Update airflow.md

content/airflow.md

@@ -80,7 +80,8 @@ CeleryExecutors has a fixed number of workers on standby to take on tasks when t
 Pros:
 + It's simple and straightforward to set up.
 + It's a good way to test DAGs while they're being developed.
-Pros:
+
+Cons:
 It isn't scalable.
 It is not possible to perform many tasks at the same time.
 Unsuitable for use in production
@@ -91,6 +92,7 @@ Unsuitable for use in production
 Pros:
 + Able to perform multiple tasks.
 + Can be used to run DAGs during development.
+
 Cons:
 + The product isn't scalable.
 + There is only one point of failure.
@@ -102,6 +104,7 @@ Cons:
 Pros:
 + It allows for scalability.
 + Celery is responsible for managing the workers. Celery creates a new one in the case of a failure.
+
 Cons:
 + Celery requires RabbitMQ/Redis for task queuing, which is redundant with what Airflow already supports.
 + The setup is also complicated due to the above-mentioned dependencies.
@@ -110,17 +113,20 @@ Cons:
 
 ## What are the pros and cons of KubernetesExecutor?
 Pros:
-It combines the benefits of CeleryExecutor and LocalExecutor in terms of scalability and simplicity.
-Fine-grained control over task-allocation resources. At the task level, the amount of CPU/memory needed can be configured.
++ It combines the benefits of CeleryExecutor and LocalExecutor in terms of scalability and simplicity.
++ Fine-grained control over task-allocation resources. At the task level, the amount of CPU/memory needed can be configured.
+
 Cons:
-Airflow is newer to Kubernetes, and the documentation is complicated.
++ Airflow is newer to Kubernetes, and the documentation is complicated.
 
 [Table of Contents](#Apache-Airflow)
 
 ## How to define a workflow in Airflow?
 Python files are used to define workflows.
 DAG (Directed Acyclic Graph)
 The DAG Python class in Airflow allows you to generate a Directed Acyclic Graph, which is a representation of the workflow.
+
+```python
 from Airflow.models import DAG
 from airflow.utils.dates import days_ago
 ​
@@ -133,7 +139,10 @@ dag_id='bash_operator_example',
 default_args=args,
 schedule_interval='* * * * *',
 )
+```
+
 You can use the start date to launch a task on a specific date.
+
 The schedule interval specifies how often each workflow is scheduled to run. '* * * * *' indicates that the tasks must run every minute.
 
 [Table of Contents](#Apache-Airflow)
@@ -142,7 +151,7 @@ The schedule interval specifies how often each workflow is scheduled to run. '*
 
 The expression `schedule_interval='30 8 * * 1-5'` is a **cron expression** used in Airflow (and Unix-like systems) to define a specific schedule for running tasks. Here's a detailed breakdown:
 
-## Cron Expression Structure
+### Cron Expression Structure
 
 A cron expression is composed of 5 fields separated by spaces:
 
@@ -154,7 +163,7 @@ A cron expression is composed of 5 fields separated by spaces:
 | **Month**     | 4        | `1-12` or `JAN-DEC`    | The month                       |
 | **Day of Week** | 5      | `0-6` or `SUN-SAT`     | The day of the week (0 = Sunday)|
 
-## Detailed Explanation of `30 8 * * 1-5`
+### Detailed Explanation of `30 8 * * 1-5`
 
 1. **`30` (Minute)**:
    - The task will run at the **30th minute** of the hour.
@@ -176,14 +185,14 @@ A cron expression is composed of 5 fields separated by spaces:
    - The range `1-5` means the task will run on **Monday to Friday**.
    - Example: It skips weekends (Saturday and Sunday).
 
-## When Will This Schedule Trigger?
+#### When Will This Schedule Trigger?
 
 This cron expression means:
 - **Time**: 8:30 AM.
 - **Days**: Monday through Friday.
 - **Frequency**: Daily (only on weekdays).
 
-## Examples of Trigger Dates
+#### Examples of Trigger Dates
 Assuming the current date is January 2025:
 - Monday, January 6, 2025, at 08:30 AM.
 - Tuesday, January 7, 2025, at 08:30 AM.