From 309b4f73009424be65d712d2798f11bf7525d952 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Rapha=C3=ABl=20Bournhonesque?=
 <raphael0202@users.noreply.github.com>
Date: Thu, 14 Nov 2024 15:29:51 +0100
Subject: [PATCH] feat: declare the schema of the Parquet file (#8)

* deps: remove ipykernel dep

* fix: remove timer decorator

* feat: declare the schema of the Parquet file

We used to use duckdb, but there were issues to to incorrect type
guessing by duckdb. By validating and modifying the data before
Parquet generation, we can control completely the schema.
---
 openfoodfacts_exports/exports/parquet.py | 763 +++++++++++++++--------
 pyproject.toml                           |   7 +-
 uv.lock                                  | 465 ++------------
 3 files changed, 576 insertions(+), 659 deletions(-)

diff --git a/openfoodfacts_exports/exports/parquet.py b/openfoodfacts_exports/exports/parquet.py
index cdd1aae..21bc4fd 100644
--- a/openfoodfacts_exports/exports/parquet.py
+++ b/openfoodfacts_exports/exports/parquet.py
@@ -3,160 +3,23 @@
 import tempfile
 from pathlib import Path
 
-import duckdb
+import orjson
 import pyarrow as pa
 import pyarrow.parquet as pq
-from pydantic import BaseModel, model_validator
 from huggingface_hub import HfApi
+from more_itertools import chunked
+from openfoodfacts.utils import jsonl_iter
+from pydantic import BaseModel, Field, field_serializer, model_validator
 
 from openfoodfacts_exports import settings
-from openfoodfacts_exports.utils import timer
 
 logger = logging.getLogger(__name__)
 
 
 PARQUET_DATASET_PATH = settings.DATASET_DIR / "openfoodfacts-products.parquet"
 
-SQL_QUERY = r"""
-SET threads to 4;
-SET preserve_insertion_order = false;
-COPY ( 
-    SELECT
-        code,
-        additives_n,
-        additives_tags,
-        allergens_from_ingredients,
-        allergens_from_user,
-        allergens_tags,
-        brands,
-        brands_tags,
-        categories_properties_tags,
-        categories,
-        checkers_tags,
-        cities_tags,
-        compared_to_category,
-        complete,
-        completeness,
-        correctors_tags,
-        countries_tags,
-        created_t,
-        creator,
-        data_quality_errors_tags,
-        data_quality_info_tags,
-        data_quality_warnings_tags,
-        data_sources_tags,
-        ecoscore_data,
-        ecoscore_grade,
-        ecoscore_score,
-        ecoscore_tags,
-        editors,
-        emb_codes,
-        emb_codes_tags,
-        entry_dates_tags,
-        environment_impact_level,
-        food_groups_tags,
-        forest_footprint_data,
-        generic_name,
-        grades,
-        images,
-        informers_tags,
-        ingredients_analysis_tags,
-        ingredients_from_palm_oil_n,
-        ingredients_n,
-        ingredients_tags,
-        ingredients_text_with_allergens,
-        ingredients_text,
-        COLUMNS('ingredients_text_\w{2}$'),
-        ingredients_with_specified_percent_n,
-        ingredients_with_unspecified_percent_n,
-        ciqual_food_name_tags,
-        ingredients_percent_analysis,
-        ingredients_original_tags,
-        ingredients_without_ciqual_codes_n,
-        ingredients_without_ciqual_codes,
-        ingredients,
-        known_ingredients_n,
-        labels_tags,
-        lang,
-        languages_tags,
-        languages_codes,
-        last_edit_dates_tags,
-        last_editor,
-        last_image_t,
-        last_modified_by,
-        last_modified_t,
-        last_updated_t,
-        link,
-        main_countries_tags,
-        manufacturing_places,
-        manufacturing_places_tags,
-        max_imgid,
-        misc_tags,
-        minerals_tags,
-        new_additives_n,
-        no_nutrition_data,
-        nova_group,
-        nova_groups,
-        nova_groups_markers,
-        nova_groups_tags,
-        nucleotides_tags,
-        nutrient_levels_tags,
-        unknown_nutrients_tags,
-        nutriments,
-        nutriscore_data,
-        nutriscore_grade,
-        nutriscore_score,
-        nutriscore_tags,
-        nutrition_data_prepared_per,
-        nutrition_data,
-        nutrition_grades_tags,
-        nutrition_score_beverage,
-        nutrition_score_warning_fruits_vegetables_nuts_estimate_from_ingredients,
-        nutrition_score_warning_no_fiber,
-        nutrition_score_warning_no_fruits_vegetables_nuts,
-        obsolete_since_date,
-        obsolete,
-        origins_tags,
-        packaging_recycling_tags,
-        packaging_shapes_tags,
-        packaging_tags,
-        packagings_materials,
-        packagings_n,
-        packagings_n,
-        photographers,
-        pnns_groups_1_tags,
-        pnns_groups_2_tags,
-        popularity_key,
-        popularity_tags,
-        product_name,
-        product_quantity_unit,
-        product_quantity,
-        purchase_places_tags,
-        quantity,
-        rev,
-        scans_n,
-        scores,
-        serving_quantity,
-        serving_size,
-        sources,
-        sources_fields,
-        specific_ingredients,
-        states_tags,
-        stores,
-        stores_tags,
-        traces_tags,
-        unique_scans_n,
-        unknown_ingredients_n,
-        vitamins_tags,
-        weighers_tags,
-        with_non_nutritive_sweeteners,
-        with_sweeteners,
-    FROM read_ndjson('{dataset_path}', ignore_errors=True)
-) TO '{output_path}' (FORMAT PARQUET)
-;
-"""
-
-_SIZE_SCHEMA = pa.struct(
+
+IMAGE_SIZE_SCHEMA = pa.struct(
     [
         pa.field("h", pa.int32(), nullable=True),
         pa.field("w", pa.int32(), nullable=True),
@@ -172,10 +35,10 @@
                 "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),
+                        pa.field("100", IMAGE_SIZE_SCHEMA, nullable=True),
+                        pa.field("200", IMAGE_SIZE_SCHEMA, nullable=True),
+                        pa.field("400", IMAGE_SIZE_SCHEMA, nullable=True),
+                        pa.field("full", IMAGE_SIZE_SCHEMA, nullable=True),
                     ]
                 ),
                 nullable=True,
@@ -186,9 +49,168 @@
     )
 )
 
-ALLOWED_IMAGE_SIZE_KEYS = {"100", "200", "400", "full"}
+INGREDIENTS_TEXT_DATATYPE = pa.list_(
+    pa.struct(
+        [
+            pa.field("lang", pa.string()),
+            pa.field("text", pa.string()),
+        ],
+    )
+)
+
+
+LANGUAGE_FIELD_DATATYPE = pa.list_(
+    pa.struct(
+        [
+            pa.field("lang", pa.string()),
+            pa.field("text", pa.string()),
+        ]
+    ),
+)
 
-SCHEMAS = {"images": IMAGES_DATATYPE}
+NUTRIMENTS_DATATYPE = pa.list_(
+    pa.struct(
+        [
+            pa.field("name", pa.string()),
+            pa.field("value", pa.float32(), nullable=True),
+            pa.field("100g", pa.float32(), nullable=True),
+            pa.field("serving", pa.float32(), nullable=True),
+            pa.field("unit", pa.string(), nullable=True),
+            pa.field("prepared_value", pa.float32(), nullable=True),
+            pa.field("prepared_100g", pa.float32(), nullable=True),
+            pa.field("prepared_serving", pa.float32(), nullable=True),
+            pa.field("prepared_unit", pa.string(), nullable=True),
+        ]
+    )
+)
+
+PACKAGING_FIELD_DATATYPE = pa.list_(
+    pa.struct(
+        [
+            pa.field("material", pa.string(), nullable=True),
+            pa.field("number_of_units", pa.int32(), nullable=True),
+            pa.field("quantity_per_unit", pa.string(), nullable=True),
+            pa.field("quantity_per_unit_unit", pa.string(), nullable=True),
+            pa.field("quantity_per_unit_value", pa.string(), nullable=True),
+            pa.field("recycling", pa.string(), nullable=True),
+            pa.field("shape", pa.string(), nullable=True),
+            pa.field("weight_measured", pa.float32(), nullable=True),
+        ]
+    )
+)
+
+PRODUCT_SCHEMA = pa.schema(
+    [
+        pa.field("additives_n", pa.int32(), nullable=True),
+        pa.field("additives_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("allergens_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("brands_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("brands", pa.string(), nullable=True),
+        pa.field("categories", pa.string(), nullable=True),
+        pa.field("categories_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("checkers_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("ciqual_food_name_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("cities_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("code", pa.string()),
+        pa.field("compared_to_category", pa.string(), nullable=True),
+        pa.field("complete", pa.int32(), nullable=True),
+        pa.field("completeness", pa.float32(), nullable=True),
+        pa.field("correctors_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("countries_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("created_t", pa.int64(), nullable=True),
+        pa.field("creator", pa.string(), nullable=True),
+        pa.field("data_quality_errors_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("data_quality_info_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("data_quality_warnings_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("data_sources_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("ecoscore_grade", pa.string(), nullable=True),
+        pa.field("ecoscore_score", pa.int32(), nullable=True),
+        pa.field("ecoscore_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("editors", pa.list_(pa.string()), nullable=True),
+        pa.field("emb_codes_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("emb_codes", pa.string(), nullable=True),
+        pa.field("entry_dates_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("food_groups_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("generic_name", pa.string(), nullable=True),
+        pa.field("images", IMAGES_DATATYPE, nullable=True),
+        pa.field("informers_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("ingredients_analysis_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("ingredients_from_palm_oil_n", pa.int32(), nullable=True),
+        pa.field("ingredients_n", pa.int32(), nullable=True),
+        pa.field("ingredients_original_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("ingredients_percent_analysis", pa.int32(), nullable=True),
+        pa.field("ingredients_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("ingredients_text", LANGUAGE_FIELD_DATATYPE, nullable=True),
+        pa.field("ingredients_with_specified_percent_n", pa.int32(), nullable=True),
+        pa.field("ingredients_with_unspecified_percent_n", pa.int32(), nullable=True),
+        pa.field("ingredients_without_ciqual_codes_n", pa.int32(), nullable=True),
+        pa.field(
+            "ingredients_without_ciqual_codes", pa.list_(pa.string()), nullable=True
+        ),
+        pa.field("ingredients", pa.string(), nullable=True),
+        pa.field("known_ingredients_n", pa.int32(), nullable=True),
+        pa.field("labels_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("labels", pa.string(), nullable=True),
+        pa.field("lang", pa.string(), nullable=True),
+        pa.field("languages_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("last_edit_dates_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("last_editor", pa.string(), nullable=True),
+        pa.field("last_image_t", pa.int64(), nullable=True),
+        pa.field("last_modified_by", pa.string(), nullable=True),
+        pa.field("last_modified_t", pa.int64(), nullable=True),
+        pa.field("last_updated_t", pa.int64(), nullable=True),
+        pa.field("link", pa.string(), nullable=True),
+        pa.field("main_countries_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("manufacturing_places_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("manufacturing_places", pa.string(), nullable=True),
+        pa.field("max_imgid", pa.int32(), nullable=True),
+        pa.field("minerals_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("misc_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("new_additives_n", pa.int32(), nullable=True),
+        pa.field("no_nutrition_data", pa.bool_(), nullable=True),
+        pa.field("nova_group", pa.int32(), nullable=True),
+        pa.field("nova_groups_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("nova_groups", pa.string(), nullable=True),
+        pa.field("nucleotides_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("nutrient_levels_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("nutriments", NUTRIMENTS_DATATYPE, nullable=True),
+        pa.field("nutriscore_grade", pa.string(), nullable=True),
+        pa.field("nutriscore_score", pa.int32(), nullable=True),
+        pa.field("nutrition_data_per", pa.string(), nullable=True),
+        pa.field("obsolete", pa.bool_()),
+        pa.field("origins_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("origins", pa.string(), nullable=True),
+        pa.field("packagings_complete", pa.bool_(), nullable=True),
+        pa.field("packaging_recycling_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("packaging_shapes_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("packaging_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("packaging_text", LANGUAGE_FIELD_DATATYPE, nullable=True),
+        pa.field("packaging", pa.string(), nullable=True),
+        pa.field("packagings", PACKAGING_FIELD_DATATYPE, nullable=True),
+        pa.field("photographers", pa.list_(pa.string()), nullable=True),
+        pa.field("popularity_key", pa.int64(), nullable=True),
+        pa.field("popularity_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("product_name", LANGUAGE_FIELD_DATATYPE, nullable=True),
+        pa.field("product_quantity_unit", pa.string(), nullable=True),
+        pa.field("product_quantity", pa.string(), nullable=True),
+        pa.field("purchase_places_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("quantity", pa.string(), nullable=True),
+        pa.field("rev", pa.int32(), nullable=True),
+        pa.field("scans_n", pa.int32(), nullable=True),
+        pa.field("serving_quantity", pa.string(), nullable=True),
+        pa.field("serving_size", pa.string(), nullable=True),
+        pa.field("states_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("stores_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("stores", pa.string(), nullable=True),
+        pa.field("traces_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("unique_scans_n", pa.int32(), nullable=True),
+        pa.field("unknown_ingredients_n", pa.int32(), nullable=True),
+        pa.field("unknown_nutrients_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("vitamins_tags", pa.list_(pa.string()), nullable=True),
+        pa.field("with_non_nutritive_sweeteners", pa.int32(), nullable=True),
+        pa.field("with_sweeteners", pa.int32(), nullable=True),
+    ]
+)
 
 
 class ImageSize(BaseModel):
@@ -196,10 +218,13 @@ class ImageSize(BaseModel):
     w: int | None = None
 
 
+ALLOWED_IMAGE_SIZE_KEYS = {"100", "200", "400", "full"}
+
+
 class Image(BaseModel):
     """`Images` schema for postprocessing used for field postprocessing."""
 
-    key: str
+    key: str | None = None
     sizes: dict[str, ImageSize | None]
     uploaded_t: int | None = None
     imgid: int | None = None
@@ -207,46 +232,326 @@ class Image(BaseModel):
 
     @model_validator(mode="after")
     def ignore_extra_sizes(self):
-        """Literal doesn't accept extra values, returning an error in case of additional
-        keys.
+        """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
 
+
+class Ingredient(BaseModel):
+    percent_max: float | None = None
+    percent_min: float | None = None
+    is_in_taxonomy: int | None = None
+    percent_estimate: float | None = None
+    vegan: str | None = None
+    id: str | None = None
+    text: str | None = None
+    vegetarian: str | None = None
+    ciqual_food_code: str | None = None
+    percent: float | None = None
+    from_palm_oil: str | None = None
+    ingredients: list["Ingredient"] | None = None
+    ecobalyse_code: str | None = None
+    processing: str | None = None
+    labels: str | None = None
+    origins: str | None = None
+    ecobalyse_proxy_code: str | None = None
+    quantity: str | None = None
+    quantity_g: float | None = None
+    ciqual_proxy_food_code: str | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def parse_nested_ingredients(cls, data: dict):
+        if "ingredients" in data and isinstance(data["ingredients"], list):
+            data["ingredients"] = [
+                cls.model_validate(ing) for ing in data["ingredients"]
+            ]
+        return data
+
+
+class LanguageField(BaseModel):
+    lang: str
+    text: str
+
+
+class NutrimentField(BaseModel):
+    name: str
+    value: float | None = None
+    per_100g: float | None = Field(default=None, alias="100g")
+    serving: float | None = None
+    unit: str | None = None
+    prepared_value: float | None = None
+    prepared_100g: float | None = None
+    prepared_serving: float | None = None
+    prepared_unit: str | None = None
+
+
+class PackagingField(BaseModel):
+    material: str | None = None
+    number_of_units: int | None = None
+    quantity_per_unit: str | None = None
+    quantity_per_unit_unit: str | None = None
+    quantity_per_unit_value: str | None = Field(
+        default=None, coerce_numbers_to_str=True
+    )
+    recycling: str | None = None
+    shape: str | None = None
+    weight_measured: float | None = None
+
+
+class Product(BaseModel):
+    additives_n: int | None = None
+    additives_tags: list[str] | None = None
+    allergens_tags: list[str] | None = None
+    brands_tags: list[str] | None = None
+    brands: str | None = None
+    categories: str | None = None
+    categories_tags: list[str] | None = None
+    checkers_tags: list[str] | None = None
+    ciqual_food_name_tags: list[str] | None = None
+    cities_tags: list[str] | None = None
+    code: str
+    compared_to_category: str | None = None
+    complete: int | None = None
+    completeness: float | None = None
+    correctors_tags: list[str] | None = None
+    countries_tags: list[str] | None = None
+    created_t: int | None = None
+    creator: str | None = None
+    data_quality_errors_tags: list[str] | None = None
+    data_quality_info_tags: list[str] | None = None
+    data_quality_warnings_tags: list[str] | None = None
+    data_sources_tags: list[str] | None = None
+    ecoscore_grade: str | None = None
+    ecoscore_score: int | None = None
+    ecoscore_tags: list[str] | None = None
+    editors: list[str] | None = None
+    emb_codes_tags: list[str] | None = None
+    emb_codes: str | None = None
+    entry_dates_tags: list[str] | None = None
+    food_groups_tags: list[str] | None = None
+    generic_name: str | None = None
+    images: list[Image] | None = None
+    informers_tags: list[str] | None = None
+    ingredients_analysis_tags: list[str] | None = None
+    ingredients_from_palm_oil_n: int | None = None
+    ingredients_n: int | None = None
+    ingredients_original_tags: list[str] | None = None
+    ingredients_percent_analysis: int | None = None
+    ingredients_tags: list[str] | None = None
+    ingredients_with_specified_percent_n: int | None = None
+    ingredients_with_unspecified_percent_n: int | None = None
+    ingredients_without_ciqual_codes_n: int | None = None
+    ingredients_without_ciqual_codes: list[str] | None = None
+    ingredients: list[Ingredient] | None = None
+    known_ingredients_n: int | None = None
+    labels_tags: list[str] | None = None
+    labels: str | None = None
+    lang: str | None = None
+    languages_tags: list[str] | None = None
+    last_edit_dates_tags: list[str] | None = None
+    last_editor: str | None = None
+    last_image_t: int | None = None
+    last_modified_by: str | None = None
+    last_modified_t: int | None = None
+    last_updated_t: int | None = None
+    link: str | None = None
+    main_countries_tags: list[str] | None = None
+    manufacturing_places_tags: list[str] | None = None
+    manufacturing_places: str | None = None
+    max_imgid: int | None = None
+    minerals_tags: list[str] | None = None
+    misc_tags: list[str] | None = None
+    new_additives_n: int | None = None
+    no_nutrition_data: bool | None = None
+    nova_group: int | None = None
+    nova_groups_tags: list[str] | None = None
+    nova_groups: str | None = None
+    nucleotides_tags: list[str] | None = None
+    nutrient_levels_tags: list[str] | None = None
+    nutriments: list[NutrimentField] | None = None
+    nutriscore_grade: str | None = None
+    nutriscore_score: int | None = None
+    nutrition_data_per: str | None = None
+    obsolete: bool = False
+    origins_tags: list[str] | None = None
+    origins: str | None = None
+    packagings_complete: bool | None = None
+    packaging_recycling_tags: list[str] | None = None
+    packaging_shapes_tags: list[str] | None = None
+    packaging_tags: list[str] | None = None
+    packaging_text: list[LanguageField] | None = None
+    packaging: str | None = None
+    packagings: list[PackagingField] | None = None
+    photographers: list[str] | None = None
+    popularity_key: int | None = None
+    popularity_tags: list[str] | None = None
+    product_name: list[LanguageField] | None = None
+    product_quantity_unit: str | None = None
+    product_quantity: str | None = Field(default=None, coerce_numbers_to_str=True)
+    purchase_places_tags: list[str] | None = None
+    quantity: str | None = None
+    rev: int | None = None
+    scans_n: int | None = None
+    serving_quantity: str | None = Field(default=None, coerce_numbers_to_str=True)
+    serving_size: str | None = None
+    states_tags: list[str] | None = None
+    stores_tags: list[str] | None = None
+    stores: str | None = None
+    traces_tags: list[str] | None = None
+    unique_scans_n: int | None = None
+    unknown_ingredients_n: int | None = None
+    unknown_nutrients_tags: list[str] | None = None
+    vitamins_tags: list[str] | None = None
+    with_non_nutritive_sweeteners: int | None = None
+    with_sweeteners: int | None = None
+    ingredients_text: list[LanguageField] | None = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def parse_bool_values(cls, data: dict):
+        """Parse boolean values from string to bool."""
+        data.pop("obsolete", None)
+        for field_name in ("no_nutrition_data",):
+            if field_name in data:
+                data[field_name] = data[field_name] in (
+                    "on",
+                    "true",
+                    1,
+                    True,
+                )
+        return data
+
     @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
+    def parse_nutriments(cls, data: dict):
+        nutriments = data.pop("nutriments", None)
+        parsed_nutriments: dict[str, dict] = {}
+        nutriments_end_mapping = {
+            "_prepared_100g": "prepared_100g",
+            "_prepared_serving": "prepared_serving",
+            "_prepared_unit": "prepared_unit",
+            "_prepared_value": "prepared_value",
+            "_unit": "unit",
+            "_value": "value",
+            "_100g": "100g",
+            "_serving": "serving",
+        }
+        if nutriments:
+            for key, value in nutriments.items():
+                for end_key, new_key in nutriments_end_mapping.items():
+                    if key.endswith(end_key):
+                        key = key.replace(end_key, "")
+                        parsed_nutriments.setdefault(key, {})
+                        parsed_nutriments[key][new_key] = value
+
+            data["nutriments"] = [
+                {"name": key, **value} for key, value in parsed_nutriments.items()
+            ]
+
+        else:
+            data["nutriments"] = None
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def parse_language_fields(cls, data: dict) -> dict:
+        """Parse language fields (such as `ingredients_text`) into a list of
+        dictionaries with `lang` and `text` keys.
+
+        In Open Food Facts, main language is stored in the field without a
+        suffix, while other languages are stored with a suffix of the
+        language code.
+
+        To make the schema compatible with Parquet, we convert these fields
+        into a list of dictionaries with `lang` and `text` keys.
+        This way, the structure is consistent across all language fields.
+
+        The main language is stored with a `lang` value of "main", while other
+        languages are stored with their language code (2-letter code).
         """
-        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('"')})
+        for field_name in ("ingredients_text", "product_name", "packaging_text"):
+            main_language_value = data.pop(field_name, None)
+            data[field_name] = []
+
+            if main_language_value:
+                data[field_name].append({"lang": "main", "text": main_language_value})
+
+            for key in list(data.keys()):
+                if key.startswith(f"{field_name}_"):
+                    lang = key.rsplit("_", maxsplit=1)[-1]
+                    value = data.pop(key)
+                    # Sometimes we have a "debug" field that is not a language
+                    # Sometimes we have a language field with a None value
+                    if len(lang) == 2 and value is not None and len(value):
+                        data[field_name].append({"lang": lang, "text": value})
+
+            if data[field_name] == {}:
+                data[field_name] = None
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def parse_images(cls, data: dict) -> dict:
+        """Parse images field into a list of dictionaries with `key`, `imgid`,
+        `sizes`, `uploaded_t`, and `uploader` keys.
+
+        In Open Food Facts, images are stored as a dictionary with the image
+        key as the key and the image data as the value.
+
+        To make the schema compatible with Parquet, we convert these fields
+        into a list of dictionaries with `key`, `imgid`, `sizes`, `uploaded_t`,
+        and `uploader` keys. We copy the image key (ex: `3`, `nutrition_fr`,...)
+        from the original dictionary and add it as a field under the `key` key.
+        """
+        images = data.pop("images", None)
+        data["images"] = []
+        if images:
+            for key, value in images.items():
+                data["images"].append({"key": key, **value})
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def parse_ecoscore_score(cls, data: dict):
+        ecoscore_score = data.get("ecoscore_score")
+        if ecoscore_score and isinstance(ecoscore_score, float):
+            # Some `ecoscore_score` are float, we need to convert them to int
+            # to prevent Pydantic from raising an error
+            data["ecoscore_score"] = int(ecoscore_score)
+
         return data
 
+    @field_serializer("ingredients")
+    def serialize_ingredients(
+        self, ingredients: list[Ingredient] | None, _info
+    ) -> str | None:
+        """Ingredients can be nested, which seems difficult to implement as an
+        Arrow struct.
+        To alleviate this, we serialize the ingredients as a JSON string."""
+        if ingredients is None:
+            return None
+        return orjson.dumps([ing.model_dump() for ing in ingredients]).decode("utf-8")
+
 
 def export_parquet(dataset_path: Path, output_path: Path) -> None:
     """Convert a JSONL dataset to Parquet format and push it to Hugging Face
-    Hub.
-    """
+    Hub."""
     logger.info("Start JSONL export to Parquet.")
     with tempfile.TemporaryDirectory() as tmp_dir:
         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_converted_parquet_path, dataset_path=dataset_path
-        )
-        postprocess_parquet(
-            parquet_path=tmp_converted_parquet_path,
-            output_path=tmp_postprocessed_parquet_path,
+            output_file_path=tmp_converted_parquet_path,
+            dataset_path=dataset_path,
+            schema=PRODUCT_SCHEMA,
         )
         # Move dataset file to output_path
-        shutil.move(tmp_postprocessed_parquet_path, output_path)
+        shutil.move(tmp_converted_parquet_path, output_path)
 
     if settings.ENABLE_HF_PUSH:
         push_parquet_file_to_hf(data_path=output_path)
@@ -258,22 +563,45 @@ def export_parquet(dataset_path: Path, output_path: Path) -> None:
 def convert_jsonl_to_parquet(
     output_file_path: Path,
     dataset_path: Path,
+    schema: pa.Schema = PRODUCT_SCHEMA,
+    batch_size: int = 1024,
 ) -> None:
-    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(
-        "{output_path}", str(output_file_path)
-    )
-    try:
-        duckdb.sql(query)
-    except duckdb.Error as e:
-        logger.error(
-            "Error executing query: %s \nError message: %s",
-            query, e
-        )
-        raise
-    logger.info("JSONL successfully converted into Parquet file.")
+    """Convert the Open Food Facts JSONL dataset to Parquet format.
+
+    Args:
+        output_file_path (Path): The path where the Parquet file will be saved.
+        dataset_path (Path): The path to the Open Food Facts JSONL dataset.
+        schema (pa.Schema): The schema of the Parquet file.
+        batch_size (int, optional): The size of the batches used to convert the
+            dataset. Defaults to 1024.
+    """
+    writer = None
+    DTYPE_MAP = {
+        "images": IMAGES_DATATYPE,
+        "nutriments": NUTRIMENTS_DATATYPE,
+        "packagings": PACKAGING_FIELD_DATATYPE,
+    }
+    for batch in chunked(jsonl_iter(dataset_path), batch_size):
+        # We use by_alias=True because some fields start with a digit
+        # (ex: nutriments.100g), and we cannot declare the schema with
+        # Pydantic without an alias.
+        products = [Product(**item).model_dump(by_alias=True) for item in batch]
+        keys = products[0].keys()
+        data = {
+            key: pa.array(
+                [product[key] for product in products],
+                # Don't let pyarrow guess type for complex types
+                type=DTYPE_MAP.get(key, None),
+            )
+            for key in keys
+        }
+        record_batch = pa.record_batch(data, schema=schema)
+        if writer is None:
+            writer = pq.ParquetWriter(output_file_path, schema=record_batch.schema)
+        writer.write_batch(record_batch)
+
+    if writer is not None:
+        writer.close()
 
 
 def push_parquet_file_to_hf(
@@ -294,70 +622,7 @@ def push_parquet_file_to_hf(
         repo_id=repo_id,
         revision=revision,
         repo_type="dataset",
-        path_in_repo="products.parquet",
+        path_in_repo="food.parquet",
         commit_message=commit_message,
     )
     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/pyproject.toml b/pyproject.toml
index db006b5..3799fc7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,9 @@ dependencies = [
     "duckdb==1.1.2",
     "huggingface-hub>=0.26.2",
     "minio>=7.2.10",
+    "more-itertools>=10.5.0",
     "openfoodfacts>=1.1.5",
+    "orjson>=3.10.11",
     "pyarrow>=18.0.0",
     "pytz>=2024.2",
     "requests>=2.32.3",
@@ -17,8 +19,6 @@ dependencies = [
     "sentry-sdk>=2.18.0",
     "toml>=0.10.2",
     "typer>=0.12.5",
-    "types-pytz>=2024.2.0.20241003",
-    "types-toml>=0.10.8.20240310",
 ]
 
 
@@ -32,9 +32,10 @@ max-doc-length = 88
 [dependency-groups]
 dev = [
     "coverage[toml]>=7.6.4",
-    "ipykernel>=6.29.5",
     "pre-commit>=4.0.1",
     "pytest-cov>=6.0.0",
     "pytest>=8.3.3",
     "ruff>=0.7.3",
+    "types-pytz>=2024.2.0.20241003",
+    "types-toml>=0.10.8.20240310",
 ]
diff --git a/uv.lock b/uv.lock
index f7461e8..656774e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -16,15 +16,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
-[[package]]
-name = "appnope"
-version = "0.1.4"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
-]
-
 [[package]]
 name = "apscheduler"
 version = "3.10.4"
@@ -72,18 +63,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5a/e4/bf8034d25edaa495da3c8a3405627d2e35758e44ff6eaa7948092646fdcc/argon2_cffi_bindings-21.2.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93", size = 53104 },
 ]
 
-[[package]]
-name = "asttokens"
-version = "2.4.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "six" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/45/1d/f03bcb60c4a3212e15f99a56085d93093a497718adf828d050b9d675da81/asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0", size = 62284 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/45/86/4736ac618d82a20d87d2f92ae19441ebc7ac9e7a581d7e58bbe79233b24a/asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24", size = 27764 },
-]
-
 [[package]]
 name = "async-timeout"
 version = "4.0.3"
@@ -258,18 +237,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
 ]
 
-[[package]]
-name = "comm"
-version = "0.2.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "traitlets" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/e9/a8/fb783cb0abe2b5fded9f55e5703015cdf1c9c85b3669087c538dd15a6a86/comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e", size = 6210 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180 },
-]
-
 [[package]]
 name = "coverage"
 version = "7.6.4"
@@ -334,40 +301,6 @@ toml = [
     { name = "tomli", marker = "python_full_version <= '3.11'" },
 ]
 
-[[package]]
-name = "debugpy"
-version = "1.8.8"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e4/5e/7667b95c9d7ddb25c047143a3a47685f9be2a5d3d177a85a730b22dc6e5c/debugpy-1.8.8.zip", hash = "sha256:e6355385db85cbd666be703a96ab7351bc9e6c61d694893206f8001e22aee091", size = 4928684 }
-wheels = [
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-    { url = "https://files.pythonhosted.org/packages/11/b3/4119fa89b66bcc64a3b186ea52ee7c22bccc5d1765ee890887678b0e3e76/debugpy-1.8.8-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6531d952b565b7cb2fbd1ef5df3d333cf160b44f37547a4e7cf73666aca5d8d", size = 3567953 },
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