Source code for airflow.operators.hive_operator

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from __future__ import unicode_literals

import os
import re

from airflow.utils import operator_helpers

from airflow.hooks.hive_hooks import HiveCliHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
from airflow.utils.operator_helpers import context_to_airflow_vars


[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) :type hql: str :param hive_cli_conn_id: reference to the Hive database. (templated) :type hive_cli_conn_id: str :param hiveconfs: if defined, these key value pairs will be passed to hive as ``-hiveconf "key"="value"`` :type hiveconfs: dict :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. :type hiveconf_jinja_translate: bool :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` :type script_begin_tag: str :param mapred_queue: queue used by the Hadoop CapacityScheduler. (templated) :type mapred_queue: str :param mapred_queue_priority: priority within CapacityScheduler queue. Possible settings include: VERY_HIGH, HIGH, NORMAL, LOW, VERY_LOW :type mapred_queue_priority: str :param mapred_job_name: This name will appear in the jobtracker. This can make monitoring easier. :type mapred_job_name: str """
[docs] template_fields = ('hql', 'schema', 'hive_cli_conn_id', 'mapred_queue', 'hiveconfs', 'mapred_job_name', 'mapred_queue_priority')
[docs] template_ext = ('.hql', '.sql',)
[docs] ui_color = '#f0e4ec'
@apply_defaults def __init__( self, hql, hive_cli_conn_id='hive_cli_default', schema='default', hiveconfs=None, hiveconf_jinja_translate=False, script_begin_tag=None, run_as_owner=False, mapred_queue=None, mapred_queue_priority=None, mapred_job_name=None, *args, **kwargs): super(HiveOperator, self).__init__(*args, **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 # 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 = None
[docs] def get_hook(self): 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): 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): self.log.info('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 = 'Airflow HiveOperator task for {}.{}.{}.{}'\ .format(ti.hostname.split('.')[0], ti.dag_id, ti.task_id, ti.execution_date.isoformat()) if self.hiveconf_jinja_translate: self.hiveconfs = context_to_airflow_vars(context) else: self.hiveconfs.update(context_to_airflow_vars(context)) self.log.info('Passing HiveConf: %s', self.hiveconfs) self.hook.run_cli(hql=self.hql, schema=self.schema, hive_conf=self.hiveconfs)
[docs] def dry_run(self): # 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): if self.hook: self.hook.kill()
[docs] def clear_airflow_vars(self): """ 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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