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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

Concept Mapping​

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

Source ConceptDataHub ConceptNotes
VerticaData Platform
TableDataset
ViewDataset
ProjectionsDataset

Metadata Ingestion Quickstart​

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

Important Capabilities​

CapabilityStatusNotes
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 above. It may also work with, but is not been tested with, 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_models: true
    include_view_lineage: true
    include_projection_lineage: true

sink:
  # sink configs

Config Details​

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

FieldDescription
host_port ✅
string
host URL
convert_urns_to_lowercase
boolean
Whether to convert dataset urns to lowercase.
Default: False
database
string
database (catalog)
include_models
boolean
Whether Models should be ingested.
Default: True
include_projection_lineage
boolean
If the source supports it, include view lineage to the underlying storage location.
Default: True
include_projections
boolean
Whether projections 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_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
If the source supports it, include view lineage to the underlying storage location.
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.
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
scheme
string
Default: 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.
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
List of regex patterns to include in ingestion
Default: ['.*']
domain.key.allow.string
string
domain.key.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
domain.key.deny
array
List of regex patterns to exclude from ingestion.
Default: []
domain.key.deny.string
string
models_pattern
AllowDenyPattern
Regex patterns for ml models to filter in ingestion.
Default: {'allow': ['.*'], 'deny': [], 'ignoreCase': True}
models_pattern.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
models_pattern.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
models_pattern.allow.string
string
models_pattern.deny
array
List of regex patterns to exclude from ingestion.
Default: []
models_pattern.deny.string
string
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.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
profile_pattern.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
profile_pattern.allow.string
string
profile_pattern.deny
array
List of regex patterns to exclude from ingestion.
Default: []
profile_pattern.deny.string
string
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.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
schema_pattern.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
schema_pattern.allow.string
string
schema_pattern.deny
array
List of regex patterns to exclude from ingestion.
Default: []
schema_pattern.deny.string
string
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.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
table_pattern.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
table_pattern.allow.string
string
table_pattern.deny
array
List of regex patterns to exclude from ingestion.
Default: []
table_pattern.deny.string
string
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.ignoreCase
boolean
Whether to ignore case sensitivity during pattern matching.
Default: True
view_pattern.allow
array
List of regex patterns to include in ingestion
Default: ['.*']
view_pattern.allow.string
string
view_pattern.deny
array
List of regex patterns to exclude from ingestion.
Default: []
view_pattern.deny.string
string
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.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.