data extract switch to simple_salesforce bulkv2

prepared_steps/1_extract_data/command.txt

@@ -1 +1 @@
-sf sfdmu run --sourceusername rene.kasseboehmer@vaillant.de --targetusername csvfile
+python .\extract_via_simple_salesforce.py --context prod

prepared_steps/1_extract_data/extract_via_simple_salesforce.py

@@ -4,10 +4,27 @@
 #   --output_path extracted_data
 
 import os
+import itertools
+import threading
+import time
+import sys
+import json
 import pandas as pd
 from dotenv import load_dotenv, find_dotenv
 from simple_salesforce import Salesforce
 
+done = False
+
+#here is the animation
+def animate():
+    for c in itertools.cycle(['|', '/', '-', '\\']):
+        if done:
+            break
+        sys.stdout.write('\rloading ' + c)
+        sys.stdout.flush()
+        time.sleep(0.1)
+    sys.stdout.write('\rDone!     ')
+
 def get_credentials(context):
     """
     Get credentials for a given context from the .env file
@@ -39,12 +56,11 @@ def get_credentials(context):
         #print(f'{context}_SF_', key, value)
         if f'{context}_SF_' in key:
             credential_key = key.split(f'{context}_SF_')[-1].upper()
-            print(credential_key)
             credentials[credential_key] = value
             
     return credentials
 
-def extract_data(object_id, output_path='output', context='qa2'):
+def extract_data(object_id, query, output_path='output', context='qa2'):
     """
     Extract data using Bulk API and save as CSV
     
@@ -54,10 +70,12 @@ def extract_data(object_id, output_path='output', context='qa2'):
         context (str): Context name for credentials (e.g., 'qa2', 'prod')
     """
     try:
+
+        global done
+        done = False
         # Get credentials based on context
         credentials = get_credentials(context)
         
-        print(credentials)
         if not all(credentials.values()):
             raise ValueError(f"Missing credentials for context: {context}")
         
@@ -66,19 +84,32 @@ def extract_data(object_id, output_path='output', context='qa2'):
             username=credentials['USERNAME'],
             password=credentials['PASSWORD'],
             security_token=credentials['SECURITY_TOKEN'],
-            domain=credentials['DOMAIN']
+            domain=credentials['DOMAIN'],
+            version='62.0'
         )
         
         # Create a simple query for the desired object
-        soql_query = f"""
+        #soql_query = f"""
-            SELECT Id, Name
+        #    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'
-            FROM SCInstalledBase__c
+        #"""
-            WHERE Country__c = 'NL' limit 1000
-        """
 
-        sf.bulk2.__getattr__("SCInstalledBase__c").download(
+        t = threading.Thread(target=animate)
-            soql_query, path="./", max_records=200000
+        t.start()
+
+        #sf.bulk2.__getattr__("SCInstalledBaseLocation__c").download(
+        #    soql_query, path="./", max_records=2000000
+        #)
+
+        results = sf.bulk2.__getattr__(object_id).query(
+            query, max_records=2000000
         )
+        print(f'Extracting: {object_id}')
+        for i, data in enumerate(results):
+            with open(f"results/{object_id}.csv", "w", encoding="utf-8") as bos:
+                bos.write(data)
+
+        time.sleep(10)
+        done = True
         
         """
         # Execute the Bulk query job
@@ -111,6 +142,7 @@ def extract_data(object_id, output_path='output', context='qa2'):
         return csv_file
         """
     except Exception as e:
+        done = True
         raise ValueError(f'Error extracting data: {str(e)}')
 
 if __name__ == '__main__':
@@ -120,18 +152,42 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Extract Salesforce data via Bulk API')
     parser.add_argument('--context', type=str, required=True,
                        help='Context name (e.g., "qa2", "prod")')
-    parser.add_argument('--object_id', type=str, required=True,
-                       help='Account, SCInstalledBaseLocation__c, SCInstalledBase__c, Product2')
     parser.add_argument('--output_path', type=str, required=False,
                        help='./')
+    parser.add_argument('--sobjects', type=str, required=False,
+                       help='Define which sobjects from queries.json to extract. (--sobjects Account,Contact,CustomObject__c) Otherwise all from queries.json will be extracted')
     
     args = parser.parse_args()
 
-    # Extract data using parameters
+    # Load queries from JSON file
+    with open('queries.json') as f:
+        queries = json.load(f)['queries']
+    
+    # Process each query in sequence
+    selected_sobjects = []
+    if args.sobjects:
+        selected_sobjects = [sobject.strip() for sobject in args.sobjects.split(',')]
+    
+    for query_def in queries:
+        sobject = query_def['sobject']
+        query_str = query_def['query']
+        
+        # Skip if --sobjects is provided and current sobject isn't in the list
+        if selected_sobjects and sobject not in selected_sobjects:
+            print(f'Skipping {sobject} as it is not specified in --sobjects')
+            continue
+        
+        try:
+            print(f'\nRunning query for {sobject}:')
             output_file = extract_data(
-        object_id=args.object_id,
+                object_id=sobject,
+                query=query_str,
                 output_path=args.output_path,
                 context=args.context
             )
-    
             print(f'File saved at: {output_file}')
+        except Exception as e:
+            print(f'Error running query for {sobject}: {str(e)}')
+            # Optionally, you can choose to skip failed queries or exit here
+
+    print('\nExtraction complete.')

prepared_steps/1_extract_data/queries.json

@@ -0,0 +1,26 @@
+{   "queries":
+    [
+        {
+            "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 = 'NL'"
+        },{
+            "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 = 'NL'"
+        },{
+            "sobject": "Asset",
+            "query": "SELECT Id, Serialnumber FROM Asset WHERE Location.ParentLocation.Name LIKE '%NL'"
+        },{
+            "sobject": "Address",
+            "query": "SELECT Id, Country, CountryCode, Street, City, ParentId, PostalCode FROM Address WHERE CountryCode = 'NL'"
+        },{
+            "sobject": "ParentLocation",
+            "query": "SELECT Id, Name FROM Location WHERE Name LIKE '%NL'"
+        },{
+            "sobject": "ChildLocation",
+            "query": "SELECT Id, ParentLocationId, Name FROM Location WHERE ParentLocation.Name LIKE '%NL'"
+        },{
+            "sobject": "Product2",
+            "query": "SELECT Id, Main_Product_Group__c, Family, MaterialType__c, Name, Product_Code__c, ProductCode, EAN_Product_Code__c FROM Product2"
+        }
+    ]
+}

prepared_steps/2_transform_via_script/LocationScript.py

@@ -5,9 +5,9 @@ country_mapping = {
     }   
 
 # Read the input CSV file, assuming the second row is the header
-read_df = pd.read_csv('../1_extract_data/target/SCInstalledBaseLocation__c_upsert_target.csv', header=0, keep_default_na=False, dtype=str)
+read_df = pd.read_csv('../1_extract_data/results/SCInstalledBaseLocation__c.csv', header=0, keep_default_na=False, dtype=str)
-read_df_ib = pd.read_csv('../1_extract_data/target/object-set-2/SCInstalledBase__c_upsert_target.csv', header=0, keep_default_na=False, dtype=str)
+read_df_ib = pd.read_csv('../1_extract_data/results/SCInstalledBase__c.csv', header=0, keep_default_na=False, dtype=str)
-read_df_product2 = pd.read_csv('../1_extract_data/target/object-set-6/Product2_upsert_target.csv', header=0, keep_default_na=False, dtype=str)
+read_df_product2 = pd.read_csv('../1_extract_data/results/Product2.csv', header=0, keep_default_na=False, dtype=str)
 
 for row in read_df.to_dict('records'):
     try:
@@ -17,11 +17,16 @@ for row in read_df.to_dict('records'):
         print(f'KeyError: {e}')
 
 # Columns for reindexing
-reindex_columns = ['City__c','Country__c','Extension__c','FlatNo__c','Floor__c','GeoX__c','GeoY__c','HouseNo__c','Id','PostalCode__c','Street__c']
+#"Id","City__c","Country__c","GeoY__c","GeoX__c","PostalCode__c","Street__c","Extension__c","HouseNo__c","FlatNo__c","Floor__c"
+reindex_columns = ['Id','City__c','Country__c','GeoY__c','GeoX__c','PostalCode__c','Street__c','Extension__c','HouseNo__c','FlatNo__c','Floor__c']
 # ArticleNo__c,CommissioningDate__c,Id,InstallationDate__c,InstalledBaseLocation__c,InstalledBaseLocation__r.Id,Name,ProductEnergy__c,ProductUnitClass__c,ProductUnitType__c,SerialNo__c,SerialNoException__c
-reindex_columns_ib = ['ArticleNo__c','CommissioningDate__c','Id','InstallationDate__c','InstalledBaseLocation__c','InstalledBaseLocation__r.Id','Name','ProductEnergy__c','ProductUnitClass__c','ProductUnitType__c','SerialNo__c','SerialNoException__c']
+#"Id","Name","CommissioningDate__c","InstallationDate__c","ProductEnergy__c","ProductUnitClass__c","ArticleNo__c","SerialNo__c","SerialNoException__c","ProductUnitType__c","InstalledBaseLocation__c"
+reindex_columns_ib = ['Id','Name','CommissioningDate__c','InstallationDate__c','ProductEnergy__c','ProductUnitClass__c','ArticleNo__c','SerialNo__c','SerialNoException__c','ProductUnitType__c','InstalledBaseLocation__c']
+#reindex_columns_ib = ['ArticleNo__c','CommissioningDate__c','Id','InstallationDate__c','InstalledBaseLocation__c','InstalledBaseLocation__r.Id','Name','ProductEnergy__c','ProductUnitClass__c','ProductUnitType__c','SerialNo__c','SerialNoException__c']
 # EAN_Product_Code__c,Family,Id,Main_Product_Group__c,MaterialType__c,Name,Product_Code__c,ProductCode
-reindex_columns_product2 = ['EAN_Product_Code__c','Family','Id','Main_Product_Group__c','MaterialType__c','Name','Product_Code__c','ProductCode']
+#"Id","Main_Product_Group__c","Family","MaterialType__c","Name","Product_Code__c","ProductCode","EAN_Product_Code__c"
+reindex_columns_product2 = ['Id','Main_Product_Group__c','Family','MaterialType__c','Name','Product_Code__c','ProductCode','EAN_Product_Code__c']
+#reindex_columns_product2 = ['EAN_Product_Code__c','Family','Id','Main_Product_Group__c','MaterialType__c','Name','Product_Code__c','ProductCode']
 
 # Reindex the columns to match the desired format
 df = read_df.reindex(reindex_columns, axis=1)
@@ -153,7 +158,7 @@ child_df['LocationType'] = 'Site'
 #ArticleNo__c,CommissioningDate__c,Id,InstallationDate__c,InstalledBaseLocation__c,InstalledBaseLocation__r.Extension__c,InstalledBaseLocation__r.FlatNo__c,InstalledBaseLocation__r.Floor__c,InstalledBaseLocation__r.Id,Name,ProductEnergy__c,ProductUnitClass__c,ProductUnitType__c,SerialNo__c,SerialNoException__c
 
 merged_df_ib = merged_df_ib.drop('InstalledBaseLocation__c', axis=1)
-merged_df_ib = merged_df_ib.drop('InstalledBaseLocation__r.Id', axis=1)
+#merged_df_ib = merged_df_ib.drop('InstalledBaseLocation__r.Id', axis=1)
 merged_df_ib = merged_df_ib.drop('Id_y', axis=1)
 print(merged_df_ib.columns)
 merged_df_ib.columns = ['Product2.Product_Code__c', 'FSL_1st_Ignition_Date__c', 'Id', 'InstallDate',  'Name', 'Kind_of_Energy__c', 'Kind_of_Installation__c', 'Main_Product_Group__c', 'SerialNumber', 'Serialnumber_Exception__c', 'Location.ExternalReference']