feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
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/**
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* Phase B analytics service. Reads pre-computed snapshots from
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feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
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* `analytics_snapshots`; recomputes on demand if older than `SNAPSHOT_TTL_MS`.
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* The recurring `analytics-refresh` BullMQ job (PR3) warms the table
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* every 15 minutes per port × per metric.
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feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
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*/
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feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
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import { and, eq, gte, isNull, sql } from 'drizzle-orm';
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feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
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import { db } from '@/lib/db';
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import { analyticsSnapshots } from '@/lib/db/schema/insights';
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feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
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import { interests } from '@/lib/db/schema/interests';
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import { invoices } from '@/lib/db/schema/financial';
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import { berthReservations } from '@/lib/db/schema/reservations';
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refactor(sales): consolidate pipeline stages + wire EOI auto-advance
The 8→9 stage refresh from earlier today only updated constants.ts and the DB —
20 component/service files still hardcoded the old enum, leaving labels blank,
filter dropdowns wrong, kanban columns mismatched, and the analytics funnel
silently dropping new-stage rows. The platform also never advanced
pipelineStage on EOI lifecycle events: documents.service.ts wrote eoiStatus
but left the user-visible stage stuck.
This commit closes both gaps:
1. Single source of truth in src/lib/constants.ts — adds STAGE_LABELS,
STAGE_BADGE, STAGE_DOT, STAGE_WEIGHTS, STAGE_TRANSITIONS plus
stageLabel / stageBadgeClass / stageDotClass / safeStage /
canTransitionStage helpers. components/clients/pipeline-constants.ts
becomes a re-export shim so existing imports keep working.
2. 18 stale-enum surfaces migrated — interest list (table, card, filters,
form, stage picker), pipeline board, client card, berth interests tab,
portal client interests page, dashboard pipeline / funnel / revenue-
forecast charts, settings pipeline_weights default, dashboard.service
weights, analytics.service funnel stages, alert-rules stale-interest
filter, interest-scoring stage rank.
3. Documents tab wired into interest detail — replaced the placeholder in
interest-tabs.tsx with InterestDocumentsTab + InterestFilesTab so the
EOI launcher is back where salespeople work.
4. Auto-advance — new advanceStageIfBehind() in interests.service.ts
(forward-only, no-op if interest is already past the target). Called
from documents.service.ts on send (→ eoi_sent), Documenso completed
webhook (→ eoi_signed), and manual signed-EOI upload (→ eoi_signed).
5. Transition guard — canTransitionStage() blocks egregious skips
(e.g. completed → open, open → contract_signed). Enforced in
changeInterestStage before the DB write.
Tests updated to reflect the 9-stage model. tsc clean, vitest 832/832,
ESLint clean on every file touched.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-01 23:33:53 +02:00
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import { PIPELINE_STAGES } from '@/lib/constants';
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feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
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export type DateRange = '7d' | '30d' | '90d' | 'today';
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feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
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export type MetricBase =
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| 'pipeline_funnel'
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| 'occupancy_timeline'
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| 'revenue_breakdown'
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| 'lead_source_attribution';
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export type MetricId = `${MetricBase}.${DateRange}`;
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export const ALL_RANGES: readonly DateRange[] = ['today', '7d', '30d', '90d'] as const;
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export const ALL_METRICS: readonly MetricBase[] = [
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'pipeline_funnel',
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'occupancy_timeline',
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'revenue_breakdown',
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'lead_source_attribution',
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] as const;
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feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
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export const SNAPSHOT_TTL_MS = 15 * 60 * 1000; // 15 minutes
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feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
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// ─── Output shapes ────────────────────────────────────────────────────────────
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feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
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|
|
export interface PipelineFunnelData {
|
|
|
|
|
|
stages: Array<{ stage: string; count: number; conversionPct: number }>;
|
2026-05-02 00:01:33 +02:00
|
|
|
|
/** Counts of terminal lost/cancelled outcomes in the range. Surfaces below
|
|
|
|
|
|
* the funnel so users can see leakage without it polluting the conversion
|
|
|
|
|
|
* math. Total = sum of these counts. */
|
|
|
|
|
|
lost: { count: number; byOutcome: Record<string, number> };
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export interface OccupancyTimelineData {
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
points: Array<{ date: string; occupied: number; total: number; occupancyPct: number }>;
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export interface RevenueBreakdownData {
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
bars: Array<{ status: string; amount: number; currency: string }>;
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export interface LeadSourceAttributionData {
|
|
|
|
|
|
slices: Array<{ source: string; count: number }>;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export type SnapshotData =
|
|
|
|
|
|
| PipelineFunnelData
|
|
|
|
|
|
| OccupancyTimelineData
|
|
|
|
|
|
| RevenueBreakdownData
|
|
|
|
|
|
| LeadSourceAttributionData;
|
|
|
|
|
|
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
// ─── Cache layer ──────────────────────────────────────────────────────────────
|
|
|
|
|
|
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
export async function readSnapshot<T extends SnapshotData>(
|
|
|
|
|
|
portId: string,
|
|
|
|
|
|
metricId: MetricId,
|
|
|
|
|
|
): Promise<T | null> {
|
|
|
|
|
|
const row = await db.query.analyticsSnapshots.findFirst({
|
|
|
|
|
|
where: and(eq(analyticsSnapshots.portId, portId), eq(analyticsSnapshots.metricId, metricId)),
|
|
|
|
|
|
});
|
|
|
|
|
|
if (!row) return null;
|
|
|
|
|
|
const age = Date.now() - row.computedAt.getTime();
|
|
|
|
|
|
if (age > SNAPSHOT_TTL_MS) return null;
|
|
|
|
|
|
return row.data as T;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export async function writeSnapshot(
|
|
|
|
|
|
portId: string,
|
|
|
|
|
|
metricId: MetricId,
|
|
|
|
|
|
data: SnapshotData,
|
|
|
|
|
|
): Promise<void> {
|
|
|
|
|
|
await db
|
|
|
|
|
|
.insert(analyticsSnapshots)
|
|
|
|
|
|
.values({ portId, metricId, data })
|
|
|
|
|
|
.onConflictDoUpdate({
|
|
|
|
|
|
target: [analyticsSnapshots.portId, analyticsSnapshots.metricId],
|
|
|
|
|
|
set: { data, computedAt: new Date() },
|
|
|
|
|
|
});
|
|
|
|
|
|
}
|
|
|
|
|
|
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
// ─── Range helpers ────────────────────────────────────────────────────────────
|
|
|
|
|
|
|
|
|
|
|
|
function rangeToCutoff(range: DateRange): Date {
|
|
|
|
|
|
const now = Date.now();
|
|
|
|
|
|
switch (range) {
|
|
|
|
|
|
case 'today':
|
|
|
|
|
|
return new Date(now - 1 * 86_400_000);
|
|
|
|
|
|
case '7d':
|
|
|
|
|
|
return new Date(now - 7 * 86_400_000);
|
|
|
|
|
|
case '30d':
|
|
|
|
|
|
return new Date(now - 30 * 86_400_000);
|
|
|
|
|
|
case '90d':
|
|
|
|
|
|
return new Date(now - 90 * 86_400_000);
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
function rangeToDays(range: DateRange): number {
|
|
|
|
|
|
switch (range) {
|
|
|
|
|
|
case 'today':
|
|
|
|
|
|
return 1;
|
|
|
|
|
|
case '7d':
|
|
|
|
|
|
return 7;
|
|
|
|
|
|
case '30d':
|
|
|
|
|
|
return 30;
|
|
|
|
|
|
case '90d':
|
|
|
|
|
|
return 90;
|
|
|
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// ─── Computations ─────────────────────────────────────────────────────────────
|
|
|
|
|
|
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
export async function computePipelineFunnel(
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
): Promise<PipelineFunnelData> {
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
const cutoff = rangeToCutoff(range);
|
2026-05-02 00:01:33 +02:00
|
|
|
|
|
|
|
|
|
|
// Stage counts EXCLUDE lost/cancelled outcomes — those never become
|
|
|
|
|
|
// conversions, so polluting the funnel with them gives meaningless math.
|
|
|
|
|
|
// Lost is reported separately in the `lost` block.
|
|
|
|
|
|
const stageRows = await db
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
.select({ stage: interests.pipelineStage, count: sql<number>`count(*)::int` })
|
|
|
|
|
|
.from(interests)
|
|
|
|
|
|
.where(
|
|
|
|
|
|
and(
|
|
|
|
|
|
eq(interests.portId, portId),
|
|
|
|
|
|
isNull(interests.archivedAt),
|
|
|
|
|
|
gte(interests.createdAt, cutoff),
|
2026-05-02 00:01:33 +02:00
|
|
|
|
sql`(${interests.outcome} IS NULL OR ${interests.outcome} = 'won')`,
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
),
|
|
|
|
|
|
)
|
|
|
|
|
|
.groupBy(interests.pipelineStage);
|
|
|
|
|
|
|
2026-05-02 00:01:33 +02:00
|
|
|
|
const counts = new Map(stageRows.map((r) => [r.stage, r.count]));
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
const top = counts.get('open') ?? 0;
|
|
|
|
|
|
|
|
|
|
|
|
const stages = PIPELINE_STAGES.map((stage) => {
|
|
|
|
|
|
const count = counts.get(stage) ?? 0;
|
|
|
|
|
|
const conversionPct = top === 0 ? 0 : Math.round((count / top) * 1000) / 10;
|
|
|
|
|
|
return { stage, count, conversionPct };
|
|
|
|
|
|
});
|
|
|
|
|
|
|
2026-05-02 00:01:33 +02:00
|
|
|
|
// Lost / cancelled summary. Same date-range filter as the funnel.
|
|
|
|
|
|
const lostRows = await db
|
|
|
|
|
|
.select({ outcome: interests.outcome, count: sql<number>`count(*)::int` })
|
|
|
|
|
|
.from(interests)
|
|
|
|
|
|
.where(
|
|
|
|
|
|
and(
|
|
|
|
|
|
eq(interests.portId, portId),
|
|
|
|
|
|
isNull(interests.archivedAt),
|
|
|
|
|
|
gte(interests.createdAt, cutoff),
|
|
|
|
|
|
sql`${interests.outcome} IS NOT NULL AND ${interests.outcome} != 'won'`,
|
|
|
|
|
|
),
|
|
|
|
|
|
)
|
|
|
|
|
|
.groupBy(interests.outcome);
|
|
|
|
|
|
|
|
|
|
|
|
const byOutcome: Record<string, number> = {};
|
|
|
|
|
|
let lostTotal = 0;
|
|
|
|
|
|
for (const row of lostRows) {
|
|
|
|
|
|
if (!row.outcome) continue;
|
|
|
|
|
|
byOutcome[row.outcome] = row.count;
|
|
|
|
|
|
lostTotal += row.count;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
return { stages, lost: { count: lostTotal, byOutcome } };
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export async function computeOccupancyTimeline(
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
): Promise<OccupancyTimelineData> {
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
const days = rangeToDays(range);
|
|
|
|
|
|
// Total berths per port (current count — assumes no churn).
|
|
|
|
|
|
const totalRow = await db.execute<{ total: number }>(
|
|
|
|
|
|
sql`SELECT count(*)::int AS total FROM berths WHERE port_id = ${portId}`,
|
|
|
|
|
|
);
|
|
|
|
|
|
const total = totalRow[0]?.total ?? 0;
|
|
|
|
|
|
|
|
|
|
|
|
// For each day in range, count berths that have an active reservation
|
|
|
|
|
|
// covering that day. A reservation is "covering" if start_date <= day
|
|
|
|
|
|
// AND (end_date IS NULL OR end_date >= day).
|
|
|
|
|
|
const points: OccupancyTimelineData['points'] = [];
|
|
|
|
|
|
for (let i = days - 1; i >= 0; i--) {
|
|
|
|
|
|
const day = new Date(Date.now() - i * 86_400_000);
|
|
|
|
|
|
const dayStr = day.toISOString().slice(0, 10);
|
|
|
|
|
|
const occRow = await db
|
|
|
|
|
|
.select({ occupied: sql<number>`count(distinct ${berthReservations.berthId})::int` })
|
|
|
|
|
|
.from(berthReservations)
|
|
|
|
|
|
.where(
|
|
|
|
|
|
and(
|
|
|
|
|
|
eq(berthReservations.portId, portId),
|
|
|
|
|
|
eq(berthReservations.status, 'active'),
|
|
|
|
|
|
sql`${berthReservations.startDate} <= ${dayStr}::date`,
|
|
|
|
|
|
sql`(${berthReservations.endDate} IS NULL OR ${berthReservations.endDate} >= ${dayStr}::date)`,
|
|
|
|
|
|
),
|
|
|
|
|
|
);
|
|
|
|
|
|
const occupied = occRow[0]?.occupied ?? 0;
|
|
|
|
|
|
const occupancyPct = total === 0 ? 0 : Math.round((occupied / total) * 1000) / 10;
|
|
|
|
|
|
points.push({ date: dayStr, occupied, total, occupancyPct });
|
|
|
|
|
|
}
|
|
|
|
|
|
return { points };
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export async function computeRevenueBreakdown(
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
): Promise<RevenueBreakdownData> {
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
const cutoff = rangeToCutoff(range);
|
|
|
|
|
|
const rows = await db
|
|
|
|
|
|
.select({
|
|
|
|
|
|
status: invoices.status,
|
|
|
|
|
|
currency: invoices.currency,
|
|
|
|
|
|
amount: sql<string>`coalesce(sum(${invoices.total}), 0)::text`,
|
|
|
|
|
|
})
|
|
|
|
|
|
.from(invoices)
|
|
|
|
|
|
.where(
|
|
|
|
|
|
and(
|
|
|
|
|
|
eq(invoices.portId, portId),
|
|
|
|
|
|
isNull(invoices.archivedAt),
|
|
|
|
|
|
gte(invoices.createdAt, cutoff),
|
|
|
|
|
|
),
|
|
|
|
|
|
)
|
|
|
|
|
|
.groupBy(invoices.status, invoices.currency);
|
|
|
|
|
|
|
|
|
|
|
|
return {
|
|
|
|
|
|
bars: rows.map((r) => ({
|
|
|
|
|
|
status: r.status,
|
|
|
|
|
|
currency: r.currency,
|
|
|
|
|
|
amount: Number(r.amount),
|
|
|
|
|
|
})),
|
|
|
|
|
|
};
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export async function computeLeadSourceAttribution(
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
|
|
|
|
): Promise<LeadSourceAttributionData> {
|
feat(analytics): real computations + 15-min snapshot refresh job
PR3 of Phase B. Replaces the no-op stubs in analytics.service.ts with
working drizzle queries and adds the recurring BullMQ job that warms
the cache.
Computations:
- computePipelineFunnel: groups interests by pipeline_stage filtered by
port + range + not archived; emits 8-row stages array with conversion
pct relative to 'open' as the funnel top.
- computeOccupancyTimeline: per day in range, counts berths covered by
an active reservation (start_date ≤ day, end_date IS NULL OR ≥ day);
emits {date, occupied, total, occupancyPct}.
- computeRevenueBreakdown: sums invoices.total grouped by status +
currency; filters out archived rows.
- computeLeadSourceAttribution: counts interests by source descending;
null source bucketed as 'unspecified'.
Public API (getPipelineFunnel, getOccupancyTimeline, etc.) reads
analytics_snapshots first; falls back to compute + writeSnapshot. TTL
15 minutes (matches the cron interval).
Cron:
- queue/scheduler.ts registers 'analytics-refresh' on maintenance with
pattern '*/15 * * * *'.
- queue/workers/maintenance.ts dispatches to refreshSnapshotsForPort
for every port; per-port try/catch so one bad port doesn't kill the
sweep.
Tests: tests/integration/analytics-service.test.ts (9 cases). Pipeline
funnel math (incl. zero state), occupancy timeline shape/percentages
with seeded reservations, revenue grouped by status + currency, lead
source attribution incl. null bucketing, cache hit (mutate snapshot
directly → next read returns mutated value), refreshSnapshotsForPort
warms every metric×range combo.
Vitest 690/690 (+9). tsc + lint clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:54:46 +02:00
|
|
|
|
const cutoff = rangeToCutoff(range);
|
|
|
|
|
|
const rows = await db
|
|
|
|
|
|
.select({ source: interests.source, count: sql<number>`count(*)::int` })
|
|
|
|
|
|
.from(interests)
|
|
|
|
|
|
.where(
|
|
|
|
|
|
and(
|
|
|
|
|
|
eq(interests.portId, portId),
|
|
|
|
|
|
isNull(interests.archivedAt),
|
|
|
|
|
|
gte(interests.createdAt, cutoff),
|
|
|
|
|
|
),
|
|
|
|
|
|
)
|
|
|
|
|
|
.groupBy(interests.source);
|
|
|
|
|
|
|
|
|
|
|
|
return {
|
|
|
|
|
|
slices: rows
|
|
|
|
|
|
.map((r) => ({
|
|
|
|
|
|
source: r.source ?? 'unspecified',
|
|
|
|
|
|
count: r.count,
|
|
|
|
|
|
}))
|
|
|
|
|
|
.sort((a, b) => b.count - a.count),
|
|
|
|
|
|
};
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// ─── Public read API (cache → compute → write back) ──────────────────────────
|
|
|
|
|
|
|
|
|
|
|
|
export async function getPipelineFunnel(
|
|
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
|
|
|
|
|
): Promise<PipelineFunnelData> {
|
|
|
|
|
|
const metricId = `pipeline_funnel.${range}` as const;
|
|
|
|
|
|
const cached = await readSnapshot<PipelineFunnelData>(portId, metricId);
|
|
|
|
|
|
if (cached) return cached;
|
|
|
|
|
|
const fresh = await computePipelineFunnel(portId, range);
|
|
|
|
|
|
await writeSnapshot(portId, metricId, fresh);
|
|
|
|
|
|
return fresh;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export async function getOccupancyTimeline(
|
|
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
|
|
|
|
|
): Promise<OccupancyTimelineData> {
|
|
|
|
|
|
const metricId = `occupancy_timeline.${range}` as const;
|
|
|
|
|
|
const cached = await readSnapshot<OccupancyTimelineData>(portId, metricId);
|
|
|
|
|
|
if (cached) return cached;
|
|
|
|
|
|
const fresh = await computeOccupancyTimeline(portId, range);
|
|
|
|
|
|
await writeSnapshot(portId, metricId, fresh);
|
|
|
|
|
|
return fresh;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export async function getRevenueBreakdown(
|
|
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
|
|
|
|
|
): Promise<RevenueBreakdownData> {
|
|
|
|
|
|
const metricId = `revenue_breakdown.${range}` as const;
|
|
|
|
|
|
const cached = await readSnapshot<RevenueBreakdownData>(portId, metricId);
|
|
|
|
|
|
if (cached) return cached;
|
|
|
|
|
|
const fresh = await computeRevenueBreakdown(portId, range);
|
|
|
|
|
|
await writeSnapshot(portId, metricId, fresh);
|
|
|
|
|
|
return fresh;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
export async function getLeadSourceAttribution(
|
|
|
|
|
|
portId: string,
|
|
|
|
|
|
range: DateRange,
|
|
|
|
|
|
): Promise<LeadSourceAttributionData> {
|
|
|
|
|
|
const metricId = `lead_source_attribution.${range}` as const;
|
|
|
|
|
|
const cached = await readSnapshot<LeadSourceAttributionData>(portId, metricId);
|
|
|
|
|
|
if (cached) return cached;
|
|
|
|
|
|
const fresh = await computeLeadSourceAttribution(portId, range);
|
|
|
|
|
|
await writeSnapshot(portId, metricId, fresh);
|
|
|
|
|
|
return fresh;
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// ─── Cron entrypoint: warm every (port × metric × range) ────────────────────
|
|
|
|
|
|
|
|
|
|
|
|
export async function refreshSnapshotsForPort(portId: string): Promise<void> {
|
|
|
|
|
|
for (const range of ALL_RANGES) {
|
|
|
|
|
|
const [funnel, occupancy, revenue, leadSource] = await Promise.all([
|
|
|
|
|
|
computePipelineFunnel(portId, range),
|
|
|
|
|
|
computeOccupancyTimeline(portId, range),
|
|
|
|
|
|
computeRevenueBreakdown(portId, range),
|
|
|
|
|
|
computeLeadSourceAttribution(portId, range),
|
|
|
|
|
|
]);
|
|
|
|
|
|
await Promise.all([
|
|
|
|
|
|
writeSnapshot(portId, `pipeline_funnel.${range}`, funnel),
|
|
|
|
|
|
writeSnapshot(portId, `occupancy_timeline.${range}`, occupancy),
|
|
|
|
|
|
writeSnapshot(portId, `revenue_breakdown.${range}`, revenue),
|
|
|
|
|
|
writeSnapshot(portId, `lead_source_attribution.${range}`, leadSource),
|
|
|
|
|
|
]);
|
|
|
|
|
|
}
|
feat(insights): Phase B schema + service skeletons
PR1 of Phase B per docs/superpowers/specs/2026-04-28-phase-b-insights-alerts-design.md.
Lays the foundation that PRs 2-10 will fill in with behaviour.
Schema (migration 0014):
- alerts table with rule-engine fields (rule_id, severity, link,
entity_type/id, fingerprint, fired/dismissed/acknowledged/resolved
timestamps, jsonb metadata). Partial-unique fingerprint index keeps
one open row per (port, rule, entity); separate indexes power
severity-filtered and time-ordered queries.
- analytics_snapshots (port_id, metric_id) -> jsonb cache + computedAt
for the 15-min recurring refresh.
- expenses: duplicate_of self-FK, dedup_scanned_at, ocr_status/raw/
confidence; partial index on (port, vendor, amount, date) where
duplicate_of IS NULL drives the dedup heuristic.
- audit_logs.search_text: GENERATED ALWAYS tsvector over
action+entity_type+entity_id+user_id, GIN-indexed (drizzle can't
model GENERATED ALWAYS in TS yet, so the migration appends manual
ALTER + the GIN index).
Service skeletons in src/lib/services/:
- alerts.service.ts: fingerprintFor, reconcileAlertsForPort (upsert +
auto-resolve), dismiss, acknowledge, listAlertsForPort.
- alert-rules.ts: RULE_REGISTRY of 10 rule evaluators (currently no-op);
PR2 fills in the bodies.
- analytics.service.ts: readSnapshot/writeSnapshot with 15-min TTL +
no-op compute* stubs for the four chart series; PR3 fills behavior.
- expense-dedup.service.ts: scanForDuplicates + markBestDuplicate
using the partial dedup index. PR8 wires the BullMQ trigger.
- expense-ocr.service.ts: OcrResult/OcrLineItem types + ocrReceipt
stub. PR9 wires Claude Vision (Haiku 4.5 + ephemeral system-prompt
cache).
- audit-search.service.ts: tsvector @@ plainto_tsquery + cursor
pagination on (createdAt, id). PR10 wires the admin UI.
tsc clean, lint clean, vitest 675/675 (one unrelated AES random-output
flake passes solo).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-28 14:43:01 +02:00
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