new changes in the api

app/core/arrow_utils.py

@@ -1,13 +1,9 @@
 """
-High-performance utilities using Apache Arrow and NumPy for geographic data.
-Provides vectorized operations for distances and coordinate processing.
+Vectorized NumPy utilities for geographic distance calculations.
 """
 
 import numpy as np
-import pyarrow as pa
-import pyarrow.parquet as pq
 import logging
-from typing import List, Dict, Any, Tuple
 
 logger = logging.getLogger(__name__)
 
@@ -35,29 +31,3 @@ def calculate_haversine_matrix_vectorized(lats: np.ndarray, lons: np.ndarray) ->
     c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
     
     return R * c
-
-def orders_to_arrow_table(orders: List[Dict[str, Any]]) -> pa.Table:
-    """
-    Convert a list of order dictionaries to an Apache Arrow Table.
-    This enables zero-copy operations and efficient columnar storage.
-    """
-    return pa.Table.from_pylist(orders)
-
-def save_optimized_route_parquet(orders: List[Dict[str, Any]], filename: str):
-    """
-    Save optimized route data to a Parquet file for high-speed analysis.
-    Useful for logging and historical simulation replays.
-    """
-    try:
-        table = orders_to_arrow_table(orders)
-        pq.write_table(table, filename)
-        logger.info(f" Saved route data to Parquet: {filename}")
-    except Exception as e:
-        logger.error(f" Failed to save Parquet: {e}")
-
-def load_route_parquet(filename: str) -> List[Dict[str, Any]]:
-    """
-    Load route data from a Parquet file and return as a list of dicts.
-    """
-    table = pq.read_table(filename)
-    return table.to_pylist()