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Overview

Control flow in Julep allows you to create sophisticated task workflows by managing the sequence of execution, handling conditions, implementing loops, and running steps in parallel. This guide covers all aspects of control flow management in Julep tasks.

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Conditional Execution

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If-Else Statements

Control execution based on conditions:

# Basic if statement
- if: _.score > 0.8
  then:
    - log: "High score achieved"

# If-else statement
- if: _.temperature > 30
  then:
    - log: "It's hot today"
  else:
    - log: "Temperature is normal"

# Nested conditions
- if: _.user_type == "premium"
  then:
    - if: _.usage > 1000
      then:
        - log: "High usage premium user"
      else:
        - log: "Normal usage premium user"
  else:
    - log: "Regular user"

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Switch Statements

Handle multiple conditions:

# Basic switch
- switch:
    - case: _.status == "pending"
      then:
        - log: "Task is pending"
    - case: _.status == "running"
      then:
        - log: "Task is running"
    - case: _  # Default case
      then:
        - log: "Unknown status"

# Switch with complex logic
- switch:
    - case: _.score > 90
      then:
        - evaluate:
            grade: "'A'"
    - case: _.score > 80
      then:
        - evaluate:
            grade: "'B'"
    - case: _.score > 70
      then:
        - evaluate:
            grade: "'C'"
    - case: _
      then:
        - evaluate:
            grade: "'F'"

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Loops and Iteration

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Foreach Loop

Iterate over collections:

# Simple foreach
- foreach:
    in: _.items
    do:
      - log: "Processing {{_}}"

# Foreach with complex processing
- foreach:
    in: _.documents
    do:
      - tool: analyze
        arguments:
          text: _.content
      - if: _.analysis.score > 0.8
        then:
          - tool: save_result
            arguments:
              data: _.analysis

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Map-Reduce

Process collections in parallel:

# Basic map-reduce
- map_reduce:
    over: _.urls
    map:
      - tool: fetch_content
        arguments:
          url: _
    reduce: results + [_]

# Map-reduce with custom reduction
- map_reduce:
    over: _.numbers
    map:
      - evaluate:
          squared: "_ ** 2"
    reduce: "max(results + [_])"
    initial: 0

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Parallel Execution

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Parallel Steps

Execute multiple steps concurrently:

# Basic parallel execution
- parallel:
    - tool: task1
      arguments:
        param: "value1"
    - tool: task2
      arguments:
        param: "value2"

# Parallel with result handling
- parallel:
    - tool: web_search
      arguments:
        query: "AI news"
    - tool: arxiv_search
      arguments:
        query: "AI research"
- evaluate:
    web_results: _[0]
    academic_results: _[1]

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Parallel Map-Reduce

Process collections with controlled parallelism:

# Parallel processing with limit
- map_reduce:
    over: _.queries
    map:
      - tool: web_search
        arguments:
          query: _
    parallelism: 5

# Complex parallel processing
- map_reduce:
    over: _.documents
    map:
      - parallel:
          - tool: analyze_sentiment
            arguments:
              text: _.content
          - tool: extract_keywords
            arguments:
              text: _.content
    parallelism: 3

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Flow Control

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Sleep and Delays

Introduce delays in workflow:

# Simple delay
- sleep:
    seconds: 30

# Conditional delay
- if: _.retry_count > 0
  then:
    - sleep:
        seconds: "_.retry_count * 10"

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Early Returns

Exit workflow early:

# Early return on condition
- if: _.error_count > 3
  then:
    - return:
        status: "error"
        message: "Too many errors"

# Return with processed data
- if: _.data_valid
  then:
    - return:
        result: _.processed_data
        metadata:
          processing_time: _.duration

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Error Handling

Handle errors in workflow:

# Try-catch pattern
- if: "try_function(_)"
  then:
    - log: "Operation successful"
  else:
    - log: "Operation failed"
    - return:
        error: _.error_message

# Retry pattern
- evaluate:
    retry_count: 0
- while: _.retry_count < 3
  do:
    - if: "try_operation(_)"
      then:
        - return: _.result
      else:
        - evaluate:
            retry_count: "_.retry_count + 1"
        - sleep:
            seconds: "2 ** _.retry_count"

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Advanced Patterns

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State Machines

Implement state machine patterns:

main:
  - evaluate:
      state: "initial"
  
  - while: _.state != "completed"
    do:
      - switch:
          - case: _.state == "initial"
            then:
              - tool: initialize
                arguments:
                  data: _.input
              - evaluate:
                  state: "processing"
          
          - case: _.state == "processing"
            then:
              - tool: process
                arguments:
                  data: _.result
              - if: _.processing_complete
                then:
                  - evaluate:
                      state: "completed"
                else:
                  - evaluate:
                      state: "processing"

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Pipeline Pattern

Create data processing pipelines:

main:
  # Data ingestion
  - parallel:
      - tool: load_source1
        arguments:
          params: _.source1_params
      - tool: load_source2
        arguments:
          params: _.source2_params
  
  # Data processing
  - map_reduce:
      over: _
      map:
        - tool: clean_data
          arguments:
            data: _
        - tool: transform_data
          arguments:
            data: _
      parallelism: 3
  
  # Data aggregation
  - tool: aggregate_results
    arguments:
      data: _
  
  # Result validation
  - if: _.validation_score > 0.9
    then:
      - tool: save_results
        arguments:
          data: _.aggregated_data
    else:
      - return:
          error: "Validation failed"

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Best Practices

  1. Flow Design

    • Keep workflows linear when possible
    • Use parallel execution judiciously
    • Handle errors at appropriate levels

  2. Performance

    • Balance parallelism with resource constraints
    • Use appropriate timeouts
    • Cache results when beneficial

  3. Maintainability

    • Document complex flow logic
    • Break down complex workflows
    • Use consistent patterns

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Example: Complex Control Flow

Here’s an example combining various control flow patterns:

main:
  # Initialize
  - evaluate:
      start_time: "datetime.now().isoformat()"
      retry_count: 0
      batch_size: 10
  
  # Batch processing with retries
  - foreach:
      in: "range(0, len(_.items), _.batch_size)"
      do:
        # Process batch in parallel
        - map_reduce:
            over: "_.items[_:_ + _.batch_size]"
            map:
              - parallel:
                  - tool: process_item
                    arguments:
                      item: _
                  - tool: validate_item
                    arguments:
                      item: _
            parallelism: 5
        
        # Handle batch results
        - foreach:
            in: _
            do:
              - if: "_.processing_result[0].success and _.processing_result[1].valid"
                then:
                  - tool: save_result
                    arguments:
                      result: _.processing_result
                else:
                  # Retry failed items
                  - while: _.retry_count < 3
                    do:
                      - evaluate:
                          retry_count: "_.retry_count + 1"
                      - sleep:
                          seconds: "2 ** _.retry_count"
                      - tool: retry_processing
                        arguments:
                          item: _
  
  # Finalize
  - return:
      results: _.processed_items
      metadata:
        start_time: _.start_time
        end_time: "datetime.now().isoformat()"
        retry_count: _.retry_count

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Next Steps

  1. Explore workflow steps
  2. Learn about task basics
  3. Understand session management