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Microsoft SQL Server

Module mssql

Certified

Important Capabilities

CapabilityStatusNotes
Data ProfilingOptionally enabled via configuration
Dataset UsageNot provided by this module, use bigquery-usage for that.
DescriptionsEnabled by default
Detect Deleted EntitiesEnabled via stateful ingestion
DomainsSupported via the domain config field
Platform InstanceEnabled by default

This plugin extracts the following:

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

We have two options for the underlying library used to connect to SQL Server: (1) python-tds and (2) pyodbc. The TDS library is pure Python and hence easier to install, but only PyODBC supports encrypted connections.

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[mssql]'

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: mssql
config:
# Coordinates
host_port: localhost:1433
database: DemoDatabase

# Credentials
username: user
password: pass

sink:
# sink configs

#------------------------------------------------------------------------
#Example: using ingestion with ODBC and encryption
#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: mssql
config:
# Coordinates
host_port: localhost:1433
database: DemoDatabase

# Credentials
username: admin
password: password

# Options
use_odbc: "True"
uri_args:
driver: "ODBC Driver 17 for SQL Server"
Encrypt: "yes"
TrustServerCertificate: "Yes"
ssl: "True"

sink:
# sink configs

Config Details

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

View All Configuration Options
FieldRequiredTypeDescriptionDefault
envstringThe environment that all assets produced by this connector belong toPROD
platformstringThe platform that this source connects toNone
platform_instancestringThe instance of the platform that all assets produced by this recipe belong toNone
optionsDict{}
include_viewsbooleanWhether views should be ingested.True
include_tablesbooleanWhether tables should be ingested.True
usernamestringusernameNone
passwordstringpasswordNone
host_portstringMSSQL host URL.localhost:1433
databasestringdatabase (catalog). If set to Null, all databases will be considered for ingestion.None
database_aliasstringAlias to apply to database when ingesting. Ignored when database is not set.None
schemestringmssql+pytds
sqlalchemy_uristringURI of database to connect to. See https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls. Takes precedence over other connection parameters.None
use_odbcbooleanSee https://docs.sqlalchemy.org/en/14/dialects/mssql.html#module-sqlalchemy.dialects.mssql.pyodbc.False
uri_argsDict[str,string]{}
stateful_ingestionSQLAlchemyStatefulIngestionConfig (see below for fields)
stateful_ingestion.enabledbooleanThe type of the ingestion state provider registered with datahub.False
stateful_ingestion.max_checkpoint_state_sizeintegerThe maximum size of the checkpoint state in bytes. Default is 16MB16777216
stateful_ingestion.state_providerDynamicTypedStateProviderConfig (see below for fields)The ingestion state provider configuration.
stateful_ingestion.state_provider.type❓ (required if stateful_ingestion.state_provider is set)stringThe type of the state provider to use. For DataHub use datahubNone
stateful_ingestion.state_provider.configGeneric dictThe 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_statebooleanIf set to True, ignores the previous checkpoint state.False
stateful_ingestion.ignore_new_statebooleanIf set to True, ignores the current checkpoint state.False
stateful_ingestion.remove_stale_metadatabooleanSoft-deletes the entities of type in the last successful run but missing in the current run with stateful_ingestion enabled.True
stateful_ingestion.fail_safe_thresholdnumberPrevents large amount of soft deletes & the state from committing from accidental changes to the source configuration if the relative change percent in entities compared to the previous state is above the 'fail_safe_threshold'.95.0
schema_patternAllowDenyPattern (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}
schema_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
schema_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
schema_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
table_patternAllowDenyPattern (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}
table_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
table_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
table_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
view_patternAllowDenyPattern (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}
view_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
view_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
view_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profile_patternAllowDenyPattern (see below for fields)Regex patterns to filter tables for profiling during ingestion. Allowed by the table_pattern.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
profile_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
profile_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
profile_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
domainDict[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.allowArray of stringList of regex patterns to include in ingestion['.*']
domain.key.denyArray of stringList of regex patterns to exclude from ingestion.[]
domain.key.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profilingGEProfilingConfig (see below for fields){'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_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, '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.enabledbooleanWhether profiling should be done.False
profiling.limitintegerMax number of documents to profile. By default, profiles all documents.None
profiling.offsetintegerOffset in documents to profile. By default, uses no offset.None
profiling.report_dropped_profilesbooleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.False
profiling.turn_off_expensive_profiling_metricsbooleanWhether 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_onlybooleanWhether to perform profiling at table-level only, or include column-level profiling as well.False
profiling.include_field_null_countbooleanWhether to profile for the number of nulls for each column.True
profiling.include_field_min_valuebooleanWhether to profile for the min value of numeric columns.True
profiling.include_field_max_valuebooleanWhether to profile for the max value of numeric columns.True
profiling.include_field_mean_valuebooleanWhether to profile for the mean value of numeric columns.True
profiling.include_field_median_valuebooleanWhether to profile for the median value of numeric columns.True
profiling.include_field_stddev_valuebooleanWhether to profile for the standard deviation of numeric columns.True
profiling.include_field_quantilesbooleanWhether to profile for the quantiles of numeric columns.False
profiling.include_field_distinct_value_frequenciesbooleanWhether to profile for distinct value frequencies.False
profiling.include_field_histogrambooleanWhether to profile for the histogram for numeric fields.False
profiling.include_field_sample_valuesbooleanWhether to profile for the sample values for all columns.True
profiling.max_number_of_fields_to_profileintegerA 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_daysnumberProfile 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, snowflake-beta and BigQuery.1
profiling.profile_table_size_limitintegerProfile 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-beta and BigQuery1
profiling.profile_table_row_limitintegerProfile 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-beta and BigQuery50000
profiling.max_workersintegerNumber of worker threads to use for profiling. Set to 1 to disable.10
profiling.query_combiner_enabledbooleanThis 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_exceptionsbooleanTrue
profiling.partition_profiling_enabledbooleanTrue
profiling.bigquery_temp_table_schemastringOn 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_datetimestringFor partitioned datasets profile only the partition which matches the datetime or profile the latest one if not set. Only Bigquery supports this.None
database_patternAllowDenyPattern (see below for fields)Regex patterns for databases to filter in ingestion.{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
database_pattern.allowArray of stringList of regex patterns to include in ingestion['.*']
database_pattern.denyArray of stringList of regex patterns to exclude from ingestion.[]
database_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True

Code Coordinates

  • Class Name: datahub.ingestion.source.sql.mssql.SQLServerSource
  • Browse on GitHub

Questions

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