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Data Sources in AVstudio

Data Sources serve as JSON-format data repositories that can be either local or populated from external sources via REST API requests.

Data Sources Overview

Types of Data Sources​

1. Local Data Sources​

  • Stored within the project
  • Data defined during creation
  • Static and immutable
  • Best for constant data

2. Remote Data Sources​

  • Data fetched via HTTP/REST API
  • Dynamic content updates
  • Processor feedback-driven updates
  • Configurable request headers

3. Mixed Data Sources​

  • Combines remote and local storage
  • Fallback to local copy if remote fails
  • Automatic failover mechanism
  • Enhanced reliability
Data Immutability

Data Source content is immutable during runtime. Updates require either a new remote fetch or manual local data modification.

Creating a Data Source​

New Data Source Form

Required Fields​

  • Name: Unique identifier for the data source
  • Type: Local, Remote, or Mixed

Remote Configuration​

Optional for Local Data Sources:

  • URL: Endpoint for data retrieval
  • Header Key: Request header name
  • Header Value: Request header value

Local Configuration​

Optional for Remote Data Sources:

  • JSON Data: Local data storage
  • Fallback Data: Backup for remote sources
Global Values

You can use project-level global values for header values and base URLs.

Advanced Features​

Additional Controls

Data Management​

  • Clear: Reset local JSON data
  • Function Attachment: Transform data structure
  • Remote Fill: Populate local storage from remote

Function Integration​

  • Attach custom functions
  • Transform data representation
  • Modify data structure
  • Apply business logic

Remote Source Management​

  • Test remote connections
  • Monitor data updates
  • Configure timeout settings
  • Handle error scenarios
Mixed Mode Behavior

When using mixed mode:

  1. System attempts remote fetch first
  2. Falls back to local copy if remote fails
  3. Automatically manages data synchronization

Best Practices​

  1. Data Structure

    • Use consistent JSON formats
    • Define clear data schemas
    • Document data structures
    • Plan for scalability

  2. Error Handling

    • Implement fallback strategies
    • Monitor remote source availability
    • Log connection issues
    • Test failure scenarios

  3. Performance

    • Optimize data payload size
    • Cache when appropriate
    • Monitor response times
    • Plan for throttling