Event Sink Filter

The Event Sink Filter is a production-ready OpenFilter component that collects events from filter pipelines and reliably delivers them to a Plainsight API's CloudEvents ingestion compatible endpoint.

Features

• CloudEvents v1.0 Compliant: Industry-standard event format with Plainsight extensions
• Intelligent Batch Processing: Automatic batching based on size (5 MiB), count (1000 events), or time (5s)
• Gzip Compression: 70-80% bandwidth reduction for efficient network usage
• Reliable Delivery: Exponential backoff retry logic for resilient event delivery
• Flexible Event Collection: Multiple extraction patterns, topic filtering
• Production Ready: Comprehensive error handling, logging, and graceful shutdown

Architecture

Main Thread (Filter)          Background Thread (EventSinkThread)
─────────────────────         ───────────────────────────────────
Receive frames                Accumulate events from queue
Extract events      ───────→  Check flush conditions:
Queue events                  - Size >= 5 MiB
                              - Count >= 1000
                              - Time >= 5s
                              ↓
                              Build CloudEvents batch
                              Gzip compress
                              HTTP POST to API
                              Retry on failure

Prerequisites

If you are exporting events to the Plainsight API, you'll need to:

• Plainsight API Token: Generate an API token from the Plainsight dashboard with filterpipeline:create scope
• Plainsight API Filter Pipeline: Create a filter pipeline in the Plainsight dashboard

Quick Start

1. Install Dependencies

python3 -m venv venv
source venv/bin/activate
make install

2. Configure Environment

Copy the example environment file and fill in your values:

cp .env.example .env
# Edit .env with your API credentials

3. Run Locally

# Run the filter
source venv/bin/activate
source .env && make run

Or in one line:

source .env && source venv/bin/activate && make run

4. Run in Docker

# Build image
make build-image

# Run with docker-compose
source .env && make run-image

Configuration

Required Parameters

Optional Parameters

Event Formats

The filter automatically detects and extracts events from frame.data.

Example

frame.data = {
  'meta': {
    'count': 5,
    'classes': ['person', 'vehicle'],
    'custom_field': 'value'
  }
}

CloudEvent Schema

Events are sent as CloudEvents v1.0 with Plainsight extensions:

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "type": "com.plainsight.event.generic",
  "source": "filter://macbookpro-lucas.local-0153261e-9551-4e4a-8740-c71b6dc9c506/ObjectDetector/main",
  "specversion": "1.0",
  "time": "2025-10-27T22:00:00Z",
  "datacontenttype": "application/json",
  "data": {
    "meta": {
      "count": 5,
      "classes": ["person", "vehicle"],
      "custom_field": "value"
    }
  },
  "pipelineid": "macbookpro-lucas.local-0153261e-9551-4e4a-8740-c71b6dc9c506",
  "filtername": "ObjectDetector",
  "filtertopic": "detections"
}

Testing

Run the comprehensive test suite:

# Run all tests
make test

# Run with coverage report
make test-coverage

Deployment

Docker Compose Example

services:
  filter_event_sink:
    image: containers.openfilter.io/plainsightai/openfilter-event-sink:1.1.1
    environment:
      LOG_LEVEL: INFO
      FILTER_ID: EventSink
      FILTER_SOURCES: tcp://upstream_filter:5550??;>VideoIn

      # Event Sink Configuration
      FILTER_API_ENDPOINT: "https://api.prod.plainsight.tech/filter-pipelines/production-line-1/events?project=your-project-uuid"
      FILTER_API_TOKEN: "${PLAINSIGHT_API_TOKEN}"
      FILTER_API_CUSTOM_HEADERS: "X-Scope-OrgID: 48eec17d-3089-4d13-a107-24f5f4cf84c7"  # Optional
      FILTER_EVENT_TOPICS: "detections,alerts"
      FILTER_FLUSH_INTERVAL_SECONDS: "5.0"

    volumes:
      - ./cache:/app/cache
      - ./telemetry:/app/telemetry
    networks:
      - filter-network

Publishing Releases

1. Update VERSION file with semver tag (e.g., v1.2.3)
2. Update RELEASE.md with version entry matching VERSION
3. Merge to main - CI will:
   • Build and publish Docker image to GAR
   • Build and publish Python wheel to GAR
   • Push docs to documentation sites

Monitoring

The filter provides structured logging:

• Info: Successful batch posts, thread lifecycle
• Warning: Retries, queue near capacity
• Error: Failed posts, dropped events, auth failures

Performance

• Throughput: 1000+ events/second with batching
• Memory: Bounded queue (10,000 events)
• Network: 70-80% bandwidth reduction with gzip

Troubleshooting

Queue Full Warnings

• Solution: Increase event_queue_size or reduce event rate
• Events will be dropped when queue is full

API Authentication Errors (401/403)

• Solution: Verify FILTER_API_TOKEN is valid and has correct scopes
• Check token expiration

Network Timeouts

• Solution: Increase request_timeout_seconds
• Check network connectivity to API endpoint

Events Not Appearing in BigQuery

• Solution: Verify filter pipeline exists in Plainsight
• Check API endpoint URL is correct
• Monitor filter logs for POST errors

Documentation

• docs/overview.md: User-facing documentation
• RELEASE.md: Release notes and changelog

License

Copyright © 2025 Plainsight AI

Support

For issues and questions:

• Create an issue in this repository
• Contact Plainsight support

Version: v1.0.1 Status: Production Ready Maintainer: Plainsight AI