Vertica
Integration Details
This plugin extracts metadata for Tables and Views on Vertica.
This plugin is in beta and has only been tested on sample data on the Vertica database.
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 |
Metadata Ingestion Quickstart
For context on getting started with ingestion, check out our metadata ingestion guide.
Module vertica
Important Capabilities
Capability | Status | Notes |
---|---|---|
Domains | ✅ | Supported via the domain config field |
Platform Instance | ✅ | Enabled by default |
Install the Plugin
pip install 'acryl-datahub[vertica]'
Quickstart 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}"
sink:
# sink configs
Config Details
- Options
- Schema
Note that a .
is used to denote nested fields in the YAML recipe.
View All Configuration Options
Field | Required | Type | Description | Default |
---|---|---|---|---|
env | string | The environment that all assets produced by this connector belong to | PROD | |
platform | string | The platform that this source connects to | None | |
platform_instance | string | The instance of the platform that all assets produced by this recipe belong to | None | |
options | Dict | {} | ||
include_views | boolean | Whether views should be ingested. | True | |
include_tables | boolean | Whether tables should be ingested. | True | |
username | string | username | None | |
password | string | password | None | |
host_port | ✅ | string | host URL | None |
database | string | database (catalog) | None | |
database_alias | string | Alias to apply to database when ingesting. | 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 | |
stateful_ingestion | SQLAlchemyStatefulIngestionConfig (see below for fields) | |||
stateful_ingestion.enabled | boolean | The type of the ingestion state provider registered with datahub. | False | |
stateful_ingestion.max_checkpoint_state_size | integer | The maximum size of the checkpoint state in bytes. Default is 16MB | 16777216 | |
stateful_ingestion.state_provider | DynamicTypedStateProviderConfig (see below for fields) | The ingestion state provider configuration. | ||
stateful_ingestion.state_provider.type | ✅ | string | The type of the state provider to use. For DataHub use datahub | None |
stateful_ingestion.state_provider.config | Generic dict | 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). | None | |
stateful_ingestion.ignore_old_state | boolean | If set to True, ignores the previous checkpoint state. | False | |
stateful_ingestion.ignore_new_state | boolean | If set to True, ignores the current checkpoint state. | False | |
stateful_ingestion.remove_stale_metadata | boolean | Soft-deletes the tables and views that were found in the last successful run but missing in the current run with stateful_ingestion enabled. | True | |
schema_pattern | AllowDenyPattern (see below for fields) | 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, 'alphabet': '[A-Za-z0-9 _.-]'} | |
schema_pattern.allow | Array of string | List of regex patterns to include in ingestion | ['.*'] | |
schema_pattern.deny | Array of string | List of regex patterns to exclude from ingestion. | [] | |
schema_pattern.ignoreCase | boolean | Whether to ignore case sensitivity during pattern matching. | True | |
schema_pattern.alphabet | string | Allowed alphabets pattern | [A-Za-z0-9 _.-] | |
table_pattern | AllowDenyPattern (see below for fields) | 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, 'alphabet': '[A-Za-z0-9 _.-]'} | |
table_pattern.allow | Array of string | List of regex patterns to include in ingestion | ['.*'] | |
table_pattern.deny | Array of string | List of regex patterns to exclude from ingestion. | [] | |
table_pattern.ignoreCase | boolean | Whether to ignore case sensitivity during pattern matching. | True | |
table_pattern.alphabet | string | Allowed alphabets pattern | [A-Za-z0-9 _.-] | |
view_pattern | AllowDenyPattern (see below for fields) | 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, 'alphabet': '[A-Za-z0-9 _.-]'} | |
view_pattern.allow | Array of string | List of regex patterns to include in ingestion | ['.*'] | |
view_pattern.deny | Array of string | List of regex patterns to exclude from ingestion. | [] | |
view_pattern.ignoreCase | boolean | Whether to ignore case sensitivity during pattern matching. | True | |
view_pattern.alphabet | string | Allowed alphabets pattern | [A-Za-z0-9 _.-] | |
profile_pattern | AllowDenyPattern (see below for fields) | Regex patterns to filter tables for profiling during ingestion. Allowed by the table_pattern . | {'allow': ['.*'], 'deny': [], 'ignoreCase': True, 'alphabet': '[A-Za-z0-9 _.-]'} | |
profile_pattern.allow | Array of string | List of regex patterns to include in ingestion | ['.*'] | |
profile_pattern.deny | Array of string | List of regex patterns to exclude from ingestion. | [] | |
profile_pattern.ignoreCase | boolean | Whether to ignore case sensitivity during pattern matching. | True | |
profile_pattern.alphabet | string | Allowed alphabets pattern | [A-Za-z0-9 _.-] | |
domain | Dict[str, AllowDenyPattern] | 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. | {} | |
domain.key .allow | Array of string | List of regex patterns to include in ingestion | ['.*'] | |
domain.key .deny | Array of string | List of regex patterns to exclude from ingestion. | [] | |
domain.key .ignoreCase | boolean | Whether to ignore case sensitivity during pattern matching. | True | |
domain.key .alphabet | string | Allowed alphabets pattern | [A-Za-z0-9 _.-] | |
profiling | GEProfilingConfig (see below for fields) | {'enabled': False, 'limit': None, 'offset': None, 'reportdropped_profiles': False, 'turn_off_expensive_profiling_metrics': False, 'profile_table_level_only': False, 'include_field_null_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, 'allow_deny_patterns': {'allow': ['.*'], 'deny': [], 'ignoreCase': True, 'alphabet': '[A-Za-z0-9 .-]'}, 'max_number_of_fields_to_profile': None, 'profile_if_updated_since_days': 1, 'profile_table_size_limit': 1, 'profile_table_row_limit': 50000, 'max_workers': 10, 'query_combiner_enabled': True, 'catch_exceptions': True, 'partition_profiling_enabled': True, 'bigquery_temp_table_schema': None, 'partition_datetime': None} | ||
profiling.enabled | boolean | Whether profiling should be done. | False | |
profiling.limit | integer | Max number of documents to profile. By default, profiles all documents. | None | |
profiling.offset | integer | Offset in documents to profile. By default, uses no offset. | None | |
profiling.report_dropped_profiles | boolean | If datasets which were not profiled are reported in source report or not. Set to True for debugging purposes. | 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. | False | |
profiling.profile_table_level_only | boolean | Whether to perform profiling at table-level only, or include column-level profiling as well. | False | |
profiling.include_field_null_count | boolean | Whether to profile for the number of nulls for each column. | True | |
profiling.include_field_min_value | boolean | Whether to profile for the min value of numeric columns. | True | |
profiling.include_field_max_value | boolean | Whether to profile for the max value of numeric columns. | True | |
profiling.include_field_mean_value | boolean | Whether to profile for the mean value of numeric columns. | True | |
profiling.include_field_median_value | boolean | Whether to profile for the median value of numeric columns. | True | |
profiling.include_field_stddev_value | boolean | Whether to profile for the standard deviation of numeric columns. | True | |
profiling.include_field_quantiles | boolean | Whether to profile for the quantiles of numeric columns. | False | |
profiling.include_field_distinct_value_frequencies | boolean | Whether to profile for distinct value frequencies. | False | |
profiling.include_field_histogram | boolean | Whether to profile for the histogram for numeric fields. | False | |
profiling.include_field_sample_values | boolean | Whether to profile for the sample values for all columns. | True | |
profiling.allow_deny_patterns | AllowDenyPattern (see below for fields) | regex patterns for filtering of tables or table columns to profile. | {'allow': ['.*'], 'deny': [], 'ignoreCase': True, 'alphabet': '[A-Za-z0-9 _.-]'} | |
profiling.allow_deny_patterns.allow | Array of string | List of regex patterns to include in ingestion | ['.*'] | |
profiling.allow_deny_patterns.deny | Array of string | List of regex patterns to exclude from ingestion. | [] | |
profiling.allow_deny_patterns.ignoreCase | boolean | Whether to ignore case sensitivity during pattern matching. | True | |
profiling.allow_deny_patterns.alphabet | string | Allowed alphabets pattern | [A-Za-z0-9 _.-] | |
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. | None | |
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 . | 1 | |
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 BigQuery | 1 | |
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 BigQuery | 50000 | |
profiling.max_workers | integer | Number of worker threads to use for profiling. Set to 1 to disable. | 10 | |
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. | True | |
profiling.catch_exceptions | boolean | True | ||
profiling.partition_profiling_enabled | boolean | True | ||
profiling.bigquery_temp_table_schema | string | On bigquery for profiling partitioned tables needs to create temporary views. You have to define a dataset where these will be created. Views will be cleaned up after profiler runs. (Great expectation tech details about this (https://legacy.docs.greatexpectations.io/en/0.9.0/reference/integrations/bigquery.html#custom-queries-with-sql-datasource). | None | |
profiling.partition_datetime | string | For partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this. | None |
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": {
"title": "Platform",
"description": "The platform that this source connects to",
"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": {
"$ref": "#/definitions/SQLAlchemyStatefulIngestionConfig"
},
"options": {
"title": "Options",
"default": {},
"type": "object"
},
"schema_pattern": {
"title": "Schema Pattern",
"description": "Regex patterns for schemas to filter in ingestion. Specify regex to only match the schema name. e.g. to match all tables in schema analytics, use the regex 'analytics'",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true,
"alphabet": "[A-Za-z0-9 _.-]"
},
"allOf": [
{
"$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,
"alphabet": "[A-Za-z0-9 _.-]"
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"view_pattern": {
"title": "View Pattern",
"description": "Regex patterns for views to filter in ingestion. Note: Defaults to table_pattern if not specified. Specify regex to match the entire view name in database.schema.view format. e.g. to match all views starting with customer in Customer database and public schema, use the regex 'Customer.public.customer.*'",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true,
"alphabet": "[A-Za-z0-9 _.-]"
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"profile_pattern": {
"title": "Profile Pattern",
"description": "Regex patterns to filter tables for profiling during ingestion. Allowed by the `table_pattern`.",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true,
"alphabet": "[A-Za-z0-9 _.-]"
},
"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"
},
"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_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,
"allow_deny_patterns": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true,
"alphabet": "[A-Za-z0-9 _.-]"
},
"max_number_of_fields_to_profile": null,
"profile_if_updated_since_days": 1,
"profile_table_size_limit": 1,
"profile_table_row_limit": 50000,
"max_workers": 10,
"query_combiner_enabled": true,
"catch_exceptions": true,
"partition_profiling_enabled": true,
"bigquery_temp_table_schema": null,
"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"
}
},
"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
},
"SQLAlchemyStatefulIngestionConfig": {
"title": "SQLAlchemyStatefulIngestionConfig",
"description": "Specialization of basic StatefulIngestionConfig to adding custom config.\nThis will be used to override the stateful_ingestion config param of StatefulIngestionConfigBase\nin the SQLAlchemyConfig.",
"type": "object",
"properties": {
"enabled": {
"title": "Enabled",
"description": "The type of the ingestion state provider registered with datahub.",
"default": false,
"type": "boolean"
},
"max_checkpoint_state_size": {
"title": "Max Checkpoint State Size",
"description": "The maximum size of the checkpoint state in bytes. Default is 16MB",
"default": 16777216,
"exclusiveMinimum": 0,
"type": "integer"
},
"state_provider": {
"title": "State Provider",
"description": "The ingestion state provider configuration.",
"allOf": [
{
"$ref": "#/definitions/DynamicTypedStateProviderConfig"
}
]
},
"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 tables and views that were found 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"
},
"alphabet": {
"title": "Alphabet",
"description": "Allowed alphabets pattern",
"default": "[A-Za-z0-9 _.-]",
"type": "string"
}
},
"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": "If datasets which were not profiled are reported in source report or not. 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_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"
},
"allow_deny_patterns": {
"title": "Allow Deny Patterns",
"description": "regex patterns for filtering of tables or table columns to profile.",
"default": {
"allow": [
".*"
],
"deny": [],
"ignoreCase": true,
"alphabet": "[A-Za-z0-9 _.-]"
},
"allOf": [
{
"$ref": "#/definitions/AllowDenyPattern"
}
]
},
"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`.",
"default": 1,
"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 `BigQuery`",
"default": 1,
"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 `BigQuery`",
"default": 50000,
"type": "integer"
},
"max_workers": {
"title": "Max Workers",
"description": "Number of worker threads to use for profiling. Set to 1 to disable.",
"default": 10,
"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"
},
"bigquery_temp_table_schema": {
"title": "Bigquery Temp Table Schema",
"description": "On bigquery for profiling partitioned tables needs to create temporary views. You have to define a dataset where these will be created. Views will be cleaned up after profiler runs. (Great expectation tech details about this (https://legacy.docs.greatexpectations.io/en/0.9.0/reference/integrations/bigquery.html#custom-queries-with-sql-datasource).",
"type": "string"
},
"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
}
}
}
Prerequisites
In order to ingest metadata from Vertica, you will need:
- Python Version 3.6+
- Vertica Server Version 10.1.1-0 and avobe. It may also work for older versions.
- Vertica Credentials (Username/Password)
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