From 95fb4c034994e67607c973d2a1e756d3b8248e76 Mon Sep 17 00:00:00 2001 From: sheikhPHD Date: Mon, 16 Mar 2026 13:09:53 +0100 Subject: [PATCH 1/3] Update ingest_data.py to support new parquet files from 6 data sources --- src/ingest_data.py | 213 ++++++++++++++++++++++++++++----------------- 1 file changed, 131 insertions(+), 82 deletions(-) diff --git a/src/ingest_data.py b/src/ingest_data.py index 369187a..1c4d32e 100644 --- a/src/ingest_data.py +++ b/src/ingest_data.py @@ -145,87 +145,106 @@ def delete_files_for_update(engine: Engine, new_or_modified_datasets: list[int]) # ============================================================================ def load_datasets_data(parquet_path: str) -> pd.DataFrame: - datsets_df = pd.read_parquet(parquet_path) - datsets_df = datsets_df[[ - 'dataset_origin', - 'dataset_id', - 'doi', - 'date_creation', - 'date_last_modified', - 'date_fetched', - 'file_number', - 'download_number', - 'view_number', - 'license', - 'dataset_url', - 'title', - 'author', - 'keywords', - 'description' - ]].rename(columns={ - 'dataset_origin': 'data_source', - 'dataset_id': 'id_in_data_source', - 'date_creation': 'date_created', - 'date_fetched': 'date_last_crawled', - 'dataset_url': 'url_in_data_source' + df = pd.read_parquet(parquet_path) + + # CHANGED: The new parquet files use different column names compared to the + # old parquet files. We rename them here to match the database field names. + # Old name → New name in parquet + # 'dataset_origin' → 'dataset_repository_name' + # 'dataset_id' → 'dataset_id_in_repository' + # 'dataset_url' → 'dataset_url_in_repository' + # 'date_last_modified' → 'date_last_updated' + # 'date_fetched' → 'date_last_fetched' + # 'file_number' → 'number_of_files' + # 'date_creation' → 'date_created' (same name, no rename needed) + df = df.rename(columns={ + 'dataset_repository_name': 'data_source', + 'dataset_id_in_repository': 'id_in_data_source', + 'dataset_url_in_repository': 'url_in_data_source', + 'date_last_updated': 'date_last_modified', + 'date_last_fetched': 'date_last_crawled', + 'number_of_files': 'file_number', }) - # Normalize author and keywords strings - # For datasets["author"] column, we can have multiple authors - # separated by a comma or a semicolon (replace semicolon with comma). - # We need to split the authors but keep them concatenated. - # We simply need to remove the space after the comma. - datsets_df['author'] = datsets_df['author'].str.replace(", ", ",").str.replace(";", ",") - datsets_df['keywords'] = datsets_df['keywords'].str.replace(", ", ",").str.replace("; ", ";").str.replace(",", ";") - - # We want to make all keywords lowercase in order to avoid duplicates - # when we create the Keyword objects in the database. - datsets_df["keywords"] = datsets_df["keywords"].str.lower() - - # Normally we'd expect all datasets to have at least one author, but it - # seems that datasets from OSF might not have an author field. - # We need to account for that by replacing NaN with an empty string. - datsets_df["author"] = datsets_df["author"].apply(lambda x: x if pd.notna(x) else "") - datsets_df["keywords"] = datsets_df["keywords"].apply(lambda x: x if pd.notna(x) else "") - - # The column 'data_source' is the name of the data source + # CHANGED: In the new parquet files, 'author_names' is a numpy array + # e.g. ["Name1", "Name2"] instead of a plain string "Name1, Name2". + # We join the list into a comma-separated string to match the old behaviour. + # We also handle the case where the field is missing (NaN) by returning "". + df['author'] = df['author_names'].apply( + lambda x: ",".join(x) if hasattr(x, '__iter__') and not isinstance(x, str) else "" + ) + + # CHANGED: In the new parquet files, 'keywords' is also a numpy array + # e.g. ["kw1", "kw2"] instead of a plain string, or None if not provided. + # We join with semicolons and lowercase everything, same as the old behaviour. + # We also handle the case where the field is missing (NaN) by returning "". + df['keywords'] = df['keywords'].apply( + lambda x: ";".join(x).lower() if hasattr(x, '__iter__') and not isinstance(x, str) else "" + ) + + # The column 'data_source' is the name of the data source. # We want to add a new column "data_source_url" that will contain the URL # of the data source. - # If the row is "zenodo", the URL will be "https://zenodo.org/" - # If the row is "figshare", the URL will be "https://figshare.com/" - # If the row is "osf", the URL will be "https://osf.io/" - datsets_df['data_source_url'] = datsets_df['data_source'].map({ + # CHANGED: Added all 6 sources (8 entries because MDDB has 3 nodes). + # Previously only zenodo, figshare, osf were here. + df['data_source_url'] = df['data_source'].map({ 'zenodo': 'https://zenodo.org/', 'figshare': 'https://figshare.com/', - 'osf': 'https://osf.io/' + 'atlas': 'https://www.dsimb.inserm.fr/ATLAS/', + 'nomad': 'https://nomad-lab.eu/', + 'gpcrmd': 'https://www.gpcrmd.org/', + 'mdposit_mmb_node': 'https://mmb.mddbr.eu/', + 'mdposit_inria_node': 'https://dynarepo.inria.fr/', + 'mdposit_cineca_node': 'https://cineca.mddbr.eu/', }) - return datsets_df + # Fill missing optional fields with safe defaults so downstream code + # does not crash when a field is absent in a particular source's parquet. + for col in ['doi', 'description', 'license', 'download_number', + 'view_number', 'file_number', 'date_created', 'date_last_modified']: + if col not in df.columns: + df[col] = None + + # ADDED: file_number cannot be NULL in the database (NOT NULL constraint). + # Some sources like GPCRmd have NaN for file_number, replace with 0. + df['file_number'] = df['file_number'].fillna(0) + + return df + def load_files_data(parquet_path: str) -> pd.DataFrame: - files_df = pd.read_parquet(parquet_path) - files_df = files_df[[ - 'dataset_origin', - 'dataset_id', - 'file_type', - 'file_name', - 'file_size', - 'file_md5', - 'file_url', - 'from_zip_file', - 'origin_zip_file' - ]].rename(columns={ - 'dataset_origin': 'data_source', - 'dataset_id': 'dataset_id_in_data_source', - 'file_type': 'type', + df = pd.read_parquet(parquet_path) + + # CHANGED: The new parquet files use different column names compared to the + # old parquet files. We rename them here to match the database field names. + # Old name → New name in parquet + # 'dataset_origin' → 'dataset_repository_name' + # 'dataset_id' → 'dataset_id_in_repository' + # 'file_type' → 'file_type' (same name, no rename needed) + # 'file_name' → 'file_name' (same name, no rename needed) + # 'file_size' → 'file_size_in_bytes' + # 'file_md5' → 'file_md5' (same name, no rename needed) + # 'file_url' → 'file_url_in_repository' + # 'origin_zip_file' → 'containing_archive_file_name' + # 'from_zip_file' → no longer exists, derived below + df = df.rename(columns={ + 'dataset_repository_name': 'data_source', + 'dataset_id_in_repository': 'dataset_id_in_data_source', 'file_name': 'name', - 'file_size': 'size_in_bytes', + 'file_type': 'type', + 'file_size_in_bytes': 'size_in_bytes', 'file_md5': 'md5', - 'file_url': 'url', - 'from_zip_file': 'is_from_zip_file', - 'origin_zip_file': 'parent_zip_file_name' + 'file_url_in_repository': 'url', + 'containing_archive_file_name': 'parent_zip_file_name', }) - return files_df + + # CHANGED: The old parquet had an explicit 'from_zip_file' boolean column. + # The new parquet does not have this column. Instead, we derive it from + # 'containing_archive_file_name': if it is not null, the file came from a zip. + df['is_from_zip_file'] = df['parent_zip_file_name'].notna() + + return df + def load_topology_data(parquet_path_topology: str) -> pd.DataFrame: """Load parquet file and return DataFrame with selected columns. @@ -275,7 +294,7 @@ def load_parameter_data(parquet_path_parameters: str) -> pd.DataFrame: 'file_name': 'name' }) - # If integrator is missing, set it no 'unknown + # If integrator is missing, set it to 'undefined' parameter_df['integrator'] = parameter_df['integrator'].apply(lambda x: x if pd.notna(x) else "undefined") return parameter_df @@ -415,7 +434,7 @@ def create_or_update_datasets_authors_origins_tables( # In our Dataset model, we have defined an attribute called author # that represents a MtM relationship with the Author model. # When we write the following, we are assigning the list of Author - # objects (collected in the authors list) to the dataset’s author + # objects (collected in the authors list) to the dataset's author # attribute. This informs SQLModel/SQLAlchemy to create the # appropriate link table entries so that the dataset is # related to all these authors. @@ -441,7 +460,7 @@ def create_or_update_datasets_authors_origins_tables( "date_last_crawled", "file_number", # "download_number", - # "view_number", + # "view_number", # "license", "url_in_data_source", "title", @@ -912,18 +931,18 @@ def data_ingestion(): - The "simulation tables" will include: - TopologyFile - ParameterFile - - TrajectoryFilen + - TrajectoryFile - Thermostat - Barostat - Integrator This function will ingest all the data in the following steps: - 1) Populate the "datsets tables". - - Load the datasets data from the parquet file. + 1) Populate the "datasets tables". + - Load the datasets data from the parquet files. - Create or update the tables. - Log the number of records created, updated, or ignored for Dataset. 2) Populate the "files" tables. - - Load the files data from the parquet file. + - Load the files data from the parquet files. - Check the datasets that have been updated or added. a) There are no new or modified datasets: - Skip the "files" ingestion. @@ -934,16 +953,42 @@ def data_ingestion(): - Create the simulation tables. 3) Log the total data ingestion time. """ - # Path to the parquet data files - files_path = "data/parquet_files/files.parquet" - datasets_path = "data/parquet_files/datasets.parquet" + # CHANGED: Replaced single hardcoded parquet paths with lists of paths, + # one per data source. Each source has its own dataset and file parquet. + # To add a new source in the future, simply add its parquet paths here. + + # --- Dataset parquet files (one per source) --- + dataset_parquets = [ + "data/parquet_files/zenodo_datasets.parquet", + "data/parquet_files/figshare_datasets.parquet", + "data/parquet_files/atlas_datasets.parquet", + "data/parquet_files/nomad_datasets.parquet", + "data/parquet_files/gpcrmd_datasets.parquet", + "data/parquet_files/mddb_datasets.parquet", + ] + + # --- File parquet files (one per source) --- + file_parquets = [ + "data/parquet_files/zenodo_files.parquet", + "data/parquet_files/figshare_files.parquet", + "data/parquet_files/atlas_files.parquet", + "data/parquet_files/nomad_files.parquet", + "data/parquet_files/gpcrmd_files.parquet", + "data/parquet_files/mddb_files.parquet", + ] start_1 = time.perf_counter() start_4 = time.perf_counter() - # Load the datasets data - datasets_df = load_datasets_data(datasets_path) + # CHANGED: Load and concatenate all dataset parquets into one DataFrame. + # Previously this was a single pd.read_parquet() call on one file. + # Now we load each source separately and stack them into one DataFrame + # using pd.concat(). The rest of the ingestion logic stays the same. + datasets_df = pd.concat( + [load_datasets_data(p) for p in dataset_parquets], + ignore_index=True + ) logger.info("Creating or updating datasets tables...") logger.info("Dataset, Author, DataSource, Keyword tables") @@ -958,8 +1003,12 @@ def data_ingestion(): start_2 = time.perf_counter() - # Load the files data - files_df = load_files_data(files_path) + # CHANGED: Same as datasets — load and concatenate all file parquets. + files_df = pd.concat( + [load_files_data(p) for p in file_parquets], + ignore_index=True + ) + logger.info("New or modified datasets found.") logger.info("Deleting files associated to modified datasets...") delete_files_for_update(engine, new_or_modified_datasets) From b5ed7828a0cd807e50c6f1db97002a601ef16763 Mon Sep 17 00:00:00 2001 From: sheikhPHD Date: Thu, 2 Apr 2026 17:34:43 +0200 Subject: [PATCH 2/3] More generic version of ingest_data.py, it passes a single parquet file as an argument from the terminal --- src/ingest_data.py | 699 ++++++++++++++++++--------------------------- 1 file changed, 277 insertions(+), 422 deletions(-) diff --git a/src/ingest_data.py b/src/ingest_data.py index 1c4d32e..5a9dfd8 100644 --- a/src/ingest_data.py +++ b/src/ingest_data.py @@ -35,15 +35,27 @@ - We transform the data into the SQLModel objects. - We create the objects in the database. -To launch this script, use the command: -uv run python src/ingest_data.py +CHANGED: The script now accepts a single parquet file path as a CLI argument. +This makes ingestion generic and granular — you choose exactly which file +to ingest, for which source, on which date. Examples: + + uv run src/ingest_data.py data/atlas/2026-02-18/atlas_datasets.parquet + uv run src/ingest_data.py data/atlas/2026-02-18/atlas_files.parquet + uv run src/ingest_data.py data/zenodo/2026-02-18/zenodo_datasets.parquet + +The script detects whether the parquet is a datasets file or a files file +by looking for '_datasets' or '_files' in the filename, then runs the +appropriate ingestion pipeline. + +File existence is checked directly against the database (Option 2 / upsert +pattern) so the datasets and files parquets are fully independent — you can +run them in any order, at any time, as many times as you want. """ # ============================================================================ # Logger configuration # ============================================================================ - logger.remove() # Terminal format logger.add( @@ -124,13 +136,13 @@ def delete_files_for_update(engine: Engine, new_or_modified_datasets: list[int]) # WARNING: This is with the assumption that the parquet files used to ingest the data # will always have ALL the files, not just new ones or a select amount of the old ones. trajectory_stmt = delete(TrajectoryFile) - result_trajectory= session.exec(trajectory_stmt) + result_trajectory = session.exec(trajectory_stmt) parameter_stmt = delete(ParameterFile) - result_parameter =session.exec(parameter_stmt) + result_parameter = session.exec(parameter_stmt) topology_stmt = delete(TopologyFile) - result_topology =session.exec(topology_stmt) + result_topology = session.exec(topology_stmt) session.commit() @@ -149,14 +161,6 @@ def load_datasets_data(parquet_path: str) -> pd.DataFrame: # CHANGED: The new parquet files use different column names compared to the # old parquet files. We rename them here to match the database field names. - # Old name → New name in parquet - # 'dataset_origin' → 'dataset_repository_name' - # 'dataset_id' → 'dataset_id_in_repository' - # 'dataset_url' → 'dataset_url_in_repository' - # 'date_last_modified' → 'date_last_updated' - # 'date_fetched' → 'date_last_fetched' - # 'file_number' → 'number_of_files' - # 'date_creation' → 'date_created' (same name, no rename needed) df = df.rename(columns={ 'dataset_repository_name': 'data_source', 'dataset_id_in_repository': 'id_in_data_source', @@ -168,25 +172,16 @@ def load_datasets_data(parquet_path: str) -> pd.DataFrame: # CHANGED: In the new parquet files, 'author_names' is a numpy array # e.g. ["Name1", "Name2"] instead of a plain string "Name1, Name2". - # We join the list into a comma-separated string to match the old behaviour. - # We also handle the case where the field is missing (NaN) by returning "". df['author'] = df['author_names'].apply( lambda x: ",".join(x) if hasattr(x, '__iter__') and not isinstance(x, str) else "" ) - # CHANGED: In the new parquet files, 'keywords' is also a numpy array - # e.g. ["kw1", "kw2"] instead of a plain string, or None if not provided. - # We join with semicolons and lowercase everything, same as the old behaviour. - # We also handle the case where the field is missing (NaN) by returning "". + # CHANGED: In the new parquet files, 'keywords' is also a numpy array. df['keywords'] = df['keywords'].apply( lambda x: ";".join(x).lower() if hasattr(x, '__iter__') and not isinstance(x, str) else "" ) - # The column 'data_source' is the name of the data source. - # We want to add a new column "data_source_url" that will contain the URL - # of the data source. - # CHANGED: Added all 6 sources (8 entries because MDDB has 3 nodes). - # Previously only zenodo, figshare, osf were here. + # Map data source name to its URL. df['data_source_url'] = df['data_source'].map({ 'zenodo': 'https://zenodo.org/', 'figshare': 'https://figshare.com/', @@ -198,15 +193,13 @@ def load_datasets_data(parquet_path: str) -> pd.DataFrame: 'mdposit_cineca_node': 'https://cineca.mddbr.eu/', }) - # Fill missing optional fields with safe defaults so downstream code - # does not crash when a field is absent in a particular source's parquet. + # Fill missing optional fields with safe defaults. for col in ['doi', 'description', 'license', 'download_number', 'view_number', 'file_number', 'date_created', 'date_last_modified']: if col not in df.columns: df[col] = None - # ADDED: file_number cannot be NULL in the database (NOT NULL constraint). - # Some sources like GPCRmd have NaN for file_number, replace with 0. + # file_number cannot be NULL in the database (NOT NULL constraint). df['file_number'] = df['file_number'].fillna(0) return df @@ -215,18 +208,7 @@ def load_datasets_data(parquet_path: str) -> pd.DataFrame: def load_files_data(parquet_path: str) -> pd.DataFrame: df = pd.read_parquet(parquet_path) - # CHANGED: The new parquet files use different column names compared to the - # old parquet files. We rename them here to match the database field names. - # Old name → New name in parquet - # 'dataset_origin' → 'dataset_repository_name' - # 'dataset_id' → 'dataset_id_in_repository' - # 'file_type' → 'file_type' (same name, no rename needed) - # 'file_name' → 'file_name' (same name, no rename needed) - # 'file_size' → 'file_size_in_bytes' - # 'file_md5' → 'file_md5' (same name, no rename needed) - # 'file_url' → 'file_url_in_repository' - # 'origin_zip_file' → 'containing_archive_file_name' - # 'from_zip_file' → no longer exists, derived below + # CHANGED: Rename columns to match the database field names. df = df.rename(columns={ 'dataset_repository_name': 'data_source', 'dataset_id_in_repository': 'dataset_id_in_data_source', @@ -238,19 +220,14 @@ def load_files_data(parquet_path: str) -> pd.DataFrame: 'containing_archive_file_name': 'parent_zip_file_name', }) - # CHANGED: The old parquet had an explicit 'from_zip_file' boolean column. - # The new parquet does not have this column. Instead, we derive it from - # 'containing_archive_file_name': if it is not null, the file came from a zip. + # CHANGED: Derive is_from_zip_file from parent_zip_file_name. df['is_from_zip_file'] = df['parent_zip_file_name'].notna() return df def load_topology_data(parquet_path_topology: str) -> pd.DataFrame: - """Load parquet file and return DataFrame with selected columns. - - Rename columns to match the SQLModel table columns. - """ + """Load parquet file and return DataFrame with selected columns.""" topology_df = pd.read_parquet(parquet_path_topology) topology_df = topology_df[[ @@ -272,10 +249,7 @@ def load_topology_data(parquet_path_topology: str) -> pd.DataFrame: return topology_df def load_parameter_data(parquet_path_parameters: str) -> pd.DataFrame: - """Load parquet file and return DataFrame with selected columns. - - Rename columns to match the SQLModel table columns. - """ + """Load parquet file and return DataFrame with selected columns.""" parameter_df = pd.read_parquet(parquet_path_parameters) parameter_df = parameter_df[[ @@ -293,30 +267,29 @@ def load_parameter_data(parquet_path_parameters: str) -> pd.DataFrame: 'dataset_id': 'dataset_id_in_data_source', 'file_name': 'name' }) - + # If integrator is missing, set it to 'undefined' - parameter_df['integrator'] = parameter_df['integrator'].apply(lambda x: x if pd.notna(x) else "undefined") + parameter_df['integrator'] = parameter_df['integrator'].apply( + lambda x: x if pd.notna(x) else "undefined" + ) return parameter_df def load_trajectory_data(parquet_path_trajectory: str) -> pd.DataFrame: - """Load parquet file and return DataFrame with selected columns. - - Rename columns to match the SQLModel table columns. - """ + """Load parquet file and return DataFrame with selected columns.""" trajectory_df = pd.read_parquet(parquet_path_trajectory) trajectory_df = trajectory_df[[ - 'dataset_origin', - 'dataset_id', - 'file_name', - 'atom_number', - 'frame_number' - ]].rename(columns={ - 'dataset_origin': 'data_source', - 'dataset_id': 'dataset_id_in_data_source', - 'file_name': 'name' - }) + 'dataset_origin', + 'dataset_id', + 'file_name', + 'atom_number', + 'frame_number' + ]].rename(columns={ + 'dataset_origin': 'data_source', + 'dataset_id': 'dataset_id_in_data_source', + 'file_name': 'name' + }) return trajectory_df @@ -332,33 +305,19 @@ def create_or_update_datasets_authors_origins_tables( """ Create or update dataset-related tables in the database. - This function processes a DataFrame containing dataset information and - updates the database accordingly. It handles the creation and updating - of Dataset, Author, DataSource, and Keyword entries. - Args: datasets_df (pd.DataFrame): DataFrame containing dataset information. engine (Engine): SQLAlchemy Engine instance to connect to the database. Returns: - None - - Process: - - Inserts new records into the Dataset, Author, DataSource, - and Keyword tables. - - Updates existing records if changes are detected. - - Logs the number of records created, updated, or ignored. - + list[int]: List of dataset IDs that were newly created or modified. """ total_rows = len(datasets_df) - # Dataset ids for new, unchanged, and modified datasets datasets_ids_new = [] datasets_ids_unchanged = [] datasets_ids_modified = [] - # The session is used to interact with the database—querying, adding, - # and committing changes. with Session(engine) as session: for _, row in tqdm( datasets_df.iterrows(), @@ -372,35 +331,23 @@ def create_or_update_datasets_authors_origins_tables( origin_url = row["data_source_url"] dict_to_add_data_source = { "url": origin_url, - "citation": None, # CURRENTLY we don't have a citation - "comment": None, # CURRENTLY we don't have a comment + "citation": None, + "comment": None, } origin_obj = get_or_create_models_with_one_attribute( session, DataSource, "name", origin_name, dict_to_add_data_source) - - # --- Handle Author(s) (many-to-many relationship) --- - # If there are multiple authors separated by a delimiter (","), - # split and process them accordingly. - # This also removes any leading/trailing whitespace from each name author_names = [name.strip() for name in row["author"].split(",") if name.strip()] - # if name.strip() condition ensures that only non-empty - # strings are included in the final list. - # If a substring is empty or consists solely of whitespace, - # name.strip() will evaluate to an empty string, - # which is considered False in a boolean context, - # and thus it will be excluded from the resulting list - - # After we have an Author object (either retrieved from the - # database or newly created), we use the following command to - # add the Author object to our authors list (list of Author - # objects for the current dataset). dict_to_add_author = { - "orcid": None, # CURRENTLY we don't have an ORCID + "orcid": None, } - authors = [get_or_create_models_with_one_attribute(session, Author, "name", name, dict_to_add_author) for name in author_names] - + authors = [ + get_or_create_models_with_one_attribute( + session, Author, "name", name, dict_to_add_author + ) + for name in author_names + ] # --- Check if the Dataset already exists --- # Uniqueness is determined by (origin, id_in_data_source) @@ -410,8 +357,8 @@ def create_or_update_datasets_authors_origins_tables( ) existing_dataset = session.exec(dataset_statement).first() - if not existing_dataset: # If the dataset doesn't exist, create it. - # --- Create the new Dataset entry --- + if not existing_dataset: + # --- Create the new Dataset entry --- new_dataset_obj = Dataset( id_in_data_source=row["id_in_data_source"], doi=row["doi"], @@ -424,44 +371,27 @@ def create_or_update_datasets_authors_origins_tables( license=row["license"], url_in_data_source=row["url_in_data_source"], title=row["title"], - # use .get() if the field might be missing keywords=row.get("keywords"), description=row.get("description"), - data_source=origin_obj, # assign the related origin + data_source=origin_obj, ) - - # Assign the many-to-many relationship for authors: - # In our Dataset model, we have defined an attribute called author - # that represents a MtM relationship with the Author model. - # When we write the following, we are assigning the list of Author - # objects (collected in the authors list) to the dataset's author - # attribute. This informs SQLModel/SQLAlchemy to create the - # appropriate link table entries so that the dataset is - # related to all these authors. new_dataset_obj.author = authors - # # Assign the many-to-many relationship for keywords: - # new_dataset_obj.keyword = keyword_entries - session.add(new_dataset_obj) session.commit() session.refresh(new_dataset_obj) datasets_ids_new.append(new_dataset_obj.dataset_id) - else: # If the dataset already exists, update it or ignore it. - # Compare fields to decide whether to update or ignore. - changed = False # We don't know yet if the dataset has changed info. + else: + # --- Update existing Dataset if fields have changed --- + changed = False - # Compare and maybe update simple fields fields_to_check = [ "doi", "date_created", "date_last_modified", "date_last_crawled", "file_number", - # "download_number", - # "view_number", - # "license", "url_in_data_source", "title", "description", @@ -469,30 +399,16 @@ def create_or_update_datasets_authors_origins_tables( ] changed = update_dataset_fields(existing_dataset, row, fields_to_check) - - # Compare many-to-many relationships for keywords and authors - # Keywords in the database - # existing_keywords = {kw.entry for kw in existing_dataset.keyword} - # # Keywords in the current row - # new_keywords = {kw.entry for kw in keyword_entries} - # if existing_keywords != new_keywords: - # existing_dataset.keyword = keyword_entries - # changed = True - - # Authors in the database existing_authors = {author.name for author in existing_dataset.author} - # Authors in the current row new_authors = {author.name for author in authors} if existing_authors != new_authors: existing_dataset.author = authors changed = True - - if changed: # If changed == True, update the dataset + if changed: session.add(existing_dataset) session.commit() datasets_ids_modified.append(existing_dataset.dataset_id) - else: datasets_ids_unchanged.append(existing_dataset.dataset_id) @@ -503,9 +419,7 @@ def create_or_update_datasets_authors_origins_tables( f"Entries ignored because they already exist in the database: {len(datasets_ids_unchanged)}" ) - datasets_ids_new_or_modified = datasets_ids_new + datasets_ids_modified - - return datasets_ids_new_or_modified + return datasets_ids_new + datasets_ids_modified # ============================================================================ @@ -515,20 +429,25 @@ def create_or_update_datasets_authors_origins_tables( def create_files_tables( files_df: pd.DataFrame, engine: Engine, - datasets_ids_new_or_modified: list[int], ) -> None: """Create the FileType and File records in the database. - We need to handle the FileType, Dataset, and recursive File relationships. + CHANGED: The datasets_ids_new_or_modified parameter has been removed. + Instead of relying on an in-memory list of modified dataset IDs passed + from a previous run, we now check directly against the database whether + each file already exists (upsert / Option 2 pattern). + + This makes the files ingestion fully independent from the datasets + ingestion — you can run them in any order, at any time, as many times + as you want, and you will never get duplicates. """ total_rows = len(files_df) - # Counters for the number of records created, updated, or ignored. files_created_count = 0 files_ignored_count = 0 - # Create a dictionary to store files by their name (if they are zip files) - parent_files_by_name = {} # key: (dataset_id, file_name), value: File file_id + # Cache for parent zip files: key=(dataset_id, file_name), value=file_id + parent_files_by_name = {} with Session(engine) as session: for _, row in tqdm( @@ -538,7 +457,7 @@ def create_files_tables( unit="row" ): - # --- Check if the File record already exists --- + # --- Find the parent Dataset --- dataset_id_in_data_source = row["dataset_id_in_data_source"] data_source = row["data_source"] dataset_stmt = select(Dataset).join(DataSource).where( @@ -547,116 +466,102 @@ def create_files_tables( ) current_dataset = session.exec(dataset_stmt).first() if not current_dataset: - logger.debug( - f"Dataset with id_in_data_source {dataset_id_in_data_source}", - f" and origin {data_source} not found." - ) - continue # Skip if not found - - # Determine if file exists already based on dataset status. - existing_file = True - # If the dataset is new or has been modified, - # then all files are new to the database - if current_dataset.dataset_id in datasets_ids_new_or_modified: - existing_file = False - - if not existing_file: - # File does not exist: create a new record with everything associated - - # --- Handle FileType (one-to-many relationship) --- - file_type_name = row["type"] - dict_to_add_file_type = { - "comment": None, # CURRENTLY we don't have a comment - } - type_obj = get_or_create_models_with_one_attribute(session, FileType, "name", file_type_name, dict_to_add_file_type) - - - # --- Handle Recursive File (parent-child relationship) --- - # We have a column "parent_zip_file_name". - # For files that are from a zip file, use this to find - # the parent file file_id. - parent_zip_file_name = row.get("parent_zip_file_name", None) - parent_zip_file_id = None # default is None - - # If the file is from a zip file, we need to find the parent file file_id - if row["is_from_zip_file"] and parent_zip_file_name: - # Construct a key that combines the dataset id and parent's file name. - # Takes the dataset_id of the child file and the parent zip file_name - key = (current_dataset.dataset_id, parent_zip_file_name) - - # Option 1: Check if we have already found the parent file in the cache. - parent_zip_file_id = parent_files_by_name.get(key, None) - - if not parent_zip_file_id: - logger.debug( - f"Parent file with dataset id {current_dataset.dataset_id}", - f" and file name {parent_zip_file_name} not found in cache.") - logger.debug("Searching in the database...") - - # Option 2: Query the DB using both the file name and dataset id. - parent_statement = ( - select(File)( - # Find the parent file by name - File.name == row["parent_zip_file_name"], - # Make sure it's in the same dataset_id as the child file - File.dataset_id == current_dataset.dataset_id - ) - ) - parent_obj = session.exec(parent_statement).first() - if parent_obj: - parent_zip_file_id = parent_obj.file_id - # Cache the found parent file for later use. - parent_files_by_name[key] = parent_obj - else: - logger.error( - f"Parent file '{parent_zip_file_name}' not found for child" - f"'{row['name']}' with dataset_id {current_dataset.dataset_id}." - ) - - new_file_obj = File( - name=row["name"], - size_in_bytes=row["size_in_bytes"], - md5=row["md5"], - url=row["url"], - is_from_zip_file=row["is_from_zip_file"], - # use the integer id from the Dataset record - dataset_id=current_dataset.dataset_id, - # use the file type id from FileType record - file_type_id=type_obj.file_type_id, - parent_zip_file_id = parent_zip_file_id + logger.debug( + f"Dataset with id_in_data_source {dataset_id_in_data_source}" + f" and origin {data_source} not found. Skipping." ) + continue - session.add(new_file_obj) - session.commit() - session.refresh(new_file_obj) # Refresh because we need the file_id for the parent zip file - files_created_count += 1 - - # If this file is a parent file (i.e. not extracted from a zip), - # then store it in the cache using its dataset_id and name. - if not row["is_from_zip_file"] and type_obj.name == "zip": - key = (current_dataset.dataset_id, row["name"]) - parent_files_by_name[key] = new_file_obj.file_id - - else: - # File exists: we ignore it. + # CHANGED: Check directly in the DB whether this file already exists. + # This replaces the old check against datasets_ids_new_or_modified. + # We identify a file as unique by its name + dataset_id combination. + existing_file_stmt = select(File).where( + File.name == row["name"], + File.dataset_id == current_dataset.dataset_id, + ) + existing_file = session.exec(existing_file_stmt).first() + + if existing_file: + # File already in DB — skip it, no duplicates created. files_ignored_count += 1 - + continue + + # --- File does not exist: create it --- + + # Handle FileType (one-to-many relationship) + file_type_name = row["type"] + dict_to_add_file_type = { + "comment": None, + } + type_obj = get_or_create_models_with_one_attribute( + session, FileType, "name", file_type_name, dict_to_add_file_type + ) + + # Handle parent zip file (recursive File relationship) + parent_zip_file_name = row.get("parent_zip_file_name", None) + parent_zip_file_id = None + + if row["is_from_zip_file"] and parent_zip_file_name: + key = (current_dataset.dataset_id, parent_zip_file_name) + + # Option 1: check the in-memory cache first (fast) + parent_zip_file_id = parent_files_by_name.get(key, None) + + if not parent_zip_file_id: + logger.debug( + f"Parent file with dataset id {current_dataset.dataset_id}" + f" and file name {parent_zip_file_name} not found in cache." + ) + logger.debug("Searching in the database...") + + # Option 2: query the DB + parent_statement = select(File).where( + File.name == row["parent_zip_file_name"], + File.dataset_id == current_dataset.dataset_id + ) + parent_obj = session.exec(parent_statement).first() + if parent_obj: + parent_zip_file_id = parent_obj.file_id + parent_files_by_name[key] = parent_obj.file_id + else: + logger.error( + f"Parent file '{parent_zip_file_name}' not found for child" + f" '{row['name']}' with dataset_id {current_dataset.dataset_id}." + ) + + new_file_obj = File( + name=row["name"], + size_in_bytes=row["size_in_bytes"], + md5=row["md5"], + url=row["url"], + is_from_zip_file=row["is_from_zip_file"], + dataset_id=current_dataset.dataset_id, + file_type_id=type_obj.file_type_id, + parent_zip_file_id=parent_zip_file_id + ) + + session.add(new_file_obj) + session.commit() + session.refresh(new_file_obj) + files_created_count += 1 + + # Cache this file if it is itself a zip (potential future parent) + if not row["is_from_zip_file"] and type_obj.name == "zip": + key = (current_dataset.dataset_id, row["name"]) + parent_files_by_name[key] = new_file_obj.file_id + logger.success("Completed creating files tables.") logger.info(f"Entries created: {files_created_count}") logger.info( f"Entries ignored because they already exist in the database: {files_ignored_count}" - ) + ) + # ============================================================================ -# TopologyFile, Parameter,File, TrajectoryFile -# Thermostat, Barostat, Integrator +# TopologyFile, ParameterFile, TrajectoryFile # ============================================================================ -def create_topology_table( - topology_df: pd.DataFrame, - engine: Engine, - # datasets_ids_new_or_modified: list[int], - ) -> None: +def create_topology_table(topology_df: pd.DataFrame, engine: Engine) -> None: """Create the TopologyFile records in the database.""" with Session(engine) as session: @@ -679,23 +584,11 @@ def create_topology_table( f"Dataset with id_in_data_source {dataset_id_in_data_source}" f" and origin {data_source} not found." ) - continue # Skip if not found + continue dataset_id = dataset_obj.dataset_id - # # Determine if file exists already based on dataset status. - # existing_file = True - # # If the dataset is new or has been modified, - # # then all files are new to the database - # if dataset_id in datasets_ids_new_or_modified: - # existing_file = False - - # if not existing_file: - gro_file_name = row["name"] statement_file = select(File).join(FileType).where( - # Here we filter out the .gro files to go faster but when - # we'll have more than just .gro files in the topology table, - # we'll remove this or refine FileType.name == "gro", File.name == gro_file_name, File.dataset_id == dataset_id @@ -706,7 +599,6 @@ def create_topology_table( f"Multiple files found with dataset_id {dataset_obj.dataset_id}" f" and file name {gro_file_name}. Skipping..." ) - # print(files) continue file_obj = session.exec(statement_file).first() if not file_obj: @@ -714,11 +606,9 @@ def create_topology_table( f"File with dataset_id {dataset_obj.dataset_id}" f" and file name {gro_file_name} not found." ) - continue # Skip if not found + continue file_id_in_files = file_obj.file_id - - # -- Create the TopologyFile -- topology_obj = TopologyFile( file_id=file_id_in_files, atom_number=row["atom_number"], @@ -732,15 +622,9 @@ def create_topology_table( session.add(topology_obj) session.commit() -def create_parameters_table( - param_df: pd.DataFrame, - engine: Engine, - # datasets_ids_new_or_modified: list[int], - ) -> None: - """ - Create the ParameterFile records in the database. - At the same time, create the Thermostat, Barostat, and Integrator records. - """ + +def create_parameters_table(param_df: pd.DataFrame, engine: Engine) -> None: + """Create the ParameterFile records in the database.""" with Session(engine) as session: for _, row in tqdm( @@ -762,15 +646,11 @@ def create_parameters_table( f"Dataset with id_in_data_source {dataset_id_in_data_source}" f" and origin {data_source} not found." ) - continue # Skip if not found + continue dataset_id = dataset_obj.dataset_id - mdp_file_name = row["name"] statement_file = select(File).join(FileType).where( - # Here we filter out the .mdp files to go faster but when - # we'll have more than just .mdp files in the parameters table, - # we'll remove this or refine FileType.name == "mdp", File.name == mdp_file_name, File.dataset_id == dataset_id @@ -781,7 +661,6 @@ def create_parameters_table( f"Multiple files found with dataset_id {dataset_obj.dataset_id}" f" and file name {mdp_file_name}. Skipping..." ) - # print(files) continue file_obj = session.exec(statement_file).first() if not file_obj: @@ -789,10 +668,9 @@ def create_parameters_table( f"File with dataset_id {dataset_obj.dataset_id}" f" and file name {mdp_file_name} not found." ) - continue # Skip if not found + continue file_id_in_files = file_obj.file_id - # -- Create the ParameterFile -- parameter_obj = ParameterFile( file_id=file_id_in_files, dt=row["dt"], @@ -806,11 +684,8 @@ def create_parameters_table( session.add(parameter_obj) session.commit() -def create_trajectory_table( - traj_df: pd.DataFrame, - engine: Engine, - # datasets_ids_new_or_modified: list[int], - ) -> None: + +def create_trajectory_table(traj_df: pd.DataFrame, engine: Engine) -> None: """Create the TrajectoryFile records in the database.""" with Session(engine) as session: @@ -834,17 +709,13 @@ def create_trajectory_table( logger.debug( f"Dataset with id_in_data_source {dataset_id_in_data_source}" f" and origin {data_source} not found." - f"Skipping {xtc_file_name} (index: {index})..." + f" Skipping {xtc_file_name} (index: {index})..." ) missing_files += 1 - continue # Skip if not found + continue dataset_id = dataset_obj.dataset_id - statement_file = select(File).join(FileType).where( - # Here we filter out the .xtc files to go faster but when - # we'll have more than just .xtc files in the trajectory table, - # we'll remove this or refine FileType.name == "xtc", File.name == xtc_file_name, File.dataset_id == dataset_id @@ -855,21 +726,18 @@ def create_trajectory_table( f"Multiple files found with dataset_id {dataset_obj.dataset_id}" f" and file name {xtc_file_name}. Skipping..." ) - # print(files) continue file_obj = session.exec(statement_file).first() if not file_obj: logger.debug( f"File with dataset_id {dataset_obj.dataset_id}" - f" and file name {xtc_file_name} not found.\n", - f"Skipping {xtc_file_name} (index: {index})..." + f" and file name {xtc_file_name} not found.\n" + f" Skipping {xtc_file_name} (index: {index})..." ) missing_files += 1 - continue # Skip if not found + continue file_id_in_files = file_obj.file_id - - # -- Create the TrajectoryFile -- traj_obj = TrajectoryFile( file_id=file_id_in_files, atom_number=row["atom_number"], @@ -882,10 +750,8 @@ def create_trajectory_table( logger.debug(f"Number of missing files: {missing_files}") -def create_simulation_tables( - engine: Engine, - ) -> None: - """Create the TopologyFile, ParameterFile, and TrajectoryFile records in the database.""" +def create_simulation_tables(engine: Engine) -> None: + """Create the TopologyFile, ParameterFile, and TrajectoryFile records.""" mdp_path = "data/parquet_files/gromacs_mdp_files.parquet" gro_path = "data/parquet_files/gromacs_gro_files.parquet" @@ -896,149 +762,138 @@ def create_simulation_tables( trajectory_df = load_trajectory_data(xtc_path) logger.info("Creating simulation tables...\n") - - # TrajectoryFile + logger.info("Creating TrajectoryFile table...") create_trajectory_table(trajectory_df, engine) logger.success("Completed creating trajectory table.\n") - - # ParameterFile, Thermostat, Barostat, Integrator - logger.info("Creating ParameterFile, Thermostat, Barostat, Integrator tables...") + + logger.info("Creating ParameterFile tables...") create_parameters_table(parameter_df, engine) logger.success("Completed creating parameters tables.\n") - - # TopologyFile + logger.info("Creating TopologyFile table...") create_topology_table(topology_df, engine) logger.success("Completed creating topology table.\n") + +# ============================================================================ +# Main ingestion entry point +# CHANGED: data_ingestion() now accepts a single parquet_path argument +# instead of having hardcoded lists of files inside the function. # ============================================================================ -def data_ingestion(): - """ - Ingest data from parquet files into the database. - - For the simplicity of understanding, we will bundle up certain tables under - the same function and "name": - - The "datasets tables" will include: - - Dataset - - Author - - DataSource - - Keyword - - The "files tables" will include: - - File - - FileType - - The "simulation tables" will include: - - TopologyFile - - ParameterFile - - TrajectoryFile - - Thermostat - - Barostat - - Integrator - - This function will ingest all the data in the following steps: - 1) Populate the "datasets tables". - - Load the datasets data from the parquet files. - - Create or update the tables. - - Log the number of records created, updated, or ignored for Dataset. - 2) Populate the "files" tables. - - Load the files data from the parquet files. - - Check the datasets that have been updated or added. - a) There are no new or modified datasets: - - Skip the "files" ingestion. - b) There are new or modified datasets: - - Delete the files associated to the modified datasets. - - Delete all the TopologyFile, ParameterFile, and TrajectoryFile tables. - - Create the files tables. - - Create the simulation tables. - 3) Log the total data ingestion time. +def data_ingestion(parquet_path: str) -> None: """ + Ingest data from a single parquet file into the database. - # CHANGED: Replaced single hardcoded parquet paths with lists of paths, - # one per data source. Each source has its own dataset and file parquet. - # To add a new source in the future, simply add its parquet paths here. - - # --- Dataset parquet files (one per source) --- - dataset_parquets = [ - "data/parquet_files/zenodo_datasets.parquet", - "data/parquet_files/figshare_datasets.parquet", - "data/parquet_files/atlas_datasets.parquet", - "data/parquet_files/nomad_datasets.parquet", - "data/parquet_files/gpcrmd_datasets.parquet", - "data/parquet_files/mddb_datasets.parquet", - ] - - # --- File parquet files (one per source) --- - file_parquets = [ - "data/parquet_files/zenodo_files.parquet", - "data/parquet_files/figshare_files.parquet", - "data/parquet_files/atlas_files.parquet", - "data/parquet_files/nomad_files.parquet", - "data/parquet_files/gpcrmd_files.parquet", - "data/parquet_files/mddb_files.parquet", - ] - - start_1 = time.perf_counter() - start_4 = time.perf_counter() - - # CHANGED: Load and concatenate all dataset parquets into one DataFrame. - # Previously this was a single pd.read_parquet() call on one file. - # Now we load each source separately and stack them into one DataFrame - # using pd.concat(). The rest of the ingestion logic stays the same. - datasets_df = pd.concat( - [load_datasets_data(p) for p in dataset_parquets], - ignore_index=True - ) - - logger.info("Creating or updating datasets tables...") - logger.info("Dataset, Author, DataSource, Keyword tables") - new_or_modified_datasets = create_or_update_datasets_authors_origins_tables(datasets_df, engine) + The function detects whether the file is a datasets parquet or a files + parquet by looking for '_datasets' or '_files' in the filename, then + runs the appropriate ingestion pipeline. - execution_time_1 = time.perf_counter() - start_1 - elapsed_time_1 = str(timedelta(seconds=execution_time_1)).split('.')[0] - logger.info(f"Datasets ingestion time: {elapsed_time_1}\n") + Args: + parquet_path (str): Path to the parquet file to ingest. + Passed from the terminal via sys.argv[1]. + Examples: + uv run src/ingest_data.py data/atlas/2026-02-18/atlas_datasets.parquet + uv run src/ingest_data.py data/atlas/2026-02-18/atlas_files.parquet + uv run src/ingest_data.py data/zenodo/2026-02-18/zenodo_datasets.parquet + """ - if len(new_or_modified_datasets) > 0: + path = Path(parquet_path) - start_2 = time.perf_counter() + # Validate the file exists before doing anything + if not path.exists(): + logger.error(f"File not found: {parquet_path}") + sys.exit(1) - # CHANGED: Same as datasets — load and concatenate all file parquets. - files_df = pd.concat( - [load_files_data(p) for p in file_parquets], - ignore_index=True - ) + if not path.suffix == ".parquet": + logger.error(f"Expected a .parquet file, got: {path.suffix}") + sys.exit(1) - logger.info("New or modified datasets found.") - logger.info("Deleting files associated to modified datasets...") - delete_files_for_update(engine, new_or_modified_datasets) + logger.info(f"Starting ingestion for: {path.name}") + start = time.perf_counter() - logger.info("Creating files tables...") - logger.info("File, FileType tables") - create_files_tables(files_df, engine, new_or_modified_datasets) + # --- Detect file type from filename and run the right pipeline --- - execution_time_2 = time.perf_counter() - start_2 - elapsed_time__2 = str(timedelta(seconds=execution_time_2)).split('.')[0] - logger.info(f"Files ingestion time: {elapsed_time__2}\n") + if "_datasets" in path.name: + # ---------------------------------------------------------------- + # DATASETS pipeline + # Ingests: Dataset, Author, DataSource tables + # ---------------------------------------------------------------- + logger.info("Detected DATASETS parquet.") + logger.info("Running datasets pipeline: Dataset, Author, DataSource tables.") - # Simulation data ingestion - start_3 = time.perf_counter() + datasets_df = load_datasets_data(parquet_path) + new_or_modified = create_or_update_datasets_authors_origins_tables(datasets_df, engine) - create_simulation_tables(engine) + if new_or_modified: + logger.info( + f"{len(new_or_modified)} new or modified datasets found." + " Run the corresponding files parquet to ingest their files." + ) + else: + logger.info("No new or modified datasets found.") + + elif "_files" in path.name: + # ---------------------------------------------------------------- + # FILES pipeline + # Ingests: File, FileType tables + # CHANGED: No longer needs datasets_ids_new_or_modified. + # Each file is checked directly against the DB (upsert pattern). + # ---------------------------------------------------------------- + logger.info("Detected FILES parquet.") + logger.info("Running files pipeline: File, FileType tables.") + + files_df = load_files_data(parquet_path) + + # CHANGED: delete_files_for_update is no longer called here. + # In the old script, files for modified datasets were deleted before + # re-inserting. Now we check existence per file in create_files_tables(), + # so we never insert a duplicate and never need a bulk delete step. + create_files_tables(files_df, engine) - execution_time_3 = time.perf_counter() - start_3 - elapsed_time_3 = str(timedelta(seconds=execution_time_3)).split('.')[0] - logger.info(f"Simulation files ingestion time: {elapsed_time_3}\n") else: - logger.info("No new or modified datasets found. Skipping files ingestion...\n") + # ---------------------------------------------------------------- + # Unknown file type — inform the user clearly + # ---------------------------------------------------------------- + logger.error( + f"Cannot determine ingestion type from filename: '{path.name}'." + ) + logger.error( + "Filename must contain '_datasets' or '_files'." + ) + logger.error( + "Examples:" + "\n data/atlas/2026-02-18/atlas_datasets.parquet" + "\n data/atlas/2026-02-18/atlas_files.parquet" + ) + sys.exit(1) + + elapsed = str(timedelta(seconds=time.perf_counter() - start)).split('.')[0] + logger.info(f"Ingestion time: {elapsed}") + logger.success("Ingestion complete.") - - # Measure the total execution time - execution_time_4 = time.perf_counter() - start_4 - elapsed_time_4 = str(timedelta(seconds=execution_time_4)).split('.')[0] - logger.info(f"Data ingestion time: {elapsed_time_4}") - logger.success("Data ingestion complete.") +# ============================================================================ +# CLI entry point +# CHANGED: We read the parquet path from sys.argv[1] instead of running +# a hardcoded data_ingestion() with no arguments. +# ============================================================================ if __name__ == "__main__": - data_ingestion() + if len(sys.argv) < 2: + logger.error("No parquet file provided.") + logger.error( + "Usage: uv run src/ingest_data.py " + ) + logger.error( + "Examples:" + "\n uv run src/ingest_data.py data/atlas/2026-02-18/atlas_datasets.parquet" + "\n uv run src/ingest_data.py data/atlas/2026-02-18/atlas_files.parquet" + "\n uv run src/ingest_data.py data/zenodo/2026-02-18/zenodo_datasets.parquet" + ) + sys.exit(1) + + parquet_path = sys.argv[1] + data_ingestion(parquet_path) \ No newline at end of file From 386e4983e359645945c324d92f940263964662d5 Mon Sep 17 00:00:00 2001 From: Pierre Poulain Date: Tue, 7 Apr 2026 18:17:19 +0200 Subject: [PATCH 3/3] fix: 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