docs db __init__

src/utils/csv/loaders.py

@@ -98,7 +98,6 @@ async def get_next_csv_atomic(pool: object, table_name: str, status: int, next_s
             raise e
 
 async def main_old_script_loader(cfg: object, id: int, pool: object, script_name: str) -> None:
-    #async def main_loader(cfg: object, id: int, pool: object) -> None:
     """
     This function retrieves CSV data, writes it to a temporary file,
     executes an external Python script to process it,

src/utils/database/__init__.py

@@ -1,22 +1,36 @@
 class WorkflowFlags:
+    """
+    Defines integer flags representing different stages in a data processing workflow.
+    Each flag is a power of 2, allowing them to be combined using bitwise operations
+    to represent multiple states simultaneously.
+    """
     CSV_RECEIVED = 0        #   0000
     DATA_LOADED = 1         #   0001
     START_ELAB = 2          #   0010
     DATA_ELABORATED = 4     #   0100
     SENT_RAW_DATA = 8       #   1000
     SENT_ELAB_DATA = 16     #  10000
-    DUMMY_ELABORATED = 32   # 100000
+    DUMMY_ELABORATED = 32   # 100000 (Used for testing or specific dummy elaborations)
 
 # Mappatura flag -> colonna timestamp
 FLAG_TO_TIMESTAMP = {
+
     WorkflowFlags.CSV_RECEIVED: "inserted_at",
     WorkflowFlags.DATA_LOADED: "loaded_at",
     WorkflowFlags.START_ELAB: "start_elab_at",
     WorkflowFlags.DATA_ELABORATED: "elaborated_at",
     WorkflowFlags.SENT_RAW_DATA: "sent_raw_at",
     WorkflowFlags.SENT_ELAB_DATA: "sent_elab_at",
-    WorkflowFlags.DUMMY_ELABORATED: "elaborated_at"
+    WorkflowFlags.DUMMY_ELABORATED: "elaborated_at" # Shares the same timestamp column as DATA_ELABORATED
 }
+"""
+A dictionary mapping each WorkflowFlag to the corresponding database column
+name that stores the timestamp when that workflow stage was reached.
+"""
 
 # Dimensione degli split della matrice per il caricamento
-BATCH_SIZE = 1000
+BATCH_SIZE = 1000
+"""
+The number of records to process in a single batch when loading data into the database.
+This helps manage memory usage and improve performance for large datasets.
+"""