202 lines
9.2 KiB
Markdown
202 lines
9.2 KiB
Markdown
# Data Foundation Capability Spec
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- Status: Draft — not approved for implementation
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- Capability: Data foundation
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- Last updated: 2026-08-20
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## Objective
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Repeatably ingest the supplied payment-attempt archive into PostgreSQL, preserve provenance and data quality evidence, model business entities separately, and provide a trustworthy base for authentication and deterministic analytics.
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## In scope
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- Streamed `.csv.gz` ingestion from a local, Git-ignored input path
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- Import runs, checksums, schema version, row counts, rejection/error reporting
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- Normalized merchant, category, terminal, payment-session, payment-attempt, and account/profile concepts
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- Versioned Gregorian/Jalali calendar dates and curated Iranian event/holiday periods
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- Rebuildable daily base summaries and metric-specific period projections for approved analytics
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- Synthetic merchant display profiles and one demo account per merchant
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- Idempotent re-runs and explicit import lifecycle
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- Database constraints/indexes driven by documented access paths
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- Development seed/reporting workflow and migration/rollback contract
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## Out of scope
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- Dashboard UI
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- Merchant-facing API endpoints
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- AI recommendations
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- Production backup mechanics (owned by platform operations)
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- Treating the challenge archive or local database as version-controlled source
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## Proposed logical model (technology-neutral)
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```mermaid
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erDiagram
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MERCHANT_CATEGORY ||--o{ MERCHANT : classifies
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MERCHANT ||--o{ TERMINAL : owns
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MERCHANT ||--o{ MERCHANT_USER : authenticates_as
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MERCHANT ||--o{ PAYMENT_SESSION : receives
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TERMINAL ||--o{ PAYMENT_SESSION : handles
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PAYMENT_SESSION ||--|{ PAYMENT_ATTEMPT : contains
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CALENDAR_DATE ||--o{ CALENDAR_DATE_EVENT : includes
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CALENDAR_EVENT ||--o{ CALENDAR_DATE_EVENT : occurs_on
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CALENDAR_DATE ||--o{ MERCHANT_DAY_SUMMARY : groups
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MERCHANT ||--o{ MERCHANT_DAY_SUMMARY : summarizes
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IMPORT_RUN ||--o{ PAYMENT_SESSION : introduced_by
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IMPORT_RUN ||--o{ PAYMENT_ATTEMPT : introduced_by
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IMPORT_RUN ||--o{ IMPORT_ISSUE : reports
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MERCHANT_CATEGORY {
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bigint id PK
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text source_category_key UK
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text title_fa
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}
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MERCHANT {
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bigint id PK
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text source_merchant_key UK
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bigint category_id FK
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text display_name
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boolean profile_is_synthetic
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}
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TERMINAL {
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bigint id PK
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text source_terminal_key UK
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bigint merchant_id FK
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}
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MERCHANT_USER {
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bigint id PK
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bigint merchant_id FK
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text username UK
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text password_hash
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boolean is_demo
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timestamptz disabled_at
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}
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PAYMENT_SESSION {
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bigint id PK
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text source_session_key UK
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bigint merchant_id FK
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bigint terminal_id FK
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bigint amount
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text session_status
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timestamptz created_at
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bigint import_run_id FK
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}
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PAYMENT_ATTEMPT {
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bigint id PK
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bigint payment_session_id FK
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int try_seq
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text try_status
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text switch_response_code
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text psp_code
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text issuer_bank_code
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text payer_card_key
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text verify_type
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bigint adjusted_fee
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int init_time_ms
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int verify_time_ms
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timestamptz try_created_at
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timestamptz verified_at
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timestamptz settled_at
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int expire_in
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}
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IMPORT_RUN {
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bigint id PK
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text source_sha256
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text source_schema_version
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text importer_revision
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text status
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bigint observed_rows
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timestamptz started_at
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timestamptz completed_at
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}
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IMPORT_ISSUE {
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bigint id PK
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bigint import_run_id FK
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text severity
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text rule_code
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bigint affected_rows
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jsonb evidence
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}
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CALENDAR_DATE {
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date date PK
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int jalali_year
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int jalali_month
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int jalali_day
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text persian_season
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int iso_weekday
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}
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CALENDAR_EVENT {
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bigint id PK
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text code UK
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text title_fa
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text event_type
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text source_version
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}
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CALENDAR_DATE_EVENT {
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date date FK
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bigint calendar_event_id FK
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}
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MERCHANT_DAY_SUMMARY {
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bigint merchant_id FK
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date local_date FK
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bigint session_count
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bigint attempt_count
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bigint successful_count
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bigint amount_sum
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bigint source_import_run_id FK
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}
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```
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This ERD is a logical baseline, not an approved physical schema. In particular, source key types, card-key retention, analytics projections/materialized views, and whether session-level fields can conflict across retries must be resolved from a profiling report before migrations are approved.
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## Import contract
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1. Accept an explicit path, defaulting locally to `data/incoming/challenge_data.csv.gz`.
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2. Calculate SHA-256 before import and identify an already completed import of the same schema/checksum.
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3. Validate headers and supported schema version before database writes.
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4. Stream/decompress and load into a staging boundary using bounded memory and PostgreSQL bulk-loading facilities.
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5. Validate uniqueness of `(session_key, try_seq)`, referential consistency, parseability, null rates, ranges, timestamp ordering, and merchant/terminal/category relationships.
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6. Promote accepted data transactionally or through a resumable, explicitly versioned workflow; a failed run cannot appear complete.
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7. Emit a machine-readable and human-readable import report. Do not log raw card keys or credentials.
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8. Populate and validate the approved calendar range, then rebuild affected analytics projections idempotently after source promotion. Projection failure must not make an incomplete projection appear fresh.
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## Synthetic merchant/account rules
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- Create exactly one initial demo user per distinct merchant unless the later identity spec explicitly changes cardinality.
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- Generate stable display names from a deterministic seed so re-imports do not rename merchants.
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- Mark every generated profile field as synthetic in storage.
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- Never derive passwords from merchant keys. Generate or seed credentials through an explicit one-off command and display/export them only through an approved secure path.
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- Password storage and session behavior belong to the identity spec; this capability only establishes referential entities and safe seeding hooks.
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## Acceptance criteria
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- A clean database can be migrated and populated from the expected archive without loading the full file into process memory.
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- Re-running the same archive is safe and does not duplicate business rows.
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- Source row count and accepted/rejected counts reconcile exactly.
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- Every attempt maps to one session, terminal, merchant, category, and import run according to validated source relationships.
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- `(source_session_key, try_seq)` is unique.
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- Missing values remain missing rather than being silently replaced with factual-looking defaults.
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- The import report captures checksum, importer revision, timings, counts, validation failures, and quality metrics.
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- Raw data and database state remain ignored by Git; code, schema, rules, docs, and safe templates are tracked.
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- Daily, Jalali-month, Persian-season, and approved calendar-event analytics reconcile to deterministic source queries for additive metrics.
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- Rates are recomputed from their numerator and denominator over the requested period rather than averaged from daily percentages.
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- Non-additive metrics such as unique payer-token counts are never produced by summing daily distinct counts.
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## Approval gates / unresolved decisions
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Resolved: use a capability-oriented modular monolith, with lightweight ports/adapters at external boundaries such as PostgreSQL and AvalAI. See ADR 0001.
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Resolved: use manual constructor injection with explicit wiring at thin `cmd/` composition roots. See ADR 0002.
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Resolved: use Ent for application persistence/modeling and pgx/PostgreSQL `COPY` for streamed bulk-ingestion staging. Migrations are explicit and versioned; production startup never performs automatic schema changes. See ADR 0003.
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Resolved: interpret timezone-naive source timestamps as `Asia/Tehran`, normalize canonical timestamps to UTC, expose ISO-8601 timestamps through APIs, and retain the immutable source/import provenance needed to reproduce the conversion. See ADR 0004.
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Resolved: retain `payer_card_key` unchanged as a restricted, merchant-scoped opaque source token for internal repeat-payer analytics. Do not HMAC it, expose it, log it, export it, or send it to AI providers. Its upstream semantics remain unverified. See ADR 0005.
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Resolved: peer benchmarks exclude the active merchant and require at least 10 other merchants plus 1,000 combined peer sessions in the approved cohort/window. Otherwise the benchmark is suppressed as insufficient data; the merchant's own analytics remain available. See ADR 0006.
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Resolved: use normalized source-of-truth entities plus versioned calendar/event dimensions, rebuildable daily base summaries, and metric-specific projections for non-additive or measured high-cost period analytics. Monthly, Persian-season, and holiday results use explicit calendar definitions. Keep fact tables unpartitioned initially and introduce partitioning only from measured need. See ADR 0007.
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No broad architecture approval gates remain for this capability. Physical columns, indexes, constraints, calendar source, projection shapes, and migration details still require profiling evidence and acceptance in the implementation-ready revision of this spec.
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