yaml242 B
Sample YAML
A YAML configuration file with nested mappings, sequences, inline lists, and comments — for testing YAML parsers.
Preview — first 17 linesyaml
# Sample YAML configuration
server:
host: localhost
port: 8080
tls:
enabled: true
minVersion: "1.2"
features:
- search
- editor
- preview
users:
- name: Ada
roles: [admin, editor]
- name: Grace
roles: [editor]
Specifications
- Structure
- mappings, sequences, inline lists, comments
- Valid
- true
What is a .yaml file?
YAML (YAML Ain't Markup Language) is a human-readable data-serialization format using indentation, key-value pairs, and lists, and is a superset of JSON. It supports comments, anchors, and multiple documents per file, favoring readability for configuration. Its indentation sensitivity makes it error-prone to hand-edit.
How to use this file
Use an example YAML file to test config parsers, indentation and anchor handling, multi-document streams, and safe-loading to avoid arbitrary object construction.
Related files
- csv10,000-Row CSVA CSV with 10,000 data rows — for testing streaming parsers, memory handling, and import performance.
- csvClean CSVA clean, well-formed CSV with a header and 20 rows — the baseline case for CSV parser testing.
- csvE-commerce Customers (CSV, 500 rows)A realistic e-commerce customer directory (500 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
- jsonE-commerce Customers (JSON, 500 records)The e-commerce customers table as a JSON array — the format twin of the CSV, for import and conversion testing.
- sqlE-commerce Database Schema (SQL)A relational SQL schema (products, customers, orders with primary and foreign keys) plus sample INSERTs — the DDL twin of the e-commerce dataset, for testing schema import and migrations.
- csvE-commerce Orders (CSV, 2000 rows)A realistic e-commerce order lines (customer_id → customers, product_id → products) (2000 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
Generated by generation/data_structured.py. Free for any use, no attribution required — license.