Variable & Value Types

Understanding the different variable and value types is essential for creating efficient and effective flows. Flow Like provides a comprehensive type system that balances simplicity with the power needed for complex operations.

Data Types

Flow Like supports various data types that map to their Rust equivalents under the hood:

Type	Rust Type	Category	Description	Example
Boolean	bool	Primitive	Represents true or false values	true, false
Integer	i64	Primitive	Whole numbers without decimal points	42, -7, 0
Float	f64	Primitive	Numbers with decimal precision	3.14, -0.01, 2.0
Byte	u8	Primitive	Single byte values (0-255)	65, 0x41
String	String	Complex	Text data of any length	"Hello, Flow Like!"
DateTime	DateTime	Complex	Date and time values with timezone information	2023-10-15T08:30:00Z
PathBuf	PathBuf	Complex	File system paths with platform-specific handling	"/home/user/documents"
Struct	Value	Complex	Custom structured data composed of multiple fields	{ "name": "Task", "complete": false }

Working with Primitive Types

Primitive types are the building blocks for more complex data structures. They are stored by value and have fixed memory requirements:

• Boolean: Used for conditional operations, flow control, and logical comparisons
• Integer: Ideal for counting, indexing, and whole number arithmetic
• Float: Appropriate for scientific calculations, percentages, and any value requiring decimal precision
• Byte: Efficient for dealing with binary data, file operations, and ASCII character representation

Working with Complex Types

Complex types manage memory differently and can represent more sophisticated data:

• String: Used for text processing, user inputs, and data that needs to be human-readable
• DateTime: Essential for scheduling, logging, time-based operations, and managing temporal data
• PathBuf: Used for file system operations, maintaining compatibility across different operating systems
• Struct: Creates custom data structures for domain-specific requirements

Value Types

Flow Like supports several value type structures that help organize and manipulate data in different ways:

Value Type	Description	Common Use Cases	Example
Normal Value	A single value of any data type	Simple variables, return values	42, "text", true
Arrays	Ordered collections of values with index-based access	Lists, sequences, batched processing	[1, 2, 3, 4], ["red", "green", "blue"]
HashSet	Unordered collections of unique values	Deduplication, membership testing, unions/intersections	{1, 2, 3} (no duplicates allowed)
HashMap	Key-value pairs with unique keys	Lookups, configuration settings, attribute storage	{"name": "Flow", "version": "1.0"}

When to Use Different Value Types

• Normal Values: Use when you only need to store a single piece of information
• Arrays: Use when order matters and you need to process items sequentially
• HashSet: Use when you need to ensure uniqueness or perform set operations
• HashMap: Use when you need to associate values with specific keys for quick retrieval

Type Conversion

You can convert between certain types as needed in your flows:

• Integers to Floats: 42 → 42.0
• Strings to other types: "42" → 42, "true" → true
• Arrays to HashSets: [1, 1, 2] → {1, 2}

Best Practices

• Choose the simplest type that meets your requirements
• Use HashSets when uniqueness is important
• Prefer strongly typed structures for complex data
• Consider performance implications when working with large collections
• Document custom struct schemas for better maintainability

Each type and structure in Flow Like has been carefully selected to provide the flexibility needed for automation while maintaining strong type safety.