diff --git a/pipelines/datasets/br_me_cnpj/constants.py b/pipelines/datasets/br_me_cnpj/constants.py index 2f3e4bef9..54229a9d8 100644 --- a/pipelines/datasets/br_me_cnpj/constants.py +++ b/pipelines/datasets/br_me_cnpj/constants.py @@ -159,10 +159,10 @@ class constants(Enum): # pylint: disable=c0103 "data_situacao_especial", ] - default_chunk_size = 2**20 # 1MB + default_chunk_size = 20 * 1024 * 1024 # 20MB default_max_retries = 32 default_max_parallel = 16 - default_timeout = 3 * 60 * 1000 # 3 minutes + default_timeout = 1 * 60 * 1000 # 1 minute diff --git a/pipelines/datasets/br_me_cnpj/flows.py b/pipelines/datasets/br_me_cnpj/flows.py index b9f558732..26e282bf8 100644 --- a/pipelines/datasets/br_me_cnpj/flows.py +++ b/pipelines/datasets/br_me_cnpj/flows.py @@ -54,21 +54,21 @@ ) tabelas = constants_cnpj.TABELAS.value[0:1] - data_source_max_date = get_data_source_max_date() + folder_date, today_date = get_data_source_max_date() dados_desatualizados = check_if_data_is_outdated( dataset_id=dataset_id, table_id=table_id, - data_source_max_date=data_source_max_date, - date_format="%Y-%m-%d", - upstream_tasks=[data_source_max_date], + data_source_max_date=folder_date, + date_format="%Y-%m", + upstream_tasks=[today_date], ) with case(dados_desatualizados, False): log_task(f"Não há atualizações para a tabela de {tabelas}!") with case(dados_desatualizados, True): - output_filepath = main(tabelas,data_atualizacao=data_source_max_date) + output_filepath = main(tabelas,folder_date=folder_date,today_date=today_date) wait_upload_table = create_table_and_upload_to_gcs( data_path=output_filepath, dataset_id=dataset_id, @@ -124,7 +124,7 @@ br_me_cnpj_empresas.storage = GCS(constants.GCS_FLOWS_BUCKET.value) br_me_cnpj_empresas.run_config = KubernetesRun(image=constants.DOCKER_IMAGE.value) -#br_me_cnpj_empresas.schedule = every_day_empresas +br_me_cnpj_empresas.schedule = every_day_empresas with Flow( name="br_me_cnpj.socios", @@ -147,21 +147,21 @@ ) tabelas = constants_cnpj.TABELAS.value[1:2] - data_source_max_date = get_data_source_max_date() + folder_date, today_date = get_data_source_max_date() dados_desatualizados = check_if_data_is_outdated( dataset_id=dataset_id, table_id=table_id, - data_source_max_date=data_source_max_date, - date_format="%Y-%m-%d", - upstream_tasks=[data_source_max_date], + data_source_max_date=folder_date, + date_format="%Y-%m", + upstream_tasks=[today_date], ) with case(dados_desatualizados, False): log_task(f"Não há atualizações para a tabela de {tabelas}!") with case(dados_desatualizados, True): - output_filepath = main(tabelas,data_atualizacao=data_source_max_date) + output_filepath = main(tabelas,folder_date=folder_date,today_date=today_date) wait_upload_table = create_table_and_upload_to_gcs( data_path=output_filepath, dataset_id=dataset_id, @@ -216,7 +216,7 @@ br_me_cnpj_socios.storage = GCS(constants.GCS_FLOWS_BUCKET.value) br_me_cnpj_socios.run_config = KubernetesRun(image=constants.DOCKER_IMAGE.value) -#br_me_cnpj_socios.schedule = every_day_socios +br_me_cnpj_socios.schedule = every_day_socios with Flow( @@ -241,21 +241,21 @@ ) tabelas = constants_cnpj.TABELAS.value[2:3] - data_source_max_date = get_data_source_max_date() + folder_date, today_date = get_data_source_max_date() dados_desatualizados = check_if_data_is_outdated( dataset_id=dataset_id, table_id=table_id, - data_source_max_date=data_source_max_date, - date_format="%Y-%m-%d", - upstream_tasks=[data_source_max_date], + data_source_max_date=folder_date, + date_format="%Y-%m", + upstream_tasks=[today_date], ) with case(dados_desatualizados, False): log_task(f"Não há atualizações para a tabela de {tabelas}!") with case(dados_desatualizados, True): - output_filepath = main(tabelas,data_atualizacao=data_source_max_date) + output_filepath = main(tabelas,folder_date=folder_date,today_date=today_date) wait_upload_table = create_table_and_upload_to_gcs( data_path=output_filepath, @@ -355,7 +355,7 @@ br_me_cnpj_estabelecimentos.run_config = KubernetesRun( image=constants.DOCKER_IMAGE.value ) -#br_me_cnpj_estabelecimentos.schedule = every_day_estabelecimentos +br_me_cnpj_estabelecimentos.schedule = every_day_estabelecimentos with Flow( @@ -379,21 +379,21 @@ ) tabelas = constants_cnpj.TABELAS.value[3:] - data_source_max_date = get_data_source_max_date() + folder_date, today_date = get_data_source_max_date() dados_desatualizados = check_if_data_is_outdated( dataset_id="br_me_cnpj", table_id="estabelecimentos", - data_source_max_date=data_source_max_date, - date_format="%Y-%m-%d", - upstream_tasks=[data_source_max_date], + data_source_max_date=folder_date, + date_format="%Y-%m", + upstream_tasks=[today_date], ) with case(dados_desatualizados, False): log_task(f"Não há atualizações para a tabela de {tabelas}!") with case(dados_desatualizados, True): - output_filepath = main(tabelas,data_atualizacao=data_source_max_date) + output_filepath = main(tabelas,folder_date=folder_date,today_date=today_date) wait_upload_table = create_table_and_upload_to_gcs( data_path=output_filepath, dataset_id=dataset_id, @@ -444,5 +444,5 @@ br_me_cnpj_simples.storage = GCS(constants.GCS_FLOWS_BUCKET.value) br_me_cnpj_simples.run_config = KubernetesRun(image=constants.DOCKER_IMAGE.value) -#br_me_cnpj_simples.schedule = every_day_simples +br_me_cnpj_simples.schedule = every_day_simples diff --git a/pipelines/datasets/br_me_cnpj/tasks.py b/pipelines/datasets/br_me_cnpj/tasks.py index b9a915b5f..9702fa6a9 100644 --- a/pipelines/datasets/br_me_cnpj/tasks.py +++ b/pipelines/datasets/br_me_cnpj/tasks.py @@ -4,10 +4,8 @@ """ import asyncio import os -from google.cloud import storage -import pathlib from typing import Union, List -from datetime import datetime +from datetime import datetime,timedelta import basedosdados as bd from prefect import task @@ -16,61 +14,44 @@ data_url, destino_output, download_unzip_csv, - download_unzip_csv_sync, process_csv_empresas, process_csv_estabelecimentos, process_csv_simples, process_csv_socios, ) from pipelines.utils.utils import log +from pipelines.constants import constants ufs = constants_cnpj.UFS.value url = constants_cnpj.URL.value headers = constants_cnpj.HEADERS.value -@task -def calculate_defasagem(): - """ - Calculates the month lag based on the current month. - - Returns: - int: Number of lagged months. - """ - current_month = datetime.now().month - current_year = datetime.now().year - - if current_year == 2023: - if current_month >= 10: - defasagem = 6 - else: - defasagem = current_month - 4 - else: - defasagem = 6 - - return defasagem - - -@task -def get_data_source_max_date(): +@task( + max_retries=3, + retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value), +) +def get_data_source_max_date() -> tuple[datetime,datetime]: """ Checks if there are available updates for a specific dataset and table. Returns: - bool: Returns True if updates are available, otherwise returns False. + tuple: Returns a tuple with the date extracted from the CNPJs API folder and today date + to be used as partition """ - # Obtém a data mais recente do site - data_obj = data_url(url=url, headers=headers) - return data_obj + + folder_date, today_date = data_url(url=url, headers=headers) + return folder_date, today_date @task -def main(tabelas:[str], data_atualizacao:datetime)-> str: +def main(tabelas:[str], folder_date:datetime, today_date:datetime)-> str: """ Performs the download, processing, and organization of CNPJ data. Args: tabelas (list): A list of tables to be processed. - data_atualizaão: Most recent database release extracted from API + folder_date (datetime): Most recent database release extracted from API + today_date (datetime): Today's date Returns: str: The path to the output folder where the data has been organized. @@ -80,35 +61,35 @@ def main(tabelas:[str], data_atualizacao:datetime)-> str: sufixo = tabela.lower() # Define o caminho para a pasta de entrada (input) - input_path = f"/tmp/data/br_me_cnpj/input/data={data_atualizacao}/" + input_path = f"/tmp/data/br_me_cnpj/input/data={today_date}/" os.makedirs(input_path, exist_ok=True) log("Pasta destino input construído") # Define o caminho para a pasta de saída (output) - output_path = destino_output(sufixo, data_atualizacao) + output_path = destino_output(sufixo, today_date) # Loop para baixar e processar os arquivos for i in range(0, 10): if tabela != "Simples": nome_arquivo = f"{tabela}{i}" - url_download = f"https://arquivos.receitafederal.gov.br/cnpj/dados_abertos_cnpj/{data_atualizacao.strftime('%Y-%m')}/{tabela}{i}.zip" + url_download = f"https://arquivos.receitafederal.gov.br/cnpj/dados_abertos_cnpj/{folder_date}/{tabela}{i}.zip" if nome_arquivo not in arquivos_baixados: arquivos_baixados.append(nome_arquivo) asyncio.run((download_unzip_csv(url_download, input_path))) if tabela == "Estabelecimentos": process_csv_estabelecimentos( - input_path, output_path, data_atualizacao, i + input_path, output_path, today_date, i ) elif tabela == "Socios": - process_csv_socios(input_path, output_path, data_atualizacao, i) + process_csv_socios(input_path, output_path, today_date, i) elif tabela == "Empresas": - process_csv_empresas(input_path, output_path, data_atualizacao, i) + process_csv_empresas(input_path, output_path, today_date, i) else: nome_arquivo = f"{tabela}" - url_download = f"https://arquivos.receitafederal.gov.br/cnpj/dados_abertos_cnpj/{data_atualizacao.strftime('%Y-%m')}/{tabela}.zip" + url_download = f"https://arquivos.receitafederal.gov.br/cnpj/dados_abertos_cnpj/{folder_date}/{tabela}.zip" if nome_arquivo not in arquivos_baixados: arquivos_baixados.append(nome_arquivo) asyncio.run((download_unzip_csv(url_download, input_path))) - process_csv_simples(input_path, output_path, data_atualizacao, sufixo) + process_csv_simples(input_path, output_path, today_date, sufixo) return output_path diff --git a/pipelines/datasets/br_me_cnpj/utils.py b/pipelines/datasets/br_me_cnpj/utils.py index a608b5c02..28734a0a1 100644 --- a/pipelines/datasets/br_me_cnpj/utils.py +++ b/pipelines/datasets/br_me_cnpj/utils.py @@ -3,20 +3,17 @@ General purpose functions for the br_me_cnpj project """ import os -import time import zipfile from asyncio import Semaphore, gather from datetime import datetime -import httpx +from httpx import AsyncClient, HTTPError, head import pandas as pd import pyarrow as pa import pyarrow.parquet as pq import re import requests from bs4 import BeautifulSoup -from loguru import logger -from requests.exceptions import ConnectionError, Timeout from tqdm import tqdm from pipelines.datasets.br_me_cnpj.constants import constants as constants_cnpj @@ -24,50 +21,56 @@ ufs = constants_cnpj.UFS.value headers = constants_cnpj.HEADERS.value +timeout = constants_cnpj.TIMEOUT.value +def data_url(url:str, headers:dict)-> tuple[datetime,datetime]: + """ + Fetches data from a URL, parses the HTML to find the latest folder date, and compares it to today's date. + Args: + url (str): The URL to fetch the data from. + headers (dict): Headers to include in the request. -def data_url(url:str, headers:dict)-> datetime: + Returns: + Tuple[datetime, datetime]: The maximum date found in the folders and today's date. + """ - max_attempts = constants_cnpj.MAX_ATTEMPTS.value - timeout = constants_cnpj.TIMEOUT.value - attempts = constants_cnpj.ATTEMPTS.value - date_pattern = re.compile(r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}") - - while attempts < max_attempts: - try: - link_data = requests.get(url, headers=headers, timeout=timeout) - link_data.raise_for_status() - break - except (ConnectionError, Timeout) as e: - log(f"Tentativa {attempts + 1} falhou. Erro: {e}") - time.sleep(1) - attempts += 1 - else: - log(f"Máximo de {max_attempts} tentativas alcançado. Verifique a conexão ou o endpoint.") - return None + link_data = requests.get(url, headers=headers, timeout=timeout, verify=False) + link_data.raise_for_status() soup = BeautifulSoup(link_data.text, "html.parser") - dates = [ - datetime.strptime(element.get_text(strip=True), "%Y-%m-%d %H:%M").date() - for element in soup.find_all('td', align="right") - if date_pattern.match(element.get_text(strip=True)) - ] - try: - max_date = max(dates) - log(f"A data máxima extraida da API é: {max_date}") - except ValueError as e: - log(f"A lista que deveria conter a data mais recente de atualização retornou nula. Verifique o endpoint \n. Erro: {e}") + max_folder_date = max( + [ + datetime.strptime(link["href"].strip("/"), "%Y-%m").strftime('%Y-%m') + for link in soup.find_all("a", href=True) + if link["href"].strip("/").startswith("202") + ] + ) + + today_date = datetime.today().strftime('%Y-%m-%d') + log(f"A data máxima extraida da API da Receita Federal que será utilizada para comparar com os metadados da BD é: {max_folder_date}") + log(f"A data de hoje gerada para criar partições no Storage é: {today_date} ") - return max_date + return max_folder_date, today_date # ! Cria o caminho do output -def destino_output(sufixo, data_coleta): +def destino_output(sufixo:str, data_coleta: datetime)-> str: + """ + Constructs the output directory path based on the suffix and collection date. + + Args: + sufixo (str): The suffix for directory structure. + data_coleta (datetime): The data collection date. + + Returns: + str: The constructed output path. + """ + output_path = f"/tmp/data/br_me_cnpj/output/{sufixo}/" # Pasta de destino para salvar o arquivo CSV if sufixo != "simples": @@ -87,78 +90,123 @@ def destino_output(sufixo, data_coleta): # ! Adiciona zero a esquerda nas colunas -def fill_left_zeros(df, column, num_digits): +def fill_left_zeros(df: datetime, column, num_digits:int)-> pd.DataFrame: + """ + Adds left zeros to the specified column of a DataFrame to meet the required digit count. + + Args: + df (pd.DataFrame): DataFrame with the target column. + column (str): Column name to fill with zeros. + num_digits (int): Total number of digits for the column. + + Returns: + pd.DataFrame: Updated DataFrame with filled zeros. + """ df[column] = df[column].astype(str).str.zfill(num_digits) return df # ! Download assincrono e em chunck`s do zip def chunk_range(content_length: int, chunk_size: int) -> list[tuple[int, int]]: - """Split the content length into a list of chunk ranges""" - return [ - (i * chunk_size, min((i + 1) * chunk_size - 1, content_length - 1)) - for i in range(content_length // chunk_size + 1) - ] + """ + Splits the content length into a list of chunk ranges for downloading. + + Args: + content_length (int): The total content length. + chunk_size (int): Size of each chunk. + Returns: + List[Tuple[int, int]]: List of start and end byte ranges for each chunk. + """ + return [(i, min(i + chunk_size - 1, content_length - 1)) for i in range(0, content_length, chunk_size)] # from https://stackoverflow.com/a/64283770 -async def download( - url: str, - chunk_size: int = 2**25, - max_retries: int = 32, - max_parallel: int = 32, - timeout: int = 3 * 60 * 2, -) -> bytes: - request_head = httpx.head(url) - log(request_head) +async def download(url, chunk_size=20 * 1024 * 1024, max_retries=5, max_parallel=5, timeout=5 * 60): + """ + Downloads a file from a URL in chunks asynchronously. - assert request_head.status_code == 200 - assert request_head.headers["accept-ranges"] == "bytes" + Args: + url (str): URL of the file to download. + chunk_size (int): Size of each chunk in bytes. + max_retries (int): Maximum retry attempts for each chunk. + max_parallel (int): Maximum number of parallel downloads. + timeout (int): Timeout for each request. - content_length = int(request_head.headers["content-length"]) + Returns: + bytes: The downloaded content in bytes. + """ - log( - f"Downloading {url} with {content_length} bytes / {chunk_size} chunks and {max_parallel} parallel downloads" - ) + try: + request_head = head(url) + log(request_head) - # TODO: pool http connections - semaphore = Semaphore(max_parallel) - tasks = [ - download_chunk(url, (start, end), max_retries, timeout, semaphore) - for start, end in chunk_range(content_length, chunk_size) - ] + assert request_head.status_code == 200 + assert request_head.headers["accept-ranges"] == "bytes" + + content_length = int(request_head.headers["content-length"]) + log(f"Baixando {url} com {content_length} bytes / {chunk_size} chunks e {max_parallel} downloads paralelos") + + semaphore = Semaphore(max_parallel) - return b"".join(await gather(*tasks)) + async with AsyncClient() as client: + tasks = [ + download_chunk(client, url, (start, end), max_retries, timeout, semaphore) + for start, end in chunk_range(content_length, chunk_size) + ] + return b"".join(await gather(*tasks)) + + except HTTPError as e: + log(f"Requisição mal sucessedida: {e}") + return b"" async def download_chunk( + client: AsyncClient, url: str, chunk_range: tuple[int, int], max_retries: int, timeout: int, - semaphore: Semaphore, + semaphore: Semaphore ) -> bytes: + """ + Downloads a chunk of a file asynchronously with retry logic. + + Args: + client (AsyncClient): HTTP client for making requests. + url (str): The URL of the file to download. + chunk_range (Tuple[int, int]): The byte range for the chunk to be downloaded. + max_retries (int): The maximum number of retries for downloading a chunk. + timeout (int): The timeout duration for each request. + semaphore (Semaphore): A semaphore to limit the number of parallel downloads. + + Returns: + bytes: The downloaded chunk content. + """ async with semaphore: - #log(f"Downloading chunk {chunk_range[0]}-{chunk_range[1]}") - for i in range(max_retries): + for attempt in range(max_retries): try: - async with httpx.AsyncClient(timeout=timeout) as client: - headers = {"Range": f"bytes={chunk_range[0]}-{chunk_range[1]}"} - response = await client.get(url, headers=headers) - response.raise_for_status() - return response.content - except httpx.HTTPError as e: - log(f"Download failed with {e}. Retrying ({i+1}/{max_retries})...") - raise httpx.HTTPError(f"Download failed after {max_retries} retries") + headers = {"Range": f"bytes={chunk_range[0]}-{chunk_range[1]}"} + response = await client.get(url, headers=headers, timeout=timeout) + response.raise_for_status() + return response.content + except HTTPError as e: + delay = 2 ** attempt + log(f"Dowload do chunk {chunk_range} ffalhou na tentativa {attempt + 1}. Tentando novamente em {delay} segundos...") + await asyncio.sleep(delay) + + raise HTTPError(f"Download do chunk {chunk_range} falhou depois de {max_retries} tentativas") # ! Executa o download do zip file -async def download_unzip_csv( - url, - pasta_destino, -): +async def download_unzip_csv(url: str, pasta_destino: str) -> None: + """ + Downloads a ZIP file from a URL and extracts its content. + + Args: + url (str): The URL of the ZIP file. + pasta_destino (str): The directory to save and extract the ZIP file. + """ log(f"Baixando o arquivo {url}") - save_path = os.path.join(pasta_destino, f"{os.path.basename(url)}.zip") content = await download(url) with open(save_path, "wb") as fd: @@ -174,32 +222,25 @@ async def download_unzip_csv( os.remove(save_path) -def download_unzip_csv_sync(url, pasta_destino, chunk_size: int = 1000): - time_before = time.perf_counter() - log(f"Baixando o arquivo {url}") - save_path = os.path.join(pasta_destino, f"{os.path.basename(url)}.zip") - - r = requests.get(url, headers=headers, stream=True, timeout=60) - with open(save_path, "wb") as fd: - for chunk in tqdm( - r.iter_content(chunk_size=chunk_size), desc="Baixando o arquivo" - ): - fd.write(chunk) - - try: - with zipfile.ZipFile(save_path) as z: - z.extractall(pasta_destino) - log("Dados extraídos com sucesso!") - log(f"Total time (asynchronous): {time.perf_counter() - time_before}.") - except zipfile.BadZipFile: - log(f"O arquivo {os.path.basename(url)} não é um arquivo ZIP válido.") - os.remove(save_path) - # ! Salva os dados CSV Estabelecimentos def process_csv_estabelecimentos( - input_path: str, output_path: str, data_coleta: str, i: int, chunk_size: int = 1000 -): + input_path: str, + output_path: str, + data_coleta: str, + i: int, + chunk_size: int = 100000 +) -> None: + """ + Processes and saves CSV data for establishments, organizing data into partitions by state. + + Args: + input_path (str): Path to the input data. + output_path (str): Directory to save processed data. + data_coleta (str): Data collection date as string. + i (int): File number or batch index. + chunk_size (int): Number of rows to process per chunk. + """ ordem = constants_cnpj.COLUNAS_ESTABELECIMENTO_ORDEM.value colunas = constants_cnpj.COLUNAS_ESTABELECIMENTO.value save_path = f"{output_path}data={data_coleta}/" @@ -266,8 +307,22 @@ def process_csv_estabelecimentos( # ! Salva os dados CSV Empresas def process_csv_empresas( - input_path: str, output_path: str, data_coleta: str, i: int, chunk_size: int = 1000 -): + input_path: str, + output_path: str, + data_coleta: str, + i: int, + chunk_size: int = 100000 +) -> None: + """ + Processes and saves CSV data for companies. + + Args: + input_path (str): Path to the input data. + output_path (str): Directory to save processed data. + data_coleta (str): Data collection date as string. + i (int): File number or batch index. + chunk_size (int): Number of rows to process per chunk. + """ colunas = constants_cnpj.COLUNAS_EMPRESAS.value save_path = f"{output_path}data={data_coleta}/empresas_{i}.csv" for nome_arquivo in os.listdir(input_path): @@ -305,7 +360,20 @@ def process_csv_empresas( # ! Salva os dados CSV Socios def process_csv_socios( input_path: str, output_path: str, data_coleta: str, i: int, chunk_size: int = 1000 -): +) -> None: + """ + Processes and saves CSV data for socios (partners). + + Args: + input_path (str): Path to the input data. + output_path (str): Directory to save processed data. + data_coleta (str): Data collection date as string. + i (int): File number or batch index. + chunk_size (int): Number of rows to process per chunk. + + Returns: + None + """ colunas = constants_cnpj.COLUNAS_SOCIOS.value save_path = f"{output_path}data={data_coleta}/socios_{i}.csv" for nome_arquivo in os.listdir(input_path): @@ -347,12 +415,21 @@ def process_csv_socios( # ! Salva os dados CSV Simples def process_csv_simples( - input_path: str, - output_path: str, - data_coleta: str, - sufixo: str, - chunk_size: int = 1000, -): + input_path: str, output_path: str, data_coleta: str, sufixo: str, chunk_size: int = 1000 +) -> None: + """ + Processes and saves CSV data for simples. + + Args: + input_path (str): Path to the input data. + output_path (str): Directory to save processed data. + data_coleta (str): Data collection date as string. + sufixo (str): Suffix used to construct the output filename. + chunk_size (int): Number of rows to process per chunk. + + Returns: + None + """ colunas = constants_cnpj.COLUNAS_SIMPLES.value save_path = f"{output_path}{sufixo}.csv" for nome_arquivo in os.listdir(input_path): @@ -391,3 +468,5 @@ def process_csv_simples( log(f"Arquivo {sufixo} salvo") os.remove(caminho_arquivo_csv) + + diff --git a/poetry.lock b/poetry.lock index e14a8591c..9097e35bc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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