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DashBoard/openspec/specs/resource-dataset-cache/spec.md
egg 71c8102de6 feat: dataset cache for hold/resource history + slow connection migration 
Two changes combined:

1. historical-query-slow-connection: Migrate all historical query pages
   to read_sql_df_slow with semaphore concurrency control (max 3),
   raise DB slow timeout to 300s, gunicorn timeout to 360s, and
   unify frontend timeouts to 360s for all historical pages.

2. hold-resource-history-dataset-cache: Convert hold-history and
   resource-history from multi-query to single-query + dataset cache
   pattern (L1 ProcessLevelCache + L2 Redis parquet/base64, TTL=900s).
   Replace old GET endpoints with POST /query + GET /view two-phase
   API. Frontend auto-retries on 410 cache_expired.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-25 13:15:02 +08:00

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ADDED Requirements

Requirement: Resource dataset cache SHALL execute a single Oracle query and cache the result

The resource_dataset_cache module SHALL query Oracle once for the full shift-status fact set and cache it for subsequent derivations.

Scenario: Primary query execution and caching

  • WHEN execute_primary_query() is called with date range, granularity, and resource filter parameters
  • THEN a deterministic query_id SHALL be computed from all primary params using SHA256
  • THEN if a cached DataFrame exists for this query_id (L1 or L2), it SHALL be used without querying Oracle
  • THEN if no cache exists, a single Oracle query SHALL fetch all shift-status records from DW_MES_RESOURCESTATUS_SHIFT for the filtered resources and date range
  • THEN the result DataFrame SHALL be stored in both L1 (ProcessLevelCache) and L2 (Redis as parquet/base64)
  • THEN the response SHALL include query_id, summary (KPI, trend, heatmap, comparison), and detail page 1

Scenario: Cache TTL and eviction

  • WHEN a DataFrame is cached
  • THEN the cache TTL SHALL be 900 seconds (15 minutes)
  • THEN L1 cache max_size SHALL be 8 entries with LRU eviction
  • THEN the Redis namespace SHALL be resource_dataset

Requirement: Resource dataset cache SHALL derive KPI summary from cached DataFrame

The module SHALL compute aggregated KPI metrics from the cached fact set.

Scenario: KPI derivation from cache

  • WHEN summary view is derived from cached DataFrame
  • THEN total hours for PRD, SBY, UDT, SDT, EGT, NST SHALL be summed
  • THEN OU% and AVAIL% SHALL be computed from the hour totals
  • THEN machine count SHALL be the distinct count of HISTORYID in the cached data

Requirement: Resource dataset cache SHALL derive trend data from cached DataFrame

The module SHALL compute time-series aggregations from the cached fact set.

Scenario: Trend derivation

  • WHEN summary view is derived with a given granularity (day/week/month/year)
  • THEN the cached DataFrame SHALL be grouped by the granularity period
  • THEN each period SHALL include PRD, SBY, UDT, SDT, EGT, NST hours and computed OU%, AVAIL%

Requirement: Resource dataset cache SHALL derive heatmap from cached DataFrame

The module SHALL compute workcenter x date OU% matrix from the cached fact set.

Scenario: Heatmap derivation

  • WHEN summary view is derived
  • THEN the cached DataFrame SHALL be grouped by (workcenter, date)
  • THEN each cell SHALL contain the OU% for that workcenter on that date
  • THEN workcenters SHALL be sorted by workcenter_seq

Requirement: Resource dataset cache SHALL derive workcenter comparison from cached DataFrame

The module SHALL compute per-workcenter aggregated metrics from the cached fact set.

Scenario: Comparison derivation

  • WHEN summary view is derived
  • THEN the cached DataFrame SHALL be grouped by workcenter
  • THEN each workcenter SHALL include total hours and computed OU%
  • THEN results SHALL be sorted by OU% descending, limited to top 15

Requirement: Resource dataset cache SHALL derive paginated detail from cached DataFrame

The module SHALL provide hierarchical detail records from the cached fact set.

Scenario: Detail derivation and pagination

  • WHEN detail view is requested with page and per_page parameters
  • THEN the cached DataFrame SHALL be used to compute per-resource metrics
  • THEN resource dimension data (WORKCENTERNAME, RESOURCEFAMILYNAME) SHALL be merged from resource_cache
  • THEN results SHALL be structured as a hierarchical tree (workcenter -> family -> resource)
  • THEN pagination SHALL apply to the flattened list

Requirement: Resource dataset cache SHALL handle cache expiry gracefully

The module SHALL return appropriate signals when cache has expired.

Scenario: Cache expired during view request

  • WHEN a view is requested with a query_id whose cache has expired
  • THEN the response SHALL return { success: false, error: "cache_expired" }
  • THEN the HTTP status SHALL be 410 (Gone)