Feat/incre load new resources

digital_land/commands.py

@@ -422,7 +422,7 @@ def dataset_create(
         path=output_path,
         specification_dir=None,  # TBD: package should use this specification object
     )
-    # don'tt use create as we don't want to create the indexes
+    # don't use create as we don't want to create the indexes
     package.create_database()
     package.disconnect()
     for path in input_paths:
@@ -474,6 +474,72 @@ def dataset_create(
     package.add_counts()
 
 
+#
+#  update dataset from processed new resources
+#
+def dataset_update(
+    input_paths,
+    output_path,
+    organisation_path,
+    pipeline,
+    dataset,
+    specification,
+    bucket_name=None,  # bucket name from bash script, need to put into cli.
+    object_key=None,  # object-key, latter part of 'bucket'
+    issue_dir="issue",
+    column_field_dir="var/column-field",
+    dataset_resource_dir="var/dataset-resource",
+):
+    """
+    Updates the current state of the sqlite files being held in S3 with new resources
+    """
+    if not output_path:
+        print("missing output path", file=sys.stderr)
+        sys.exit(2)
+
+    if not bucket_name or not object_key:
+        print("Missing bucket name or object_key to get sqlite files", file=sys.stderr)
+        sys.exit(2)
+
+    # Set up initial objects
+    column_field_dir = Path(column_field_dir)
+    dataset_resource_dir = Path(dataset_resource_dir)
+    organisation = Organisation(
+        organisation_path=organisation_path, pipeline_dir=Path(pipeline.path)
+    )
+    package = DatasetPackage(
+        dataset,
+        organisation=organisation,
+        path=output_path,
+        specification_dir=None,  # TBD: package should use this specification object
+    )
+    # Copy files from S3 and load into tables
+    table_name = dataset
+    package.load_from_s3(
+        bucket_name=bucket_name, object_key=object_key, table_name=table_name
+    )
+
+    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):
+        package.load_old_entities(old_entity_path)
+
+    issue_paths = os.path.join(issue_dir, dataset)
+    if os.path.exists(issue_paths):
+        for issue_path in os.listdir(issue_paths):
+            package.load_issues(os.path.join(issue_paths, issue_path))
+    else:
+        logging.warning("No directory for this dataset in the provided issue_directory")
+
+    package.add_counts()
+
+
 def dataset_dump(input_path, output_path):
     cmd = f"sqlite3 -header -csv {input_path} 'select * from entity;' > {output_path}"
     logging.info(cmd)

digital_land/package/sqlite.py

@@ -3,6 +3,8 @@
 import csv
 import sqlite3
 import logging
+import boto3
+import botocore.exceptions
 from .package import Package
 from decimal import Decimal
 
@@ -298,15 +300,46 @@ def create_database(self):
         if os.path.exists(self.path):
             os.remove(self.path)
 
+        self.set_up_connection()
+
+        self.create_tables()
+
+    def set_up_connection(self):
         self.connect()
 
         if self._spatialite:
             self.connection.execute("select InitSpatialMetadata(1)")
 
-        self.create_tables()
-
     def create(self):
         self.create_database()
         self.load()
         self.create_indexes()
         self.disconnect()
+
+    def load_from_s3(self, bucket_name, object_key, table_name):
+        # Ensure parameters are valid
+        if not isinstance(bucket_name, str) or not isinstance(object_key, str):
+            raise ValueError("Bucket name and object key must be strings.")
+
+        local_path = os.path.dirname(self.path)
+        s3 = boto3.client("s3")
+
+        file_key = f"{table_name}.sqlite3"
+        local_file_path = os.path.join(local_path, file_key)
+
+        try:
+            os.makedirs(local_path, exist_ok=True)  # Ensure local directory exists
+            s3.download_file(bucket_name, object_key + "/" + file_key, local_file_path)
+        except botocore.exceptions.NoCredentialsError:
+            logger.error(
+                "❌ AWS credentials not found. Run `aws configure` to set them."
+            )
+        except botocore.exceptions.ParamValidationError as e:
+            logger.error(f"❌ Parameter validation error: {e}")
+        except botocore.exceptions.ClientError as e:
+            logger.error(f"❌ AWS S3 error: {e}")
+
+        self.set_up_connection()
+        self.load()
+        # self.create_indexes()# Do we need this?
+        self.disconnect()

setup.py

@@ -58,6 +58,8 @@ def get_long_description():
         "dask[dataframe]",
         "pyarrow",
         "pygit2",
+        "boto3",
+        "moto",
     ],
     entry_points={"console_scripts": ["digital-land=digital_land.cli:cli"]},
     setup_requires=["pytest-runner"],

tests/integration/package/test_dataset.py

@@ -371,6 +371,7 @@ def test_entry_date_upsert_uploads_blank_fields(
     # run upload to fact table not fact resource for testing the upsert
     package.connect()
     package.create_cursor()
+
     fact_fields = package.specification.schema["fact"]["fields"]
     fact_conflict_fields = ["fact"]
     fact_update_fields = [
@@ -406,3 +407,114 @@ def test_entry_date_upsert_uploads_blank_fields(
     ]
 
     assert actual_result == expected_result, "actual result does not match query"
+
+
+def test_insert_newest_date(
+    specification_dir,
+    organisation_csv,
+    blank_patch_csv,
+    transformed_fact_resources,
+    transformed_fact_resources_with_blank,
+    tmp_path,
+):
+    dataset = "conservation-area"
+    sqlite3_path = os.path.join(tmp_path, f"{dataset}.sqlite3")
+
+    organisation = Organisation(
+        organisation_path=organisation_csv,
+        pipeline_dir=Path(os.path.dirname(blank_patch_csv)),
+    )
+    package = DatasetPackage(
+        "conservation-area",
+        organisation=organisation,
+        path=sqlite3_path,
+        specification_dir=specification_dir,  # TBD: package should use this specification object
+    )
+
+    # create package
+    package.create()
+
+    # run upload to fact table not fact resource for testing the upsert
+    package.connect()
+    package.create_cursor()
+
+    fact_resource_fields = package.specification.schema["fact-resource"]["fields"]
+    for row in transformed_fact_resources:
+        package.insert("fact-resource", fact_resource_fields, row, upsert=True)
+    package.commit()
+    package.disconnect()
+
+    # retrieve results
+    package.connect()
+    package.create_cursor()
+    package.cursor.execute("SELECT * FROM fact_resource;")
+    cols = [column[0] for column in package.cursor.description]
+    actual_result = pd.DataFrame.from_records(
+        package.cursor.fetchall(), columns=cols
+    ).to_dict(orient="records")
+    expected_result = [
+        {
+            "end_date": "",
+            "fact": "1f90248fd06e49accd42b80e43d58beeac300f942f1a9f71da4b64865356b1f3",
+            "entry_date": "2021-09-06",
+            "entry_number": None,
+            "priority": None,
+            "resource": "",
+            "start_date": "",
+        },
+        {
+            "end_date": "",
+            "fact": "1f90248fd06e49accd42b80e43d58beeac300f942f1a9f71da4b64865356b1f3",
+            "entry_date": "2022-11-02",
+            "entry_number": None,
+            "priority": None,
+            "resource": "",
+            "start_date": "",
+        },
+    ]
+
+    assert actual_result == expected_result, "actual result does not match query"
+
+    # create package
+    package.create()
+
+    # run upload to fact table not fact resource for testing the upsert
+    package.connect()
+    package.create_cursor()
+
+    fact_resource_fields = package.specification.schema["fact-resource"]["fields"]
+    for row in transformed_fact_resources_with_blank:
+        package.insert("fact-resource", fact_resource_fields, row, upsert=True)
+    package.commit()
+    package.disconnect()
+
+    # retrieve results
+    package.connect()
+    package.create_cursor()
+    package.cursor.execute("SELECT * FROM fact_resource;")
+    cols = [column[0] for column in package.cursor.description]
+    actual_result = pd.DataFrame.from_records(
+        package.cursor.fetchall(), columns=cols
+    ).to_dict(orient="records")
+    expected_result = [
+        {
+            "end_date": "2021-12-31",
+            "fact": "1f90248fd06e49accd42b80e43d58beeac300f942f1a9f71da4b64865356b1f3",
+            "entry_date": "2021-09-06",
+            "entry_number": None,
+            "priority": None,
+            "resource": "",
+            "start_date": "",
+        },
+        {
+            "end_date": "",
+            "fact": "1f90248fd06e49accd42b80e43d58beeac300f942f1a9f71da4b64865356b1f3",
+            "entry_date": "2022-11-02",
+            "entry_number": None,
+            "priority": None,
+            "resource": "",
+            "start_date": "",
+        },
+    ]
+
+    assert actual_result == expected_result, "actual result does not match query"