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Indexing Pipeline

TODO: Complete Data Processing Pipeline Documentation​

How data flows through the system from collection to delivery.

Required Sections​

  • Pipeline Stages

    • Ingestion: Consuming data from sources
    • Normalization: Converting diverse formats to canonical schema
    • Validation: Checking data quality and consistency
    • Enrichment: Adding derived fields, joining data
    • Computation: Calculating risk scores, updating reputation
    • Storage: Persisting processed data
    • Publication: Serving via APIs, oracle, alerts

  • Data Flow Specifics

    • Per-operator processing
    • Batch vs streaming decisions
    • Aggregation time windows (e.g., risk score updated per epoch or per block?)
    • Dependency handling (does new data invalidate old computations?)

  • Processing Latency

    • Target latency from event → indexed data
    • Latency from indexed data → API response
    • Latency from event → oracle publication
    • Latency from event → alert delivery

  • Failure Handling

    • Processing errors & retries
    • Partial failures (one operator fails, others succeed)
    • Data consistency during failures
    • Alerting on pipeline problems

  • Scalability

    • Horizontal scaling strategy
    • Bottlenecks & optimization opportunities
    • Cost model (per-operator, per-byte, per-event)

  • Monitoring & Debugging

    • Per-stage metrics
    • Data lineage tracking
    • Replay & rollback capabilities
    • Audit trails

Examples​

  • Risk score recalculation trigger (e.g., slashing event detected)
  • Operator reputation update process
  • Alert delivery pipeline

Status: NOT STARTED — Requires pipeline architecture specification