from __future__ import annotations

from hdbcli import dbapi
from mcp.server.fastmcp import Context

from hana_performance_mcp.formatting import format_bytes
from hana_performance_mcp.server import get_pool


async def get_table_statistics(
    schema_name: str,
    table_name: str | None = None,
    ctx: Context = None,
) -> str:
    """Retrieve SAP HANA table statistics for a schema, or column-level detail for a specific table.

    Args:
        schema_name: The database schema to inspect.
        table_name: Optional table name. If provided, returns column-level detail.
                    If omitted, returns an overview of the top 50 tables by size.
    """
    pool = get_pool(ctx)

    try:
        if table_name is None:
            return _schema_overview(pool, schema_name)
        return _table_detail(pool, schema_name, table_name)
    except dbapi.Error as e:
        return f"HANA Error ({e.errorcode}): {e.errortext}"
    except Exception as e:
        return f"Error: {e}"


def _schema_overview(pool, schema_name: str) -> str:
    with pool.get_cursor() as cursor:
        cursor.execute(
            "SELECT TABLE_NAME, TABLE_TYPE, IS_COLUMN_TABLE, RECORD_COUNT, "
            "TABLE_SIZE, IS_PARTITIONED, READ_COUNT, WRITE_COUNT, LOADED "
            "FROM M_TABLES WHERE SCHEMA_NAME = ? "
            "ORDER BY TABLE_SIZE DESC LIMIT 50",
            (schema_name,),
        )
        columns = [desc[0] for desc in cursor.description]
        rows = cursor.fetchall()

    if not rows:
        return f"No tables found in schema '{schema_name}'."

    lines = [
        f"## Table Statistics for Schema: {schema_name} (top {len(rows)} by size)\n"
    ]

    lines.append(
        f"{'Table':<40} {'Type':<8} {'Col?':<5} {'Rows':>12} "
        f"{'Size':>12} {'Part?':<6} {'Reads':>10} {'Writes':>10} {'Loaded':<8}"
    )
    lines.append("-" * 125)

    for row in rows:
        t = dict(zip(columns, row))
        name = t["TABLE_NAME"] or "N/A"
        if len(name) > 38:
            name = name[:35] + "..."
        ttype = t["TABLE_TYPE"] or "N/A"
        col = "Yes" if t["IS_COLUMN_TABLE"] == "TRUE" else "No"
        records = t["RECORD_COUNT"] if t["RECORD_COUNT"] is not None else 0
        size = format_bytes(t["TABLE_SIZE"])
        part = "Yes" if t["IS_PARTITIONED"] == "TRUE" else "No"
        reads = t["READ_COUNT"] if t["READ_COUNT"] is not None else 0
        writes = t["WRITE_COUNT"] if t["WRITE_COUNT"] is not None else 0
        loaded = t["LOADED"] or "N/A"

        lines.append(
            f"{name:<40} {ttype:<8} {col:<5} {records:>12} "
            f"{size:>12} {part:<6} {reads:>10} {writes:>10} {loaded:<8}"
        )

    return "\n".join(lines)


def _table_detail(pool, schema_name: str, table_name: str) -> str:
    with pool.get_cursor() as cursor:
        cursor.execute(
            "SELECT TABLE_NAME, TABLE_TYPE, IS_COLUMN_TABLE, RECORD_COUNT, "
            "TABLE_SIZE, IS_PARTITIONED, READ_COUNT, WRITE_COUNT, LOADED "
            "FROM M_TABLES WHERE SCHEMA_NAME = ? AND TABLE_NAME = ?",
            (schema_name, table_name),
        )
        table_cols = [desc[0] for desc in cursor.description]
        table_row = cursor.fetchone()

        if not table_row:
            return f"Table '{schema_name}.{table_name}' not found."

        cursor.execute(
            "SELECT COLUMN_NAME, DATA_TYPE_NAME, DISTINCT_COUNT, "
            "MEMORY_SIZE_IN_TOTAL, COMPRESSION_TYPE, LOADED, "
            "INTERNAL_ATTRIBUTE_TYPE "
            "FROM M_CS_COLUMNS "
            "WHERE SCHEMA_NAME = ? AND TABLE_NAME = ? "
            "ORDER BY MEMORY_SIZE_IN_TOTAL DESC",
            (schema_name, table_name),
        )
        col_columns = [desc[0] for desc in cursor.description]
        col_rows = cursor.fetchall()

    t = dict(zip(table_cols, table_row))
    lines = [f"## Table Detail: {schema_name}.{table_name}\n"]
    lines.append(f"- Type: {t['TABLE_TYPE']} | Column Store: {'Yes' if t['IS_COLUMN_TABLE'] == 'TRUE' else 'No'}")
    lines.append(f"- Records: {t['RECORD_COUNT']} | Size: {format_bytes(t['TABLE_SIZE'])}")
    lines.append(f"- Partitioned: {'Yes' if t['IS_PARTITIONED'] == 'TRUE' else 'No'} | Loaded: {t['LOADED'] or 'N/A'}")
    lines.append(f"- Reads: {t['READ_COUNT']} | Writes: {t['WRITE_COUNT']}")

    if not col_rows:
        lines.append("\nNo column statistics available (table may not be column-store).")
        return "\n".join(lines)

    lines.append(f"\n### Column Details ({len(col_rows)} columns)\n")
    lines.append(
        f"{'Column':<35} {'Data Type':<18} {'Distinct':>10} "
        f"{'Memory':>12} {'Compression':<15} {'Loaded':<8} {'Attr Type':<10}"
    )
    lines.append("-" * 120)

    for row in col_rows:
        c = dict(zip(col_columns, row))
        name = c["COLUMN_NAME"] or "N/A"
        if len(name) > 33:
            name = name[:30] + "..."
        dtype = c["DATA_TYPE_NAME"] or "N/A"
        distinct = c["DISTINCT_COUNT"] if c["DISTINCT_COUNT"] is not None else "N/A"
        mem = format_bytes(c["MEMORY_SIZE_IN_TOTAL"])
        compression = c["COMPRESSION_TYPE"] or "N/A"
        loaded = c["LOADED"] or "N/A"
        attr_type = c["INTERNAL_ATTRIBUTE_TYPE"] or "N/A"

        lines.append(
            f"{name:<35} {dtype:<18} {distinct:>10} "
            f"{mem:>12} {compression:<15} {loaded:<8} {attr_type:<10}"
        )

    return "\n".join(lines)