Historian Module (Reference) provides comprehensive time-series data collection, storage, and retrieval capabilities for FrameworX solutions.

Key Concepts:

  • Historian Tag: Individual data points mapped for archiving
  • Historian Table: Logical grouping of tags
  • Storage Location: Data repository
  • Historian Engine: Data collection engine

In this page:


Historian → Tutorial | Concept | How-to Guide | Reference


Historian Module Configuration

Designer UI Section

Configuration Sections

SectionPathPurpose
Storage LocationsHistorian → Storage LocationsDefine data repositories
Historian TablesHistorian → Historian TablesConfigure archiving groups
Historian TagsHistorian → Historian TagsMap tags to tables
Historian MonitorHistorian → Historian MonitorRuntime status and trends

Configuration Workflow

StepActionPurpose
1. Define StorageConfigure default databaseSet primary repository
2. Add LocationsConfigure external historiansMultiple storage targets
3. Create TablesDefine historian tablesGroup tags logically
4. Map TagsAssign tags to tablesConfigure collection
5. Set TriggersConfigure archiving eventsControl data collection


Storage Options


SQL Databases

Dataset.DB.TagHistorian

  • SQLite (default, 10GB limit)
  • SQL Server
  • PostgreSQL
  • Others...



TagProvider Storage Service

External historian integration:

  • CanaryLabs - Industrial historian
  • InfluxDB - Time-series database
  • GE Proficy - Enterprise historian



ScriptClass Custom Storage

For specialized requirements:

  • Cloud storage adapters
  • Custom file formats
  • API integrations


Runtime Architecture

Process Execution

The Historian Module runs as an isolated server process:

  • Event detection and triggering
  • Data archiving operations
  • Store and Forward management
  • Query response handling
  • Synchronization tasks

Control Operations

Access via Runtime → Runtime Diagnostics:

  • Run/Pause/Stop execution
  • Monitor performance metrics
  • View error messages
  • Check module status

Archiving Control

Trigger-Based

Event-driven collection:

  • Time triggers (Server.Minute)
  • Tag triggers (value change)
  • Expression triggers
  • Batch/process events

SaveOnChange

Continuous monitoring:

  • Individual tag tracking
  • Value change detection
  • Deadband filtering
  • Asynchronous storage

TimeDeadband

Minimum interval control:

  • Prevents excessive writes
  • Reduces storage requirements
  • Maintains data integrity

Display Integration

TrendChart Control

Built-in visualization:

  • Real-time trending
  • Historical analysis
  • Multiple pen support
  • Zoom and pan features

Historian Monitor

Runtime monitoring interface:

  • Table status display
  • Error tracking
  • Performance metrics
  • Remote web access

Performance Optimization

Table Design

  • Group by update frequency
  • Limit tags per table (50-100)
  • Match process dynamics
  • Consider query patterns

Storage Strategy

  • Choose appropriate schema
  • Set retention policies
  • Enable compression
  • Plan capacity

Deadband Configuration

  • Value deadbands for noise
  • Time deadbands for frequency
  • Deviation for exceptions
  • Rate of change for upsets

Common Configurations

High-Speed Data

Storage: Local SQLite
Schema: Normalized
Trigger: SaveOnChange
TimeDeadband: 1 second
Retention: 30 days

Production Records

Storage: SQL Server
Schema: Standard
Trigger: Batch events
SaveQuality: True
Retention: 365 days

Compliance Data

Storage: CanaryLabs
Store&Forward: True
SaveQuality: True
Retention: 7 years
Audit: Enabled

Best Practices Checklist

Design

  • Plan storage architecture - Match technology to requirements
  • Group tags logically - Similar rates and relationships
  • Size tables appropriately - Balance performance
  • Configure deadbands - Optimize storage
  • Set retention policies - Manage growth

Operations

  • Monitor regularly - Check error counts
  • Test failover - Verify Store and Forward
  • Archive old data - Maintain performance
  • Document configuration - Track changes
  • Backup settings - Protect configuration

Performance

  • Optimize queries - Use appropriate time ranges
  • Index databases - Improve retrieval
  • Cache frequently - Reduce database load
  • Batch operations - Minimize transactions

Troubleshooting

Data Not Storing

  • Verify trigger configuration
  • Check table assignment
  • Confirm database connection
  • Review error messages

Incomplete Data

  • Check module started status
  • Verify tag quality
  • Review deadband settings
  • Examine triggers

Slow Retrieval

  • Enable caching
  • Optimize query ranges
  • Check database indexes
  • Review network latency

Store and Forward Issues

  • Verify feature enabled
  • Check local disk space
  • Monitor buffer size
  • Review sync status


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