High availability configuration in Apache Airflow - Mini Project: Build & Apply

High Availability Configuration in Apache Airflow

📖 Scenario: You are setting up Apache Airflow for a company that needs its workflows to run without interruption. To achieve this, you will configure Airflow for high availability (HA) so that if one scheduler or webserver fails, another can take over seamlessly.

🎯 Goal: Build a simple Airflow configuration that enables high availability by setting up multiple schedulers and webservers with a shared metadata database.

📋 What You'll Learn

Create an Airflow configuration dictionary with basic settings

Add a configuration variable to enable multiple schedulers

Configure the webserver to run with multiple workers

Print the final Airflow configuration dictionary

💡 Why This Matters

🌍 Real World

High availability ensures that Airflow workflows keep running even if one scheduler or webserver fails, which is critical for business processes that depend on timely data pipelines.

💼 Career

DevOps engineers and data engineers often configure Airflow for high availability to maintain reliable and fault-tolerant workflow orchestration in production environments.

1

Create the initial Airflow configuration dictionary

Create a dictionary called airflow_config with these exact entries: "core": {"executor": "LocalExecutor", "sql_alchemy_conn": "sqlite:///airflow.db"} and "webserver": {"web_server_port": 8080}.

Apache Airflow

# Create the airflow_config dictionary with core and webserver sections
# Your code here

Hint

Use a dictionary with keys core and webserver. Each key maps to another dictionary with the specified settings.

2

Enable multiple schedulers for high availability

Add a new key "scheduler" to the airflow_config dictionary with the entry "num_runs": 0 to enable continuous running schedulers.

Apache Airflow

airflow_config = {
    "core": {
        "executor": "LocalExecutor",
        "sql_alchemy_conn": "sqlite:///airflow.db"
    },
    "webserver": {
        "web_server_port": 8080
    }
}
# Add scheduler configuration to airflow_config
# Your code here

Hint

Add a new dictionary entry for scheduler with num_runs set to 0 to keep schedulers running continuously.

3

Configure the webserver for multiple workers

Add the key "workers" with value 4 inside the "webserver" dictionary in airflow_config to allow multiple webserver workers.

Apache Airflow

airflow_config = {
    "core": {
        "executor": "LocalExecutor",
        "sql_alchemy_conn": "sqlite:///airflow.db"
    },
    "webserver": {
        "web_server_port": 8080
    },
    "scheduler": {
        "num_runs": 0
    }
}
# Add workers = 4 inside the webserver dictionary
# Your code here

Hint

Inside the webserver dictionary, add "workers": 4 to allow four webserver workers.

4

Print the final Airflow configuration

Write a print statement to display the airflow_config dictionary.

Apache Airflow

airflow_config = {
    "core": {
        "executor": "LocalExecutor",
        "sql_alchemy_conn": "sqlite:///airflow.db"
    },
    "webserver": {
        "web_server_port": 8080,
        "workers": 4
    },
    "scheduler": {
        "num_runs": 0
    }
}
# Print the airflow_config dictionary
# Your code here

Hint

Use print(airflow_config) to display the dictionary.

Practice

1. What is the main purpose of configuring high availability in Apache Airflow?

easy

A. To reduce the number of DAGs in the system

B. To keep Airflow running even if some components fail

C. To speed up the Airflow UI loading time

D. To limit the number of users accessing Airflow

Solution

1. Step 1: Understand high availability concept

   High availability means the system keeps working even if parts fail.

2. Step 2: Apply to Airflow context

   In Airflow, this means tasks and services continue running despite failures.

3. Final Answer:

   To keep Airflow running even if some components fail -> Option B

4. Quick Check:

   High availability = system stays up despite failures [OK]

Hint: High availability means no downtime during failures [OK]

Common Mistakes:

• Confusing high availability with performance optimization
• Thinking it limits users or DAGs
• Assuming it only affects the UI

2. Which executor is commonly used in Airflow for high availability setups?

easy

A. SequentialExecutor

B. DebugExecutor

C. LocalExecutor

D. CeleryExecutor

Solution

1. Step 1: Recall executor types in Airflow

   SequentialExecutor runs tasks one by one, LocalExecutor runs tasks locally in parallel.

2. Step 2: Identify executor for distributed task handling

   CeleryExecutor uses workers and message brokers for distributed task execution, suitable for high availability.

3. Final Answer:

   CeleryExecutor -> Option D

4. Quick Check:

   Distributed task handling = CeleryExecutor [OK]

Hint: CeleryExecutor enables distributed task execution [OK]

Common Mistakes:

• Choosing SequentialExecutor which is single-threaded
• Confusing LocalExecutor with distributed execution
• Selecting DebugExecutor which is for testing only

3. Given this Airflow configuration snippet for high availability, what is the role of Redis?

[celery]
broker_url = redis://localhost:6379/0

medium

A. Stores task results permanently

B. Manages the Airflow metadata database

C. Acts as a message broker between scheduler and workers

D. Runs the Airflow webserver

Solution

1. Step 1: Identify Redis role in CeleryExecutor

   Redis is used as a message broker to pass task messages between scheduler and workers.

2. Step 2: Differentiate Redis from other components

   Redis is not for storing results permanently or running webserver or metadata DB.

3. Final Answer:

   Acts as a message broker between scheduler and workers -> Option C

4. Quick Check:

   Redis = message broker in CeleryExecutor [OK]

Hint: Redis brokers messages between scheduler and workers [OK]

Common Mistakes:

• Thinking Redis stores metadata or results
• Confusing Redis with PostgreSQL database
• Assuming Redis runs the webserver

4. You configured Airflow with CeleryExecutor but tasks are not being distributed to workers. Which configuration mistake could cause this?

medium

A. Using SQLite as the metadata database

B. Setting broker_url to a Redis server

C. Running multiple scheduler instances

D. Enabling high availability mode in webserver

Solution

1. Step 1: Understand metadata database requirements

   CeleryExecutor requires a robust metadata database like PostgreSQL, not SQLite.

2. Step 2: Identify why SQLite causes issues

   SQLite locks the database and does not support concurrent writes well, blocking task distribution.

3. Final Answer:

   Using SQLite as the metadata database -> Option A

4. Quick Check:

   SQLite blocks concurrency, breaks CeleryExecutor [OK]

Hint: Use PostgreSQL, not SQLite, for CeleryExecutor metadata DB [OK]

Common Mistakes:

• Assuming Redis broker_url is wrong when it is correct
• Thinking multiple schedulers cause task distribution failure
• Confusing webserver settings with task distribution

5. To achieve high availability for Airflow's scheduler, which setup is recommended?

hard

A. Run multiple scheduler instances with PostgreSQL backend and CeleryExecutor

B. Run a single scheduler instance with SQLite backend

C. Run multiple webservers without a scheduler

D. Use LocalExecutor with a single scheduler instance

Solution

1. Step 1: Understand scheduler high availability needs

   Multiple scheduler instances increase reliability and avoid single point of failure.

2. Step 2: Combine with proper backend and executor

   PostgreSQL supports concurrency well; CeleryExecutor enables distributed task execution.

3. Step 3: Eliminate incorrect options

   SQLite and single scheduler limit availability; webservers alone don't schedule tasks; LocalExecutor is not distributed.

4. Final Answer:

   Run multiple scheduler instances with PostgreSQL backend and CeleryExecutor -> Option A

5. Quick Check:

   Multiple schedulers + PostgreSQL + CeleryExecutor = HA scheduler [OK]

Hint: Multiple schedulers + PostgreSQL + CeleryExecutor = high availability [OK]

Common Mistakes:

• Using SQLite which blocks concurrency
• Running only one scheduler instance
• Confusing webserver role with scheduler