Unleashing the Magic of Job Schedulers: How to Tame Your Code and Save Your Sanity
Tags: beginners, webdev, dataengineering, schedule
Published: 2023-04-13 06:00:05

_Once upon a time, in a land far, far away, software engineers were manually running their code on their machines like it was the Wild West🐎. But then, a hero emerged_ - **the job scheduler!**🔫 A tool that revolutionized the way developers manage their tasks, making their lives easier and their code more organized. So, let's dive into the magical world of job schedulers and discover how they can save you from the chaos of manual task management.

## From Granny-tech to Anime-inspired😎
**[IBM TWS](https://www.ibm.com/docs/en/workload-automation/8.6.0?topic=SSGSPN_8.6.0/com.ibm.tivoli.itws.doc_8.6/eqqg1mst82.html)** was one of the earliest job schedulers used in enterprise IT environments to manage batch jobs and automate workflows. TWS used a central server to manage and schedule jobs across multiple platforms and operating systems. It was a powerful tool that allowed users to create and schedule jobs across different platforms and applications.

While TWS was a powerful and widely used job scheduler in its time, it was also complex and difficult to configure and maintain. Developers wanted a more flexible and scalable solution that could handle complex dependencies and workflows. This led to the development of more modern schedulers.

**[Airflow](https://airflow.apache.org/)** uses a directed acyclic graph (DAG) to define workflows, allowing users to define complex dependencies and schedule tasks based on their relationships.

**[Mage](https://docs.mage.ai/introduction/overview)** is a proprietary job scheduler developed by Shopify. It was designed to handle the high volume of tasks and dependencies in their e-commerce platform. Mage is known for its scalability and ability to handle complex workflows across different data centres.

Another popular scheduler is **[HashiCorp Nomad](https://www.nomadproject.io/)**, which takes a more modern, cloud-native approach to job scheduling. Nomad is designed to be highly scalable and flexible, allowing users to run jobs on any infrastructure, from bare metal to virtual machines to containers.

While modern schedulers have become increasingly popular, the use of _Cron Jobs_ still remains relevant. _Cron Jobs_ is a simple and widely used scheduling tool in Unix-based systems. They allow users to schedule repetitive tasks at specific intervals using a standard syntax.

_Since I plunged from TWS to Airflow, let's dive into their comparison!_

## Ready to Migrate? Your Smooth Transition Checklist!📝
- Identify the existing job workflows running on TWS
- Define the job workflows in Airflow using Directed Acyclic Graphs (DAGs)
- Configure connections for any external systems used in the job workflows
- Migrate job scripts from TWS to Airflow, with support for various languages such as Python, Bash, and SQL
- Set up scheduling rules in Airflow that match those in TWS
- Test and validate the workflows for functionality and performance
- Switch over from TWS to Airflow, with close monitoring during the transition period
- Monitor and maintain the workflows, with necessary adjustments made as needed.

## Pennywise or Penny Foolish??💸

- **Reduced licensing costs**: Airflow is free and open-source, while TWS requires users to pay licensing fees.

- **Lower hardware costs**: Airflow can run on a cluster of servers or on cloud platforms, which can reduce hardware costs compared to TWS's centralized server requirement.

- **Easier maintenance**: Airflow has a simple web-based interface and a user-friendly workflow definition language, which can reduce labour costs compared to TWS's complex configuration and maintenance requirements.

- **Better resource utilization**: Airflow can dynamically allocate resources based on job requirements, which can improve resource utilization and reduce costs compared to TWS's potential for uneven resource utilization.

Overall, migrating to Airflow can help organizations save money by reducing licensing and hardware costs, improving productivity, and optimizing resource utilization, all while providing greater flexibility and agility.

## Limitations of Airflow🚫
- Resource Intensive
- Steep Learning Curve
- Limited Native Support for Real-time Workflows
- Limited Monitoring Capabilities
- Lack of Native Support for Kubernetes

## Conclusion🌐
In summary, the modernization of job schedulers in software engineering has improved scalability, flexibility, and cost-effectiveness. New job schedulers allow for greater automation, better visualization, and more efficient management of tasks. Migrating to modern schedulers can optimize processes, reduce costs, and improve overall performance, making it crucial to stay up-to-date with the latest tools and technologies.
_But they will keep on coming my friend so hold you're horses!_🏇

_**Keep Learning, Keep Growing!**_🙌🌻

Cheers, 
[Ujjwal](https://www.linkedin.com/in/ujjwal-tyagi-datawizard/)🤙