Source code for airflow.providers.apache.hive.operators.hive

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import os
import re
from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence

from airflow.configuration import conf
from airflow.models import BaseOperator
from airflow.providers.apache.hive.hooks.hive import HiveCliHook
from airflow.utils import operator_helpers
from airflow.utils.operator_helpers import context_to_airflow_vars

    from airflow.utils.context import Context

[docs]class HiveOperator(BaseOperator): """ Executes hql code or hive script in a specific Hive database. :param hql: the hql to be executed. Note that you may also use a relative path from the dag file of a (template) hive script. (templated) :param hive_cli_conn_id: Reference to the :ref:`Hive CLI connection id <howto/connection:hive_cli>`. (templated) :param hiveconfs: if defined, these key value pairs will be passed to hive as ``-hiveconf "key"="value"`` :param hiveconf_jinja_translate: when True, hiveconf-type templating ${var} gets translated into jinja-type templating {{ var }} and ${hiveconf:var} gets translated into jinja-type templating {{ var }}. Note that you may want to use this along with the ``DAG(user_defined_macros=myargs)`` parameter. View the DAG object documentation for more details. :param script_begin_tag: If defined, the operator will get rid of the part of the script before the first occurrence of `script_begin_tag` :param run_as_owner: Run HQL code as a DAG's owner. :param mapred_queue: queue used by the Hadoop CapacityScheduler. (templated) :param mapred_queue_priority: priority within CapacityScheduler queue. Possible settings include: VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW :param mapred_job_name: This name will appear in the jobtracker. This can make monitoring easier. """
[docs] template_fields: Sequence[str] = ( 'hql', 'schema', 'hive_cli_conn_id', 'mapred_queue', 'hiveconfs', 'mapred_job_name', 'mapred_queue_priority',
[docs] template_ext: Sequence[str] = ( '.hql', '.sql',
[docs] template_fields_renderers = {'hql': 'hql'}
[docs] ui_color = '#f0e4ec'
def __init__( self, *, hql: str, hive_cli_conn_id: str = 'hive_cli_default', schema: str = 'default', hiveconfs: Optional[Dict[Any, Any]] = None, hiveconf_jinja_translate: bool = False, script_begin_tag: Optional[str] = None, run_as_owner: bool = False, mapred_queue: Optional[str] = None, mapred_queue_priority: Optional[str] = None, mapred_job_name: Optional[str] = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) self.hql = hql self.hive_cli_conn_id = hive_cli_conn_id self.schema = schema self.hiveconfs = hiveconfs or {} self.hiveconf_jinja_translate = hiveconf_jinja_translate self.script_begin_tag = script_begin_tag self.run_as = None if run_as_owner: self.run_as = self.dag.owner self.mapred_queue = mapred_queue self.mapred_queue_priority = mapred_queue_priority self.mapred_job_name = mapred_job_name job_name_template = conf.get( 'hive', 'mapred_job_name_template', fallback="Airflow HiveOperator task for {hostname}.{dag_id}.{task_id}.{execution_date}", ) if job_name_template is None: raise ValueError("Job name template should be set !") self.mapred_job_name_template: str = job_name_template # assigned lazily - just for consistency we can create the attribute with a # `None` initial value, later it will be populated by the execute method. # This also makes `on_kill` implementation consistent since it assumes `self.hook` # is defined. self.hook: Optional[HiveCliHook] = None
[docs] def get_hook(self) -> HiveCliHook: """Get Hive cli hook""" return HiveCliHook( hive_cli_conn_id=self.hive_cli_conn_id, run_as=self.run_as, mapred_queue=self.mapred_queue, mapred_queue_priority=self.mapred_queue_priority, mapred_job_name=self.mapred_job_name,
[docs] def prepare_template(self) -> None: if self.hiveconf_jinja_translate: self.hql = re.sub(r"(\$\{(hiveconf:)?([ a-zA-Z0-9_]*)\})", r"{{ \g<3> }}", self.hql) if self.script_begin_tag and self.script_begin_tag in self.hql: self.hql = "\n".join(self.hql.split(self.script_begin_tag)[1:])
[docs] def execute(self, context: "Context") -> None:'Executing: %s', self.hql) self.hook = self.get_hook() # set the mapred_job_name if it's not set with dag, task, execution time info if not self.mapred_job_name: ti = context['ti'] self.hook.mapred_job_name = self.mapred_job_name_template.format( dag_id=ti.dag_id, task_id=ti.task_id, execution_date=ti.execution_date.isoformat(), hostname=ti.hostname.split('.')[0], ) if self.hiveconf_jinja_translate: self.hiveconfs = context_to_airflow_vars(context) else: self.hiveconfs.update(context_to_airflow_vars(context))'Passing HiveConf: %s', self.hiveconfs) self.hook.run_cli(hql=self.hql, schema=self.schema, hive_conf=self.hiveconfs)
[docs] def dry_run(self) -> None: # Reset airflow environment variables to prevent # existing env vars from impacting behavior. self.clear_airflow_vars() self.hook = self.get_hook() self.hook.test_hql(hql=self.hql)
[docs] def on_kill(self) -> None: if self.hook: self.hook.kill()
[docs] def clear_airflow_vars(self) -> None: """Reset airflow environment variables to prevent existing ones from impacting behavior.""" blank_env_vars = { value['env_var_format']: '' for value in operator_helpers.AIRFLOW_VAR_NAME_FORMAT_MAPPING.values() } os.environ.update(blank_env_vars)

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