updating beam pipeline to use GroupByKey

analyse_properties/main.py

@@ -1,25 +1,49 @@
+import argparse
 from datetime import datetime
 import hashlib
 import itertools
+import json
+import logging
 import pathlib
 
 import apache_beam as beam
+from apache_beam.options.pipeline_options import PipelineOptions, SetupOptions
 
 # from analyse_properties.debug import DebugShowEmptyColumn, DebugShowColumnWithValueIn
 
 
 def slice_by_range(element, *ranges):
-    """Slice a list with multiple ranges."""
+    """
+    Slice a list with multiple ranges.
+
+    Args:
+        element : The element.
+        *ranges (tuple): Tuples containing a start,end index to slice the element.
+            E.g (0, 3), (5, 6) - Keeps columns 0,1,2,5. Drops everything else.
+
+    Returns:
+        list: The list sliced by the ranges
+    """
     return itertools.chain(*(itertools.islice(element, *r) for r in ranges))
 
 
 class DropRecordsSingleEmptyColumn(beam.DoFn):
-    """If a given item in a list is empty, drop this entry from the PCollection."""
-
     def __init__(self, index):
         self.index = index
 
     def process(self, element):
+        """
+        Drop the entire row if a given column is empty.
+
+        Args:
+            element : The element
+
+        Returns:
+            None: If the length of the column is 0, drop the element.
+
+        Yields:
+            element: If the length of the column isn't 0, keep the element.
+        """
         column = element[self.index]
         if len(column) == 0:
             return None
@@ -75,9 +99,9 @@ class GenerateUniqueID(beam.DoFn):
             ",".join(element[2:]) if not self.all_columns else ",".join(element)
         )
         hashed_string = hashlib.md5(unique_string.encode())
-        # append the hash to the end
-        element.append(hashed_string.hexdigest())
-        yield element
+        # add the hash as a key to the data.
+        new_element = (hashed_string.hexdigest(), list(element))
+        yield new_element
 
 
 class DeduplicateByID(beam.DoFn):
@@ -87,8 +111,8 @@ class DeduplicateByID(beam.DoFn):
     """
 
     def process(self, element):
-        if len(element[1]) > 0:
-            deduplicated_element = (element[0], [element[1][0]])
+        if len(list(element[1])) > 0:
+            deduplicated_element = (element[0], [list(element[1])[0]])
             yield deduplicated_element
         else:
             yield element
@@ -99,7 +123,6 @@ class RemoveUniqueID(beam.DoFn):
 
     def process(self, element):
         element_no_id = element[-1][0]
-        element_no_id.pop(-1)
         yield element_no_id
 
 
@@ -166,16 +189,16 @@ class ConvertDataToDict(beam.DoFn):
         # Create the dict to hold all the information about the property.
         json_object = {
             "property_id": element[0],
-            "readable_address": None,
-            "flat_appartment": element[-1][0][4],
-            "builing": element[-1][0][10],
-            "number": element[-1][0][3],
-            "street": element[-1][0][5],
-            "locality": element[-1][0][6],
-            "town": element[-1][0][7],
-            "district": element[-1][0][8],
-            "county": element[-1][0][9],
-            "postcode": element[-1][0][2],
+            # "readable_address": None,
+            "flat_appartment": list(element[-1])[0][4],
+            "builing": list(element[-1])[0][10],
+            "number": list(element[-1])[0][3],
+            "street": list(element[-1])[0][5],
+            "locality": list(element[-1])[0][6],
+            "town": list(element[-1])[0][7],
+            "district": list(element[-1])[0][8],
+            "county": list(element[-1])[0][9],
+            "postcode": list(element[-1])[0][2],
             "property_transactions": property_transactions,
             "latest_transaction_year": self.get_latest_transaction(
                 [
@@ -186,37 +209,59 @@ class ConvertDataToDict(beam.DoFn):
         }
 
         # Create a human readable address to go in the dict.
-        json_object["readable_address"] = self.get_readable_address(
-            [
-                json_object["flat_appartment"],
-                json_object["builing"],
-                f'{json_object["number"]} {json_object["street"]}',
-                json_object["postcode"],
-            ],
-            [
-                json_object["locality"],
-                json_object["town"],
-                json_object["district"],
-                json_object["county"],
-            ],
-        )
+        # json_object["readable_address"] = self.get_readable_address(
+        #     [
+        #         json_object["flat_appartment"],
+        #         json_object["builing"],
+        #         f'{json_object["number"]} {json_object["street"]}',
+        #         json_object["postcode"],
+        #     ],
+        #     [
+        #         json_object["locality"],
+        #         json_object["town"],
+        #         json_object["district"],
+        #         json_object["county"],
+        #     ],
+        # )
         yield json_object
 
 
-def main():
-    # Load in the data from a csv file.
+def run(argv=None, save_main_session=True):
+    """Entrypoint and definition of the pipeline."""
+    # Default input/output files
     input_file = (
         pathlib.Path(__file__).parents[1]
         / "data"
         / "input"
         / "pp-monthly-update-new-version.csv"
     )
+    output_file = (
+        pathlib.Path(__file__).parents[1]
+        / "data"
+        / "output"
+        / "pp-monthly-update-new-version"
+    )
 
-    with beam.Pipeline() as pipeline:
+    # Arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input", dest="input", default=str(input_file), help="Input file."
+    )
+    parser.add_argument(
+        "--output", dest="output", default=str(output_file), help="Output file."
+    )
+    known_args, pipeline_args = parser.parse_known_args(argv)
+
+    # Pipeline options. save_main_session needed for DataFlow for global imports.
+    pipeline_options = PipelineOptions(pipeline_args)
+    pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+
+    # Load in the data from a csv file.
+    with beam.Pipeline(options=pipeline_options) as pipeline:
         # Load the data
         load = (
             pipeline
-            | "Read input data" >> beam.io.ReadFromText(str(input_file))
+            | "Read input data" >> beam.io.ReadFromText(known_args.input)
             | "Split by ','" >> beam.Map(lambda element: element.split(","))
             | "Remove leading and trailing quotes"
             >> beam.Map(lambda element: [el.strip('"') for el in element])
@@ -234,6 +279,7 @@ def main():
             | "Drop Empty Postcodes" >> beam.ParDo(DropRecordsSingleEmptyColumn(2))
             | "Drop empty PAON if missing SAON"
             >> beam.ParDo(DropRecordsTwoEmptyColumn(3, 4))
+            # | beam.ParDo(DebugShowColumnWithValueIn(2, "B16 0AE"))
             | "Split PAON into two columns if separated by comma"
             >> beam.ParDo(SplitColumn(3, ","))
         )
@@ -244,19 +290,20 @@ def main():
             | "Generate unique ID for all columns"
             >> beam.ParDo(GenerateUniqueID(all_columns=True))
             | "Group by the ID for all columns"
-            >> beam.GroupBy(lambda element: element[-1])
+            >> 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 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.GroupBy(lambda element: element[-1])
+            >> beam.GroupByKey()
+            # | beam.Map(print)
         )
 
         # Format the data into a dict.
@@ -267,19 +314,18 @@ def main():
         )
 
         # Save the data to a .json file.
-        output_file = (
-            pathlib.Path(__file__).parents[1] / "data" / "output" / "pp-complete"
-        )
-        output = (
+        (
             formatted
             | "Combine into one PCollection" >> beam.combiners.ToList()
+            | "Format output" >> beam.Map(json.dumps)
             | "Save to .json file"
             >> beam.io.WriteToText(
-                file_path_prefix=str(output_file),
+                file_path_prefix=known_args.output,
                 file_name_suffix=".json",
             )
         )
 
 
 if __name__ == "__main__":
-    main()
+    logging.getLogger().setLevel(logging.INFO)
+    run()

download_data.sh

@@ -1,5 +1,5 @@
 # Full data set
-# wget https://storage.googleapis.com/street-group-technical-test-dmot/pp-complete.csv -P data/input
+# wget https://storage.googleapis.com/street-group-technical-test-dmot/input/pp-complete.csv -P data/input
 
 # Monthly update data set
-wget https://storage.googleapis.com/street-group-technical-test-dmot/pp-monthly-update-new-version.csv -P data/input
+wget https://storage.googleapis.com/street-group-technical-test-dmot/input/pp-monthly-update-new-version.csv -P data/input

notes/documentation/dataflow.md

@@ -0,0 +1,24 @@
+# DataFlow
+
+<https://cloud.google.com/dataflow/docs/quickstarts/quickstart-python>
+
+Export env variable:
+
+`export GOOGLE_APPLICATION_CREDENTIALS="/home/dtomlinson/git-repos/work/street_group/street_group_tech_test/street-group-0c490d23a9d0.json"`
+
+Run the pipeline:
+
+python -m analyse_properties.main \
+    --region europe-west2 \
+    --input gs://street-group-technical-test-dmot/input/pp-monthly-update-new-version.csv \
+    --output gs://street-group-technical-test-dmot/output/pp-monthly-update-new-version \
+    --runner DataflowRunner \
+    --project street-group \
+    --temp_location gs://street-group-technical-test-dmot/tmp
+
+
+## Errors
+
+Unsubscriptable error on window:
+
+<https://stackoverflow.com/questions/42276520/what-does-object-of-type-unwindowedvalues-has-no-len-mean>

prospector.yaml

@@ -20,6 +20,7 @@ pylint:
     - super-init-not-called
     - arguments-differ
     - inconsistent-return-statements
+    - expression-not-assigned
   enable:
 
   options: