Data Contracts 🚧

🚧 Roadmap Item - Data contracts bind data models to storage, retention, and processing policies, ensuring consistent data handling across your industrial systems.

Data contracts define the operational aspects of your data models - where data gets stored, how long it's retained, and what processing rules apply. They bridge the gap between logical data structure (models) and physical data management.

Overview

Data contracts are stored in the datacontracts: configuration section and reference specific versions of data models:

datacontracts:
  - name: contract_name
    version: v1
    model: ModelName:v1
    sinks:
      timescaledb: true
    retention_days: 365

Core Properties

Name and Versioning

datacontracts:
  - name: _temperature
    version: v1
    model: Temperature:v1

Naming Convention:

• Contract names start with underscore (_temperature, _pump)

• Versions follow semantic versioning (v1, v2, etc.)

• Model references include version (Temperature:v1)

Model Binding

Each contract binds to exactly one data model version:

datacontracts:
  - name: _pump
    version: v1
    model: Pump:v1  # Specific model version

This binding is immutable - to change the model, create a new contract version.

Data Sinks

Contracts specify where data gets stored and processed:

datacontracts:
  - name: _temperature
    version: v1
    model: Temperature:v1
    sinks:
      timescaledb: true
      custom_analytics: false
      cloud_storage: true

Available Sinks:

• timescaledb: Automatic TimescaleDB hypertable creation

• custom_dfc: Custom data flow configurations

• cloud_storage: S3-compatible storage

• analytics_pipeline: Stream analytics processing

Retention Policies

Define how long data is kept:

datacontracts:
  - name: _historian
    version: v1
    model: HistorianData:v1
    sinks:
      timescaledb: true
    retention_days: 2555  # ~7 years

Complete Examples

Simple Temperature Contract

datamodels:
  - name: Temperature
    version: v1
    structure:
      temperature_in_c:
        type: timeseries
        constraints:
          unit: "°C"

datacontracts:
  - name: _temperature
    version: v1
    model: Temperature:v1
    sinks:
      timescaledb: true
    retention_days: 365

Complex Pump Contract

datamodels:
  - name: Motor
    version: v1
    structure:
      current:
        type: timeseries
      rpm:
        type: timeseries

  - name: Pump
    version: v1
    structure:
      pressure:
        type: timeseries
        constraints:
          unit: kPa
          min: 0
      temperature:
        type: timeseries
        constraints:
          unit: "°C"
      running:
        type: timeseries
        constraints:
          allowed: [true, false]
      diagnostics:
        vibration:
          type: timeseries
          constraints:
            unit: "mm/s"
      motor:
        _model: Motor:v1
      total_power:
        type: timeseries
        constraints:
          unit: kW
      serial_number:
        type: timeseries

datacontracts:
  - name: _pump
    version: v1
    model: Pump:v1
    sinks:
      timescaledb: true
      analytics_pipeline: true
    retention_days: 1825  # 5 years

Generated Database Schema

When a contract with TimescaleDB sink is deployed, UMH automatically creates:

Hypertable Structure

For the _pump:v1 contract:

-- Auto-generated hypertable: pump_v1
CREATE TABLE pump_v1 (
    time TIMESTAMPTZ NOT NULL,
    location JSONB NOT NULL,
    
    -- Top-level fields
    pressure DOUBLE PRECISION,
    temperature DOUBLE PRECISION,
    running BOOLEAN,
    total_power DOUBLE PRECISION,
    serial_number TEXT,
    
    -- Sub-model fields (flattened)
    motor_current DOUBLE PRECISION,
    motor_rpm DOUBLE PRECISION,
    
    -- Folder fields (flattened with path)
    diagnostics_vibration DOUBLE PRECISION
);

-- Hypertable conversion
SELECT create_hypertable('pump_v1', 'time');

-- Indexes for location-based queries
CREATE INDEX idx_pump_v1_location ON pump_v1 USING GIN (location);

Location Structure

The location field stores ISA-95 hierarchy:

{
  "level0": "corpA",
  "level1": "plant-A", 
  "level2": "line-4",
  "level3": "pump41"
}

Contract Evolution

Version Management

Contracts support controlled evolution:

# Version 1
datacontracts:
  - name: _pump
    version: v1
    model: Pump:v1
    sinks:
      timescaledb: true

# Version 2 - Extended retention
datacontracts:
  - name: _pump
    version: v2
    model: Pump:v2  # Updated model
    sinks:
      timescaledb: true
      analytics_pipeline: true  # New sink
    retention_days: 2555  # Extended retention

Backward Compatibility

• Multiple contract versions can coexist

• Existing stream processors continue using their bound contract version

• Database schemas adapt automatically for new fields

• No downtime required for contract updates

Sink Configuration Details

TimescaleDB Sink

sinks:
  timescaledb: true

Behavior:

• Auto-creates hypertable {contract_name}_{version}

• Generates appropriate column types from model constraints

• Creates location indexes for ISA-95 queries

• Handles sub-model field flattening automatically

Custom Data Flow Sink

sinks:
  custom_dfc:
    endpoint: "https://analytics.company.com/api/pump-data"
    auth_token: "${DFC_AUTH_TOKEN}"
    batch_size: 1000

Behavior:

• Sends data to external systems

• Supports authentication and batching

• Configurable retry policies

• Schema validation before transmission

Cloud Storage Sink

sinks:
  cloud_storage:
    bucket: "industrial-data-lake"
    prefix: "pump-data/{year}/{month}/{day}/"
    format: "parquet"
    compression: "gzip"

Behavior:

• Partitioned storage by time

• Multiple format support (JSON, Parquet, Avro)

• Compression options

• Automated lifecycle management

Validation and Enforcement

Schema Enforcement

All data contracts are registered in Redpanda Schema Registry:

• Publish-time validation: Messages are validated before acceptance

• Consumer protection: Invalid messages are rejected automatically

• Evolution safety: Schema changes must maintain compatibility

Runtime Validation

The UNS output plugin enforces contract compliance:

# Invalid message - rejected
Topic: umh.v1.corpA.plant-A.line-4.pump41._pump.invalid_field
Reason: Field 'invalid_field' not defined in Pump:v1 model

# Valid message - accepted
Topic: umh.v1.corpA.plant-A.line-4.pump41._pump.pressure
Payload: {"value": 42.5, "timestamp_ms": 1733904005123}

Best Practices

Contract Design

• Single responsibility: One contract per logical entity type

• Semantic naming: Use descriptive, underscore-prefixed names

• Version explicitly: Always specify model and contract versions

• Plan for growth: Consider future sink requirements

Retention Planning

• Match business needs: Align retention with regulatory requirements

• Consider storage costs: Balance retention vs. storage expenses

• Plan for archival: Design archival strategies for historical data

Sink Selection

• TimescaleDB for time-series: Optimal for sensor data and analytics

• Cloud storage for archives: Long-term, cost-effective storage

• Custom DFC for integration: External system connectivity

Schema Evolution

• Additive changes: Add fields rather than modifying existing ones

• Test compatibility: Validate schema evolution before deployment

• Document changes: Maintain clear change logs

Integration with Stream Processors

Data contracts are consumed by stream processors:

streamprocessors:
  - name: pump41_sp
    contract: _pump:v1  # References this contract
    location:
      level0: corpA
      level1: plant-A
      level2: line-4
      level3: pump41
    # ... mapping configuration

The stream processor:

1. Validates output against the contract's model schema

2. Routes data to configured sinks

3. Applies retention policies automatically

4. Enforces location hierarchy requirements

Related Documentation

• Data Models - Defining reusable data structures

• Stream Processors - Implementing contract instances

• Stream Processor Implementation - Detailed runtime configuration