update address & location IoT

prepared_steps/1_extract_data/queries.json

@@ -2,10 +2,10 @@
     [
         {
             "sobject": "SCInstalledBaseLocation__c",
-            "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 = '{country}' AND Id = 'a1B1r0000099EsfEAE' limit 1"
+            "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 = '{country}' limit 3"
         },{
             "sobject": "SCInstalledBase__c",
-            "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 = '{country}' AND InstalledBaseLocation__c = 'a1B1r0000099EsfEAE'"
+            "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 = '{country}' limit 3"
         },{
             "sobject": "Asset",
             "query": "SELECT Id, Serialnumber FROM Asset WHERE Location.ParentLocation.Name LIKE '%{country}'"

prepared_steps/2_transform_via_script/TransformScript.py

@@ -13,13 +13,8 @@ read_df_ibr = pd.read_csv('../1_extract_data/results/SCInstalledBaseRole__c.csv'
 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_resourceassignment = pd.read_csv('../1_extract_data/results/SCResourceAssignment__c.csv', header=0, keep_default_na=False, dtype=str)
-
+read_df_address_iot = pd.read_csv('../1_extract_data/results/Address.csv', header=0, keep_default_na=False, dtype=str)
-for row in read_df.to_dict('records'):
+read_df_location_iot = pd.read_csv('../1_extract_data/results/ParentLocation.csv', header=0, keep_default_na=False, dtype=str)
-    try:
-        # Your processing logic here
-        pass
-    except KeyError as e:
-        print(f'KeyError: {e}')
 
 # Columns for reindexing
 reindex_columns = ['Id','City__c','Country__c','GeoY__c','GeoX__c','PostalCode__c','Street__c','Extension__c','HouseNo__c','FlatNo__c','Floor__c']
@@ -29,6 +24,8 @@ reindex_columns_ibr = ['Id', 'InstalledBaseLocation__c', 'Role__c', 'ValidFrom__
 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_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_columns_address_iot = ['Id', 'Country', 'CountryCode', 'Street', 'City', 'ParentId', 'PostalCode']
+reindex_columns_location_iot = ['Id', 'Name']
 
 # Reindex the columns to match the desired format
 df = read_df.reindex(reindex_columns, axis=1)
@@ -38,6 +35,29 @@ df_ibr = read_df_ibr.reindex(reindex_columns_ibr, 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_resourceassignment = read_df_resourceassignment.reindex(reindex_columns_resourceassignment, axis=1)
+df_address_iot = read_df_address_iot.reindex(reindex_columns_address_iot, axis=1)
+df_location_iot = read_df_location_iot.reindex(reindex_columns_location_iot, axis=1)    
+
+##--------------------------------------------------------------------------##
+## Update for IoT Addresses and Locations
+##--------------------------------------------------------------------------##
+
+df_address_iot['PKey__c'] = (
+    df_address_iot['Street'].astype(str) + ', ' +
+    df_address_iot['PostalCode'].astype(str) + ' ' +
+    df_address_iot['City'].astype(str) + ', ' +
+    df_address_iot['CountryCode'].astype(str)
+)
+
+merged_df_location_iot = pd.merge(df_location_iot,
+                        df_address_iot[['Id', 'ParentId', 'PKey__c']],
+                        left_on='Id',
+                        right_on='ParentId',
+                        how='left')
+
+merged_df_location_iot = merged_df_location_iot.drop('ParentId', axis=1)
+
+merged_df_location_iot.columns = ['Id', 'Name', 'VisitorAddressId', 'PKey__c']
 
 ##--------------------------------------------------------------------------##
 ## Address and Location (Parent and Child)
@@ -305,13 +325,14 @@ df_pricelistitem.columns = columns_pricebookentry
 
 
 # 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('../4_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('../4_upsert_address_and_parent_location/Location.csv', index=False)
-child_df.to_csv('../5_upsert_child_location/Location.csv', index=False)
+child_df.to_csv('../6_upsert_child_location/Location.csv', index=False)
-merged_df_ib.to_csv('../7_upsert_assets/Asset.csv', index=False)
+merged_df_ib.to_csv('../8_upsert_assets/Asset.csv', index=False)
-df_ibr.to_csv('../10_upsert_associated_location/AssociatedLocation.csv', index=False)
+df_ibr.to_csv('../11_upsert_associated_location/AssociatedLocation.csv', index=False)
-df_pricelist.to_csv('../11_insert_pricebook2_and_pricebookentries/Pricebook2.csv', index=False)
+df_pricelist.to_csv('../12_insert_pricebook2_and_pricebookentries/Pricebook2.csv', index=False)
-df_pricelistitem.to_csv('../11_insert_pricebook2_and_pricebookentries/PricebookEntry.csv', index=False)
+df_pricelistitem.to_csv('../12_insert_pricebook2_and_pricebookentries/PricebookEntry.csv', index=False)
+merged_df_location_iot.to_csv('../3_update_address_and_location_data_for_migration/Location.csv', index=False)
 
 ## end mapping
 

prepared_steps/3_update_address_and_location_data_for_migration/export.json

@@ -0,0 +1,16 @@
+{
+    "allOrNone": true,
+    "objects": [
+        {
+            "query": "SELECT  Id FROM Address WHERE CountryCode = 'NL'",
+            "operation": "Readonly",
+            "master": false
+        },{
+            "query": "SELECT Id,Name,VisitorAddressId,PKey__c FROM Location",
+            "operation": "Update",
+            "externalId": "Id",
+            "master": true,
+            "useSourceCSVFile": true
+        }
+    ]
+}

prepared_steps/4_upsert_address_and_parent_location/export.json

@@ -5,11 +5,12 @@
     "objects": [
         {
             "query": "SELECT Name,DuplicateCheck__c,IsInventoryLocation,IsMobile,LocationType FROM Location",
-            "operation": "Insert",
+            "operation": "Upsert",
             "externalId": "Name"
         },{
             "query": "SELECT City, CountryCode, PostalCode, Street, Latitude, Longitude, PKey__c, Country, Pkey__c, ParentId$Location FROM Address",
-            "operation": "Insert"
+            "operation": "Upsert",
+            "externalId": "PKey__c"
         }
     ]
 }

prepared_steps/8_upsert_assets/command.txt

@@ -0,0 +1 @@
+sf sfdmu run --sourceusername csvfile --targetusername rene.kasseboehmer@vaillant.de.devrene