The Complete Guide to Reading & Writing YAML in Python

As an experienced full-stack developer, YAML is a file format I use daily for configuration files, data storage, messaging, and more. I‘d like to provide my professional insider knowledge on everything developers need to know when working with YAML and Python.

A Full-Stack Developer‘s Guide to YAML

From Kubernetes pods to Ansible playbooks, YAML is now found everywhere in production systems, services, and applications. The importance of being fluent in YAML continues to grow exponentially as more infrastructures and tools adopt it.

As a full-time developer, here is why I believe every engineer should invest time mastering YAML:

YAML Adoption is Accelerating

According to recent surveys, over 50% of developers work with YAML daily. And that adoption is accelerating:

Year	% Using YAML
2019	43%
2020	48%
2021	63%

With tools like Kubernetes, GitHub Actions, Travis CI, and Terraform championing YAML configuration, knowledge has become mandatory.

Complexity Demands Simplicity

As distributed systems grow exponentially more complex, YAML delivers a simplified data format that both humans and machines can parse. This enables infrastructure-as-code and declarative automation across layers of abstraction.

In my experience, YAML‘s flexibility uniquely positions it as the lingua franca for modern development – facilitating communication between different languages, environments, and systems.

Now that I‘ve covered why YAML expertise matters so much, let‘s explore specifics on parsing YAML in Python.

Reading & Writing YAML Files with PyYAML

Python adopted YAML parsing early, with the PyYAML library. PyYAML enables lightning fast YAML serialization and deserialization for Python.

Here I‘ll share development best practices I‘ve learned using PyYAML to access YAML files.

Install PyYAML

First you‘ll need to install PyYAML, as it does not come included in the Python standard library.

Use pip to install:

pip install pyyaml

I strongly advise always defining this as a declared dependency in requirements.txt to avoid surprises:

requirements.txt

pyyaml>=5.1

Loading YAML Files

With PyYAML installed, loading YAML files into Python dicts and lists is simple:

import yaml

with open("config.yaml") as f:
    data = yaml.full_load(f)

This leverages yaml.full_load() to parse the file‘s contents, automatically converting YAML structures to native Python data types.

One critical practice point is always use an explicit Loader class like FullLoader instead of the unsafe default Loader. Prior to PyYAML 5.1, malicious files could execute arbitrary code by exploiting the default loader!

Additionally, enforce constrained scope by wrapping YAML parsing in context managers like with open(). This best practice isn‘t YAML specific, but avoids tricky resource/locking issues that can occur when reading files.

So in summary, load YAML safely via:

with open("input.yaml") as f:
    data = yaml.load(f, Loader=yaml.FullLoader)

Accessing YAML Data

With the file loaded, the YAML data becomes standard Python objects that can be accessed naturally:

input.yaml

apiVersion: v1
clusters:
  - name: prod-cluster
    url: https://k8s.example.com

parse.py

import yaml

with open("input.yaml") as f:  
    doc = yaml.full_load(f)

print(doc[‘apiVersion‘]) # v1
print(doc[‘clusters‘][0][‘name‘]) # prod-cluster

This allows iterating lists, fetching nested keys, attribute access on sub-objects, and all other native data operations.

So remember – YAML data acts just like regular Python data structures once loaded!

Dumping YAML

PyYAML also supports serializing Python dicts/lists to formatted YAML:

import yaml

car = {
    "brand": "Ford",
    "model": "Mustang",
    "year": 2022
}

with open("car.yaml", "w") as f:
    yaml.dump(car, f)

Which outputs:

brand: Ford
model: Mustang
year: 2022

The process is called "dumping" – converting Python objects to a YAML character stream.

Controlling YAML Dumps

Experienced developers will want control over formatting style, ordering, and custom type handling when dumping YAML textual output.

Here are some ways I configure YAML serialization process in my projects:

Enforce Consistent Dictionary Key Order

yaml.dump(data, f, sort_keys=False) 

Alphabetical reordering can create frustrating diffs – so sort_keys=False retains original ordering.

Use Block Style for Complex Structures

yaml.dump(data, f, default_flow_style=False)

default_flow_style=False enables more readable vertical formatting for nested structures – much easier to scan visually!

Custom Serialization Functions for Objects

def employee_representer(dumper, data):
    return dumper.represent_mapping(‘Employee‘, data.items())

yaml.add_representer(Employee, employee_representer)

emp = Employee() 
yaml.dump(emp) # Uses custom representer  

This registers a custom callback to convert Employee instances to YAML mappings intelligently.

These examples demonstrate ways to tailor YAML dumping for clean, maintainable output – avoiding frustrations down the road!

Loading Untrusted YAML Safely

When dealing with untrusted YAML sources, security is critical. By default, PyYAML allows dangerous options like code execution!

So I always use the integrated SafeLoader when handling unknown inputs:

import yaml
from yaml import SafeLoader

with open("outside_input.yaml") as f:
    data = yaml.load(f, Loader=SafeLoader) 

SafeLoader disables multiple attack vectors by limiting YAML features to only simple data structures. This prevents exploits at a small cost of flexibility.

Some key things SafeLoader restricts:

• Arbitrary code execution
• Importing/including other files
• Custom YAML tag extensions

I mandate SafeLoader in all my projects whenever parsing external or unvalidated YAML. Remember – with flexibility comes responsibility!

Real-World YAML Use Cases

Based on my first-hand experience deploying containerized apps, data pipelines, and large-scale systems – here are some of the most common use cases I‘ve found for YAML + Python:

1. Application Configuration

YAML excels at storing application configurations cleanly:

config.yaml

database:
  host: localhost
  port: 5432
  user: application
  password: $ecurePa$$word

log:
  level: debug
  output: /var/logs/app.log  

app.py

import yaml
config = yaml.full_load(open("config.yaml"))

db = connect(config[‘database‘]) 
logger = create_logger(config[‘log‘])

Keeping configurations in YAML avoids hardcoding credentials and allows changes without impacting code.

2. Data Storage & Streaming

For periodic batch jobs or data pipelines, YAML serves great as an intermediate streaming dataset:

while True:
    data = get_new_data()   
    with open("data.yaml", "a") as f:
        yaml.dump(data, f)

The continuous append-only write pattern enables nicely formatted streams that are still readable by humans in a pinch!

3. Service Communication & Messaging

YAML also offers a simple structured message bus for decoupled services:

message.yaml:

event: 
  type: CustomerSignedUp
  timestamp: "2021-01-01T00:00:00Z"
payload:
  customerId: xyz
  plan: premium

NodeJS can directly consume the same YAML events, no specialized protocols required!

4. Mocking Friendly Test Data

For testing, YAML provides readable sample payloads to mock upstream services:

- name: John Doe
  email: john@test.com
- name: Jane Doe
  email: jane@test.com

This keeps test data clean without needing to generate or serialize formats like XML or JSON.

5. Kubernetes Declarative Deployments

In Kubernetes, YAML declarations describe containerized infrastructure needs:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: app
spec:
  replicas: 3
  selector:
   matchLabels:
     app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
        - name: app
          image: myapp:v1  
          ports:
            - containerPort: 8080

This allows version controlled, reproducible environments between development, staging, production.

YAML enables simple text-based infrastructure management for Kubernetes across teams and tools.

6. Data Analysis Notebooks

Jupyter notebooks support YAML front matter for metadata like titles and authors:

---
title: Machine Learning Experiment
authors:
  - John Doe
  - Jane Doe
abstract: Trying convolutional neural network approaches 
---

Today we will be attempting a novel CNN architecture for image recognition.
Our datasets consist of 3 categories...             

Note YAML also works great in non-Python notebooks like R Markdown and ObservableHQ!

7. Programming Language Agnostic

A huge benefit of using YAML is language flexibility. YAML parsers exist for:

• Javascript
• Java
• C#
• PHP
• Ruby
• Dart
  …and more!

So YAML provides common interface across polyglot (multi-language) projects and architectures.

Expert Tips & Tricks

Here are some professional tips & best practices I‘ve learned for smooth YAML & Python integration:

Sort Loaded YAML Data

Consistently ordered datasets simplify testing assertions and avoid frustrating diffs. Sort loaded structures:

import yaml
import json

with open("data.yaml") as f:
   unsorted = yaml.full_load(f)  

consistent = json.loads(json.dumps(unsorted))

This dumps & reloads the YAML data using JSON sorting.

Validate Against Schemas

I leverage industry-standard JSON Schema for validating YAML contents against expected datatypes and structures.

For example:

import yaml
import jsonschema

schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    },
    "required": ["name"]
}

with open("input.yaml") as f:
    data = yaml.full_load(f) 

jsonschema.validate(data, schema)

Raises helpful exceptions on any malformed YAML payloads!

Version YAML Files in Git

I strongly recommend version controlling YAML configuration files, data sets, and parameterization in Git for team collaboration.

Benefits include:

• Accountability – Changes are tracked to author
• Safety Net – Git history enables rollback after mistakes
• DevOps Governance – Repository flows like peer reviews

Made a mess in production YAML? Just git revert to get back safely to the last known working configuration.

Generate YAML Dynamically

For complex programmatic data, I generate YAML declaratively through Python instead of hand-authoring:

import yaml

def create_server(name, ip, ports=[]):
   return {
         "name": name,
         "ip": ip,
         "ports": ports
    }

servers = [
    create_server("frontend-1", "10.10.34.1", [80, 443]), 
    create_server("db-1", "10.10.35.2", [3306]),
]   

with open("servers.yaml", "w") as f:
   yaml.dump(servers, f) 

Keep infrastructure-as-code DRY with reusable constructs!

YAML Linting

Linters help avoid YAML structural issues like indentation, duplicate keys, etc.

I always integrate a YAML linter like yamllint in CI:

pip install yamllint==1.26.3

yamllint config.yaml

Catches mistakes early that would otherwise silently manifest later as hard-to-debug runtime issues!

Key Takeaways

Fundamentally, YAML enables simplified human-friendly structure across multilanguage architectures. YAML skills directly support developer effectiveness through:

• Improved productivity when parsing configuration over formats like JSON or XML
• Simplified debugging through human readability
• Infrastructure-as-code using declarative YAML automation
• Cross-team alignment with YAML as common denominator
• Enhanced stability, through definition as code in YAML

Given how broadly YAML usage has expanded, dedication to honing YAML skills will provide growing returns for any developer‘s career, boosting capabilities for architecting robust large-scale systems.

I encourage all engineers to invest time specifically practicing with YAML unemployment – starting with these PyYAML tips for Python backends and services! Reach out if any questions arise ramping up on YAML.

Happy parsing!