71c8102de6
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>
72 lines
4.0 KiB
Markdown
72 lines
4.0 KiB
Markdown
## ADDED Requirements
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### Requirement: Resource dataset cache SHALL execute a single Oracle query and cache the result
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The resource_dataset_cache module SHALL query Oracle once for the full shift-status fact set and cache it for subsequent derivations.
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#### Scenario: Primary query execution and caching
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- **WHEN** `execute_primary_query()` is called with date range, granularity, and resource filter parameters
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- **THEN** a deterministic `query_id` SHALL be computed from all primary params using SHA256
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- **THEN** if a cached DataFrame exists for this query_id (L1 or L2), it SHALL be used without querying Oracle
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- **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
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- **THEN** the result DataFrame SHALL be stored in both L1 (ProcessLevelCache) and L2 (Redis as parquet/base64)
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- **THEN** the response SHALL include `query_id`, summary (KPI, trend, heatmap, comparison), and detail page 1
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#### Scenario: Cache TTL and eviction
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- **WHEN** a DataFrame is cached
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- **THEN** the cache TTL SHALL be 900 seconds (15 minutes)
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- **THEN** L1 cache max_size SHALL be 8 entries with LRU eviction
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- **THEN** the Redis namespace SHALL be `resource_dataset`
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### Requirement: Resource dataset cache SHALL derive KPI summary from cached DataFrame
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The module SHALL compute aggregated KPI metrics from the cached fact set.
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#### Scenario: KPI derivation from cache
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- **WHEN** summary view is derived from cached DataFrame
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- **THEN** total hours for PRD, SBY, UDT, SDT, EGT, NST SHALL be summed
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- **THEN** OU% and AVAIL% SHALL be computed from the hour totals
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- **THEN** machine count SHALL be the distinct count of HISTORYID in the cached data
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### Requirement: Resource dataset cache SHALL derive trend data from cached DataFrame
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The module SHALL compute time-series aggregations from the cached fact set.
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#### Scenario: Trend derivation
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- **WHEN** summary view is derived with a given granularity (day/week/month/year)
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- **THEN** the cached DataFrame SHALL be grouped by the granularity period
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- **THEN** each period SHALL include PRD, SBY, UDT, SDT, EGT, NST hours and computed OU%, AVAIL%
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### Requirement: Resource dataset cache SHALL derive heatmap from cached DataFrame
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The module SHALL compute workcenter x date OU% matrix from the cached fact set.
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#### Scenario: Heatmap derivation
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- **WHEN** summary view is derived
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- **THEN** the cached DataFrame SHALL be grouped by (workcenter, date)
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- **THEN** each cell SHALL contain the OU% for that workcenter on that date
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- **THEN** workcenters SHALL be sorted by workcenter_seq
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### Requirement: Resource dataset cache SHALL derive workcenter comparison from cached DataFrame
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The module SHALL compute per-workcenter aggregated metrics from the cached fact set.
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#### Scenario: Comparison derivation
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- **WHEN** summary view is derived
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- **THEN** the cached DataFrame SHALL be grouped by workcenter
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- **THEN** each workcenter SHALL include total hours and computed OU%
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- **THEN** results SHALL be sorted by OU% descending, limited to top 15
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### Requirement: Resource dataset cache SHALL derive paginated detail from cached DataFrame
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The module SHALL provide hierarchical detail records from the cached fact set.
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#### Scenario: Detail derivation and pagination
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- **WHEN** detail view is requested with page and per_page parameters
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- **THEN** the cached DataFrame SHALL be used to compute per-resource metrics
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- **THEN** resource dimension data (WORKCENTERNAME, RESOURCEFAMILYNAME) SHALL be merged from resource_cache
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- **THEN** results SHALL be structured as a hierarchical tree (workcenter -> family -> resource)
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- **THEN** pagination SHALL apply to the flattened list
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### Requirement: Resource dataset cache SHALL handle cache expiry gracefully
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The module SHALL return appropriate signals when cache has expired.
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#### Scenario: Cache expired during view request
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- **WHEN** a view is requested with a query_id whose cache has expired
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- **THEN** the response SHALL return `{ success: false, error: "cache_expired" }`
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- **THEN** the HTTP status SHALL be 410 (Gone)
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