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Redshift

Certified

Important Capabilities​

CapabilityStatusNotes
Column-level Lineage✅Optionally enabled via configuration (mixed or sql_based lineage needs to be enabled)
Data Profiling✅Optionally enabled via configuration
Dataset Usage✅Enabled by default, can be disabled via configuration include_usage_statistics
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 dependencies.

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 retain at most seven days of log history, and sometimes closer to 2-5 days. 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:
        # check all available options here: https://pypi.org/project/redshift-connector/
        ssl_insecure: "false" # Specifies if IDP hosts server certificate will be verified

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
convert_urns_to_lowercase
boolean
Whether to convert dataset urns to lowercase.
Default: False
database
string
database
Default: dev
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.
enable_stateful_lineage_ingestion
boolean
Enable stateful lineage ingestion. This will store lineage window timestamps after successful lineage ingestion. and will not run lineage ingestion for same timestamps in subsequent run.
Default: True
enable_stateful_profiling
boolean
Enable stateful profiling. This will store profiling timestamps per dataset after successful profiling. and will not run profiling again in subsequent run if table has not been updated.
Default: True
enable_stateful_usage_ingestion
boolean
Enable stateful lineage ingestion. This will store usage window timestamps after successful usage ingestion. and will not run usage ingestion for same timestamps in subsequent run.
Default: True
end_time
string(date-time)
Latest date of lineage/usage to consider. Default: Current time in UTC
extra_client_options
object
Default: {}
extract_column_level_lineage
boolean
Whether to extract column level lineage. This config works with rest-sink only.
Default: True
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_table_rename_lineage
boolean
Whether we should follow alter table ... rename to statements when computing lineage.
Default: False
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_view_column_lineage
boolean
Populates column-level lineage for view->view and table->view lineage using DataHub's sql parser. Requires include_view_lineage to be enabled.
Default: True
include_view_lineage
boolean
Populates view->view and table->view lineage using DataHub's sql parser.
Default: True
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. This config works with rest-sink only.
Default: False
match_fully_qualified_names
boolean
Whether schema_pattern is matched against fully qualified schema name <database>.<schema>.
Default: False
options
object
Any options specified here will be passed to SQLAlchemy.create_engine as kwargs. To set connection arguments in the URL, specify them under connect_args.
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)
resolve_temp_table_in_lineage
boolean
Whether to resolve temp table appear in lineage to upstream permanent tables.
Default: False
scheme
string
Default: redshift+redshift_connector
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 lineage/usage to consider. Default: Last full day in UTC (or hour, depending on bucket_duration). You can also specify relative time with respect to end_time such as '-7 days' Or '-7d'.
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
use_file_backed_cache
boolean
Whether to use a file backed cache for the view definitions.
Default: True
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
domain.key.deny
array
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
profile_pattern.deny
array
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
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.allow_double_stars
boolean
Allow double stars in the include path. This can affect performance significantly if enabled
Default: False
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
s3_lineage_config.path_specs.file_types
array
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
Regex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
schema_pattern.allow
array
schema_pattern.deny
array
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
table_pattern.deny
array
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
user_email_pattern.deny
array
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
view_pattern.deny
array
view_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profiling
GEProfilingConfig
Default: {'enabled': False, 'operation_config': {'lower_fre...
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)
If specified, profile only the partition which matches this datetime. If not specified, profile the latest partition. Only Bigquery supports this.
profiling.partition_profiling_enabled
boolean
Whether to profile partitioned tables. Only BigQuery supports this. If enabled, latest partition data is used for profiling.
Default: True
profiling.profile_external_tables
boolean
Whether to profile external tables. Only Snowflake and Redshift supports this.
Default: False
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 and MySQL.
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.sample_size
integer
Number of rows to be sampled from table for column level profiling.Applicable only if use_sampling is set to True.
Default: 10000
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
profiling.use_sampling
boolean
Whether to profile column level stats on sample of table. Only BigQuery and Snowflake support this. If enabled, profiling is done on rows sampled from table. Sampling is not done for smaller tables.
Default: True
profiling.operation_config
OperationConfig
Experimental feature. To specify operation configs.
profiling.operation_config.lower_freq_profile_enabled
boolean
Whether to do profiling at lower freq or not. This does not do any scheduling just adds additional checks to when not to run profiling.
Default: False
profiling.operation_config.profile_date_of_month
integer
Number between 1 to 31 for date of month (both inclusive). If not specified, defaults to Nothing and this field does not take affect.
profiling.operation_config.profile_day_of_week
integer
Number between 0 to 6 for day of week (both inclusive). 0 is Monday and 6 is Sunday. If not specified, defaults to Nothing and this field does not take affect.
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.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

Questions

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