Optimizing Resources With Airflow: A Guide to Workload Optimization and SLA Management

Optimizing resources with Airflow

When it comes to managing vast pipelines of automated operations, scores of companies turn to Apache Airflow. Airflow employs Python code to manage task chains for workload automation use cases ranging from ETL data pipelines to machine learning lifecycles.

Organizations rarely automate their workflows in one fell swoop, however. Piecemeal arrangements with multiple environments and use cases are near universal. Plus, while Airflow helps overhaul task automation and data pipelines, the platform lacks predictive powers. This results in visibility issues and an inability to forecast task completion, leaving teams no room to adapt and correct course.

This opacity places companies in a bind when it comes to meeting their service level agreements (SLAs). Automated Analytics & Intelligence (AAI) for Airflow—and its “single pane of glass” display—guards against any service delivery failures or runaway events, empowering companies to deliver on their obligations, both formal and informal.

Cloud-native automation platform challenges

Although cloud-native workload automation platforms like Airflow have definite benefits, they inadvertently introduce the following new challenges:

• Visibility loss—Processes become scattered across a myriad of environments and screens. A quick, comprehensive scan becomes near impossible.
• Lack of monitoring—It is nearly impossible to see dependencies across the workstream. This makes it challenging to monitor operations and track down the root cause of errors.
• Concealment of SLAs—While teams may feel they have a grasp on their SLAs, a sudden workload automation platform issue can reveal hidden or unaccounted-for commitments.

Put simply, a business governed by diverse and disconnected workload tools calls to mind the classic recipe for chaos: too many cooks in the kitchen.

SLAs, under the microscope

While formal SLAs serve as a core feature of technical support and managed service contracts, the concept extends beyond mere IT service pacts.

In essence, any action step that depends upon the completion of a prior action step contains an implied SLA.

For example, a data analyst commits to scrutinizing critical information. But if they don’t receive the data in a timely fashion, they’re handcuffed—unable to act. This notion of “SLA” extends to obligations like:

• Shipping deadlines
• Division-of-labor agreements
• IT infrastructure provisioning

When taking stock of your team’s SLAs, ask yourself: “Who screams the loudest when an application goes down or when data is delivered late or is inaccessible?” The answer will likely reveal an array of unspoken SLAs (and their dependencies), which probably surpass those articulated in formal SLA contracts.

Common Airflow challenges

Airflow attempts to summon order from chaos by furnishing a versatile Python framework in which engineers can schedule, audit, and generate workflows. In addition, its open-source nature renders it compatible with nearly the full spectrum of technologies.

To accomplish its mission, Airflow employs DAGs (Directed Acyclic Graphs). DAGs perform these actions:

• Draw on Python files to convert jobs and their dependencies into usable code.
• Define tasks by their dependencies (i.e., step 2 relies on step 1 to run).
• Display tasks in a way that illustrates their relationships and interdependence.

But because Airflow treats DAG runs simply as “a task that must be completed within X time frame,” the platform struggles to link these DAGs to specific business processes. As a result, observability, especially in moments of crisis, becomes murky.

Missing insights

This obscurity results from a few inherent “blind spots” native to Airflow:

• Inability to predict whether a process will achieve (or exceed) its SLA.
• Lack of comprehensive visibility across multiple platforms.
• Absence of dependency charting (it only features a simple Gantt View).

Consider an SLA provision that “[X task] must complete within a maximum time range, relative to the DAG start time.”

Without comprehensive clarity, there’s no way to predict whether a task will overflow the time limit defined in the SLA (and trigger a cascading domino effect of trouble). Warnings only arrive once the deadline has passed.

This would be equivalent to a shipping dock worker latching the trailer door and dispatching a truck for delivery before the manufacturing line finishes the freight.

How AAI enhances Airflow

AAI enhances Airflow with a layer of deep visibility. By doing so, it eliminates fruitless searching. Equally important, AAI introduces DAG forecasting, which serves as a crystal ball for engineers to foresee (and avoid) impending patches of workflow quicksand.

AAI also imposes uniformity upon its data—no need to spend time deciphering reports or performing frantic conversions.

SLA management with AAI

When asked whether they are monitoring SLAs, many Airflow users answer yes. However, this may not really be true. The definition of an SLA within Airflow differs to that within AAI:

• In Airflow, SLA refers to a timeframe in which a task or DAG should finish.
• In AAI, SLA refers to a timeframe in which a business process should execute.

Because it effectively defines SLAs as “run durations,” Airflow will sound the alarm only if a job runs over its allotted time, even if the task never initiated. Airflow also doesn’t account for these aspects:

• Tasks initiated manually
• DAGs triggered by events

This opens the door for late or misfired alerts—and the ensuing confusion. It also leaves engineers scrambling to investigate SLA misses after the fact.

With AAI, on the other hand, users receive the benefit of predictive forecasting. Alarms sound whenever an SLA (of any type) is in peril, granting users time to intervene.

Crucially, this forecasting power requires no coding knowledge whatsoever—it’s baked into the platform’s DNA.

AAI: Enhanced service delivery and visibility

Companies rely on Airflow for a reason: its automation offers powerful control over the complex task chains that define modern business. But even elite systems leave room for enhancement.

In Airflow’s case, both its visibility shortfalls and SLA monitoring invite upgrades.

Fortunately, AAI has risen to the challenge. By delivering unparalleled visibility enshrined in a digestible “single pane of glass” view, it empowers engineers to readily diagnose issues and respond with precision. And like a tornado siren, its predictive monitoring capacity allows users to respond rapidly, while sidestepping costly post-disaster cleanup.

For more on AAI’s Airflow integration, watch the demo. To discover how to optimize your Airflow usage, we invite you to expand your understanding of Automation Analytics & Intelligence on Broadcom Software Academy.

Sources

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Apache Airflow. Use Cases. https://airflow.apache.org/use-cases/

Apache Airflow. 2023 Airflow Survey Results. https://airflow.apache.org/survey/

CIO.com. What is an SLA? Best practices for service-level agreements. https://www.cio.com/article/274740/outsourcing-sla-definitions-and-solutions.html

Apache Airflow. What is Airflow? https://airflow.apache.org/docs/apache-airflow/stable/index.html

UC Davis Health. Directed Acyclic Graphs (DAGs) and Regression for Causal Inference. https://health.ucdavis.edu/media-resources/ctsc/documents/pdfs/directed-acyclic-graphs20220209.pdf

Argo Workflows. DAG. https://argo-workflows.readthedocs.io/en/latest/walk-through/dag/

Association for Project Management. What is a Gantt chart? https://www.apm.org.uk/resources/find-a-resource/gantt-chart/

Forbes. How Automation Tools are Transforming the Workplace. https://www.forbes.com/councils/theyec/2022/04/20/how-automation-tools-are-transforming-the-modern-workplace/