File Processing

File Processing System

Overview

The HoneyGrid is generated from several large data files that require significant processing:

• USPS carrier route boundaries (GeoJSON format)
• Carrier route demographics (CSV format)
• New mover data (CSV format)

These files contain millions of records and complex geographical data that need to be indexed into H3 cells for efficient querying and analysis. Processing this data presents several challenges in a serverless environment:

1. Size Constraints: Workers have limited memory and compute time per request
2. Processing Duration: Full processing can take hours, exceeding Worker time limits
3. State Management: Need to track progress and handle failures
4. Resource Efficiency: Must process data in chunks to optimize memory usage

To address these challenges, we use Durable Objects as our processing engine. DOs provide:

• Dedicated compute and storage
• Persistent state across requests
• Automatic scaling
• Built-in SQLite database with Drizzle ORM

While each DO request is limited to 30 seconds of compute time, we use the alarm API to maintain long-running processes, effectively creating a persistent processing environment.

R2 Directory Structure

We organize data files in the R2 bucket using a structured approach:

honeygrid-source-data/
├── DD-MM-YYYY/          # Date-based directories for each data batch
│   ├── route-boundaries/     # Directory for boundary GeoJSON files
│   │   ├── boundary_file1.json
│   │   └── boundary_file2.json
│   ├── route-metadata/       # Directory for route metadata CSV files
│   │   ├── MVP Segment 1.csv
│   │   └── MVP Segment 2.csv
│   └── new-mover-metadata/   # Directory for new mover CSV files
│       └── NewMoverPostalRouteDetailReport.csv
└── other-date/          # Another date-based directory
    └── ...

This directory structure provides:

1. Temporal Organization: Groups data by upload/processing date
2. Type Separation: Keeps different file types separate for easier management
3. Batch Processing: Enables processing entire data sets as a unit
4. Version Control: Maintains historical data sets for analysis

File Upload and Processing Workflow

The file processing system follows a decoupled workflow designed to handle large datasets efficiently and reliably.

1. File Management Options

We provide two methods for adding files to the system:

1. Direct R2 Upload

   • Upload files directly to R2 using the Cloudflare dashboard
   • Organize files into appropriate directories
   • Use the admin UI to process these directories

2. Admin UI Upload

   • Upload individual files through the HoneyGrid admin interface
   • Files are automatically placed in the correct directory structure
   • Supports individual file uploads for testing and small updates

2. Processing Flow

The processing flow follows these decoupled steps:

1. Directory Selection

   • Browse the R2 bucket structure
   • Select one or more directories to process
   • These directories can contain any combination of boundary, metadata, or new mover files

2. Grid Version Creation

   • System generates a new grid version ID
   • Selected directories are associated with this version

3. File Registration

   • Files from selected directories are registered with the grid version
   • File types are determined from directory paths
   • Metadata about each file is stored

4. Orchestrator Workflow

   • A Workflow is started to coordinate processing
   • Manages file processing and grid building steps sequentially:
     • Processes all boundary files first.
     • Waits for boundary file processing to complete.
     • Processes all demographic/new mover files next.
     • Waits for demographic/new mover file processing to complete.
     • Initiates the grid building process.
   • Provides high-level status tracking via events sent from the file processors.

5. File Processing (Sequential)

   • Boundary Files: Processed first by BoundaryFileProcessor Durable Objects.
     • Content is parsed and validated.
     • Data is split into manageable chunks and sub-chunks to handle complex geometries.
     • Processed boundary data (H3 cells, overlap fractions) is stored in KV.
   • Demographic/New Mover Files: Processed second by DemographicFileProcessor Durable Objects after boundaries are complete.
     • Content is parsed and validated.
     • Data is split into manageable chunks.
     • Extracts demographic and geographic metadata.
     • Processed data is stored in the processor’s internal SQLite staging database.

6. Grid Building / Data Export

   • After all file processing is complete, the grid building workflow is initiated.
   • The GridBuildingWorkflow coordinates final data integration.
   • It reads boundary data from KV.
   • It uses RPC to trigger the DemographicFileProcessor to export its staged data (primary tables and junction tables) directly to the main D1 database.
   • Implements retries for export operations to ensure reliability.
   • Final grid is stored in D1 for querying.

Core Components

Two-Phase Processing Architecture

Our system follows a two-phase processing architecture:

1. Phase 1: Data Processing

   • Each file processor handles its specific data format
   • BoundaryFileProcessor creates cell relationships and stores them in KV. It uses a sub-chunking strategy to handle large geometries.
   • DemographicFileProcessor processes demographic data and stores it in a local SQLite database that mirrors an extended version of the main D1 schema.
   • Both maintain hash-based caching in KV to avoid redundant processing.

2. Phase 2: Grid Building

   • GridBuildingWorkflow coordinates final data integration.
   • It reads boundary data from KV for cell relationships.
   • It uses RPC to directly invoke methods on DOs for database operations (e.g., calling exportToD1).
   • It builds and manages relationships between cells, routes, zips, etc.

This approach separates processing from database operations, improving modularity and reliability.

BaseFileProcessor

The BaseFileProcessor is an abstract class that serves as the foundation for all file processors. It handles the complex orchestration of file processing, including state management, chunking, and error handling, while allowing specific implementations to define their data processing logic.

Key Responsibilities:

• State Management: Manages the processor’s lifecycle through a state machine with statuses: pending, processing, completed, failed, stopped. The state is persisted in durable storage.
• Lifecycle Control: Provides methods to start, stop, resume, and restart processing.
• File Chunking: Breaks large files into manageable pieces, which are stored in an internal SQLite Chunks table.
• Long-Running Processes: Uses the alarm API to process chunks sequentially over time, avoiding execution limits.
• Error Handling: Implements configurable retry logic with exponential backoff for chunk processing.
• Progress Tracking: Reports progress updates to a central GridStateManager via a queue, providing real-time status for the UI.
• Workflow Integration: Sends completion or failure events to the main orchestrator workflow.

// Simplified representation of the BaseFileProcessor
abstract class BaseFileProcessor<T extends BaseRowData> extends DurableObject {
  // --- State ---
  protected data: FileProcessorState // Manages status, progress, errors etc.

  // --- Configuration ---
  protected maxRetries = 3
  protected backoffDelays = [1000, 5000, 15000] // In ms
  protected alarmInterval = 1000 // In ms
  protected chunkSize = 20

  // --- Core Lifecycle Methods ---
  public async start(fileName: string, fileType: FileType, gridVersionId: string): Promise<boolean>
  public async stop(): Promise<FileProcessorState>
  public async resume(): Promise<FileProcessorState>
  public async restart(): Promise<FileProcessorState>
  public async deleteStorage(): Promise<void>
  public async getStatus(): Promise<FileProcessorState>

  // --- Internal Processing Logic ---
  public async alarm(): Promise<void> // Triggered to process the next chunk
  private async processNextChunk(): Promise<boolean>

  // --- Abstract Methods for Child Classes ---
  /**
   * Split file content into processable chunks
   * @param content Raw file content as string
   * @returns An object containing chunks and any validation errors
   */
  protected abstract createChunks(content: string): Promise<{
    chunks: Array<Array<RouteData<T>>>
    validationErrors?: Array<{ index: number; error: string }>
  }>

  /**
   * Process a single chunk of data
   * @param chunk Array of data items to process
   */
  protected abstract processChunk(chunk: RouteData<T>[]): Promise<void>
}

Processor-Specific Implementations

BoundaryFileProcessor

The BoundaryFileProcessor handles GeoJSON processing and storing the cell relationships. Due to the high memory and CPU cost of geometric calculations, it employs a sub-chunking strategy to stay within Worker limits.

• GeoJSON Processing: Parses and validates GeoJSON data from the boundary files.
• H3 Conversion: Converts route boundaries to H3 cells.
• Sub-Chunking: For each route, it first generates a list of unique H3 cells at resolution 8. This list is then broken into smaller “sub-chunks” (RES8_SUB_CHUNK_SIZE). The processor’s state, including the queue of remaining sub-chunks, is saved to durable storage between alarm invocations. This allows it to process extremely complex geometries piece by piece.
• Fractional Overlap: For each cell in a sub-chunk, it calculates the precise intersection area and the fractional overlap relative to the total route area.
• KV Storage: Once all sub-chunks for a route are processed, the final list of ProcessedCell objects is aggregated and stored in the CARRIER_ROUTE_BOUNDARIES KV namespace.
• Guardrails: Implements checks like MAX_POLYGON_VERTICES and MAX_H3_CELLS to prevent memory exhaustion and runaway processing, throwing specific errors (TooManyVerticesError, TooManyCellsError) if limits are exceeded.

DemographicFileProcessor

The DemographicFileProcessor handles CSV processing and storing demographic data. It uses a sophisticated internal database to calculate cell metrics before exporting the final data.

• CSV Processing: Parses and validates demographic data files, handling common formatting issues.
• Internal Staging Database: Uses an internal SQLite instance with a schema that is an extended version of the main D1 schema. The key difference is the honeygridCellsExtended table, which includes a route_contributions JSON column.
• Data Allocation: For each carrier route, it retrieves the corresponding H3 cell boundary data from KV. It then allocates demographic data (e.g., households, income) to each H3 cell based on the fractional overlap calculated by the BoundaryFileProcessor.
• Stateful Aggregation: The route_contributions field is critical. When a cell is affected by multiple overlapping routes, this JSON field stores the contribution from each route. The processor then uses complex SQL queries with json_each and json_set to recalculate the cell’s total aggregates (summing counts, and creating weighted averages for metrics like income) every time a new route’s data is added. This ensures that aggregates are always accurate and reflect all contributing data.
• Data Export: After processing is complete, it provides two export methods callable via RPC from the orchestrator workflow: exportToD1 and exportToSqlFile.

The route_contributions logic enables:

• Accurate aggregation of overlapping areas
• Ability to recalculate if a route’s data changes
• Transparency in how values were derived
• Proper weighting of averages based on overlap area

Internal Cell Processing Logic

The core logic for inserting and updating cells within the DemographicFileProcessor’s internal SQLite database is complex. It ensures that as data from multiple routes is processed, the aggregates for each cell are correctly recalculated.

// Inside DemographicFileProcessor's processDemographics method
await this.db
  .insert(schema.honeygridCellsExtended)
  .values({
    id: cell.cellId,
    resolution: parseInt(cell.cellId.charAt(0)),
    //... initial values
    route_contributions: {
      /* ... contribution from the first route ... */
    },
  })
  .onConflictDoUpdate({
    target: schema.honeygridCellsExtended.id,
    set: {
      // Merge the new route's data into the route_contributions JSON object
      route_contributions: sql`json_set(
        COALESCE(route_contributions, '{}'),
        ${sql.raw(`'$.${routeId}'`)},
        json(${JSON.stringify({
          households,
          sfdu,
          mfdu,
          income: data.Income,
          home_value: data.HomeValue,
          fraction: cell.fraction,
        })})
      )`,
      // Recalculate 'households' by summing all contributions in the updated JSON
      households: sql`(
        SELECT SUM(json_extract(value, '$.households'))
        FROM json_each(json_set(
          COALESCE(route_contributions, '{}'),
          ${sql.raw(`'$.${routeId}'`)},
          json(${JSON.stringify({ households })})
        ))
      )`,
      // ... similar recalculation for sfdu and mfdu ...

      // Recalculate 'income' as a weighted average based on fraction
      income: sql`(
        SELECT ROUND(
          SUM(json_extract(value, '$.income') * json_extract(value, '$.fraction')) /
          SUM(json_extract(value, '$.fraction'))
        )
        FROM json_each(json_set(
          COALESCE(route_contributions, '{}'),
          ${sql.raw(`'$.${routeId}'`)},
          json(${JSON.stringify({ income: data.Income, fraction: cell.fraction })})
        ))
      )`,
      // ... similar weighted average for home_value ...
    },
  })

Storage and Export Architecture

Our storage strategy uses a multi-tier approach to separate staging data from final, queryable data.

1. KV Storage (Boundary Data & Caching)

• CARRIER_ROUTE_BOUNDARIES: Stores the final processed boundary data (H3 cells + fractions per route). This is the source of truth for geometric relationships.
• Other KVs: Used for caching and tracking processing state.

2. Durable Object SQLite (Staging for Demographics)

• The DemographicFileProcessor uses its internal SQLite instance as a powerful staging area.
• The schema here is extended, notably with the honeygridCellsExtended table containing the route_contributions JSON column. This allows for complex, stateful calculations that would be inefficient to perform directly in D1.
• This data is temporary and is cleared or overwritten on subsequent processing runs for the same grid version.

3. D1 Database (Final Storage)

The central D1 database stores the final, aggregated grid data, optimized for querying.

• It receives data exports from the DemographicFileProcessor.
• The honeygridCells table in D1 is simpler than its DO counterpart; it does not contain the route_contributions column, only the final aggregated values.
• Data is inserted using onConflictDoUpdate clauses to handle merging data from different processing jobs if necessary.

// D1 schema for H3 cells (final processed data)
const honeygridCells = table('HoneygridCell', {
  id: text().primaryKey().notNull(), // H3 index
  resolution: integer().notNull(),
  households: integer(),
  sfdu: integer(), // Single Family Dwelling Units
  mfdu: integer(), // Multi-Family Dwelling Units
  new_mover_total: integer(),
  new_mover_sfdu: integer(),
  new_mover_mfdu: integer(),
  income: integer(),
  home_value: integer(),
  // Note: No route_contributions field in the final D1 schema
})

// Primary and junction tables for geographic metadata
const carrierRoutes = table('CarrierRoutes', {
  /* ... */
})
const zipCodes = table('ZipCodes', {
  /* ... */
})
// ... etc.

Data Export Methods

The DemographicFileProcessor provides two methods for exporting its internally staged data.

exportToD1()

This method exports all staged data (cells, routes, lookups, junctions) from the DO’s SQLite database to the main D1 database.

• Batching & Retries: It uses a generic exportWithRetry helper that processes records in small batches (e.g., size 10) and implements exponential backoff for retries to handle D1’s rate limits and ensure reliability.
• Conflict Handling:
  • For lookup tables (cities, counties, zips) and junction tables, it uses ON CONFLICT DO NOTHING.
  • For carrierRoutes, it uses ON CONFLICT DO UPDATE to refresh route metadata.
  • For honeygridCells, it uses an additive ON CONFLICT DO UPDATE strategy. If a cell already exists in D1, it adds the new values to the existing ones. This is a simpler merge strategy than the full recalculation done inside the DO.

// Simplified D1 export logic for honeygridCells
d1Client
  .insert(schema.honeygridCells)
  .values(batch)
  .onConflictDoUpdate({
    target: schema.honeygridCells.id,
    set: {
      // Additive merge for counts
      households: sql`COALESCE(households, 0) + excluded.households`,
      sfdu: sql`COALESCE(sfdu, 0) + excluded.sfdu`,
      mfdu: sql`COALESCE(mfdu, 0) + excluded.mfdu`,
      // Recalculate weighted averages based on the sum of households
      income: sql`ROUND(((COALESCE(income, 0) * COALESCE(households, 0)) + (excluded.income * excluded.households)) / NULLIF(COALESCE(households, 0) + excluded.households, 0))`,
      home_value: sql`ROUND(((COALESCE(home_value, 0) * COALESCE(households, 0)) + (excluded.home_value * excluded.households)) / NULLIF(COALESCE(households, 0) + excluded.households, 0))`,
    },
  })

exportToSqlFile()

This method provides a way to dump the entire processed dataset from the DO’s internal SQLite database into a single .sql file.

• Purpose: Useful for debugging, local development, or seeding databases without direct D1 access.
• Multipart Upload: To handle potentially very large SQL files, this method streams the SQL statements and uses R2’s multipart upload API. This avoids loading the entire SQL file into memory.
• Output: The resulting .sql file is uploaded to the HONEYGRID_SOURCE_DATA R2 bucket.

GridBuildingWorkflow

The GridBuildingWorkflow coordinates the final grid building process. It waits for completion events from all file processors before initiating the final data export steps.

async run(event: WorkflowEvent<GridBuildingParams>, step: WorkflowStep) {
  const { gridVersionId, fileNames } = event.payload;

  // 1. Wait for all individual file processors to complete by listening for their events
  const completionEvents = fileNames.map(fileName =>
    step.waitFor<FileCompletionEvent>(`file-complete-${fileName}`)
  );
  await step.whenAll(completionEvents);

  // 2. Export demographic data directly to D1
  await step.do("export demographic data to D1", async () => {
    // Get DO stub for the correct grid version
    const processorId = this.env.DEMOGRAPHIC_FILE_PROCESSOR.idFromName(gridVersionId);
    const processor = this.env.DEMOGRAPHIC_FILE_PROCESSOR.get(processorId);

    // Direct RPC call - no fetch needed!
    const stats = await processor.exportToD1();
    console.log(`Demographic export stats: ${JSON.stringify(stats)}`);
  });

  // 3. (Optional) Export data to SQL file
  await step.do("export data to SQL file", async () => {
  const processorId = this.env.DEMOGRAPHIC_FILE_PROCESSOR.idFromName(gridVersionId);
    const processor = this.env.DEMOGRAPHIC_FILE_PROCESSOR.get(processorId);

  const sqlFileName = `honeygrid-export-${gridVersionId}.sql`;
  await processor.exportToSqlFile(sqlFileName);
  console.log(`Exported data to ${sqlFileName}`);
  });

  // 4. Update grid status
  await step.do("update grid status", async () => {
    // Mark grid as complete
  });
}

Error Handling and Best Practices

• Retries: All critical operations, especially database exports and chunk processing, implement robust retry logic with exponential backoff.
• State Management: Error states are saved in the processor’s durable state, including error messages and timestamps, which are visible in the admin UI.
• Database Operations:
  • Use transactions for consistency where needed.
  • Use appropriate ON CONFLICT clauses for both internal SQLite and final D1 operations.
• Cleanup: The deleteStorage() method on the base processor can be called to completely wipe a DO’s state, including its internal SQLite database, to ensure a clean slate for reprocessing.
• Performance: Sub-chunking in the BoundaryFileProcessor and batching in the DemographicFileProcessor’s export methods are key to managing performance and staying within platform limits.
• Conflict Handling:
  • For lookup tables (cities, counties, zips) and junction tables, it uses ON CONFLICT DO NOTHING.
  • For carrierRoutes, it uses ON CONFLICT DO UPDATE to refresh route metadata.
  • For honeygridCells, it uses a specific ON CONFLICT DO UPDATE strategy. Count-based fields (households, sfdu, mfdu) are merged additively. Value-based fields (income, home_value) are recalculated as a weighted average based on the total households, which is a simpler merge strategy than the full recalculation done inside the DO.

Observability with Workers Analytics Engine

Simplified Direct-Write Implementation

To avoid complexity while gaining observability, implement direct writes to Workers Analytics Engine (WAE) without buffering. This approach leverages WAE’s built-in sampling capabilities for high-throughput scenarios.

Core Implementation

interface WAEMetricsCollector {
  recordRouteProcessed(routeId: string, processingTime: number, cellsGenerated: number): void
  recordError(routeId: string, errorType: string, retryCount: number): void
  recordProcessingStage(routeId: string, stage: string, duration: number): void
  recordFileProcessed(fileName: string, routeCount: number, totalTime: number): void
}

class WAEDirectMetricsCollector implements WAEMetricsCollector {
  constructor(private env: Env) {}

  recordRouteProcessed(routeId: string, processingTime: number, cellsGenerated: number): void {
    // Direct write to WAE - leverages automatic sampling for high volume
    this.env.ANALYTICS_ENGINE.writeDataPoint({
      doubles: [processingTime, cellsGenerated, 1],
      blobs: ['route_processed', routeId, 'boundary_processing'],
      indexes: [routeId],
    })
  }

  recordError(routeId: string, errorType: string, retryCount: number): void {
    this.env.ANALYTICS_ENGINE.writeDataPoint({
      doubles: [1, retryCount],
      blobs: ['error', errorType, routeId],
      indexes: [routeId, errorType],
    })
  }

  recordProcessingStage(routeId: string, stage: string, duration: number): void {
    this.env.ANALYTICS_ENGINE.writeDataPoint({
      doubles: [duration],
      blobs: ['stage_duration', stage, routeId],
      indexes: [routeId, stage],
    })
  }

  recordFileProcessed(fileName: string, routeCount: number, totalTime: number): void {
    this.env.ANALYTICS_ENGINE.writeDataPoint({
      doubles: [routeCount, totalTime],
      blobs: ['file_processed', fileName],
      indexes: [fileName],
    })
  }
}

Integration with Existing Processors

// Enhanced BoundaryFileProcessor with WAE metrics
class WAEMonitorableBoundaryFileProcessor extends BoundaryFileProcessor {
  private metrics: WAEMetricsCollector

  constructor(ctx: DurableObjectState, env: Env) {
    super(ctx, env)
    this.metrics = new WAEDirectMetricsCollector(env)
  }

  protected async processSingleRes8Cell(
    cellId: string,
    routeFeature: Feature<Polygon | MultiPolygon>,
    routeId: string,
    routeTotalArea: number
  ): Promise<ProcessedCell | null> {
    const startTime = Date.now()

    try {
      const result = await super.processSingleRes8Cell(
        cellId,
        routeFeature,
        routeId,
        routeTotalArea
      )

      const processingTime = Date.now() - startTime

      if (result) {
        this.metrics.recordRouteProcessed(routeId, processingTime, 1)
      }

      return result
    } catch (error) {
      const processingTime = Date.now() - startTime
      this.metrics.recordError(routeId, error.name, 0)
      throw error
    }
  }
}

// Enhanced DemographicFileProcessor with WAE metrics
class WAEMonitorableDemographicFileProcessor extends DemographicFileProcessor {
  private metrics: WAEMetricsCollector

  constructor(ctx: DurableObjectState, env: Env) {
    super(ctx, env)
    this.metrics = new WAEDirectMetricsCollector(env)
  }

  protected async processChunk(
    chunk: Array<{ routeId: string; data: DemographicItem }>
  ): Promise<void> {
    const startTime = Date.now()

    try {
      await super.processChunk(chunk)

      const processingTime = Date.now() - startTime
      const routeCount = chunk.length

      // Record file-level metrics
      if (this.data.fileInfo) {
        this.metrics.recordFileProcessed(this.data.fileInfo.fileName, routeCount, processingTime)
      }

      // Record per-route stage metrics
      for (const { routeId } of chunk) {
        this.metrics.recordProcessingStage(
          routeId,
          'demographic_processing',
          processingTime / routeCount
        )
      }
    } catch (error) {
      // Record error metrics for all routes in the chunk
      for (const { routeId } of chunk) {
        this.metrics.recordError(routeId, error.name, 0)
      }
      throw error
    }
  }
}

Key Benefits of Direct-Write Approach

1. Simplified Architecture: No buffering, flushing, or background processes
2. Immediate Visibility: Metrics appear in WAE instantly
3. WAE-Optimized: Leverages WAE’s built-in sampling for high-throughput scenarios
4. Minimal Performance Impact: Direct writes are very fast
5. No Memory Overhead: No metric buffering in memory

WAE Configuration

wrangler.jsonc

{
  "analytics_engine_datasets": [
    {
      "binding": "ANALYTICS_ENGINE",
      "dataset": "honeygrid_processing",
    },
  ],
}

Example Queries

-- Real-time processing status
SELECT
  blob2 as route_id,
  SUM(double1) * _sample_interval as total_processing_time,
  SUM(double2) * _sample_interval as total_cells_generated
FROM analytics_engine_dataset
WHERE blob1 = 'route_processed'
  AND timestamp > NOW() - INTERVAL '5' MINUTE
GROUP BY blob2

-- Error rate by route
SELECT
  blob3 as route_id,
  SUM(double1) * _sample_interval as error_count,
  blob2 as error_type
FROM analytics_engine_dataset
WHERE blob1 = 'error'
  AND timestamp > NOW() - INTERVAL '1' HOUR
GROUP BY blob3, blob2
ORDER BY error_count DESC

-- Processing stage performance
SELECT
  blob2 as stage,
  QUANTILEWEIGHTED(double1, 0.95) as p95_duration,
  AVG(double1) as avg_duration
FROM analytics_engine_dataset
WHERE blob1 = 'stage_duration'
  AND timestamp > NOW() - INTERVAL '24' HOUR
GROUP BY blob2

Cost Estimate

• Data Volume: ~25k data points per processing job × 100 jobs/month = 2.5M data points
• Storage: ~50GB for 3-month retention
• Monthly Cost: ~$1.75 ($0.50 ingestion + $1.25 storage)

Limitations & Considerations

• 25 Data Points per Worker Invocation: May need selective metric recording for high-frequency operations
• Network Latency: Each write involves a network call
• Mitigation: WAE’s automatic sampling handles high-volume scenarios gracefully

This direct-write approach provides immediate observability with minimal complexity while leveraging WAE’s powerful sampling and querying capabilities.

Implementation Status

✅ Workers Analytics Engine integration has been implemented with the following components:

1. WAE Metrics Collector (src/api/utils/wae-metrics-collector.ts)

• WAEDirectMetricsCollector: Direct-write implementation without buffering
• NoOpMetricsCollector: For testing or when WAE is disabled
• Comprehensive interface supporting route, error, stage, and file-level metrics

2. BoundaryFileProcessor Integration

• Records route setup time
• Tracks route completion metrics
• Captures error events with context
• Monitors processing stages

3. DemographicFileProcessor Integration

• Tracks demographic setup and allocation times
• Records export performance metrics
• Captures processing errors with retry counts
• Monitors file-level processing times

4. Configuration (wrangler.jsonc)

• WAE dataset binding configured
• Dataset name: honeygrid_processing

Sample Queries

Real-time Route Processing Status

SELECT
  blob2 as route_id,
  SUM(double1) * _sample_interval as total_processing_time,
  SUM(double2) * _sample_interval as total_cells_generated
FROM analytics_engine_dataset
WHERE blob1 = 'route_processed'
  AND timestamp > NOW() - INTERVAL '5' MINUTE
GROUP BY blob2
ORDER BY total_processing_time DESC

Error Analysis by Route

SELECT
  blob3 as route_id,
  SUM(double1) * _sample_interval as error_count,
  blob2 as error_type,
  AVG(double2) as avg_retry_count
FROM analytics_engine_dataset
WHERE blob1 = 'error'
  AND timestamp > NOW() - INTERVAL '1' HOUR
GROUP BY blob3, blob2
ORDER BY error_count DESC

Processing Stage Performance

SELECT
  blob2 as stage,
  QUANTILEWEIGHTED(double1, 0.95) as p95_duration,
  AVG(double1) as avg_duration,
  COUNT(*) * _sample_interval as sample_count
FROM analytics_engine_dataset
WHERE blob1 = 'stage_duration'
  AND timestamp > NOW() - INTERVAL '24' HOUR
GROUP BY blob2
ORDER BY p95_duration DESC

Benefits Achieved

✅ Immediate Observability

• Metrics appear in WAE instantly after processing
• No buffering delays or periodic flush cycles
• Real-time visibility into processing performance

✅ Minimal Performance Impact

• Direct WAE writes are very fast (< 1ms per metric)
• No additional memory usage for buffering
• No background processes or timers

✅ WAE-Optimized

• Leverages automatic sampling for high-volume scenarios
• Uses efficient data structure (doubles, blobs, indexes)
• Compatible with WAE’s 3-month retention and SQL querying

✅ Comprehensive Coverage

• Route-level processing metrics
• Stage-by-stage performance tracking
• Error tracking with context and retry counts
• File-level processing summaries
• Export performance monitoring

Cost Estimate

• Data Volume: ~25k data points per processing job × 100 jobs/month = 2.5M data points
• Storage: ~50GB for 3-month retention
• Monthly Cost: ~$1.75 ($0.50 ingestion + $1.25 storage)

This implementation provides enterprise-grade observability for the HoneyGrid processing system while maintaining the performance characteristics required for high-throughput data processing.