform_instance_map [✅]map(str,string)None
sqlalchemy_uri [✅]stringURI 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_time [✅]string(date-time)Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
table_lineage_mode [✅]EnumWhich table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]stl_scan_based
username [✅]stringusernameNone
env [✅]stringThe environment that all assets produced by this connector belong toPROD
database_pattern [✅]AllowDenyPatternRegex patterns for databases to filter in ingestion.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
database_pattern.allow [❓ (required if database_pattern is set)]array(string)None
database_pattern.deny [❓ (required if database_pattern is set)]array(string)None
database_pattern.ignoreCase [❓ (required if database_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
s3_lineage_config [✅]S3LineageProviderConfigCommon config for S3 lineage generationNone
s3_lineage_config.path_specs [❓ (required if s3_lineage_config is set)]array(object)None
s3_lineage_config.path_specs.default_extension [❓ (required if path_specs is set)]stringFor 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_compression [❓ (required if path_specs is set)]booleanEnable or disable processing compressed files. Currently .gz and .bz files are supported.True
s3_lineage_config.path_specs.exclude [❓ (required if path_specs is set)]array(string)None
s3_lineage_config.path_specs.file_types [❓ (required if path_specs is set)]array(string)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.sample_files [❓ (required if path_specs is set)]booleanNot 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_name [❓ (required if path_specs is set)]stringDisplay name of the dataset.Combination of named variables from include path and stringsNone
schema_pattern [✅]AllowDenyPattern{'allow': ['.*'], 'deny': ['information_schema'], '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
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.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
bucket_duration [✅]EnumSize of the time window to aggregate usage stats.DAY
capture_lineage_query_parser_failures [✅]booleanWhether to capture lineage query parser errors with dataset properties for debuggingsNone
database [✅]stringdatabase (catalog). If set to Null, all databases will be considered for ingestion.None
database_alias [✅]string[Deprecated] Alias to apply to database when ingesting.None
default_schema [✅]stringThe default schema to use if the sql parser fails to parse the schema with sql_based lineage collectorpublic
email_domain [✅]stringEmail domain of your organisation so users can be displayed on UI appropriately.None
end_time [✅]string(date-time)Latest date of usage to consider. Default: Current time in UTCNone
format_sql_queries [✅]booleanWhether to format sql queriesNone
host_port [✅]stringhost URLNone
include_copy_lineage [✅]booleanWhether lineage should be collected from copy commandsTrue
include_operational_stats [✅]booleanWhether to display operational stats.True
include_read_operational_stats [✅]booleanWhether to report read operational stats. Experimental.None
include_table_lineage [✅]booleanWhether table lineage should be ingested.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_top_n_queries [✅]booleanWhether to ingest the top_n_queries.True
include_unload_lineage [✅]booleanWhether lineage should be collected from unload commandsTrue
include_view_lineage [✅]booleanInclude table lineage for viewsNone
include_views [✅]booleanWhether views should be ingested.True
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
password [✅]string(password)passwordNone
platform_instance [✅]stringThe instance of the platform that all assets produced by this recipe belong toNone
platform_instance_map [✅]map(str,string)None
sqlalchemy_uri [✅]stringURI 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_time [✅]string(date-time)Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)None
table_lineage_mode [✅]EnumWhich table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]stl_scan_based
top_n_queries [✅]integerNumber of top queries to save to each table.10
username [✅]stringusernameNone
env [✅]stringThe environment that all assets produced by this connector belong toPROD
database_pattern [✅]AllowDenyPatternRegex patterns for databases to filter in ingestion.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
database_pattern.allow [❓ (required if database_pattern is set)]array(string)None
database_pattern.deny [❓ (required if database_pattern is set)]array(string)None
database_pattern.ignoreCase [❓ (required if database_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
s3_lineage_config [✅]S3LineageProviderConfigCommon config for S3 lineage generationNone
s3_lineage_config.path_specs [❓ (required if s3_lineage_config is set)]array(object)None
s3_lineage_config.path_specs.default_extension [❓ (required if path_specs is set)]stringFor 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_compression [❓ (required if path_specs is set)]booleanEnable or disable processing compressed files. Currently .gz and .bz files are supported.True
s3_lineage_config.path_specs.exclude [❓ (required if path_specs is set)]array(string)None
s3_lineage_config.path_specs.file_types [❓ (required if path_specs is set)]array(string)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.sample_files [❓ (required if path_specs is set)]booleanNot 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_name [❓ (required if path_specs is set)]stringDisplay name of the dataset.Combination of named variables from include path and stringsNone
schema_pattern [✅]AllowDenyPattern{'allow': ['.*'], 'deny': ['information_schema'], '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
user_email_pattern [✅]AllowDenyPatternregex patterns for user emails to filter in usage.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
user_email_pattern.allow [❓ (required if user_email_pattern is set)]array(string)None
user_email_pattern.deny [❓ (required if user_email_pattern is set)]array(string)None
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.usage.redshift_usage.RedshiftUsageSource
  • Browse on GitHub

Questions

If you've got any questions on configuring ingestion for Redshift, feel free to ping us on our Slack