Airflow task group parameters

weight_rule handles prioritization at task level. You seem to have misunderstood default_args. I don't want to test the whole dag. The following code solved the issue. utils. At last I wrote a custom filter 'tojson' in Jinja2/filter. Execute a task on AWS ECS (Elastic Container Service). Jan 14, 2023 · 2. UI - manual trigger from tree view UI - create new DAG run from browse > DAG runs > create new record. Parameter tuning Airflow has many parameters that impact its performance. You can change n to control how many tasks you want to retry before the current task. 1 Feb 22, 2022 · In the app/folder there is a dags/ folder containing all DAGs and the airflow. BaseSensorOperator. There are a few parameters to know with task groups. The BashOperator is commonly used to execute shell commands, including dbt commands. Pushing Data to XCom. dag ( [dag_id, description, schedule, ]) Python dag decorator which wraps a function into an Airflow DAG. Airflow taskgroup parameters. PythonOperator - calls an arbitrary Python function. cfg template file is available on the Airflow github. models import Variable. DagRunState | None) – If passed, it only take into account instances of a specific state. XComs are principally defined by a key, value, and Mar 13, 2019 · priority_weight can be used to prioritize all the instances of certain DAG over other DAGs. Can be used to parametrize TaskGroup. Apr 14, 2019 · Even if you use something like the following to get an access to XCOM values generated by some upstream task: from airflow. Tensor: """ Generates a vector of length k with normally distributed random numbers. Mar 1, 2022 · My code is as follows: The yaml file contains all jobs for each task group and the gluejob script location as keyvalue pair. timedelta object, representing the maximum runtime allowed for a task. I also don't want to have to test the tasks individually. dates import days_ago. decorators. 0 dag and task decorators. May 30, 2019 · pool: the pool to execute the task in. from airflow. It has to be meaningful as you will see it on the user interface but not only… (teasing 😉) Look at the following code: This tutorial builds on the regular Airflow Tutorial and focuses specifically on writing data pipelines using the TaskFlow API paradigm which is introduced as part of Airflow 2. dag_run_conf_overrides_params , so if that flag Airflow DAG concurrency is a crucial aspect of managing workflow execution. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. 1 and Jinja2 does not contain the builtin filter named 'tojson' until version 2. decorators import task, task_group. import configparser. baseoperator. models import TaskInstance. The tasks share similar/close business logic with the DAG so it make sense to find these tasks within the specific DAG. Nov 24, 2022 · The execution date of DAG A is one hour before DAG B, and you set the execution delta to 2 hours, meaning DAG A external sensor is trying to find DAG B with an execution date of 0 4 * * *, which doesn't exist. db import provide_session. Bases: airflow. Jun 30, 2022 · Is there a way to test all tasks within a TaskGroup in Airflow without running each task separately? I have multiple task groups within a dag. Given a number of tasks, builds a dependency chain. xCom is used precisely for exchanging information between various tasks. operators. To quote. state. Use the trigger rule for the task, to skip the task based on previous parameter. This is not applicable in the older versions (1. This function accepts values of BaseOperator (aka tasks), EdgeModifiers (aka Labels), XComArg, TaskGroups, or lists containing any mix of these types (or a mix in the same list). expand(seconds=seconds_list) printer. I have implemented the following code: from airflow. The top row is a chart of DAG Runs by duration, and below, task instances. Jul 23, 2023 · a. You can explore the mandatory/optional parameters for the Airflow Operator encapsulated by the decorator to have a better idea of the signature for the specific task. context (airflow. decorators import task from airflow import DAG from datetime import datetime as dt import pendulum local_tz Fortunately, Airflow has multiple options for building conditional logic and/or branching into your DAGs. orm. Feb 27, 2024 · So I am trying to assign the values into the pipeline_parameters. EcsHook] This is the base operator for all Elastic Container Service operators. This should help ! Adding an example as requested by author, here is the code. However, this seem to add another layer to the tasks. However, without the trigger_rule argument to Task-C we would end up with Task-B downstream marked as skipped. task_group. ). Sep 11, 2017 · Thanks to @Chengzhi and @Daniel. Advanced Decorators Feb 27, 2024 · happens on the for loop trying to iterate on task. I'm interested in creating dynamic processes, so I saw the partial () and expand () methods in the 2. mime. hooks. Decision flow to find if parallelism configuration of airflow is Nov 17, 2021 · variables = ['first', 'second', 'third'] def run_dag_task(variable): task = dag_task(variable) return task. Define the dependencies one by one. Task groups can also contain other task groups, creating a hierarchical structure of tasks. Now you are trying to do it all in one line. If a task exceeds this duration, Airflow raises an Oct 29, 2021 · Note that I am using the function taking dag and task_group parameters to create task group tasks because I want to create the same set of tasks for another dag too. expand(string=strings_list) I tried recuperating the list from the list_generator task and iterating over it, but it May 28, 2022 · 1. This will increase the task concurrency set at the scheduler level. If both external_task_group_id and external_task_id are None (default), the sensor waits for the DAG. # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. Two ways to change your DAG behavior: Use Airflow variables like mentioned by Bryan in his answer. This sensor is particularly useful in complex workflows where tasks in different DAGs have dependencies on each other. Create and use params in Airflow. @task_group. JSON can be passed either from. example_task_group. def fn(): pass. The airflow. I don't get it: I would hope for same behavior, but not this. When the number of running task instances reaches the defined concurrency limit, additional tasks Aug 14, 2019 · Pass {'retry_upstream_depth': n} value to the params parameter of your task operator. Impersonation. May 20, 2019 · I want to create for every group of sublists a task in airflow like: something_cool = PythonOperator(. timedelta object. example_dags. You'll finally have two DAG files that invokes the TaskGroups help us visually group similar or dependent tasks together in the DAG view. Nov 5, 2023 · Introduce a branch operator, in the function present the condition. signature(python_callable) # Don't allow context argument defaults other than None to Oct 28, 2022 · What have I tried. Logging: Ensure that each retry is logged appropriately. Now, say I want the first task to finish before commencing the second task, and to finish the second task before Image 7 - DAG view showing the tasks will run in parallel (image by author) The start task will now run first, followed by the other four tasks that connect to the APIs and run in parallel. base. Cross-DAG Dependencies. """ import smtplib, ssl from email. 6) can change based on the output/result of previous tasks, see Dynamic Task Apr 13, 2023 · import torch from typing import Optional from airflow. task_id='cool', python_callable=do_something_cool(sub_list), dag=dag) would the best way to do this is to write a loop? in my case, the main list is very long and writing out each operator would be very hard. However, it is not possible to go from a list to a list. Is this grouping necessary and what is wrong with the first method? Parameter tuning Airflow has many parameters that impact its performance. Step 1: Define the dbt DAG Mar 15, 2019 · 0. Here's an example of how to set it: Source code for airflow. If None (default value) the sensor waits for the DAG. t2 depends upon some parameters acquired during t1, meaning t2 should be downstream task to t1. When the decorated function is called, a task group will be created to represent a collection of closely related tasks on the same DAG that should be grouped together when the DAG is displayed graphically. '#task1 > task2 >. I tried to use the expand method, but the task_group decorator doesn't seem to be implementing it. 0 as a way to group related tasks within a DAG. A DAG (Directed Acyclic Graph) is the core concept of Airflow, collecting Tasks together, organized with dependencies and relationships to say how they should run. short_circuit_task ( [python_callable, multiple_outputs]) Wrap a function into an ShortCircuitOperator. Airflow also offers better visual representation of dependencies for tasks on the same DAG. Mar 8, 2021 · 1 Answer. Note that you have to default arguments to None. Control the parallelism of your task groups: You can create a new pool task_groups_pool with 1 slot, and use it for the tasks of the task groups, in this case you will not have more than one task of all the task groups running at Subclasses should implement this, running whatever logic is necessary to choose a branch and returning a task_id or list of task_ids. Advanced Decorators Dynamic DAG Generation. Jan 7, 2017 · Workers consume "work tasks" from the queue. models. (There is a long discussion in the Github repo about "making the concept less nebulous". 1. Click on the failed task which is going to clear, heading of its displaying popup which has the clear option will be [taskname] on [executiondatewithtime] Open the task log, the Dynamic DAG Generation. Oct 11, 2021 · Documentation on the nature of context is pretty sparse at the moment. EmailOperator - sends an email. Jan 19, 2022 · To be able to create tasks dynamically we have to use external resources like GCS, database or Airflow Variables. Sep 12, 2022 · Mapping a task group is not possible yet, but this is a feature that will be available soon in the next versions. In this example, we set `dag_concurrency` to 3 May 25, 2021 · Is there any way to fix this error? such as creating custom task_id dynamically? I know this is possible using PythonOperator . Defaults to EcsTaskDefinitionStates. Jul 5, 2023 · Conclusion: Enhancing task monitoring in Apache Airflow through email notifications provides valuable insights into task execution status and outcomes. The tasks in the group are never to be executed as a stand alone. Creates a unique ID for upstream dependencies of this TaskGroup. Either directly if implemented using external to Airflow technology, or as as Airflow Sensor task (maybe in a separate DAG). Execution is always as part of the DAG itself. session. branch (BranchPythonOperator) and @task. from airflow import DAG. Waits for a different DAG, task group, or task to complete for a specific logical date. If you need different schedules and you don't want to repeat the code twice, you can take this DAG factory idea and build your own factory for these two DAGs. If a task exceeds this time limit, it will be killed by Airflow. short_circuit (ShortCircuitOperator), other available branching operators, and additional resources to implement conditional logic in your Airflow DAGs. When configuring tasks in Airflow, it's crucial to consider the execution_timeout attribute to ensure that tasks do not run indefinitely and potentially consume excessive resources. The ability to update params while triggering a DAG depends on the flag core. Sep 6, 2018 · Just commenting the tasks you want to skip. amazon. Airflow context is only accessible from tasks in runtime, and TaskGroup is not a task, it's just a collection of tasks used to group the tasks in the UI. What we’re building today is a simple DAG with two groups of tasks, using the @taskgroup decorator from the TaskFlow API from Airflow 2. ACTIVE as the success state, but accepts a parameter to change that. task_group(python_callable: Callable[FParams, FReturn]) → _TaskGroupFactory[FParams, FReturn] Python TaskGroup decorator. dag_id When using Aug 6, 2020 · 2. This ensures uniqueness of group_id and task_id throughout the DAG. Running dbt as an Airflow Task: To run dbt as an Airflow task, you need to define an Airflow Operator that executes the dbt CLI command to run your dbt models. 3 version of airflow. Aug 10, 2023 · I am trying to create airflow task group dynamically based on user input provided. Airflow has a very extensive set of operators available, with some built-in to the core or pre-installed providers. For example, your task mappings are constrained by datatypes supported by XCom, namely Python dict and lists. Sorted by: 1. from datetime import timedelta, datetime. The order of your tasks is as follows: task_1 >> task_2 >> task_depends_on_previous_tasks AWS ECS Task Definition State Sensor¶ To poll the task definition state until it reaches a terminal state you can use EcsTaskDefinitionStateSensor. Jan 12, 2021 · 6. With Airflow 2. The reason Airflow allows so many adjustments is that, as an agnostic orchestrator, Airflow is used for a wide variety of use cases. Context) – Context dictionary as passed to execute() execute (context) [source] ¶ Derive when creating an operator. import pendulum. Here is the full code of my task group: from airflow. ecs. Jul 6, 2021 · 4. python import task, get_current_context. The execution_timeout parameter is set at the task level and accepts a datetime. Valid states are either EcsTaskDefinitionStates. Example: Let’s create an Airflow DAG that runs a dbt model as a task. dag = DAG() @provide_session. I'm struggling to understand how to read DAG config parameters inside a task using Airflow 2. You can pass DAG and task-level params by using the params parameter. In a few places in the documentation it's referred to as a "context dictionary" or even an "execution context dictionary", but never really spelled out what that is. 0 and contrasts this with DAGs written using the traditional paradigm. 13. This is not possible because we are only able to set a dependency for a lists to a single task and from a single task to a list. or from. Aug 30, 2023 · However, when I annotate run_glue_ingestion with @task_group() instead of @task. providers. Use the @task decorator to execute an arbitrary Python function. Jun 21, 2019 · def notify_email(context): import inspect """Send custom email alerts. XCom is a built-in Airflow feature. base_sensor_operator. Task instances can impersonate Unix users if the run_as_user parameter is Oct 29, 2022 · This parent group takes the list of IDs I add a loop and for each parent ID, I create a TaskGroup containing your 2 Aiflow tasks (print operators) For the TaskGroup related to a parent ID, the TaskGroup ID is built from it in order to be unique in the DAG Jan 10, 2014 · Module Contents. This attribute accepts a datetime. Tuning these settings can impact DAG parsing and task scheduling performance, parallelism in your Airflow environment, and more. decorators import task @task def process_data(data): # Process data logic here return processed_data In this example, process_data becomes an Airflow task by simply adding the @task decorator. 8. cfg file, which is used to configure the Airflow setup. The bigger issue with writing the same DAG with different URLs is that you're breaking the DRY (Don't Repeat Yourself) principle. in this case, your external sensor task fails on timeout. XComs allow tasks to exchange task metadata or small amounts of data. task_group. I will add an abbreviated version of my code below that I have tried. Create and return the EcsHook’s client. 0. Session | None) – SQLAlchemy ORM Session. task_id dag_instance=context['dag_id']. 5 and above we can make decorators to create a task group @task_group. task_id__1. When two DAGs have dependency relationships, it is worth considering combining them into a single DAG, which is usually simpler to understand. py,because in airflow the default Jinja2 version is 2. This then gives the user full control over the actual group_id and task_id. Common Issues and Solutions. If a pipeline is late, you can quickly see where the different steps are and identify the blocking ones. We go through the argument # list and "fill in" defaults to arguments that are known context keys, # since values for those will be provided when the task is run. A more serious solution but with more effort will probably be to create the DAG dynamically based on a parameter of start_from_task and in this case the dependencies will be built using this parameter. Waits for a different DAG or a task in a different DAG to complete for a specific execution_date. baseoperator import BaseOperator. They provide a logical structure for organizing tasks, making DAG definitions more modular and Aug 24, 2021 · With Airflow 2. Must overwrite in child classes. Use Airflow JSON Conf to pass JSON data to a single DAG run. import sys. Airflow operates under the airflow:airflow user and group by default, particularly on Redhat based systems. Jan 31, 2023 · example_2: You explicitly state via arguments you want only dag_run from the task instance context variables. XComs can be "pushed", meaning sent by a task, or "pulled", meaning received by a task. Implements the @task_group function decorator. python import BranchPythonOperator, PythonOperator. 9 . decorators import task @task def generate_normal_vector(k: int, filepath: Optional[str] = None) -> torch. Apr 23, 2021 · So basically we can catch the actual exception in our code and raise mentioned Airflow exception which "force" task state change from failed to skipped. aws. Parameters. def get_files_list(session): from airflow. get_previous_ti (state = None, session = NEW_SESSION) [source] ¶ Return the task instance for the task that ran before this task instance Jun 12, 2023 · To repair and rerun all failed tasks in a DatabricksWorkflowTaskGroup, go to the “launch” task in the Airflow UI and click on the “Repair all tasks” button. INACTIVE. If you want to implement a DAG where number of Tasks (or Task Groups as of Airflow 2. Trigger the DAG once again and inspect the Tree view - you’ll see that the tasks have started running at the same time: Jul 9, 2021 · 3. Apache Airflow's xcom_push method allows tasks to communicate by pushing messages to XComs, which can then be pulled by downstream tasks. The ASF licenses this file # to you under the Apache License, Version 2. Below is sample code: from datetime import datetime. Example: task_id. The TaskFlow API is simple and allows for a proper code structure, favoring a clear separation of concerns. By leveraging Airflow’s email capabilities, you can receive timely notifications for successful task completions and failures, enabling effective monitoring and issue resolution. Params are ideal to store information that is specific to individual DAG runs like changing dates 2. base_aws. external_task_id ( str or None) – The task_id that contains the task you want to wait for. for variable in variables: run_dag_task(variable) This code would run the three tasks for the three variables in parallel. Here's how you can manage and monitor these retry delays: Configuring Retries: Set the retries parameter in your task definition to specify the number of retry attempts. Configuration settings are primarily derived from /etc/sysconfig/airflow, but can also be set at AIRFLOW_HOME or AIRFLOW_CONFIG. Dec 5, 2022 · Dynamic task mapping (DTM) is a major feature that adds a lot of flexibility to how you build your DAGs. Some popular operators from core include: BashOperator - executes a bash command. Jul 15, 2021 · Points in favor of adding the tasks with Task Group to the current DAG: The tasks are a subunit of the DAG. decorators import dag, task. @task. The group_id parameter is the unique identifier of a Taskgroup. This wraps a function into an Airflow TaskGroup. There are two ways I will show how you can do this. sensors. pass. Quoting the docstring comment from DAG params. default_args = {. Using the @task allows to dynamically generate task_id by calling the decorated function. We can also create multiple TaskGroups and can have them nested. group_id. AwsBaseOperator [ airflow. ACTIVE or EcsTaskDefinitionStates. But params is accessible from the TaskGroup tasks: @task_group () def mygroup (params=None): @task def task1 (): return params ["a"] task1 () answered Jan 14, 2023 at 20:50. Params are arguments which you can pass to an Airflow DAG or task at runtime and are stored in the Airflow context dictionary for each DAG run. Use the retry_delay parameter to define the delay duration between retries. task_group import TaskGroup. Once you have the context dict, the 'params' key contains the arguments sent to the Dag via REST API. Params are Airflow's concept of providing runtime configuration to tasks when a dag gets triggered manually. DAGs. If this TaskGroup has immediate upstream TaskGroups or tasks, a proxy node called. For example. TaskGroup to reduce the total number of edges needed to be displayed. Mismatched execution_date: The sensor's execution_date must align with the target task's schedule. Parameters: k (int): Length of the vector. max_active_tis_per_dag: controls the number of concurrent running task instances across dag_runs per task. You can use TaskFlow decorator functions (for example, @task) to pass data between tasks by providing the output of one task as an argument to another task. Sep 21, 2022 · When using task decorator as-is like. the default operator is the PythonOperator. This is particularly useful for sharing small pieces of data such as file paths or configuration parameters. In this guide, you'll learn how you can use @task. import yaml. Since # we're not actually running the function, None is good enough here. When they finish processing their task, the Airflow Sensor gets triggered and the execution flow continues. edited Sep 23, 2022 at 7:25. Values for external_task_group_id and external_task_id can’t be set at the same time. Let`s see some of the parameters to configure a TaskGroup. *) of Airflow. As like example given below, but here we want number of task groups created based on user input provided (without Sep 6, 2021 · In my case, I have a function to create a task group with the necessary parameters. See Templates reference. When used as the @task_group() form, all arguments are forwarded to the underlying TaskGroup class. They are defined by a key, value, and timestamp. session (sqlalchemy. They enable users to group related tasks, simplifying the Graph view and making complex workflows more manageable. Aug 17, 2021 · 2. However, it does not provide infinite flexibility and break you free of being beholden to Airflow's patterns. Feb 7, 2024 · Let’s explore a few critical parameters related to DAG (Directed Acyclic Graph) concurrency, task parallelism, and maximum active runs per DAG. Feb 12, 2024 · Task Groups were introduced in Apache Airflow 2. For instance, consider a pool with 2 slots, Pool(pool='maintenance Sep 15, 2022 · With Airflow 2. Aug 11, 2016 · What is the way to pass parameter into dependent tasks in Airflow? I have a lot of bashes files, and i'm trying to migrate this approach to airflow, but i don't know how to pass some properties between tasks. Hussein Awala. This document describes creation of DAGs that have a structure generated dynamically, but where the number of tasks in the DAG does not change between DAG Runs. get_current_context(). Apache Airflow Task Groups are a powerful feature for organizing tasks within a DAG. The details panel will update when selecting a DAG Run by clicking on a duration bar: In Apache Airflow, the execution_timeout parameter is used to specify the maximum amount of time that a task can run. task3 > task4. signature = inspect. context. You have 3 options: Hover to the failed task which is going to clear, in its displaying tag there will be a value with key Run:, it is its Execution date and time. It gives me this: Doing it this way, the glue jobs are getting triggered (they fail due to some issues with the script). 6) can change based on the output/result of previous tasks, see Dynamic Task from airflow. upstream_join_id will be created in Graph view to join the outgoing edges from this. The function's parameters can be used to pass arguments to the task, enhancing the workflow's dynamic nature. something = task1() I can trigger the dag using the UI or the console and pass to it some (key,value) config, for example: How Oct 9, 2023 · 2. Here’s a basic example DAG: It defines four Tasks - A, B, C, and D - and dictates the order in which they have to run, and which tasks depend on what others. airflow. Here is the overview of my pseudo code: def create_task_group(group_id, a, b, c): with TaskGroup(group_id=group_id) as my_task_group: # add some tasks. By default, child tasks and TaskGroups have their task_id and group_id prefixed with the group_id of their parent TaskGroup. To disable the prefixing, pass prefix_group_id=False when creating the TaskGroup. Sep 24, 2023 · Generating groups based on unknown inputs with dynamic task mapping. But I'm trying to do this using TaskFlow API instead. However, it is sometimes not practical to put all related tasks on the same DAG. This is a real example: Nov 6, 2023 · Task groups are a way of grouping tasks together in a DAG, so that they appear as a single node in the Airflow UI. python_operator import PythonOperator from airflow. I had to solve my problem using Airflow Variables: You can see the code here: from airflow. Pools can be used to limit parallelism for only a subset of tasks. multipart import MIMEMultipart sender_email = '[email protected]' receiver_email = '[email protected]' password = "abc" message = MIMEMultipart("alternative") #task_instance = context['task']. Incorrect external_dag_id or external_task_id: Verify that the IDs match the target DAG and task. 0 Params. For example DAG D1 has two tasks t1 and t2. This is particularly useful when several tasks that belong to the same pool don’t carry the same “computational weight”. To push data to XCom within a task, use the xcom_push method: A bar chart and grid representation of the DAG that spans across time. state (airflow. Task groups can have their own dependencies, retries, trigger rules, and other parameters, just like regular tasks. 0, SubDags are being relegated and now replaced with the Task Group feature. The docs of _get_unique_task_id states: Generate unique task id given a DAG (or if run in a DAG context) Ids are generated by appending a unique number to the end of the original task id. text import MIMEText from email. Python Version: 3. you could set check_existence=True to fail immediately instead of waiting for 10 retries. group level, the params dict But, strangely or not, it works if I try to do it on task gdp_task with same returned dict (d_params from task1). """ from __future__ import annotations import functools import inspect import warnings from typing import TYPE_CHECKING, Any, Callable The TaskFlow API is a functional API for using decorators to define DAGs and tasks, which simplifies the process for passing data between tasks and defining dependencies. Airflow tasks will each occupy a single pool slot by default, but they can be configured to occupy more with the pool_slots argument if required. I have implemented a task group that is expected to be reused across multiple DAGs, in one of which utilizing it in a mapping manner makes more sense. Params are configured while defining the dag & tasks, that can be altered while doing a manual trigger. default_args is just a shorthand (code-cleanup / refactoring / brevity) to pass common (which have same value for all operators of DAG, like owner) args to all your operator s, by setting them up as defaults and passing to the DAG itself. python. Example: t1 = BaseOperator(pool='my_custom_pool', max_active_tis_per_dag=12) Options that are specified across an entire Airflow setup: sensor_task ( [python_callable]) Wrap a function into an Airflow operator. from typing import Sequence. The name is an abbreviation of “cross-communication”. You may use user_defined_macros parameter when instantiating DAG and pass your decision function here. glue import AwsGlueJobOperator. t1 >> t2. It determines the maximum number of task instances that can run simultaneously within a single DAG. Sensor Timing Out: Ensure that the execution_timeout parameter is set appropriately. from os import path. 8 Airflow Version: 2. chain(*tasks)[source] ¶. example_3: You can also fetch the task instance context variables from inside a task using airflow. XComs let tasks exchange messages, allowing more nuanced forms of control and shared state. This is controlled by the concurrency parameter in the DAG definition. Jan 7, 2021 · There is a new function get_current_context() to fetch the context in Airflow 2. This should be possible via xcom. We can increase the concurrency of the task by increasing the number of schedulers. When an XCom is pushed, it is stored in the Airflow metadata database and made available to all other airflow. See below for an example. Task Groups are defined using the task_group decorator, which groups tasks into a collapsible hierarchy in the Airflow UI. 0+ multiple schedulers can be run within Airflow. Consider this simple DAG definition file: @task(start_date=days_ago(1)) def task1(): return 1. seconds_list = list_generator(n=5) strings_list = sleeper_stringer_group. Then I iterate to create each DAG with the function to create the task group (s) with different parameters. Apache Airflow's ExternalTaskSensor is a powerful feature that allows one DAG to wait for a task or a task group to complete in another DAG before proceeding. These are the most important parameters that must be set in order to be able to run 1000 parallel tasks with Celery Executor: executor . xq qu ip dy zp ha ca ox fd vj