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Reference Solution: AVEVA PI System Event Frames and Process Data to Wide Table Format

This article captures the design considerations for creating a solution to obtain PI Event Frame data and PI asset attribute data for the Event Frame duration and create a payload for a cloud data store

What Does This Article Cover?

An Intelligence Hub solution can be created to obtain Event Frame data from AVEVA PI and use the event frame start and end datetime to obtain process values for the Event Frame's asset.  The combined payload could be formatted and sent to a wide table in a data warehouse or data lake.  The following describes how this solution can be created.

Design Assumptions

The following are some of the design considerations.

  • This approach uses the Intelligence Hub PI Connection Event Frame Input that can be configured to use most Event Frame queries that can be defined in PI System Explorer.​
  • The query can be configured to return the instances of one or more Event Frame templates.​
  • An Event Frame returns one value for Event Frame attributes.  It might be beneficial to obtain multiple values for many attributes over the time span of the Event Frame.  For example obtain the value of all asset attributes configured for a reactor every minute during the duration of a batch phase.​ 

 

Overall Solution Configuration

The following describes the Intelligence Hub configuration objects.​

  • Create a Pipeline Polling stage configured with the optimal polling frequency considering latency, PI query performance and size of the payload​.
  • Obtain Event Frame data from PI using the PI Connection Event Frame Input and a parameterized datetime in the query filter.  Persist the last datetime index in a Pipeline state variable. ​
  • Use the Event Frame start and end datetime to obtain the historical values for the Event Frame’s asset.  The PI Connection Asset Raw Values Input can be used and it can be parameterized to use the data from the Event Frames.


Pipeline Configuration

The following describes the Intelligence Hub Pipeline design​.

  • Update the datetime start variable.
  • Read the Event Frame date.
  • Breakup the array of Event Frames.
  • Read all process values ​based on the Event Frame's start and end datetime.
  • Move the Event Frame context to metadata .
  • Break up the array of process values .
  • Assign the Event Frame context to each element of array.
  • Buffer transactions using a strategy that aligns with the solution requirements for latency.  It may be beneficial to buffer transactions for a cloud data warehouse, like Snowflake for example. 
  • Consider the use of the Buffer Key based on use case​.
  • Create the desired file format ​for the data lake if applicable.
  • Write to the data lake configuring the desired file name format.

 

Other Considerations 

The following should be considered related to this design ​

  • In the simple reference solution the schema of the outgoing payload was not defined.  An Intelligence Hub Model could be defined and using in the Pipeline to enforce the schema.​​
  • The Intelligence Hub PI Connection Event Frame Input type can obtain Event Frame Acknowledge information, Annotations, and the associated element.

 

Solution Video

The following video captures a reference solution.

 

Additional Resources