[a r] Index dataset description from Terra API (#5547)

deployments/anvilbox/environment.py

@@ -141,6 +141,7 @@ def env() -> Mapping[str, Optional[str]]:
         'AZUL_TDR_SOURCE_LOCATION': 'us-central1',
         'AZUL_TDR_SERVICE_URL': 'https://jade.datarepo-dev.broadinstitute.org',
         'AZUL_SAM_SERVICE_URL': 'https://sam.dsde-dev.broadinstitute.org',
+        'AZUL_DUOS_SERVICE_URL': 'https://consent.dsde-dev.broadinstitute.org',
 
         **(
             {

deployments/anvildev/environment.py

@@ -115,6 +115,7 @@ def env() -> Mapping[str, Optional[str]]:
         'AZUL_TDR_SOURCE_LOCATION': 'us-central1',
         'AZUL_TDR_SERVICE_URL': 'https://jade.datarepo-dev.broadinstitute.org',
         'AZUL_SAM_SERVICE_URL': 'https://sam.dsde-dev.broadinstitute.org',
+        'AZUL_DUOS_SERVICE_URL': 'https://consent.dsde-dev.broadinstitute.org',
 
         'AZUL_ENABLE_MONITORING': '1',
 

lambdas/service/app.py

@@ -227,7 +227,7 @@
         # changes and reset the minor version to zero. Otherwise, increment only
         # the minor version for backwards compatible changes. A backwards
         # compatible change is one that does not require updates to clients.
-        'version': '2.0'
+        'version': '2.1'
     },
     'tags': [
         {

lambdas/service/openapi.json

@@ -3,7 +3,7 @@
     "info": {
         "title": "azul_service",
         "description": "\n# Overview\n\nAzul is a REST web service for querying metadata associated with\nboth experimental and analysis data from a data repository. In order\nto deliver response times that make it suitable for interactive use\ncases, the set of metadata properties that it exposes for sorting,\nfiltering, and aggregation is limited. Azul provides a uniform view\nof the metadata over a range of diverse schemas, effectively\nshielding clients from changes in the schemas as they occur over\ntime. It does so, however, at the expense of detail in the set of\nmetadata properties it exposes and in the accuracy with which it\naggregates them.\n\nAzul denormalizes and aggregates metadata into several different\nindices for selected entity types. Metadata entities can be queried\nusing the [Index](#operations-tag-Index) endpoints.\n\nA set of indices forms a catalog. There is a default catalog called\n`dcp2` which will be used unless a\ndifferent catalog name is specified using the `catalog` query\nparameter. Metadata from different catalogs is completely\nindependent: a response obtained by querying one catalog does not\nnecessarily correlate to a response obtained by querying another\none. Two catalogs can contain metadata from the same sources or\ndifferent sources. It is only guaranteed that the body of a\nresponse by any given endpoint adheres to one schema,\nindependently of which catalog was specified in the request.\n\nAzul provides the ability to download data and metadata via the\n[Manifests](#operations-tag-Manifests) endpoints. The\n`curl` format manifests can be used to\ndownload data files. Other formats provide various views of the\nmetadata. Manifests can be generated for a selection of files using\nfilters. These filters are interchangeable with the filters used by\nthe [Index](#operations-tag-Index) endpoints.\n\nAzul also provides a [summary](#operations-Index-get_index_summary)\nview of indexed data.\n\n## Data model\n\nAny index, when queried, returns a JSON array of hits. Each hit\nrepresents a metadata entity. Nested in each hit is a summary of the\nproperties of entities associated with the hit. An entity is\nassociated either by a direct edge in the original metadata graph,\nor indirectly as a series of edges. The nested properties are\ngrouped by the type of the associated entity. The properties of all\ndata files associated with a particular sample, for example, are\nlisted under `hits[*].files` in a `/index/samples` response. It is\nimportant to note that while each _hit_ represents a discrete\nentity, the properties nested within that hit are the result of an\naggregation over potentially many associated entities.\n\nTo illustrate this, consider a data file that is part of two\nprojects (a project is a group of related experiments, typically by\none laboratory, institution or consortium). Querying the `files`\nindex for this file yields a hit looking something like:\n\n```\n{\n    \"projects\": [\n        {\n            \"projectTitle\": \"Project One\"\n            \"laboratory\": ...,\n            ...\n        },\n        {\n            \"projectTitle\": \"Project Two\"\n            \"laboratory\": ...,\n            ...\n        }\n    ],\n    \"files\": [\n        {\n            \"format\": \"pdf\",\n            \"name\": \"Team description.pdf\",\n            ...\n        }\n    ]\n}\n```\n\nThis example hit contains two kinds of nested entities (a hit in an\nactual Azul response will contain more): There are the two projects\nentities, and the file itself. These nested entities contain\nselected metadata properties extracted in a consistent way. This\nmakes filtering and sorting simple.\n\nAlso notice that there is only one file. When querying a particular\nindex, the corresponding entity will always be a singleton like\nthis.\n",
-        "version": "2.0"
+        "version": "2.1"
     },
     "tags": [
         {

src/azul/__init__.py

@@ -322,6 +322,10 @@ def tdr_service_url(self) -> mutable_furl:
     def sam_service_url(self) -> mutable_furl:
         return furl(self.environ['AZUL_SAM_SERVICE_URL'])
 
+    @property
+    def duos_service_url(self) -> mutable_furl:
+        return furl(self.environ['AZUL_DUOS_SERVICE_URL'])
+
     @property
     def dss_query_prefix(self) -> str:
         return self.environ.get('AZUL_DSS_QUERY_PREFIX', '')

src/azul/plugins/metadata/anvil/indexer/transform.py

@@ -125,7 +125,7 @@ def field_types(cls) -> FieldTypes:
         return {
             'activities': cls._activity_types(),
             'biosamples': cls._biosample_types(),
-            'datasets': cls._dataset_types(),
+            'datasets': {**cls._dataset_types(), **cls._sparse_dataset_types()},
             'diagnoses': cls._diagnosis_types(),
             'donors': cls._donor_types(),
             'files': cls._aggregate_file_types(),
@@ -221,6 +221,13 @@ def _biosample_types(cls) -> FieldTypes:
             'donor_age_at_collection': pass_thru_json,
         }
 
+    @classmethod
+    def _sparse_dataset_types(cls) -> FieldTypes:
+        return {
+            'document_id': null_str,
+            'description': null_str,
+        }
+
     @classmethod
     def _dataset_types(cls) -> FieldTypes:
         return {
@@ -370,6 +377,9 @@ def _biosample(self, biosample: EntityReference) -> MutableJSON:
     def _dataset(self, dataset: EntityReference) -> MutableJSON:
         return self._entity(dataset, self._dataset_types())
 
+    def _sparse_dataset(self, dataset: EntityReference) -> MutableJSON:
+        return self._entity(dataset, self._sparse_dataset_types())
+
     def _diagnosis(self, diagnosis: EntityReference) -> MutableJSON:
         return self._entity(diagnosis,
                             self._diagnosis_types(),
@@ -446,17 +456,22 @@ def entity_type(cls) -> str:
         return 'datasets'
 
     def _transform(self, entity: EntityReference) -> Contribution:
-        contents = dict(
-            activities=self._entities(self._activity, chain.from_iterable(
-                self._entities_by_type[activity_type]
-                for activity_type in self._activity_polymorphic_types
-            )),
-            biosamples=self._entities(self._biosample, self._entities_by_type['biosample']),
-            datasets=[self._dataset(entity)],
-            diagnoses=self._entities(self._diagnosis, self._entities_by_type['diagnosis']),
-            donors=self._entities(self._donor, self._entities_by_type['donor']),
-            files=self._entities(self._file, self._entities_by_type['file']),
-        )
+        try:
+            dataset = self._dataset(entity)
+        except KeyError:
+            contents = dict(datasets=[self._sparse_dataset(entity)])
+        else:
+            contents = dict(
+                activities=self._entities(self._activity, chain.from_iterable(
+                    self._entities_by_type[activity_type]
+                    for activity_type in self._activity_polymorphic_types
+                )),
+                biosamples=self._entities(self._biosample, self._entities_by_type['biosample']),
+                datasets=[dataset],
+                diagnoses=self._entities(self._diagnosis, self._entities_by_type['diagnosis']),
+                donors=self._entities(self._donor, self._entities_by_type['donor']),
+                files=self._entities(self._file, self._entities_by_type['file']),
+            )
         return self._contribution(contents, entity)
 
 

src/azul/plugins/repository/tdr_anvil/__init__.py

@@ -60,6 +60,7 @@
     MutableJSONs,
 )
 from azul.uuids import (
+    change_version,
     validate_uuid_prefix,
 )
 
@@ -98,9 +99,21 @@ class BundleEntityType(Enum):
     specifically tailored traversal implementation. Supplementary bundles always
     consist of exactly two entities: one file (the bundle entity) and one
     dataset.
+
+    The `dataset.description` field is unusual in that it is not stored in
+    BigQuery and must be retrieved via Terra's DUOS API. There is only one
+    dataset per snapshot, which is referenced in all primary and supplementary
+    bundles. Therefore, only one request to DUOS per *snapshot* is necessary,
+    but if `description` is retrieved at the same time as the other dataset
+    fields, we will make one request per *bundle* instead, potentially
+    overloading the DUOS service. Our solution is to retrieve `description` only
+    in a dedicated bundle format, once per snapshot, and merge it with the other
+    dataset fields during aggregation. This bundle contains only a single
+    dataset entity with only the `description` field populated.
     """
     primary: EntityType = 'biosample'
     supplementary: EntityType = 'file'
+    dataset: EntityType = 'dataset'
 
 
 class AnvilBundleFQIDJSON(SourcedBundleFQIDJSON):
@@ -178,26 +191,43 @@ def _list_bundles(self,
         validate_uuid_prefix(partition_prefix)
         primary = BundleEntityType.primary.value
         supplementary = BundleEntityType.supplementary.value
-        rows = self._run_sql(f'''
+        dataset = BundleEntityType.dataset.value
+        rows = list(self._run_sql(f'''
             SELECT datarepo_row_id, {primary!r} AS entity_type
             FROM {backtick(self._full_table_name(spec, primary))}
             WHERE STARTS_WITH(datarepo_row_id, '{partition_prefix}')
             UNION ALL
             SELECT datarepo_row_id, {supplementary!r} AS entity_type
             FROM {backtick(self._full_table_name(spec, supplementary))} AS supp
             WHERE supp.is_supplementary AND STARTS_WITH(datarepo_row_id, '{partition_prefix}')
-        ''')
-        return [
-            AnvilBundleFQID(source=source,
-                            # Reversibly tweak the entity UUID to prevent
-                            # collisions between entity IDs and bundle IDs
-                            uuid=uuids.change_version(row['datarepo_row_id'],
-                                                      self.datarepo_row_uuid_version,
-                                                      self.bundle_uuid_version),
-                            version=self._version,
-                            entity_type=BundleEntityType(row['entity_type']))
-            for row in rows
-        ]
+            UNION ALL
+            SELECT datarepo_row_id, {dataset!r} AS entity_type
+            FROM {backtick(self._full_table_name(spec, dataset))}
+        '''))
+        bundles = []
+        dataset_count = 0
+        for row in rows:
+            # We intentionally omit the WHERE clause for datasets so that we can
+            # verify our assumption that each snapshot only contains rows for a
+            # single dataset. This verification is performed independently and
+            # concurrently for every partition, but only one partition actually
+            # emits the bundle.
+            if row['entity_type'] == dataset:
+                require(0 == dataset_count)
+                dataset_count += 1
+                if not row['datarepo_row_id'].startswith(partition_prefix):
+                    continue
+            bundles.append(AnvilBundleFQID(
+                source=source,
+                # Reversibly tweak the entity UUID to prevent
+                # collisions between entity IDs and bundle IDs
+                uuid=uuids.change_version(row['datarepo_row_id'],
+                                          self.datarepo_row_uuid_version,
+                                          self.bundle_uuid_version),
+                version=self._version,
+                entity_type=BundleEntityType(row['entity_type'])
+            ))
+        return bundles
 
     def list_partitions(self,
                         source: TDRSourceRef
@@ -250,6 +280,9 @@ def _emulate_bundle(self, bundle_fqid: AnvilBundleFQID) -> TDRAnvilBundle:
         elif bundle_fqid.entity_type is BundleEntityType.supplementary:
             log.info('Bundle %r is a supplementary bundle', bundle_fqid.uuid)
             return self._supplementary_bundle(bundle_fqid)
+        elif bundle_fqid.entity_type is BundleEntityType.dataset:
+            log.info('Bundle %r is a dataset description', bundle_fqid.uuid)
+            return self._dataset_description(bundle_fqid)
         else:
             assert False, bundle_fqid.entity_type
 
@@ -334,6 +367,19 @@ def _supplementary_bundle(self, bundle_fqid: AnvilBundleFQID) -> TDRAnvilBundle:
         result.add_links({Link(**link_args)}, entities_by_key)
         return result
 
+    def _dataset_description(self, bundle_fqid: AnvilBundleFQID) -> TDRAnvilBundle:
+        description = self.tdr.get_duos(bundle_fqid.source)['studyDescription']
+        entity_id = change_version(bundle_fqid.uuid,
+                                   self.bundle_uuid_version,
+                                   self.datarepo_row_uuid_version)
+        entity = EntityReference(entity_type=bundle_fqid.entity_type.value,
+                                 entity_id=entity_id)
+        bundle = TDRAnvilBundle(fqid=bundle_fqid)
+        bundle.add_entity(entity=entity,
+                          version=self._version,
+                          row={'description': description})
+        return bundle
+
     def _bundle_entity(self, bundle_fqid: AnvilBundleFQID) -> KeyReference:
         source = bundle_fqid.source
         bundle_uuid = bundle_fqid.uuid

src/azul/terra.py

@@ -555,6 +555,9 @@ def _job_info(self, job: QueryJob) -> JSON:
     def _repository_endpoint(self, *path: str) -> mutable_furl:
         return config.tdr_service_url.set(path=('api', 'repository', 'v1', *path))
 
+    def _duos_endpoint(self, *path: str) -> mutable_furl:
+        return config.duos_service_url.set(path=('api', *path))
+
     def _check_response(self,
                         endpoint: furl,
                         response: urllib3.HTTPResponse
@@ -659,3 +662,10 @@ def for_registered_user(cls, authentication: OAuth2) -> 'TDRClient':
 
     def drs_client(self) -> DRSClient:
         return DRSClient(http_client=self._http_client)
+
+    def get_duos(self, source: SourceRef) -> MutableJSON:
+        response = self._retrieve_source(source)
+        duos_id = response['duosFirecloudGroup']['duosId']
+        url = self._duos_endpoint('dataset', 'registration', duos_id)
+        response = self._request('GET', url)
+        return self._check_response(url, response)

test/indexer/__init__.py

@@ -19,6 +19,9 @@
 from elasticsearch.helpers import (
     scan,
 )
+from more_itertools import (
+    one,
+)
 
 from azul import (
     CatalogName,
@@ -30,6 +33,7 @@
     SourcedBundleFQID,
 )
 from azul.indexer.document import (
+    DocumentType,
     IndexName,
 )
 from azul.indexer.index_service import (
@@ -137,9 +141,22 @@ def _get_all_hits(self):
                          index=','.join(self.index_service.index_names(self.catalog)),
                          preserve_order=True))
         for hit in hits:
+            entity_type, doc_type = self._parse_index_name(hit)
+            if (
+                entity_type == 'datasets'
+                and doc_type is DocumentType.contribution
+                and 'description' in one(hit['_source']['contents']['datasets'])
+            ):
+                # Sparse dataset contributions contain no lists
+                continue
             self._verify_sorted_lists(hit['_source'])
         return hits
 
+    def _parse_index_name(self, hit) -> tuple[str, DocumentType]:
+        index_name = IndexName.parse(hit['_index'])
+        index_name.validate()
+        return index_name.entity_type, index_name.doc_type
+
     def _load_canned_result(self, bundle_fqid: BundleFQID) -> MutableJSONs:
         """
         Load the canned index documents for the given canned bundle and fix the