begin refactoring

data_analysis/02-3_oracle_workflow_test.py

@@ -3,8 +3,11 @@ from __future__ import annotations
 
 import datetime
 import json
+import shutil
+import tempfile
 import time
 import typing
+import uuid
 from collections.abc import Sequence
 from pathlib import Path
 from pprint import pprint
@@ -19,10 +22,34 @@ from sqlalchemy import event
 
 from umbreit import db, types
 
+oracledb.defaults.arraysize = 1000
+oracledb.defaults.prefetchrows = 1000
+
+
 # %%
 # import importlib
 # types = importlib.reload(types)
 # db = importlib.reload(db)
+# %%
+def create_tmp_dir() -> Path:
+    tmp_pth = Path(tempfile.mkdtemp())
+    assert tmp_pth.exists()
+    return tmp_pth
+
+
+TMP_DIR = create_tmp_dir()
+
+
+def clear_tmp_dir() -> None:
+    shutil.rmtree(TMP_DIR)
+    TMP_DIR.mkdir()
+
+
+def remove_tmp_dir() -> None:
+    shutil.rmtree(TMP_DIR)
+
+
+print(f"Created temp directory under: >{TMP_DIR}<")
 
 # %%
 p_cfg = io.search_file_iterative(
@@ -48,13 +75,22 @@ conn_string = (
     f"oracle+oracledb://{USER_NAME}:{USER_PASS}@{HOST}:{PORT}?service_name={SERVICE}"
 )
 # engine = sql.create_engine(conn_string)
-engine = sql.create_engine(conn_string, execution_options={"stream_results": True})
+engine = sql.create_engine(
+    conn_string,
+    execution_options={"stream_results": True},
+)
 
 
-@event.listens_for(engine, "after_cursor_execute")
-def set_fetch_sizes(conn, cursor, statement, parameters, context, executemany):
-    cursor.arraysize = 1000
-    cursor.prefetchrows = 1000
+# @event.listens_for(engine, "after_cursor_execute")
+# def set_fetch_sizes(conn, cursor, statement, parameters, context, executemany):
+#     cursor.arraysize = 1000
+#     cursor.prefetchrows = 1000
+
+
+# @event.listens_for(engine, "before_cursor_execute")
+# def set_fetch_sizes(conn, cursor, statement, parameters, context, executemany):
+#     cursor.arraysize = 1000
+#     cursor.prefetchrows = 1000
 
 
 # %%
@@ -373,7 +409,7 @@ df.filter(pl.col("MELDENUMMER") == 18).select(pl.col("MENGE_VORMERKER").is_null(
 # BEDP_MENGE_BEDARF_VM as reference or ground truth not suitable
 df_diff_VM_bedp_tinfo = df.filter(pl.col("BEDP_MENGE_BEDARF_VM") > pl.col("MENGE_VORMERKER"))
 p_save_diff_VM_bedp_tinfo = (
-    Path.cwd() / "diff_BEDP-MENGE-BEDARF-VM_TINF-MENGE-VORMERKER_20251211-1.xlsx"
+    Path.cwd() / "diff_BEDP-MENGE-BEDARF-VM_TINF-MENGE-VORMERKER_20260130-1.xlsx"
 )
 
 df_diff_VM_bedp_tinfo.to_pandas().to_excel(p_save_diff_VM_bedp_tinfo, index=False)
@@ -404,6 +440,45 @@ RESULT_COLUMN_ORDER: typing.Final[tuple[str, ...]] = tuple(
     db.EXT_DOPT_ERGEBNIS.columns.keys()
 )
 ORDER_QTY_EXPR_KWARGS: typing.Final[types.OrderQtyExprKwArgs] = types.OrderQtyExprKwArgs()
+SAVE_TMP_FILES: typing.Final[bool] = True
+TMPFILE_WF100_SUB1_WDB = "WF-100_Sub1-WDB"
+TMPFILE_WF200_SUB1 = "WF-200_Sub1"
+
+
+def save_tmp_file(
+    data: pl.DataFrame,
+    filename: str | None,
+) -> None:
+    if filename is None:
+        filename = str(uuid.uuid4())
+    pth = (TMP_DIR / filename).with_suffix(".arrow")
+
+    n: int = 1
+    while pth.exists():
+        filename_new = pth.stem + f"_{n}"
+        pth = (TMP_DIR / filename_new).with_suffix(".arrow")
+        n += 1
+
+    data.write_ipc(pth)
+
+
+def load_tmp_file(
+    filename: str,
+) -> pl.DataFrame:
+    pth = (TMP_DIR / filename).with_suffix(".arrow")
+    if not pth.exists():
+        raise FileNotFoundError(f"File >{pth.name}< not found")
+
+    return pl.read_ipc(pth)
+
+
+def load_all_tmp_files() -> dict[str, pl.DataFrame]:
+    all_dfs: dict[str, pl.DataFrame] = {}
+    for file in TMP_DIR.glob("*.arrow"):
+        df = pl.read_ipc(file)
+        all_dfs[file.stem] = df
+
+    return all_dfs
 
 
 def get_starting_date(
@@ -430,6 +505,7 @@ def get_raw_data() -> pl.DataFrame:
         db.ext_titel_info.c.VERLAGSNR,
         db.ext_titel_info.c.MENGE_VORMERKER,
         db.ext_titel_info.c.MANDFUEHR,
+        db.ext_titel_info.c.EINKAEUFER,
     ).select_from(db.ext_bedpbed.join(db.ext_titel_info, join_condition, isouter=True))
 
     return pl.read_database(
@@ -599,6 +675,7 @@ class PipelineResult:
                 "VERLAGSNR",
                 "MENGE_VORMERKER",
                 "MANDFUEHR",
+                "EINKAEUFER",
             ]
         )
 
@@ -681,7 +758,7 @@ def wf900(
     filter_meldenummer_null = pl.col("MELDENUMMER").is_not_null()
     filter_mandant = pl.col(MANDANT_CRITERION).is_in((1, 90))
     res = _apply_several_filters(
-        pipe_res.open,
+        pipe_result.open,
         (
             filter_meldenummer_null,
             filter_mandant,
@@ -842,8 +919,7 @@ def _wf100_petersen_sub1_wdb(
 ) -> PipelineResult:
     ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_100)
     # input: pre-filtered entries (WDB titles and #VM > 0)
-    # more then 1 VM
-    # !! show these entries
+    # more than 1 VM: show these entries
     filter_number_vm = pl.col(VM_CRITERION) > 1
     res = _apply_several_filters(
         pipe_result.open,
@@ -883,11 +959,26 @@ def _wf100_petersen_sub1_wdb(
         .group_by(sub1.c.BEDP_TITELNR)
         .having(count_col > 1)
     )
+    if SAVE_TMP_FILES:
+        stmt = (
+            sql.select(
+                sub1.c.BEDP_TITELNR,
+                count_col.label("count"),
+            )
+            .select_from(sub1)
+            .group_by(sub1.c.BEDP_TITELNR)
+        )
+    # !! this is a sub result which must be used in the result set
+    # !! for testing and feedback by the customer
     relevant_titles = pl.read_database(
         stmt,
         engine,
     )
-    print(relevant_titles)
+
+    if SAVE_TMP_FILES:
+        save_tmp_file(relevant_titles, TMPFILE_WF100_SUB1_WDB)
+        relevant_titles = relevant_titles.filter(pl.col.COUNT > 1)
+
     entries_to_show = pipe_result.open.filter(
         pl.col.BEDP_TITELNR.is_in(relevant_titles["BEDP_TITELNR"].unique().implode())
     )
@@ -1003,10 +1094,27 @@ def _wf200_sub1(
         .group_by(sub1.c.BEDP_TITELNR)
         .having(unique_count_col >= 3)
     )
+    if SAVE_TMP_FILES:
+        stmt = (
+            sql.select(
+                sub1.c.BEDP_TITELNR,
+                sql.func.count().label("count"),
+                unique_count_col.label("customer_count"),
+            )
+            .select_from(sub1)
+            .group_by(sub1.c.BEDP_TITELNR)
+        )
+    # !! this is a sub result which must be used in the result set
+    # !! for testing and feedback by the customer
     relevant_titles = pl.read_database(
         stmt,
         engine,
     )
+
+    if SAVE_TMP_FILES:
+        save_tmp_file(relevant_titles, TMPFILE_WF200_SUB1)
+        relevant_titles = relevant_titles.filter(pl.col.CUSTOMER_COUNT >= 3)
+
     entries_to_show = pipe_result.open.filter(
         pl.col.BEDP_TITELNR.is_in(relevant_titles["BEDP_TITELNR"].unique().implode())
     )
@@ -1031,63 +1139,26 @@ def _wf200_sub1(
 
 # %%
 # SAVING/LOADING
-p_save = Path.cwd() / "raw_data_from_sql_query_20260116-1.arrow"
-df = pl.read_ipc(p_save)
+READ_DATABASE = False
+OVERWRITE = True
+FILENAME = "raw_data_from_sql_query_20260202-1.arrow"
+p_save = Path.cwd() / FILENAME
+if READ_DATABASE:
+    df = get_raw_data()
+    if not p_save.exists() or OVERWRITE:
+        df.write_ipc(p_save)
+else:
+    df = pl.read_ipc(p_save)
+# %%
+df
+# %%
+# initialise pipeline
+raw_data = df.clone()
 print(f"Number of entries: {len(df)}")
-
+clear_tmp_dir()
+clear_result_data()
 # %%
 df.head()
-# %%
-# df.filter(pl.col.BEDP_TITELNR == 4314750)
-# # %%
-# RELEVANT_DATE = get_starting_date(180)
-# join_condition = db.tmp_data.c.BEDP_TITELNR == db.EXT_BESPBES_INFO.c.BESP_TITELNR
-# filter_ = sql.and_(
-#     db.EXT_BESPBES_INFO.c.BES_DATUM >= RELEVANT_DATE,
-#     db.EXT_BESPBES_INFO.c.BESP_TITELNR == 4314750,
-# )
-# stmt = sql.select(db.EXT_BESPBES_INFO).where(filter_)
-# relevant_titles = pl.read_database(
-#     stmt,
-#     engine,
-# )
-# print(relevant_titles)
-# %%
-# removed_rows = []
-
-# raw_data = df.clone()
-# print(f"Length raw data: {len(raw_data)}")
-# filter_mandant = pl.col("BEDP_MAN").is_in((1, 90))
-# filter_ignore_MNR26 = pl.col("MELDENUMMER") != 26
-
-
-# filtered = raw_data.filter(filter_mandant)
-# filtered_n = raw_data.filter(~filter_mandant)
-# num_filter = len(filtered)
-# num_filter_n = len(filtered_n)
-# removed_rows.append(filtered_n)
-# print(f"Length filtered: {num_filter}")
-# print(f"Length filtered out: {num_filter_n}")
-# print(f"Length all: {num_filter + num_filter_n}")
-# raw_data = filtered
-# out = pl.concat(removed_rows)
-# print(f"Length out: {len(out)}")
-
-# # %%
-# print("---------------------------------------")
-# filtered = raw_data.filter(filter_ignore_MNR26)
-# filtered_n = raw_data.filter(~filter_ignore_MNR26)
-# num_filter = len(filtered)
-# num_filter_n = len(filtered_n)
-# len(filtered_n)
-# # %%
-# removed_rows.append(filtered_n)
-# print(f"Length filtered: {num_filter}")
-# print(f"Length filtered out: {num_filter_n}")
-# print(f"Length all: {num_filter + num_filter_n}")
-# out = pl.concat(removed_rows)
-# print(f"Length out: {len(out)}")
-
 # %%
 raw_data = df.clone()
 # pipe_res = get_empty_pipeline_result(raw_data)
@@ -1101,15 +1172,6 @@ print(f"Number of entries open data: {len(pipe_res.open):>10}")
 
 # %%
 pipe_res.results
-
-# %%
-# // test result writing
-res = pipe_res.results.clone()
-res.height
-
-# raw_data.filter(pl.col("BEDARFNR") == 166982).filter(pl.col("BEDP_SEQUENZ") == 1)
-# %%
-# pipe_res.open.filter(pl.col("BEDP_MENGE_BEDARF_VM") > pl.col("MENGE_VORMERKER"))
 # %%
 pipe_res = wf910(pipe_res)
 print(f"Length of base data: {len(raw_data):>18}")
@@ -1117,8 +1179,6 @@ print(f"Number of entries pipe data: {len(pipe_res):>10}")
 print(f"Number of entries result data: {len(pipe_res.results):>8}")
 print(f"Number of entries open data: {len(pipe_res.open):>10}")
 # %%
-# pipe_res.results.select(pl.col("vorlage").value_counts())
-# %%
 pipe_res = wf100_umbreit(pipe_res, VM_CRITERION)
 print(f"Length of base data: {len(raw_data):>18}")
 print(f"Number of entries pipe data: {len(pipe_res):>10}")
@@ -1146,6 +1206,120 @@ print(f"Number of entries open data: {len(pipe_res.open):>10}")
 pipe_res.open.filter(pl.col.MELDENUMMER.is_in((17, 18)))
 # %%
 pipe_res.results.select(pl.col("VORLAGE").value_counts())
+# %%
+pipe_res.results.height
+# %%
+# // aggregate test results
+all_tmps = load_all_tmp_files()
+print(len(all_tmps))
+
+
+# %%
+def prepare_tmp_data() -> list[pl.DataFrame]:
+    all_tmps = load_all_tmp_files()
+    WF_100_TMP_RENAME = {"COUNT": "WF-100_WDB_Anz-Best-Petersen_verg_6_Monate"}
+    WF_200_TMP_RENAME = {
+        "COUNT": "WF-200_Anz-Best-Kunde_verg_3_Monate",
+        "CUSTOMER_COUNT": "WF-200_Anz-Kunden_verg_3_Monate",
+    }
+
+    WF_100: list[pl.DataFrame] = []
+    WF_200: list[pl.DataFrame] = []
+
+    for name, df in all_tmps.items():
+        if TMPFILE_WF100_SUB1_WDB in name:
+            rename_schema = WF_100_TMP_RENAME
+            df = df.rename(rename_schema)
+            WF_100.append(df)
+        elif TMPFILE_WF200_SUB1 in name:
+            rename_schema = WF_200_TMP_RENAME
+            df = df.rename(rename_schema)
+            WF_200.append(df)
+
+    tmp_WF_collects = (WF_100, WF_200)
+    all_tmps_preproc: list[pl.DataFrame] = []
+
+    for collect in tmp_WF_collects:
+        if len(collect) > 1:
+            df = pl.concat(collect)
+        elif len(collect) == 1:
+            df = collect[0].clone()
+        else:
+            raise RuntimeError()
+
+        all_tmps_preproc.append(df)
+
+    return all_tmps_preproc
+
+
+def generate_test_result_data(
+    raw_data: pl.DataFrame,
+    pipe_result: PipelineResult,
+) -> pl.DataFrame:
+    all_tmps_preproc = prepare_tmp_data()
+
+    res_table = pipe_result.results.clone()
+    res_title_info = res_table.join(
+        raw_data,
+        left_on=["BEDARF_NR", "BEDARF_SEQUENZ"],
+        right_on=["BEDARFNR", "BEDP_SEQUENZ"],
+        how="inner",
+    )
+    exclude_cols = ("BEDARF_NR", "BEDARF_SEQUENZ")
+    res_title_info = res_title_info.select(pl.exclude(exclude_cols))
+    columns = [
+        "VORLAGE",
+        "WF_ID",
+        "BEST_MENGE",
+        "FREIGABE_AUTO",
+        "BEDP_MENGE_BEDARF_VM",
+        "MENGE_VORMERKER",
+        "BEDP_TITELNR",
+        "BEDP_MAN",
+        "MELDENUMMER",
+        "VERLAGSNR",
+        "EINKAEUFER",
+        "MANDFUEHR",
+    ]
+    res_title_info = res_title_info.select(columns)
+
+    test_results = res_title_info.clone()
+    for df in all_tmps_preproc:
+        test_results = test_results.join(df, on="BEDP_TITELNR", how="left")
+
+    test_results = test_results.sort(by=["WF_ID", "BEDP_MAN"], descending=False)
+    test_results = test_results.select(pl.int_range(1, pl.len() + 1).alias("Index"), pl.all())
+
+    return test_results
+
+
+# %%
+test_results = generate_test_result_data(raw_data, pipe_res)
+test_results.head()
+# %%
+date_str = datetime.datetime.now().strftime("%Y-%m-%d")
+p_save = Path.cwd() / f"Testdatensatz_WF-100-200_{date_str}.xlsx"
+test_results.to_pandas().set_index("Index").to_excel(
+    p_save,
+    freeze_panes=(1, 1),
+    sheet_name=f"Ergebnisse_Testphase_{date_str}",
+)
+#####################################################################
+# %%
+# ** deviating titles where BEDP_MENGE_BEDARF_VM > MENGE_VORMERKER
+deviation_vm = test_results.with_columns(pl.col.MENGE_VORMERKER.fill_null(0)).filter(
+    pl.col.BEDP_MENGE_BEDARF_VM > pl.col.MENGE_VORMERKER
+)
+deviation_vm = test_results.filter(pl.col.BEDP_TITELNR.is_in(dev["BEDP_TITELNR"].implode()))
+
+date_str = datetime.datetime.now().strftime("%Y-%m-%d")
+p_save = Path.cwd() / f"Abweichungen-VM_{date_str}.xlsx"
+deviation_vm.to_pandas().set_index("Index").to_excel(p_save, freeze_panes=(1, 1))
+# ** WF-200 potentially triggered
+raw_data.filter(pl.col.MELDENUMMER.is_in((17, 18))).filter(
+    pl.col.BEDP_TITELNR.is_duplicated()
+).sort("BEDP_TITELNR")
+
 # %%
 # ---------------------------------------------------------------------------- #
 # Workflow 200 (Umbreit only)
@@ -1613,3 +1787,5 @@ pipe_res_main.open
 # %%
 pipe_res.results
 # %%
+remove_tmp_dir()
+# %%

src/umbreit/constants.py

@@ -0,0 +1,30 @@
+from pathlib import Path
+from typing import Final
+
+from dopt_basics import configs
+from dopt_basics import io as io_
+
+LIB_PATH: Final[Path] = Path(__file__).parent
+
+# // database connections
+p_cfg = io_.search_file_iterative(
+    starting_path=LIB_PATH,
+    glob_pattern="CRED*.toml",
+    stop_folder_name="umbreit-py",
+)
+if p_cfg is None:
+    raise FileNotFoundError("Config was not found")
+
+CFG = configs.load_toml(p_cfg)
+HOST = CFG["server"]["host"]
+PORT = CFG["server"]["port"]
+SERVICE = CFG["server"]["service"]
+USER_NAME = CFG["user"]["name"]
+USER_PASS = CFG["user"]["pass"]
+
+# TODO remove or change
+# ** Oracle client libs
+USE_THICK_MODE: Final[bool] = False
+P_ORACLE_CLIENT_LIBS = Path(r"C:\Databases\Oracle\instantclient_19_29")
+assert P_ORACLE_CLIENT_LIBS.exists(), "Client libs not found"
+assert P_ORACLE_CLIENT_LIBS.is_dir()

src/umbreit/db.py

@@ -178,17 +178,8 @@ EXT_AUFPAUF_schema_map: PolarsSchema = {
 EXT_AUFPAUF_null_values: PolarsNullValues = {}
 
 # // queries and temp data
-raw_data_query_schema_map: PolarsSchema = {
-    "BEDARFNR": pl.UInt32,
-    "BEDP_SEQUENZ": pl.UInt32,
-    "BEDP_TITELNR": pl.UInt32,
-    "BEDP_MAN": pl.UInt8,
-    "BEDP_MENGE_BEDARF_VM": pl.UInt32,
-    "MELDENUMMER": pl.UInt8,
-    "VERLAGSNR": pl.UInt32,
-    "MENGE_VORMERKER": pl.UInt32,
-    "MANDFUEHR": pl.UInt8,
-}
+raw_data_query_schema_map: PolarsSchema = ext_bedpbed_schema_map.copy()
+raw_data_query_schema_map.update(ext_titel_info_schema_map)
 
 tmp_data = Table(
     "EXT_TMP_BEDP_TINFO",

src/umbreit/pipeline.py

@@ -0,0 +1,750 @@
+from __future__ import annotations
+
+import datetime
+import shutil
+import tempfile
+import uuid
+from collections.abc import Sequence
+from pathlib import Path
+from typing import Final, Literal, Protocol, TypeAlias, overload
+
+import dopt_basics.datetime as dt
+import oracledb
+import polars as pl
+import polars.selectors as cs
+import sqlalchemy as sql
+
+from umbreit import constants, db, types
+from umbreit.constants import HOST, PORT, SERVICE, USER_NAME, USER_PASS
+
+oracledb.defaults.arraysize = 1000
+oracledb.defaults.prefetchrows = 1000
+if constants.USE_THICK_MODE:
+    oracledb.init_oracle_client(lib_dir=str(constants.P_ORACLE_CLIENT_LIBS))
+
+
+def create_tmp_dir() -> Path:
+    tmp_pth = Path(tempfile.mkdtemp())
+    assert tmp_pth.exists()
+    return tmp_pth
+
+
+def clear_tmp_dir() -> None:
+    shutil.rmtree(TMP_DIR)
+    TMP_DIR.mkdir()
+
+
+def remove_tmp_dir() -> None:
+    shutil.rmtree(TMP_DIR)
+
+
+TMP_DIR = create_tmp_dir()
+
+CONN_STRING: Final[str] = (
+    f"oracle+oracledb://{USER_NAME}:{USER_PASS}@{HOST}:{PORT}?service_name={SERVICE}"
+)
+engine = sql.create_engine(
+    CONN_STRING,
+    execution_options={"stream_results": True},
+)
+
+VM_CRITERION: Final[str] = "BEDP_MENGE_BEDARF_VM"
+MANDANT_CRITERION: Final[str] = "BEDP_MAN"
+ORDER_QTY_CRIT: Final[str] = "BEDP_MENGE_BEDARF_VM"
+RESULT_COLUMN_ORDER: Final[tuple[str, ...]] = tuple(db.EXT_DOPT_ERGEBNIS.columns.keys())
+ORDER_QTY_EXPR_KWARGS: Final[types.OrderQtyExprKwArgs] = types.OrderQtyExprKwArgs()
+SAVE_TMP_FILES: Final[bool] = True
+TMPFILE_WF100_SUB1_WDB = "WF-100_Sub1-WDB"
+TMPFILE_WF200_SUB1 = "WF-200_Sub1"
+
+
+def save_tmp_file(
+    data: pl.DataFrame,
+    filename: str | None,
+) -> None:
+    if filename is None:
+        filename = str(uuid.uuid4())
+    pth = (TMP_DIR / filename).with_suffix(".arrow")
+
+    n: int = 1
+    while pth.exists():
+        filename_new = pth.stem + f"_{n}"
+        pth = (TMP_DIR / filename_new).with_suffix(".arrow")
+        n += 1
+
+    data.write_ipc(pth)
+
+
+def load_tmp_file(
+    filename: str,
+) -> pl.DataFrame:
+    pth = (TMP_DIR / filename).with_suffix(".arrow")
+    if not pth.exists():
+        raise FileNotFoundError(f"File >{pth.name}< not found")
+
+    return pl.read_ipc(pth)
+
+
+def load_all_tmp_files() -> dict[str, pl.DataFrame]:
+    all_dfs: dict[str, pl.DataFrame] = {}
+    for file in TMP_DIR.glob("*.arrow"):
+        df = pl.read_ipc(file)
+        all_dfs[file.stem] = df
+
+    return all_dfs
+
+
+def get_starting_date(
+    days_from_now: int,
+) -> datetime.date:
+    current_dt = dt.current_time_tz(cut_microseconds=True)
+    td = dt.timedelta_from_val(days_from_now, dt.TimeUnitsTimedelta.DAYS)
+
+    return (current_dt - td).date()
+
+
+def get_raw_data() -> pl.DataFrame:
+    join_condition = db.ext_bedpbed.c.BEDP_TITELNR == db.ext_titel_info.c.TI_NUMMER
+    stmt = sql.select(
+        db.ext_bedpbed.c.BEDARFNR,
+        db.ext_bedpbed.c.BEDP_SEQUENZ,
+        db.ext_bedpbed.c.BEDP_TITELNR,
+        db.ext_bedpbed.c.BEDP_MAN,
+        sql.case(
+            (db.ext_bedpbed.c.BEDP_MENGE_BEDARF_VM <= -1, sql.null()),
+            else_=db.ext_bedpbed.c.BEDP_MENGE_BEDARF_VM,
+        ).label("BEDP_MENGE_BEDARF_VM"),
+        db.ext_titel_info.c.MELDENUMMER,
+        db.ext_titel_info.c.VERLAGSNR,
+        db.ext_titel_info.c.MENGE_VORMERKER,
+        db.ext_titel_info.c.MANDFUEHR,
+        db.ext_titel_info.c.EINKAEUFER,
+    ).select_from(db.ext_bedpbed.join(db.ext_titel_info, join_condition, isouter=True))
+
+    return pl.read_database(
+        stmt,
+        engine,
+        schema_overrides=db.raw_data_query_schema_map,
+    )
+
+
+def save_tmp_data(df: pl.DataFrame) -> None:
+    with engine.begin() as conn:
+        conn.execute(sql.delete(db.tmp_data))
+
+    with engine.begin() as conn:
+        conn.execute(sql.insert(db.tmp_data), df.to_dicts())
+
+
+def get_tmp_data() -> pl.DataFrame:
+    return pl.read_database(
+        sql.select(db.tmp_data),
+        engine,
+        schema_overrides=db.tmp_data_schema_map,
+    )
+
+
+def get_result_data() -> pl.DataFrame:
+    return pl.read_database(
+        sql.select(db.EXT_DOPT_ERGEBNIS),
+        engine,
+        schema_overrides=db.results_schema_map,
+    )
+
+
+def save_result_data(results: pl.DataFrame) -> None:
+    with engine.begin() as conn:
+        conn.execute(sql.insert(db.EXT_DOPT_ERGEBNIS), results.to_dicts())
+
+
+def clear_result_data() -> None:
+    with engine.begin() as conn:
+        conn.execute(sql.delete(db.EXT_DOPT_ERGEBNIS))
+
+
+def save_result_data_native(results: pl.DataFrame) -> None:
+    results = results.with_columns(
+        [
+            pl.when(pl.col(c)).then(pl.lit("Y")).otherwise(pl.lit("N")).alias(c)
+            for c in results.select(cs.boolean()).columns
+        ]
+    )
+    stmt = """
+    INSERT INTO "EXT_DOPT_ERGEBNIS" ("BEDARF_NR", "BEDARF_SEQUENZ", "VORLAGE", "WF_ID", 
+    "BEST_MENGE", "FREIGABE_AUTO") 
+    VALUES (:1, :2, :3, :4, :5, :6)
+    """
+    with engine.begin() as conn:
+        raw_conn = conn.connection.connection
+        with raw_conn.cursor() as cursor:
+            cursor.executemany(stmt, results.to_pandas(use_pyarrow_extension_array=True))
+
+
+def _apply_several_filters(
+    df: pl.DataFrame,
+    filters: Sequence[pl.Expr],
+) -> types.FilterResult:
+    df_current = df
+    removed_rows: list[pl.DataFrame] = []
+
+    for filter in filters:
+        removed = df_current.filter(~filter)
+        removed_rows.append(removed)
+
+        df_current = df_current.filter(filter)
+
+    df_removed = pl.concat(removed_rows)
+
+    return types.FilterResult(in_=df_current, out_=df_removed)
+
+
+class PipelineResult:
+    __slots__ = ("_results", "_open", "_subtracted_indices")
+    _index_cols: tuple[str, ...] = ("BEDARFNR", "BEDP_SEQUENZ")
+
+    def __init__(
+        self,
+        data: pl.DataFrame,
+    ) -> None:
+        self._open = data
+        schema = db.results_schema_map.copy()
+        del schema["ID"]
+        self._results = pl.DataFrame(schema=schema)
+
+        schema = {}
+        for col in self._index_cols:
+            schema[col] = db.raw_data_query_schema_map[col]
+        self._subtracted_indices = pl.DataFrame(schema=schema)
+
+    def __len__(self) -> int:
+        return len(self._results) + len(self._open)
+
+    @property
+    def open(self) -> pl.DataFrame:
+        return self._open
+
+    @property
+    def results(self) -> pl.DataFrame:
+        return self._results
+
+    @property
+    def subtracted_indices(self) -> pl.DataFrame:
+        return self._subtracted_indices
+
+    def update_open(
+        self,
+        data: pl.DataFrame,
+    ) -> None:
+        self._open = data
+
+    def _subtract_data(
+        self,
+        data: pl.DataFrame,
+    ) -> None:
+        self._open = self._open.join(data, on=self._index_cols, how="anti")
+        self._subtracted_indices = pl.concat(
+            (self._subtracted_indices, data[self._index_cols])
+        )
+
+    def _add_results(
+        self,
+        data: pl.DataFrame,
+    ) -> None:
+        res = pl.concat([self._results, data])
+        self._results = res
+
+    def merge_pipeline(
+        self,
+        pipeline: PipelineResult,
+    ) -> None:
+        self._subtract_data(pipeline.subtracted_indices)
+        self._add_results(pipeline.results)
+
+    def write_results(
+        self,
+        data: pl.DataFrame,
+        vorlage: bool,
+        wf_id: types.Workflows,
+        freigabe_auto: types.Freigabe,
+        order_qty_expr: pl.Expr,
+    ) -> None:
+        results = data.rename(db.map_data_to_result)
+        results = results.with_columns(
+            [
+                pl.lit(vorlage).alias("VORLAGE").cast(db.results_schema_map["VORLAGE"]),
+                pl.lit(wf_id.value).alias("WF_ID").cast(db.results_schema_map["WF_ID"]),
+                order_qty_expr,
+                pl.lit(freigabe_auto.value)
+                .alias("FREIGABE_AUTO")
+                .cast(db.results_schema_map["FREIGABE_AUTO"]),
+            ]
+        )
+        results = results.drop(
+            [
+                "BEDP_TITELNR",
+                "BEDP_MAN",
+                "BEDP_MENGE_BEDARF_VM",
+                "MELDENUMMER",
+                "VERLAGSNR",
+                "MENGE_VORMERKER",
+                "MANDFUEHR",
+                "EINKAEUFER",
+            ]
+        )
+
+        self._subtract_data(data)
+        self._add_results(results)
+
+
+class ExprOrderQty(Protocol): ...
+
+
+class ExprOrderQty_Base(ExprOrderQty, Protocol):
+    def __call__(self) -> pl.Expr: ...
+
+
+ExprOrderQty_Base_Types: TypeAlias = (
+    Literal[types.Workflows.ID_200]
+    | Literal[types.Workflows.ID_900]
+    | Literal[types.Workflows.ID_910]
+)
+
+
+class ExprOrderQty_WF100(ExprOrderQty, Protocol):
+    def __call__(self, empty: bool) -> pl.Expr: ...
+
+
+@overload
+def get_expr_order_qty(
+    wf_id: Literal[types.Workflows.ID_100],
+) -> ExprOrderQty_WF100: ...
+
+
+@overload
+def get_expr_order_qty(
+    wf_id: ExprOrderQty_Base_Types,
+) -> ExprOrderQty_Base: ...
+
+
+def get_expr_order_qty(
+    wf_id: types.Workflows,
+) -> ExprOrderQty:
+    empty_expr = (
+        pl.lit(0)
+        .alias(ORDER_QTY_CRIT)
+        .alias("BEST_MENGE")
+        .cast(db.results_schema_map["BEST_MENGE"])
+    )
+
+    def _empty() -> pl.Expr:
+        return empty_expr
+
+    func: ExprOrderQty
+    match wf_id:
+        case types.Workflows.ID_100:
+
+            def _func(empty: bool) -> pl.Expr:
+                order_qty_expr: pl.Expr
+                if empty:
+                    order_qty_expr = empty_expr
+                else:
+                    order_qty_expr = pl.col(ORDER_QTY_CRIT).alias("BEST_MENGE")
+                return order_qty_expr
+
+            func = _func
+
+        case types.Workflows.ID_200 | types.Workflows.ID_900 | types.Workflows.ID_910:
+            func = _empty
+        case _:
+            raise NotImplementedError(
+                f"Order expression for WF-ID {wf_id.value} is not implemented"
+            )
+
+    return func
+
+
+def wf900(
+    pipe_result: PipelineResult,
+) -> PipelineResult:
+    """filter 'Meldenummer' and fill non-feasible entries"""
+    ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_900)
+    filter_meldenummer_null = pl.col("MELDENUMMER").is_not_null()
+    filter_mandant = pl.col(MANDANT_CRITERION).is_in((1, 90))
+    res = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer_null,
+            filter_mandant,
+        ),
+    )
+    pipe_result.write_results(
+        data=res.out_,
+        vorlage=False,
+        wf_id=types.Workflows.ID_900,
+        freigabe_auto=types.Freigabe.WF_900,
+        order_qty_expr=ORDER_QTY_FUNC(),
+    )
+
+    pipe_result.update_open(
+        res.in_.with_columns(
+            pl.col("MENGE_VORMERKER").fill_null(0),
+            pl.col("BEDP_MENGE_BEDARF_VM").fill_null(0),
+        )
+    )
+
+    return pipe_result
+
+
+def wf910(
+    pipe_result: PipelineResult,
+) -> PipelineResult:
+    ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_910)
+    filter_ignore_MNR26 = pl.col("MELDENUMMER") != 26
+
+    res = _apply_several_filters(pipe_result.open, filters=(filter_ignore_MNR26,))
+    pipe_result.write_results(
+        data=res.out_,
+        vorlage=False,
+        wf_id=types.Workflows.ID_910,
+        freigabe_auto=types.Freigabe.WF_910,
+        order_qty_expr=ORDER_QTY_FUNC(),
+    )
+
+    return pipe_result
+
+
+# this a main routine:
+# receives and gives back result objects
+def wf100_umbreit(
+    pipe_result: PipelineResult,
+    vm_criterion: str,
+) -> PipelineResult:
+    ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_100)
+
+    filter_meldenummer = pl.col("MELDENUMMER") == 18
+    filter_mandant = pl.col(MANDANT_CRITERION) == 1
+    filter_number_vm = pl.col(vm_criterion) > 0
+
+    res = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer,
+            filter_mandant,
+            filter_number_vm,
+        ),
+    )
+    pipe_result.write_results(
+        data=res.in_,
+        vorlage=False,
+        wf_id=types.Workflows.ID_100,
+        freigabe_auto=types.Freigabe.WF_100,
+        order_qty_expr=ORDER_QTY_FUNC(empty=False),
+    )
+
+    return pipe_result
+
+
+def wf100_petersen(
+    pipe_result: PipelineResult,
+    vm_criterion: str,
+) -> PipelineResult:
+    ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_100)
+    # difference WDB and others
+
+    # // WDB branch
+    # order quantity 0, no further action in other WFs
+    filter_meldenummer = pl.col("MELDENUMMER") == 18
+    filter_mandant = pl.col(MANDANT_CRITERION) == 90
+    filter_WDB = pl.col("VERLAGSNR").is_in((76008, 76070))
+    filter_number_vm = pl.col(vm_criterion) == 0
+    res = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer,
+            filter_mandant,
+            filter_WDB,
+            filter_number_vm,
+        ),
+    )
+    pipe_result.write_results(
+        data=res.in_,
+        vorlage=False,
+        wf_id=types.Workflows.ID_100,
+        freigabe_auto=types.Freigabe.WF_100,
+        order_qty_expr=ORDER_QTY_FUNC(empty=True),
+    )
+    filter_number_vm = pl.col(vm_criterion) > 0
+    res_candidates = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer,
+            filter_mandant,
+            filter_WDB,
+            filter_number_vm,
+        ),
+    )
+    wdb_sub_pipe = PipelineResult(res_candidates.in_)
+    wdb_sub_pipe = _wf100_petersen_sub1_wdb(wdb_sub_pipe)
+    assert wdb_sub_pipe.open.height == 0, "Sub pipe not fully processed"
+    pipe_result.merge_pipeline(wdb_sub_pipe)
+
+    # // other branch
+    # show always entries with #VM > 1
+    filter_number_vm = pl.col(vm_criterion) > 1
+    res = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer,
+            filter_mandant,
+            filter_number_vm,
+        ),
+    )
+    pipe_result.write_results(
+        data=res.in_,
+        vorlage=True,
+        wf_id=types.Workflows.ID_100,
+        freigabe_auto=types.Freigabe.WF_100,
+        order_qty_expr=ORDER_QTY_FUNC(empty=False),
+    )
+
+    filter_number_vm = pl.col(vm_criterion) > 0
+    res = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer,
+            filter_mandant,
+            filter_number_vm,
+        ),
+    )
+    pipe_result.write_results(
+        data=res.in_,
+        vorlage=False,
+        wf_id=types.Workflows.ID_100,
+        freigabe_auto=types.Freigabe.WF_100,
+        order_qty_expr=ORDER_QTY_FUNC(empty=False),
+    )
+
+    return pipe_result
+
+
+def _wf100_petersen_sub1_wdb(
+    pipe_result: PipelineResult,
+) -> PipelineResult:
+    ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_100)
+    # input: pre-filtered entries (WDB titles and #VM > 0)
+    # more than 1 VM: show these entries
+    filter_number_vm = pl.col(VM_CRITERION) > 1
+    res = _apply_several_filters(
+        pipe_result.open,
+        (filter_number_vm,),
+    )
+    pipe_result.write_results(
+        data=res.in_,
+        vorlage=True,
+        wf_id=types.Workflows.ID_100,
+        freigabe_auto=types.Freigabe.WF_100,
+        order_qty_expr=ORDER_QTY_FUNC(empty=False),
+    )
+    # filtered out entries (WDB with #VM == 1) must be analysed for orders in the
+    # past 6 months
+    save_tmp_data(pipe_result.open)
+    RELEVANT_DATE = get_starting_date(180)
+    join_condition = db.tmp_data.c.BEDP_TITELNR == db.EXT_BESPBES_INFO.c.BESP_TITELNR
+    filter_ = db.EXT_BESPBES_INFO.c.BES_DATUM >= RELEVANT_DATE
+    stmt = (
+        sql.select(
+            db.tmp_data,
+            db.EXT_BESPBES_INFO.c.BESP_MENGE,
+            db.EXT_BESPBES_INFO.c.BESP_STATUS,
+        )
+        .select_from(db.tmp_data.join(db.EXT_BESPBES_INFO, join_condition))
+        .where(filter_)
+    )
+    sub1 = stmt.subquery()
+
+    count_col = sql.func.count()
+    stmt = (
+        sql.select(
+            sub1.c.BEDP_TITELNR,
+            count_col.label("count"),
+        )
+        .select_from(sub1)
+        .group_by(sub1.c.BEDP_TITELNR)
+        .having(count_col > 1)
+    )
+    if SAVE_TMP_FILES:
+        stmt = (
+            sql.select(
+                sub1.c.BEDP_TITELNR,
+                count_col.label("count"),
+            )
+            .select_from(sub1)
+            .group_by(sub1.c.BEDP_TITELNR)
+        )
+    # !! this is a sub result which must be used in the result set
+    # !! for testing and feedback by the customer
+    relevant_titles = pl.read_database(
+        stmt,
+        engine,
+    )
+
+    if SAVE_TMP_FILES:
+        save_tmp_file(relevant_titles, TMPFILE_WF100_SUB1_WDB)
+        relevant_titles = relevant_titles.filter(pl.col.COUNT > 1)
+
+    entries_to_show = pipe_result.open.filter(
+        pl.col.BEDP_TITELNR.is_in(relevant_titles["BEDP_TITELNR"].unique().implode())
+    )
+    pipe_result.write_results(
+        data=entries_to_show,
+        vorlage=True,
+        wf_id=types.Workflows.ID_100,
+        freigabe_auto=types.Freigabe.WF_100,
+        order_qty_expr=ORDER_QTY_FUNC(empty=False),
+    )
+    pipe_result.write_results(
+        data=pipe_result.open,
+        vorlage=False,
+        wf_id=types.Workflows.ID_100,
+        freigabe_auto=types.Freigabe.WF_100,
+        order_qty_expr=ORDER_QTY_FUNC(empty=False),
+    )
+    return pipe_result
+
+
+def wf200_umbreit(
+    pipe_result: PipelineResult,
+) -> PipelineResult:
+    relevant_mnr: tuple[int, ...] = (17, 18)
+    filter_meldenummer = pl.col("MELDENUMMER").is_in(relevant_mnr)
+    filter_mandant = pl.col("BEDP_MAN") == 1
+
+    res = _apply_several_filters(
+        pipe_result.open,
+        (filter_meldenummer, filter_mandant),
+    )
+    sub_pipe = PipelineResult(res.in_)
+    sub_pipe = _wf200_sub1(sub_pipe)
+    assert sub_pipe.open.height == 0
+    pipe_result.merge_pipeline(sub_pipe)
+
+    return pipe_result
+
+
+def wf200_petersen(
+    pipe_result: PipelineResult,
+) -> PipelineResult:
+    ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_200)
+    RELEVANT_MNR: tuple[int, ...] = (17, 18)
+    # // WDB branch
+    filter_meldenummer = pl.col("MELDENUMMER").is_in(RELEVANT_MNR)
+    filter_mandant = pl.col(MANDANT_CRITERION) == 90
+    filter_WDB = pl.col("VERLAGSNR").is_in((76008, 76070))
+    res = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer,
+            filter_mandant,
+            filter_WDB,
+        ),
+    )
+    # ignore these
+    pipe_result.write_results(
+        data=res.in_,
+        vorlage=False,
+        wf_id=types.Workflows.ID_200,
+        freigabe_auto=types.Freigabe.WF_200,
+        order_qty_expr=ORDER_QTY_FUNC(),
+    )
+    # // other branch
+    res = _apply_several_filters(
+        pipe_result.open,
+        (
+            filter_meldenummer,
+            filter_mandant,
+        ),
+    )
+    sub_pipe = PipelineResult(res.in_)
+    sub_pipe = _wf200_sub1(sub_pipe)
+    assert sub_pipe.open.height == 0, "Sub pipe not fully processed"
+    pipe_result.merge_pipeline(sub_pipe)
+
+    return pipe_result
+
+
+def _wf200_sub1(
+    pipe_result: PipelineResult,
+) -> PipelineResult:
+    save_tmp_data(pipe_result.open)
+    ORDER_QTY_FUNC = get_expr_order_qty(types.Workflows.ID_200)
+    RELEVANT_DATE = get_starting_date(90)
+
+    join_condition = db.tmp_data.c.BEDP_TITELNR == db.EXT_AUFPAUF.c.TITELNR
+    filter_ = sql.and_(
+        db.EXT_AUFPAUF.c.AUFTRAGS_DATUM >= RELEVANT_DATE,
+        db.EXT_AUFPAUF.c.KUNDE_RECHNUNG.not_in((608991, 260202)),
+        db.EXT_AUFPAUF.c.AUFTRAGS_ART.in_((1, 99)),
+    )
+    stmt = (
+        sql.select(
+            db.tmp_data,
+            db.EXT_AUFPAUF.c.KUNDE_RECHNUNG,
+            db.EXT_AUFPAUF.c.AUFTRAGS_ART,
+        )
+        .select_from(db.tmp_data.join(db.EXT_AUFPAUF, join_condition))
+        .where(filter_)
+    )
+    sub1 = stmt.subquery()
+
+    unique_count_col = sql.func.count(sub1.c.KUNDE_RECHNUNG.distinct())
+    stmt = (
+        sql.select(
+            sub1.c.BEDP_TITELNR,
+            sql.func.count().label("count"),
+            unique_count_col.label("customer_count"),
+        )
+        .select_from(sub1)
+        .group_by(sub1.c.BEDP_TITELNR)
+        .having(unique_count_col >= 3)
+    )
+    if SAVE_TMP_FILES:
+        stmt = (
+            sql.select(
+                sub1.c.BEDP_TITELNR,
+                sql.func.count().label("count"),
+                unique_count_col.label("customer_count"),
+            )
+            .select_from(sub1)
+            .group_by(sub1.c.BEDP_TITELNR)
+        )
+    # !! this is a sub result which must be used in the result set
+    # !! for testing and feedback by the customer
+    relevant_titles = pl.read_database(
+        stmt,
+        engine,
+    )
+
+    if SAVE_TMP_FILES:
+        save_tmp_file(relevant_titles, TMPFILE_WF200_SUB1)
+        relevant_titles = relevant_titles.filter(pl.col.CUSTOMER_COUNT >= 3)
+
+    entries_to_show = pipe_result.open.filter(
+        pl.col.BEDP_TITELNR.is_in(relevant_titles["BEDP_TITELNR"].unique().implode())
+    )
+
+    pipe_result.write_results(
+        data=entries_to_show,
+        vorlage=True,
+        wf_id=types.Workflows.ID_200,
+        freigabe_auto=types.Freigabe.WF_200,
+        order_qty_expr=ORDER_QTY_FUNC(),
+    )
+    pipe_result.write_results(
+        data=pipe_result.open,
+        vorlage=False,
+        wf_id=types.Workflows.ID_200,
+        freigabe_auto=types.Freigabe.WF_200,
+        order_qty_expr=ORDER_QTY_FUNC(),
+    )
+
+    return pipe_result