How to Create Logical Expressions in Datablit?

Logical expressions are combinations of event fields, signals, and operators that always return either true or false. They are used throughout DataBlit for filtering events, creating rules, and defining conditions.

Overview

Logical expressions evaluate to boolean values (true or false)
They can reference event properties, signal values, and parameters
Used in signal filters, rule definitions, and conditional logic
Based on the Expr language specification

Basic Syntax

Logical expressions follow a simple, readable syntax similar to common programming languages:

// Basic comparison
event == 'ORDER_PLACED'
order_amount > 100

// Multiple conditions
no_of_orders_24h > 10 && os_name == 'android'
user_type == 'premium' || total_spent > 500

Operators

Comparison Operators

Operator	Description	Example
==	Equal to	event == 'PURCHASE'
!=	Not equal to	user_type != 'guest'
>	Greater than	order_amount > 100
>=	Greater than or equal	age >= 18
<	Less than	session_duration < 300
<=	Less than or equal	items_in_cart <= 5

Logical Operators

Operator	Description	Example
&&	Logical AND	is_premium && order_count > 5
\|\|	Logical OR	is_mobile \|\| is_tablet
!	Logical NOT	!is_guest

String Operations

Operation	Description	Example
contains()	Check if string contains substring	device_name.contains('iPhone')
startsWith()	Check if string starts with prefix	url.startsWith('https://')
endsWith()	Check if string ends with suffix	filename.endsWith('.jpg')
matches()	Regex pattern matching	email.matches('^[^@]+@[^@]+\.[^@]+$')

Array Operations

Operation	Description	Example
in	Check if value exists in array	category in ['electronics', 'books']
size()	Get array length	tags.size() > 3
all()	Check if all elements match condition	prices.all(price > 0)
any()	Check if any element matches condition	categories.any(cat == 'electronics')

Data Types

Supported Types

String: Text values (use single quotes: 'value')
Number: Integer and floating-point numbers (42, 3.14)
Boolean: true or false
Array: Lists of values ([1, 2, 3], ['a', 'b'])
Null: null value

Type Conversion

Expressions automatically handle type conversions where appropriate:

// String to number comparison
'100' > 50  // true

// Number to string comparison
100 == '100'  // true

Field References

Event Properties

Reference event properties directly by their field names:

// Basic event properties
event == 'PURCHASE'
user_id == 'user123'
timestamp > 1640995200

// Nested properties (if supported)
user_profile_age > 18
order_items_count > 0

Signal Values

Reference signal values using their keys:

// Signal references
no_of_orders_24h > 10
total_spent_7d > 500
avg_session_duration > 300

Parameters

Reference parameters passed during evaluation:

// Parameter references
os_name == params.os_name
device_type == params.device_type

Common Patterns

User Segmentation

// High-value customers
total_spent_30d > 1000 && order_count_30d > 5

// New users
days_since_signup <= 7 && total_orders == 0

// Inactive users
days_since_last_activity > 30

Event Filtering

// Purchase events with minimum amount
event == 'PURCHASE' && order_amount >= 50

// Mobile app events
platform == 'mobile' && (os_name == 'ios' || os_name == 'android')

// Premium user actions
user_type == 'premium' && event in ['PURCHASE', 'SUBSCRIPTION']

Complex Conditions

// Multi-condition logic
(is_premium && order_amount > 100) ||
(total_spent_90d > 500 && loyalty_level == 'gold')

// Time-based conditions
hour_of_day >= 9 && hour_of_day <= 17 &&
day_of_week in [1, 2, 3, 4, 5]  // Business hours

Best Practices

1. Use Clear Field Names

// Good
order_amount > 100
user_type == 'premium'

// Avoid
oa > 100
ut == 'p'

2. Use Parentheses for Clarity

// Good - explicit precedence
(a && b) || (c && d)

// Avoid - relies on operator precedence
a && b || c && d

3. Test Your Expressions

Always test your logical expressions with sample data to ensure they work as expected.

Error Handling

Common Issues

Field not found: Ensure field names match exactly (case-sensitive)
Type mismatch: Check data types in your expressions
Syntax errors: Validate expression syntax before saving

Debugging Tips

Start with simple expressions and build complexity gradually
Test with known data values
Use the evaluation feature to test expressions
Check field names and data types

Examples by Use Case

E-commerce

// Abandoned cart detection
event == 'CART_ADDED' &&
days_since_last_activity > 1 &&
cart_value > 0

// High-value product views
event == 'PRODUCT_VIEW' &&
product_price > 1000 &&
user_type == 'premium'

Gaming

// Active players
days_since_last_login <= 7 &&
total_playtime > 3600

// High-level players
player_level >= 50 &&
total_achievements > 20

SaaS

// Trial users near expiration
subscription_status == 'trial' &&
days_until_expiry <= 3

// Power users
feature_usage_count > 100 &&
last_login_days <= 7

Reference

For more detailed information about the expression language syntax and features, visit the Expr Language Documentation.

Next Steps

Learn about Data Signals to create custom metrics
Explore Rules to build complex business logic
Understand Event Property Conversion for data transformation