adding docker commands for spark

analyse_properties/main.py

@@ -8,6 +8,7 @@ import pathlib
 
 import apache_beam as beam
 from apache_beam.io import fileio
+from apache_beam.options.pipeline_options import PipelineOptions
 
 # from analyse_properties.debug import DebugShowEmptyColumn, DebugShowColumnWithValueIn
 
@@ -175,7 +176,7 @@ class ConvertDataToDict(beam.DoFn):
         # Create the dict to hold all the information about the property.
         json_object = {
             "property_id": element[0],
-            "readable_address": None,
+            # "readable_address": None,
             "flat_appartment": element[-1][0][4],
             "builing": element[-1][0][10],
             "number": element[-1][0][3],
@@ -195,98 +196,117 @@ 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.
-    csv_data = resources.path(
-        # "analyse_properties.data.input",
-        # "pp-monthly-update-new-version.csv"
-        "analyse_properties.data.input", "pp-complete.csv"
+    # csv_data = resources.path(
+    #     "analyse_properties.data.input",
+    #     "pp-monthly-update-new-version.csv"
+    #     # "analyse_properties.data.input",
+    #     # "pp-complete.csv",
+    # )
+
+    options = PipelineOptions(
+        [
+            "--runner=PortableRunner",
+            "--job_endpoint=localhost:8099",
+            "--environment_type=LOOPBACK",
+        ]
     )
 
-    with beam.Pipeline() as pipeline:
+    with beam.Pipeline(options=options) as pipeline:
         # Load the data
-        with csv_data as csv_data_file:
-            # https://github.com/apache/beam/blob/v2.32.0/sdks/python/apache_beam/io/fileio_test.py#L155-L170
-            load = (
-                pipeline
-                | fileio.MatchFiles(str(csv_data_file))
-                | fileio.ReadMatches()
-                | beam.FlatMap(csv_reader)
-            )
+        # with csv_data as csv_data_file:
+        #     # https://github.com/apache/beam/blob/v2.32.0/sdks/python/apache_beam/io/fileio_test.py#L155-L170
+        #     load = (
+        #         pipeline
+        #         | fileio.MatchFiles(str(csv_data_file))
+        #         | fileio.ReadMatches()
+        #         | beam.FlatMap(csv_reader)
+        #     )
+
+        load = pipeline | beam.Create(
+            [
+                "🍓Strawberry,🥕Carrot,🍆Eggplant",
+                "🍅Tomato,🥔Potato",
+            ]
+        )
 
         # Clean the data by dropping unneeded rows.
-        clean_drop = (
-            load
-            | "Drop unneeded columns"
-            >> beam.Map(lambda element: list(slice_by_range(element, (1, 4), (7, 14))))
-            | "Convert to Upper Case"
-            >> beam.Map(lambda element: [e.upper() for e in element])
-            | "Strip leading/trailing whitespace"
-            >> beam.Map(lambda element: [e.strip() for e in element])
-            | "Drop Empty Postcodes" >> beam.ParDo(DropRecordsSingleEmptyColumn(2))
-            | "Drop empty PAON if missing SAON"
-            >> beam.ParDo(DropRecordsTwoEmptyColumn(3, 4))
-            | "Split PAON into two columns if separated by comma"
-            >> beam.ParDo(SplitColumn(3, ","))
-        )
+        # clean_drop = (
+        #     load
+        #     | "Drop unneeded columns"
+        #     >> beam.Map(lambda element: list(slice_by_range(element, (1, 4), (7, 14))))
+        #     | "Convert to Upper Case"
+        #     >> beam.Map(lambda element: [e.upper() for e in element])
+        #     | "Strip leading/trailing whitespace"
+        #     >> beam.Map(lambda element: [e.strip() for e in element])
+        #     | "Drop Empty Postcodes" >> beam.ParDo(DropRecordsSingleEmptyColumn(2))
+        #     | "Drop empty PAON if missing SAON"
+        #     >> beam.ParDo(DropRecordsTwoEmptyColumn(3, 4))
+        #     | "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.GroupBy(lambda element: element[-1])
-            | "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 unique ID for all columns"
+        #     >> beam.ParDo(GenerateUniqueID(all_columns=True))
+        #     | "Group by the ID for all columns"
+        #     >> beam.GroupBy(lambda element: element[-1])
+        #     | "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.GroupBy(lambda element: element[-1])
-        )
+        # # 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])
+        # )
 
-        # Format the data into a dict.
-        formatted = (
-            prepare
-            | "Convert the prepared data into a dict object"
-            >> beam.ParDo(ConvertDataToDict())
-        )
+        # # Format the data into a dict.
+        # formatted = (
+        #     prepare
+        #     | "Convert the prepared data into a dict object"
+        #     >> beam.ParDo(ConvertDataToDict())
+        # )
 
-        # Save the data to a .json file.
-        output_file = pathlib.Path(__file__).parent / "data" / "output" / "pp-complete"
-        output = (
-            formatted
-            | "Combine into one PCollection" >> beam.combiners.ToList()
-            | "Save to .json file"
-            >> beam.io.WriteToText(
-                file_path_prefix=str(output_file),
-                file_name_suffix=".json",
-                shard_name_template="",
-            )
-        )
+        # # Save the data to a .json file.
+        # output_file = pathlib.Path(__file__).parent / "data" / "output" / "pp-complete"
+        # # output_file = "/tmp/file"
+
+        # (
+        #     formatted
+        #     | "Combine into one PCollection" >> beam.combiners.ToList()
+        #     | beam.Map(print)
+        #     # | "Save to .json file"
+        #     # >> beam.io.WriteToText(
+        #     #     file_path_prefix=str(output_file),
+        #     #     file_name_suffix=".json",
+        #     #     shard_name_template="",
+        #     # )
+        # )
 
 
 if __name__ == "__main__":

notes/docker-commands-spark.txt

@@ -0,0 +1,14 @@
+docker run --rm \
+    -p 8098:8098 -p 8097:8097 -p 8099:8099 \
+    --name=beam_spark \
+apache/beam_spark_job_server:latest
+
+
+docker pull apache/beam_spark_job_server:2.33.0_rc1
+
+
+docker run --rm \
+    -e SPARK_DRIVER_MEMORY=8g \
+    -p 8098:8098 -p 8097:8097 -p 8099:8099 \
+    --name=beam_spark \
+apache/beam_spark_job_server:latest