updating for open data format change

setup.py

@@ -52,7 +52,7 @@
         "montydb": ["montydb>=2.3.12"],
         "notebook_runner": ["IPython>=8.11", "nbformat>=5.0", "regex>=2020.6"],
         "azure": ["azure-storage-blob>=12.16.0", "azure-identity>=1.12.0"],
-        "open_data": ["pandas>=2.1.4"],
+        "open_data": ["pandas>=2.1.4", "jsonlines>=4.0.0"],
         "testing": [
             "pytest",
             "pytest-cov",

src/maggma/stores/open_data.py

@@ -1,9 +1,9 @@
 import gzip
 from datetime import datetime
-from io import BytesIO
+from io import BytesIO, StringIO
 from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union
 
-import orjson
+import jsonlines
 import pandas as pd
 from boto3 import Session
 from botocore import UNSIGNED
@@ -31,7 +31,7 @@
     @property
     def _collection(self):
         """
-        Returns a handle to the pymongo collection object
+        Returns a handle to the pymongo collection object.
 
         Raises:
             NotImplementedError: always as this concept does not make sense for this type of store
@@ -41,7 +41,7 @@
     @property
     def name(self) -> str:
         """
-        Return a string representing this data source
+        Return a string representing this data source.
         """
         return "imem://"
 
@@ -151,6 +151,15 @@
         ret = self._query(criteria=criteria, properties=properties, sort=sort, skip=skip, limit=limit)
         return (row.to_dict() for _, row in ret.iterrows())
 
+    @staticmethod
+    def add_missing_items(to_dt: pd.DataFrame, from_dt: pd.DataFrame, on: List[str]) -> pd.DataFrame:
+        orig_columns = to_dt.columns
+        merged = to_dt.merge(from_dt, on=on, how="left", suffixes=("", "_B"))
+        for column in from_dt.columns:
+            if column not in on:
+                merged[column].update(merged.pop(column + "_B"))
+        return merged[orig_columns]
+
     def update(self, docs: Union[List[Dict], Dict], key: Union[List, str, None] = None, clear_first: bool = False):
         """
         Update documents into the Store
@@ -164,20 +173,17 @@
             clear_first: if True clears the underlying data first, fully replacing the data with docs;
                 if False performs an upsert based on the parameters
         """
+        if key is not None:
+            raise NotImplementedError("updating store based on a key different than the store key is not supported")
+
         df = pd.DataFrame(docs)
         if self._data is None or clear_first:
             if not df.empty:
                 self._data = df
             return
+        key = [self.key]
 
-        if key is None:
-            key = self.key
-
-        merged = self._data.merge(df, on=key, how="left", suffixes=("", "_B"))
-        for column in df.columns:
-            if column not in key:
-                merged[column] = merged[column + "_B"].combine_first(merged[column])
-        merged = merged[self._data.columns]
+        merged = PandasMemoryStore.add_missing_items(to_dt=self._data, from_dt=df, on=key)
         non_matching = df[~df.set_index(key).index.isin(self._data.set_index(key).index)]
         self._data = pd.concat([merged, non_matching], ignore_index=True)
 
@@ -372,7 +378,15 @@
         """
         try:
             response = self.client.get_object(Bucket=self.bucket, Key=self._get_full_key_path())
-            return pd.read_json(response["Body"], orient="records")
+            df = pd.read_json(response["Body"], orient="records", lines=True)
+            if self.last_updated_field in df.columns:
+                df[self.last_updated_field] = df[self.last_updated_field].apply(
+                    lambda x: datetime.fromisoformat(x["$date"].rstrip("Z"))
+                    if isinstance(x, dict) and "$date" in x
+                    else x
+                )
+            return df
+
         except ClientError as ex:
             if ex.response["Error"]["Code"] == "NoSuchKey":
                 return []
@@ -386,17 +400,22 @@
         """
         super().update(self._retrieve_manifest(), clear_first=True)
 
-    def store_manifest(self, data: List[Dict]) -> None:
+    def store_manifest(self, data: pd.DataFrame) -> None:
         """Stores the provided data into the index stored in S3.
         This overwrites and fully replaces all of the contents of the previous index stored in S3.
         It also rewrites the memory index with the provided data.
 
         Args:
             data (List[Dict]): The data to store in the index.
         """
+        string_io = StringIO()
+        with jsonlines.Writer(string_io, dumps=json_util.dumps) as writer:
+            for _, row in data.iterrows():
+                writer.write(row.to_dict())
+
         self.client.put_object(
             Bucket=self.bucket,
-            Body=orjson.dumps(data, default=json_util.default),
+            Body=BytesIO(string_io.getvalue().encode("utf-8")),
             Key=self._get_full_key_path(),
         )
         super().update(data, clear_first=True)
@@ -458,8 +477,9 @@
         sub_dir: Optional[str] = None,
         key: str = "fs_id",
         searchable_fields: Optional[List[str]] = None,
-        object_file_extension: str = ".json.gz",
+        object_file_extension: str = ".jsonl.gz",
         access_as_public_bucket: bool = False,
+        object_grouping: Optional[List[str]] = None,
         **kwargs,
     ):
         """Initializes an OpenDataStore
@@ -481,10 +501,11 @@
         self.searchable_fields = searchable_fields if searchable_fields is not None else []
         self.object_file_extension = object_file_extension
         self.access_as_public_bucket = access_as_public_bucket
+        self.object_grouping = object_grouping if object_grouping is not None else ["nelements", "symmetry_number"]
+
         if access_as_public_bucket:
             kwargs["s3_resource_kwargs"] = kwargs["s3_resource_kwargs"] if "s3_resource_kwargs" in kwargs else {}
             kwargs["s3_resource_kwargs"]["config"] = Config(signature_version=UNSIGNED)
-
         kwargs["index"] = index
         kwargs["bucket"] = bucket
         kwargs["compress"] = compress
@@ -495,16 +516,71 @@
         kwargs["unpack_data"] = True
         self.kwargs = kwargs
         super().__init__(**kwargs)
+        self.searchable_fields = list(
+            set(self.object_grouping) | set(self.searchable_fields) | {self.key, self.last_updated_field}
+        )
+
+    def query(
+        self,
+        criteria: Optional[Dict] = None,
+        properties: Union[Dict, List, None] = None,
+        sort: Optional[Dict[str, Union[Sort, int]]] = None,
+        skip: int = 0,
+        limit: int = 0,
+    ) -> Iterator[Dict]:
+        """
+        Queries the Store for a set of documents.
+
+        Args:
+            criteria: PyMongo filter for documents to search in.
+            properties: properties to return in grouped documents.
+            sort: Dictionary of sort order for fields. Keys are field names and values
+                are 1 for ascending or -1 for descending.
+            skip: number documents to skip.
+            limit: limit on total number of documents returned.
+
+        """
+        prop_keys = set()
+        if isinstance(properties, dict):
+            prop_keys = set(properties.keys())
+        elif isinstance(properties, list):
+            prop_keys = set(properties)
+
+        for _, docs in self.index.groupby(
+            keys=self.object_grouping, criteria=criteria, sort=sort, limit=limit, skip=skip
+        ):
+            group_doc = None  # S3 backed group doc
+            for _, doc in docs.iterrows():
+                data = doc
+                if properties is None or not prop_keys.issubset(set(doc.keys())):
+                    if not group_doc:
+                        group_doc = self._read_doc_from_s3(self._get_full_key_path(docs))
+                    if group_doc.empty:
+                        continue
+                    data = group_doc.query(f"{self.key} == '{doc[self.key]}'")
+                    data = data.to_dict(orient="index")[0]
+                if properties is None:
+                    yield data
+                else:
+                    yield {p: data[p] for p in prop_keys if p in data}
+
+    def _read_doc_from_s3(self, file_id: str) -> pd.DataFrame:
+        try:
+            response = self.s3_bucket.Object(file_id).get()
+            return pd.read_json(response["Body"], orient="records", lines=True, compression={"method": "gzip"})
+        except ClientError as ex:
+            if ex.response["Error"]["Code"] == "NoSuchKey":
+                return pd.DataFrame()
+            raise
 
     def _get_full_key_path(self, id: str) -> str:
-        if self.index.collection_name == "thermo" and self.key == "thermo_id":
-            material_id, thermo_type = id.split("_", 1)
-            return f"{self.sub_dir}{thermo_type}/{material_id}{self.object_file_extension}"
-        if self.index.collection_name == "xas" and self.key == "spectrum_id":
-            material_id, spectrum_type, absorbing_element, edge = id.rsplit("-", 3)
-            return f"{self.sub_dir}{edge}/{spectrum_type}/{absorbing_element}/{material_id}{self.object_file_extension}"
-        if self.index.collection_name == "synth_descriptions" and self.key == "doi":
-            return f"{self.sub_dir}{id.replace('/', '_')}{self.object_file_extension}"
+        raise NotImplementedError("Not implemented for this store")
+
+    def _get_full_key_path(self, index: pd.DataFrame) -> str:
+        id = ""
+        for group in self.object_grouping:
+            id = f"{id}{group}={index[group].iloc[0]}/"
+        id = id.rstrip("/")
         return f"{self.sub_dir}{id}{self.object_file_extension}"
 
     def _get_compression_function(self) -> Callable:
@@ -513,36 +589,83 @@
     def _get_decompression_function(self) -> Callable:
         return gzip.decompress
 
-    def _read_data(self, data: bytes, compress_header: str = "gzip") -> List[Dict]:
-        if compress_header is not None:
-            data = self._get_decompression_function()(data)
-        return orjson.loads(data)
+    def _gather_indexable_data(self, df: pd.DataFrame, search_keys: List[str]) -> pd.DataFrame:
+        return df[search_keys]
 
-    def _gather_indexable_data(self, doc: Dict, search_keys: List[str]) -> Dict:
-        index_doc = {k: doc[k] for k in search_keys}
-        index_doc[self.key] = doc[self.key]  # Ensure key is in metadata
-        # Ensure last updated field is in metada if it's present in the data
-        if self.last_updated_field in doc:
-            index_doc[self.last_updated_field] = doc[self.last_updated_field]
-        return index_doc
+    def update(
+        self,
+        docs: Union[List[Dict], Dict],
+        key: Union[List, str, None] = None,
+        additional_metadata: Union[str, List[str], None] = None,
+    ):
+        if additional_metadata is not None:
+            raise NotImplementedError("updating store with additional metadata is not supported")
+        super().update(docs=docs, key=key)
+
+    def _write_to_s3_and_index(self, docs: List[Dict], search_keys: List[str]):
+        """Implements updating of the provided documents in S3 and the index.
+
+        Args:
+            docs (List[Dict]): The documents to update
+            search_keys (List[str]): The keys of the information to be updated in the index
+        """
+        # group docs to update by object grouping
+        og = list(set(self.object_grouping) | set(search_keys))
+        df = pd.json_normalize(docs, sep="_")[og]
+        df_grouped = df.groupby(self.object_grouping)
+        existing = self.index._data
+        docs_df = pd.DataFrame(docs)
+        for group, _ in df_grouped:
+            query_str = " and ".join([f"{col} == {val!r}" for col, val in zip(self.object_grouping, group)])
+            sub_df = df.query(query_str)
+            sub_docs_df = docs_df[docs_df[self.key].isin(sub_df[self.key].unique())]
+            merged_df = sub_df
+            if existing is not None:
+                # fetch subsection of existing and docs_df and do outer merge with indicator=True
+                sub_existing = existing.query(query_str)
+                merged_df = sub_existing.merge(sub_df, on=og, how="outer", indicator=True)
+                # if there's any rows in existing only
+                if not merged_df[merged_df["_merge"] == "left_only"].empty:
+                    ## fetch the S3 data and populate those rows in sub_docs_df
+                    s3_df = self._read_doc_from_s3(self._get_full_key_path(sub_existing))
+                    # sub_docs
+                    sub_docs_df = sub_docs_df.merge(merged_df[[self.key, "_merge"]], on=self.key, how="right")
+                    sub_docs_df.update(s3_df, overwrite=False)
+                    sub_docs_df = sub_docs_df.drop("_merge", axis=1)
+
+                merged_df = merged_df.drop("_merge", axis=1)
+            # write doc based on subsection
+            self._write_doc_and_update_index(sub_docs_df, merged_df)
+
+    def _write_doc_and_update_index(self, items: pd.DataFrame, index: pd.DataFrame) -> None:
+        self.write_doc_to_s3(items, index)
+        self.index.update(index)
+
+    def write_doc_to_s3(self, doc, search_keys):
+        if not isinstance(doc, pd.DataFrame):
+            raise NotImplementedError("doc parameter must be a Pandas DataFrame for the implementation for this store")
+        if not isinstance(search_keys, pd.DataFrame):
+            raise NotImplementedError(
+                "search_keys parameter must be a Pandas DataFrame for the implementation for this store"
+            )
+        # def write_doc_to_s3(self, items: pd.DataFrame, index: pd.DataFrame) -> None:
+        string_io = StringIO()
+        with jsonlines.Writer(string_io, dumps=json_util.dumps) as writer:
+            for _, row in doc.iterrows():
+                writer.write(row.to_dict())
 
-    def write_doc_to_s3(self, doc: Dict, search_keys: List[str]) -> Dict:
-        search_doc = self._gather_indexable_data(doc, search_keys)
+        data = self._get_compression_function()(string_io.getvalue().encode("utf-8"))
 
-        data = orjson.dumps(doc, default=json_util.default)
-        data = self._get_compression_function()(data)
         self._get_bucket().upload_fileobj(
             Fileobj=BytesIO(data),
-            Key=self._get_full_key_path(str(doc[self.key])),
+            Key=self._get_full_key_path(search_keys),
         )
-        return search_doc
 
-    def _index_for_doc_from_s3(self, bucket, key: str) -> Dict:
-        response = bucket.Object(key).get()
-        doc = self._read_data(response["Body"].read())
+    def _index_for_doc_from_s3(self, key: str) -> pd.DataFrame:
+        doc = self._read_doc_from_s3(key)
         return self._gather_indexable_data(doc, self.searchable_fields)
 
-    def rebuild_index_from_s3_data(self) -> List[Dict]:
+    def rebuild_index_from_s3_data(self) -> pd.DataFrame:
         """
         Rebuilds the index Store from the data in S3
         Stores only the key, last_updated_field and searchable_fields in the index.
@@ -561,12 +684,12 @@
             for file in page["Contents"]:
                 key = file["Key"]
                 if key != self.index._get_full_key_path():
-                    index_doc = self._index_for_doc_from_s3(bucket, key)
-                    all_index_docs.append(index_doc)
-        self.index.store_manifest(all_index_docs)
-        return all_index_docs
+                    all_index_docs.append(self._index_for_doc_from_s3(key))
+        ret = pd.concat(all_index_docs, ignore_index=True)
+        self.index.store_manifest(ret)
+        return ret
 
-    def rebuild_index_from_data(self, docs: List[Dict]) -> List[Dict]:
+    def rebuild_index_from_data(self, docs: pd.DataFrame) -> pd.DataFrame:
         """
         Rebuilds the index Store from the provided data.
         The provided data needs to include all of the documents in this data set.
@@ -578,10 +701,7 @@
         Returns:
             List[Dict]: The set of docs representing the index data.
         """
-        all_index_docs = []
-        for doc in docs:
-            index_doc = self._gather_indexable_data(doc, self.searchable_fields)
-            all_index_docs.append(index_doc)
+        all_index_docs = self._gather_indexable_data(docs, self.searchable_fields)
         self.index.store_manifest(all_index_docs)
         return all_index_docs
 
@@ -593,6 +713,7 @@
                 self.endpoint_url,
                 self.key,
                 self.sub_dir,
+                tuple(self.object_grouping),
             )
         )
 
@@ -614,5 +735,6 @@
             "searchable_fields",
             "sub_dir",
             "last_updated_field",
+            "object_grouping",
         ]
         return all(getattr(self, f) == getattr(other, f) for f in fields)