Use parquet to speed up dataset building.

digital_land/cli.py

@@ -22,6 +22,7 @@
     operational_issue_save_csv,
     convert,
     dataset_create,
+    # dataset_parquet_create,
     pipeline_run,
     collection_add_source,
     add_endpoints_and_lookups,
@@ -140,6 +141,8 @@ def convert_cmd(input_path, output_path):
 @column_field_dir
 @dataset_resource_dir
 @issue_dir
+@click.option("--cache-dir", type=click.Path(), default="var/cache/parquet")
+@click.option("--resource-path", type=click.Path(), default="collection/resource.csv")
 @click.argument("input-paths", nargs=-1, type=click.Path(exists=True))
 @click.pass_context
 def dataset_create_cmd(
@@ -150,6 +153,8 @@ def dataset_create_cmd(
     column_field_dir,
     dataset_resource_dir,
     issue_dir,
+    cache_dir,
+    resource_path,
 ):
     return dataset_create(
         input_paths=input_paths,
@@ -161,6 +166,8 @@ def dataset_create_cmd(
         column_field_dir=column_field_dir,
         dataset_resource_dir=dataset_resource_dir,
         issue_dir=issue_dir,
+        cache_dir=cache_dir,
+        resource_path=resource_path,
     )
 
 

digital_land/commands.py

@@ -12,6 +12,8 @@
 import geojson
 import shapely
 
+import subprocess
+
 from digital_land.package.organisation import OrganisationPackage
 from digital_land.check import duplicate_reference_check
 from digital_land.specification import Specification
@@ -26,6 +28,7 @@
 )
 from digital_land.organisation import Organisation
 from digital_land.package.dataset import DatasetPackage
+from digital_land.package.datasetparquet import DatasetParquetPackage
 from digital_land.phase.combine import FactCombinePhase
 from digital_land.phase.concat import ConcatFieldPhase
 from digital_land.phase.convert import ConvertPhase, execute
@@ -357,7 +360,11 @@ def dataset_create(
     issue_dir="issue",
     column_field_dir="var/column-field",
     dataset_resource_dir="var/dataset-resource",
+    cache_dir="var/cache/parquet",
+    resource_path="collection/resource.csv",
 ):
+    cache_dir = os.path.join(cache_dir, dataset)
+
     if not output_path:
         print("missing output path", file=sys.stderr)
         sys.exit(2)
@@ -377,10 +384,8 @@ def dataset_create(
     package.create()
     for path in input_paths:
         path_obj = Path(path)
-        package.load_transformed(path)
         package.load_column_fields(column_field_dir / dataset / path_obj.name)
         package.load_dataset_resource(dataset_resource_dir / dataset / path_obj.name)
-    package.load_entities()
 
     old_entity_path = os.path.join(pipeline.path, "old-entity.csv")
     if os.path.exists(old_entity_path):
@@ -395,9 +400,29 @@ def dataset_create(
 
     package.add_counts()
 
+    # Repeat for parquet
+    # Set up cache directory to store parquet files. The sqlite files created from this will be saved in the dataset
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+    pqpackage = DatasetParquetPackage(
+        dataset,
+        organisation=organisation,
+        path=output_path,
+        cache_dir=cache_dir,
+        resource_path=resource_path,
+        specification_dir=None,  # TBD: package should use this specification object
+    )
+    pqpackage.create_temp_table(input_paths)
+    pqpackage.load_facts()
+    pqpackage.load_fact_resource()
+    pqpackage.load_entities()
+    pqpackage.pq_to_sqlite()
+    pqpackage.close_conn()
+
 
 def dataset_dump(input_path, output_path):
-    cmd = f"sqlite3 -header -csv {input_path} 'select * from entity;' > {output_path}"
+    cmd = f"sqlite3 -header -csv {input_path} 'select * from entity order by entity;' > {output_path}"
     logging.info(cmd)
     os.system(cmd)
 
@@ -409,7 +434,7 @@ def dataset_dump_flattened(csv_path, flattened_dir, specification, dataset):
     elif isinstance(csv_path, Path):
         dataset_name = csv_path.stem
     else:
-        logging.error(f"Can't extract  datapackage name from {csv_path}")
+        logging.error(f"Can't extract datapackage name from {csv_path}")
         sys.exit(-1)
 
     flattened_csv_path = os.path.join(flattened_dir, f"{dataset_name}.csv")
@@ -456,6 +481,7 @@ def dataset_dump_flattened(csv_path, flattened_dir, specification, dataset):
     batch_size = 100000
     temp_geojson_files = []
     geography_entities = [e for e in entities if e["typology"] == "geography"]
+
     for i in range(0, len(geography_entities), batch_size):
         batch = geography_entities[i : i + batch_size]
         feature_collection = process_data_in_batches(batch, flattened_dir, dataset_name)
@@ -470,6 +496,13 @@ def dataset_dump_flattened(csv_path, flattened_dir, specification, dataset):
 
     if all(os.path.isfile(path) for path in temp_geojson_files):
         rfc7946_geojson_path = os.path.join(flattened_dir, f"{dataset_name}.geojson")
+        env = os.environ.copy()
+
+        out, _ = subprocess.Popen(
+            ["ogr2ogr", "--version"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.DEVNULL,
+        ).communicate()
         env = (
             dict(os.environ, OGR_GEOJSON_MAX_OBJ_SIZE="0")
             if get_gdal_version() >= Version("3.5.2")
@@ -874,9 +907,10 @@ def process_data_in_batches(entities, flattened_dir, dataset_name):
                 logging.error(f"Error loading wkt from entity {entity['entity']}")
                 logging.error(e)
         else:
-            logging.error(
-                f"No geometry or point data for entity {entity['entity']} with typology 'geography'"
-            )
+            pass
+            # logging.error(
+            #     f"No geometry or point data for entity {entity['entity']} with typology 'geography'"
+            # )
 
     if features:
         feature_collection = geojson.FeatureCollection(

digital_land/expectations/operation.py

@@ -102,7 +102,7 @@ def count_lpa_boundary(
 
     result = comparison_rules[comparison_rule]
 
-    message = f"there were {actual} entities found"
+    message = f"there were {actual} enttities found"
 
     details = {
         "actual": actual,