README
¶
df library examples
this directory contains a comprehensive set of examples demonstrating the features and capabilities of the df library. the examples are ordered from basic to advanced concepts, making it easy for new users to learn progressively.
learning path
the examples are numbered to provide a clear learning progression. start with df_01_bindunbind and work your way through the numbered sequence to build understanding incrementally.
example overview
foundation examples (01-04)
df_01_bindunbind - basic bind/unbind operations
start here - learn the core df.Bind(), df.Unbind(), and df.New[T]() functions for converting between go structs and map[string]any. demonstrates the fundamental bidirectional data mapping that powers all other df features.
df_02_tags - struct tag configuration
learn how to customize field behavior using df struct tags. covers custom field names, required fields, secret fields, and field exclusion. essential for real-world data binding scenarios.
df_03_defaults - configuration merging and defaults
discover df.Merge() for intelligent configuration overlays. perfect for building 12-factor apps with layered configuration (defaults + environment + user overrides). crucial for production configuration management.
df_04_fileio - json/yaml file i/o
master direct file operations with df.BindFromJSON/YAML() and df.UnbindToJSON/YAML(). eliminates boilerplate for config file loading and saving. essential for application startup and configuration persistence.
intermediate examples (05-08)
df_05_inspect - debugging and validation
learn df.Inspect() for human-readable output of configuration structures. invaluable for debugging, with secret field filtering and customizable formatting. use this whenever you need to understand what your data looks like.
df_06_validation - error handling and validation
comprehensive coverage of error handling patterns, validation strategies, and production-ready error management. read this before building production systems to handle edge cases gracefully.
df_07_converters - custom type conversion
implement the Converter interface for specialized data types (emails, temperatures, timestamps). use when you have domain-specific types that need validation or special formatting during binding.
df_08_marshalers - custom marshaling/unmarshaling
implement Marshaler/Unmarshaler interfaces for complete control over binding. necessary for legacy data formats, complex validation, or computed fields that don't map directly to struct fields.
advanced examples (09-12)
df_09_dynamic - polymorphic data binding
learn the df.Dynamic interface for runtime type discrimination. essential for plugin architectures, workflow engines, and any system where different data can represent different types based on a discriminator field.
df_10_field_binders - field-specific polymorphic control
master FieldDynamicBinders for per-field type mappings. use when different struct fields need to support different sets of polymorphic types, providing namespace isolation and fine-grained control.
df_11_pointers - object references
understand df.Pointer[T] for type-safe object references with cycle support. crucial for data structures with relationships (users, documents, dependencies) where objects reference each other.
df_12_advanced_linker - complex object graph management
master advanced reference resolution with NewLinker(), caching, and performance optimization. necessary for large-scale applications with complex object relationships and performance requirements.
application architecture examples (13+)
df_13_container - dependency injection and service management
learn the Registry and Service types for building scalable application containers. covers object registration, lifecycle management (startable/stoppable), dependency injection through factories, and container introspection. essential for building modular, testable applications.
usage patterns
for application developers
start with examples 01-06 to cover the most common use cases: basic binding, configuration management, file i/o, and error handling.
for library authors
review examples 07-08 to understand how to integrate custom types with the df ecosystem through converters and marshalers.
for framework builders
study examples 09-12 to learn advanced patterns for building extensible, plugin-based systems with complex data relationships.
for application architects
review example 13+ to understand how to structure scalable applications with dependency injection, service lifecycle management, and modular architectures.
running examples
each example is self-contained and can be run independently:
cd df_01_bindunbind
go run main.go
each example includes a detailed README explaining the concepts, use cases, and code structure.
next steps
after working through these examples, you'll have a comprehensive understanding of the df library's capabilities. consider:
- integration: apply these patterns to your existing applications
- performance: use the advanced linker patterns for large-scale data
- extensibility: implement custom converters and marshalers for your domain types
- architecture: leverage dynamic binding for plugin-based systems
the df library is designed to scale from simple configuration binding to complex, dynamic application architectures. these examples provide the foundation for building robust, maintainable systems.
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