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Redshift

There are 3 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
  • Table, row, and column statistics via optional SQL profiling
  • Table lineage
  • Usage statistics

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.

Profiling​

Profiling runs sql queries on the redshift cluster to get statistics about the tables. To be able to do that, the user needs to have read access to the tables that should be profiled.

If you don't want to grant read access to the tables you can enable table level profiling which will get table statistics without reading the data.

profiling:
  profile_table_level_only: true

Read more...

redshift-legacy

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-legacy

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...

Module redshift​

Certified

Important Capabilities​

CapabilityStatusNotes
Data Profiling✅Optionally enabled via configuration
Dataset Usage✅Optionally enabled via configuration
Descriptions✅Enabled by default
Detect Deleted Entities✅Enabled via stateful ingestion
Domains✅Supported via the domain config field
Platform Instance✅Enabled by default
Table-Level Lineage✅Optionally enabled via configuration

This plugin extracts the following:

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

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.

Profiling​

Profiling runs sql queries on the redshift cluster to get statistics about the tables. To be able to do that, the user needs to have read access to the tables that should be profiled.

If you don't want to grant read access to the tables you can enable table level profiling which will get table statistics without reading the data.

profiling:
  profile_table_level_only: true

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.

  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_table_lineage: true
    include_usage_statistics: true
    # The following options are only used when include_usage_statistics is true
    # it appends the domain after the resdhift username which is extracted from the Redshift audit history
    # in the format username@email_domain
    email_domain: mydomain.com

    profiling:
      enabled: true
      # Only collect table level profiling information
      profile_table_level_only: 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.

FieldDescription
host_port ✅
string
host URL
bucket_duration
Enum
Size of the time window to aggregate usage stats.
Default: DAY
capture_lineage_query_parser_failures
boolean
Whether to capture lineage query parser errors with dataset properties for debugging
Default: False
database
string
database
Default: dev
database_alias
string
[Deprecated] Alias to apply to database when ingesting.
default_schema
string
The default schema to use if the sql parser fails to parse the schema with sql_based lineage collector
Default: public
email_domain
string
Email domain of your organisation so users can be displayed on UI appropriately.
end_time
string(date-time)
Latest date of usage to consider. Default: Current time in UTC
extra_client_options
object
Default: {}
format_sql_queries
boolean
Whether to format sql queries
Default: False
include_copy_lineage
boolean
Whether lineage should be collected from copy commands
Default: True
include_operational_stats
boolean
Whether to display operational stats.
Default: True
include_read_operational_stats
boolean
Whether to report read operational stats. Experimental.
Default: False
include_table_lineage
boolean
Whether table lineage should be ingested.
Default: True
include_table_location_lineage
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_tables
boolean
Whether tables should be ingested.
Default: True
include_top_n_queries
boolean
Whether to ingest the top_n_queries.
Default: True
include_unload_lineage
boolean
Whether lineage should be collected from unload commands
Default: True
include_usage_statistics
boolean
Generate usage statistic. email_domain config parameter needs to be set if enabled
Default: False
include_views
boolean
Whether views should be ingested.
Default: True
incremental_lineage
boolean
When enabled, emits lineage as incremental to existing lineage already in DataHub. When disabled, re-states lineage on each run.
Default: True
options
object
Any options specified here will be passed to SQLAlchemy.create_engine as kwargs.
password
string(password)
password
platform_instance
string
The instance of the platform that all assets produced by this recipe belong to
platform_instance_map
map(str,string)
scheme
string
Default: redshift+psycopg2
sql_parser_use_external_process
boolean
When enabled, sql parser will run in isolated in a separate process. This can affect processing time but can protect from sql parser's mem leak.
Default: False
sqlalchemy_uri
string
URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.
start_time
string(date-time)
Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)
store_last_lineage_extraction_timestamp
boolean
Enable checking last lineage extraction date in store.
Default: False
store_last_profiling_timestamps
boolean
Enable storing last profile timestamp in store.
Default: False
store_last_usage_extraction_timestamp
boolean
Enable checking last usage timestamp in store.
Default: True
table_lineage_mode
Enum
Which table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]
Default: stl_scan_based
top_n_queries
integer
Number of top queries to save to each table.
Default: 10
username
string
username
env
string
The environment that all assets produced by this connector belong to
Default: PROD
domain
map(str,AllowDenyPattern)
A class to store allow deny regexes
domain.key.allow
array(string)
domain.key.deny
array(string)
domain.key.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profile_pattern
AllowDenyPattern
Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allow
array(string)
profile_pattern.deny
array(string)
profile_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
s3_lineage_config
S3LineageProviderConfig
Common config for S3 lineage generation
Default: {'path_specs': [], 'strip_urls': True}
s3_lineage_config.strip_urls
boolean
Strip filename from s3 url. It only applies if path_specs are not specified.
Default: True
s3_lineage_config.path_specs
array(object)
s3_lineage_config.path_specs.include ❓
string
Path to table. 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.
s3_lineage_config.path_specs.default_extension
string
For files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.
s3_lineage_config.path_specs.enable_compression
boolean
Enable or disable processing compressed files. Currently .gz and .bz files are supported.
Default: True
s3_lineage_config.path_specs.exclude
array(string)
s3_lineage_config.path_specs.file_types
array(string)
s3_lineage_config.path_specs.sample_files
boolean
Not 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 enabled
Default: True
s3_lineage_config.path_specs.table_name
string
Display name of the dataset.Combination of named variables from include path and strings
schema_pattern
AllowDenyPattern
Default: {'allow': ['.*'], 'deny': ['information_schema'], ...
schema_pattern.allow
array(string)
schema_pattern.deny
array(string)
schema_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern
AllowDenyPattern
Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow
array(string)
table_pattern.deny
array(string)
table_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
user_email_pattern
AllowDenyPattern
regex patterns for user emails to filter in usage.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
user_email_pattern.allow
array(string)
user_email_pattern.deny
array(string)
user_email_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
view_pattern
AllowDenyPattern
Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allow
array(string)
view_pattern.deny
array(string)
view_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling
GEProfilingConfig
Default: {'enabled': False, 'limit': None, 'offset': None, ...
profiling.catch_exceptions
boolean
Default: True
profiling.enabled
boolean
Whether profiling should be done.
Default: False
profiling.field_sample_values_limit
integer
Upper limit for number of sample values to collect for all columns.
Default: 20
profiling.include_field_distinct_count
boolean
Whether to profile for the number of distinct values for each column.
Default: True
profiling.include_field_distinct_value_frequencies
boolean
Whether to profile for distinct value frequencies.
Default: False
profiling.include_field_histogram
boolean
Whether to profile for the histogram for numeric fields.
Default: False
profiling.include_field_max_value
boolean
Whether to profile for the max value of numeric columns.
Default: True
profiling.include_field_mean_value
boolean
Whether to profile for the mean value of numeric columns.
Default: True
profiling.include_field_median_value
boolean
Whether to profile for the median value of numeric columns.
Default: True
profiling.include_field_min_value
boolean
Whether to profile for the min value of numeric columns.
Default: True
profiling.include_field_null_count
boolean
Whether to profile for the number of nulls for each column.
Default: True
profiling.include_field_quantiles
boolean
Whether to profile for the quantiles of numeric columns.
Default: False
profiling.include_field_sample_values
boolean
Whether to profile for the sample values for all columns.
Default: True
profiling.include_field_stddev_value
boolean
Whether to profile for the standard deviation of numeric columns.
Default: True
profiling.limit
integer
Max number of documents to profile. By default, profiles all documents.
profiling.max_number_of_fields_to_profile
integer
A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.
profiling.max_workers
integer
Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
profiling.offset
integer
Offset in documents to profile. By default, uses no offset.
profiling.partition_datetime
string(date-time)
For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.
profiling.partition_profiling_enabled
boolean
Default: True
profiling.profile_if_updated_since_days
number
Profile table only if it has been updated since these many number of days. If set to null, no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery.
profiling.profile_table_level_only
boolean
Whether to perform profiling at table-level only, or include column-level profiling as well.
Default: False
profiling.profile_table_row_count_estimate_only
boolean
Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.
Default: False
profiling.profile_table_row_limit
integer
Profile tables only if their row count is less then specified count. If set to null, no limit on the row count of tables to profile. Supported only in snowflake and BigQuery
Default: 5000000
profiling.profile_table_size_limit
integer
Profile tables only if their size is less then specified GBs. If set to null, no limit on the size of tables to profile. Supported only in snowflake and BigQuery
Default: 5
profiling.query_combiner_enabled
boolean
This feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.
Default: True
profiling.report_dropped_profiles
boolean
Whether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.
Default: False
profiling.turn_off_expensive_profiling_metrics
boolean
Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.
Default: False
stateful_ingestion
StatefulStaleMetadataRemovalConfig
Base specialized config for Stateful Ingestion with stale metadata removal capability.
stateful_ingestion.enabled
boolean
The type of the ingestion state provider registered with datahub.
Default: False
stateful_ingestion.ignore_new_state
boolean
If set to True, ignores the current checkpoint state.
Default: False
stateful_ingestion.ignore_old_state
boolean
If set to True, ignores the previous checkpoint state.
Default: False
stateful_ingestion.remove_stale_metadata
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

Code Coordinates​

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

Module redshift-legacy​

Certified

Important Capabilities​

CapabilityStatusNotes
Data Profiling✅Optionally enabled via configuration
Dataset Usage❌Not provided by this module, use redshift-usage for that.
Descriptions✅Enabled by default
Detect Deleted Entities✅Enabled via stateful ingestion
Domains✅Supported via the domain config field
Platform Instance✅Enabled by default
Table-Level Lineage✅Optionally 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-legacy]'

Config Details​

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

FieldDescription
host_port ✅
string
host URL
bucket_duration
Enum
Size of the time window to aggregate usage stats.
Default: DAY
capture_lineage_query_parser_failures
boolean
Whether to capture lineage query parser errors with dataset properties for debuggings
Default: False
database
string
database (catalog)
database_alias
string
[Deprecated] Alias to apply to database when ingesting.
default_schema
string
The default schema to use if the sql parser fails to parse the schema with sql_based lineage collector
Default: public
end_time
string(date-time)
Latest date of usage to consider. Default: Current time in UTC
include_copy_lineage
boolean
Whether lineage should be collected from copy commands
Default: True
include_table_lineage
boolean
Whether table lineage should be ingested.
Default: True
include_table_location_lineage
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_tables
boolean
Whether tables should be ingested.
Default: True
include_unload_lineage
boolean
Whether lineage should be collected from unload commands
Default: True
include_views
boolean
Whether views should be ingested.
Default: True
options
object
Any options specified here will be passed to SQLAlchemy.create_engine as kwargs.
password
string(password)
password
platform_instance
string
The instance of the platform that all assets produced by this recipe belong to
platform_instance_map
map(str,string)
sqlalchemy_uri
string
URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.
start_time
string(date-time)
Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)
table_lineage_mode
Enum
Which table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]
Default: stl_scan_based
username
string
username
env
string
The environment that all assets produced by this connector belong to
Default: PROD
domain
map(str,AllowDenyPattern)
A class to store allow deny regexes
domain.key.allow
array(string)
domain.key.deny
array(string)
domain.key.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profile_pattern
AllowDenyPattern
Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allow
array(string)
profile_pattern.deny
array(string)
profile_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
s3_lineage_config
S3LineageProviderConfig
Common config for S3 lineage generation
s3_lineage_config.path_specs
array(object)
s3_lineage_config.path_specs.include ❓
string
Path to table. 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.
s3_lineage_config.path_specs.default_extension
string
For files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.
s3_lineage_config.path_specs.enable_compression
boolean
Enable or disable processing compressed files. Currently .gz and .bz files are supported.
Default: True
s3_lineage_config.path_specs.exclude
array(string)
s3_lineage_config.path_specs.file_types
array(string)
s3_lineage_config.path_specs.sample_files
boolean
Not 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 enabled
Default: True
s3_lineage_config.path_specs.table_name
string
Display name of the dataset.Combination of named variables from include path and strings
schema_pattern
AllowDenyPattern
Default: {'allow': ['.*'], 'deny': ['information_schema'], ...
schema_pattern.allow
array(string)
schema_pattern.deny
array(string)
schema_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern
AllowDenyPattern
Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow
array(string)
table_pattern.deny
array(string)
table_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
view_pattern
AllowDenyPattern
Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allow
array(string)
view_pattern.deny
array(string)
view_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling
GEProfilingConfig
Default: {'enabled': False, 'limit': None, 'offset': None, ...
profiling.catch_exceptions
boolean
Default: True
profiling.enabled
boolean
Whether profiling should be done.
Default: False
profiling.field_sample_values_limit
integer
Upper limit for number of sample values to collect for all columns.
Default: 20
profiling.include_field_distinct_count
boolean
Whether to profile for the number of distinct values for each column.
Default: True
profiling.include_field_distinct_value_frequencies
boolean
Whether to profile for distinct value frequencies.
Default: False
profiling.include_field_histogram
boolean
Whether to profile for the histogram for numeric fields.
Default: False
profiling.include_field_max_value
boolean
Whether to profile for the max value of numeric columns.
Default: True
profiling.include_field_mean_value
boolean
Whether to profile for the mean value of numeric columns.
Default: True
profiling.include_field_median_value
boolean
Whether to profile for the median value of numeric columns.
Default: True
profiling.include_field_min_value
boolean
Whether to profile for the min value of numeric columns.
Default: True
profiling.include_field_null_count
boolean
Whether to profile for the number of nulls for each column.
Default: True
profiling.include_field_quantiles
boolean
Whether to profile for the quantiles of numeric columns.
Default: False
profiling.include_field_sample_values
boolean
Whether to profile for the sample values for all columns.
Default: True
profiling.include_field_stddev_value
boolean
Whether to profile for the standard deviation of numeric columns.
Default: True
profiling.limit
integer
Max number of documents to profile. By default, profiles all documents.
profiling.max_number_of_fields_to_profile
integer
A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.
profiling.max_workers
integer
Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
profiling.offset
integer
Offset in documents to profile. By default, uses no offset.
profiling.partition_datetime
string(date-time)
For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.
profiling.partition_profiling_enabled
boolean
Default: True
profiling.profile_if_updated_since_days
number
Profile table only if it has been updated since these many number of days. If set to null, no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery.
profiling.profile_table_level_only
boolean
Whether to perform profiling at table-level only, or include column-level profiling as well.
Default: False
profiling.profile_table_row_count_estimate_only
boolean
Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.
Default: False
profiling.profile_table_row_limit
integer
Profile tables only if their row count is less then specified count. If set to null, no limit on the row count of tables to profile. Supported only in snowflake and BigQuery
Default: 5000000
profiling.profile_table_size_limit
integer
Profile tables only if their size is less then specified GBs. If set to null, no limit on the size of tables to profile. Supported only in snowflake and BigQuery
Default: 5
profiling.query_combiner_enabled
boolean
This feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.
Default: True
profiling.report_dropped_profiles
boolean
Whether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.
Default: False
profiling.turn_off_expensive_profiling_metrics
boolean
Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.
Default: False
stateful_ingestion
StatefulStaleMetadataRemovalConfig
Base specialized config for Stateful Ingestion with stale metadata removal capability.
stateful_ingestion.enabled
boolean
The type of the ingestion state provider registered with datahub.
Default: False
stateful_ingestion.ignore_new_state
boolean
If set to True, ignores the current checkpoint state.
Default: False
stateful_ingestion.ignore_old_state
boolean
If set to True, ignores the previous checkpoint state.
Default: False
stateful_ingestion.remove_stale_metadata
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

Code Coordinates​

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

Module redshift-usage-legacy​

Certified

Important Capabilities​

CapabilityStatusNotes
Platform Instance✅Enabled 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-legacy]'

Config Details​

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

FieldDescription
email_domain ✅
string
Email domain of your organisation so users can be displayed on UI appropriately.
host_port ✅
string
host URL
bucket_duration
Enum
Size of the time window to aggregate usage stats.
Default: DAY
capture_lineage_query_parser_failures
boolean
Whether to capture lineage query parser errors with dataset properties for debuggings
Default: False
database
string
database (catalog)
database_alias
string
[Deprecated] Alias to apply to database when ingesting.
default_schema
string
The default schema to use if the sql parser fails to parse the schema with sql_based lineage collector
Default: public
end_time
string(date-time)
Latest date of usage to consider. Default: Current time in UTC
format_sql_queries
boolean
Whether to format sql queries
Default: False
include_copy_lineage
boolean
Whether lineage should be collected from copy commands
Default: True
include_operational_stats
boolean
Whether to display operational stats.
Default: True
include_read_operational_stats
boolean
Whether to report read operational stats. Experimental.
Default: False
include_table_lineage
boolean
Whether table lineage should be ingested.
Default: True
include_table_location_lineage
boolean
If the source supports it, include table lineage to the underlying storage location.
Default: True
include_tables
boolean
Whether tables should be ingested.
Default: True
include_top_n_queries
boolean
Whether to ingest the top_n_queries.
Default: True
include_unload_lineage
boolean
Whether lineage should be collected from unload commands
Default: True
include_views
boolean
Whether views should be ingested.
Default: True
options
object
Any 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.
Default: {}
password
string(password)
password
platform_instance
string
The instance of the platform that all assets produced by this recipe belong to
platform_instance_map
map(str,string)
sqlalchemy_uri
string
URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.
start_time
string(date-time)
Earliest date of usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration)
table_lineage_mode
Enum
Which table lineage collector mode to use. Available modes are: [stl_scan_based, sql_based, mixed]
Default: stl_scan_based
top_n_queries
integer
Number of top queries to save to each table.
Default: 10
username
string
username
env
string
The environment that all assets produced by this connector belong to
Default: PROD
domain
map(str,AllowDenyPattern)
A class to store allow deny regexes
domain.key.allow
array(string)
domain.key.deny
array(string)
domain.key.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profile_pattern
AllowDenyPattern
Regex patterns to filter tables (or specific columns) for profiling during ingestion. Note that only tables allowed by the table_pattern will be considered.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allow
array(string)
profile_pattern.deny
array(string)
profile_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
s3_lineage_config
S3LineageProviderConfig
Common config for S3 lineage generation
s3_lineage_config.path_specs
array(object)
s3_lineage_config.path_specs.include ❓
string
Path to table. 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.
s3_lineage_config.path_specs.default_extension
string
For files without extension it will assume the specified file type. If it is not set the files without extensions will be skipped.
s3_lineage_config.path_specs.enable_compression
boolean
Enable or disable processing compressed files. Currently .gz and .bz files are supported.
Default: True
s3_lineage_config.path_specs.exclude
array(string)
s3_lineage_config.path_specs.file_types
array(string)
s3_lineage_config.path_specs.sample_files
boolean
Not 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 enabled
Default: True
s3_lineage_config.path_specs.table_name
string
Display name of the dataset.Combination of named variables from include path and strings
schema_pattern
AllowDenyPattern
Default: {'allow': ['.*'], 'deny': ['information_schema'], ...
schema_pattern.allow
array(string)
schema_pattern.deny
array(string)
schema_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern
AllowDenyPattern
Regex patterns for tables to filter in ingestion. Specify regex to match the entire table name in database.schema.table format. e.g. to match all tables starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
table_pattern.allow
array(string)
table_pattern.deny
array(string)
table_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
user_email_pattern
AllowDenyPattern
regex patterns for user emails to filter in usage.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
user_email_pattern.allow
array(string)
user_email_pattern.deny
array(string)
user_email_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
view_pattern
AllowDenyPattern
Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
view_pattern.allow
array(string)
view_pattern.deny
array(string)
view_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling
GEProfilingConfig
Default: {'enabled': False, 'limit': None, 'offset': None, ...
profiling.catch_exceptions
boolean
Default: True
profiling.enabled
boolean
Whether profiling should be done.
Default: False
profiling.field_sample_values_limit
integer
Upper limit for number of sample values to collect for all columns.
Default: 20
profiling.include_field_distinct_count
boolean
Whether to profile for the number of distinct values for each column.
Default: True
profiling.include_field_distinct_value_frequencies
boolean
Whether to profile for distinct value frequencies.
Default: False
profiling.include_field_histogram
boolean
Whether to profile for the histogram for numeric fields.
Default: False
profiling.include_field_max_value
boolean
Whether to profile for the max value of numeric columns.
Default: True
profiling.include_field_mean_value
boolean
Whether to profile for the mean value of numeric columns.
Default: True
profiling.include_field_median_value
boolean
Whether to profile for the median value of numeric columns.
Default: True
profiling.include_field_min_value
boolean
Whether to profile for the min value of numeric columns.
Default: True
profiling.include_field_null_count
boolean
Whether to profile for the number of nulls for each column.
Default: True
profiling.include_field_quantiles
boolean
Whether to profile for the quantiles of numeric columns.
Default: False
profiling.include_field_sample_values
boolean
Whether to profile for the sample values for all columns.
Default: True
profiling.include_field_stddev_value
boolean
Whether to profile for the standard deviation of numeric columns.
Default: True
profiling.limit
integer
Max number of documents to profile. By default, profiles all documents.
profiling.max_number_of_fields_to_profile
integer
A positive integer that specifies the maximum number of columns to profile for any table. None implies all columns. The cost of profiling goes up significantly as the number of columns to profile goes up.
profiling.max_workers
integer
Number of worker threads to use for profiling. Set to 1 to disable.
Default: 20
profiling.offset
integer
Offset in documents to profile. By default, uses no offset.
profiling.partition_datetime
string(date-time)
For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.
profiling.partition_profiling_enabled
boolean
Default: True
profiling.profile_if_updated_since_days
number
Profile table only if it has been updated since these many number of days. If set to null, no constraint of last modified time for tables to profile. Supported only in snowflake and BigQuery.
profiling.profile_table_level_only
boolean
Whether to perform profiling at table-level only, or include column-level profiling as well.
Default: False
profiling.profile_table_row_count_estimate_only
boolean
Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.
Default: False
profiling.profile_table_row_limit
integer
Profile tables only if their row count is less then specified count. If set to null, no limit on the row count of tables to profile. Supported only in snowflake and BigQuery
Default: 5000000
profiling.profile_table_size_limit
integer
Profile tables only if their size is less then specified GBs. If set to null, no limit on the size of tables to profile. Supported only in snowflake and BigQuery
Default: 5
profiling.query_combiner_enabled
boolean
This feature is still experimental and can be disabled if it causes issues. Reduces the total number of queries issued and speeds up profiling by dynamically combining SQL queries where possible.
Default: True
profiling.report_dropped_profiles
boolean
Whether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.
Default: False
profiling.turn_off_expensive_profiling_metrics
boolean
Whether to turn off expensive profiling or not. This turns off profiling for quantiles, distinct_value_frequencies, histogram & sample_values. This also limits maximum number of fields being profiled to 10.
Default: False
stateful_ingestion
StatefulStaleMetadataRemovalConfig
Base specialized config for Stateful Ingestion with stale metadata removal capability.
stateful_ingestion.enabled
boolean
The type of the ingestion state provider registered with datahub.
Default: False
stateful_ingestion.ignore_new_state
boolean
If set to True, ignores the current checkpoint state.
Default: False
stateful_ingestion.ignore_old_state
boolean
If set to True, ignores the previous checkpoint state.
Default: False
stateful_ingestion.remove_stale_metadata
boolean
Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.
Default: True

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.