Apache Airflow Dags

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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.

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. It will also say how often to run the DAG - maybe "every 5 minutes starting tomorrow", or "every day since January 1st, 2020".

The DAG itself doesn't care about what is happening inside the tasks; it is merely concerned with how to execute them - the order to run them in, how many times to retry them, if they have timeouts, and so on.

Bake DAGs in Docker image¶

There are three ways to declare a DAG - either you can use a context manager,which will add the DAG to anything inside it implicitly:.

Or, you can use a standard constructor, passing the dag into anyoperators you use:.

Or, you can use the @dag decorator to turn a function into a DAG generator:. DAGs are nothing without Tasks to run, and those will usually either come in the form of either Operators, Sensors or TaskFlow.

A Task/Operator does not usually live alone; it has dependencies on other tasks (those upstream of it), and other tasks depend on it (those downstream of it).

Airflow DAGs Best Practices

Declaring these dependencies between tasks is what makes up the DAG structure (the edges of the directed acyclic graph). There are two main ways to declare individual task dependencies. The recommended one is to use the >> and << operators:.

Or, you can also use the more explicit set_upstream and set_downstream methods:. There are also shortcuts to declaring more complex dependencies.

If you want to make two lists of tasks depend on all parts of each other, you can't use either of the approaches above, so you need to use cross_downstream:.

And if you want to chain together dependencies, you can use chain:. Chain can also do pairwise dependencies for lists the same size (this is different to the cross dependencies done by cross_downstream!):.

Airflow loads DAGs from Python source files, which it looks for inside its configured DAG_FOLDER.

It will take each file, execute it, and then load any DAG objects from that file. This means you can define multiple DAGs per Python file, or even spread one very complex DAG across multiple Python files using imports.

Airflow Task #1 - Get Current Datetime

Note, though, that when Airflow comes to load DAGs from a Python file, it will only pull any objects at the top level that are a DAG instance. For example, take this DAG file:.

While both DAG constructors get called when the file is accessed, only dag_1 is at the top level (in the globals()), and so only it is added to Airflow.

dag_2 is not loaded. When searching for DAGs inside the DAG_FOLDER, Airflow only considers Python files that contain the strings airflow and dag (case-insensitively) as an optimization.

To consider all Python files instead, disable the DAG_DISCOVERY_SAFE_MODE configuration flag. You can also provide an .airflowignore file inside your DAG_FOLDER, or any of its subfolders, which describes files for the loader to ignore.

It covers the directory it's in plus all subfolders underneath it, and should be one regular expression per line, with # indicating comments.

DAGs will run in one of two ways:. When they are triggered either manually or via the API. On a defined schedule, which is defined as part of the DAG. DAGs do not require a schedule, but it's very common to define one.

You define it via the schedule_interval argument, like this:. The schedule_interval argument takes any value that is a valid Crontab schedule value, so you could also do:.

Every time you run a DAG, you are creating a new instance of that DAG which Airflow calls a DAG Run. DAG Runs can run in parallel for the same DAG, and each has a defined execution_date, which identifies the logical date and time it is running for - not the actual time when it was started.

Airflow Task #3 - Save Processed Datetime

As an example of why this is useful, consider writing a DAG that processes a daily set of experimental data.

  • It's been rewritten, and you want to run it on the previous 3 months of data - no problem, since Airflow can backfill the DAG and run copies of it for every day in those previous 3 months, all at once.

  • Those DAG Runs will all have been started on the same actual day, but their execution_date values will cover those last 3 months, and that's what all the tasks, operators and sensors inside the DAG look at when they run.

In much the same way a DAG instantiates into a DAG Run every time it's run, Tasks specified inside a DAG also instantiate into Task Instances along with it.

Note that every single Operator/Task must be assigned to a DAG in order to run. Airflow has several ways of calculating the DAG without you passing it explicitly:.

If you declare your Operator inside a withDAG block.

If you declare your Operator inside a @dag decorator,. If you put your Operator upstream or downstream of a Operator that has a DAG.

Otherwise, you must pass it into each Operator with dag=. Often, many Operators inside a DAG need the same set of default arguments (such as their start_date). Rather than having to specify this individually for every Operator, you can instead pass default_args to the DAG when you create it, and it will auto-apply them to any operator tied to it:.

As well as the more traditional ways of declaring a single DAG using a context manager or the DAG() constructor, you can also decorate a function with @dag to turn it into a DAG generator function:.

As well as being a new way of making DAGs cleanly, the decorator also sets up any parameters you have in your function as DAG parameters, letting you set those parameters when triggering the DAG. You can then access the parameters from Python code, or from {{context.params}} inside a Jinja template.

Airflow will only load DAGs that appear in the top level of a DAG file. This means you cannot just declare a function with @dag - you must also call it at least once in your DAG file and assign it to a top-level object, as you can see in the example above.

By default, a DAG will only run a Task when all the Tasks it depends on are successful. There are several ways of modifying this, however:.

Branching, where you can select which Task to move onto based on a condition. Latest Only, a special form of branching that only runs on DAGs running against the present. Depends On Past, where tasks can depend on themselves from a previous run. Trigger Rules, which let you set the conditions under which a DAG will run a task. You can make use of branching in order to tell the DAG not to run all dependent tasks, but instead to pick and choose one or more paths to go down.

This is where the branching Operators come in. The BranchPythonOperator is much like the PythonOperator except that it expects a python_callable that returns a task_id (or list of task_ids). The task_id returned is followed, and all of the other paths are skipped. The task_id returned by the Python function has to reference a task directly downstream from the BranchPythonOperator task.

What You'll Do Today

When a Task is downstream of both the branching operator and downstream of one of more of the selected tasks, it will not be skipped:.

  • The paths of the branching task are branch_a, join and branch_b.

  • Since join is a downstream task of branch_a, it will be still be run, even though it was not returned as part of the branch decision.

  • The BranchPythonOperator can also be used with XComs allowing branching context to dynamically decide what branch to follow based on upstream tasks.

If you wish to implement your own operators with branching functionality, you can inherit from BaseBranchOperator, which behaves similarly to BranchPythonOperator but expects you to provide an implementation of the method choose_branch.

Mounting DAGs using Git-Sync sidecar with Persistence enabled¶

As with the callable for BranchPythonOperator, this method should return the ID of a downstream task, or a list of task IDs, which will be run, and all others will be skipped:. Airflow's DAG Runs are often run for a date that is not the same as the current date - for example, running one copy of a DAG for every day in the last month to backfill some data.

DAG Run Status¶

There are situations, though, where you don't want to let some (or all) parts of a DAG run for a previous date; in this case, you can use the LatestOnlyOperator. This special Operator skips all tasks downstream of itself if you are not on the "latest" DAG run (if the wall-clock time right now is between its execution_time and the next scheduled execution_time, and it was not an externally-triggered run).

Here's an example:. In the case of this DAG:. task1 is directly downstream of latest_only and will be skipped for all runs except the latest. task2 is entirely independent of latest_only and will run in all scheduled periods. task3 is downstream of task1 and task2 and because of the default trigger rule being all_success will receive a cascaded skip from task1.

task4 is downstream of task1 and task2, but it will not be skipped, since its trigger_rule is set to all_done.

You can also say a task can only run if the previous run of the task in the previous DAG Run succeeded. To use this, you just need to set the depends_on_past argument on your Task to True. Note that if you are running the DAG at the very start of its life - specifically, that the execution_date matches the start_date - then the Task will still run, as there is no previous run to depend on.

ETL Frameworks to Scale Your Data Pipelines

By default, Airflow will wait for all upstream tasks for a task to be successful before it runs that task. However, this is just the default behaviour, and you can control it using the trigger_rule argument to a Task.

  • The options for trigger_rule are:. all_success (default): All upstream tasks have succeeded.

  • all_failed: All upstream tasks are in a failed or upstream_failed state.

  • all_done: All upstream tasks are done with their execution.

  • one_failed: At least one upstream task has failed (does not wait for all upstream tasks to be done).

The DAG decorator¶

one_success: At least one upstream task has succeeded (does not wait for all upstream tasks to be done). none_failed: All upstream tasks have not failed or upstream_failed - that is, all upstream tasks have succeeded or been skipped.

none_failed_or_skipped: All upstream tasks have not failed or upstream_failed, and at least one upstream task has succeeded. none_skipped: No upstream task is in a skipped state - that is, all upstream tasks are in a success, failed, or upstream_failed state.

dummy: No dependencies at all, run this task at any time. You can also combine this with the Depends On Past functionality if you wish.

It's important to be aware of the interaction between trigger rules and skipped tasks, especially tasks that are skipped as part of a branching operation.

You almost never want to use all_success or all_failed downstream of a branching operation. Skipped tasks will cascade through trigger rules all_success and all_failed, and cause them to skip as well.

Consider the following DAG:. join is downstream of follow_branch_a and branch_false. The join task will show up as skipped because its trigger_rule is set to all_success by default, and the skip caused by the branching operation cascades down to skip a task marked as all_success.

By setting trigger_rule to none_failed_or_skipped in the join task, we can instead get the intended behaviour:. Since a DAG is defined by Python code, there is no need for it to be purely declarative; you are free to use loops, functions, and more to define your DAG.

For example, here is a DAG that uses a for loop to define some Tasks:. In general, we advise you to try and keep the topology (the layout) of your DAG tasks relatively stable; dynamic DAGs are usually better used for dynamically loading configuration options or changing operator options.

If you want to see a visual representation of a DAG, you have two options:. You can load up the Airflow UI, navigate to your DAG, and select "Graph View". You can run airflowdagsshow, which renders it out as an image file.

Packaging DAGs¶

We generally recommend you use the Graph View, as it will also show you the state of all the Task Instances within any DAG Run you select. Of course, as you develop out your DAGs they are going to get increasingly complex, so we provide a few ways to modify these DAG views to make them easier to understand.

A TaskGroup can be used to organize tasks into hierarchical groups in Graph View. It is useful for creating repeating patterns and cutting down visual clutter.

Unlike SubDAGs, TaskGroups are purely a UI grouping concept. Tasks in TaskGroups live on the same original DAG, and honor all the DAG settings and pool configurations. Dependency relationships can be applied across all tasks in a TaskGroup with the >> and << operators.

For example, the following code puts task1 and task2 in TaskGroup group1 and then puts both tasks upstream of task3:.

If you want to see a more advanced use of TaskGroup, you can look at the example_task_group.py example DAG that comes with Airflow.

  • By default, child tasks/TaskGroups have their IDs prefixed with the group_id of their parent TaskGroup. This helps to ensure uniqueness of group_id and task_id throughout the DAG.

  • To disable the prefixing, pass prefix_group_id=False when creating the TaskGroup, but note that you will now be responsible for ensuring every single task and group has a unique ID of its own.

  • As well as grouping tasks into groups, you can also label the dependency edges between different tasks in the Graph View - this can be especially useful for branching areas of your DAG, so you can label the conditions under which certain branches might run.

  • To add labels, you can use them directly inline with the >> and << operators:. Or, you can pass a Label object to set_upstream/set_downstream:. Here's an example DAG which illustrates labeling different branches:.

Re-run DAG¶

It's possible to add documentation or notes to your DAGs & task objects that are visible in the web interface ("Graph View" & "Tree View" for DAGs, "Task Instance Details" for tasks).

There are a set of special task attributes that get rendered as rich content if defined:. Please note that for DAGs, doc_md is the only attribute interpreted.

Mounting DAGs from a private Github repo using Git-Sync sidecar¶

This is especially useful if your tasks are built dynamically from configuration files, as it allows you to expose the configuration that led to the related tasks in Airflow:.

Sometimes, you will find that you are regularly adding exactly the same set of tasks to every DAG, or you want to group a lot of tasks into a single, logical unit.

  • This is what SubDAGs are for. For example, here's a DAG that has a lot of parallel tasks in two sections:.

  • We can combine all of the parallel task-* operators into a single SubDAG, so that the resulting DAG resembles the following:.

  • Note that SubDAG operators should contain a factory method that returns a DAG object.

  • This will prevent the SubDAG from being treated like a separate DAG in the main UI - remember, if Airflow sees a DAG at the top level of a Python file, it will load it as its own DAG.

  • This SubDAG can then be referenced in your main DAG file:. You can zoom into a SubDagOperator from the graph view of the main DAG to show the tasks contained within the SubDAG:.

  • Some other tips when using SubDAGs:. By convention, a SubDAG's dag_id should be prefixed by the name of its parent DAG and a dot (parent.child).

  • You should share arguments between the main DAG and the SubDAG by passing arguments to the SubDAG operator (as demonstrated above). SubDAGs must have a schedule and be enabled.

  • If the SubDAG's schedule is set to None or @once, the SubDAG will succeed without having done anything. Clearing a SubDagOperator also clears the state of the tasks within it.

  • Marking success on a SubDagOperator does not affect the state of the tasks within it. Refrain from using Depends On Past in tasks within the SubDAG as this can be confusing. You can specify an executor for the SubDAG.

  • It is common to use the SequentialExecutor if you want to run the SubDAG in-process and effectively limit its parallelism to one.

Using LocalExecutor can be problematic as it may over-subscribe your worker, running multiple tasks in a single slot.

See airflow/example_dags for a demonstration.

Note that Pools are not honored by SubDagOperator, and soresources could be consumed by SubdagOperators beyond any limits you may have set. While simpler DAGs are usually only in a single Python file, it is not uncommon that more complex DAGs might be spread across multiple files and have dependencies that should be shipped with them ("vendored").

You can either do this all inside of the DAG_FOLDER, with a standard filesystem layout, or you can package the DAG and all of its Python files up as a single zip file.

For instance, you could ship two dags along with a dependency they need as a zip file with the following contents:. Note that packaged DAGs come with some caveats:. They cannot be used if you have picking enabled for serialization. They cannot contain compiled libraries (e.g. libz.so), only pure Python. They will be inserted into Python's sys.path and importable by any other code in the Airflow process, so ensure the package names don't clash with other packages already installed on your system.

In general, if you have a complex set of compiled dependencies and modules, you are likely better off using the Python virtualenv system and installing the necessary packages on your target systems with pip.

Mounting DAGs using Git-Sync sidecar without Persistence¶

Greetings Data Practitioners.It's 2021, Data Engineering is new the Data Science.There is an increase in time and investment spent in the Data Engineering space.

How often have you built models or performed analysis to only realize there was something off with the data you used?

— Pretty often if you’ve been in the industry for a while. To clear our doubts, we often have to consult data engineers to verify the integrity of our data. If we’re the data engineers ourselves, we’ll have to dig into data pipelines to understand the ETL of our data.

Mounting DAGs from an externally populated PVC¶

As the data industry matures, more priorities are being placed on Data Pipelines and Infrastructure.

  • This is what today’s write-up is about. Apache Airflow is one of the most popular workflow management systems for us to manage data pipelines.

  • Long gone are the times where crontabs are being utilized as schedulers of our pipelines.

Apache Airflow is one of the best solutions for batch pipelines. If your company is serious about data, adopting Airflow could bring huge benefits for future projects.

Tech Giants like Facebook and Airbnb use Apache Airflow in their day-to-day data tasks. This is an article on building frameworks on Apache Airflow.Hence, it is assumed that you understand the basic concepts of Airflow like —.

Loading DAGs¶

If you’re unfamiliar, have a read-through on the comprehensive guide I’ve made on the basics of Apache Airflow. It also goes without saying that you understand basic Python.

First of all, you may be asking— Why do we even need a Framework? Having a framework allows us to build better-structured pipelines. It is easier to maintain and scale.

Not to mention it also complies with business rules and programming practices. When you start to have a huge number of pipelines, frameworks save you time in deploying them, allowing you to focus your efforts on developing code for your pipelines. Here’s how a Pipeline in Apache Airflow is deployed—.

A DAG.py file is created in the DAG folder in Airflow, containing the imports for operators, DAG configurations like schedule and DAG name, and defining the dependency and sequence of tasks.

Operators are created in the Operator folder in Airflow. They contain Python Classes that have logic to perform tasks. They are called in the DAG.py file.

Here’s a sample DAG and Operator —.

Looks pretty neat. However, things start to get messy when there are multiple tasks in the pipeline. Each task calls upon a different operator and they all require different variables.

Your DAG.py file will soon be flooded with lines of unreadable code.File paths, SQL queries, and even random variables, which is just plain confusing to the reader when it starts to pile up.

How to Run the Airflow DAG

Let's use a framework to deploy our pipeline. Instead of a .py file, a folder is created for each DAG. Inside the folder, a .py file, task_config.json, and job_config.json are created. The .py file is a one-liner calling the create_dag_operator.py.

The create_dag_operator.py contains all logic on designing the task flow of our pipeline. It also extracts all Operator required variables from the job and task config files.

From here, it imports all other custom operators during the task flow construction. Here’s how the pipeline is deployed with the framework —.

From here, you can clearly see that the CreateDagOperator does all the heavy lifting.

How to Connect the Airflow DAG

Observe how simple the DAG file is. The config files are simple python dictionaries. Job Config mainly consists of DAG configurations like name, schedule, and start date.

Task Config consists of Task Configurations and Instance Variables like task name and whatever needs to be passed through the called Operator.

In the CreateDagOperator, task flow dependencies, DAG creation, and task definitions are defined.

All required variables are extracted from the config files and instantiated here.

The framework keeps things neat and simple for members of your team.

They know exactly what inputs they have to make in the config file, while only developing logic in the CreateDagOperator.

Of course, this is just an example of a basic pipeline.

For special jobs and custom task flows, simply make a new static method in the CreateDagOperator and call it in the DAG file.

Here’s how your folder structure should look like with the framework —.

Expanding on the framework, you can create folders in your sample_dag folder for different purposes.

For example, if your pipelines somehow involve .csv files, you can create a folder named ‘resources’ and have your .csv file placed in there.

You can then call it from the CreateDagOperator.

You can then have your config files in a folder named ‘config’.

This provides a much more systematic way of organizing your files.What I’ve provided here today is the basic structure of the framework.

It is entirely up to you to expand on it, which is where the fun comes into play, isn't it?

An actual example of folder structure here —. Congratulations if you have made it this far. You must be really serious about data. You should be — data will soon be the fuel of our economy.

Table of Contents

In this article, you have learned —. Basic Structure of Apache Airflow. Building a basic Framework on Apache Airflow. As usual, I end with a quote. Every company has big data in its future, and every company will eventually be in the data business.

Davenport, American academic and Author. You can also support me by signing up for a medium membership through my link.

You will be able to read an unlimited amount of stories from me and other incredible writers! I am working on more stories, writings, and guides in the data industry.

You can absolutely expect more posts like this. In the meantime, feel free to check out my other articles to temporarily fill your hunger for data. Thanks for reading! If you want to get in touch with me, feel free to reach me at [email protected] or my LinkedIn Profile.

You can also view the code for previous write-ups in my Github.

Since organizations are increasingly relying on data, Data Pipelines are becoming an integral element of their everyday operations.

The amount of data used in various business activities has grown dramatically over time, from Megabytes per day to Gigabytes per minute.

Despite the fact that dealing with this data flood may appear to be a significant challenge, these growing data volumes may be managed with the right equipment.

  • This article introduces us to the Airflow DAGs and their best practices. When Airbnb ran into similar issues in 2014, its Engineers developed Airflow – a Workflow Management Platform that allowed them to write and schedule as well as monitor the workflows using the built-in interface.

  • Apache Airflow leverages workflows as DAGs (Directed Acyclic Graphs) to build a Data Pipeline. Airflow DAG is a collection of tasks organized in such a way that their relationships and dependencies are reflected.

  • This guide will present a comprehensive understanding of the Airflow DAGs, its architecture, as well as the best practices for writing Airflow DAGs. Continue reading to know more.

  • Introduction to Airflow DAG – Directed Acyclic Graph. Airflow DAGs Best Practices.

  • Basic understanding of Data Pipelines. Apache Airflow is an open-source, distributed Workflow Management Platform developed for Data Orchestration.

  • The Airflow project was initially started by Maxime Beauchemin at Airbnb.

  • Following the success of the project, the Apache Software Foundation swiftly adopted the Airflow project, first as an incubator project in 2016 and then as a top-level project in 2019. Airflow provides users to write programmatically, schedule, and monitor Data Pipelines.

The key feature of Airflow is that it enables users to build scheduled Data Pipelines using a flexible Python framework easily.

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Introduction to Airflow DAG – Directed Acyclic Graph

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One needs to understand the following aspects to get a clear picture of what Airflow DAGs actually are.

The increasing data volumes necessitate a Data Pipeline to handle Data Storage, Analysis, Visualization, and more.

A Data Pipeline is a collection of all the necessary steps that together are responsible for a certain process.

Apache Airflow is a platform that allows users to develop and monitor batch Data Pipelines.

A basic Data Pipeline, for example, consists of two tasks, each performing its own function.

However, new data cannot be pushed in between the pipelines until it has undergone the transformations.

In the graph-based representation, the tasks are represented as nodes, while directed edges represent dependencies between tasks.

The direction of the edge represents the dependency.

For example, an edge pointing from Task 1 to Task 2 (above image) implies that Task 1 must be finished before Task 2 can begin.

This graph is called a Directed Graph. There are two types of Directed Graphs: Cyclic and Acyclic.

In a Cyclic Graph, the cycles prevent the task execution due to the circular dependencies.

Due to the interdependency of Tasks 2 and 3, there is no clear execution path.

In the Acyclic Graph, there is a clear path to execute the three different tasks.

Summary and Next Steps

In Apache Airflow, DAG stands for Directed Acyclic Graph. DAG is a collection of tasks organized in such a way that their relationships and dependencies are reflected.

One of the advantages of this DAG model is that it gives a reasonably simple technique for executing the pipeline. Another advantage is that it clearly divides pipelines into discrete incremental tasks rather than relying on a single monolithic script to perform all the work.

The acyclic feature is particularly significant since it is simple and prevents tasks from being entangled in circular dependencies.

  • Airflow employs the acyclic characteristic of DAGs to resolve and execute these task graphs efficiently.

  • Apache Airflow lets users set a scheduled interval for each DAG, which dictates when the Airflow runs the pipeline.

  • Airflow is organized into3 main components:. Webserver: Visualizes the Airflow DAGs parsed by the scheduler and provides the main interface for users to monitor DAG runs and their results.

Scheduler:Parses Airflow DAGs, verifies their scheduled intervals, and begins scheduling DAG tasks for execution by passing them to Airflow Workers. Worker: Picks up tasks that are scheduled for execution and executes them.

Declaring a DAG¶

Other components:. Database: A separate service you have to provide to Airflow to store metadata from the Webserver and Scheduler. Follow the below-mentioned practices to implement Airflow DAGs in your system. It’s easy to get into a tangle while creating Airflow DAGs. DAG code, for example, may easily become unnecessarily intricate or difficult to comprehend, especially if DAGs are produced by members of a team who have very different programming styles.

Use Style Conventions: Adopting a uniform, clean programming style and applying it consistently across all your Airflow DAGs is one of the first steps toward building clean and consistent DAGs.

When writing the code, the simplest method to make it clearer and easier to comprehend is to utilize a commonly used style.

Manage Credentials Centrally: Airflow DAGs interact with many different systems, leading to many different types of credentials such as databases, cloud storage, and so on. Fortunately, retrieving connection data from the Airflow connections store makes it simple to retain credentials for custom code. Group Related Tasks using Task Groups: Because of the sheer number of tasks required, complex Airflow DAGs can be difficult to comprehend.

Airflow 2’s new feature called Task Groups assists in managing these complicated systems. Task Groups efficiently divide tasks into smaller groups, making the DAG structure more manageable and understandable.

  • Aside from developing excellent DAG code, one of the most difficult aspects of writing a successful DAG is making your tasks reproducible.

  • This means that users can simply rerun a task and get the same result even if the task is executed at various times.

  • Always Require Tasks to be Idempotent: Idempotency is one of the most important characteristics of a good Airflow task.

  • No matter how many times you execute an idempotent task, the result is always the same. Idempotency guarantees consistency and resilience in the face of failure. Task Results should be Deterministic: To build reproducible tasks and DAGs, they must be deterministic.

  • The deterministic task should always return the same output for any given input. Design Tasks using Functional Paradigms: It is easier to design tasks using the functional programming paradigm.

  • Functional programming is a method of building computer programs that treat computation primarily as the application of mathematical functions while avoiding the use of changeable data and mutable states. Airflow DAGs that handle large volumes of data should be carefully designed as efficiently as feasible.

Limit the Data being Processed: Limiting Data Processing to the minimum data necessary to get the intended outcome is the most effective approach to managing data. This entails thoroughly considering the Data Sources and assessing whether or not they are all necessary.

Incremental Processing: The primary idea behind incremental processing is to divide your data into (time-based) divisions and treat each of the DAG runs separately.

Users may reap the benefits of incremental processing by executing filtering/aggregation processes in the incremental stage of the process and doing large-scale analysis on the reduced output. Avoid Storing Data on a Local File System: Handling data within Airflow sometimes might be tempting to write data to the local system. As a result, downstream tasks may not be able to access them since Airflow runs its tasks multiple tasks in parallel.

The simplest method to prevent this problem is to utilize shared storage that all Airflow workers can access to perform tasks simultaneously. When dealing with large volumes of data, it can possibly overburden the Airflow Cluster.

Dynamic DAGs¶

As a result, properly managing resources can aid in the reduction of this burden.

Managing Concurrency using Pools: When performing many processes in parallel, it’s possible that numerous tasks will require access to the same resource. Airflow uses resource pools to regulate how many tasks have access to a given resource.

  • Each pool has a set number of slots that offer access to the associated resource.

  • Detecting Long-running Tasks using SLAs and Alerts: Airflow’s SLA (Service-level Agreement) mechanism allows users to track how jobs are performing.

Using this mechanism, users can effectively designate SLA timeouts to DAGs, with Airflow alerting them if even one of the DAGs tasks takes longer than the specified SLA timeout. This blog taught us that the workflows in Apache Airflow are represented as DAGs, which clearly define tasks and their dependencies.

Similarly, we also learned about some of the best practices while writing Airflow DAGs. Today, many large organizations rely on Airflow for orchestrating numerous critical data processes.

Simplify Airflow ETL with Hevo’s No-code Data Pipeline

It is important to consolidate data from Airflow and other Data Sources into a Cloud Data Warehouse or a destination of your choice for further Business Analytics.

This is where Hevo comes in. visit our website to explore hevo. Hevo Data with its strong integration with 100+ Sources & BI tools, allows you to not only export data from sources & load data to the destinations, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools.

Give Hevo Data a try and sign up for a 14-day free trial today. Hevo offers plans & pricing for different use cases and business needs, check them out! Share your experience of understanding Airflow DAGs in the comments section below. In the previous article, you've seen how to install Apache Airflow locally in a new Python virtual environment and how to do the initial setup. Today you'll write your first data pipeline (DAG) in Airflow, and it won't take you more than 10 minutes. The data pipeline will get the current datetime from the Terminal, process it, and save it to a CSV file. Pretty simple, but you'll learn how Airflow's Bash and Python operators work, and also how to communicate between tasks using Xcoms, and how to schedule your data pipelines.

Let's dive straight in! Don't feel like reading? Watch my video instead:. As the title suggests, you'll write your first DAG that implements the following data pipeline:.

Gets the current datetime information from the Terminal. Processes the returned datetime string. Saves the datetime information to a CSV file. You can get the current datetime information through the Terminal by running the following command:. We'll start by creating a new file in ~/airflow/dags. Create the dags folder before starting and open it in any code editor.

I'm using PyCharm, but you're free to use anything else. Inside the dags folder create a new Python file called first_dag.py.

  • You're ready to get started - let's begin with the boilerplate.

  • Copy the following code to first_dag.py:. We've made a lot of imports, and these are the modules and operators we'll use throughout the file.

  • Every Airflow DAG is defined with Python's context manager syntax (with). Here's a description for each parameter:.