[check_for_updates] br_anp_precos_combustiveis

pipelines/datasets/br_anp_precos_combustiveis/constants.py

@@ -48,6 +48,10 @@ class constants(Enum):  # pylint: disable=c0103
         "https://www.gov.br/anp/pt-br/centrais-de-conteudo/dados-abertos/arquivos/shpc/qus/ultimas-4-semanas-diesel-gnv.csv",
     ]
 
+    URLS_DATA = [
+        "https://www.gov.br/anp/pt-br/centrais-de-conteudo/dados-abertos/arquivos/shpc/qus/ultimas-4-semanas-glp.csv"
+    ]
+
     PATH_INPUT = "/tmp/input/"
 
     PATH_OUTPUT = "/tmp/output/"

pipelines/datasets/br_anp_precos_combustiveis/flows.py

@@ -13,11 +13,13 @@
 from pipelines.utils.constants import constants as utils_constants
 from pipelines.utils.decorators import Flow
 from pipelines.utils.execute_dbt_model.constants import constants as dump_db_constants
+from pipelines.datasets.br_anp_precos_combustiveis.utils import download_files
 from pipelines.datasets.br_anp_precos_combustiveis.tasks import (
-    tratamento,
+    download_and_transform,
     data_max_bd_mais,
     data_max_bd_pro,
     make_partitions,
+    check_for_updates,
 )
 from pipelines.datasets.br_anp_precos_combustiveis.schedules import (
     every_week_anp_microdados,
@@ -26,6 +28,11 @@
     create_table_and_upload_to_gcs,
     rename_current_flow_run_dataset_table,
     get_current_flow_labels,
+    log_task,
+)
+
+from pipelines.datasets.br_anp_precos_combustiveis.constants import (
+    constants as anatel_constants,
 )
 
 with Flow(
@@ -37,7 +44,7 @@
     table_id = Parameter("table_id", default="microdados", required=True)
 
     materialization_mode = Parameter(
-        "materialization_mode", default="prod", required=False
+        "materialization_mode", default="dev", required=False
     )
 
     materialize_after_dump = Parameter(
@@ -51,103 +58,68 @@
     )
     update_metadata = Parameter("update_metadata", default=True, required=False)
 
-    df = tratamento(upstream_tasks=[rename_flow_run])
-    output_path = make_partitions(df=df, upstream_tasks=[df])
-    get_date_max_mais = data_max_bd_mais()
-    get_date_max_pro = data_max_bd_pro(df=df)
-
-    # pylint: disable=C0103
-    wait_upload_table = create_table_and_upload_to_gcs(
-        data_path=output_path,
-        dataset_id=dataset_id,
-        table_id=table_id,
-        dump_mode="append",
-        wait=output_path,
-    )
-
-    # ! BD MAIS - Atrasado
-    with case(materialize_after_dump, True):
-        # Trigger DBT flow run
-        current_flow_labels = get_current_flow_labels()
-        materialization_flow = create_flow_run(
-            flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value,
-            project_name=constants.PREFECT_DEFAULT_PROJECT.value,
-            parameters={
-                "dataset_id": dataset_id,
-                "table_id": table_id,
-                "mode": materialization_mode,
-                "dbt_alias": dbt_alias,
-            },
-            labels=current_flow_labels,
-            run_name=f"Materialize {dataset_id}.{table_id}",
+    dados_desatualizados = check_for_updates(dataset_id=dataset_id, table_id=table_id)
+    log_task(f"Checando se os dados estão desatualizados: {dados_desatualizados}")
+    with case(dados_desatualizados, False):
+        log_task(
+            "Dados atualizados, não é necessário fazer o download",
+            upstream_tasks=[dados_desatualizados],
         )
 
-        wait_for_materialization = wait_for_flow_run(
-            materialization_flow,
-            stream_states=True,
-            stream_logs=True,
-            raise_final_state=True,
-        )
-        wait_for_materialization.max_retries = (
-            dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_ATTEMPTS.value
-        )
-        wait_for_materialization.retry_delay = timedelta(
-            seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value
-        )
+    with case(dados_desatualizados, True):
+        df = download_and_transform(upstream_tasks=[rename_flow_run])
+        output_path = make_partitions(df=df, upstream_tasks=[df])
+        get_date_max_pro = data_max_bd_pro(df=df)
 
-        with case(update_metadata, True):
-            update_django_metadata(
-                dataset_id,
-                table_id,
-                metadata_type="DateTimeRange",
-                bq_last_update=False,
-                api_mode="prod",
-                date_format="yy-mm-dd",
-                _last_date=get_date_max_mais,
-                upstream_tasks=[wait_upload_table],
-            )
-
-    # ! BD PRO - Atualizado
-    with case(materialize_after_dump, True):
-        # Trigger DBT flow run
-        current_flow_labels = get_current_flow_labels()
-        materialization_flow = create_flow_run(
-            flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value,
-            project_name=constants.PREFECT_DEFAULT_PROJECT.value,
-            parameters={
-                "dataset_id": dataset_id,
-                "table_id": table_id + "_atualizado",
-                "mode": materialization_mode,
-                "dbt_alias": dbt_alias,
-            },
-            labels=current_flow_labels,
-            run_name=f"Materialize {dataset_id}.{table_id}" "_atualizado",
+        # pylint: disable=C0103
+        wait_upload_table = create_table_and_upload_to_gcs(
+            data_path=output_path,
+            dataset_id=dataset_id,
+            table_id=table_id,
+            dump_mode="append",
+            wait=output_path,
         )
 
-        wait_for_materialization = wait_for_flow_run(
-            materialization_flow,
-            stream_states=True,
-            stream_logs=True,
-            raise_final_state=True,
-        )
-        wait_for_materialization.max_retries = (
-            dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_ATTEMPTS.value
-        )
-        wait_for_materialization.retry_delay = timedelta(
-            seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value
-        )
+        with case(materialize_after_dump, True):
+            # Trigger DBT flow run
+            current_flow_labels = get_current_flow_labels()
+            materialization_flow = create_flow_run(
+                flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value,
+                project_name=constants.PREFECT_DEFAULT_PROJECT.value,
+                parameters={
+                    "dataset_id": dataset_id,
+                    "table_id": table_id,
+                    "mode": materialization_mode,
+                    "dbt_alias": dbt_alias,
+                },
+                labels=current_flow_labels,
+                run_name=f"Materialize {dataset_id}.{table_id}",
+            )
 
-        with case(update_metadata, True):
-            update_django_metadata(
-                dataset_id,
-                table_id + "_atualizado",
-                metadata_type="DateTimeRange",
-                bq_last_update=False,
-                api_mode="prod",
-                date_format="yy-mm-dd",
-                _last_date=get_date_max_pro,
-                upstream_tasks=[wait_upload_table],
+            wait_for_materialization = wait_for_flow_run(
+                materialization_flow,
+                stream_states=True,
+                stream_logs=True,
+                raise_final_state=True,
+            )
+            wait_for_materialization.max_retries = (
+                dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_ATTEMPTS.value
             )
+            wait_for_materialization.retry_delay = timedelta(
+                seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value
+            )
+
+            with case(update_metadata, True):
+                update_django_metadata(
+                    dataset_id,
+                    table_id,
+                    metadata_type="DateTimeRange",
+                    bq_last_update=False,
+                    api_mode="prod",
+                    date_format="yy-mm-dd",
+                    _last_date=get_date_max_pro,
+                    upstream_tasks=[wait_upload_table],
+                )
 
 anp_microdados.storage = GCS(constants.GCS_FLOWS_BUCKET.value)
 anp_microdados.run_config = KubernetesRun(image=constants.DOCKER_IMAGE.value)

pipelines/datasets/br_anp_precos_combustiveis/schedules.py

@@ -4,15 +4,14 @@
 """
 
 from datetime import timedelta, datetime
-
 from prefect.schedules import Schedule
-from prefect.schedules.clocks import IntervalClock
+from prefect.schedules.clocks import CronClock
 from pipelines.constants import constants
 
 every_week_anp_microdados = Schedule(
     clocks=[
-        IntervalClock(
-            interval=timedelta(days=1),
+        CronClock(
+            cron="0 10 * * *",
             start_date=datetime(2021, 1, 1),
             labels=[constants.BASEDOSDADOS_PROD_AGENT_LABEL.value],
             parameter_defaults={

pipelines/datasets/br_anp_precos_combustiveis/tasks.py

@@ -12,99 +12,84 @@
     get_id_municipio,
     open_csvs,
     partition_data,
+    merge_table_id_municipio,
+    orderning_data_coleta,
+    creating_column_ano,
+    rename_and_reordening,
+    rename_columns,
+    rename_and_to_create_endereco,
+    lower_colunm_produto,
 )
 from pipelines.datasets.br_anp_precos_combustiveis.constants import (
     constants as anatel_constants,
 )
-from pipelines.utils.utils import log
+from pipelines.utils.utils import log, extract_last_date
 from pipelines.constants import constants
 
 
+@task
+def check_for_updates(dataset_id, table_id):
+    """
+    Checks if there are available updates for a specific dataset and table.
+
+    Returns:
+        bool: Returns True if updates are available, otherwise returns False.
+    """
+    # Obtém a data mais recente do site
+    download_files(anatel_constants.URLS_DATA.value, anatel_constants.PATH_INPUT.value)
+    df = pd.read_csv(anatel_constants.URL_GLP.value, sep=";", encoding="utf-8")
+    data_obj = df["Data da Coleta"].max()
+    data_obj = datetime.strptime(data_obj, "%d/%m/%Y").strftime("%Y-%m-%d")
+    data_obj = datetime.strptime(data_obj, "%Y-%m-%d").date()
+
+    # Obtém a última data no site BD
+    data_bq_obj = extract_last_date(
+        dataset_id, table_id, "yy-mm-dd", "basedosdados", data="data_coleta"
+    )
+
+    # Registra a data mais recente do site
+    log(f"Última data no site do ANP: {data_obj}")
+    log(f"Última data no site da BD: {data_bq_obj}")
+
+    # Compara as datas para verificar se há atualizações
+    if data_obj > data_bq_obj:
+        return True  # Há atualizações disponíveis
+    else:
+        return False  # Não há novas atualizações disponíveis
+
+
 @task(
     max_retries=constants.TASK_MAX_RETRIES.value,
     retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value),
 )
-def tratamento():
-    download_files(
-        anatel_constants.URLS.value,
-        anatel_constants.PATH_INPUT.value,
-    )
+def download_and_transform():
+    download_files(anatel_constants.URLS.value, anatel_constants.PATH_INPUT.value)
 
     precos_combustiveis = open_csvs(
-        url_diesel_gnv=anatel_constants.URL_DIESEL_GNV.value,
-        url_gasolina_etanol=anatel_constants.URL_GASOLINA_ETANOL.value,
-        url_glp=anatel_constants.URL_GLP.value,
+        anatel_constants.URL_DIESEL_GNV.value,
+        anatel_constants.URL_GASOLINA_ETANOL.value,
+        anatel_constants.URL_GLP.value,
     )
 
-    id_municipio = get_id_municipio()
-    log("Iniciando tratamento dos dados precos_combustiveis")
-    precos_combustiveis = pd.merge(
-        id_municipio,
-        precos_combustiveis,
-        how="right",
-        left_on=["nome", "sigla_uf"],
-        right_on=["Municipio", "Estado - Sigla"],
-    )
-    log("----" * 150)
-    log("Dados mergeados")
-    precos_combustiveis.rename(columns={"Municipio": "nome"}, inplace=True)
-    precos_combustiveis.dropna(subset=["Valor de Venda"], inplace=True)
-    precos_combustiveis["endereco_revenda"] = (
-        precos_combustiveis["Nome da Rua"].fillna("")
-        + ","
-        + " "
-        + precos_combustiveis["Numero Rua"].fillna("")
-        + ","
-        + " "
-        + precos_combustiveis["Complemento"].fillna("")
-    )
-    precos_combustiveis.drop(columns=["sigla_uf"], inplace=True)
-    precos_combustiveis.rename(columns={"Data da Coleta": "data_coleta"}, inplace=True)
-    precos_combustiveis["data_coleta"] = (
-        precos_combustiveis["data_coleta"].str[6:10]
-        + "-"
-        + precos_combustiveis["data_coleta"].str[3:5]
-        + "-"
-        + precos_combustiveis["data_coleta"].str[0:2]
-    )
-    precos_combustiveis["Produto"] = precos_combustiveis["Produto"].str.lower()
-    precos_combustiveis["ano"] = precos_combustiveis["data_coleta"].str[0:4]
-    precos_combustiveis["ano"].replace("nan", "", inplace=True)
-    precos_combustiveis.rename(columns=anatel_constants.RENAME.value, inplace=True)
-    precos_combustiveis = precos_combustiveis[anatel_constants.ORDEM.value]
-    precos_combustiveis["ano"] = precos_combustiveis["ano"].apply(
-        lambda x: str(x).replace(".0", "")
-    )
-    precos_combustiveis["cep_revenda"] = precos_combustiveis["cep_revenda"].apply(
-        lambda x: str(x).replace("-", "")
-    )
-    precos_combustiveis["unidade_medida"] = precos_combustiveis["unidade_medida"].map(
-        {"R$ / litro": "R$/litro", "R$ / m³": "R$/m3", "R$ / 13 kg": "R$/13kg"}
-    )
-    precos_combustiveis["nome_estabelecimento"] = precos_combustiveis[
-        "nome_estabelecimento"
-    ].apply(lambda x: str(x).replace(",", ""))
-    precos_combustiveis["preco_compra"] = precos_combustiveis["preco_compra"].apply(
-        lambda x: str(x).replace(",", ".")
-    )
-    precos_combustiveis["preco_venda"] = precos_combustiveis["preco_venda"].apply(
-        lambda x: str(x).replace(",", ".")
-    )
-    precos_combustiveis["preco_venda"] = precos_combustiveis["preco_venda"].replace(
-        "nan", ""
-    )
-    precos_combustiveis["preco_compra"] = precos_combustiveis["preco_compra"].replace(
-        "nan", ""
+    df = get_id_municipio(id_municipio=precos_combustiveis)
+
+    df = merge_table_id_municipio(
+        id_municipio=df, pd_precos_combustiveis=precos_combustiveis
     )
-    precos_combustiveis.replace(np.nan, "", inplace=True)
-    log("----" * 150)
-    log("Dados tratados com sucesso")
-    log("----" * 150)
-    log("Iniciando particionamento dos dados")
-    log("----" * 150)
-    log(precos_combustiveis["data_coleta"].unique())
-
-    return precos_combustiveis
+
+    df = rename_and_to_create_endereco(precos_combustiveis=df)
+
+    df = orderning_data_coleta(precos_combustiveis=df)
+
+    df = lower_colunm_produto(precos_combustiveis=df)
+
+    df = creating_column_ano(precos_combustiveis=df)
+
+    df = rename_and_reordening(precos_combustiveis=df)
+
+    df = rename_columns(precos_combustiveis=df)
+
+    return df
 
 
 @task(