Source code for airflow.models

# -*- coding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from future.standard_library import install_aliases

install_aliases()
from builtins import str
from builtins import object, bytes
import copy
from collections import namedtuple
from datetime import datetime, timedelta
import dill
import functools
import getpass
import imp
import importlib
import inspect
import zipfile
import jinja2
import json
import logging
import os
import pickle
import re
import signal
import socket
import sys
import textwrap
import traceback
import warnings
import hashlib

from urllib.parse import urlparse

from sqlalchemy import (
    Column, Integer, String, DateTime, Text, Boolean, ForeignKey, PickleType,
    Index, Float)
from sqlalchemy import func, or_, and_
from sqlalchemy.ext.declarative import declarative_base, declared_attr
from sqlalchemy.dialects.mysql import LONGTEXT
from sqlalchemy.orm import reconstructor, relationship, synonym

from croniter import croniter
import six

from airflow import settings, utils
from airflow.executors import DEFAULT_EXECUTOR, LocalExecutor
from airflow import configuration
from airflow.exceptions import AirflowException, AirflowSkipException, AirflowTaskTimeout
from airflow.dag.base_dag import BaseDag, BaseDagBag
from airflow.ti_deps.deps.not_in_retry_period_dep import NotInRetryPeriodDep
from airflow.ti_deps.deps.prev_dagrun_dep import PrevDagrunDep
from airflow.ti_deps.deps.trigger_rule_dep import TriggerRuleDep
from airflow.ti_deps.dep_context import DepContext, QUEUE_DEPS, RUN_DEPS
from airflow.utils.dates import cron_presets, date_range as utils_date_range
from airflow.utils.db import provide_session
from airflow.utils.decorators import apply_defaults
from airflow.utils.email import send_email
from airflow.utils.helpers import (
    as_tuple, is_container, is_in, validate_key, pprinttable)
from airflow.utils.logging import LoggingMixin
from airflow.utils.operator_resources import Resources
from airflow.utils.state import State
from airflow.utils.timeout import timeout
from airflow.utils.trigger_rule import TriggerRule

Base = declarative_base()
ID_LEN = 250
XCOM_RETURN_KEY = 'return_value'

Stats = settings.Stats

ENCRYPTION_ON = False
try:
    from cryptography.fernet import Fernet
    FERNET = Fernet(configuration.get('core', 'FERNET_KEY').encode('utf-8'))
    ENCRYPTION_ON = True
except:
    pass

if 'mysql' in settings.SQL_ALCHEMY_CONN:
    LongText = LONGTEXT
else:
    LongText = Text

# used by DAG context_managers
_CONTEXT_MANAGER_DAG = None


def clear_task_instances(tis, session, activate_dag_runs=True):
    """
    Clears a set of task instances, but makes sure the running ones
    get killed.
    """
    job_ids = []
    for ti in tis:
        if ti.state == State.RUNNING:
            if ti.job_id:
                ti.state = State.SHUTDOWN
                job_ids.append(ti.job_id)
        # todo: this creates an issue with the webui tests
        # elif ti.state != State.REMOVED:
        #     ti.state = State.NONE
        #     session.merge(ti)
        else:
            session.delete(ti)
    if job_ids:
        from airflow.jobs import BaseJob as BJ
        for job in session.query(BJ).filter(BJ.id.in_(job_ids)).all():
            job.state = State.SHUTDOWN
    if activate_dag_runs:
        execution_dates = {ti.execution_date for ti in tis}
        dag_ids = {ti.dag_id for ti in tis}
        drs = session.query(DagRun).filter(
            DagRun.dag_id.in_(dag_ids),
            DagRun.execution_date.in_(execution_dates),
        ).all()
        for dr in drs:
            dr.state = State.RUNNING
            dr.start_date = datetime.now()


[docs]class DagBag(BaseDagBag, LoggingMixin): """ A dagbag is a collection of dags, parsed out of a folder tree and has high level configuration settings, like what database to use as a backend and what executor to use to fire off tasks. This makes it easier to run distinct environments for say production and development, tests, or for different teams or security profiles. What would have been system level settings are now dagbag level so that one system can run multiple, independent settings sets. :param dag_folder: the folder to scan to find DAGs :type dag_folder: unicode :param executor: the executor to use when executing task instances in this DagBag :param include_examples: whether to include the examples that ship with airflow or not :type include_examples: bool :param sync_to_db: whether to sync the properties of the DAGs to the metadata DB while finding them, typically should be done by the scheduler job only :type sync_to_db: bool """ def __init__( self, dag_folder=None, executor=DEFAULT_EXECUTOR, include_examples=configuration.getboolean('core', 'LOAD_EXAMPLES')): dag_folder = dag_folder or settings.DAGS_FOLDER self.logger.info("Filling up the DagBag from {}".format(dag_folder)) self.dag_folder = dag_folder self.dags = {} # the file's last modified timestamp when we last read it self.file_last_changed = {} self.executor = executor self.import_errors = {} if include_examples: example_dag_folder = os.path.join( os.path.dirname(__file__), 'example_dags') self.collect_dags(example_dag_folder) self.collect_dags(dag_folder)
[docs] def size(self): """ :return: the amount of dags contained in this dagbag """ return len(self.dags)
[docs] def get_dag(self, dag_id): """ Gets the DAG out of the dictionary, and refreshes it if expired """ # If asking for a known subdag, we want to refresh the parent root_dag_id = dag_id if dag_id in self.dags: dag = self.dags[dag_id] if dag.is_subdag: root_dag_id = dag.parent_dag.dag_id # If the dag corresponding to root_dag_id is absent or expired orm_dag = DagModel.get_current(root_dag_id) if orm_dag and ( root_dag_id not in self.dags or ( orm_dag.last_expired and dag.last_loaded < orm_dag.last_expired ) ): # Reprocess source file found_dags = self.process_file( filepath=orm_dag.fileloc, only_if_updated=False) # If the source file no longer exports `dag_id`, delete it from self.dags if found_dags and dag_id in [dag.dag_id for dag in found_dags]: return self.dags[dag_id] elif dag_id in self.dags: del self.dags[dag_id] return self.dags.get(dag_id)
[docs] def process_file(self, filepath, only_if_updated=True, safe_mode=True): """ Given a path to a python module or zip file, this method imports the module and look for dag objects within it. """ found_dags = [] # todo: raise exception? if not os.path.isfile(filepath): return found_dags try: # This failed before in what may have been a git sync # race condition file_last_changed_on_disk = datetime.fromtimestamp(os.path.getmtime(filepath)) if only_if_updated \ and filepath in self.file_last_changed \ and file_last_changed_on_disk == self.file_last_changed[filepath]: return found_dags except Exception as e: logging.exception(e) return found_dags mods = [] if not zipfile.is_zipfile(filepath): if safe_mode and os.path.isfile(filepath): with open(filepath, 'rb') as f: content = f.read() if not all([s in content for s in (b'DAG', b'airflow')]): self.file_last_changed[filepath] = file_last_changed_on_disk return found_dags self.logger.debug("Importing {}".format(filepath)) org_mod_name, _ = os.path.splitext(os.path.split(filepath)[-1]) mod_name = ('unusual_prefix_' + hashlib.sha1(filepath.encode('utf-8')).hexdigest() + '_' + org_mod_name) if mod_name in sys.modules: del sys.modules[mod_name] with timeout(configuration.getint('core', "DAGBAG_IMPORT_TIMEOUT")): try: m = imp.load_source(mod_name, filepath) mods.append(m) except Exception as e: self.logger.exception("Failed to import: " + filepath) self.import_errors[filepath] = str(e) self.file_last_changed[filepath] = file_last_changed_on_disk else: zip_file = zipfile.ZipFile(filepath) for mod in zip_file.infolist(): head, _ = os.path.split(mod.filename) mod_name, ext = os.path.splitext(mod.filename) if not head and (ext == '.py' or ext == '.pyc'): if mod_name == '__init__': self.logger.warning("Found __init__.{0} at root of {1}". format(ext, filepath)) if safe_mode: with zip_file.open(mod.filename) as zf: self.logger.debug("Reading {} from {}". format(mod.filename, filepath)) content = zf.read() if not all([s in content for s in (b'DAG', b'airflow')]): self.file_last_changed[filepath] = ( file_last_changed_on_disk) # todo: create ignore list return found_dags if mod_name in sys.modules: del sys.modules[mod_name] try: sys.path.insert(0, filepath) m = importlib.import_module(mod_name) mods.append(m) except Exception as e: self.logger.exception("Failed to import: " + filepath) self.import_errors[filepath] = str(e) self.file_last_changed[filepath] = file_last_changed_on_disk for m in mods: for dag in list(m.__dict__.values()): if isinstance(dag, DAG): if not dag.full_filepath: dag.full_filepath = filepath dag.is_subdag = False self.bag_dag(dag, parent_dag=dag, root_dag=dag) found_dags.append(dag) found_dags += dag.subdags self.file_last_changed[filepath] = file_last_changed_on_disk return found_dags
@provide_session
[docs] def kill_zombies(self, session=None): """ Fails tasks that haven't had a heartbeat in too long """ from airflow.jobs import LocalTaskJob as LJ self.logger.info("Finding 'running' jobs without a recent heartbeat") TI = TaskInstance secs = ( configuration.getint('scheduler', 'scheduler_zombie_task_threshold')) limit_dttm = datetime.now() - timedelta(seconds=secs) self.logger.info( "Failing jobs without heartbeat after {}".format(limit_dttm)) tis = ( session.query(TI) .join(LJ, TI.job_id == LJ.id) .filter(TI.state == State.RUNNING) .filter( or_( LJ.state != State.RUNNING, LJ.latest_heartbeat < limit_dttm, )) .all() ) for ti in tis: if ti and ti.dag_id in self.dags: dag = self.dags[ti.dag_id] if ti.task_id in dag.task_ids: task = dag.get_task(ti.task_id) ti.task = task ti.handle_failure("{} killed as zombie".format(ti)) self.logger.info( 'Marked zombie job {} as failed'.format(ti)) Stats.incr('zombies_killed') session.commit()
[docs] def bag_dag(self, dag, parent_dag, root_dag): """ Adds the DAG into the bag, recurses into sub dags. """ self.dags[dag.dag_id] = dag dag.resolve_template_files() dag.last_loaded = datetime.now() for task in dag.tasks: settings.policy(task) for subdag in dag.subdags: subdag.full_filepath = dag.full_filepath subdag.parent_dag = dag subdag.is_subdag = True self.bag_dag(subdag, parent_dag=dag, root_dag=root_dag) self.logger.debug('Loaded DAG {dag}'.format(**locals()))
[docs] def collect_dags( self, dag_folder=None, only_if_updated=True): """ Given a file path or a folder, this method looks for python modules, imports them and adds them to the dagbag collection. Note that if a .airflowignore file is found while processing, the directory, it will behaves much like a .gitignore does, ignoring files that match any of the regex patterns specified in the file. """ start_dttm = datetime.now() dag_folder = dag_folder or self.dag_folder # Used to store stats around DagBag processing stats = [] FileLoadStat = namedtuple( 'FileLoadStat', "file duration dag_num task_num dags") if os.path.isfile(dag_folder): self.process_file(dag_folder, only_if_updated=only_if_updated) elif os.path.isdir(dag_folder): patterns = [] for root, dirs, files in os.walk(dag_folder, followlinks=True): ignore_file = [f for f in files if f == '.airflowignore'] if ignore_file: f = open(os.path.join(root, ignore_file[0]), 'r') patterns += [p for p in f.read().split('\n') if p] f.close() for f in files: try: filepath = os.path.join(root, f) if not os.path.isfile(filepath): continue mod_name, file_ext = os.path.splitext( os.path.split(filepath)[-1]) if file_ext != '.py' and not zipfile.is_zipfile(filepath): continue if not any( [re.findall(p, filepath) for p in patterns]): ts = datetime.now() found_dags = self.process_file( filepath, only_if_updated=only_if_updated) td = datetime.now() - ts td = td.total_seconds() + ( float(td.microseconds) / 1000000) stats.append(FileLoadStat( filepath.replace(dag_folder, ''), td, len(found_dags), sum([len(dag.tasks) for dag in found_dags]), str([dag.dag_id for dag in found_dags]), )) except Exception as e: logging.warning(e) Stats.gauge( 'collect_dags', (datetime.now() - start_dttm).total_seconds(), 1) Stats.gauge( 'dagbag_size', len(self.dags), 1) Stats.gauge( 'dagbag_import_errors', len(self.import_errors), 1) self.dagbag_stats = sorted( stats, key=lambda x: x.duration, reverse=True)
[docs] def dagbag_report(self): """Prints a report around DagBag loading stats""" report = textwrap.dedent("""\n ------------------------------------------------------------------- DagBag loading stats for {dag_folder} ------------------------------------------------------------------- Number of DAGs: {dag_num} Total task number: {task_num} DagBag parsing time: {duration} {table} """) stats = self.dagbag_stats return report.format( dag_folder=self.dag_folder, duration=sum([o.duration for o in stats]), dag_num=sum([o.dag_num for o in stats]), task_num=sum([o.dag_num for o in stats]), table=pprinttable(stats), )
def deactivate_inactive_dags(self): active_dag_ids = [dag.dag_id for dag in list(self.dags.values())] session = settings.Session() for dag in session.query( DagModel).filter(~DagModel.dag_id.in_(active_dag_ids)).all(): dag.is_active = False session.merge(dag) session.commit() session.close() def paused_dags(self): session = settings.Session() dag_ids = [dp.dag_id for dp in session.query(DagModel).filter( DagModel.is_paused.is_(True))] session.commit() session.close() return dag_ids
class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True) username = Column(String(ID_LEN), unique=True) email = Column(String(500)) superuser = False def __repr__(self): return self.username def get_id(self): return str(self.id) def is_superuser(self): return self.superuser
[docs]class Connection(Base): """ Placeholder to store information about different database instances connection information. The idea here is that scripts use references to database instances (conn_id) instead of hard coding hostname, logins and passwords when using operators or hooks. """ __tablename__ = "connection" id = Column(Integer(), primary_key=True) conn_id = Column(String(ID_LEN)) conn_type = Column(String(500)) host = Column(String(500)) schema = Column(String(500)) login = Column(String(500)) _password = Column('password', String(5000)) port = Column(Integer()) is_encrypted = Column(Boolean, unique=False, default=False) is_extra_encrypted = Column(Boolean, unique=False, default=False) _extra = Column('extra', String(5000)) _types = [ ('fs', 'File (path)'), ('ftp', 'FTP',), ('google_cloud_platform', 'Google Cloud Platform'), ('hdfs', 'HDFS',), ('http', 'HTTP',), ('hive_cli', 'Hive Client Wrapper',), ('hive_metastore', 'Hive Metastore Thrift',), ('hiveserver2', 'Hive Server 2 Thrift',), ('jdbc', 'Jdbc Connection',), ('mysql', 'MySQL',), ('postgres', 'Postgres',), ('oracle', 'Oracle',), ('vertica', 'Vertica',), ('presto', 'Presto',), ('s3', 'S3',), ('samba', 'Samba',), ('sqlite', 'Sqlite',), ('ssh', 'SSH',), ('cloudant', 'IBM Cloudant',), ('mssql', 'Microsoft SQL Server'), ('mesos_framework-id', 'Mesos Framework ID'), ('jira', 'JIRA',), ] def __init__( self, conn_id=None, conn_type=None, host=None, login=None, password=None, schema=None, port=None, extra=None, uri=None): self.conn_id = conn_id if uri: self.parse_from_uri(uri) else: self.conn_type = conn_type self.host = host self.login = login self.password = password self.schema = schema self.port = port self.extra = extra def parse_from_uri(self, uri): temp_uri = urlparse(uri) hostname = temp_uri.hostname or '' if '%2f' in hostname: hostname = hostname.replace('%2f', '/').replace('%2F', '/') conn_type = temp_uri.scheme if conn_type == 'postgresql': conn_type = 'postgres' self.conn_type = conn_type self.host = hostname self.schema = temp_uri.path[1:] self.login = temp_uri.username self.password = temp_uri.password self.port = temp_uri.port def get_password(self): if self._password and self.is_encrypted: if not ENCRYPTION_ON: raise AirflowException( "Can't decrypt encrypted password for login={}, \ FERNET_KEY configuration is missing".format(self.login)) return FERNET.decrypt(bytes(self._password, 'utf-8')).decode() else: return self._password def set_password(self, value): if value: try: self._password = FERNET.encrypt(bytes(value, 'utf-8')).decode() self.is_encrypted = True except NameError: self._password = value self.is_encrypted = False @declared_attr def password(cls): return synonym('_password', descriptor=property(cls.get_password, cls.set_password)) def get_extra(self): if self._extra and self.is_extra_encrypted: if not ENCRYPTION_ON: raise AirflowException( "Can't decrypt `extra` params for login={},\ FERNET_KEY configuration is missing".format(self.login)) return FERNET.decrypt(bytes(self._extra, 'utf-8')).decode() else: return self._extra def set_extra(self, value): if value: try: self._extra = FERNET.encrypt(bytes(value, 'utf-8')).decode() self.is_extra_encrypted = True except NameError: self._extra = value self.is_extra_encrypted = False @declared_attr def extra(cls): return synonym('_extra', descriptor=property(cls.get_extra, cls.set_extra)) def get_hook(self): try: if self.conn_type == 'mysql': from airflow.hooks.mysql_hook import MySqlHook return MySqlHook(mysql_conn_id=self.conn_id) elif self.conn_type == 'google_cloud_platform': from airflow.contrib.hooks.bigquery_hook import BigQueryHook return BigQueryHook(bigquery_conn_id=self.conn_id) elif self.conn_type == 'postgres': from airflow.hooks.postgres_hook import PostgresHook return PostgresHook(postgres_conn_id=self.conn_id) elif self.conn_type == 'hive_cli': from airflow.hooks.hive_hooks import HiveCliHook return HiveCliHook(hive_cli_conn_id=self.conn_id) elif self.conn_type == 'presto': from airflow.hooks.presto_hook import PrestoHook return PrestoHook(presto_conn_id=self.conn_id) elif self.conn_type == 'hiveserver2': from airflow.hooks.hive_hooks import HiveServer2Hook return HiveServer2Hook(hiveserver2_conn_id=self.conn_id) elif self.conn_type == 'sqlite': from airflow.hooks.sqlite_hook import SqliteHook return SqliteHook(sqlite_conn_id=self.conn_id) elif self.conn_type == 'jdbc': from airflow.hooks.jdbc_hook import JdbcHook return JdbcHook(jdbc_conn_id=self.conn_id) elif self.conn_type == 'mssql': from airflow.hooks.mssql_hook import MsSqlHook return MsSqlHook(mssql_conn_id=self.conn_id) elif self.conn_type == 'oracle': from airflow.hooks.oracle_hook import OracleHook return OracleHook(oracle_conn_id=self.conn_id) elif self.conn_type == 'vertica': from airflow.contrib.hooks.vertica_hook import VerticaHook return VerticaHook(vertica_conn_id=self.conn_id) elif self.conn_type == 'cloudant': from airflow.contrib.hooks.cloudant_hook import CloudantHook return CloudantHook(cloudant_conn_id=self.conn_id) elif self.conn_type == 'jira': from airflow.contrib.hooks.jira_hook import JiraHook return JiraHook(jira_conn_id=self.conn_id) except: pass def __repr__(self): return self.conn_id @property def extra_dejson(self): """Returns the extra property by deserializing json.""" obj = {} if self.extra: try: obj = json.loads(self.extra) except Exception as e: logging.exception(e) logging.error("Failed parsing the json for conn_id %s", self.conn_id) return obj
class DagPickle(Base): """ Dags can originate from different places (user repos, master repo, ...) and also get executed in different places (different executors). This object represents a version of a DAG and becomes a source of truth for a BackfillJob execution. A pickle is a native python serialized object, and in this case gets stored in the database for the duration of the job. The executors pick up the DagPickle id and read the dag definition from the database. """ id = Column(Integer, primary_key=True) pickle = Column(PickleType(pickler=dill)) created_dttm = Column(DateTime, default=func.now()) pickle_hash = Column(Text) __tablename__ = "dag_pickle" def __init__(self, dag): self.dag_id = dag.dag_id if hasattr(dag, 'template_env'): dag.template_env = None self.pickle_hash = hash(dag) self.pickle = dag
[docs]class TaskInstance(Base): """ Task instances store the state of a task instance. This table is the authority and single source of truth around what tasks have run and the state they are in. The SqlAchemy model doesn't have a SqlAlchemy foreign key to the task or dag model deliberately to have more control over transactions. Database transactions on this table should insure double triggers and any confusion around what task instances are or aren't ready to run even while multiple schedulers may be firing task instances. """ __tablename__ = "task_instance" task_id = Column(String(ID_LEN), primary_key=True) dag_id = Column(String(ID_LEN), primary_key=True) execution_date = Column(DateTime, primary_key=True) start_date = Column(DateTime) end_date = Column(DateTime) duration = Column(Float) state = Column(String(20)) try_number = Column(Integer, default=0) hostname = Column(String(1000)) unixname = Column(String(1000)) job_id = Column(Integer) pool = Column(String(50)) queue = Column(String(50)) priority_weight = Column(Integer) operator = Column(String(1000)) queued_dttm = Column(DateTime) pid = Column(Integer) __table_args__ = ( Index('ti_dag_state', dag_id, state), Index('ti_state', state), Index('ti_state_lkp', dag_id, task_id, execution_date, state), Index('ti_pool', pool, state, priority_weight), ) def __init__(self, task, execution_date, state=None): self.dag_id = task.dag_id self.task_id = task.task_id self.execution_date = execution_date self.task = task self.queue = task.queue self.pool = task.pool self.priority_weight = task.priority_weight_total self.try_number = 0 self.unixname = getpass.getuser() self.run_as_user = task.run_as_user if state: self.state = state self.hostname = '' self.init_on_load() @reconstructor
[docs] def init_on_load(self): """ Initialize the attributes that aren't stored in the DB. """ self.test_mode = False # can be changed when calling 'run'
[docs] def command( self, mark_success=False, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, local=False, pickle_id=None, raw=False, job_id=None, pool=None, cfg_path=None): """ Returns a command that can be executed anywhere where airflow is installed. This command is part of the message sent to executors by the orchestrator. """ return " ".join(self.command_as_list( mark_success=mark_success, ignore_all_deps=ignore_all_deps, ignore_depends_on_past=ignore_depends_on_past, ignore_task_deps=ignore_task_deps, ignore_ti_state=ignore_ti_state, local=local, pickle_id=pickle_id, raw=raw, job_id=job_id, pool=pool, cfg_path=cfg_path))
[docs] def command_as_list( self, mark_success=False, ignore_all_deps=False, ignore_task_deps=False, ignore_depends_on_past=False, ignore_ti_state=False, local=False, pickle_id=None, raw=False, job_id=None, pool=None, cfg_path=None): """ Returns a command that can be executed anywhere where airflow is installed. This command is part of the message sent to executors by the orchestrator. """ dag = self.task.dag should_pass_filepath = not pickle_id and dag if should_pass_filepath and dag.full_filepath != dag.filepath: path = "DAGS_FOLDER/{}".format(dag.filepath) elif should_pass_filepath and dag.full_filepath: path = dag.full_filepath else: path = None return TaskInstance.generate_command( self.dag_id, self.task_id, self.execution_date, mark_success=mark_success, ignore_all_deps=ignore_all_deps, ignore_task_deps=ignore_task_deps, ignore_depends_on_past=ignore_depends_on_past, ignore_ti_state=ignore_ti_state, local=local, pickle_id=pickle_id, file_path=path, raw=raw, job_id=job_id, pool=pool, cfg_path=cfg_path)
@staticmethod
[docs] def generate_command(dag_id, task_id, execution_date, mark_success=False, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, local=False, pickle_id=None, file_path=None, raw=False, job_id=None, pool=None, cfg_path=None ): """ Generates the shell command required to execute this task instance. :param dag_id: DAG ID :type dag_id: unicode :param task_id: Task ID :type task_id: unicode :param execution_date: Execution date for the task :type execution_date: datetime :param mark_success: Whether to mark the task as successful :type mark_success: bool :param ignore_all_deps: Ignore all ignoreable dependencies. Overrides the other ignore_* parameters. :type ignore_all_deps: boolean :param ignore_depends_on_past: Ignore depends_on_past parameter of DAGs (e.g. for Backfills) :type ignore_depends_on_past: boolean :param ignore_task_deps: Ignore task-specific dependencies such as depends_on_past and trigger rule :type ignore_task_deps: boolean :param ignore_ti_state: Ignore the task instance's previous failure/success :type ignore_ti_state: boolean :param local: Whether to run the task locally :type local: bool :param pickle_id: If the DAG was serialized to the DB, the ID associated with the pickled DAG :type pickle_id: unicode :param file_path: path to the file containing the DAG definition :param raw: raw mode (needs more details) :param job_id: job ID (needs more details) :param pool: the Airflow pool that the task should run in :type pool: unicode :return: shell command that can be used to run the task instance """ iso = execution_date.isoformat() cmd = ["airflow", "run", str(dag_id), str(task_id), str(iso)] cmd.extend(["--mark_success"]) if mark_success else None cmd.extend(["--pickle", str(pickle_id)]) if pickle_id else None cmd.extend(["--job_id", str(job_id)]) if job_id else None cmd.extend(["-A "]) if ignore_all_deps else None cmd.extend(["-i"]) if ignore_task_deps else None cmd.extend(["-I"]) if ignore_depends_on_past else None cmd.extend(["--force"]) if ignore_ti_state else None cmd.extend(["--local"]) if local else None cmd.extend(["--pool", pool]) if pool else None cmd.extend(["--raw"]) if raw else None cmd.extend(["-sd", file_path]) if file_path else None cmd.extend(["--cfg_path", cfg_path]) if cfg_path else None return cmd
@property def log_filepath(self): iso = self.execution_date.isoformat() log = os.path.expanduser(configuration.get('core', 'BASE_LOG_FOLDER')) return ( "{log}/{self.dag_id}/{self.task_id}/{iso}.log".format(**locals())) @property def log_url(self): iso = self.execution_date.isoformat() BASE_URL = configuration.get('webserver', 'BASE_URL') return BASE_URL + ( "/admin/airflow/log" "?dag_id={self.dag_id}" "&task_id={self.task_id}" "&execution_date={iso}" ).format(**locals()) @property def mark_success_url(self): iso = self.execution_date.isoformat() BASE_URL = configuration.get('webserver', 'BASE_URL') return BASE_URL + ( "/admin/airflow/action" "?action=success" "&task_id={self.task_id}" "&dag_id={self.dag_id}" "&execution_date={iso}" "&upstream=false" "&downstream=false" ).format(**locals()) @provide_session
[docs] def current_state(self, session=None): """ Get the very latest state from the database, if a session is passed, we use and looking up the state becomes part of the session, otherwise a new session is used. """ TI = TaskInstance ti = session.query(TI).filter( TI.dag_id == self.dag_id, TI.task_id == self.task_id, TI.execution_date == self.execution_date, ).all() if ti: state = ti[0].state else: state = None return state
@provide_session
[docs] def error(self, session=None): """ Forces the task instance's state to FAILED in the database. """ logging.error("Recording the task instance as FAILED") self.state = State.FAILED session.merge(self) session.commit()
@provide_session
[docs] def refresh_from_db(self, session=None, lock_for_update=False): """ Refreshes the task instance from the database based on the primary key :param lock_for_update: if True, indicates that the database should lock the TaskInstance (issuing a FOR UPDATE clause) until the session is committed. """ TI = TaskInstance qry = session.query(TI).filter( TI.dag_id == self.dag_id, TI.task_id == self.task_id, TI.execution_date == self.execution_date) if lock_for_update: ti = qry.with_for_update().first() else: ti = qry.first() if ti: self.state = ti.state self.start_date = ti.start_date self.end_date = ti.end_date self.try_number = ti.try_number self.hostname = ti.hostname self.pid = ti.pid else: self.state = None
@provide_session
[docs] def clear_xcom_data(self, session=None): """ Clears all XCom data from the database for the task instance """ session.query(XCom).filter( XCom.dag_id == self.dag_id, XCom.task_id == self.task_id, XCom.execution_date == self.execution_date ).delete() session.commit()
@property def key(self): """ Returns a tuple that identifies the task instance uniquely """ return self.dag_id, self.task_id, self.execution_date def set_state(self, state, session): self.state = state self.start_date = datetime.now() self.end_date = datetime.now() session.merge(self) session.commit() @property def is_premature(self): """ Returns whether a task is in UP_FOR_RETRY state and its retry interval has elapsed. """ # is the task still in the retry waiting period? return self.state == State.UP_FOR_RETRY and not self.ready_for_retry() @provide_session
[docs] def are_dependents_done(self, session=None): """ Checks whether the dependents of this task instance have all succeeded. This is meant to be used by wait_for_downstream. This is useful when you do not want to start processing the next schedule of a task until the dependents are done. For instance, if the task DROPs and recreates a table. """ task = self.task if not task.downstream_task_ids: return True ti = session.query(func.count(TaskInstance.task_id)).filter( TaskInstance.dag_id == self.dag_id, TaskInstance.task_id.in_(task.downstream_task_ids), TaskInstance.execution_date == self.execution_date, TaskInstance.state == State.SUCCESS, ) count = ti[0][0] return count == len(task.downstream_task_ids)
@property @provide_session def previous_ti(self, session=None): """ The task instance for the task that ran before this task instance """ dag = self.task.dag if dag: dr = self.get_dagrun(session=session) # LEGACY: most likely running from unit tests if not dr: # Means that this TI is NOT being run from a DR, but from a catchup previous_scheduled_date = dag.previous_schedule(self.execution_date) if not previous_scheduled_date: return None return TaskInstance(task=self.task, execution_date=previous_scheduled_date) dr.dag = dag if dag.catchup: last_dagrun = dr.get_previous_scheduled_dagrun(session=session) else: last_dagrun = dr.get_previous_dagrun(session=session) if last_dagrun: return last_dagrun.get_task_instance(self.task_id, session=session) return None @provide_session
[docs] def are_dependencies_met( self, dep_context=None, session=None, verbose=False): """ Returns whether or not all the conditions are met for this task instance to be run given the context for the dependencies (e.g. a task instance being force run from the UI will ignore some dependencies). :param dep_context: The execution context that determines the dependencies that should be evaluated. :type dep_context: DepContext :param session: database session :type session: Session :param verbose: whether or not to print details on failed dependencies :type verbose: boolean """ dep_context = dep_context or DepContext() failed = False for dep_status in self.get_failed_dep_statuses( dep_context=dep_context, session=session): failed = True if verbose: logging.info("Dependencies not met for {}, dependency '{}' FAILED: {}" .format(self, dep_status.dep_name, dep_status.reason)) if failed: return False if verbose: logging.info("Dependencies all met for {}".format(self)) return True
@provide_session def get_failed_dep_statuses( self, dep_context=None, session=None): dep_context = dep_context or DepContext() for dep in dep_context.deps | self.task.deps: for dep_status in dep.get_dep_statuses( self, session, dep_context): logging.debug("{} dependency '{}' PASSED: {}, {}" .format(self, dep_status.dep_name, dep_status.passed, dep_status.reason)) if not dep_status.passed: yield dep_status def __repr__(self): return ( "<TaskInstance: {ti.dag_id}.{ti.task_id} " "{ti.execution_date} [{ti.state}]>" ).format(ti=self)
[docs] def next_retry_datetime(self): """ Get datetime of the next retry if the task instance fails. For exponential backoff, retry_delay is used as base and will be converted to seconds. """ delay = self.task.retry_delay if self.task.retry_exponential_backoff: delay_backoff_in_seconds = delay.total_seconds() ** self.try_number delay = timedelta(seconds=delay_backoff_in_seconds) if self.task.max_retry_delay: delay = min(self.task.max_retry_delay, delay) return self.end_date + delay
[docs] def ready_for_retry(self): """ Checks on whether the task instance is in the right state and timeframe to be retried. """ return (self.state == State.UP_FOR_RETRY and self.next_retry_datetime() < datetime.now())
@provide_session
[docs] def pool_full(self, session): """ Returns a boolean as to whether the slot pool has room for this task to run """ if not self.task.pool: return False pool = ( session .query(Pool) .filter(Pool.pool == self.task.pool) .first() ) if not pool: return False open_slots = pool.open_slots(session=session) return open_slots <= 0
@provide_session
[docs] def get_dagrun(self, session): """ Returns the DagRun for this TaskInstance :param session: :return: DagRun """ dr = session.query(DagRun).filter( DagRun.dag_id == self.dag_id, DagRun.execution_date == self.execution_date ).first() return dr
@provide_session
[docs] def run( self, verbose=True, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, mark_success=False, test_mode=False, job_id=None, pool=None, session=None): """ Runs the task instance. :param verbose: whether to turn on more verbose loggin :type verbose: boolean :param ignore_all_deps: Ignore all of the non-critical dependencies, just runs :type ignore_all_deps: boolean :param ignore_depends_on_past: Ignore depends_on_past DAG attribute :type ignore_depends_on_past: boolean :param ignore_task_deps: Don't check the dependencies of this TI's task :type ignore_task_deps: boolean :param ignore_ti_state: Disregards previous task instance state :type ignore_ti_state: boolean :param mark_success: Don't run the task, mark its state as success :type mark_success: boolean :param test_mode: Doesn't record success or failure in the DB :type test_mode: boolean :param pool: specifies the pool to use to run the task instance :type pool: str """ task = self.task self.pool = pool or task.pool self.test_mode = test_mode self.refresh_from_db(session=session, lock_for_update=True) self.job_id = job_id self.hostname = socket.getfqdn() self.operator = task.__class__.__name__ if not ignore_all_deps and not ignore_ti_state and self.state == State.SUCCESS: Stats.incr('previously_succeeded', 1, 1) queue_dep_context = DepContext( deps=QUEUE_DEPS, ignore_all_deps=ignore_all_deps, ignore_ti_state=ignore_ti_state, ignore_depends_on_past=ignore_depends_on_past, ignore_task_deps=ignore_task_deps) if not self.are_dependencies_met( dep_context=queue_dep_context, session=session, verbose=True): session.commit() return hr = "\n" + ("-" * 80) + "\n" # Line break # For reporting purposes, we report based on 1-indexed, # not 0-indexed lists (i.e. Attempt 1 instead of # Attempt 0 for the first attempt). msg = "Starting attempt {attempt} of {total}".format( attempt=self.try_number % (task.retries + 1) + 1, total=task.retries + 1) self.start_date = datetime.now() dep_context = DepContext( deps=RUN_DEPS - QUEUE_DEPS, ignore_all_deps=ignore_all_deps, ignore_depends_on_past=ignore_depends_on_past, ignore_task_deps=ignore_task_deps, ignore_ti_state=ignore_ti_state) runnable = self.are_dependencies_met( dep_context=dep_context, session=session, verbose=True) if not runnable and not mark_success: # FIXME: we might have hit concurrency limits, which means we probably # have been running prematurely. This should be handled in the # scheduling mechanism. self.state = State.NONE msg = ("FIXME: Rescheduling due to concurrency limits reached at task " "runtime. Attempt {attempt} of {total}. State set to NONE.").format( attempt=self.try_number % (task.retries + 1) + 1, total=task.retries + 1) logging.warning(hr + msg + hr) self.queued_dttm = datetime.now() msg = "Queuing into pool {}".format(self.pool) logging.info(msg) session.merge(self) session.commit() return # Another worker might have started running this task instance while # the current worker process was blocked on refresh_from_db if self.state == State.RUNNING: msg = "Task Instance already running {}".format(self) logging.warn(msg) session.commit() return # print status message logging.info(hr + msg + hr) self.try_number += 1 if not test_mode: session.add(Log(State.RUNNING, self)) self.state = State.RUNNING self.pid = os.getpid() self.end_date = None if not test_mode: session.merge(self) session.commit() # Closing all pooled connections to prevent # "max number of connections reached" settings.engine.dispose() if verbose: if mark_success: msg = "Marking success for " else: msg = "Executing " msg += "{self.task} on {self.execution_date}" context = {} try: logging.info(msg.format(self=self)) if not mark_success: context = self.get_template_context() task_copy = copy.copy(task) self.task = task_copy def signal_handler(signum, frame): '''Setting kill signal handler''' logging.error("Killing subprocess") task_copy.on_kill() raise AirflowException("Task received SIGTERM signal") signal.signal(signal.SIGTERM, signal_handler) # Don't clear Xcom until the task is certain to execute self.clear_xcom_data() self.render_templates() task_copy.pre_execute(context=context) # If a timeout is specified for the task, make it fail # if it goes beyond result = None if task_copy.execution_timeout: try: with timeout(int( task_copy.execution_timeout.total_seconds())): result = task_copy.execute(context=context) except AirflowTaskTimeout: task_copy.on_kill() raise else: result = task_copy.execute(context=context) # If the task returns a result, push an XCom containing it if result is not None: self.xcom_push(key=XCOM_RETURN_KEY, value=result) task_copy.post_execute(context=context) Stats.incr('operator_successes_{}'.format( self.task.__class__.__name__), 1, 1) self.state = State.SUCCESS except AirflowSkipException: self.state = State.SKIPPED except (Exception, KeyboardInterrupt) as e: self.handle_failure(e, test_mode, context) raise # Recording SUCCESS self.end_date = datetime.now() self.set_duration() if not test_mode: session.add(Log(self.state, self)) session.merge(self) session.commit() # Success callback try: if task.on_success_callback: task.on_success_callback(context) except Exception as e3: logging.error("Failed when executing success callback") logging.exception(e3) session.commit()
def dry_run(self): task = self.task task_copy = copy.copy(task) self.task = task_copy self.render_templates() task_copy.dry_run() def handle_failure(self, error, test_mode=False, context=None): logging.exception(error) task = self.task session = settings.Session() self.end_date = datetime.now() self.set_duration() Stats.incr('operator_failures_{}'.format(task.__class__.__name__), 1, 1) if not test_mode: session.add(Log(State.FAILED, self)) # Log failure duration session.add(TaskFail(task, self.execution_date, self.start_date, self.end_date)) # Let's go deeper try: if task.retries and self.try_number % (task.retries + 1) != 0: self.state = State.UP_FOR_RETRY logging.info('Marking task as UP_FOR_RETRY') if task.email_on_retry and task.email: self.email_alert(error, is_retry=True) else: self.state = State.FAILED if task.retries: logging.info('All retries failed; marking task as FAILED') else: logging.info('Marking task as FAILED.') if task.email_on_failure and task.email: self.email_alert(error, is_retry=False) except Exception as e2: logging.error( 'Failed to send email to: ' + str(task.email)) logging.exception(e2) # Handling callbacks pessimistically try: if self.state == State.UP_FOR_RETRY and task.on_retry_callback: task.on_retry_callback(context) if self.state == State.FAILED and task.on_failure_callback: task.on_failure_callback(context) except Exception as e3: logging.error("Failed at executing callback") logging.exception(e3) if not test_mode: session.merge(self) session.commit() logging.error(str(error)) @provide_session def get_template_context(self, session=None): task = self.task from airflow import macros tables = None if 'tables' in task.params: tables = task.params['tables'] ds = self.execution_date.isoformat()[:10] ts = self.execution_date.isoformat() yesterday_ds = (self.execution_date - timedelta(1)).isoformat()[:10] tomorrow_ds = (self.execution_date + timedelta(1)).isoformat()[:10] prev_execution_date = task.dag.previous_schedule(self.execution_date) next_execution_date = task.dag.following_schedule(self.execution_date) ds_nodash = ds.replace('-', '') ts_nodash = ts.replace('-', '').replace(':', '') yesterday_ds_nodash = yesterday_ds.replace('-', '') tomorrow_ds_nodash = tomorrow_ds.replace('-', '') ti_key_str = "{task.dag_id}__{task.task_id}__{ds_nodash}" ti_key_str = ti_key_str.format(**locals()) params = {} run_id = '' dag_run = None if hasattr(task, 'dag'): if task.dag.params: params.update(task.dag.params) dag_run = ( session.query(DagRun) .filter_by( dag_id=task.dag.dag_id, execution_date=self.execution_date) .first() ) run_id = dag_run.run_id if dag_run else None session.expunge_all() session.commit() if task.params: params.update(task.params) class VariableAccessor: """ Wrapper around Variable. This way you can get variables in templates by using {var.variable_name}. """ def __init__(self): self.var = None def __getattr__(self, item): self.var = Variable.get(item) return self.var def __repr__(self): return str(self.var) class VariableJsonAccessor: def __init__(self): self.var = None def __getattr__(self, item): self.var = Variable.get(item, deserialize_json=True) return self.var def __repr__(self): return str(self.var) return { 'dag': task.dag, 'ds': ds, 'ds_nodash': ds_nodash, 'ts': ts, 'ts_nodash': ts_nodash, 'yesterday_ds': yesterday_ds, 'yesterday_ds_nodash': yesterday_ds_nodash, 'tomorrow_ds': tomorrow_ds, 'tomorrow_ds_nodash': tomorrow_ds_nodash, 'END_DATE': ds, 'end_date': ds, 'dag_run': dag_run, 'run_id': run_id, 'execution_date': self.execution_date, 'prev_execution_date': prev_execution_date, 'next_execution_date': next_execution_date, 'latest_date': ds, 'macros': macros, 'params': params, 'tables': tables, 'task': task, 'task_instance': self, 'ti': self, 'task_instance_key_str': ti_key_str, 'conf': configuration, 'test_mode': self.test_mode, 'var': { 'value': VariableAccessor(), 'json': VariableJsonAccessor() } } def render_templates(self): task = self.task jinja_context = self.get_template_context() if hasattr(self, 'task') and hasattr(self.task, 'dag'): if self.task.dag.user_defined_macros: jinja_context.update( self.task.dag.user_defined_macros) rt = self.task.render_template # shortcut to method for attr in task.__class__.template_fields: content = getattr(task, attr) if content: rendered_content = rt(attr, content, jinja_context) setattr(task, attr, rendered_content) def email_alert(self, exception, is_retry=False): task = self.task title = "Airflow alert: {self}".format(**locals()) exception = str(exception).replace('\n', '<br>') try_ = task.retries + 1 body = ( "Try {self.try_number} out of {try_}<br>" "Exception:<br>{exception}<br>" "Log: <a href='{self.log_url}'>Link</a><br>" "Host: {self.hostname}<br>" "Log file: {self.log_filepath}<br>" "Mark success: <a href='{self.mark_success_url}'>Link</a><br>" ).format(**locals()) send_email(task.email, title, body) def set_duration(self): if self.end_date and self.start_date: self.duration = (self.end_date - self.start_date).total_seconds() else: self.duration = None
[docs] def xcom_push( self, key, value, execution_date=None): """ Make an XCom available for tasks to pull. :param key: A key for the XCom :type key: string :param value: A value for the XCom. The value is pickled and stored in the database. :type value: any pickleable object :param execution_date: if provided, the XCom will not be visible until this date. This can be used, for example, to send a message to a task on a future date without it being immediately visible. :type execution_date: datetime """ if execution_date and execution_date < self.execution_date: raise ValueError( 'execution_date can not be in the past (current ' 'execution_date is {}; received {})'.format( self.execution_date, execution_date)) XCom.set( key=key, value=value, task_id=self.task_id, dag_id=self.dag_id, execution_date=execution_date or self.execution_date)
[docs] def xcom_pull( self, task_ids, dag_id=None, key=XCOM_RETURN_KEY, include_prior_dates=False): """ Pull XComs that optionally meet certain criteria. The default value for `key` limits the search to XComs that were returned by other tasks (as opposed to those that were pushed manually). To remove this filter, pass key=None (or any desired value). If a single task_id string is provided, the result is the value of the most recent matching XCom from that task_id. If multiple task_ids are provided, a tuple of matching values is returned. None is returned whenever no matches are found. :param key: A key for the XCom. If provided, only XComs with matching keys will be returned. The default key is 'return_value', also available as a constant XCOM_RETURN_KEY. This key is automatically given to XComs returned by tasks (as opposed to being pushed manually). To remove the filter, pass key=None. :type key: string :param task_ids: Only XComs from tasks with matching ids will be pulled. Can pass None to remove the filter. :type task_ids: string or iterable of strings (representing task_ids) :param dag_id: If provided, only pulls XComs from this DAG. If None (default), the DAG of the calling task is used. :type dag_id: string :param include_prior_dates: If False, only XComs from the current execution_date are returned. If True, XComs from previous dates are returned as well. :type include_prior_dates: bool """ if dag_id is None: dag_id = self.dag_id pull_fn = functools.partial( XCom.get_one, execution_date=self.execution_date, key=key, dag_id=dag_id, include_prior_dates=include_prior_dates) if is_container(task_ids): return tuple(pull_fn(task_id=t) for t in task_ids) else: return pull_fn(task_id=task_ids)
class TaskFail(Base): """ TaskFail tracks the failed run durations of each task instance. """ __tablename__ = "task_fail" task_id = Column(String(ID_LEN), primary_key=True) dag_id = Column(String(ID_LEN), primary_key=True) execution_date = Column(DateTime, primary_key=True) start_date = Column(DateTime) end_date = Column(DateTime) duration = Column(Float) def __init__(self, task, execution_date, start_date, end_date): self.dag_id = task.dag_id self.task_id = task.task_id self.execution_date = execution_date self.start_date = start_date self.end_date = end_date self.duration = (self.end_date - self.start_date).total_seconds() class Log(Base): """ Used to actively log events to the database """ __tablename__ = "log" id = Column(Integer, primary_key=True) dttm = Column(DateTime) dag_id = Column(String(ID_LEN)) task_id = Column(String(ID_LEN)) event = Column(String(30)) execution_date = Column(DateTime) owner = Column(String(500)) extra = Column(Text) def __init__(self, event, task_instance, owner=None, extra=None, **kwargs): self.dttm = datetime.now() self.event = event self.extra = extra task_owner = None if task_instance: self.dag_id = task_instance.dag_id self.task_id = task_instance.task_id self.execution_date = task_instance.execution_date task_owner = task_instance.task.owner if 'task_id' in kwargs: self.task_id = kwargs['task_id'] if 'dag_id' in kwargs: self.dag_id = kwargs['dag_id'] if 'execution_date' in kwargs: if kwargs['execution_date']: self.execution_date = kwargs['execution_date'] self.owner = owner or task_owner @functools.total_ordering
[docs]class BaseOperator(object): """ Abstract base class for all operators. Since operators create objects that become node in the dag, BaseOperator contains many recursive methods for dag crawling behavior. To derive this class, you are expected to override the constructor as well as the 'execute' method. Operators derived from this task should perform or trigger certain tasks synchronously (wait for completion). Example of operators could be an operator the runs a Pig job (PigOperator), a sensor operator that waits for a partition to land in Hive (HiveSensorOperator), or one that moves data from Hive to MySQL (Hive2MySqlOperator). Instances of these operators (tasks) target specific operations, running specific scripts, functions or data transfers. This class is abstract and shouldn't be instantiated. Instantiating a class derived from this one results in the creation of a task object, which ultimately becomes a node in DAG objects. Task dependencies should be set by using the set_upstream and/or set_downstream methods. Note that this class is derived from SQLAlchemy's Base class, which allows us to push metadata regarding tasks to the database. Deriving this classes needs to implement the polymorphic specificities documented in SQLAlchemy. This should become clear while reading the code for other operators. :param task_id: a unique, meaningful id for the task :type task_id: string :param owner: the owner of the task, using the unix username is recommended :type owner: string :param retries: the number of retries that should be performed before failing the task :type retries: int :param retry_delay: delay between retries :type retry_delay: timedelta :param retry_exponential_backoff: allow progressive longer waits between retries by using exponential backoff algorithm on retry delay (delay will be converted into seconds) :type retry_exponential_backoff: bool :param max_retry_delay: maximum delay interval between retries :type max_retry_delay: timedelta :param start_date: The ``start_date`` for the task, determines the ``execution_date`` for the first task instance. The best practice is to have the start_date rounded to your DAG's ``schedule_interval``. Daily jobs have their start_date some day at 00:00:00, hourly jobs have their start_date at 00:00 of a specific hour. Note that Airflow simply looks at the latest ``execution_date`` and adds the ``schedule_interval`` to determine the next ``execution_date``. It is also very important to note that different tasks' dependencies need to line up in time. If task A depends on task B and their start_date are offset in a way that their execution_date don't line up, A's dependencies will never be met. If you are looking to delay a task, for example running a daily task at 2AM, look into the ``TimeSensor`` and ``TimeDeltaSensor``. We advise against using dynamic ``start_date`` and recommend using fixed ones. Read the FAQ entry about start_date for more information. :type start_date: datetime :param end_date: if specified, the scheduler won't go beyond this date :type end_date: datetime :param depends_on_past: when set to true, task instances will run sequentially while relying on the previous task's schedule to succeed. The task instance for the start_date is allowed to run. :type depends_on_past: bool :param wait_for_downstream: when set to true, an instance of task X will wait for tasks immediately downstream of the previous instance of task X to finish successfully before it runs. This is useful if the different instances of a task X alter the same asset, and this asset is used by tasks downstream of task X. Note that depends_on_past is forced to True wherever wait_for_downstream is used. :type wait_for_downstream: bool :param queue: which queue to target when running this job. Not all executors implement queue management, the CeleryExecutor does support targeting specific queues. :type queue: str :param dag: a reference to the dag the task is attached to (if any) :type dag: DAG :param priority_weight: priority weight of this task against other task. This allows the executor to trigger higher priority tasks before others when things get backed up. :type priority_weight: int :param pool: the slot pool this task should run in, slot pools are a way to limit concurrency for certain tasks :type pool: str :param sla: time by which the job is expected to succeed. Note that this represents the ``timedelta`` after the period is closed. For example if you set an SLA of 1 hour, the scheduler would send dan email soon after 1:00AM on the ``2016-01-02`` if the ``2016-01-01`` instance has not succeeded yet. The scheduler pays special attention for jobs with an SLA and sends alert emails for sla misses. SLA misses are also recorded in the database for future reference. All tasks that share the same SLA time get bundled in a single email, sent soon after that time. SLA notification are sent once and only once for each task instance. :type sla: datetime.timedelta :param execution_timeout: max time allowed for the execution of this task instance, if it goes beyond it will raise and fail. :type execution_timeout: datetime.timedelta :param on_failure_callback: a function to be called when a task instance of this task fails. a context dictionary is passed as a single parameter to this function. Context contains references to related objects to the task instance and is documented under the macros section of the API. :type on_failure_callback: callable :param on_retry_callback: much like the ``on_failure_callback`` excepts that it is executed when retries occur. :param on_success_callback: much like the ``on_failure_callback`` excepts that it is executed when the task succeeds. :type on_success_callback: callable :param trigger_rule: defines the rule by which dependencies are applied for the task to get triggered. Options are: ``{ all_success | all_failed | all_done | one_success | one_failed | dummy}`` default is ``all_success``. Options can be set as string or using the constants defined in the static class ``airflow.utils.TriggerRule`` :type trigger_rule: str :param resources: A map of resource parameter names (the argument names of the Resources constructor) to their values. :type resources: dict :param run_as_user: unix username to impersonate while running the task :type run_as_user: str """ # For derived classes to define which fields will get jinjaified template_fields = [] # Defines wich files extensions to look for in the templated fields template_ext = [] # Defines the color in the UI ui_color = '#fff' ui_fgcolor = '#000' @apply_defaults def __init__( self, task_id, owner=configuration.get('operators', 'DEFAULT_OWNER'), email=None, email_on_retry=True, email_on_failure=True, retries=0, retry_delay=timedelta(seconds=300), retry_exponential_backoff=False, max_retry_delay=None, start_date=None, end_date=None, schedule_interval=None, # not hooked as of now depends_on_past=False, wait_for_downstream=False, dag=None, params=None, default_args=None, adhoc=False, priority_weight=1, queue=configuration.get('celery', 'default_queue'), pool=None, sla=None, execution_timeout=None, on_failure_callback=None, on_success_callback=None, on_retry_callback=None, trigger_rule=TriggerRule.ALL_SUCCESS, resources=None, run_as_user=None, *args, **kwargs): if args or kwargs: # TODO remove *args and **kwargs in Airflow 2.0 warnings.warn( 'Invalid arguments were passed to {c}. Support for ' 'passing such arguments will be dropped in Airflow 2.0. ' 'Invalid arguments were:' '\n*args: {a}\n**kwargs: {k}'.format( c=self.__class__.__name__, a=args, k=kwargs), category=PendingDeprecationWarning ) validate_key(task_id) self.task_id = task_id self.owner = owner self.email = email self.email_on_retry = email_on_retry self.email_on_failure = email_on_failure self.start_date = start_date if start_date and not isinstance(start_date, datetime): logging.warning( "start_date for {} isn't datetime.datetime".format(self)) self.end_date = end_date if not TriggerRule.is_valid(trigger_rule): raise AirflowException( "The trigger_rule must be one of {all_triggers}," "'{d}.{t}'; received '{tr}'." .format(all_triggers=TriggerRule.all_triggers, d=dag.dag_id, t=task_id, tr=trigger_rule)) self.trigger_rule = trigger_rule self.depends_on_past = depends_on_past self.wait_for_downstream = wait_for_downstream if wait_for_downstream: self.depends_on_past = True if schedule_interval: logging.warning( "schedule_interval is used for {}, though it has " "been deprecated as a task parameter, you need to " "specify it as a DAG parameter instead".format(self)) self._schedule_interval = schedule_interval self.retries = retries self.queue = queue self.pool = pool self.sla = sla self.execution_timeout = execution_timeout self.on_failure_callback = on_failure_callback self.on_success_callback = on_success_callback self.on_retry_callback = on_retry_callback if isinstance(retry_delay, timedelta): self.retry_delay = retry_delay else: logging.debug("retry_delay isn't timedelta object, assuming secs") self.retry_delay = timedelta(seconds=retry_delay) self.retry_exponential_backoff = retry_exponential_backoff self.max_retry_delay = max_retry_delay self.params = params or {} # Available in templates! self.adhoc = adhoc self.priority_weight = priority_weight self.resources = Resources(**(resources or {})) self.run_as_user = run_as_user # Private attributes self._upstream_task_ids = [] self._downstream_task_ids = [] if not dag and _CONTEXT_MANAGER_DAG: dag = _CONTEXT_MANAGER_DAG if dag: self.dag = dag self._comps = { 'task_id', 'dag_id', 'owner', 'email', 'email_on_retry', 'retry_delay', 'retry_exponential_backoff', 'max_retry_delay', 'start_date', 'schedule_interval', 'depends_on_past', 'wait_for_downstream', 'adhoc', 'priority_weight', 'sla', 'execution_timeout', 'on_failure_callback', 'on_success_callback', 'on_retry_callback', } def __eq__(self, other): return ( type(self) == type(other) and all(self.__dict__.get(c, None) == other.__dict__.get(c, None) for c in self._comps)) def __ne__(self, other): return not self == other def __lt__(self, other): return self.task_id < other.task_id def __hash__(self): hash_components = [type(self)] for c in self._comps: val = getattr(self, c, None) try: hash(val) hash_components.append(val) except TypeError: hash_components.append(repr(val)) return hash(tuple(hash_components)) # Composing Operators ----------------------------------------------- def __rshift__(self, other): """ Implements Self >> Other == self.set_downstream(other) If "Other" is a DAG, the DAG is assigned to the Operator. """ if isinstance(other, DAG): # if this dag is already assigned, do nothing # otherwise, do normal dag assignment if not (self.has_dag() and self.dag is other): self.dag = other else: self.set_downstream(other) return other def __lshift__(self, other): """ Implements Self << Other == self.set_upstream(other) If "Other" is a DAG, the DAG is assigned to the Operator. """ if isinstance(other, DAG): # if this dag is already assigned, do nothing # otherwise, do normal dag assignment if not (self.has_dag() and self.dag is other): self.dag = other else: self.set_upstream(other) return other def __rrshift__(self, other): """ Called for [DAG] >> [Operator] because DAGs don't have __rshift__ operators. """ self.__lshift__(other) return self def __rlshift__(self, other): """ Called for [DAG] << [Operator] because DAGs don't have __lshift__ operators. """ self.__rshift__(other) return self # /Composing Operators --------------------------------------------- @property def dag(self): """ Returns the Operator's DAG if set, otherwise raises an error """ if self.has_dag(): return self._dag else: raise AirflowException( 'Operator {} has not been assigned to a DAG yet'.format(self)) @dag.setter def dag(self, dag): """ Operators can be assigned to one DAG, one time. Repeat assignments to that same DAG are ok. """ if not isinstance(dag, DAG): raise TypeError( 'Expected DAG; received {}'.format(dag.__class__.__name__)) elif self.has_dag() and self.dag is not dag: raise AirflowException( "The DAG assigned to {} can not be changed.".format(self)) elif self.task_id not in dag.task_dict: dag.add_task(self) self._dag = dag
[docs] def has_dag(self): """ Returns True if the Operator has been assigned to a DAG. """ return getattr(self, '_dag', None) is not None
@property def dag_id(self): if self.has_dag(): return self.dag.dag_id else: return 'adhoc_' + self.owner @property def deps(self): """ Returns the list of dependencies for the operator. These differ from execution context dependencies in that they are specific to tasks and can be extended/overriden by subclasses. """ return { NotInRetryPeriodDep(), PrevDagrunDep(), TriggerRuleDep(), } @property def schedule_interval(self): """ The schedule interval of the DAG always wins over individual tasks so that tasks within a DAG always line up. The task still needs a schedule_interval as it may not be attached to a DAG. """ if self.has_dag(): return self.dag._schedule_interval else: return self._schedule_interval @property def priority_weight_total(self): return sum([ t.priority_weight for t in self.get_flat_relatives(upstream=False) ]) + self.priority_weight
[docs] def pre_execute(self, context): """ This is triggered right before self.execute, it's mostly a hook for people deriving operators. """ pass
[docs] def execute(self, context): """ This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates. Refer to get_template_context for more context. """ raise NotImplementedError()
[docs] def post_execute(self, context): """ This is triggered right after self.execute, it's mostly a hook for people deriving operators. """ pass
[docs] def on_kill(self): """ Override this method to cleanup subprocesses when a task instance gets killed. Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up or it will leave ghost processes behind. """ pass
def __deepcopy__(self, memo): """ Hack sorting double chained task lists by task_id to avoid hitting max_depth on deepcopy operations. """ sys.setrecursionlimit(5000) # TODO fix this in a better way cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result for k, v in list(self.__dict__.items()): if k not in ('user_defined_macros', 'params'): setattr(result, k, copy.deepcopy(v, memo)) result.params = self.params if hasattr(self, 'user_defined_macros'): result.user_defined_macros = self.user_defined_macros return result
[docs] def render_template_from_field(self, attr, content, context, jinja_env): """ Renders a template from a field. If the field is a string, it will simply render the string and return the result. If it is a collection or nested set of collections, it will traverse the structure and render all strings in it. """ rt = self.render_template if isinstance(content, six.string_types): result = jinja_env.from_string(content).render(**context) elif isinstance(content, (list, tuple)): result = [rt(attr, e, context) for e in content] elif isinstance(content, dict): result = { k: rt("{}[{}]".format(attr, k), v, context) for k, v in list(content.items())} else: param_type = type(content) msg = ( "Type '{param_type}' used for parameter '{attr}' is " "not supported for templating").format(**locals()) raise AirflowException(msg) return result
[docs] def render_template(self, attr, content, context): """ Renders a template either from a file or directly in a field, and returns the rendered result. """ jinja_env = self.dag.get_template_env() \ if hasattr(self, 'dag') \ else jinja2.Environment(cache_size=0) exts = self.__class__.template_ext if ( isinstance(content, six.string_types) and any([content.endswith(ext) for ext in exts])): return jinja_env.get_template(content).render(**context) else: return self.render_template_from_field(attr, content, context, jinja_env)
[docs] def prepare_template(self): """ Hook that is triggered after the templated fields get replaced by their content. If you need your operator to alter the content of the file before the template is rendered, it should override this method to do so. """ pass
def resolve_template_files(self): # Getting the content of files for template_field / template_ext for attr in self.template_fields: content = getattr(self, attr) if content is not None and \ isinstance(content, six.string_types) and \ any([content.endswith(ext) for ext in self.template_ext]): env = self.dag.get_template_env() try: setattr(self, attr, env.loader.get_source(env, content)[0]) except Exception as e: logging.exception(e) self.prepare_template() @property def upstream_list(self): """@property: list of tasks directly upstream""" return [self.dag.get_task(tid) for tid in self._upstream_task_ids] @property def upstream_task_ids(self): return self._upstream_task_ids @property def downstream_list(self): """@property: list of tasks directly downstream""" return [self.dag.get_task(tid) for tid in self._downstream_task_ids] @property def downstream_task_ids(self): return self._downstream_task_ids
[docs] def clear( self, start_date=None, end_date=None, upstream=False, downstream=False): """ Clears the state of task instances associated with the task, following the parameters specified. """ session = settings.Session() TI = TaskInstance qry = session.query(TI).filter(TI.dag_id == self.dag_id) if start_date: qry = qry.filter(TI.execution_date >= start_date) if end_date: qry = qry.filter(TI.execution_date <= end_date) tasks = [self.task_id] if upstream: tasks += [ t.task_id for t in self.get_flat_relatives(upstream=True)] if downstream: tasks += [ t.task_id for t in self.get_flat_relatives(upstream=False)] qry = qry.filter(TI.task_id.in_(tasks)) count = qry.count() clear_task_instances(qry, session) session.commit() session.close() return count
[docs] def get_task_instances(self, session, start_date=None, end_date=None): """ Get a set of task instance related to this task for a specific date range. """ TI = TaskInstance end_date = end_date or datetime.now() return session.query(TI).filter( TI.dag_id == self.dag_id, TI.task_id == self.task_id, TI.execution_date >= start_date, TI.execution_date <= end_date, ).order_by(TI.execution_date).all()
[docs] def get_flat_relatives(self, upstream=False, l=None): """ Get a flat list of relatives, either upstream or downstream. """ if not l: l = [] for t in self.get_direct_relatives(upstream): if not is_in(t, l): l.append(t) t.get_flat_relatives(upstream, l) return l
[docs] def detect_downstream_cycle(self, task=None): """ When invoked, this routine will raise an exception if a cycle is detected downstream from self. It is invoked when tasks are added to the DAG to detect cycles. """ if not task: task = self for t in self.get_direct_relatives(): if task is t: msg = "Cycle detected in DAG. Faulty task: {0}".format(task) raise AirflowException(msg) else: t.detect_downstream_cycle(task=task) return False
[docs] def run( self, start_date=None, end_date=None, ignore_first_depends_on_past=False, ignore_ti_state=False, mark_success=False): """ Run a set of task instances for a date range. """ start_date = start_date or self.start_date end_date = end_date or self.end_date or datetime.now() for dt in self.dag.date_range(start_date, end_date=end_date): TaskInstance(self, dt).run( mark_success=mark_success, ignore_depends_on_past=( dt == start_date and ignore_first_depends_on_past), ignore_ti_state=ignore_ti_state)
def dry_run(self): logging.info('Dry run') for attr in self.template_fields: content = getattr(self, attr) if content and isinstance(content, six.string_types): logging.info('Rendering template for {0}'.format(attr)) logging.info(content)
[docs] def get_direct_relatives(self, upstream=False): """ Get the direct relatives to the current task, upstream or downstream. """ if upstream: return self.upstream_list else: return self.downstream_list
def __repr__(self): return "<Task({self.__class__.__name__}): {self.task_id}>".format( self=self) @property def task_type(self): return self.__class__.__name__ def append_only_new(self, l, item): if any([item is t for t in l]): raise AirflowException( 'Dependency {self}, {item} already registered' ''.format(**locals())) else: l.append(item) def _set_relatives(self, task_or_task_list, upstream=False): try: task_list = list(task_or_task_list) except TypeError: task_list = [task_or_task_list] for t in task_list: if not isinstance(t, BaseOperator): raise AirflowException( "Relationships can only be set between " "Operators; received {}".format(t.__class__.__name__)) # relationships can only be set if the tasks share a single DAG. Tasks # without a DAG are assigned to that DAG. dags = set(t.dag for t in [self] + task_list if t.has_dag()) if len(dags) > 1: raise AirflowException( 'Tried to set relationships between tasks in ' 'more than one DAG: {}'.format(dags)) elif len(dags) == 1: dag = list(dags)[0] else: raise AirflowException( "Tried to create relationships between tasks that don't have " "DAGs yet. Set the DAG for at least one " "task and try again: {}".format([self] + task_list)) if dag and not self.has_dag(): self.dag = dag for task in task_list: if dag and not task.has_dag(): task.dag = dag if upstream: task.append_only_new(task._downstream_task_ids, self.task_id) self.append_only_new(self._upstream_task_ids, task.task_id) else: self.append_only_new(self._downstream_task_ids, task.task_id) task.append_only_new(task._upstream_task_ids, self.task_id) self.detect_downstream_cycle()
[docs] def set_downstream(self, task_or_task_list): """ Set a task, or a task task to be directly downstream from the current task. """ self._set_relatives(task_or_task_list, upstream=False)
[docs] def set_upstream(self, task_or_task_list): """ Set a task, or a task task to be directly upstream from the current task. """ self._set_relatives(task_or_task_list, upstream=True)
[docs] def xcom_push( self, context, key, value, execution_date=None): """ See TaskInstance.xcom_push() """ context['ti'].xcom_push( key=key, value=value, execution_date=execution_date)
[docs] def xcom_pull( self, context, task_ids, dag_id=None, key=XCOM_RETURN_KEY, include_prior_dates=None): """ See TaskInstance.xcom_pull() """ return context['ti'].xcom_pull( key=key, task_ids=task_ids, dag_id=dag_id, include_prior_dates=include_prior_dates)
class DagModel(Base): __tablename__ = "dag" """ These items are stored in the database for state related information """ dag_id = Column(String(ID_LEN), primary_key=True) # A DAG can be paused from the UI / DB # Set this default value of is_paused based on a configuration value! is_paused_at_creation = configuration.getboolean('core', 'dags_are_paused_at_creation') is_paused = Column(Boolean, default=is_paused_at_creation) # Whether the DAG is a subdag is_subdag = Column(Boolean, default=False) # Whether that DAG was seen on the last DagBag load is_active = Column(Boolean, default=False) # Last time the scheduler started last_scheduler_run = Column(DateTime) # Last time this DAG was pickled last_pickled = Column(DateTime) # Time when the DAG last received a refresh signal # (e.g. the DAG's "refresh" button was clicked in the web UI) last_expired = Column(DateTime) # Whether (one of) the scheduler is scheduling this DAG at the moment scheduler_lock = Column(Boolean) # Foreign key to the latest pickle_id pickle_id = Column(Integer) # The location of the file containing the DAG object fileloc = Column(String(2000)) # String representing the owners owners = Column(String(2000)) def __repr__(self): return "<DAG: {self.dag_id}>".format(self=self) @classmethod def get_current(cls, dag_id): session = settings.Session() obj = session.query(cls).filter(cls.dag_id == dag_id).first() session.expunge_all() session.commit() session.close() return obj @functools.total_ordering
[docs]class DAG(BaseDag, LoggingMixin): """ A dag (directed acyclic graph) is a collection of tasks with directional dependencies. A dag also has a schedule, a start end an end date (optional). For each schedule, (say daily or hourly), the DAG needs to run each individual tasks as their dependencies are met. Certain tasks have the property of depending on their own past, meaning that they can't run until their previous schedule (and upstream tasks) are completed. DAGs essentially act as namespaces for tasks. A task_id can only be added once to a DAG. :param dag_id: The id of the DAG :type dag_id: string :param description: The description for the DAG to e.g. be shown on the webserver :type description: string :param schedule_interval: Defines how often that DAG runs, this timedelta object gets added to your latest task instance's execution_date to figure out the next schedule :type schedule_interval: datetime.timedelta or dateutil.relativedelta.relativedelta or str that acts as a cron expression :param start_date: The timestamp from which the scheduler will attempt to backfill :type start_date: datetime.datetime :param end_date: A date beyond which your DAG won't run, leave to None for open ended scheduling :type end_date: datetime.datetime :param template_searchpath: This list of folders (non relative) defines where jinja will look for your templates. Order matters. Note that jinja/airflow includes the path of your DAG file by default :type template_searchpath: string or list of stings :param user_defined_macros: a dictionary of macros that will be exposed in your jinja templates. For example, passing ``dict(foo='bar')`` to this argument allows you to ``{{ foo }}`` in all jinja templates related to this DAG. Note that you can pass any type of object here. :type user_defined_macros: dict :param default_args: A dictionary of default parameters to be used as constructor keyword parameters when initialising operators. Note that operators have the same hook, and precede those defined here, meaning that if your dict contains `'depends_on_past': True` here and `'depends_on_past': False` in the operator's call `default_args`, the actual value will be `False`. :type default_args: dict :param params: a dictionary of DAG level parameters that are made accessible in templates, namespaced under `params`. These params can be overridden at the task level. :type params: dict :param concurrency: the number of task instances allowed to run concurrently :type concurrency: int :param max_active_runs: maximum number of active DAG runs, beyond this number of DAG runs in a running state, the scheduler won't create new active DAG runs :type max_active_runs: int :param dagrun_timeout: specify how long a DagRun should be up before timing out / failing, so that new DagRuns can be created :type dagrun_timeout: datetime.timedelta :param sla_miss_callback: specify a function to call when reporting SLA timeouts. :type sla_miss_callback: types.FunctionType :param orientation: Specify DAG orientation in graph view (LR, TB, RL, BT) :type orientation: string :param catchup: Perform scheduler catchup (or only run latest)? Defaults to True "type catchup: bool" """ def __init__( self, dag_id, description='', schedule_interval=timedelta(days=1), start_date=None, end_date=None, full_filepath=None, template_searchpath=None, user_defined_macros=None, default_args=None, concurrency=configuration.getint('core', 'dag_concurrency'), max_active_runs=configuration.getint( 'core', 'max_active_runs_per_dag'), dagrun_timeout=None, sla_miss_callback=None, orientation=configuration.get('webserver', 'dag_orientation'), catchup=configuration.getboolean('scheduler', 'catchup_by_default'), params=None): self.user_defined_macros = user_defined_macros self.default_args = default_args or {} self.params = params or {} # merging potentially conflicting default_args['params'] into params if 'params' in self.default_args: self.params.update(self.default_args['params']) del self.default_args['params'] validate_key(dag_id) # Properties from BaseDag self._dag_id = dag_id self._full_filepath = full_filepath if full_filepath else '' self._concurrency = concurrency self._pickle_id = None self._description = description # set file location to caller source path self.fileloc = inspect.getsourcefile(inspect.stack()[1][0]) self.task_dict = dict() self.start_date = start_date self.end_date = end_date self.schedule_interval = schedule_interval if schedule_interval in cron_presets: self._schedule_interval = cron_presets.get(schedule_interval) elif schedule_interval == '@once': self._schedule_interval = None else: self._schedule_interval = schedule_interval if isinstance(template_searchpath, six.string_types): template_searchpath = [template_searchpath] self.template_searchpath = template_searchpath self.parent_dag = None # Gets set when DAGs are loaded self.last_loaded = datetime.now() self.safe_dag_id = dag_id.replace('.', '__dot__') self.max_active_runs = max_active_runs self.dagrun_timeout = dagrun_timeout self.sla_miss_callback = sla_miss_callback self.orientation = orientation self.catchup = catchup self.partial = False self._comps = { 'dag_id', 'task_ids', 'parent_dag', 'start_date', 'schedule_interval', 'full_filepath', 'template_searchpath', 'last_loaded', } def __repr__(self): return "<DAG: {self.dag_id}>".format(self=self) def __eq__(self, other): return ( type(self) == type(other) and # Use getattr() instead of __dict__ as __dict__ doesn't return # correct values for properties. all(getattr(self, c, None) == getattr(other, c, None) for c in self._comps)) def __ne__(self, other): return not self == other def __lt__(self, other): return self.dag_id < other.dag_id def __hash__(self): hash_components = [type(self)] for c in self._comps: # task_ids returns a list and lists can't be hashed if c == 'task_ids': val = tuple(self.task_dict.keys()) else: val = getattr(self, c, None) try: hash(val) hash_components.append(val) except TypeError: hash_components.append(repr(val)) return hash(tuple(hash_components)) # Context Manager ----------------------------------------------- def __enter__(self): global _CONTEXT_MANAGER_DAG self._old_context_manager_dag = _CONTEXT_MANAGER_DAG _CONTEXT_MANAGER_DAG = self return self def __exit__(self, _type, _value, _tb): global _CONTEXT_MANAGER_DAG _CONTEXT_MANAGER_DAG = self._old_context_manager_dag # /Context Manager ---------------------------------------------- def date_range(self, start_date, num=None, end_date=datetime.now()): if num: end_date = None return utils_date_range( start_date=start_date, end_date=end_date, num=num, delta=self._schedule_interval) def following_schedule(self, dttm): if isinstance(self._schedule_interval, six.string_types): cron = croniter(self._schedule_interval, dttm) return cron.get_next(datetime) elif isinstance(self._schedule_interval, timedelta): return dttm + self._schedule_interval def previous_schedule(self, dttm): if isinstance(self._schedule_interval, six.string_types): cron = croniter(self._schedule_interval, dttm) return cron.get_prev(datetime) elif isinstance(self._schedule_interval, timedelta): return dttm - self._schedule_interval
[docs] def normalize_schedule(self, dttm): """ Returns dttm + interval unless dttm is first interval then it returns dttm """ following = self.following_schedule(dttm) # in case of @once if not following: return dttm if self.previous_schedule(following) != dttm: return following return dttm
@provide_session
[docs] def get_last_dagrun(self, session=None, include_externally_triggered=False): """ Returns the last dag run for this dag, None if there was none. Last dag run can be any type of run eg. scheduled or backfilled. Overriden DagRuns are ignored """ DR = DagRun qry = session.query(DR).filter( DR.dag_id == self.dag_id, ) if not include_externally_triggered: qry = qry.filter(DR.external_trigger.is_(False)) qry = qry.order_by(DR.execution_date.desc()) last = qry.first() return last
@property def dag_id(self): return self._dag_id @dag_id.setter def dag_id(self, value): self._dag_id = value @property def full_filepath(self): return self._full_filepath @full_filepath.setter def full_filepath(self, value): self._full_filepath = value @property def concurrency(self): return self._concurrency @concurrency.setter def concurrency(self, value): self._concurrency = value @property def description(self): return self._description @property def pickle_id(self): return self._pickle_id @pickle_id.setter def pickle_id(self, value): self._pickle_id = value @property def tasks(self): return list(self.task_dict.values()) @tasks.setter def tasks(self, val): raise AttributeError( 'DAG.tasks can not be modified. Use dag.add_task() instead.') @property def task_ids(self): return list(self.task_dict.keys()) @property def active_task_ids(self): return list(k for k, v in self.task_dict.items() if not v.adhoc) @property def active_tasks(self): return [t for t in self.tasks if not t.adhoc] @property def filepath(self): """ File location of where the dag object is instantiated """ fn = self.full_filepath.replace(settings.DAGS_FOLDER + '/', '') fn = fn.replace(os.path.dirname(__file__) + '/', '') return fn @property def folder(self): """ Folder location of where the dag object is instantiated """ return os.path.dirname(self.full_filepath) @property def owner(self): return ", ".join(list(set([t.owner for t in self.tasks]))) @property @provide_session def concurrency_reached(self, session=None): """ Returns a boolean indicating whether the concurrency limit for this DAG has been reached """ TI = TaskInstance qry = session.query(func.count(TI.task_id)).filter( TI.dag_id == self.dag_id, TI.task_id.in_(self.task_ids), TI.state == State.RUNNING, ) return qry.scalar() >= self.concurrency @property @provide_session def is_paused(self, session=None): """ Returns a boolean indicating whether this DAG is paused """ qry = session.query(DagModel).filter( DagModel.dag_id == self.dag_id) return qry.value('is_paused') @provide_session
[docs] def get_active_runs(self, session=None): """ Returns a list of "running" tasks :param session: :return: List of execution dates """ runs = DagRun.find(dag_id=self.dag_id, state=State.RUNNING) active_dates = [] for run in runs: active_dates.append(run.execution_date) return active_dates
@provide_session
[docs] def get_dagrun(self, execution_date, session=None): """ Returns the dag run for a given execution date if it exists, otherwise none. :param execution_date: The execution date of the DagRun to find. :param session: :return: The DagRun if found, otherwise None. """ dagrun = ( session.query(DagRun) .filter( DagRun.dag_id == self.dag_id, DagRun.execution_date == execution_date) .first()) return dagrun
@property def latest_execution_date(self): """ Returns the latest date for which at least one dag run exists """ session = settings.Session() execution_date = session.query(func.max(DagRun.execution_date)).filter( DagRun.dag_id == self.dag_id ).scalar() session.commit() session.close() return execution_date @property def subdags(self): """ Returns a list of the subdag objects associated to this DAG """ # Check SubDag for class but don't check class directly, see # https://github.com/airbnb/airflow/issues/1168 l = [] for task in self.tasks: if ( task.__class__.__name__ == 'SubDagOperator' and hasattr(task, 'subdag')): l.append(task.subdag) l += task.subdag.subdags return l def resolve_template_files(self): for t in self.tasks: t.resolve_template_files()
[docs] def crawl_for_tasks(objects): """ Typically called at the end of a script by passing globals() as a parameter. This allows to not explicitly add every single task to the dag explicitly. """ raise NotImplementedError("")
[docs] def get_template_env(self): """ Returns a jinja2 Environment while taking into account the DAGs template_searchpath and user_defined_macros """ searchpath = [self.folder] if self.template_searchpath: searchpath += self.template_searchpath env = jinja2.Environment( loader=jinja2.FileSystemLoader(searchpath), extensions=["jinja2.ext.do"], cache_size=0) if self.user_defined_macros: env.globals.update(self.user_defined_macros) return env
[docs] def set_dependency(self, upstream_task_id, downstream_task_id): """ Simple utility method to set dependency between two tasks that already have been added to the DAG using add_task() """ self.get_task(upstream_task_id).set_downstream( self.get_task(downstream_task_id))
def get_task_instances( self, session, start_date=None, end_date=None, state=None): TI = TaskInstance if not start_date: start_date = (datetime.today() - timedelta(30)).date() start_date = datetime.combine(start_date, datetime.min.time()) end_date = end_date or datetime.now() tis = session.query(TI).filter( TI.dag_id == self.dag_id, TI.execution_date >= start_date, TI.execution_date <= end_date, TI.task_id.in_([t.task_id for t in self.tasks]), ) if state: tis = tis.filter(TI.state == state) tis = tis.all() return tis @property def roots(self): return [t for t in self.tasks if not t.downstream_list]
[docs] def topological_sort(self): """ Sorts tasks in topographical order, such that a task comes after any of its upstream dependencies. Heavily inspired by: http://blog.jupo.org/2012/04/06/topological-sorting-acyclic-directed-graphs/ :returns: list of tasks in topological order """ # copy the the tasks so we leave it unmodified graph_unsorted = self.tasks[:] graph_sorted = [] # special case if len(self.tasks) == 0: return tuple(graph_sorted) # Run until the unsorted graph is empty. while graph_unsorted: # Go through each of the node/edges pairs in the unsorted # graph. If a set of edges doesn't contain any nodes that # haven't been resolved, that is, that are still in the # unsorted graph, remove the pair from the unsorted graph, # and append it to the sorted graph. Note here that by using # using the items() method for iterating, a copy of the # unsorted graph is used, allowing us to modify the unsorted # graph as we move through it. We also keep a flag for # checking that that graph is acyclic, which is true if any # nodes are resolved during each pass through the graph. If # not, we need to bail out as the graph therefore can't be # sorted. acyclic = False for node in list(graph_unsorted): for edge in node.upstream_list: if edge in graph_unsorted: break # no edges in upstream tasks else: acyclic = True graph_unsorted.remove(node) graph_sorted.append(node) if not acyclic: raise AirflowException("A cyclic dependency occurred in dag: {}" .format(self.dag_id)) return tuple(graph_sorted)
@provide_session def set_dag_runs_state( self, state=State.RUNNING, session=None): drs = session.query(DagModel).filter_by(dag_id=self.dag_id).all() dirty_ids = [] for dr in drs: dr.state = state dirty_ids.append(dr.dag_id) DagStat.clean_dirty(dirty_ids, session=session)
[docs] def clear( self, start_date=None, end_date=None, only_failed=False, only_running=False, confirm_prompt=False, include_subdags=True, reset_dag_runs=True, dry_run=False): """ Clears a set of task instances associated with the current dag for a specified date range. """ session = settings.Session() TI = TaskInstance tis = session.query(TI) if include_subdags: # Crafting the right filter for dag_id and task_ids combo conditions = [] for dag in self.subdags + [self]: conditions.append( TI.dag_id.like(dag.dag_id) & TI.task_id.in_(dag.task_ids) ) tis = tis.filter(or_(*conditions)) else: tis = session.query(TI).filter(TI.dag_id == self.dag_id) tis = tis.filter(TI.task_id.in_(self.task_ids)) if start_date: tis = tis.filter(TI.execution_date >= start_date) if end_date: tis = tis.filter(TI.execution_date <= end_date) if only_failed: tis = tis.filter(TI.state == State.FAILED) if only_running: tis = tis.filter(TI.state == State.RUNNING) if dry_run: tis = tis.all() session.expunge_all() return tis count = tis.count() do_it = True if count == 0: print("Nothing to clear.") return 0 if confirm_prompt: ti_list = "\n".join([str(t) for t in tis]) question = ( "You are about to delete these {count} tasks:\n" "{ti_list}\n\n" "Are you sure? (yes/no): ").format(**locals()) do_it = utils.helpers.ask_yesno(question) if do_it: clear_task_instances(tis, session) if reset_dag_runs: self.set_dag_runs_state(session=session) else: count = 0 print("Bail. Nothing was cleared.") session.commit() session.close() return count
def __deepcopy__(self, memo): # Swiwtcharoo to go around deepcopying objects coming through the # backdoor cls = self.__class__ result = cls.__new__(cls) memo[id(self)] = result for k, v in list(self.__dict__.items()): if k not in ('user_defined_macros', 'params'): setattr(result, k, copy.deepcopy(v, memo)) result.user_defined_macros = self.user_defined_macros result.params = self.params return result
[docs] def sub_dag(self, task_regex, include_downstream=False, include_upstream=True): """ Returns a subset of the current dag as a deep copy of the current dag based on a regex that should match one or many tasks, and includes upstream and downstream neighbours based on the flag passed. """ dag = copy.deepcopy(self) regex_match = [ t for t in dag.tasks if re.findall(task_regex, t.task_id)] also_include = [] for t in regex_match: if include_downstream: also_include += t.get_flat_relatives(upstream=False) if include_upstream: also_include += t.get_flat_relatives(upstream=True) # Compiling the unique list of tasks that made the cut dag.task_dict = {t.task_id: t for t in regex_match + also_include} for t in dag.tasks: # Removing upstream/downstream references to tasks that did not # made the cut t._upstream_task_ids = [ tid for tid in t._upstream_task_ids if tid in dag.task_ids] t._downstream_task_ids = [ tid for tid in t._downstream_task_ids if tid in dag.task_ids] if len(dag.tasks) < len(self.tasks): dag.partial = True return dag
def has_task(self, task_id): return task_id in (t.task_id for t in self.tasks) def get_task(self, task_id): if task_id in self.task_dict: return self.task_dict[task_id] raise AirflowException("Task {task_id} not found".format(**locals())) @provide_session def pickle_info(self, session=None): d = {} d['is_picklable'] = True try: dttm = datetime.now() pickled = pickle.dumps(self) d['pickle_len'] = len(pickled) d['pickling_duration'] = "{}".format(datetime.now() - dttm) except Exception as e: logging.debug(e) d['is_picklable'] = False d['stacktrace'] = traceback.format_exc() return d @provide_session def pickle(self, session=None): dag = session.query( DagModel).filter(DagModel.dag_id == self.dag_id).first() dp = None if dag and dag.pickle_id: dp = session.query(DagPickle).filter( DagPickle.id == dag.pickle_id).first() if not dp or dp.pickle != self: dp = DagPickle(dag=self) session.add(dp) self.last_pickled = datetime.now() session.commit() self.pickle_id = dp.id return dp
[docs] def tree_view(self): """ Shows an ascii tree representation of the DAG """ def get_downstream(task, level=0): print((" " * level * 4) + str(task)) level += 1 for t in task.upstream_list: get_downstream(t, level) for t in self.roots: get_downstream(t)
[docs] def add_task(self, task): """ Add a task to the DAG :param task: the task you want to add :type task: task """ if not self.start_date and not task.start_date: raise AirflowException("Task is missing the start_date parameter") if not task.start_date: task.start_date = self.start_date if task.task_id in self.task_dict: # TODO: raise an error in Airflow 2.0 warnings.warn( 'The requested task could not be added to the DAG because a ' 'task with task_id {} is already in the DAG. Starting in ' 'Airflow 2.0, trying to overwrite a task will raise an ' 'exception.'.format(task.task_id), category=PendingDeprecationWarning) else: self.tasks.append(task) self.task_dict[task.task_id] = task task.dag = self self.task_count = len(self.tasks)
[docs] def add_tasks(self, tasks): """ Add a list of tasks to the DAG :param task: a lit of tasks you want to add :type task: list of tasks """ for task in tasks: self.add_task(task)
def db_merge(self): BO = BaseOperator session = settings.Session() tasks = session.query(BO).filter(BO.dag_id == self.dag_id).all() for t in tasks: session.delete(t) session.commit() session.merge(self) session.commit()
[docs] def run( self, start_date=None, end_date=None, mark_success=False, include_adhoc=False, local=False, executor=None, donot_pickle=configuration.getboolean('core', 'donot_pickle'), ignore_task_deps=False, ignore_first_depends_on_past=False, pool=None): """ Runs the DAG. """ from airflow.jobs import BackfillJob if not executor and local: executor = LocalExecutor() elif not executor: executor = DEFAULT_EXECUTOR job = BackfillJob( self, start_date=start_date, end_date=end_date, mark_success=mark_success, include_adhoc=include_adhoc, executor=executor, donot_pickle=donot_pickle, ignore_task_deps=ignore_task_deps, ignore_first_depends_on_past=ignore_first_depends_on_past, pool=pool) job.run()
[docs] def cli(self): """ Exposes a CLI specific to this DAG """ from airflow.bin import cli parser = cli.CLIFactory.get_parser(dag_parser=True) args = parser.parse_args() args.func(args, self)
@provide_session
[docs] def create_dagrun(self, run_id, state, execution_date=None, start_date=None, external_trigger=False, conf=None, session=None): """ Creates a dag run from this dag including the tasks associated with this dag. Returns the dag run. :param run_id: defines the the run id for this dag run :type run_id: string :param execution_date: the execution date of this dag run :type execution_date: datetime :param state: the state of the dag run :type state: State :param start_date: the date this dag run should be evaluated :type start_date: datetime :param external_trigger: whether this dag run is externally triggered :type external_trigger: bool :param session: database session :type session: Session """ run = DagRun( dag_id=self.dag_id, run_id=run_id, execution_date=execution_date, start_date=start_date, external_trigger=external_trigger, conf=conf, state=state ) session.add(run) session.commit() run.dag = self # create the associated task instances # state is None at the moment of creation run.verify_integrity(session=session) run.refresh_from_db() DagStat.set_dirty(self.dag_id, session=session) # add a placeholder row into DagStat table if not session.query(DagStat).filter(DagStat.dag_id == self.dag_id).first(): session.add(DagStat(dag_id=self.dag_id, state=state, count=0, dirty=True)) session.commit() return run
@staticmethod @provide_session
[docs] def sync_to_db(dag, owner, sync_time, session=None): """ Save attributes about this DAG to the DB. Note that this method can be called for both DAGs and SubDAGs. A SubDag is actually a SubDagOperator. :param dag: the DAG object to save to the DB :type dag: DAG :own :param sync_time: The time that the DAG should be marked as sync'ed :type sync_time: datetime :return: None """ orm_dag = session.query( DagModel).filter(DagModel.dag_id == dag.dag_id).first() if not orm_dag: orm_dag = DagModel(dag_id=dag.dag_id) logging.info("Creating ORM DAG for %s", dag.dag_id) orm_dag.fileloc = dag.fileloc orm_dag.is_subdag = dag.is_subdag orm_dag.owners = owner orm_dag.is_active = True orm_dag.last_scheduler_run = sync_time session.merge(orm_dag) session.commit() for subdag in dag.subdags: DAG.sync_to_db(subdag, owner, sync_time, session=session)
@staticmethod @provide_session
[docs] def deactivate_unknown_dags(active_dag_ids, session=None): """ Given a list of known DAGs, deactivate any other DAGs that are marked as active in the ORM :param active_dag_ids: list of DAG IDs that are active :type active_dag_ids: list[unicode] :return: None """ if len(active_dag_ids) == 0: return for dag in session.query( DagModel).filter(~DagModel.dag_id.in_(active_dag_ids)).all(): dag.is_active = False session.merge(dag)
@staticmethod @provide_session
[docs] def deactivate_stale_dags(expiration_date, session=None): """ Deactivate any DAGs that were last touched by the scheduler before the expiration date. These DAGs were likely deleted. :param expiration_date: set inactive DAGs that were touched before this time :type expiration_date: datetime :return: None """ for dag in session.query( DagModel).filter(DagModel.last_scheduler_run < expiration_date, DagModel.is_active).all(): logging.info("Deactivating DAG ID %s since it was last touched " "by the scheduler at %s", dag.dag_id, dag.last_scheduler_run.isoformat()) dag.is_active = False session.merge(dag) session.commit()
class Chart(Base): __tablename__ = "chart" id = Column(Integer, primary_key=True) label = Column(String(200)) conn_id = Column(String(ID_LEN), nullable=False) user_id = Column(Integer(), ForeignKey('users.id'), nullable=True) chart_type = Column(String(100), default="line") sql_layout = Column(String(50), default="series") sql = Column(Text, default="SELECT series, x, y FROM table") y_log_scale = Column(Boolean) show_datatable = Column(Boolean) show_sql = Column(Boolean, default=True) height = Column(Integer, default=600) default_params = Column(String(5000), default="{}") owner = relationship( "User", cascade=False, cascade_backrefs=False, backref='charts') x_is_date = Column(Boolean, default=True) iteration_no = Column(Integer, default=0) last_modified = Column(DateTime, default=func.now()) def __repr__(self): return self.label class KnownEventType(Base): __tablename__ = "known_event_type" id = Column(Integer, primary_key=True) know_event_type = Column(String(200)) def __repr__(self): return self.know_event_type class KnownEvent(Base): __tablename__ = "known_event" id = Column(Integer, primary_key=True) label = Column(String(200)) start_date = Column(DateTime) end_date = Column(DateTime) user_id = Column(Integer(), ForeignKey('users.id'),) known_event_type_id = Column(Integer(), ForeignKey('known_event_type.id'),) reported_by = relationship( "User", cascade=False, cascade_backrefs=False, backref='known_events') event_type = relationship( "KnownEventType", cascade=False, cascade_backrefs=False, backref='known_events') description = Column(Text) def __repr__(self): return self.label class Variable(Base): __tablename__ = "variable" id = Column(Integer, primary_key=True) key = Column(String(ID_LEN), unique=True) _val = Column('val', Text) is_encrypted = Column(Boolean, unique=False, default=False) def __repr__(self): # Hiding the value return '{} : {}'.format(self.key, self._val) def get_val(self): if self._val and self.is_encrypted: if not ENCRYPTION_ON: raise AirflowException( "Can't decrypt _val for key={}, FERNET_KEY configuration \ missing".format(self.key)) return FERNET.decrypt(bytes(self._val, 'utf-8')).decode() else: return self._val def set_val(self, value): if value: try: self._val = FERNET.encrypt(bytes(value, 'utf-8')).decode() self.is_encrypted = True except NameError: self._val = value self.is_encrypted = False @declared_attr def val(cls): return synonym('_val', descriptor=property(cls.get_val, cls.set_val)) @classmethod def setdefault(cls, key, default, deserialize_json=False): """ Like a Python builtin dict object, setdefault returns the current value for a key, and if it isn't there, stores the default value and returns it. :param key: Dict key for this Variable :type key: String :param: default: Default value to set and return if the variable isn't already in the DB :type: default: Mixed :param: deserialize_json: Store this as a JSON encoded value in the DB and un-encode it when retrieving a value :return: Mixed """ default_sentinel = object() obj = Variable.get(key, default_var=default_sentinel, deserialize_json=False) if obj is default_sentinel: if default is not None: Variable.set(key, default, serialize_json=deserialize_json) return default else: raise ValueError('Default Value must be set') else: if deserialize_json: return json.loads(obj.val) else: return obj.val @classmethod @provide_session def get(cls, key, default_var=None, deserialize_json=False, session=None): obj = session.query(cls).filter(cls.key == key).first() if obj is None: if default_var is not None: return default_var else: raise KeyError('Variable {} does not exist'.format(key)) else: if deserialize_json: return json.loads(obj.val) else: return obj.val @classmethod @provide_session def set(cls, key, value, serialize_json=False, session=None): if serialize_json: stored_value = json.dumps(value) else: stored_value = value session.query(cls).filter(cls.key == key).delete() session.add(Variable(key=key, val=stored_value)) session.flush() class XCom(Base): """ Base class for XCom objects. """ __tablename__ = "xcom" id = Column(Integer, primary_key=True) key = Column(String(512)) value = Column(PickleType(pickler=dill)) timestamp = Column( DateTime, default=func.now(), nullable=False) execution_date = Column(DateTime, nullable=False) # source information task_id = Column(String(ID_LEN), nullable=False) dag_id = Column(String(ID_LEN), nullable=False) __table_args__ = ( Index('idx_xcom_dag_task_date', dag_id, task_id, execution_date, unique=False), ) def __repr__(self): return '<XCom "{key}" ({task_id} @ {execution_date})>'.format( key=self.key, task_id=self.task_id, execution_date=self.execution_date) @classmethod @provide_session def set( cls, key, value, execution_date, task_id, dag_id, session=None): """ Store an XCom value. """ session.expunge_all() # remove any duplicate XComs session.query(cls).filter( cls.key == key, cls.execution_date == execution_date, cls.task_id == task_id, cls.dag_id == dag_id).delete() session.commit() # insert new XCom session.add(XCom( key=key, value=value, execution_date=execution_date, task_id=task_id, dag_id=dag_id)) session.commit() @classmethod @provide_session def get_one( cls, execution_date, key=None, task_id=None, dag_id=None, include_prior_dates=False, session=None): """ Retrieve an XCom value, optionally meeting certain criteria """ filters = [] if key: filters.append(cls.key == key) if task_id: filters.append(cls.task_id == task_id) if dag_id: filters.append(cls.dag_id == dag_id) if include_prior_dates: filters.append(cls.execution_date <= execution_date) else: filters.append(cls.execution_date == execution_date) query = ( session.query(cls.value) .filter(and_(*filters)) .order_by(cls.execution_date.desc(), cls.timestamp.desc()) .limit(1)) result = query.first() if result: return result.value @classmethod @provide_session def get_many( cls, execution_date, key=None, task_ids=None, dag_ids=None, include_prior_dates=False, limit=100, session=None): """ Retrieve an XCom value, optionally meeting certain criteria """ filters = [] if key: filters.append(cls.key == key) if task_ids: filters.append(cls.task_id.in_(as_tuple(task_ids))) if dag_ids: filters.append(cls.dag_id.in_(as_tuple(dag_ids))) if include_prior_dates: filters.append(cls.execution_date <= execution_date) else: filters.append(cls.execution_date == execution_date) query = ( session.query(cls) .filter(and_(*filters)) .order_by(cls.execution_date.desc(), cls.timestamp.desc()) .limit(limit)) return query.all() @classmethod @provide_session def delete(cls, xcoms, session=None): if isinstance(xcoms, XCom): xcoms = [xcoms] for xcom in xcoms: if not isinstance(xcom, XCom): raise TypeError( 'Expected XCom; received {}'.format(xcom.__class__.__name__) ) session.delete(xcom) session.commit() class DagStat(Base): __tablename__ = "dag_stats" dag_id = Column(String(ID_LEN), primary_key=True) state = Column(String(50), primary_key=True) count = Column(Integer, default=0) dirty = Column(Boolean, default=False) def __init__(self, dag_id, state, count, dirty=False): self.dag_id = dag_id self.state = state self.count = count self.dirty = dirty @staticmethod @provide_session def set_dirty(dag_id, session=None): for dag in session.query(DagStat).filter(DagStat.dag_id == dag_id): dag.dirty = True session.commit() @staticmethod @provide_session def clean_dirty(dag_ids, session=None): """ Cleans out the dirty/out-of-sync rows from dag_stats table :param dag_ids: dag_ids that may be dirty :type dag_ids: list :param full_query: whether to check dag_runs for new drs not in dag_stats :type full_query: bool """ dag_ids = set(dag_ids) qry = ( session.query(DagStat) .filter(and_(DagStat.dag_id.in_(dag_ids), DagStat.dirty == True)) ) dirty_ids = {dag.dag_id for dag in qry.all()} qry.delete(synchronize_session='fetch') session.commit() qry = ( session.query(DagRun.dag_id, DagRun.state, func.count('*')) .filter(DagRun.dag_id.in_(dirty_ids)) .group_by(DagRun.dag_id, DagRun.state) ) for dag_id, state, count in qry: session.add(DagStat(dag_id=dag_id, state=state, count=count)) session.commit() class DagRun(Base): """ DagRun describes an instance of a Dag. It can be created by the scheduler (for regular runs) or by an external trigger """ __tablename__ = "dag_run" ID_PREFIX = 'scheduled__' ID_FORMAT_PREFIX = ID_PREFIX + '{0}' DEADLOCK_CHECK_DEP_CONTEXT = DepContext(ignore_in_retry_period=True) id = Column(Integer, primary_key=True) dag_id = Column(String(ID_LEN)) execution_date = Column(DateTime, default=func.now()) start_date = Column(DateTime, default=func.now()) end_date = Column(DateTime) _state = Column('state', String(50), default=State.RUNNING) run_id = Column(String(ID_LEN)) external_trigger = Column(Boolean, default=True) conf = Column(PickleType) dag = None __table_args__ = ( Index('dr_run_id', dag_id, run_id, unique=True), ) def __repr__(self): return ( '<DagRun {dag_id} @ {execution_date}: {run_id}, ' 'externally triggered: {external_trigger}>' ).format( dag_id=self.dag_id, execution_date=self.execution_date, run_id=self.run_id, external_trigger=self.external_trigger) def get_state(self): return self._state def set_state(self, state): if self._state != state: self._state = state # something really weird goes on here: if you try to close the session # dag runs will end up detached session = settings.Session() DagStat.set_dirty(self.dag_id, session=session) @declared_attr def state(self): return synonym('_state', descriptor=property(self.get_state, self.set_state)) @classmethod def id_for_date(cls, date, prefix=ID_FORMAT_PREFIX): return prefix.format(date.isoformat()[:19]) @provide_session def refresh_from_db(self, session=None): """ Reloads the current dagrun from the database :param session: database session """ DR = DagRun exec_date = func.cast(self.execution_date, DateTime) dr = session.query(DR).filter( DR.dag_id == self.dag_id, func.cast(DR.execution_date, DateTime) == exec_date, DR.run_id == self.run_id ).one() self.id = dr.id self.state = dr.state @staticmethod @provide_session def find(dag_id=None, run_id=None, execution_date=None, state=None, external_trigger=None, session=None): """ Returns a set of dag runs for the given search criteria. :param dag_id: the dag_id to find dag runs for :type dag_id: integer, list :param run_id: defines the the run id for this dag run :type run_id: string :param execution_date: the execution date :type execution_date: datetime :param state: the state of the dag run :type state: State :param external_trigger: whether this dag run is externally triggered :type external_trigger: bool :param session: database session :type session: Session """ DR = DagRun qry = session.query(DR) if dag_id: qry = qry.filter(DR.dag_id == dag_id) if run_id: qry = qry.filter(DR.run_id == run_id) if execution_date: if isinstance(execution_date, list): qry = qry.filter(DR.execution_date.in_(execution_date)) else: qry = qry.filter(DR.execution_date == execution_date) if state: qry = qry.filter(DR.state == state) if external_trigger: qry = qry.filter(DR.external_trigger == external_trigger) dr = qry.order_by(DR.execution_date).all() return dr @provide_session def get_task_instances(self, state=None, session=None): """ Returns the task instances for this dag run """ TI = TaskInstance tis = session.query(TI).filter( TI.dag_id == self.dag_id, TI.execution_date == self.execution_date, ) if state: if isinstance(state, six.string_types): tis = tis.filter(TI.state == state) else: # this is required to deal with NULL values if None in state: tis = tis.filter( or_(TI.state.in_(state), TI.state.is_(None)) ) else: tis = tis.filter(TI.state.in_(state)) if self.dag and self.dag.partial: tis = tis.filter(TI.task_id.in_(self.dag.task_ids)) return tis.all() @provide_session def get_task_instance(self, task_id, session=None): """ Returns the task instance specified by task_id for this dag run :param task_id: the task id """ TI = TaskInstance ti = session.query(TI).filter( TI.dag_id == self.dag_id, TI.execution_date == self.execution_date, TI.task_id == task_id ).one() return ti def get_dag(self): """ Returns the Dag associated with this DagRun. :return: DAG """ if not self.dag: raise AirflowException("The DAG (.dag) for {} needs to be set" .format(self)) return self.dag @provide_session def get_previous_dagrun(self, session=None): """The previous DagRun, if there is one""" return session.query(DagRun).filter( DagRun.dag_id == self.dag_id, DagRun.execution_date < self.execution_date ).order_by( DagRun.execution_date.desc() ).first() @provide_session def get_previous_scheduled_dagrun(self, session=None): """The previous, SCHEDULED DagRun, if there is one""" dag = self.get_dag() return session.query(DagRun).filter( DagRun.dag_id == self.dag_id, DagRun.execution_date == dag.previous_schedule(self.execution_date) ).first() @provide_session def update_state(self, session=None): """ Determines the overall state of the DagRun based on the state of its TaskInstances. :returns State: """ dag = self.get_dag() tis = self.get_task_instances(session=session) logging.info("Updating state for {} considering {} task(s)" .format(self, len(tis))) for ti in list(tis): # skip in db? if ti.state == State.REMOVED: tis.remove(ti) else: ti.task = dag.get_task(ti.task_id) # pre-calculate # db is faster start_dttm = datetime.now() unfinished_tasks = self.get_task_instances( state=State.unfinished(), session=session ) none_depends_on_past = all(not t.task.depends_on_past for t in unfinished_tasks) # small speed up if unfinished_tasks and none_depends_on_past: # todo: this can actually get pretty slow: one task costs between 0.01-015s no_dependencies_met = all( # Use a special dependency context that ignores task's up for retry # dependency, since a task that is up for retry is not necessarily # deadlocked. not t.are_dependencies_met(dep_context=self.DEADLOCK_CHECK_DEP_CONTEXT, session=session) for t in unfinished_tasks) duration = (datetime.now() - start_dttm).total_seconds() * 1000 Stats.timing("dagrun.dependency-check.{}.{}". format(self.dag_id, self.execution_date), duration) # future: remove the check on adhoc tasks (=active_tasks) if len(tis) == len(dag.active_tasks): root_ids = [t.task_id for t in dag.roots] roots = [t for t in tis if t.task_id in root_ids] # if all roots finished and at least on failed, the run failed if (not unfinished_tasks and any(r.state in (State.FAILED, State.UPSTREAM_FAILED) for r in roots)): logging.info('Marking run {} failed'.format(self)) self.state = State.FAILED # if all roots succeeded, the run succeeded elif all(r.state in (State.SUCCESS, State.SKIPPED) for r in roots): logging.info('Marking run {} successful'.format(self)) self.state = State.SUCCESS # if *all tasks* are deadlocked, the run failed elif unfinished_tasks and none_depends_on_past and no_dependencies_met: logging.info( 'Deadlock; marking run {} failed'.format(self)) self.state = State.FAILED # finally, if the roots aren't done, the dag is still running else: self.state = State.RUNNING # todo: determine we want to use with_for_update to make sure to lock the run session.merge(self) session.commit() return self.state @provide_session def verify_integrity(self, session=None): """ Verifies the DagRun by checking for removed tasks or tasks that are not in the database yet. It will set state to removed or add the task if required. """ dag = self.get_dag() tis = self.get_task_instances(session=session) # check for removed tasks task_ids = [] for ti in tis: task_ids.append(ti.task_id) try: dag.get_task(ti.task_id) except AirflowException: if self.state is not State.RUNNING and not dag.partial: ti.state = State.REMOVED # check for missing tasks for task in dag.tasks: if task.adhoc: continue if task.task_id not in task_ids: ti = TaskInstance(task, self.execution_date) session.add(ti) session.commit() @staticmethod def get_running_tasks(session, dag_id, task_ids): """ Returns the number of tasks running in the given DAG. :param session: ORM session :param dag_id: ID of the DAG to get the task concurrency of :type dag_id: unicode :param task_ids: A list of valid task IDs for the given DAG :type task_ids: list[unicode] :return: The number of running tasks :rtype: int """ qry = session.query(func.count(TaskInstance.task_id)).filter( TaskInstance.dag_id == dag_id, TaskInstance.task_id.in_(task_ids), TaskInstance.state == State.RUNNING, ) return qry.scalar() @staticmethod def get_run(session, dag_id, execution_date): """ :param dag_id: DAG ID :type dag_id: unicode :param execution_date: execution date :type execution_date: datetime :return: DagRun corresponding to the given dag_id and execution date if one exists. None otherwise. :rtype: DagRun """ qry = session.query(DagRun).filter( DagRun.dag_id == dag_id, DagRun.external_trigger == False, DagRun.execution_date == execution_date, ) return qry.first() @property def is_backfill(self): if "backfill" in self.run_id: return True return False class Pool(Base): __tablename__ = "slot_pool" id = Column(Integer, primary_key=True) pool = Column(String(50), unique=True) slots = Column(Integer, default=0) description = Column(Text) def __repr__(self): return self.pool @provide_session def used_slots(self, session): """ Returns the number of slots used at the moment """ running = ( session .query(TaskInstance) .filter(TaskInstance.pool == self.pool) .filter(TaskInstance.state == State.RUNNING) .count() ) return running @provide_session def queued_slots(self, session): """ Returns the number of slots used at the moment """ return ( session .query(TaskInstance) .filter(TaskInstance.pool == self.pool) .filter(TaskInstance.state == State.QUEUED) .count() ) @provide_session def open_slots(self, session): """ Returns the number of slots open at the moment """ used_slots = self.used_slots(session=session) queued_slots = self.queued_slots(session=session) return self.slots - used_slots - queued_slots class SlaMiss(Base): """ Model that stores a history of the SLA that have been missed. It is used to keep track of SLA failures over time and to avoid double triggering alert emails. """ __tablename__ = "sla_miss" task_id = Column(String(ID_LEN), primary_key=True) dag_id = Column(String(ID_LEN), primary_key=True) execution_date = Column(DateTime, primary_key=True) email_sent = Column(Boolean, default=False) timestamp = Column(DateTime) description = Column(Text) notification_sent = Column(Boolean, default=False) def __repr__(self): return str(( self.dag_id, self.task_id, self.execution_date.isoformat())) class ImportError(Base): __tablename__ = "import_error" id = Column(Integer, primary_key=True) timestamp = Column(DateTime) filename = Column(String(1024)) stacktrace = Column(Text)