adding latest beam pipeline code for dataflow with group optimisation

analyse_properties/main.py

@@ -85,7 +85,7 @@ class SplitColumn(beam.DoFn):
             yield element
 
 
-class GenerateUniqueID(beam.DoFn):
+class CreateMappingTable(beam.DoFn):
     """
     Generate a unique ID for the PCollection, either for all the columns or for the
     uniquely identifying data only.
@@ -104,6 +104,49 @@ class GenerateUniqueID(beam.DoFn):
         yield new_element
 
 
+# class CreateMappingTable(beam.DoFn):
+#     def process(self, element):
+#         unique_string = ",".join(element)
+#         hashed_string = hashlib.md5(unique_string.encode())
+#         new_element = {hashed_string.hexdigest(): list(element)}
+#         yield new_element
+
+
+class CreateUniquePropertyID(beam.DoFn):
+    def process(self, element):
+        unique_string = ",".join(element[-1][2:])
+        hashed_string = hashlib.md5(unique_string.encode())
+        new_element = (hashed_string.hexdigest(), element[0])
+        yield new_element
+
+
+class DeduplicateIDs(beam.DoFn):
+    def process(self, element):
+        deduplicated_list = list(set(element[-1]))
+        new_element = (element[0], deduplicated_list)
+        yield new_element
+
+
+# class InsertDataForID(beam.DoFn):
+#     def __init__(self, mapping_table):
+#         self.mapping_table = mapping_table
+
+#     def process(self, element):
+#         replaced_list = [self.mapping_table[x] for x in element[-1]]
+#         new_element = (element[0], replaced_list)
+#         yield new_element
+
+
+def insert_data_for_id(element, mapping_table):
+    replaced_list = []
+    for data_id in element[-1]:
+        replaced_list.append(mapping_table[data_id])
+    # replaced_list = [mapping_table[x] for x in element[-1]]
+    new_element = (element[0], replaced_list)
+    yield new_element
+
+
+# old
 class DeduplicateByID(beam.DoFn):
     """
     If the PCollection has multiple entries after being grouped by ID for all columns,
@@ -280,37 +323,66 @@ def run(argv=None, save_main_session=True):
             | "Drop empty PAON if missing SAON"
             >> beam.ParDo(DropRecordsTwoEmptyColumn(3, 4))
             # | beam.ParDo(DebugShowColumnWithValueIn(2, "B16 0AE"))
+            # | beam.ParDo(DebugShowColumnWithValueIn(2, "B90 3LA"))
             | "Split PAON into two columns if separated by comma"
             >> beam.ParDo(SplitColumn(3, ","))
         )
 
-        # Clean the data by creating an ID, and deduplicating to eliminate repeated rows.
-        clean_deduplicate = (
-            clean_drop
-            | "Generate unique ID for all columns"
-            >> beam.ParDo(GenerateUniqueID(all_columns=True))
-            | "Group by the ID for all columns"
-            >> beam.GroupByKey()
-            | "Deduplicate by the ID for all columns" >> beam.ParDo(DeduplicateByID())
-        )
+        # # Clean the data by creating an ID, and deduplicating to eliminate repeated rows.
+        # clean_deduplicate = (
+        #     clean_drop
+        #     | "Generate ID using all columns"
+        #     >> beam.ParDo(GenerateUniqueID(all_columns=True))
+        #     | "Group by the ID for all columns" >> beam.GroupByKey()
+        #     | "Deduplicate by the ID for all columns" >> beam.ParDo(DeduplicateByID())
+        # )
 
-        # Prepare the data by generating an ID using the uniquely identifying
-        # information only and grouping them by this ID.
-        prepare = (
-            clean_deduplicate
-            | "Remove previous unique ID" >> beam.ParDo(RemoveUniqueID())
-            | "Generate unique ID ignoring price & date"
-            >> beam.ParDo(GenerateUniqueID())
-            | "Group by the ID ignoring price & date"
-            >> beam.GroupByKey()
+        # Create a mapping table
+        mapping_table_raw = (
+            clean_drop
+            | "Create a mapping table with key of id_all_columns and value of cleaned data."
+            >> beam.ParDo(CreateMappingTable(all_columns=True))
             # | beam.Map(print)
         )
 
+        mapping_table_condensed = (
+            mapping_table_raw
+            | "Condense mapping table into single dict" >> beam.combiners.ToDict()
+            # | beam.Map(print)
+        )
+
+        prepared = (
+            mapping_table_raw
+            | "Create unique ID ignoring price & date"
+            >> beam.ParDo(CreateUniquePropertyID())
+            | "Group IDs using all columns by IDs ignoring price & date"
+            >> beam.GroupByKey()
+            | "Deduplicate to eliminate repeated transactions"
+            >> beam.ParDo(DeduplicateIDs())
+            | "Insert the raw data using the mapping table"
+            >> beam.FlatMap(
+                insert_data_for_id, beam.pvalue.AsSingleton(mapping_table_condensed)
+            )
+            # | beam.Map(print)
+        )
+
+        # # Prepare the data by generating an ID using the uniquely identifying
+        # # information only and grouping them by this ID.
+        # prepare = (
+        #     clean_deduplicate
+        #     | "Remove previous unique ID" >> beam.ParDo(RemoveUniqueID())
+        #     | "Generate unique ID ignoring price & date old"
+        #     >> beam.ParDo(GenerateUniqueID())
+        #     | "Group by the ID ignoring price & date" >> beam.GroupByKey()
+        #     # | beam.Map(print)
+        # )
+
         # Format the data into a dict.
         formatted = (
-            prepare
+            prepared
             | "Convert the prepared data into a dict object"
             >> beam.ParDo(ConvertDataToDict())
+            # | beam.Map(print)
         )
 
         # Save the data to a .json file.