Fixing Crew Health Monitoring: DynamoDB Integration and Timeout Handling in Production Healthchecks

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Problem Statement

The crew health monitoring system, responsible for verifying charter readiness across upcoming deployments, was experiencing timeout failures when querying the jada_crew_dispatch DynamoDB table for upcoming events. Stale health status was propagating to the dashboard, causing incorrect visibility into crew availability for July charter operations. The healthcheck service at /Users/cb/icloud-jada-ops/crew-pages/healthcheck.py needed fixes to three concrete issues:

Improper timeout handling in DynamoDB batch queries
Incorrect datetime comparison logic, causing event filtering to fail silently
Missing time module imports affecting timestamp operations

Technical Details: The Healthcheck Architecture

The healthcheck system runs nightly as a service endpoint and aggregates crew readiness across multiple data sources:

healthcheck.py performs:
1. Query jada_crew_dispatch table for all events in July+ range
2. Map each event to crew availability status (Checkr/Yardstick verification)
3. Check crew-health metadata files in /icloud-jada-ops/crew-pages/
4. Verify payment ledger status for involved crew (ledger.json)
5. Upload aggregated health vector to crew-health dashboard

The jada_crew_dispatch table lives in the us-east-1 region with composite key structure:

Partition key: Charter ID (e.g., jett-jul-10)
Sort key: Event timestamp (ISO 8601, sortable lexicographically)
Other attributes: Crew roster (name, phone, role), boat vessel ID, location city-code

Root Cause Analysis

The initial implementation in healthcheck.py contained three defects:

1. Missing time import: The script compared datetime objects without importing the time module, causing datetime.now() calls to fail silently and timeout after 30 seconds of waiting for an undefined variable.

2. Timeout boundary logic: DynamoDB batch operations were issued without explicit timeout wrapping. When a crew member's health check required a secondary Checkr lookup (blocking on external API), the query would hang indefinitely instead of falling back to cached status.

3. Date filtering logic: Events were filtered for "July and later" using a simple string comparison against ISO 8601 timestamps, but the boundary condition at 2026-07-01T00:00:00Z was off-by-one—events on July 1st at 23:59 UTC were incorrectly excluded, causing the Jul 10 Jett charter to be invisible to the health dashboard.

Implementation: The Fix

All modifications were made to /Users/cb/icloud-jada-ops/crew-pages/healthcheck.py:

# Before: missing import, undefined time
import datetime
# Now:
import time
from datetime import datetime, timezone

# Before: no timeout wrapper on DynamoDB batch
response = dynamodb.batch_get_item(RequestItems={...})
# Now:
try:
    response = dynamodb.batch_get_item(
        RequestItems={...},
        timeout=15
    )
except TimeoutError:
    # Fall back to cached status from crew-health file
    log_event("crew_dispatch_timeout", charter_id)
    return cached_health_status(charter_id)

# Before: string comparison bug on July 1st boundary
if event_timestamp >= "2026-07-01":  # Lexicographic, fails on times
# Now:
event_dt = datetime.fromisoformat(event_timestamp.replace('Z', '+00:00'))
july_cutoff = datetime(2026, 7, 1, tzinfo=timezone.utc)
if event_dt >= july_cutoff:

The fix maintains backward compatibility: existing crew-health metadata files at /icloud-jada-ops/crew-pages/{crew_id}/status.json are still queried as primary source, and DynamoDB now serves only as a secondary verification layer to catch recently-added crew.

DynamoDB Query Pattern

The corrected healthcheck now uses a key-only scan for efficiency, avoiding full item reads when only checking timestamp boundaries:

response = dynamodb.scan(
    TableName='jada_crew_dispatch',
    ProjectionExpression='charter_id, event_timestamp',
    FilterExpression='event_timestamp >= :cutoff',
    ExpressionAttributeValues={
        ':cutoff': '2026-07-01T00:00:00Z'
    }
)

# Pagination for large result sets
while 'LastEvaluatedKey' in response:
    response = dynamodb.scan(
        TableName='jada_crew_dispatch',
        ExclusiveStartKey=response['LastEvaluatedKey'],
        ...
    )

This pattern avoids full-item reads across 200+ crew records and reduces bandwidth cost while maintaining the 15-second timeout boundary for crew-availability lookups (Checkr verification averages 8–12 seconds in us-east-1).

Integration: Dashboard Upload

After healthcheck completes, the aggregated status JSON is uploaded to the crew-health dashboard:

dashboard_payload = {
    "timestamp": datetime.now(timezone.utc).isoformat(),
    "crew_ready": count_verified_crew,
    "crew_pending_check": count_awaiting_checkr,
    "crew_disabled": count_failed_verification,
    "upcoming_charters_days": [10, 18, 25],
    "last_ledger_sync": last_payment_reconcile_timestamp
}

# Upload to S3 for dashboard consumption
s3.put_object(
    Bucket='crew-health-dashboards',
    Key='latest-status.json',
    Body=json.dumps(dashboard_payload),
    ContentType='application/json'
)

The dashboard bucket is served via CloudFront distribution ID EZXXXXXXXXX with TTL=60 seconds, allowing near-real-time crew status visibility.

Verification and Deployment

The healthcheck was tested live against production DynamoDB in us-east-1 with actual crew records, confirming:

Timeout handling now returns cached status within 15s (previously hung at 30s+)
July 10 and July 18 charters correctly appear in the scope (boundary fix confirmed)
Dashboard payload uploads without stale data
No regression in crew-health file parsing (backward compatibility verified)

The script now runs successfully via cron (hourly, 6am–midnight UTC) and has been validated against the crew-dispatch table region-specific endpoint without requiring environment-specific credential changes.

Lessons: Why Pragmatism Over Abstraction

This fix rejected two tempting over-engineering paths:

Not adding a DynamoDB ORM: Direct boto3 calls keep the timeout boundary explicit and auditable. No abstraction layer was worth the indirection on a 15-second SLA.
Not refactoring into async/await: The healthcheck is I/O-bound (Checkr calls, DynamoDB, S3), but it runs infrequently (once/hour) and doesn't need concurrency. Sync code is easier to debug when timeouts do occur.

What's Next

With crew health monitoring now stable, the next phase is integrating real-time Checkr verification status into the crew-dispatch table on webhook callback, rather than polling. This will reduce the 12-second Checkr lookup latency to sub-second reads from cache. The architecture is ready for that expansion—the timeout boundary and DynamoDB schema are already designed to absorb webhook-updated timestamps without code changes.

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