## concurrency and parallelism, a technical primer

1. Concurrency:
   - Definition: Concurrency refers to the ability of a system to perform multiple tasks or processes simultaneously, but not necessarily at the same instant.
   - Key Points:
     - Tasks can progress independently and interleave their execution.
     - Concurrency improves responsiveness and efficiency by allowing tasks to progress concurrently.
     - Can be achieved on a single processing unit by switching between tasks.
   - Example: Multithreading in Python
     ```python
     import threading
     
     def task1():
         print("Task 1 started")
         # Perform task 1
         print("Task 1 completed")
     
     def task2():
         print("Task 2 started")
         # Perform task 2
         print("Task 2 completed")
     
     thread1 = threading.Thread(target=task1)
     thread2 = threading.Thread(target=task2)
     
     thread1.start()
     thread2.start()
     
     thread1.join()
     thread2.join()
     ```

2. Parallelism:
   - Definition: Parallelism refers to the actual simultaneous execution of multiple tasks or processes on different processing units or cores.
   - Key Points:
     - Tasks are executed simultaneously on different processing units.
     - Parallelism requires hardware support, such as multiple processors or cores.
     - Aims to improve performance and solve problems faster by leveraging multiple computing resources.
   - Example: Multiprocessing in Python
     ```python
     import multiprocessing
     
     def task1():
         print("Task 1 started")
         # Perform task 1
         print("Task 1 completed")
     
     def task2():
         print("Task 2 started")
         # Perform task 2
         print("Task 2 completed")
     
     process1 = multiprocessing.Process(target=task1)
     process2 = multiprocessing.Process(target=task2)
     
     process1.start()
     process2.start()
     
     process1.join()
     process2.join()
     ```

3. Concurrency in Pipelines:
   - Definition: Concurrency in pipelines allows multiple tasks or processes to progress independently through different stages of execution.
   - Examples:
     - CI/CD Pipeline: Stages like code compilation, unit testing, and packaging can operate concurrently on different builds.
     - ETL Pipeline: Stages like data extraction, transformation, and loading can process different batches of data concurrently.
   - Key Points:
     - Stages in a pipeline can operate concurrently on different data or tasks.
     - Concurrency improves throughput and performance by efficiently utilizing resources.
     - Stages may or may not execute in parallel, depending on available resources and dependencies.

4. DAGs (Directed Acyclic Graphs) in Apache Airflow:
   - Definition: A DAG represents a collection of tasks organized based on their dependencies and relationships.
   - Key Points:
     - Tasks in a DAG are independent units of work that can be executed concurrently.
     - Airflow's scheduler can execute multiple tasks in parallel, leveraging available resources.
     - DAGs enable task parallelism, task concurrency, and DAG concurrency.
   - Example: Simple DAG in Apache Airflow
     ```python
     from airflow import DAG
     from airflow.operators.python_operator import PythonOperator
     from datetime import datetime, timedelta
     
     default_args = {
         'owner': 'airflow',
         'depends_on_past': False,
         'start_date': datetime(2023, 1, 1),
         'email_on_failure': False,
         'email_on_retry': False,
         'retries': 1,
         'retry_delay': timedelta(minutes=5),
     }
     
     dag = DAG(
         'example_dag',
         default_args=default_args,
         description='A simple DAG example',
         schedule_interval=timedelta(days=1),
     )
     
     def task1():
         print("Task 1 executed")
     
     def task2():
         print("Task 2 executed")
     
     task1_operator = PythonOperator(
         task_id='task1',
         python_callable=task1,
         dag=dag,
     )
     
     task2_operator = PythonOperator(
         task_id='task2',
         python_callable=task2,
         dag=dag,
     )
     
     task1_operator >> task2_operator
     ```

This guide provides a concise overview of concurrency and parallelism, along with technical examples in Python for multithreading, multiprocessing, and a simple DAG in Apache Airflow. It highlights the key differences between concurrency and parallelism and illustrates how concurrency is utilized in pipelines and DAGs to enable efficient execution of tasks.