Kafka KerberosAuth#
- pydantic model onetl.connection.db_connection.kafka.kafka_kerberos_auth.KafkaKerberosAuth#
Connect to Kafka using
sasl.mechanism="GSSAPI".For more details see:
Examples
Auth in Kafka with keytab, automatically deploy keytab files to all Spark hosts (driver and executors):
from onetl.connection import Kafka auth = Kafka.KerberosAuth( principal="user", keytab="/path/to/keytab", deploy_keytab=True, )
Auth in Kafka with keytab, keytab is already deployed on all Spark hosts (driver and executors):
from onetl.connection import Kafka auth = Kafka.KerberosAuth( principal="user", keytab="/path/to/keytab", deploy_keytab=False, )
Auth in Kafka with existing Kerberos ticket (only Spark session created with
master=local):from onetl.connection import Kafka auth = Kafka.KerberosAuth( principal="user", use_keytab=False, use_ticket_cache=True, )
Pass custom options for JAAS config and Kafka SASL:
from onetl.connection import Kafka auth = Kafka.KerberosAuth.parse( { "principal": "user", "keytab": "/path/to/keytab", # options without sasl.kerberos. prefix are passed to JAAS config # names are in camel case! "isInitiator": True, # options with sasl.kerberos. prefix are passed to Kafka client config "sasl.kerberos.kinit.cmd": "/usr/bin/kinit", } )
- field principal: str [Required]#
- field keytab: LocalPath | None = None (alias 'keyTab')#
- field deploy_keytab: bool = True#
- field service_name: str = 'kafka' (alias 'serviceName')#
- field renew_ticket: bool = True (alias 'renewTicket')#
- field store_key: bool = True (alias 'storeKey')#
- field use_keytab: bool = True (alias 'useKeyTab')#
- field use_ticket_cache: bool = False (alias 'useTicketCache')#