Historians: Read Tag Metadata
This article provides Starter Solutions for obtaining metadata for points or tags from supported historians.
What this article covers
High-volume historian pipelines often capture value changes at high frequency and write to narrow tables for throughput and low latency. Those pipelines typically do not process tag metadata. A parallel metadata pipeline can supply the destination system with details such as point or tag description, units of measure, data type, and more. This article outlines starter solutions for processing point or tag metadata, with examples for specific historians.
General design considerations for tag metadata
- Define scope: Identify the points or tags for the solution
- Query strategy: Decide how points or tags will be queried (e.g., using naming conventions)
- Start small, measure, then scale: Begin with 10s or 100s of points or tags and scale from there to determine the optimal query duration. Optimally, the duration of the query should be approximately a few seconds.
- Scale resources or split queries: Determine if resources need to be scaled to enable the query to be more efficient. Consider that it might not be possible to obtain all desired points or tags with one query.
- Assess Connection impact: Consider how the new Connection Input impacts other Inputs sharing the same Connection.
- Account for edit frequency: Decide how often tag metadata changes and rapidly how edits need to be processed. Choose an appropriate polling interval.
- Choose orchestration: The overall data pipeline solution might be triggered on a schedule, polled at a frequency, or called externally. Metadata is typically processed infrequently relative to time-series values.
- Changes to point or tag metadata: These changes might update existing records in the destination system rather than creating new records.
- Select outputs: Write results to a database, data lake, or data warehouse, or make available by the Intelligence Hub REST Data Server.
- Shape the payload: A wide-table format (columns per metadata attribute) is common. Use an Intelligence Hub Model to define column names and data types.
AVEVA PI System Data Archive: point metadata
These considerations apply to obtaining metadata for PI Points from PI System Data Archive.
- Use the PI Connection Point Browse Input Type for obtaining Point metadata.
- Consider using the changes data property to query for only metadata that has been changed since the last time the metadata was processed.
A project file may be downloaded [here].
Navigate to the interactive demonstration [here].
Canary Historian design considerations for obtaining tag metadata
These considerations pertain to obtaining metadata tags from Canary Historian.- Use an Intelligence Hub REST Client Connection to exchange data with Canary Historian.
- The Canary Historian Views Service API getTagProperties method can be used to tag metadata.
A project file may be downloaded [here].
Navigate to the interactive demonstration [here].
Aspen InfoPlus.21 (IP.21): tag metadata
These considerations pertain to obtaining tag metadata from IP.21.
- Write a query that returns tag metadata via the IP.21 Connection.
A project file may be downloaded [here].
Navigate to the interactive demonstration [here].
Summary
Use these considerations to implement tag metadata pipelines alongside value change pipelines in Intelligence Hub. Align scope, query strategy, cadence, modeling, and destination behavior with downstream requirements.
Additional Resources