Extract Part Of A Date

Extract Part Of A Date

Prerequisites

Before using this node, ensure you have:

• Basic understanding of workflow creation in Agentic WorkFlow
• Appropriate browser permissions configured (if applicable)
• Required dependencies installed and configured

Overview

The Extract Part Of A Date node provides comprehensive date parsing and component extraction capabilities for breaking down dates into their constituent parts. This node handles multiple date formats including ISO 8601, locale-specific, and custom formats, timezone conversions, and specialized date components, making it essential for date analysis, filtering, and conditional workflow logic based on temporal data.

Purpose and Functionality

Extract Part Of A Date serves as a powerful date parsing tool that allows you to:

• Extract individual components like year, month, day, hour, minute, second
• Parse dates from multiple input formats and string representations
• Handle timezone information and convert between timezones
• Calculate derived values like day of week, quarter, week number
• Support batch extraction from multiple dates simultaneously

Key Features

• Comprehensive Component Extraction: Extract any date/time component including derived values
• Multi-Format Parsing: Handle multiple date string formats and automatic format detection
• Timezone Processing: Extract and convert timezone information
• Derived Calculations: Calculate day of week, quarter, week of year, and more
• Batch Processing: Extract components from multiple dates in one operation

Primary Use Cases

• Date Filtering: Extract components for conditional workflow logic
• Analytics and Reporting: Break down dates for time-based analysis
• Data Validation: Verify date components meet specific criteria
• Scheduling Logic: Extract time components for scheduling decisions

Parameters & Configuration

Required Parameters

Parameter	Type	Description	Example
input_date	string/date	Date to extract components from	"2024-01-15T14:30:45Z"
components	array	List of components to extract	["year", "month", "day", "hour"]

Optional Parameters

Parameter	Type	Default	Description	Example
input_format	string	"auto"	Expected input format or “auto” for detection	"YYYY-MM-DD HH:mm:ss"
timezone	string	"UTC"	Timezone for extraction	"America/New_York"
locale	string	"en-US"	Locale for text-based components	"fr-FR"
output_format	string	"object"	Output format: “object”, “array”, or “flat”	"flat"

Available Components

Component	Description	Example Output
year	Four-digit year	2024
month	Month number (1-12)	1
month_name	Full month name	"January"
month_short	Abbreviated month name	"Jan"
day	Day of month (1-31)	15
day_of_week	Day of week (0=Sunday, 6=Saturday)	1
day_name	Full day name	"Monday"
day_short	Abbreviated day name	"Mon"
hour	Hour (0-23)	14
minute	Minute (0-59)	30
second	Second (0-59)	45
millisecond	Millisecond (0-999)	123
quarter	Quarter of year (1-4)	1
week_of_year	Week number in year	3
day_of_year	Day number in year (1-366)	15
timezone_offset	Timezone offset in minutes	-300
timezone_name	Timezone abbreviation	"EST"
is_weekend	Boolean indicating weekend	false
is_leap_year	Boolean indicating leap year	true

Advanced Configuration

{
  "input_date": "2024-01-15T14:30:45.123Z",
  "components": [
    "year", "month", "day", "hour", "minute",
    "day_name", "quarter", "week_of_year", "is_weekend"
  ],
  "timezone": "America/New_York",
  "locale": "en-US",
  "output_format": "object",
  "custom_components": {
    "fiscal_year": {
      "start_month": 4,
      "calculation": "year + (month >= 4 ? 0 : -1)"
    },
    "business_quarter": {
      "q1_start": 4,
      "calculation": "custom"
    }
  }
}

Input/Output Specifications

Input Data Structure

{
  "date": "2024-01-15T14:30:45.123Z",
  "extraction_config": {
    "components": ["year", "month", "day", "day_name", "quarter"],
    "timezone": "America/New_York",
    "locale": "en-US"
  }
}

Output Data Structure

{
  "original_date": "2024-01-15T14:30:45.123Z",
  "extracted_components": {
    "year": 2024,
    "month": 1,
    "day": 15,
    "hour": 9,
    "minute": 30,
    "day_name": "Monday",
    "quarter": 1,
    "week_of_year": 3,
    "is_weekend": false,
    "timezone_name": "EST",
    "timezone_offset": -300
  },
  "metadata": {
    "input_timezone": "UTC",
    "output_timezone": "America/New_York",
    "locale_used": "en-US",
    "parsing_time": 8,
    "components_extracted": 10
  }
}

Practical Examples

Example 1: Basic Component Extraction

Scenario: Extract year, month, and day from a timestamp for date-based filtering

Configuration:

{
  "input_date": "2024-01-15T14:30:45Z",
  "components": ["year", "month", "day"],
  "output_format": "flat"
}

Input Data:

{
  "event_timestamp": "2024-01-15T14:30:45Z"
}

Expected Output:

{
  "year": 2024,
  "month": 1,
  "day": 15,
  "metadata": {
    "components_extracted": 3,
    "parsing_time": 5
  }
}

Example 2: Comprehensive Date Analysis

Scenario: Extract multiple components for analytics dashboard

Configuration:

{
  "input_date": "2024-01-15T14:30:45Z",
  "components": [
    "year", "month", "month_name", "day", "day_name",
    "quarter", "week_of_year", "is_weekend", "hour"
  ],
  "timezone": "America/New_York",
  "locale": "en-US"
}

Workflow Integration:

Data Source → Extract Part Of A Date → Analytics Filter
     ↓              ↓                      ↓
  timestamps    date_components      filtered_data

Complete Example:

{
  "input": {
    "user_activity": [
      {"timestamp": "2024-01-15T14:30:45Z", "action": "login"},
      {"timestamp": "2024-01-15T16:45:30Z", "action": "purchase"}
    ]
  },
  "processing": {
    "extract_from_each": "timestamp",
    "components": ["hour", "day_name", "is_weekend"]
  },
  "output": {
    "enriched_activity": [
      {
        "timestamp": "2024-01-15T14:30:45Z",
        "action": "login",
        "hour": 9,
        "day_name": "Monday",
        "is_weekend": false
      },
      {
        "timestamp": "2024-01-15T16:45:30Z",
        "action": "purchase",
        "hour": 11,
        "day_name": "Monday",
        "is_weekend": false
      }
    ]
  }
}

Examples

Basic Usage

This example demonstrates the fundamental usage of the ExtractPartOfADate node in a typical workflow scenario.

Configuration:

{
  "date": "example_value",
  "format": true
}

Input Data:

{
  "data": "sample input data"
}

Expected Output:

{
  "result": "processed output data"
}

Advanced Usage

This example shows more complex configuration options and integration patterns.

Configuration:

{
  "parameter1": "advanced_value",
  "parameter2": false,
  "advancedOptions": {
    "option1": "value1",
    "option2": 100
  }
}

Integration Example

Example showing how this node integrates with other workflow nodes:

1. Previous Node → ExtractPartOfADate → Next Node
2. Data flows through the workflow with appropriate transformations
3. Error handling and validation at each step

Integration Patterns

Common Node Combinations

Pattern 1: Date-Based Filtering

• Nodes: Data Source → Extract Part Of A Date → Filter → Processing
• Use Case: Filter data based on specific date components
• Configuration Tips: Extract only the components needed for filtering to optimize performance

Pattern 2: Analytics Enrichment

• Nodes: Raw Data → Extract Part Of A Date → Edit Fields → Analytics Dashboard
• Use Case: Enrich data with temporal components for analysis
• Data Flow: Raw timestamps → date components → structured data → visualizations

Best Practices

• Performance: Extract only the components you need to minimize processing time
• Error Handling: Validate date formats before extraction to prevent parsing errors
• Data Validation: Check for null or invalid dates in input data
• Resource Management: Use batch processing for large datasets

Troubleshooting

Common Issues

Issue: Date Parsing Failures

• Symptoms: Node fails to parse input dates or returns null values
• Causes: Unsupported date format or invalid date strings
• Solutions:
  1. Specify input_format explicitly instead of using “auto”
  2. Validate date strings before extraction
  3. Use standard ISO date formats when possible
• Prevention: Standardize date formats in your data pipeline

Issue: Timezone Conversion Problems

• Symptoms: Incorrect time components or unexpected timezone results
• Causes: Invalid timezone identifiers or daylight saving time issues
• Solutions:
  1. Use valid IANA timezone identifiers
  2. Check timezone_offset in output for verification
  3. Test around DST transition dates
• Prevention: Validate timezone strings and handle DST transitions

Performance Issues

• Slow Extraction: Reduce the number of components or use batch processing
• Memory Usage: Process large date arrays in chunks
• Locale Loading: Cache locale data for frequently used locales

Limitations & Constraints

Technical Limitations

• Date Range: Supports dates from 1900-01-01 to 2100-12-31
• Component Limit: Maximum 20 components per extraction operation

Format Limitations

• Auto-Detection: May not correctly identify all custom date formats
• Ambiguous Dates: MM/DD/YYYY vs DD/MM/YYYY requires explicit format specification
• Locale Support: Some locales may have limited component name translations

Data Limitations

• Input Size: Maximum 1000 dates per batch operation
• Processing Time: Complex extractions timeout after 30 seconds
• Memory Usage: Large component extractions may require chunking

Key Terminology

Field Transformation: Process of modifying data field names, types, or values

Type Conversion: Converting data from one type to another (string, number, boolean, etc.)

Data Validation: Process of ensuring data meets specified criteria and constraints

Schema: Structure definition that describes the format and constraints of data

Serialization: Process of converting data structures into a format for storage or transmission

Search & Discovery

Keywords

• data processing
• field manipulation
• type conversion
• data validation
• formatting
• transformation

Common Search Terms

• “transform”
• “convert”
• “format”
• “edit”
• “modify”
• “process”
• “validate”
• “clean”
• “restructure”

Primary Use Cases

• data cleaning
• format conversion
• field manipulation
• data validation
• report generation
• data processing

Learning Path

Skill Level: Beginner

Enhanced Cross-References

Workflow Patterns

• Data Processing Patterns
• Content Manipulation Patterns
• Data Validation Workflows

Related Tutorials

• Data Processing Fundamentals
• Advanced Data Manipulation

Practical Examples

• Real-World Use Cases
• Integration Examples
• Best Practice Examples

Related Nodes

Similar Functionality

• AddToADate: Use when you need different approach to similar functionality
• SubstractFromDate: Use when you need different approach to similar functionality

Complementary Nodes

• FormatDate: Works well together in workflows
• GetCurrentDate: Works well together in workflows
• EditFields: Works well together in workflows

Common Workflow Patterns

• GetCurrentDate → ExtractPartOfADate → EditFields: Common integration pattern
• Http-Request → ExtractPartOfADate → Filter: Common integration pattern

See Also

• Data Processing Patterns
• Data Transformation Guide
• Data Mapping Expressions
• Field Validation Examples

Decision Guides:

• Data Transformation Decision Guide

General Resources:

• Workflow Patterns
• Integration Examples
• Node Types Overview

Additional Resources

• Date Component Analysis Tutorial
• Timezone Handling Guide
• Analytics Dashboard Examples