Update scripts to be integrated as a workflow in the provision pipeline

affro_cluster.py

@@ -5,10 +5,10 @@ from matching_cluster import *
 from create_input_cluster import *
 import json
 
-dix_org = load_json('dictionaries/dix_acad.json')
-dix_mult = load_json('dictionaries/dix_mult.json')
-dix_city = load_json('dictionaries/dix_city.json')
-dix_country = load_json('dictionaries/dix_country.json')
+dix_org = load_json('dix_acad.json')
+dix_mult = load_json('dix_mult.json')
+dix_city = load_json('dix_city.json')
+dix_country = load_json('dix_country.json')
 
     
 def affro(raw_aff_string):

functions_cluster.py

@@ -32,12 +32,12 @@ def replace_double_consonants(text):
     result = re.sub(pattern, r'\1', text, flags=re.IGNORECASE)
     return result
 
-remove_list = [replace_double_consonants(x) for x in load_txt('txt_files/remove_list.txt')]
-stop_words = load_txt('txt_files/stop_words.txt')
-university_terms = [replace_double_consonants(x) for x in load_txt('txt_files/university_terms.txt')]
-city_names = [replace_double_consonants(x) for x in load_txt('txt_files/city_names.txt')]
+remove_list = [replace_double_consonants(x) for x in load_txt('remove_list.txt')]
+stop_words = load_txt('stop_words.txt')
+university_terms = [replace_double_consonants(x) for x in load_txt('university_terms.txt')]
+city_names = [replace_double_consonants(x) for x in load_txt('city_names.txt')]
 
-categ_dicts = load_json('dictionaries/dix_categ.json')
+categ_dicts = load_json('dix_categ.json')
 
 
 def is_contained(s, w):

update_records.py

@@ -4,18 +4,11 @@ from pyspark.sql import SparkSession
 from affro_cluster import *
 
 folder_path = '/user/zeppelin/miriam.baglioni/AffStringFromIISDataset2'
+hdfs_output_path = '/tmp/affro/results'
 #folder_path = 'check'
 
 json_file_names = []
 
-# Loop through all files in the directory
-for file_name in os.listdir(folder_path):
-    # Check if the file is a JSON file (you can adjust the check as needed)
-    if file_name != '_SUCCESS':
-        json_file_names.append(file_name)
-
-# json_file_names now contains the names of all JSON files in the folder
-
 # Initialize Spark session
 spark = SparkSession.builder.appName("JSONProcessing").getOrCreate()
 
@@ -90,27 +83,12 @@ def update_record(record):
         print(f"Error processing record with id {record.get('id')}: {str(e)}")
         return None
 
+df = spark.read.json(folder_path)
 
+# Apply the update_record function
+updated_rdd = df.rdd.map(lambda row: update_record(row.asDict()))
 
-for file in json_file_names:
-    print('start processing '+str(file))
-    df = spark.read.json(folder_path + '/' + file)
+# Convert updated RDD to JSON strings
+json_rdd = updated_rdd.map(lambda record: json.dumps(record))
 
-    # Apply the update_record function
-    updated_rdd = df.rdd.map(lambda row: update_record(row.asDict()))
-
-    # Convert updated RDD to JSON strings
-    json_rdd = updated_rdd.map(lambda record: json.dumps(record))
-
-    # Collect the data and write to an output file with a unique name
-    json_data = json_rdd.collect()
-
-    # Create a new filename by appending "_output.json" to the original filename (without extension)
-    output_file_name = file+'_output.json'
-    print('end processing '+str(file))
-
-    with open(output_file_name, 'w') as f:
-        for i, item in enumerate(json_data):
-            print('write '+str(i))
-
-            f.write(item + '\n')
+json_rdd.saveAsTextFile(hdfs_output_path)