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Hive

Module hive​

Certified

Important Capabilities​

CapabilityStatusNotes
Domains✅Supported via the domain config field
Platform Instance✅Enabled by default

This plugin extracts the following:

  • Metadata for databases, schemas, and tables
  • Column types associated with each table
  • Detailed table and storage information
  • Table, row, and column statistics via optional SQL profiling.

CLI based Ingestion​

Install the Plugin​

pip install 'acryl-datahub[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: hive
  config:
    # Coordinates
    host_port: localhost:10000
    database: DemoDatabase # optional, if not specified, ingests from all databases

    # Credentials
    username: user # optional
    password: pass # optional

    # For more details on authentication, see the PyHive docs:
    # https://github.com/dropbox/PyHive#passing-session-configuration.
    # LDAP, Kerberos, etc. are supported using connect_args, which can be
    # added under the `options` config parameter.
    #options:
    #  connect_args:
    #    auth: KERBEROS
    #    kerberos_service_name: hive
    #scheme: 'hive+http' # set this if Thrift should use the HTTP transport
    #scheme: 'hive+https' # set this if Thrift should use the HTTP with SSL transport
    #scheme: 'sparksql' # set this for Spark Thrift Server

sink:
  # sink configs

# ---------------------------------------------------------
# Recipe (Azure HDInsight)
# Connecting to Microsoft Azure HDInsight using TLS.
# ---------------------------------------------------------

source:
  type: hive
  config:
    # Coordinates
    host_port: <cluster_name>.azurehdinsight.net:443

    # Credentials
    username: admin
    password: password

    # Options
    options:
      connect_args:
        http_path: "/hive2"
        auth: BASIC

sink:
  # sink configs

# ---------------------------------------------------------
# Recipe (Databricks)
# Ensure that databricks-dbapi is installed. If not, use ```pip install databricks-dbapi``` to install.
# Use the ```http_path``` from your Databricks cluster in the following recipe.
# See (https://docs.databricks.com/integrations/bi/jdbc-odbc-bi.html#get-server-hostname-port-http-path-and-jdbc-url) for instructions to find ```http_path```.
# ---------------------------------------------------------

source:
  type: hive
  config:
    host_port: <databricks workspace URL>:443
    username: token / username
    password: <api token> / password
    scheme: 'databricks+pyhive'

    options:
      connect_args:
        http_path: 'sql/protocolv1/o/xxxyyyzzzaaasa/1234-567890-hello123'

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
host_port [✅]stringhost URLNone
databasestringdatabase (catalog)None
database_aliasstringAlias to apply to database when ingesting.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
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
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
usernamestringusernameNone
envstringThe environment that all assets produced by this connector belong toPROD
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.hive.HiveSource
  • Browse on GitHub

Questions​

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