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working connection to Wittenbecher's external database

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This commit is contained in:
Florian Förster
2026-06-15 16:32:58 +02:00
parent 85e45e4c94
commit 9af7136d19
5 changed files with 473 additions and 6 deletions
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26
database/create.sql Normal file
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@@ -0,0 +1,26 @@
CREATE TABLE IF NOT EXISTS ls.production_order (
"Suchschluessel" TEXT PRIMARY KEY, -- ERP, eindeutiger key für einen Auftrag
"Grund der Ausloesung Beschreibung" TEXT, -- ERP, nicht relevant
"Produktionsplan Name" TEXT, -- ERP, nicht relevant
"Produktionsplan Suchschluessel" TEXT, -- ERP, nicht relevant "Suchschluessel" im Frontend
"Status" TEXT, -- ERP, entweder "CL", oder "OR". Zur zeit wollen wir bei uns nur Aufträge mit Status "OR" anschauen
"Freigabe" TEXT, -- ERP, "N" oder "Y", wir schauen nur Aufträge mit "Y" an
"Startdatum" TEXT, -- ERP
"Enddatum" TEXT, -- ERP
"Arbeitsgang_Arbeitsanweisung" TEXT, -- ERP, nicht relevant
"Arbeitsgang_Artikelname" TEXT, -- ERP, das zeigen wir im Frontend unter "Artikelname" an
"Arbeitsgang_Artikelnummer" TEXT, -- ERP, das zeigen wir im Frontend unter "Artikelnummer" an
"Arbeitsgang_Menge" TEXT, -- ERP, das zeigen wir im Frontend unter "Menge" an
"Arbeitsgang_Rohmaterial" TEXT, -- ERP, Json, Liste mit Rohmaterialien. Wir klassifizieren das mittels regex und zeigen die Klassen für Material und Form an (siehe ret_materials und ret_forms). Außerdem steht hier manchmal noch ein größe mit dabei (siehe ret_dim_1)
"ret_forms" TEXT, -- KMI, Form des Rohmaterials, das zeigen wir im Frontend unter "Form" an
"ret_materials" TEXT, -- KMI, Rohmaterial, das zeigen wir im Frontend unter "Material" an
"ret_dim_1" INTEGER, -- KMI, Länge des Rohmaterials, das zeigen wir im Frontend unter "Dim1 an
"Bestelldatum" TEXT, -- ERP
"Kunden-ID" TEXT, -- ERP
"Kundenprioritaet" INTEGER, -- ERP
"order" INTEGER, -- KMI, Reihenfolge die durch unsere Heuristik berechnet wurde
"clusters" INTEGER, -- KMI, wir clustern die Aufträge nach material und form
"chunk_id" INTEGER, -- KMI Aufträge mit ähnlicher "prio" werden in chunks zusammengefasst, sodass man innerhalb dieser chunks nach cluster sortieren kann (siehe chunk 7 im bild unten). So werden Rüstzeiten minimiert.
"prio" FLOAT, -- KMI prioritäts score für den Auftrag auf Basis von Kundenprioritaet und Enddatum
"erfassungspunkt" TEXT
);

291
pdm.lock generated
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@@ -5,7 +5,7 @@
groups = ["default", "dev", "lint", "nb", "tests"] groups = ["default", "dev", "lint", "nb", "tests"]
strategy = ["inherit_metadata"] strategy = ["inherit_metadata"]
lock_version = "4.5.0" lock_version = "4.5.0"
content_hash = "sha256:3a107981dc4305f031f87c89e3a57a6bb823954d397a52d074fef1c72ac639d0" content_hash = "sha256:e2ae321e3a1a60b798f31e8215052031fd4d08c5175ebe6b87a6391df86214ae"
[[metadata.targets]] [[metadata.targets]]
requires_python = ">=3.11" requires_python = ">=3.11"
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[[package]] [[package]]
name = "h11" name = "h11"
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@@ -1714,6 +1793,106 @@ files = [
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] ]
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name = "stack-data" name = "stack-data"
version = "0.6.3" version = "0.6.3"
@@ -2549,7 +2830,7 @@ name = "typing-extensions"
version = "4.15.0" version = "4.15.0"
requires_python = ">=3.9" requires_python = ">=3.9"
summary = "Backported and Experimental Type Hints for Python 3.9+" summary = "Backported and Experimental Type Hints for Python 3.9+"
groups = ["dev", "nb"] groups = ["default", "dev", "nb"]
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@@ -2574,8 +2855,8 @@ name = "tzdata"
version = "2026.2" version = "2026.2"
requires_python = ">=2" requires_python = ">=2"
summary = "Provider of IANA time zone data" summary = "Provider of IANA time zone data"
groups = ["nb"] groups = ["default", "nb"]
marker = "python_version >= \"3.9\"" marker = "sys_platform == \"win32\" or python_version >= \"3.9\""
files = [ files = [
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160
prototpyes/01_connect.py Normal file
View File

@@ -0,0 +1,160 @@
# %%
import dataclasses as dc
import datetime
import os
import random
from pprint import pprint
import dotenv
import psycopg
import sqlalchemy as sql
dotenv.load_dotenv()
# %%
DB_HOST = os.getenv("POSTGRES_HOST", None)
DB_PORT = os.getenv("POSTGRES_PORT", None)
DB_USER = os.getenv("POSTGRES_USER", None)
DB_PASS = os.getenv("POSTGRESS_PASS", None)
assert DB_HOST
assert DB_PORT
assert DB_USER
assert DB_PASS
RNG = random.Random(42)
CONN_STRING = f"postgresql+psycopg://{DB_USER}:{DB_PASS}@{DB_HOST}:{DB_PORT}/ls"
# conn_info = "dbname=deine_db user=postgres password=dein_passwort host=localhost port=5432"
# %%
metadata = sql.MetaData()
prod_order_t = sql.Table(
"production_order",
metadata,
sql.Column("Suchschluessel", sql.Text, primary_key=True),
sql.Column("Grund_der_Ausloesung_Beschreibung", sql.Text),
sql.Column("Produktionsplan_Name", sql.Text),
sql.Column("Produktionsplan_Suchschluessel", sql.Text),
sql.Column("Status", sql.Text),
sql.Column("Freigabe", sql.Text),
sql.Column("Startdatum", sql.Text),
sql.Column("Enddatum", sql.Text),
sql.Column("Arbeitsgang_Arbeitsanweisung", sql.Text),
sql.Column("Arbeitsgang_Artikelname", sql.Text),
sql.Column("Arbeitsgang_Artikelnummer", sql.Text),
sql.Column("Arbeitsgang_Menge", sql.Text),
sql.Column("Arbeitsgang_Rohmaterial", sql.Text),
sql.Column("ret_forms", sql.Text),
sql.Column("ret_materials", sql.Text),
sql.Column("ret_dim_1", sql.Integer),
sql.Column("Bestelldatum", sql.Text),
sql.Column("Kunden_ID", sql.Text),
sql.Column("Kundenprioritaet", sql.Integer),
sql.Column("order", sql.Integer),
sql.Column("clusters", sql.Integer),
sql.Column("chunk_id", sql.Integer),
sql.Column("prio", sql.Float),
sql.Column("erfassungspunkt", sql.Text),
schema="ls",
)
# %%
@dc.dataclass(slots=True, kw_only=True)
class DbEntry:
Suchschluessel: str
Grund_der_Ausloesung_Beschreibung: str
Produktionsplan_Name: str
Produktionsplan_Suchschluessel: str
Status: str
Freigabe: str
Startdatum: str
Enddatum: str
Arbeitsgang_Arbeitsanweisung: str
Arbeitsgang_Artikelname: str
Arbeitsgang_Artikelnummer: str
Arbeitsgang_Menge: str
Arbeitsgang_Rohmaterial: str
ret_forms: str
ret_materials: str
ret_dim_1: int
Bestelldatum: str
Kunden_ID: str
Kundenprioritaet: int
order: int
clusters: int
chunk_id: int
prio: float
erfassungspunkt: str
def random_prod_no() -> str:
num = RNG.randint(100000, 9999999)
return f"PRO{num}WS"
def random_entry() -> DbEntry:
_prod_no = random_prod_no()
prod_no = f"random__{_prod_no}"
dummy = "DUMMY"
datestr = datetime.datetime.now().date().isoformat()
rand_prio = RNG.randint(1, 10) / 10
return DbEntry(
Suchschluessel=prod_no,
Grund_der_Ausloesung_Beschreibung=dummy,
Produktionsplan_Name=prod_no,
Produktionsplan_Suchschluessel=prod_no,
Status="test",
Freigabe="N",
Startdatum=datestr,
Enddatum=datestr,
Arbeitsgang_Arbeitsanweisung="TEST",
Arbeitsgang_Artikelname="TEST",
Arbeitsgang_Artikelnummer="TEST_ARTIKEL",
Arbeitsgang_Menge="TEST_MENGE",
Arbeitsgang_Rohmaterial="ROHMATERIAL_XYZ",
ret_forms="FORMS",
ret_materials="MATERIALS",
ret_dim_1=1200,
Bestelldatum=datestr,
Kunden_ID=f"KUNDE_{prod_no}",
Kundenprioritaet=2,
order=2,
clusters=3,
chunk_id=4,
prio=rand_prio,
erfassungspunkt="erfassungspunkt_xyz_dummy",
)
# %%
engine = sql.create_engine(CONN_STRING, echo=True)
# %%
stmt = sql.text("SELECT * FROM ls.production_order")
with engine.connect() as conn:
res = conn.execute(stmt)
results = res.all()
# %%
pprint(list(res.keys()))
# for entry in list(res.keys()):
# print(f"{entry}: str")
# %%
NUM_DATAPOINTS = 100
_insert_data = [random_entry() for _ in range(NUM_DATAPOINTS)]
insert_data = [dc.asdict(x) for x in _insert_data]
stmt = sql.insert(prod_order_t)
with engine.begin() as conn:
res = conn.execute(stmt, insert_data)
# %%
stmt = sql.text("TRUNCATE ls.production_order;")
with engine.begin() as conn:
conn.execute(stmt)
# %%
engine.dispose()
# %%

2
pyproject.toml
View File

@@ -5,7 +5,7 @@ description = "PLEASE ADD PROJECT DESCRIPTION"
authors = [ authors = [
{name = "PLEASE ADD AUTHOR", email = "PLEASE ADD EMAIL ADDRESS"}, {name = "PLEASE ADD AUTHOR", email = "PLEASE ADD EMAIL ADDRESS"},
] ]
dependencies = [] dependencies = ["psycopg[binary,pool]>=3.3.4", "python-dotenv>=1.2.2", "sqlalchemy[asyncio]>=2.0.50"]
requires-python = ">=3.11" requires-python = ">=3.11"
readme = "README.md" readme = "README.md"
license = {text = "LicenseRef-Proprietary"} license = {text = "LicenseRef-Proprietary"}

0
src/py311/__init__.py → src/kmi_wb_postgres/__init__.py
View File