Counts files to db migration

Makefile

@@ -334,7 +334,7 @@ data-loaded-image-%: ${BUILD_LOCATION_ABSOLUTE}/db/docker.built ${BUILD_SCRIPTS_
         docker container start temporary-spt-db-preloading && \
         bash ${BUILD_SCRIPTS_LOCATION_ABSOLUTE}/poll_container_readiness_direct.sh temporary-spt-db-preloading && \
         pipeline_cmd="cd /working_dir; cp -r /mount_sources/build .; cp -r /mount_sources/test .; bash build/build_scripts/import_test_dataset$*.sh "; \
-        docker run \
+        docker container run \
         --rm \
         --network container:temporary-spt-db-preloading \
         --mount type=bind,src=${PWD},dst=/mount_sources \

build/apiserver/compose.yaml

@@ -25,12 +25,6 @@ services:
       SINGLE_CELL_DATABASE_HOST: spt-db-testing-only
       SINGLE_CELL_DATABASE_USER: postgres
       SINGLE_CELL_DATABASE_PASSWORD: postgres
-      USE_ALTERNATIVE_TESTING_DATABASE: 1
-      DISABLE_FAST_CACHE_RECREATION: 1
-    volumes:
-      - type: bind
-        source: ../../test/ondemand/test_expression_data
-        target: /ondemand/source_data/
     pull_policy: never
 
   testing-api-server:

build/ondemand/Makefile

@@ -32,23 +32,20 @@ teardown-unit-testing: ${UNIT_TESTS}
 >@docker compose down && docker compose rm --force --stop ; echo "$$?" > status_code
 >@${MESSAGE} end "Down." "Error."
 >@rm -f status_code
->@rm -f ${TEST_LOCATION_ABSOLUTE}/ondemand/module_tests/expected_binary_expression_data_cohoused/.spt_db.config.generated
 >@rm -f ${TEST_LOCATION_ABSOLUTE}/ondemand/test_expression_data/.spt_db.config.generated
 
 teardown-module-testing: ${MODULE_TESTS}
 >@${MESSAGE} start "\u2517\u2501"
 >@docker compose down && docker compose rm --force --stop ; echo "$$?" > status_code
 >@${MESSAGE} end "Down." "Error."
 >@rm -f status_code
->@rm -f ${TEST_LOCATION_ABSOLUTE}/ondemand/module_tests/expected_binary_expression_data_cohoused/.spt_db.config.generated
 >@rm -f ${TEST_LOCATION_ABSOLUTE}/ondemand/test_expression_data/.spt_db.config.generated
 
 teardown-testing: ${TESTS}
 >@${MESSAGE} start "\u2517\u2501"
 >@docker compose down && docker compose rm --force --stop ; echo "$$?" > status_code
 >@${MESSAGE} end "Down." "Error."
 >@rm -f status_code
->@rm -f ${TEST_LOCATION_ABSOLUTE}/ondemand/module_tests/expected_binary_expression_data_cohoused/.spt_db.config.generated
 >@rm -f ${TEST_LOCATION_ABSOLUTE}/ondemand/test_expression_data/.spt_db.config.generated
 
 ${TESTS}: setup-testing
@@ -87,6 +84,5 @@ clean:
 >@rm -f ${WHEEL_FILENAME}
 >@rm -f status_code
 >@rm -rf ../../test/ondemand/expression_data/
->@rm -f ../../test/ondemand/module_tests/expected_binary_expression_data_cohoused/.spt_db.config.generated
 >@rm -rf ../../test/test_data/fast_cache_testing
 >@for f in dlogs.db.txt dlogs.api.txt dlogs.od.txt; do rm -f $$f; done;

build/ondemand/compose.yaml

@@ -12,12 +12,6 @@ services:
       SINGLE_CELL_DATABASE_HOST: spt-db-testing-only
       SINGLE_CELL_DATABASE_USER: postgres
       SINGLE_CELL_DATABASE_PASSWORD: postgres
-      USE_ALTERNATIVE_TESTING_DATABASE: 1
-      DISABLE_FAST_CACHE_RECREATION: 1
-    volumes:
-      - type: bind
-        source: ../../test/ondemand/module_tests/expected_binary_expression_data_cohoused
-        target: /ondemand/source_data/
     pull_policy: never
 
   testing-database:

spatialprofilingtoolbox/db/data_model/performance_tweaks.sql

@@ -44,3 +44,9 @@ CREATE TABLE pending_feature_computation (
     feature_specification VARCHAR(512) REFERENCES feature_specification(identifier),
     time_initiated VARCHAR(512)
 );
+
+CREATE TABLE ondemand_studies_index (
+    specimen VARCHAR(512),
+    blob_type VARCHAR(512),
+    blob_contents bytea
+);

spatialprofilingtoolbox/db/database_connection.py

@@ -163,14 +163,14 @@ def retrieve_study_names(database_config_file: str | None) -> list[str]:
     with DBCursor(database_config_file=database_config_file) as cursor:
         cursor.execute('SELECT study FROM study_lookup;')
         rows = cursor.fetchall()
-    return sorted([row[0] for row in rows])
+    return sorted([str(row[0]) for row in rows])
 
 
 def get_specimen_names(cursor) -> list[str]:
     query = 'SELECT specimen FROM specimen_collection_process;'
     cursor.execute(query)
     rows = cursor.fetchall()
-    return sorted([row[0] for row in rows])
+    return sorted([str(row[0]) for row in rows])
 
 
 def retrieve_study_from_specimen(database_config_file: str | None, specimen: str) -> str:
@@ -189,6 +189,17 @@ def retrieve_study_from_specimen(database_config_file: str | None, specimen: str
     return study
 
 
+def retrieve_primary_study(database_config_file: str, component_study: str) -> str | None:
+    studies = retrieve_study_names(database_config_file)
+    for study in studies:
+        with DBCursor(database_config_file=database_config_file, study=study) as cursor:
+            query = 'SELECT COUNT(*) FROM study_component sc WHERE sc.component_study=%s ;'
+            cursor.execute(query, (component_study,))
+            count = tuple(cursor.fetchall())[0][0]
+            if count == 1:
+                return study
+    return None
+
 def create_database(database_config_file: str | None, database_name: str) -> None:
     if database_config_file is None:
         message = 'Data import requires a database configuration file.'

spatialprofilingtoolbox/db/ondemand_studies_index.py

@@ -0,0 +1,53 @@
+"""Count number of cached files of given types in cached-files area of database."""
+
+from spatialprofilingtoolbox.db.database_connection import DBCursor
+from spatialprofilingtoolbox.db.database_connection import retrieve_study_names
+
+def get_counts(database_config_file: str, blob_type: str, study: str | None = None) -> dict[str, int]:
+    if study is None:
+        studies = tuple(retrieve_study_names(database_config_file))
+    else:
+        studies = (study,)
+    counts: dict[str, int] = {}
+    for study in studies:
+        with DBCursor(database_config_file=database_config_file, study=study) as cursor:
+            cursor.execute(f'''
+                SELECT COUNT(*) FROM ondemand_studies_index osi
+                WHERE osi.blob_type='{blob_type}' ;
+            ''')
+            count = tuple(cursor.fetchall())[0][0]
+            counts[study] = count
+    return counts
+
+
+def drop_cache_files(database_config_file: str, blob_type: str, study: str | None = None) -> None:
+    if study is None:
+        studies = tuple(retrieve_study_names(database_config_file))
+    else:
+        studies = (study,)
+    for _study in studies:
+        with DBCursor(database_config_file=database_config_file, study=_study) as cursor:
+            cursor.execute(f'''
+                DELETE FROM ondemand_studies_index osi
+                WHERE osi.blob_type='{blob_type}' ;
+            ''')
+
+
+def retrieve_expressions_index(database_config_file: str, study: str) -> str:
+    with DBCursor(database_config_file=database_config_file, study=study) as cursor:
+        cursor.execute('''
+            SELECT blob_contents FROM ondemand_studies_index osi
+            WHERE osi.blob_type='expressions_index' ;
+        ''')
+        result_blob = bytearray(tuple(cursor.fetchall())[0][0])
+    return result_blob.decode(encoding='utf-8')
+
+
+def retrieve_indexed_samples(database_config_file: str, study: str) -> tuple[str, ...]:
+    with DBCursor(database_config_file=database_config_file, study=study) as cursor:
+        cursor.execute('''
+            SELECT specimen FROM ondemand_studies_index osi
+            WHERE osi.blob_type='feature_matrix' ;
+        ''')
+        specimens = tuple(r[0] for r in cursor.fetchall())
+    return specimens

spatialprofilingtoolbox/ondemand/compressed_matrix_writer.py

@@ -2,88 +2,87 @@
 
 from typing import cast
 import json
-from os.path import isfile
-from os.path import join
-from os import getcwd
 
-from spatialprofilingtoolbox.ondemand.defaults import EXPRESSIONS_INDEX_FILENAME
+from spatialprofilingtoolbox.db.ondemand_studies_index import get_counts
+from spatialprofilingtoolbox.db.database_connection import DBCursor
+from spatialprofilingtoolbox.db.database_connection import retrieve_primary_study
 from spatialprofilingtoolbox.standalone_utilities.log_formats import colorized_logger
 
 logger = colorized_logger(__name__)
 
 
 class CompressedMatrixWriter:
     """Write the compressed in-memory binary format matrices to file."""
+    database_config_file: str
 
-    @classmethod
-    def write_specimen(cls, data: dict[int, int], study_index: int, specimen_index: int) -> None:
-        cls._write_data_array(data, study_index, specimen_index)
+    def __init__(self, database_config_file: str | None) -> None:
+        self.database_config_file = cast(str, database_config_file)
 
-    @classmethod
-    def _write_data_array(cls,
+    def write_specimen(self, data: dict[int, int], study_name: str, specimen: str) -> None:
+        self._write_data_array(data, study_name, specimen)
+
+    def _write_data_array(self,
         data_array: dict[int, int],
-        study_index: int,
-        specimen_index: int,
+        study_name: str,
+        specimen: str,
     ) -> None:
-        filename = cls._format_filename(study_index, specimen_index)
-        cls._write_data_array_to_file(cast(dict[int, int], data_array), filename)
-
-    @classmethod
-    def _format_filename(cls, study_index: int, specimen_index: int) -> str:
-        return '.'.join([
-            cls.get_data_array_filename_base(),
-            str(study_index),
-            str(specimen_index),
-            'bin',
-        ])
+        self._write_data_array_to_db(data_array, study_name, specimen)
 
-    @classmethod
-    def write_index(cls,
+    def write_index(self,
         specimens_by_measurement_study: dict[str, list[str]],
         target_index_lookups: dict,
         target_by_symbols: dict,
     ) -> None:
-        index = []
         study_names = sorted(list(specimens_by_measurement_study.keys()))
-        for study_index, study_name in enumerate(sorted(study_names)):
+        for measurement_study_name in sorted(study_names):
             index_item: dict[str, str | list] = {}
-            index_item['specimen measurement study name'] = study_name
-            index_item['expressions files'] = []
-            specimens = sorted(specimens_by_measurement_study[study_name])
-            for specimen_index, specimen in enumerate(specimens):
-                filename = '.'.join([
-                    cls.get_data_array_filename_base(),
-                    str(study_index),
-                    str(specimen_index),
-                    'bin',
-                ])
-                index_item['expressions files'].append({
-                    'specimen': specimen,
-                    'filename': filename,
-                })
-            index_item['target index lookup'] = target_index_lookups[study_name]
-            index_item['target by symbol'] = target_by_symbols[study_name]
-            index.append(index_item)
-        filename = join(cls.get_data_directory(), EXPRESSIONS_INDEX_FILENAME)
-        with open(filename, 'wt', encoding='utf-8') as index_file:
-            index_file.write(json.dumps({'': index}, indent=4))
-        logger.debug('Wrote expression index file %s .', filename)
-
-    @classmethod
-    def get_data_directory(cls) -> str:
-        return getcwd()
+            index_item['specimen measurement study name'] = measurement_study_name
+            index_item['target index lookup'] = target_index_lookups[measurement_study_name]
+            index_item['target by symbol'] = target_by_symbols[measurement_study_name]
+            index_str = json.dumps({'': [index_item]})
+            index_str_as_bytes = index_str.encode('utf-8')
+            study = retrieve_primary_study(self.database_config_file, measurement_study_name)
+            with DBCursor(database_config_file=self.database_config_file, study=study) as cursor:
+                insert_query = '''
+                    INSERT INTO
+                    ondemand_studies_index (
+                        specimen,
+                        blob_type,
+                        blob_contents
+                    )
+                    VALUES (%s, %s, %s) ;
+                '''
+                cursor.execute(insert_query, (None, 'expressions_index', index_str_as_bytes))
+                cursor.close()
+            logger.debug(f'Wrote expression index to database {study} .')
 
-    @classmethod
-    def _write_data_array_to_file(cls, data_array: dict[int, int], filename: str) -> None:
-        with open(filename, 'wb') as file:
-            for histological_structure_id, entry in data_array.items():
-                file.write(histological_structure_id.to_bytes(8, 'little'))
-                file.write(entry.to_bytes(8, 'little'))
-
-    @classmethod
-    def already_exists(cls, data_directory: str):
-        return isfile(join(data_directory, EXPRESSIONS_INDEX_FILENAME))
+    def _write_data_array_to_db(
+        self,
+        data_array: dict[int, int],
+        measurement_study_name: str,
+        specimen: str,
+    ) -> None:
+        blob = bytearray()
+        for histological_structure_id, entry in data_array.items():
+            blob.extend(histological_structure_id.to_bytes(8, 'little'))
+            blob.extend(entry.to_bytes(8, 'little'))
+        study_name = retrieve_primary_study(self.database_config_file, measurement_study_name)
+        with DBCursor(database_config_file=self.database_config_file, study=study_name) as cursor:
+            insert_query = '''
+                INSERT INTO
+                ondemand_studies_index (
+                    specimen,
+                    blob_type,
+                    blob_contents)
+                VALUES (%s, %s, %s) ;
+            '''
+            cursor.execute(insert_query, (specimen, 'feature_matrix', blob))
+            cursor.close()
 
-    @classmethod
-    def get_data_array_filename_base(cls):
-        return 'expression_data_array'
+    def expressions_indices_already_exist(self, study: str | None = None):
+        counts = get_counts(self.database_config_file, 'expressions_index', study=study)
+        for _study, count in counts.items():
+            if count > 1:
+                message = f'Too many ({count}) expression index files for study {_study}.'
+                raise ValueError(message)
+        return all(count == 1 for count in counts.values())