diff --git a/openfoodfacts_exports/exports/parquet.py b/openfoodfacts_exports/exports/parquet.py index 08d095d..cdd1aae 100644 --- a/openfoodfacts_exports/exports/parquet.py +++ b/openfoodfacts_exports/exports/parquet.py @@ -4,9 +4,13 @@ from pathlib import Path import duckdb +import pyarrow as pa +import pyarrow.parquet as pq +from pydantic import BaseModel, model_validator from huggingface_hub import HfApi from openfoodfacts_exports import settings +from openfoodfacts_exports.utils import timer logger = logging.getLogger(__name__) @@ -152,32 +156,110 @@ ; """ +_SIZE_SCHEMA = pa.struct( + [ + pa.field("h", pa.int32(), nullable=True), + pa.field("w", pa.int32(), nullable=True), + ] +) + +IMAGES_DATATYPE = pa.list_( + pa.struct( + [ + pa.field("key", pa.string(), nullable=True), + pa.field("imgid", pa.int32(), nullable=True), + pa.field( + "sizes", + pa.struct( + [ + pa.field("100", _SIZE_SCHEMA, nullable=True), + pa.field("200", _SIZE_SCHEMA, nullable=True), + pa.field("400", _SIZE_SCHEMA, nullable=True), + pa.field("full", _SIZE_SCHEMA, nullable=True), + ] + ), + nullable=True, + ), + pa.field("uploaded_t", pa.int64(), nullable=True), + pa.field("uploader", pa.string(), nullable=True), + ] + ) +) + +ALLOWED_IMAGE_SIZE_KEYS = {"100", "200", "400", "full"} + +SCHEMAS = {"images": IMAGES_DATATYPE} + + +class ImageSize(BaseModel): + h: int | None = None + w: int | None = None + + +class Image(BaseModel): + """`Images` schema for postprocessing used for field postprocessing.""" + + key: str + sizes: dict[str, ImageSize | None] + uploaded_t: int | None = None + imgid: int | None = None + uploader: str | None = None + + @model_validator(mode="after") + def ignore_extra_sizes(self): + """Literal doesn't accept extra values, returning an error in case of additional + keys. + """ + self.sizes = { + k: v for k, v in self.sizes.items() if k in ALLOWED_IMAGE_SIZE_KEYS + } + return self + + @model_validator(mode="before") + @classmethod + def parse_int_from_string(cls, data: dict): + """Some int are considered as string like '"1517312996"', leading to + int parsing issues + """ + imgid = data.get("imgid") + uploaded_t = data.get("uploaded_t") + if imgid and isinstance(imgid, str): + data.update({"imgid": imgid.strip('"')}) + if uploaded_t and isinstance(uploaded_t, str): + data.update({"uploaded_t": uploaded_t.strip('"')}) + return data + def export_parquet(dataset_path: Path, output_path: Path) -> None: """Convert a JSONL dataset to Parquet format and push it to Hugging Face - Hub.""" - logger.info("Starting conversion of JSONL to Parquet") + Hub. + """ + logger.info("Start JSONL export to Parquet.") with tempfile.TemporaryDirectory() as tmp_dir: - tmp_file_path = Path(tmp_dir) / "converted_data.parquet" + tmp_converted_parquet_path = Path(tmp_dir) / "converted_data.parquet" + tmp_postprocessed_parquet_path = Path(tmp_dir) / "postprocessed_data.parquet" convert_jsonl_to_parquet( - output_file_path=tmp_file_path, dataset_path=dataset_path + output_file_path=tmp_converted_parquet_path, dataset_path=dataset_path + ) + postprocess_parquet( + parquet_path=tmp_converted_parquet_path, + output_path=tmp_postprocessed_parquet_path, ) # Move dataset file to output_path - shutil.move(tmp_file_path, output_path) + shutil.move(tmp_postprocessed_parquet_path, output_path) if settings.ENABLE_HF_PUSH: push_parquet_file_to_hf(data_path=output_path) else: logger.info("Hugging Face push is disabled.") - - logger.info("JSONL to Parquet conversion completed.") + logger.info("JSONL to Parquet conversion and postprocessing completed.") def convert_jsonl_to_parquet( output_file_path: Path, dataset_path: Path, ) -> None: - logger.info("Start JSONL to Parquet conversion process.") + logger.info("Start conversion from JSONL to Parquet.") if not dataset_path.exists(): raise FileNotFoundError(f"{str(dataset_path)} was not found.") query = SQL_QUERY.replace("{dataset_path}", str(dataset_path)).replace( @@ -186,7 +268,10 @@ def convert_jsonl_to_parquet( try: duckdb.sql(query) except duckdb.Error as e: - logger.error(f"Error executing query: {query}\nError message: {e}") + logger.error( + "Error executing query: %s \nError message: %s", + query, e + ) raise logger.info("JSONL successfully converted into Parquet file.") @@ -197,7 +282,7 @@ def push_parquet_file_to_hf( revision: str = "main", commit_message: str = "Database updated", ) -> None: - logger.info(f"Start pushing data to Hugging Face at {repo_id}") + logger.info("Start pushing data to Hugging Face at %s", repo_id) if not data_path.exists(): raise FileNotFoundError(f"Data is missing: {data_path}") if data_path.suffix != ".parquet": @@ -212,4 +297,67 @@ def push_parquet_file_to_hf( path_in_repo="products.parquet", commit_message=commit_message, ) - logger.info(f"Data succesfully pushed to Hugging Face at {repo_id}") + logger.info("Data succesfully pushed to Hugging Face at %s", repo_id) + + +@timer +def postprocess_parquet( + parquet_path: Path, output_path: Path, batch_size: int = 10000 +) -> None: + logger.info("Start postprocessing parquet") + parquet_file = pq.ParquetFile(parquet_path) + updated_schema = update_schema(parquet_file.schema.to_arrow_schema()) + with pq.ParquetWriter(output_path, schema=updated_schema) as writer: + for batch in parquet_file.iter_batches(batch_size=batch_size): + batch = postprocess_arrow_batch(batch) + writer.write_batch(batch) + logger.info("Parquet post processing done.") + + +def update_schema(schema: pa.Schema) -> pa.Schema: + for field_name, field_datatype in SCHEMAS.items(): + schema = _update_schema_by_field( + schema=schema, field_name=field_name, field_datatype=field_datatype + ) + return schema + + +def _update_schema_by_field( + schema: pa.Schema, field_name: str, field_datatype: pa.DataType +) -> pa.schema: + field_index = schema.get_field_index(field_name) + schema = schema.remove(field_index) + schema = schema.insert(field_index, pa.field(field_name, field_datatype)) + return schema + + +def postprocess_arrow_batch(batch: pa.RecordBatch) -> pa.RecordBatch: + """Add new processing features here.""" + batch = postprocess_images(batch) + return batch + + +def postprocess_images( + batch: pa.RecordBatch, datatype: pa.DataType = IMAGES_DATATYPE +) -> pa.RecordBatch: + """The `Images` field is a nested JSON with inconsistent data type. + We extract and structure the data as a list of dict using Pydantic. + Each dict corresponds to an image from the same product. + + ### Notes: + The process is quite long (20 - 30 min). + Possibilities: concurrency, pyarrow.compute, ... + """ + # Duckdb converted the json filed into a map_array: + # https://arrow.apache.org/docs/python/generated/pyarrow.MapArray.html#pyarrow-maparray + postprocessed_images = [ + [Image(key=key, **value).model_dump() for key, value in image] if image else [] + for image in batch["images"].to_pylist() + ] + images_col_index = batch.schema.get_field_index("images") + batch = batch.set_column( + images_col_index, + pa.field("images", datatype), + pa.array(postprocessed_images, type=datatype), + ) + return batch diff --git a/openfoodfacts_exports/utils.py b/openfoodfacts_exports/utils.py index 8e3cb40..8991827 100644 --- a/openfoodfacts_exports/utils.py +++ b/openfoodfacts_exports/utils.py @@ -1,4 +1,5 @@ import logging +import time import sentry_sdk import toml @@ -10,6 +11,9 @@ from openfoodfacts_exports import settings +logger = logging.getLogger(__name__) + + def init_sentry(integrations: list[Integration] | None = None): if settings.SENTRY_DSN: integrations = integrations or [] @@ -37,3 +41,13 @@ def get_package_version() -> str: def get_minio_client() -> Minio: """Return a Minio client with AWS credentials from environment.""" return Minio("s3.amazonaws.com", credentials=EnvAWSProvider()) + + +def timer(func): + def wrapper(*args, **kwargs): + timestamp= time.time() + output = func(*args, **kwargs) + latency = time.time() - timestamp + logger.info(f"Latency of 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