Inferensys

Glossary

DataSet

A defined collection of field-level data values, configured in a Publisher, that is encoded and delivered as a single payload in a PubSub Network Message.
Data scientist building training data pipeline on laptop, data preprocessing visible, technical workspace.
PUBSUB PAYLOAD DEFINITION

What is a DataSet?

A DataSet is a configured collection of field-level data values within an OPC UA Publisher, encoded and transmitted as a single, atomic payload in a PubSub Network Message.

A DataSet is a publisher-defined, logical grouping of Variable Node values from the OPC UA Address Space that are sampled and transmitted together. It serves as the fundamental unit of payload in the PubSub model, decoupling the structure of industrial data from the transport protocol. The DataSetMetaData describes the exact names, types, and order of fields, enabling any subscriber to decode the binary or JSON payload without prior knowledge of the publisher's internal schema.

Within the DataSetWriter, the publisher configures how the DataSet is serialized—using UA Binary or JSON Encoding—and assigns it to a specific WriterGroup for network delivery. This mechanism allows a single publisher to aggregate disparate data points, such as temperature readings and actuator states, into a cohesive message for efficient, deterministic distribution over transports like MQTT or TSN.

OPC UA PUBSUB FUNDAMENTALS

Key Characteristics of a DataSet

A DataSet defines the logical structure and content of a payload in OPC UA PubSub, abstracting the data from its physical transport. Understanding its core characteristics is essential for designing efficient, deterministic industrial communication.

01

Field-Level Data Aggregation

A DataSet is a collection of Variable Node values, aggregated from an OPC UA Server's Address Space. Instead of publishing individual value changes, the Publisher groups related data points—such as temperature, pressure, and speed from a single machine module—into one cohesive payload. This reduces message overhead and ensures data consistency by delivering a snapshot of correlated process variables simultaneously.

02

DataSetMetaData: The Semantic Contract

Every DataSet is described by DataSetMetaData, which provides the schema and semantic context required for a Subscriber to decode the payload. The metadata includes:

  • Name and Description: Human-readable identifiers.
  • Field Definitions: The name, data type, and engineering units for each value.
  • Configuration Version: A version stamp that signals structural changes, ensuring Subscribers can detect and handle schema mismatches gracefully.
03

Encoding Agnosticism

The logical DataSet is decoupled from its wire format. The same DataSet can be serialized into a Network Message using different encodings based on the transport profile:

  • UA Binary: A compact, high-performance format for deterministic field-level communication (e.g., OPC UA FX over TSN).
  • JSON: A human-readable format ideal for cloud integration and web-friendly transports (e.g., PubSub over MQTT). This allows a single data definition to serve both real-time control and enterprise analytics.
04

DataSetWriter: The Publishing Agent

A DataSetWriter is the Publisher's internal component responsible for assembling and sending a specific DataSet. It manages the timing of data collection from the local Address Space and constructs the Network Message. Multiple DataSetWriters can exist within a single Publisher, each handling a distinct DataSet, allowing a single device to publish different data streams at independent rates.

05

Key Frame and Delta Frame Dynamics

To optimize bandwidth, DataSet messages can be sent as Key Frames or Delta Frames:

  • Key Frame: Contains the complete, current value of every field in the DataSet. Essential for initial synchronization and recovery.
  • Delta Frame: Contains only the fields whose values have changed since the last message. This drastically reduces payload size for high-frequency, sparsely changing data. Subscribers reconstruct the full DataSet by applying Delta Frames to the last received Key Frame.
06

Deterministic Cyclic Publishing

For real-time control, a DataSet can be configured for cyclic publishing at a strict, deterministic interval. The DataSetWriter samples the configured Variables at a precise Sampling Offset and publishes the Network Message at a guaranteed Publishing Offset within the communication cycle. This time-aware behavior, fundamental to OPC UA FX over TSN, ensures bounded latency and jitter for controller-to-controller communication.

DATASET CLARIFICATIONS

Frequently Asked Questions

Clear answers to common questions about the structure, configuration, and role of DataSets in OPC UA PubSub communication.

An OPC UA DataSet is a defined, ordered collection of field-level data values, configured within a Publisher, that is encoded and delivered as a single atomic payload in a PubSub Network Message. It works by grouping related Variable values from the Address Space into a structured DataSetMetaData definition. At runtime, the Publisher samples the current values of these fields, encodes them according to a specified DataSetField layout, and hands the resulting binary or JSON blob to the Message Mapping layer for transport. This decouples the semantic grouping of data from the network framing, allowing a single DataSet to be sent over MQTT, AMQP, or OPC UA UDP without altering its internal structure. The subscriber then decodes the DataSet payload back into individual field values, preserving their original data types and timestamps.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.