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EPG Collector: The Complete Guide to Gathering TV Program Data

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Overview: What an EPG Collector Does

An EPG collector gathers program schedule data from various sources (network streams, XMLTV, OTA EIT, web scraping, APIs), converts it to a common format, deduplicates and merges entries, enriches metadata, and provides a consumable feed (often XMLTV, JSON, or database-backed APIs) for downstream systems like middleware, set-top boxes, or media players.

Key goals:

  • Accurate start/stop times (including time-zone and DST handling)
  • Consistent program identifiers to prevent duplicates and maintain continuity
  • Up-to-date schedules with change detection and quick updates
  • Rich metadata (descriptions, genres, cast, images, ratings)
  • Scalability and reliability for many channels and regions

Planning and Requirements

  1. Define scope
    • Number of channels and services (local, national, international)
    • Languages and regions
    • Update frequency (real-time, hourly, daily)
    • Output format (XMLTV, JSON, database)
  2. Identify consumers
    • Middleware systems, apps, EPG displays, DVR schedulers
  3. Determine metadata needs
    • Descriptions, episode numbers, seasons, images, parental ratings
  4. Infrastructure choices
    • On-premise vs cloud
    • Database selection (relational for structured schedules, NoSQL for flexible metadata)
    • Storage for images and large assets (object storage like S3)
  5. Compliance and licensing
    • Check terms of use for source data (some web sources forbid scraping)
    • Consider content rights for images and artwork

Choosing Data Sources

Common EPG data sources:

  • XMLTV files provided by third parties
  • Broadcaster/satellite/cable EPG feeds (often in XML or proprietary formats)
  • Over-the-air (OTA) EIT (Event Information Table) via DVB ISDB or ATSC — requires tuner hardware and parsing
  • Public APIs (e.g., schedules APIs, TV metadata providers)
  • Web scraping of broadcaster websites or TV listings sites
  • Community-maintained sources and guides

Choose multiple complementary sources per region to improve completeness and accuracy. For mission-critical systems, prefer official broadcaster feeds or licensed providers.

Fetching Methods

  1. Polling feeds
    • Regularly download XMLTV/JSON feeds via HTTP(S).
    • Use conditional requests (If-Modified-Since / ETag) to save bandwidth.
  2. Streaming and push feeds
    • Some providers offer push notifications, webhooks, or streaming updates — integrate these for low-latency updates.
  3. OTA capture
    • Use DVB / ATSC tuners with software (e.g., dvbtee, dvbapi) to parse EIT tables and capture live metadata.
  4. Scraping
    • Use robust scraping tools (headless browsers, rate limiting, rotating IPs) and respect robots.txt and terms of service.
  5. APIs
    • Authenticate and respect rate limits; cache responses and refresh selectively.

Implement retry logic, exponential backoff, and monitoring for fetch failures.

Parsing and Normalization

Raw sources vary widely in structure and quality. Normalization steps:

  • Convert all inputs to a canonical schema (e.g., XMLTV or custom JSON schema).
  • Normalize date-times to UTC and store original time-zone and DST offsets.
  • Parse episode/season info (SxxExx) and structure it consistently.
  • Map genres and categories to a controlled vocabulary.
  • Extract and canonicalize program identifiers (IMDB ID, EIDR, proprietary IDs).
  • Clean descriptions: strip HTML, decode entities, trim whitespace.
  • Standardize titles (handle alternate titles and localized variants).

Example normalization mapping:

  • source.start_time -> program.start (UTC ISO8601)
  • source.channel_id -> channel.external_id
  • source.desc_html -> program.description (plain text)

Deduplication and Merging

When multiple sources provide data for the same event, merge intelligently:

  • Use a deterministic key: channel + start_time + duration +/- tolerance (e.g., 30s) or unique IDs when available.
  • Prefer authoritative sources for core fields (times, title) and richer sources for metadata (images, cast).
  • Track source provenance and confidence scores per field to resolve conflicts.
  • Keep history of merges to audit changes and rollback if needed.

Storage and Data Modeling

Storage choices depend on scale and query patterns:

  • Relational DB (Postgres, MySQL)
    • Good for transactions, complex joins, and ensuring data integrity.
    • Schema: channels, programs, episodes, metadata, source_logs.
  • NoSQL (MongoDB, DynamoDB)
    • Flexible schema for heterogeneous metadata and fast reads.
  • Time-series DB for logging updates (InfluxDB, Prometheus for metrics).
  • Object storage for artwork and large assets (S3-compatible).

Store both:

  • Canonical normalized feed used by consumers
  • Raw source payloads for debugging and auditing

Scheduling and Update Strategy

  • Full refresh vs incremental updates:
    • Full refreshes are simple but heavy; use for initial sync or daily rebuilds.
    • Incremental updates (diffs) are efficient for regular operation.
  • Prioritize near-term schedules (next 24–72 hours) for frequent updates; apply less frequent refreshes to long-range schedules.
  • Implement fast re-scan for breaking schedule changes (e.g., live sports overruns).
  • Use job queues and workers to parallelize fetch/parse/merge tasks.

Handling Time Zones and Daylight Saving Time

Time handling is critical:

  • Store canonical times in UTC and preserve original timezone info.
  • Use reliable libraries (e.g., pytz or zoneinfo in Python, ICU libraries) to apply DST rules per region.
  • Beware of sources that provide local times without timezone markers — require channel-level timezone mapping.
  • For live events that overrun, implement rules to extend the program end time and shift subsequent events.

Enrichment: Images, Credits, Ratings

  • Fetch and cache artwork (posters, thumbnails) with consistent sizes and aspect ratios.
  • Use external metadata providers (TMDB, IMDb, TheTVDB, Gracenote) for cast, episode synopses, and ratings — observe licensing.
  • Match by normalized title, season/episode, and year; fall back to fuzzy matching when exact IDs are absent.
  • Store metadata provenance and timestamps for each enrichment action.

Validation, QA, and Monitoring

  • Implement automated validation rules:
    • No negative durations; start < stop
    • Titles present; descriptions not empty for prime-time
    • No overlapping events on same channel (or flag overlaps as potential overruns)
  • Monitor freshness: track last update per channel and alert when stale.
  • Track ingestion success rates and parsing errors.
  • Provide a dashboard with sample events, change logs, and error counts.
  • Build tests that replay sample raw feeds and ensure output matches expected normalized data.

Caching and Distribution

  • Expose feeds via:
    • XMLTV files updated regularly
    • JSON APIs with endpoints for channels, time ranges, and search
    • GraphQL endpoints for flexible queries
  • Use CDN caching for static feed files and images; set appropriate cache headers for clients.
  • Support ETag/If-Modified-Since for clients polling feeds.
  • Secure API keys and credentials; rotate keys periodically.
  • Respect copyright and terms of service for source content and images.
  • If storing user data (e.g., personalized favorites), follow privacy best practices and data minimization.

Scaling and Reliability

  • Design for horizontal scalability: stateless fetch/parse workers, scalable databases, and distributed caches.
  • Use circuit breakers and back-pressure to handle source outages.
  • Implement graceful degradation: serve last-known-good schedules when live updates fail.
  • Automate backups of canonical data and raw sources.

Example Minimal Architecture

  • Scheduler service that queues fetch jobs
  • Fetcher workers that download raw feeds
  • Parser workers that normalize and validate
  • Merger service that resolves conflicts and writes canonical records
  • API server serving XMLTV/JSON and managing CDN cache invalidation
  • Monitoring/Alerting stack and object storage for assets

Troubleshooting Common Issues

  • Missing episodes: check source completeness, fuzzy-match thresholds, episode numbering schemes.
  • Time drift/incorrect DST: verify channel timezone mappings and source time formats.
  • Duplicate events: tighten deduplication keys, increase confidence scoring for source precedence.
  • Slow updates: parallelize workers, implement incremental diffs, or adopt push-based sources.

Best Practices Summary

  • Use multiple trusted data sources and merge them with provenance.
  • Normalize times to UTC and handle DST per-channel.
  • Prioritize near-term schedule accuracy and allow coarser long-range updates.
  • Cache artwork and metadata; respect licensing.
  • Monitor freshness and parsing health; fail gracefully with last-known-good data.

If you’d like, I can:

  • Provide example XMLTV mappings and a sample normalized JSON schema.
  • Draft a Docker-based deployment plan with container images for fetcher/parser/api.
  • Help design a deduplication algorithm or SQL schema for storing EPG data.

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