Vertica

Integration Details

The DataHub Vertica Plugin extracts the following:

• Metadata for databases, schemas, views, tables, and projections
• Table level lineage
• Metadata for ML Models
• Metadata for Vertica OAuth

Concept Mapping

This ingestion source maps the following Source System Concepts to DataHub Concepts:

Source Concept	DataHub Concept	Notes
Vertica	Data Platform
Table	Dataset
View	Dataset
Projections	Dataset

Metadata Ingestion Quickstart

For context on getting started with ingestion, check out our metadata ingestion guide.

Module vertica

Important Capabilities

Capability	Status	Notes
Data Profiling	✅	Optionally enabled via configuration
Detect Deleted Entities	✅	Optionally enabled via stateful_ingestion.remove_stale_metadata
Domains	✅	Supported via the domain config field
Platform Instance	✅	Enabled by default
Table-Level Lineage	✅	Enabled by default, can be disabled via configuration include_view_lineage and include_projection_lineage

Prerequisites

In order to ingest metadata from Vertica, you will need:

• Vertica Server Version 10.1.1-0 and avobe. It may also work for older versions.
• Vertica Credentials (Username/Password)

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[vertica]'

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: vertica
  config:
    # Coordinates
    host_port: localhost:5433
    database: DATABASE_NAME

    # Credentials
    username: "${VERTICA_USER}"
    password: "${VERTICA_PASSWORD}"
    
    include_tables: true
    include_views: true
    include_projections: true
    include_oauth: true
    include_models: true
    include_view_lineage: true
    include_projection_lineage: true

sink:
  # sink configs

Config Details

Options

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

View All Configuration Options

Field [Required]	Type	Description	Default	Notes
database [✅]	string	database (catalog)	None
database_alias [✅]	string	Alias to apply to database when ingesting.	None
host_port [✅]	string	host URL	None
include_models [✅]	boolean	Whether Models should be ingested.	True
include_oauth [✅]	boolean	Whether Oauth should be ingested.	True
include_projection_lineage [✅]	boolean	If the source supports it, include view lineage to the underlying storage location.	True
include_projections [✅]	boolean	Whether projections should be ingested.	True
include_table_location_lineage [✅]	boolean	If the source supports it, include table lineage to the underlying storage location.	True
include_tables [✅]	boolean	Whether tables should be ingested.	True
include_view_lineage [✅]	boolean	If the source supports it, include view lineage to the underlying storage location.	True
include_views [✅]	boolean	Whether views should be ingested.	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.	None
password [✅]	string(password)	password	None
platform_instance [✅]	string	The instance of the platform that all assets produced by this recipe belong to	None
scheme [✅]	string		vertica+vertica_python
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.	None
username [✅]	string	username	None
env [✅]	string	The environment that all assets produced by this connector belong to	PROD
domain [✅]	map(str,AllowDenyPattern)	A class to store allow deny regexes	None
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)]	boolean	Whether to ignore case sensitivity during pattern matching.	True
models_pattern [✅]	AllowDenyPattern	Regex patterns for ml models to filter in ingestion.	{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
models_pattern.allow [❓ (required if models_pattern is set)]	array(string)		None
models_pattern.deny [❓ (required if models_pattern is set)]	array(string)		None
models_pattern.ignoreCase [❓ (required if models_pattern is set)]	boolean	Whether to ignore case sensitivity during pattern matching.	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.	{'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)]	boolean	Whether to ignore case sensitivity during pattern matching.	True
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'	{'allow': ['.*'], 'deny': [], '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)]	boolean	Whether to ignore case sensitivity during pattern matching.	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.*'	{'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)]	boolean	Whether to ignore case sensitivity during pattern matching.	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.*'	{'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)]	boolean	Whether 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)]	boolean		True
profiling.enabled [❓ (required if profiling is set)]	boolean	Whether profiling should be done.	None
profiling.field_sample_values_limit [❓ (required if profiling is set)]	integer	Upper limit for number of sample values to collect for all columns.	20
profiling.include_field_distinct_count [❓ (required if profiling is set)]	boolean	Whether to profile for the number of distinct values for each column.	True
profiling.include_field_distinct_value_frequencies [❓ (required if profiling is set)]	boolean	Whether to profile for distinct value frequencies.	None
profiling.include_field_histogram [❓ (required if profiling is set)]	boolean	Whether to profile for the histogram for numeric fields.	None
profiling.include_field_max_value [❓ (required if profiling is set)]	boolean	Whether to profile for the max value of numeric columns.	True
profiling.include_field_mean_value [❓ (required if profiling is set)]	boolean	Whether to profile for the mean value of numeric columns.	True
profiling.include_field_median_value [❓ (required if profiling is set)]	boolean	Whether to profile for the median value of numeric columns.	True
profiling.include_field_min_value [❓ (required if profiling is set)]	boolean	Whether to profile for the min value of numeric columns.	True
profiling.include_field_null_count [❓ (required if profiling is set)]	boolean	Whether to profile for the number of nulls for each column.	True
profiling.include_field_quantiles [❓ (required if profiling is set)]	boolean	Whether to profile for the quantiles of numeric columns.	None
profiling.include_field_sample_values [❓ (required if profiling is set)]	boolean	Whether to profile for the sample values for all columns.	True
profiling.include_field_stddev_value [❓ (required if profiling is set)]	boolean	Whether to profile for the standard deviation of numeric columns.	True
profiling.limit [❓ (required if profiling is set)]	integer	Max number of documents to profile. By default, profiles all documents.	None
profiling.max_number_of_fields_to_profile [❓ (required if profiling is set)]	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.	None
profiling.max_workers [❓ (required if profiling is set)]	integer	Number of worker threads to use for profiling. Set to 1 to disable.	20
profiling.offset [❓ (required if profiling is set)]	integer	Offset 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)]	boolean		True
profiling.profile_if_updated_since_days [❓ (required if profiling is set)]	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.	None
profiling.profile_table_level_only [❓ (required if profiling is set)]	boolean	Whether 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)]	boolean	Use 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)]	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	5000000
profiling.profile_table_size_limit [❓ (required if profiling is set)]	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	5
profiling.query_combiner_enabled [❓ (required if profiling is set)]	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.	True
profiling.report_dropped_profiles [❓ (required if profiling is set)]	boolean	Whether 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)]	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.	None
stateful_ingestion [✅]	StatefulStaleMetadataRemovalConfig	Base specialized config for Stateful Ingestion with stale metadata removal capability.	None
stateful_ingestion.enabled [❓ (required if stateful_ingestion is set)]	boolean	The type of the ingestion state provider registered with datahub.	None
stateful_ingestion.ignore_new_state [❓ (required if stateful_ingestion is set)]	boolean	If set to True, ignores the current checkpoint state.	None
stateful_ingestion.ignore_old_state [❓ (required if stateful_ingestion is set)]	boolean	If set to True, ignores the previous checkpoint state.	None
stateful_ingestion.remove_stale_metadata [❓ (required if stateful_ingestion is set)]	boolean	Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.	True

Schema

The JSONSchema for this configuration is inlined below.

{
  "title": "VerticaConfig",
  "description": "Base configuration class for stateful ingestion for source configs to inherit from.",
  "type": "object",
  "properties": {
    "env": {
      "title": "Env",
      "description": "The environment that all assets produced by this connector belong to",
      "default": "PROD",
      "type": "string"
    },
    "platform_instance": {
      "title": "Platform Instance",
      "description": "The instance of the platform that all assets produced by this recipe belong to",
      "type": "string"
    },
    "stateful_ingestion": {
      "$ref": "#/definitions/StatefulStaleMetadataRemovalConfig"
    },
    "options": {
      "title": "Options",
      "description": "Any options specified here will be passed to SQLAlchemy's create_engine as kwargs. See https://docs.sqlalchemy.org/en/14/core/engines.html#sqlalchemy.create_engine for details.",
      "type": "object"
    },
    "schema_pattern": {
      "title": "Schema Pattern",
      "description": "Regex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'",
      "default": {
        "allow": [
          ".*"
        ],
        "deny": [],
        "ignoreCase": true
      },
      "allOf": [
        {
          "$ref": "#/definitions/AllowDenyPattern"
        }
      ]
    },
    "table_pattern": {
      "title": "Table Pattern",
      "description": "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
      },
      "allOf": [
        {
          "$ref": "#/definitions/AllowDenyPattern"
        }
      ]
    },
    "view_pattern": {
      "title": "View Pattern",
      "description": "Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'",
      "default": {
        "allow": [
          ".*"
        ],
        "deny": [],
        "ignoreCase": true
      },
      "allOf": [
        {
          "$ref": "#/definitions/AllowDenyPattern"
        }
      ]
    },
    "profile_pattern": {
      "title": "Profile Pattern",
      "description": "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
      },
      "allOf": [
        {
          "$ref": "#/definitions/AllowDenyPattern"
        }
      ]
    },
    "domain": {
      "title": "Domain",
      "description": "Attach domains to databases, schemas or tables during ingestion using regex patterns. Domain key can be a guid like *urn:li:domain:ec428203-ce86-4db3-985d-5a8ee6df32ba* or a string like \"Marketing\".) If you provide strings, then datahub will attempt to resolve this name to a guid, and will error out if this fails. There can be multiple domain keys specified.",
      "default": {},
      "type": "object",
      "additionalProperties": {
        "$ref": "#/definitions/AllowDenyPattern"
      }
    },
    "include_views": {
      "title": "Include Views",
      "description": "Whether views should be ingested.",
      "default": true,
      "type": "boolean"
    },
    "include_tables": {
      "title": "Include Tables",
      "description": "Whether tables should be ingested.",
      "default": true,
      "type": "boolean"
    },
    "include_table_location_lineage": {
      "title": "Include Table Location Lineage",
      "description": "If the source supports it, include table lineage to the underlying storage location.",
      "default": true,
      "type": "boolean"
    },
    "profiling": {
      "title": "Profiling",
      "default": {
        "enabled": false,
        "limit": null,
        "offset": null,
        "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": null,
        "profile_if_updated_since_days": null,
        "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": null
      },
      "allOf": [
        {
          "$ref": "#/definitions/GEProfilingConfig"
        }
      ]
    },
    "username": {
      "title": "Username",
      "description": "username",
      "type": "string"
    },
    "password": {
      "title": "Password",
      "description": "password",
      "type": "string",
      "writeOnly": true,
      "format": "password"
    },
    "host_port": {
      "title": "Host Port",
      "description": "host URL",
      "type": "string"
    },
    "database": {
      "title": "Database",
      "description": "database (catalog)",
      "type": "string"
    },
    "database_alias": {
      "title": "Database Alias",
      "description": "Alias to apply to database when ingesting.",
      "type": "string"
    },
    "scheme": {
      "title": "Scheme",
      "default": "vertica+vertica_python",
      "type": "string"
    },
    "sqlalchemy_uri": {
      "title": "Sqlalchemy Uri",
      "description": "URI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.",
      "type": "string"
    },
    "models_pattern": {
      "title": "Models Pattern",
      "description": "Regex patterns for ml models to filter in ingestion. ",
      "default": {
        "allow": [
          ".*"
        ],
        "deny": [],
        "ignoreCase": true
      },
      "allOf": [
        {
          "$ref": "#/definitions/AllowDenyPattern"
        }
      ]
    },
    "include_projections": {
      "title": "Include Projections",
      "description": "Whether projections should be ingested.",
      "default": true,
      "type": "boolean"
    },
    "include_models": {
      "title": "Include Models",
      "description": "Whether Models should be ingested.",
      "default": true,
      "type": "boolean"
    },
    "include_oauth": {
      "title": "Include Oauth",
      "description": "Whether Oauth should be ingested.",
      "default": true,
      "type": "boolean"
    },
    "include_view_lineage": {
      "title": "Include View Lineage",
      "description": "If the source supports it, include view lineage to the underlying storage location.",
      "default": true,
      "type": "boolean"
    },
    "include_projection_lineage": {
      "title": "Include Projection Lineage",
      "description": "If the source supports it, include view lineage to the underlying storage location.",
      "default": true,
      "type": "boolean"
    }
  },
  "required": [
    "host_port"
  ],
  "additionalProperties": false,
  "definitions": {
    "DynamicTypedStateProviderConfig": {
      "title": "DynamicTypedStateProviderConfig",
      "type": "object",
      "properties": {
        "type": {
          "title": "Type",
          "description": "The type of the state provider to use. For DataHub use `datahub`",
          "type": "string"
        },
        "config": {
          "title": "Config",
          "description": "The configuration required for initializing the state provider. Default: The datahub_api config if set at pipeline level. Otherwise, the default DatahubClientConfig. See the defaults (https://github.com/datahub-project/datahub/blob/master/metadata-ingestion/src/datahub/ingestion/graph/client.py#L19)."
        }
      },
      "required": [
        "type"
      ],
      "additionalProperties": false
    },
    "StatefulStaleMetadataRemovalConfig": {
      "title": "StatefulStaleMetadataRemovalConfig",
      "description": "Base specialized config for Stateful Ingestion with stale metadata removal capability.",
      "type": "object",
      "properties": {
        "enabled": {
          "title": "Enabled",
          "description": "The type of the ingestion state provider registered with datahub.",
          "default": false,
          "type": "boolean"
        },
        "ignore_old_state": {
          "title": "Ignore Old State",
          "description": "If set to True, ignores the previous checkpoint state.",
          "default": false,
          "type": "boolean"
        },
        "ignore_new_state": {
          "title": "Ignore New State",
          "description": "If set to True, ignores the current checkpoint state.",
          "default": false,
          "type": "boolean"
        },
        "remove_stale_metadata": {
          "title": "Remove Stale Metadata",
          "description": "Soft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.",
          "default": true,
          "type": "boolean"
        }
      },
      "additionalProperties": false
    },
    "AllowDenyPattern": {
      "title": "AllowDenyPattern",
      "description": "A class to store allow deny regexes",
      "type": "object",
      "properties": {
        "allow": {
          "title": "Allow",
          "description": "List of regex patterns to include in ingestion",
          "default": [
            ".*"
          ],
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "deny": {
          "title": "Deny",
          "description": "List of regex patterns to exclude from ingestion.",
          "default": [],
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "ignoreCase": {
          "title": "Ignorecase",
          "description": "Whether to ignore case sensitivity during pattern matching.",
          "default": true,
          "type": "boolean"
        }
      },
      "additionalProperties": false
    },
    "GEProfilingConfig": {
      "title": "GEProfilingConfig",
      "type": "object",
      "properties": {
        "enabled": {
          "title": "Enabled",
          "description": "Whether profiling should be done.",
          "default": false,
          "type": "boolean"
        },
        "limit": {
          "title": "Limit",
          "description": "Max number of documents to profile. By default, profiles all documents.",
          "type": "integer"
        },
        "offset": {
          "title": "Offset",
          "description": "Offset in documents to profile. By default, uses no offset.",
          "type": "integer"
        },
        "report_dropped_profiles": {
          "title": "Report Dropped Profiles",
          "description": "Whether to report datasets or dataset columns which were not profiled. Set to `True` for debugging purposes.",
          "default": false,
          "type": "boolean"
        },
        "turn_off_expensive_profiling_metrics": {
          "title": "Turn Off Expensive Profiling Metrics",
          "description": "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,
          "type": "boolean"
        },
        "profile_table_level_only": {
          "title": "Profile Table Level Only",
          "description": "Whether to perform profiling at table-level only, or include column-level profiling as well.",
          "default": false,
          "type": "boolean"
        },
        "include_field_null_count": {
          "title": "Include Field Null Count",
          "description": "Whether to profile for the number of nulls for each column.",
          "default": true,
          "type": "boolean"
        },
        "include_field_distinct_count": {
          "title": "Include Field Distinct Count",
          "description": "Whether to profile for the number of distinct values for each column.",
          "default": true,
          "type": "boolean"
        },
        "include_field_min_value": {
          "title": "Include Field Min Value",
          "description": "Whether to profile for the min value of numeric columns.",
          "default": true,
          "type": "boolean"
        },
        "include_field_max_value": {
          "title": "Include Field Max Value",
          "description": "Whether to profile for the max value of numeric columns.",
          "default": true,
          "type": "boolean"
        },
        "include_field_mean_value": {
          "title": "Include Field Mean Value",
          "description": "Whether to profile for the mean value of numeric columns.",
          "default": true,
          "type": "boolean"
        },
        "include_field_median_value": {
          "title": "Include Field Median Value",
          "description": "Whether to profile for the median value of numeric columns.",
          "default": true,
          "type": "boolean"
        },
        "include_field_stddev_value": {
          "title": "Include Field Stddev Value",
          "description": "Whether to profile for the standard deviation of numeric columns.",
          "default": true,
          "type": "boolean"
        },
        "include_field_quantiles": {
          "title": "Include Field Quantiles",
          "description": "Whether to profile for the quantiles of numeric columns.",
          "default": false,
          "type": "boolean"
        },
        "include_field_distinct_value_frequencies": {
          "title": "Include Field Distinct Value Frequencies",
          "description": "Whether to profile for distinct value frequencies.",
          "default": false,
          "type": "boolean"
        },
        "include_field_histogram": {
          "title": "Include Field Histogram",
          "description": "Whether to profile for the histogram for numeric fields.",
          "default": false,
          "type": "boolean"
        },
        "include_field_sample_values": {
          "title": "Include Field Sample Values",
          "description": "Whether to profile for the sample values for all columns.",
          "default": true,
          "type": "boolean"
        },
        "field_sample_values_limit": {
          "title": "Field Sample Values Limit",
          "description": "Upper limit for number of sample values to collect for all columns.",
          "default": 20,
          "type": "integer"
        },
        "max_number_of_fields_to_profile": {
          "title": "Max Number Of Fields To Profile",
          "description": "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.",
          "exclusiveMinimum": 0,
          "type": "integer"
        },
        "profile_if_updated_since_days": {
          "title": "Profile If Updated Since Days",
          "description": "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`.",
          "exclusiveMinimum": 0,
          "type": "number"
        },
        "profile_table_size_limit": {
          "title": "Profile Table Size Limit",
          "description": "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,
          "type": "integer"
        },
        "profile_table_row_limit": {
          "title": "Profile Table Row Limit",
          "description": "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,
          "type": "integer"
        },
        "profile_table_row_count_estimate_only": {
          "title": "Profile Table Row Count Estimate Only",
          "description": "Use an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres. ",
          "default": false,
          "type": "boolean"
        },
        "max_workers": {
          "title": "Max Workers",
          "description": "Number of worker threads to use for profiling. Set to 1 to disable.",
          "default": 20,
          "type": "integer"
        },
        "query_combiner_enabled": {
          "title": "Query Combiner Enabled",
          "description": "*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,
          "type": "boolean"
        },
        "catch_exceptions": {
          "title": "Catch Exceptions",
          "default": true,
          "type": "boolean"
        },
        "partition_profiling_enabled": {
          "title": "Partition Profiling Enabled",
          "default": true,
          "type": "boolean"
        },
        "partition_datetime": {
          "title": "Partition Datetime",
          "description": "For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.",
          "type": "string",
          "format": "date-time"
        }
      },
      "additionalProperties": false
    }
  }
}

Code Coordinates

• Class Name: datahub.ingestion.source.sql.vertica.VerticaSource
• Browse on GitHub

Questions

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