SAP HANA
Module hana
Important Capabilities
Capability | Status | Notes |
---|---|---|
Data Profiling | ✅ | Optionally enabled via configuration |
Detect Deleted Entities | ✅ | Enabled via stateful ingestion |
Domains | ✅ | Supported via the domain config field |
Platform Instance | ✅ | Enabled by default |
CLI based Ingestion
Install the Plugin
pip install 'acryl-datahub[hana]'
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: hana
config:
# Coordinates
host_port: localhost:39041
database: dbname
# Credentials
username: ${HANA_USER}
password: ${HANA_PASS}
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 | Notes |
---|---|---|---|---|
database | string | database (catalog) | None | |
database_alias | string | Alias to apply to database when ingesting. | None | |
host_port | string | localhost:39041 | ||
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_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 | hana+hdbcli | ||
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 | array(string) | None | ||
domain.key .deny | array(string) | None | ||
domain.key .ignoreCase | 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 | array(string) | None | ||
profile_pattern.deny | array(string) | None | ||
profile_pattern.ignoreCase | 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 | array(string) | None | ||
schema_pattern.deny | array(string) | None | ||
schema_pattern.ignoreCase | 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 | array(string) | None | ||
table_pattern.deny | array(string) | None | ||
table_pattern.ignoreCase | 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 | array(string) | None | ||
view_pattern.deny | array(string) | None | ||
view_pattern.ignoreCase | 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 | boolean | True | ||
profiling.enabled | boolean | Whether profiling should be done. | None | |
profiling.field_sample_values_limit | integer | Upper limit for number of sample values to collect for all columns. | 20 | |
profiling.include_field_distinct_count | boolean | Whether to profile for the number of distinct values for each column. | True | |
profiling.include_field_distinct_value_frequencies | boolean | Whether to profile for distinct value frequencies. | None | |
profiling.include_field_histogram | boolean | Whether to profile for the histogram for numeric fields. | None | |
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_min_value | boolean | Whether to profile for the min value of numeric columns. | True | |
profiling.include_field_null_count | boolean | Whether to profile for the number of nulls for each column. | True | |
profiling.include_field_quantiles | boolean | Whether to profile for the quantiles of numeric columns. | None | |
profiling.include_field_sample_values | boolean | Whether to profile for the sample values for all columns. | True | |
profiling.include_field_stddev_value | boolean | Whether to profile for the standard deviation of numeric columns. | True | |
profiling.limit | integer | Max number of documents to profile. By default, profiles all documents. | None | |
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.max_workers | integer | Number of worker threads to use for profiling. Set to 1 to disable. | 20 | |
profiling.offset | integer | Offset in documents to profile. By default, uses no offset. | None | |
profiling.partition_datetime | 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 | boolean | True | ||
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 . | None | |
profiling.profile_table_level_only | boolean | Whether to perform profiling at table-level only, or include column-level profiling as well. | None | |
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. | None | |
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 | 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 | 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. | True | |
profiling.report_dropped_profiles | 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 | 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 | boolean | The type of the ingestion state provider registered with datahub. | None | |
stateful_ingestion.ignore_new_state | boolean | If set to True, ignores the current checkpoint state. | None | |
stateful_ingestion.ignore_old_state | boolean | If set to True, ignores the previous checkpoint state. | None | |
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. | True |
The JSONSchema for this configuration is inlined below.
{
"title": "HanaConfig",
"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",
"default": "localhost:39041",
"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": "hana+hdbcli",
"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"
}
},
"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
}
}
}
Integration Details
The implementation uses the SQLAlchemy Dialect for SAP HANA. The SQLAlchemy Dialect for SAP HANA is an open-source project hosted at GitHub that is actively maintained by SAP SE, and is not part of a licensed SAP HANA edition or option. It is provided under the terms of the project license. Please notice that sqlalchemy-hana isn't an official SAP product and isn't covered by SAP support.
Compatibility
Under the hood, SQLAlchemy Dialect for SAP HANA uses the SAP HANA Python Driver hdbcli. Therefore it is compatible with HANA or HANA express versions since HANA SPS 2.
Questions
If you've got any questions on configuring this source, feel free to ping us on our Slack!
Code Coordinates
- Class Name:
datahub.ingestion.source.sql.hana.HanaSource
- Browse on GitHub
Questions
If you've got any questions on configuring ingestion for SAP HANA, feel free to ping us on our Slack