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Presto on Hive

Module presto-on-hive

Certified

Important Capabilities

CapabilityStatusNotes
Data ProfilingOptionally enabled via configuration
Detect Deleted EntitiesEnabled via stateful ingestion

This plugin extracts the following:

  • Metadata for Presto views and Hive tables (external / managed)
  • Column types associated with each table / view
  • Detailed table / view property info

CLI based Ingestion

Install the Plugin

pip install 'acryl-datahub[presto-on-hive]'

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: presto-on-hive
config:
# Hive metastore DB connection
host_port: localhost:5432
database: metastore

# specify the schema where metastore tables reside
schema_pattern:
allow:
- "^public"

# credentials
username: user # optional
password: pass # optional

#scheme: 'postgresql+psycopg2' # set this if metastore db is using postgres
#scheme: 'mysql+pymysql' # set this if metastore db is using mysql, default if unset

# set this to have advanced filters on what to ingest
#views_where_clause_suffix: AND d."name" in ('db1')
#tables_where_clause_suffix: AND d."name" in ('db1')

sink:
# sink configs

Config Details

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

View All Configuration Options
Field [Required]TypeDescriptionDefaultNotes
databasestringdatabase (catalog)None
database_aliasstringAlias to apply to database when ingesting.None
host_portstringHost URL and port to connect to. Example: localhost:3306localhost:3306
include_catalog_name_in_idsbooleanAdd the Presto catalog name (e.g. hive) to the generated dataset urns. urn:li:dataset:(urn:li:dataPlatform:hive,hive.user.logging_events,PROD) versus urn:li:dataset:(urn:li:dataPlatform:hive,user.logging_events,PROD)None
include_table_location_lineagebooleanIf the source supports it, include table lineage to the underlying storage location.True
include_tablesbooleanWhether tables should be ingested.True
include_viewsbooleanWhether views should be ingested.True
ingestion_job_idstringNone
metastore_db_namestringName of the Hive metastore's database (usually: metastore). For backward compatibility, if this field is not provided, the database field will be used. If both the 'database' and 'metastore_db_name' fields are set then the 'database' field will be used to filter the hive/presto/trino databaseNone
modeEnumThe ingested data will be stored under this platform. Valid options: ['hive', 'presto', 'presto-on-hive', 'trino']presto-on-hive
optionsobjectAny 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
passwordstring(password)passwordNone
platform_instancestringThe instance of the platform that all assets produced by this recipe belong toNone
schemas_where_clause_suffixstringWhere clause to specify what Hive schemas should be ingested.None
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
tables_where_clause_suffixstringWhere clause to specify what Hive tables should be ingested.None
use_catalog_subtypebooleanContainer Subtype name to be 'Database' or 'Catalog' Valid options: ['True', 'False']True
use_dataset_pascalcase_subtypebooleanDataset Subtype name to be 'Table' or 'View' Valid options: ['True', 'False']None
usernamestringusernameNone
views_where_clause_suffixstringWhere clause to specify what Presto views should be ingested.None
envstringThe environment that all assets produced by this connector belong toPROD
database_patternAllowDenyPatternRegex patterns for hive/presto database to filter in ingestion. Specify regex to only match the database name. e.g. to match all tables in database analytics, use the regex 'analytics'{'allow': ['.*'], 'deny': [], 'ignoreCase': True}
database_pattern.allowarray(string)None
database_pattern.denyarray(string)None
database_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
domainmap(str,AllowDenyPattern)A class to store allow deny regexesNone
domain.key.allowarray(string)None
domain.key.denyarray(string)None
domain.key.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profile_patternAllowDenyPatternRegex 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.allowarray(string)None
profile_pattern.denyarray(string)None
profile_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
schema_patternAllowDenyPatternRegex 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(string)None
schema_pattern.denyarray(string)None
schema_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
table_patternAllowDenyPatternRegex 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(string)None
table_pattern.denyarray(string)None
table_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
view_patternAllowDenyPatternRegex 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(string)None
view_pattern.denyarray(string)None
view_pattern.ignoreCasebooleanWhether to ignore case sensitivity during pattern matching.True
profilingGEProfilingConfig{'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_exceptionsbooleanTrue
profiling.enabledbooleanWhether profiling should be done.None
profiling.field_sample_values_limitintegerUpper limit for number of sample values to collect for all columns.20
profiling.include_field_distinct_countbooleanWhether to profile for the number of distinct values for each column.True
profiling.include_field_distinct_value_frequenciesbooleanWhether to profile for distinct value frequencies.None
profiling.include_field_histogrambooleanWhether to profile for the histogram for numeric fields.None
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_min_valuebooleanWhether to profile for the min value of numeric columns.True
profiling.include_field_null_countbooleanWhether to profile for the number of nulls for each column.True
profiling.include_field_quantilesbooleanWhether to profile for the quantiles of numeric columns.None
profiling.include_field_sample_valuesbooleanWhether to profile for the sample values for all columns.True
profiling.include_field_stddev_valuebooleanWhether to profile for the standard deviation of numeric columns.True
profiling.limitintegerMax number of documents to profile. By default, profiles all documents.None
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.max_workersintegerNumber of worker threads to use for profiling. Set to 1 to disable.20
profiling.offsetintegerOffset in documents to profile. By default, uses no offset.None
profiling.partition_datetimestring(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_enabledbooleanTrue
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 and BigQuery.None
profiling.profile_table_level_onlybooleanWhether to perform profiling at table-level only, or include column-level profiling as well.None
profiling.profile_table_row_count_estimate_onlybooleanUse an approximate query for row count. This will be much faster but slightly less accurate. Only supported for Postgres.None
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 and BigQuery5000000
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 and BigQuery5
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.report_dropped_profilesbooleanWhether to report datasets or dataset columns which were not profiled. Set to True for debugging purposes.None
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.None
stateful_ingestionStatefulStaleMetadataRemovalConfigBase specialized config for Stateful Ingestion with stale metadata removal capability.None
stateful_ingestion.enabledbooleanThe type of the ingestion state provider registered with datahub.None
stateful_ingestion.ignore_new_statebooleanIf set to True, ignores the current checkpoint state.None
stateful_ingestion.ignore_old_statebooleanIf set to True, ignores the previous checkpoint state.None
stateful_ingestion.remove_stale_metadatabooleanSoft-deletes the entities present in the last successful run but missing in the current run with stateful_ingestion enabled.True

Code Coordinates

  • Class Name: datahub.ingestion.source.sql.presto_on_hive.PrestoOnHiveSource
  • Browse on GitHub

Questions

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