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pre for location (van)

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This commit is contained in:
Rene Kaßeböhmer
2025-04-22 15:55:47 +02:00
parent 3f9fa0bbc7
commit 90a5e6b629
6 changed files with 42 additions and 731 deletions
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4
.gitignore vendored
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@ -22,3 +22,7 @@ SCPriceList__c.csv
SCPriceListItem__c.csv SCPriceListItem__c.csv
results/ results/
mapping.csv mapping.csv
SCInstalledBaseRole__c.csv
SCPriceList__c.csv
SCPriceListItem__c.csv
SCResourceAssignment__c.csv

72
prepared_steps/1_extract_data/export.json
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@ -1,72 +0,0 @@
{
"allOrNone": true,
"useSeparatedCSVFiles": true,
"pollingQueryTimeoutMs": 1000000,
"bulkApiVersion": "2.0",
"queryBulkApiThreshold ": 100,
"objectSets": [
{
"objects": [
{
"query": "SELECT Id, City__c, Country__c, GeoY__c, GeoX__c, PostalCode__c, Street__c, Extension__c, HouseNo__c, FlatNo__c, Floor__c FROM SCInstalledBaseLocation__c WHERE Country__c = 'NL' limit 1000",
"externalId": "Name",
"operation": "Readonly"
}
]
},{
"objects": [
{
"query": "SELECT Id, Name, CommissioningDate__c,InstallationDate__c,ProductEnergy__c, ProductUnitClass__c,ArticleNo__c,SerialNo__c, SerialNoException__c, ProductUnitType__c, InstalledBaseLocation__c FROM SCInstalledBase__c WHERE Country__c = 'NL'",
"externalId": "Name",
"operation": "Readonly",
"master":true,
"excludedFromUpdateFields": ["InstalledBaseLocation__c"],
"skipRecordsComparison": true,
"parallelRestJobs": 4,
"restApiBatchSize": 9500,
"fieldMapping": [
{
"sourceField": "InstalledBaseLocation__c",
"targetField": "Id",
"targetObject": "SCInstalledBaseLocation__c"
}
]
}
]
},{
"objects": [
{
"query": "SELECT Id, Serialnumber FROM Asset WHERE Location.ParentLocation.Name LIKE '%NL'",
"externalId": "Name",
"operation": "Readonly"
}
]
},{
"objects": [
{
"query": "SELECT Id, Country, CountryCode, Street, City, ParentId, PostalCode FROM Address WHERE CountryCode = 'NL'",
"externalId": "Name",
"operation": "Readonly",
"excludedFromUpdateFields": ["ParentId"]
}
]
},{
"objects": [
{
"query": "SELECT Id, ParentLocationId, Name FROM Location WHERE Name LIKE '%NL' OR ParentLocation.Name = '%NL'",
"externalId": "Name",
"operation": "Readonly"
}
]
},{
"objects": [
{
"query": "SELECT Id, Main_Product_Group__c, Family, MaterialType__c, Name, Product_Code__c, ProductCode, EAN_Product_Code__c FROM Product2",
"externalId": "Name",
"operation": "Readonly"
}
]
}
]
}

17
prepared_steps/1_extract_data/extract_via_simple_salesforce.py
View File

@ -41,10 +41,12 @@ def get_credentials(context):
credentials = { credentials = {
'USERNAME': None, 'USERNAME': None,
'PASSWORD': None, 'PASSWORD': None,
'SECURITY_TOKEN': None, 'SECURITY_TOKEN': None
'DOMAIN': 'test'
} }
if context != 'PROD':
credentials['DOMAIN'] = 'test'
# Load the .env file explicitly from one directory above # Load the .env file explicitly from one directory above
env_file = find_dotenv("../.env") env_file = find_dotenv("../.env")
load_dotenv(env_file) load_dotenv(env_file)
@ -52,6 +54,8 @@ def get_credentials(context):
# Load all environment variables # Load all environment variables
env_vars = os.environ env_vars = os.environ
for key, value in env_vars.items(): for key, value in env_vars.items():
#print(f'{context}_SF_', key, value) #print(f'{context}_SF_', key, value)
if f'{context}_SF_' in key: if f'{context}_SF_' in key:
@ -79,6 +83,15 @@ def extract_data(object_id, query, output_path='output', context='qa2'):
if not all(credentials.values()): if not all(credentials.values()):
raise ValueError(f"Missing credentials for context: {context}") raise ValueError(f"Missing credentials for context: {context}")
if(context == 'prod'):
# Initialize Salesforce bulk connector
sf = Salesforce(
username=credentials['USERNAME'],
password=credentials['PASSWORD'],
security_token=credentials['SECURITY_TOKEN'],
version='62.0'
)
else:
# Initialize Salesforce bulk connector # Initialize Salesforce bulk connector
sf = Salesforce( sf = Salesforce(
username=credentials['USERNAME'], username=credentials['USERNAME'],

3
prepared_steps/1_extract_data/queries.json
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@ -30,6 +30,9 @@
},{ },{
"sobject": "SCPriceListItem__c", "sobject": "SCPriceListItem__c",
"query": "SELECT Id, Article__r.Name, Price__c, PriceUnit__c, Pricelist__c, ValidFrom__c, ValidTo__c, Article__r.EANCode__c, Pricelist__r.Brand__r.Name, Pricelist__r.Country__c FROM SCPriceListItem__c WHERE Country__c = '{country}' AND PriceList__c != null AND (ValidTo__c >= TODAY OR ValidFrom__c >= TODAY) AND Article__c = 'a0Gw000001R9slpEAB'" "query": "SELECT Id, Article__r.Name, Price__c, PriceUnit__c, Pricelist__c, ValidFrom__c, ValidTo__c, Article__r.EANCode__c, Pricelist__r.Brand__r.Name, Pricelist__r.Country__c FROM SCPriceListItem__c WHERE Country__c = '{country}' AND PriceList__c != null AND (ValidTo__c >= TODAY OR ValidFrom__c >= TODAY) AND Article__c = 'a0Gw000001R9slpEAB'"
},{
"sobject": "SCResourceAssignment__c",
"query": "SELECT Id, ValidTo__c, ValidFrom__c, Country__c, City__c, District__c, Street__c, HouseNo__c, Extension__c, FlatNo__c, Floor__c, GeoY__c, GeoX__c, Resource__c, Stock__c FROM SCResourceAssignment__c WHERE Country__c = '{country}' AND ValidTo__c >= TODAY"
} }
] ]
} }

10
prepared_steps/2_transform_via_script/TransformScript.py
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@ -12,6 +12,7 @@ read_df_product2 = pd.read_csv('../1_extract_data/results/Product2.csv', header=
read_df_ibr = pd.read_csv('../1_extract_data/results/SCInstalledBaseRole__c.csv', header=0, keep_default_na=False, dtype=str) read_df_ibr = pd.read_csv('../1_extract_data/results/SCInstalledBaseRole__c.csv', header=0, keep_default_na=False, dtype=str)
read_df_pricelist = pd.read_csv('../1_extract_data/results/SCPriceList__c.csv', header=0, keep_default_na=False, dtype=str) read_df_pricelist = pd.read_csv('../1_extract_data/results/SCPriceList__c.csv', header=0, keep_default_na=False, dtype=str)
read_df_pricelistitem = pd.read_csv('../1_extract_data/results/SCPriceListItem__c.csv', header=0, keep_default_na=False, dtype=str) read_df_pricelistitem = pd.read_csv('../1_extract_data/results/SCPriceListItem__c.csv', header=0, keep_default_na=False, dtype=str)
read_df_resourceassignment = pd.read_csv('../1_extract_data/results/SCResourceAssignment__c.csv', header=0, keep_default_na=False, dtype=str)
for row in read_df.to_dict('records'): for row in read_df.to_dict('records'):
try: try:
@ -27,6 +28,7 @@ reindex_columns_product2 = ['Id','Main_Product_Group__c','Family','MaterialType_
reindex_columns_ibr = ['Id', 'InstalledBaseLocation__c', 'Role__c', 'ValidFrom__c', 'ValidTo__c', 'Account__c'] reindex_columns_ibr = ['Id', 'InstalledBaseLocation__c', 'Role__c', 'ValidFrom__c', 'ValidTo__c', 'Account__c']
reindex_columns_pricelist = ['Id', 'Name', 'Brand__r.Name', 'Country__c'] reindex_columns_pricelist = ['Id', 'Name', 'Brand__r.Name', 'Country__c']
reindex_columns_pricelistitem = ['Id', 'Article__r.Name', 'Article__r.EANCode__c', 'Price__c', 'PriceUnit__c', 'Pricelist__c', 'ValidFrom__c', 'ValidTo__c', 'Pricelist__r.Brand__r.Name', 'Pricelist__r.Country__c'] reindex_columns_pricelistitem = ['Id', 'Article__r.Name', 'Article__r.EANCode__c', 'Price__c', 'PriceUnit__c', 'Pricelist__c', 'ValidFrom__c', 'ValidTo__c', 'Pricelist__r.Brand__r.Name', 'Pricelist__r.Country__c']
reindex_columns_resourceassignment = ['Id', 'ValidTo__c', 'ValidFrom__c', 'Country__c', 'City__c', 'District__c', 'Street__c', 'HouseNo__c', 'Extension__c', 'FlatNo__c', 'Floor__c', 'GeoY__c', 'GeoX__c', 'Resource__c', 'Stock__c']
# Reindex the columns to match the desired format # Reindex the columns to match the desired format
df = read_df.reindex(reindex_columns, axis=1) df = read_df.reindex(reindex_columns, axis=1)
@ -35,6 +37,7 @@ df_product2 = read_df_product2.reindex(reindex_columns_product2, axis=1)
df_ibr = read_df_ibr.reindex(reindex_columns_ibr, axis=1) df_ibr = read_df_ibr.reindex(reindex_columns_ibr, axis=1)
df_pricelist = read_df_pricelist.reindex(reindex_columns_pricelist, axis=1) df_pricelist = read_df_pricelist.reindex(reindex_columns_pricelist, axis=1)
df_pricelistitem = read_df_pricelistitem.reindex(reindex_columns_pricelistitem, axis=1) df_pricelistitem = read_df_pricelistitem.reindex(reindex_columns_pricelistitem, axis=1)
df_resourceassignment = read_df_resourceassignment.reindex(reindex_columns_resourceassignment, axis=1)
##--------------------------------------------------------------------------## ##--------------------------------------------------------------------------##
## Address and Location (Parent and Child) ## Address and Location (Parent and Child)
@ -294,6 +297,13 @@ columns_pricebookentry = ['IsActive', 'Product2.Product_Code__c', 'UnitPrice', '
df_pricelistitem.columns = columns_pricebookentry df_pricelistitem.columns = columns_pricebookentry
##--------------------------------------------------------------------------##
## Location (Van)
##--------------------------------------------------------------------------##
# Write each DataFrame to a separate CSV file # Write each DataFrame to a separate CSV file
address_df.to_csv('../3_upsert_address_and_parent_location/Address.csv', index=False) address_df.to_csv('../3_upsert_address_and_parent_location/Address.csv', index=False)
parent_df.to_csv('../3_upsert_address_and_parent_location/Location.csv', index=False) parent_df.to_csv('../3_upsert_address_and_parent_location/Location.csv', index=False)

647
prepared_steps/2_transform_via_script/mapping.csv
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@ -1,647 +0,0 @@
Product_Code__c,Main_Product_Group__c,ArticleNo__c,ProductUnitClass__c,ProductUnitType__c
,,,,
,,,01,011
,,,01,012
,,,02,021
,,,02,022
,,,04,041
,,,05,051
,,,05,053
,,,05,054
,,,06,061
,,,07,071
,,,07,072
,,,07,073
,,,07,076
,,,08,081
,,,08,084
,,,10,102
,,,10,103
,,,10,106
,,,10,107
,,,11,
,,,11,114
,,,12,121
,,,12,122
,,,40,409
,,,99,999
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0010005973,E1,0010005973,,
311430,E1,311430,,
304052,E1,304052,,
311555,E1,311555,,
311432,E1,311432,,
304050,E1,304050,,
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311430,E1,311430,04,041
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304052,E1,304052,04,041
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304053,E1,304053,04,041
311257,E1,311257,04,041
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311483,E1,311483,04,041
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311555,E1,311555,04,041
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304050,E1,304050,04,041
311556,E1,311556,04,041
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311244,E1,311244,04,041
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304039,E1,304039,04,041
311231,E1,311231,04,041
311412,E1,311412,04,041
311481,E1,311481,04,041
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0010006837,E3,0010006837,07,071
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8000015096,E5,8000015096,05,054
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0020145020,E5,0020145020,07,073
0020053780,F1,0020053780,,
,F1,,,
0020028505,F1,0020028505,,
0020171320,F1,0020171320,,
,F1,,01,011
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0020117049,F1,0020117049,07,073
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0020231665,F1,0020231665,07,073
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307401,F1,307401,10,103
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0020242486,F1,0020242486,10,103
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0010005352,F2,0010005352,07,072
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0010002617,F2,0010002617,07,072
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0010006856,F6,0010006856,,
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0010013172,F6,0010013172,07,071
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0020266791,K1,0020266791,,
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336006,K1,336006,12,121
031600,Q1,031600,01,012
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0010006227,V1,0010006227,01,012
0010002828,XQ,0010002828,01,012
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0010005425,XQ,0010005425,02,022
0010002542,XQ,0010002542,02,022
0010002543,XQ,0010002543,02,022
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1 Product_Code__c Main_Product_Group__c ArticleNo__c ProductUnitClass__c ProductUnitType__c
2
3 01 011
4 01 012
5 02 021
6 02 022
7 04 041
8 05 051
9 05 053
10 05 054
11 06 061
12 07 071
13 07 072
14 07 073
15 07 076
16 08 081
17 08 084
18 10 102
19 10 103
20 10 106
21 10 107
22 11
23 11 114
24 12 121
25 12 122
26 40 409
27 99 999
28 0010007402 A1 0010007402
29 308501 A1 308501
30 300161 A1 300161
31 308500 A1 308500
32 0010002664 A1 0010002664
33 300160 A1 300160
34 0010009493 A1 0010009493
35 0010009492 A1 0010009492
36 0010002628 A1 0010002628
37 0010004142 A1 0010004142
38 0010002627 A1 0010002627
39 0010002662 A1 0010002662
40 0010009494 A1 0010009494
41 0010009491 A1 0010009491
42 0010002661 A1 0010002661
43 0010004141 A1 0010004141
44 0010011721 A1 0010011721
45 0010011722 A1 0010011722
46 0010011731 A1 0010011731
47 0010002628 A1 0010002628 01 011
48 0010021961 A1 0010021961 01 011
49 0010021905 A1 0010021905 01 012
50 0010021908 A1 0010021908 01 012
51 0010018504 A1 0010018504 01 012
52 0010021906 A1 0010021906 01 012
53 0010011702 A1 0010011702 01 012
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61 0010008850 A1 0010008850 01 012
62 0010011704 A1 0010011704 01 012
63 0010017821 A1 0010017821 01 012
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66 0010008849 A1 0010008849 01 012
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73 0010009493 A1 0010009493 01 012
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76 0010005829 A1 0010005829 01 012
77 0010017375 A1 0010017375 01 012
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79 0010011707 A1 0010011707 01 012
80 0010015667 A1 0010015667 01 012
81 0010025033 A1 0010025033 01 012
82 0010020371 A1 0010020371 01 012
83 LT95SG12 A1 LT95SG12 01 012
84 0010014587 A1 0010014587 01 012
85 2000465503 A1 2000465503 01 012
86 0010025034 A1 0010025034 01 012
87 0010025031 A1 0010025031 01 012
88 0010021910 A1 0010021910 01 012
89 0010025032 A1 0010025032 01 012
90 2000465502 A1 2000465502 01 012
91 0010004142 A1 0010004142 01 012
92 0010008852 A1 0010008852 01 012
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94 0010002662 A1 0010002662 01 012
95 0010002628 A1 0010002628 01 012
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102 308501 A1 308501 01 012
103 0010002665 A1 0010002665 01 012
104 0010015668 A1 0010015668 01 012
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106 0010020374 A1 0010020374 01 012
107 308500 A1 308500 01 012
108 0010010779 A1 0010010779 01 012
109 0010003810 A1 0010003810 01 012
110 0010011701 A1 0010011701 01 012
111 0010002661 A1 0010002661 01 012
112 0010009492 A1 0010009492 01 012
113 0010011708 A1 0010011708 01 012
114 0010009494 A1 0010009494 01 012
115 0010007968 A1 0010007968 01 012
116 0010025035 A1 0010025035 01 012
117 0010021526 A1 0010021526 01 012
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120 0010010755 A1 0010010755 01 012
121 0010011705 A1 0010011705 01 012
122 0010002660 A1 0010002660 01 012
123 0010021527 A1 0010021527 01 012
124 300161 A1 300161 01 012
125 0010016403 A1 0010016403 01 012
126 2000465698 A1 2000465698 01 012
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128 0010029560 A1 0010029560 01 012
129 0010029557 A1 0010029557 01 012
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131 0010016400 A1 0010016400 01 012
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141 0010024619 A1 0010024619 01 012
142 0010024617 A1 0010024617 01 012
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151 0010043923 A1 0010043923 01 012
152 0010043922 A1 0010043922 01 012
153 0010043924 A1 0010043924 01 012
154 0010043921 A1 0010043921 01 012
155 0010024613 A1 0010024613 01 012
156 0010016396 A1 0010016396 01 012
157 0010016401 A1 0010016401 01 012
158 0010016397 A1 0010016397 01 012
159 0010016399 A1 0010016399 01 012
160 0010015191 A1 0010015191 01 012
161 0010016398 A1 0010016398 01 012
162 0010016402 A1 0010016402 01 012
163 0010011633 A1 0010011633 01 012
164 0010021849 A1 0010021849 01 012
165 0010002627 A1 0010002627 05 051
166 0010021910 A1 0010021910 05 051
167 0010021905 A1 0010021905 07 073
168 0010011709 A1 0010011709 07 073
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