Merge remote-tracking branch 'origin/master'
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@@ -297,12 +297,76 @@ func GetProductSeriesMappingsHandler(pg *sql.DB) http.HandlerFunc {
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dim3ByToken, _ := loadDimTokenIDs(ctx, pg, "dimval3", setToSortedSlice(dim3Set))
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existing, _ := loadProductSeriesAssignments(ctx, pg, codes)
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// Per-request cache for per-mmitem dimval3 inference to avoid repeated dfblob scans.
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inferCache := map[string]int64{}
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inferDim3ForMmitem := func(mmitemID int64, token string) int64 {
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mmitemID = int64(mmitemID)
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tok := strings.ToUpper(normalizeDimParam(token))
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if mmitemID <= 0 || tok == "" {
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return 0
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}
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key := fmt.Sprintf("%d|%s", mmitemID, tok)
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if v, ok := inferCache[key]; ok {
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return v
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}
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// Use the same pattern approach as resolveDimvalFromFileNameToken, but scoped to src_id.
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patterns := buildNameLikePatterns(tok)
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if len(patterns) == 0 {
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inferCache[key] = 0
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return 0
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}
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var dimv string
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err := pg.QueryRowContext(ctx, `
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SELECT x.dimv
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FROM (
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SELECT COALESCE(dimval3::text, '') AS dimv, COUNT(*) AS cnt
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FROM dfblob
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WHERE src_table='mmitem'
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AND typ='img'
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AND src_id=$7
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AND COALESCE(dimval3::text, '') <> ''
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AND (
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UPPER(COALESCE(file_name,'')) LIKE $1 OR
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UPPER(COALESCE(file_name,'')) LIKE $2 OR
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UPPER(COALESCE(file_name,'')) LIKE $3 OR
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UPPER(COALESCE(file_name,'')) LIKE $4 OR
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UPPER(COALESCE(file_name,'')) LIKE $5 OR
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UPPER(COALESCE(file_name,'')) LIKE $6
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)
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GROUP BY COALESCE(dimval3::text, '')
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) x
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ORDER BY x.cnt DESC, x.dimv
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LIMIT 1
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`, patterns[0], patterns[1], patterns[2], patterns[3], patterns[4], patterns[5], mmitemID).Scan(&dimv)
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if err != nil {
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inferCache[key] = 0
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return 0
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}
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dimv = strings.TrimSpace(dimv)
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if dimv == "" || dimv == "0" {
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inferCache[key] = 0
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return 0
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}
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id, err := strconv.ParseInt(dimv, 10, 64)
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if err != nil || id <= 0 {
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inferCache[key] = 0
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return 0
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}
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inferCache[key] = id
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return id
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}
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out := make([]productSeriesMappingRow, 0, len(grouped))
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for _, row := range grouped {
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row.MmitemID = mmitemByCode[row.ProductCode]
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row.Dim1ID = dim1ByToken[row.ColorCode]
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if row.Dim3Code != "" {
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row.Dim3ID = dim3ByToken[row.Dim3Code]
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// dimval3 tokens can be ambiguous globally; prefer per-mmitem inference.
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if inferred := inferDim3ForMmitem(row.MmitemID, row.Dim3Code); inferred > 0 {
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row.Dim3ID = inferred
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} else {
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row.Dim3ID = dim3ByToken[row.Dim3Code]
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}
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}
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row.MappingReady = row.MmitemID > 0 && row.Dim1ID > 0 && (row.Dim3Code == "" || row.Dim3ID > 0)
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if !row.MappingReady {
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@@ -515,7 +579,45 @@ WHERE dim_column=$1 AND token = ANY($2)
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}
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out[strings.TrimSpace(token)] = id
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}
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return out, rows.Err()
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if err := rows.Err(); err != nil {
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return out, err
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}
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// Best-effort fallback: infer missing token->dim_id from dfblob file_name patterns.
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// NOTE: For dimval3, the same token can map to different dim ids per product in this
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// installation, so we do NOT infer/persist globally here. Per-product inference is
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// handled in the row loop (using mmitem_id) to avoid wrong matches.
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for _, rawTok := range tokens {
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tok := strings.ToUpper(normalizeDimParam(rawTok))
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if tok == "" {
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continue
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}
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if _, ok := out[tok]; ok {
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continue
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}
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if column == "dimval3" {
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// avoid global inference for dimval3
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continue
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}
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v := resolveDimvalFromFileNameToken(pg, column, tok)
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if v == "" {
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continue
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}
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id, err := strconv.ParseInt(v, 10, 64)
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if err != nil || id <= 0 {
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continue
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}
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// Persist for future requests (best-effort).
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_, _ = pg.ExecContext(ctx, `
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INSERT INTO mk_dim_token_map (dim_column, token, dim_id, updated_at)
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VALUES ($1,$2,$3,now())
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ON CONFLICT (dim_column, token)
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DO UPDATE SET dim_id = EXCLUDED.dim_id, updated_at = EXCLUDED.updated_at
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`, column, tok, id)
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out[tok] = id
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}
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return out, nil
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}
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func loadProductSeriesAssignments(ctx context.Context, pg *sql.DB, codes []string) (map[string][]int64, error) {
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