mitai-jinkendo/backend/data_layer/activity_session_metrics.py

"""
Activity session metrics (EAV) and resolved attribute schema — Layer 1.

See: .claude/docs/technical/ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE.md
"""
from __future__ import annotations

import logging
from decimal import Decimal
from typing import Any, Dict, List, Mapping, Optional, Sequence

from data_layer.activity_data_canon import (
    ACTIVITY_LOG_LEGACY_COLUMN_FOR_EAV_PRIMARY_PARAM,
    ACTIVITY_MODULE_REGISTRY_FIELD_KEYS,
)

logger = logging.getLogger(__name__)

# Diese Spalten nicht aus CSV-Parameter-Zuordnung überschreiben (kommen aus Typ-Mapping / System).
ACTIVITY_LOG_PATCH_FORBIDDEN = frozenset(
    {
        "id",
        "profile_id",
        "date",
        "created",
        "training_type_id",
        "training_category",
        "training_subcategory",
        "source",
    }
)


class ActivitySessionMetricsError(Exception):
    """Raised by Layer 1; routers map to HTTP (404/400)."""

    def __init__(self, status_code: int, detail: str):
        self.status_code = status_code
        self.detail = detail
        super().__init__(detail)


def _effective_training_category(
    cur, training_category: Optional[str], training_type_id: Optional[int]
) -> Optional[str]:
    if training_category:
        return training_category.strip() or None
    if training_type_id is None:
        return None
    cur.execute("SELECT category FROM training_types WHERE id = %s", (training_type_id,))
    row = cur.fetchone()
    if row and row.get("category"):
        return row["category"]
    return None


def merge_parameter_schema_rows(
    category_rows: Sequence[Dict[str, Any]],
    type_rows: Sequence[Dict[str, Any]],
) -> List[Dict[str, Any]]:
    """
    Pure merge: category assignments + type assignments → sorted schema list.
    Row shapes match SELECTs in resolve_activity_attribute_schema (cat_sort / typ_* aliases).
    """
    merged: Dict[int, Dict[str, Any]] = {}

    for r in category_rows:
        pid = r["training_parameter_id"]
        merged[pid] = {
            "training_parameter_id": pid,
            "key": r["key"],
            "name_de": r["name_de"],
            "name_en": r["name_en"],
            "description_de": r.get("description_de"),
            "description_en": r.get("description_en"),
            "param_category": r["param_category"],
            "data_type": r["data_type"],
            "unit": r["unit"],
            "validation_rules": r["validation_rules"] or {},
            "source_field": r["source_field"],
            "sort_order": r["cat_sort"],
            "required": bool(r["cat_required"]),
            "ui_group": r["cat_ui_group"],
        }

    for r in type_rows:
        pid = r["training_parameter_id"]
        base = merged.get(pid)
        if base is None:
            merged[pid] = {
                "training_parameter_id": pid,
                "key": r["key"],
                "name_de": r["name_de"],
                "name_en": r["name_en"],
                "description_de": r.get("description_de"),
                "description_en": r.get("description_en"),
                "param_category": r["param_category"],
                "data_type": r["data_type"],
                "unit": r["unit"],
                "validation_rules": r["validation_rules"] or {},
                "source_field": r["source_field"],
                "sort_order": r["typ_sort"] if r["typ_sort"] is not None else 0,
                "required": bool(r["typ_required"]) if r["typ_required"] is not None else False,
                "ui_group": r["typ_ui_group"],
            }
        else:
            if r["typ_sort"] is not None:
                base["sort_order"] = r["typ_sort"]
            if r["typ_required"] is not None:
                base["required"] = bool(r["typ_required"])
            if r["typ_ui_group"] is not None:
                base["ui_group"] = r["typ_ui_group"]

    out = list(merged.values())
    out.sort(key=lambda x: (x["sort_order"], x["key"]))
    return out


def resolve_activity_attribute_schema(
    cur,
    training_category: Optional[str],
    training_type_id: Optional[int],
) -> List[Dict[str, Any]]:
    """
    Merged parameter definitions for UI / validation (category base + type overrides/additions).
    Sorted by sort_order, then key.
    """
    cat = _effective_training_category(cur, training_category, training_type_id)
    category_rows: List[Dict[str, Any]] = []
    type_rows: List[Dict[str, Any]] = []

    if cat:
        cur.execute(
            """
            SELECT
                tcp.training_parameter_id,
                tcp.sort_order AS cat_sort,
                tcp.required AS cat_required,
                tcp.ui_group AS cat_ui_group,
                tp.key, tp.name_de, tp.name_en,
                tp.description_de, tp.description_en,
                tp.category AS param_category,
                tp.data_type, tp.unit, tp.validation_rules, tp.source_field
            FROM training_category_parameter tcp
            JOIN training_parameters tp ON tp.id = tcp.training_parameter_id
            WHERE tcp.training_category = %s AND tp.is_active = true
            """,
            (cat,),
        )
        category_rows = list(cur.fetchall())

    if training_type_id is not None:
        cur.execute(
            """
            SELECT
                ttp.training_parameter_id,
                ttp.sort_order AS typ_sort,
                ttp.required AS typ_required,
                ttp.ui_group AS typ_ui_group,
                tp.key, tp.name_de, tp.name_en,
                tp.description_de, tp.description_en,
                tp.category AS param_category,
                tp.data_type, tp.unit, tp.validation_rules, tp.source_field
            FROM training_type_parameter ttp
            JOIN training_parameters tp ON tp.id = ttp.training_parameter_id
            WHERE ttp.training_type_id = %s AND tp.is_active = true
            """,
            (training_type_id,),
        )
        type_rows = list(cur.fetchall())

    return merge_parameter_schema_rows(category_rows, type_rows)


def _metric_human_labels(schema_row: Mapping[str, Any]) -> Dict[str, Any]:
    """Bezeichnung + Kurzbeschreibung aus training_parameters (KI / Export)."""
    return {
        "name_de": schema_row.get("name_de"),
        "name_en": schema_row.get("name_en"),
        "description_de": schema_row.get("description_de"),
        "description_en": schema_row.get("description_en"),
    }


def _validation_rules_dict(raw: Any) -> Dict[str, Any]:
    if isinstance(raw, dict):
        return raw
    return {}


def _validate_single_value(data_type: str, value: Any, rules: Dict[str, Any]) -> None:
    if data_type == "integer":
        if not isinstance(value, int) or isinstance(value, bool):
            raise ActivitySessionMetricsError(400, f"Erwartet integer, erhalten: {type(value).__name__}")
        if "min" in rules and value < rules["min"]:
            raise ActivitySessionMetricsError(400, f"Wert unter min ({rules['min']})")
        if "max" in rules and value > rules["max"]:
            raise ActivitySessionMetricsError(400, f"Wert über max ({rules['max']})")
    elif data_type == "float":
        if isinstance(value, bool) or not isinstance(value, (int, float, Decimal)):
            raise ActivitySessionMetricsError(400, f"Erwartet Zahl, erhalten: {type(value).__name__}")
        v = float(value)
        if "min" in rules and v < float(rules["min"]):
            raise ActivitySessionMetricsError(400, f"Wert unter min ({rules['min']})")
        if "max" in rules and v > float(rules["max"]):
            raise ActivitySessionMetricsError(400, f"Wert über max ({rules['max']})")
    elif data_type == "string":
        if not isinstance(value, str):
            raise ActivitySessionMetricsError(400, f"Erwartet string, erhalten: {type(value).__name__}")
        if rules.get("not_empty") and not value.strip():
            raise ActivitySessionMetricsError(400, "Leerer String nicht erlaubt")
        if "max_length" in rules and len(value) > int(rules["max_length"]):
            raise ActivitySessionMetricsError(400, f"String zu lang (max {rules['max_length']})")
        allowed = rules.get("allowed_values")
        if allowed and value not in allowed:
            raise ActivitySessionMetricsError(400, "Wert nicht in erlaubter Menge")
    elif data_type == "boolean":
        if not isinstance(value, bool):
            raise ActivitySessionMetricsError(400, f"Erwartet boolean, erhalten: {type(value).__name__}")
    else:
        raise ActivitySessionMetricsError(400, f"Unbekannter data_type: {data_type}")


def _row_value_tuple(data_type: str, value: Any) -> tuple:
    if data_type == "integer":
        return (None, int(value), None, None)
    if data_type == "float":
        return (float(value), None, None, None)
    if data_type == "string":
        return (None, None, str(value), None)
    if data_type == "boolean":
        return (None, None, None, bool(value))
    raise ValueError(data_type)


def _coerce_raw_value_for_parameter(data_type: str, raw: Any) -> Any:
    """Wert aus activity_log-Spalte in den Typ bringen, den training_parameters.data_type erwartet."""
    if data_type == "integer":
        if isinstance(raw, bool):
            raise TypeError("boolean nicht als integer erlaubt")
        if isinstance(raw, str):
            s = raw.strip().replace(",", ".")
            return int(round(float(s)))
        return int(round(float(raw)))
    if data_type == "float":
        if isinstance(raw, str):
            s = raw.strip().replace(",", ".")
            return float(s)
        return float(raw)
    if data_type == "string":
        return str(raw) if raw is not None else ""
    if data_type == "boolean":
        if isinstance(raw, bool):
            return raw
        s = str(raw).strip().lower()
        if s in ("true", "1", "t", "yes"):
            return True
        if s in ("false", "0", "f", "no", ""):
            return False
        raise TypeError(f"boolean-Koercion nicht möglich: {raw!r}")
    raise ValueError(data_type)


def upsert_session_metrics_from_csv_mapped(
    cur,
    profile_id: str,
    activity_log_id: str,
    mapped: Mapping[str, Any],
    training_category: Optional[str],
    training_type_id: Optional[int],
) -> None:
    """
    EAV für Trainingsparameter aus CSV.

    Es werden nur Parameter geschrieben, die in ``resolve_activity_attribute_schema`` (Kategorie +
    Trainingstyp) vorkommen. CSV-Spalten-Mappings sind import-spezifisch und definieren **nicht** das
    UI-/Auswertungs-Schema — fehlende tcp/ttp-Zuordnung bedeutet: kein EAV für diesen Key (Werte ggf.
    nur in ``activity_log``-Kernfeldern).

    Kernfelder schreibt der Executor nach ``activity_log``; hier keine EAV-Zeilen für Registry-Keys.

    Hat ein Parameter ``source_field`` (Semantik aus ``activity_log``), wird EAV nur dann **nicht**
    geschrieben, wenn diese Spalte nach dem Import bereits befüllt ist — sonst gäbe es doppelte
    Speicherung und der Merge würde ohnehin die Spalte bevorzugen. Ist die Spalte leer (z. B. Feld
    nur noch über EAV / Custom-Mapping, ohne Registry-Patch), schreibt der Import den Wert aus
    ``mapped`` nach EAV — analog zum Lesepfad (Spalte zuerst, sonst EAV).
    """
    cur.execute("SELECT * FROM activity_log WHERE id = %s", (activity_log_id,))
    row = cur.fetchone()
    if not row or str(row["profile_id"]) != str(profile_id):
        return
    header = dict(row)
    schema = resolve_activity_attribute_schema(cur, training_category, training_type_id)
    for spec in schema:
        pkey = spec["key"]
        if pkey not in mapped:
            continue
        raw = mapped[pkey]
        if raw is None or raw == "":
            continue
        if pkey in ACTIVITY_MODULE_REGISTRY_FIELD_KEYS:
            continue
        sf_raw = spec.get("source_field")
        if sf_raw is not None and str(sf_raw).strip():
            col = str(sf_raw).strip()
            if col in header and header[col] is not None:
                continue
        tid = spec["training_parameter_id"]
        dt = spec["data_type"]
        rules = _validation_rules_dict(spec["validation_rules"])
        try:
            coerced = _coerce_raw_value_for_parameter(dt, raw)
            _validate_single_value(dt, coerced, rules)
        except (ActivitySessionMetricsError, TypeError, ValueError) as ex:
            logger.warning("CSV EAV skipped %s: %s", pkey, ex)
            continue
        vn, vi, vt, vb = _row_value_tuple(dt, coerced)
        cur.execute(
            """
            INSERT INTO activity_session_metrics (
                activity_log_id, training_parameter_id,
                value_num, value_int, value_text, value_bool, updated_at
            ) VALUES (%s, %s, %s, %s, %s, %s, NOW())
            ON CONFLICT (activity_log_id, training_parameter_id)
            DO UPDATE SET
                value_num = EXCLUDED.value_num,
                value_int = EXCLUDED.value_int,
                value_text = EXCLUDED.value_text,
                value_bool = EXCLUDED.value_bool,
                updated_at = NOW()
            """,
            (activity_log_id, tid, vn, vi, vt, vb),
        )


def merge_column_backed_and_eav_metrics(
    header: Mapping[str, Any],
    schema: Sequence[Dict[str, Any]],
    eav_metrics: Sequence[Dict[str, Any]],
) -> List[Dict[str, Any]]:
    """
    Effektive Metrikliste **nur** für Parameter aus ``schema`` (Kategorie + Trainingstyp / tcp+ttp).

    Kanon beim Lesen: **activity_log** schlägt EAV, sobald ein passender Spaltenwert existiert und
    koerzierbar ist — in dieser Reihenfolge:

    1. ``source_field`` → Spalte
    2. Parameter-Key = Registry-Kernfeld (``ACTIVITY_MODULE_REGISTRY_FIELD_KEYS``) → gleichnamige Spalte
    3. EAV-primäre Keys → Legacy-Spalte laut ``ACTIVITY_LOG_LEGACY_COLUMN_FOR_EAV_PRIMARY_PARAM``
    4. sonst EAV

    EAV-Zeilen zu Parametern, die nicht im Schema sind, werden nicht ausgegeben.
    """
    eav_by_key = {m["key"]: m for m in eav_metrics}
    merged: List[Dict[str, Any]] = []
    keys_handled: set[str] = set()

    for s in schema:
        k = s["key"]
        tid = s["training_parameter_id"]
        dt = s["data_type"]
        unit = s.get("unit")
        sf = s.get("source_field")

        used_column = False
        if sf and isinstance(sf, str) and str(sf).strip():
            col = str(sf).strip()
            if col in header and header[col] is not None:
                try:
                    val = _coerce_raw_value_for_parameter(dt, header[col])
                    merged.append(
                        {
                            "training_parameter_id": tid,
                            "key": k,
                            "data_type": dt,
                            "unit": unit,
                            "value": val,
                            **_metric_human_labels(s),
                        }
                    )
                    used_column = True
                    keys_handled.add(k)
                except (TypeError, ValueError):
                    pass

        if used_column:
            continue

        if k in ACTIVITY_MODULE_REGISTRY_FIELD_KEYS and k in header and header[k] is not None:
            try:
                val = _coerce_raw_value_for_parameter(dt, header[k])
                merged.append(
                    {
                        "training_parameter_id": tid,
                        "key": k,
                        "data_type": dt,
                        "unit": unit,
                        "value": val,
                        **_metric_human_labels(s),
                    }
                )
                keys_handled.add(k)
                continue
            except (TypeError, ValueError):
                pass

        legacy_col = ACTIVITY_LOG_LEGACY_COLUMN_FOR_EAV_PRIMARY_PARAM.get(k)
        if legacy_col and legacy_col in header and header[legacy_col] is not None:
            try:
                val = _coerce_raw_value_for_parameter(dt, header[legacy_col])
                merged.append(
                    {
                        "training_parameter_id": tid,
                        "key": k,
                        "data_type": dt,
                        "unit": unit,
                        "value": val,
                        **_metric_human_labels(s),
                    }
                )
                keys_handled.add(k)
                continue
            except (TypeError, ValueError):
                pass

        if k in eav_by_key:
            row = dict(eav_by_key[k])
            row.update(_metric_human_labels(s))
            merged.append(row)
            keys_handled.add(k)

    merged.sort(key=lambda x: x["key"])
    return merged


def sync_column_backed_session_metrics(cur, profile_id: str, activity_log_id: str) -> None:
    """
    [Veraltet / nicht mehr in Schreibpfaden aufgerufen]

    Früher: EAV spiegelte activity_log-Spalten für Parameter mit source_field.
    Kanon: Spaltenwerte werden bei merge_column_backed_and_eav_metrics beim Lesen berücksichtigt; keine
    doppelte Speicherung. Funktion bleibt für optionale Admin-/Reparatur-Skripte.
    """
    cur.execute("SELECT * FROM activity_log WHERE id = %s", (activity_log_id,))
    row = cur.fetchone()
    if not row or str(row["profile_id"]) != str(profile_id):
        return
    header = dict(row)
    schema = resolve_activity_attribute_schema(
        cur, header.get("training_category"), header.get("training_type_id")
    )
    for spec in schema:
        sf = spec.get("source_field")
        if sf is None or (isinstance(sf, str) and not str(sf).strip()):
            continue
        col = str(sf).strip()
        if col not in header:
            continue
        raw = header[col]
        tid = spec["training_parameter_id"]
        dt = spec["data_type"]
        rules = _validation_rules_dict(spec["validation_rules"])

        if raw is None:
            cur.execute(
                """
                DELETE FROM activity_session_metrics
                WHERE activity_log_id = %s AND training_parameter_id = %s
                """,
                (activity_log_id, tid),
            )
            continue

        try:
            coerced = _coerce_raw_value_for_parameter(dt, raw)
            _validate_single_value(dt, coerced, rules)
        except (ActivitySessionMetricsError, TypeError, ValueError) as ex:
            logger.warning(
                "sync_column_backed_session_metrics: überspringe %s (Spalte %s): %s",
                spec.get("key"),
                col,
                ex,
            )
            continue

        vn, vi, vt, vb = _row_value_tuple(dt, coerced)
        cur.execute(
            """
            INSERT INTO activity_session_metrics (
                activity_log_id, training_parameter_id,
                value_num, value_int, value_text, value_bool, updated_at
            ) VALUES (%s, %s, %s, %s, %s, %s, NOW())
            ON CONFLICT (activity_log_id, training_parameter_id)
            DO UPDATE SET
                value_num = EXCLUDED.value_num,
                value_int = EXCLUDED.value_int,
                value_text = EXCLUDED.value_text,
                value_bool = EXCLUDED.value_bool,
                updated_at = NOW()
            """,
            (activity_log_id, tid, vn, vi, vt, vb),
        )


def fetch_activity_session_metrics(cur, activity_log_id: str) -> List[Dict[str, Any]]:
    cur.execute(
        """
        SELECT
            m.id,
            m.activity_log_id,
            m.training_parameter_id,
            m.value_num,
            m.value_int,
            m.value_text,
            m.value_bool,
            tp.key,
            tp.data_type,
            tp.unit
        FROM activity_session_metrics m
        JOIN training_parameters tp ON tp.id = m.training_parameter_id
        WHERE m.activity_log_id = %s
        ORDER BY tp.key
        """,
        (activity_log_id,),
    )
    rows = cur.fetchall()
    out: List[Dict[str, Any]] = []
    for r in rows:
        dt = r["data_type"]
        if dt == "integer":
            val = int(r["value_int"]) if r["value_int"] is not None else None
        elif dt == "float":
            val = float(r["value_num"]) if r["value_num"] is not None else None
        elif dt == "string":
            val = r["value_text"]
        else:
            val = r["value_bool"]
        out.append(
            {
                "training_parameter_id": r["training_parameter_id"],
                "key": r["key"],
                "data_type": dt,
                "unit": r["unit"],
                "value": val,
            }
        )
    return out


def replace_activity_session_metrics(
    cur,
    profile_id: str,
    activity_log_id: str,
    metrics: Sequence[Dict[str, Any]],
) -> List[Dict[str, Any]]:
    """
    Full replace of EAV rows for this session. metrics: [{ "parameter_key": str, "value": ... }, ...]
    """
    cur.execute(
        """
        SELECT id, profile_id, training_category, training_type_id
        FROM activity_log WHERE id = %s
        """,
        (activity_log_id,),
    )
    row = cur.fetchone()
    if not row or str(row["profile_id"]) != str(profile_id):
        raise ActivitySessionMetricsError(404, "Aktivität nicht gefunden")

    schema = resolve_activity_attribute_schema(
        cur, row.get("training_category"), row.get("training_type_id")
    )
    by_key = {s["key"]: s for s in schema}
    payload_by_key: Dict[str, Dict[str, Any]] = {}
    for item in metrics:
        raw_k = item.get("parameter_key")
        if raw_k is None or not str(raw_k).strip():
            raise ActivitySessionMetricsError(400, "parameter_key fehlt")
        k = str(raw_k).strip()
        if k not in by_key:
            raise ActivitySessionMetricsError(400, f"Unbekannter oder nicht zugewiesener Parameter: {k}")
        payload_by_key[k] = item

    for s in schema:
        if not s["required"]:
            continue
        itk = s["key"]
        hit = payload_by_key.get(itk)
        if hit is None or hit.get("value") is None:
            raise ActivitySessionMetricsError(400, f"Pflichtfeld fehlt: {itk}")

    cur.execute(
        "DELETE FROM activity_session_metrics WHERE activity_log_id = %s",
        (activity_log_id,),
    )

    for item in metrics:
        k = str(item["parameter_key"]).strip()
        spec = by_key[k]
        val = item.get("value")
        if val is None:
            if spec["required"]:
                raise ActivitySessionMetricsError(400, f"Pflichtfeld fehlt: {k}")
            continue
        rules = _validation_rules_dict(spec["validation_rules"])
        _validate_single_value(spec["data_type"], val, rules)
        vn, vi, vt, vb = _row_value_tuple(spec["data_type"], val)
        cur.execute(
            """
            INSERT INTO activity_session_metrics (
                activity_log_id, training_parameter_id,
                value_num, value_int, value_text, value_bool, updated_at
            ) VALUES (%s, %s, %s, %s, %s, %s, NOW())
            """,
            (activity_log_id, spec["training_parameter_id"], vn, vi, vt, vb),
        )

    # Kein sync_column_backed nach PUT /metrics: der Request ist maßgeblich für EAV. Ein Spalten-Sync würde
    # Werte aus nicht mitgeschriebenen activity_log-Spalten wieder verwerfen.

    return fetch_activity_session_metrics(cur, activity_log_id)


def get_activity_session_logical_unit(cur, profile_id: str, activity_log_id: str) -> Dict[str, Any]:
    cur.execute("SELECT * FROM activity_log WHERE id = %s", (activity_log_id,))
    row = cur.fetchone()
    if not row or str(row["profile_id"]) != str(profile_id):
        raise ActivitySessionMetricsError(404, "Aktivität nicht gefunden")

    header = dict(row)
    schema = resolve_activity_attribute_schema(
        cur, header.get("training_category"), header.get("training_type_id")
    )
    metrics = fetch_activity_session_metrics(cur, activity_log_id)
    merged_metrics = merge_column_backed_and_eav_metrics(header, schema, metrics)
    return {
        "header": header,
        "schema": schema,
        "metrics": merged_metrics,
    }


def enrich_sessions_with_metrics(cur, sessions: List[Dict[str, Any]]) -> None:
    """
    Mutates each session dict: adds key 'session_metrics' (list).

    Kombiniert EAV mit activity_log-Spalten für Parameter mit source_field (kanonisch: Spalte),
    analog zu get_activity_session_logical_unit – ohne doppelte EAV-Speicherung beim Import.
    """
    if not sessions:
        return
    ids = [str(s["id"]) for s in sessions if s.get("id")]
    if not ids:
        return
    ph = ",".join(["%s"] * len(ids))

    cur.execute(
        f"SELECT * FROM activity_log WHERE id IN ({ph})",
        ids,
    )
    headers_by_id: Dict[str, Dict[str, Any]] = {}
    for r in cur.fetchall():
        h = dict(r)
        headers_by_id[str(h["id"])] = h

    cur.execute(
        f"""
        SELECT
            m.activity_log_id,
            m.training_parameter_id,
            tp.key,
            tp.data_type,
            tp.unit,
            m.value_num,
            m.value_int,
            m.value_text,
            m.value_bool
        FROM activity_session_metrics m
        JOIN training_parameters tp ON tp.id = m.training_parameter_id
        WHERE m.activity_log_id IN ({ph})
        ORDER BY m.activity_log_id, tp.key
        """,
        ids,
    )
    by_act: Dict[str, List[Dict[str, Any]]] = {}
    for r in cur.fetchall():
        aid = str(r["activity_log_id"])
        dt = r["data_type"]
        if dt == "integer":
            val = int(r["value_int"]) if r["value_int"] is not None else None
        elif dt == "float":
            val = float(r["value_num"]) if r["value_num"] is not None else None
        elif dt == "string":
            val = r["value_text"]
        else:
            val = r["value_bool"]
        by_act.setdefault(aid, []).append(
            {
                "training_parameter_id": r["training_parameter_id"],
                "key": r["key"],
                "data_type": dt,
                "unit": r["unit"],
                "value": val,
            }
        )

    schema_cache: Dict[tuple[Any, Any], List[Dict[str, Any]]] = {}

    def _schema(cat: Any, tid: Any) -> List[Dict[str, Any]]:
        cache_key = (cat, tid)
        if cache_key not in schema_cache:
            schema_cache[cache_key] = resolve_activity_attribute_schema(cur, cat, tid)
        return schema_cache[cache_key]

    for s in sessions:
        aid = str(s.get("id"))
        header = headers_by_id.get(aid)
        if not header:
            s["session_metrics"] = []
            continue
        schema = _schema(header.get("training_category"), header.get("training_type_id"))
        eav_list = by_act.get(aid, [])
        merged = merge_column_backed_and_eav_metrics(header, schema, eav_list)
        s["session_metrics"] = [
            {
                "key": m["key"],
                "data_type": m["data_type"],
                "unit": m["unit"],
                "value": m["value"],
                "name_de": m.get("name_de"),
                "name_en": m.get("name_en"),
                "description_de": m.get("description_de"),
                "description_en": m.get("description_en"),
            }
            for m in merged
        ]