Templates e Snippets

import requests
import json
import time
import logging
from typing import Dict, List, Optional, Any
from dataclasses import dataclass

@dataclass
class DataSnapConfig:
    token: str
    base_url: str = "https://api.datasnap.cloud"
    timeout: int = 30
    retry_attempts: int = 3

class DataSnapClient:
    """Cliente base reutilizável para integração com DataSnap"""
    
    def __init__(self, config: DataSnapConfig):
        self.config = config
        self.session = requests.Session()
        self.session.headers.update({
            "Authorization": f"Bearer {config.token}",
            "User-Agent": "DataSnap-Client/1.0"
        })
        
        # Configurar logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    def _make_request(self, method: str, url: str, **kwargs) -> Dict[str, Any]:
        """Método base para requisições com retry automático"""
        for attempt in range(self.config.retry_attempts):
            try:
                response = self.session.request(
                    method, 
                    f"{self.config.base_url}{url}",
                    timeout=self.config.timeout,
                    **kwargs
                )
                response.raise_for_status()
                return response.json()
                
            except requests.exceptions.HTTPError as e:
                if e.response.status_code >= 400:  # Erro HTTP
                    wait_time = 2 ** attempt
                    self.logger.warning(f"Rate limit hit, waiting {wait_time}s")
                    time.sleep(wait_time)
                    continue
                raise
            except requests.exceptions.RequestException as e:
                if attempt == self.config.retry_attempts - 1:
                    raise
                time.sleep(2 ** attempt)
                
    def upload_file(self, schema_slug: str, file_path: str) -> Dict[str, Any]:
        """Upload de arquivo com validação"""
        with open(file_path, 'rb') as f:
            files = {'files': f}
            # Remove Content-Type header para multipart
            headers = {"Authorization": f"Bearer {self.config.token}"}
            
            response = self.session.post(
                f"{self.config.base_url}/api/v1/schemas/{schema_slug}/files",
                files=files,
                headers=headers,
                timeout=self.config.timeout
            )
            response.raise_for_status()
            return response.json()
    
    def query(self, schema_slug: str, query_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute consulta com validação de parâmetros"""
        required_fields = ['select']
        for field in required_fields:
            if field not in query_data:
                raise ValueError(f"Campo obrigatório ausente: {field}")
                
        return self._make_request(
            'POST',
            f'/api/v1/schemas/{schema_slug}/query',
            json=query_data
        )

# Exemplo de uso
config = DataSnapConfig(token="seu_token_aqui")
client = DataSnapClient(config)

const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');

class DataSnapClient {
    constructor(config = {}) {
        this.config = {
            baseUrl: 'https://api.datasnap.cloud',
            timeout: 30000,
            retryAttempts: 3,
            ...config
        };
        
        this.axios = axios.create({
            baseURL: this.config.baseUrl,
            timeout: this.config.timeout,
            headers: {
                'Authorization': `Bearer ${this.config.token}`,
                'User-Agent': 'DataSnap-Client-JS/1.0'
            }
        });
        
        // Interceptor para retry automático
        this.setupRetryInterceptor();
    }
    
    setupRetryInterceptor() {
        this.axios.interceptors.response.use(
            response => response,
            async error => {
                const { config } = error;
                
                if (!config.retryCount) {
                    config.retryCount = 0;
                }
                
                if (
                    config.retryCount < this.config.retryAttempts &&
                    (error.response?.status >= 400 || error.code === 'ECONNRESET')
                ) {
                    config.retryCount++;
                    const delay = Math.pow(2, config.retryCount) * 1000;
                    
                    await new Promise(resolve => setTimeout(resolve, delay));
                    return this.axios(config);
                }
                
                return Promise.reject(error);
            }
        );
    }
    
    async uploadFile(schemaSlug, filePath) {
        const formData = new FormData();
        formData.append('files', fs.createReadStream(filePath));
        
        const response = await this.axios.post(
            `/api/v1/schemas/${schemaSlug}/files`,
            formData,
            { headers: formData.getHeaders() }
        );
        
        return response.data;
    }
    
    async query(schemaSlug, queryData) {
        if (!queryData.select) {
            throw new Error('Campo "select" é obrigatório');
        }
        
        const response = await this.axios.post(
            `/api/v1/schemas/${schemaSlug}/query`,
            queryData
        );
        
        return response.data;
    }
}

module.exports = DataSnapClient;

def validate_jsonl_file(file_path: str, max_errors: int = 10) -> Dict[str, Any]:
    """
    Valida arquivo JSONL e retorna relatório detalhado
    """
    errors = []
    valid_lines = 0
    total_lines = 0
    
    with open(file_path, 'r', encoding='utf-8') as f:
        for line_num, line in enumerate(f, 1):
            total_lines += 1
            line = line.strip()
            
            if not line:  # Pular linhas vazias
                continue
                
            try:
                json.loads(line)
                valid_lines += 1
            except json.JSONDecodeError as e:
                errors.append({
                    'line': line_num,
                    'error': str(e),
                    'content': line[:100] + '...' if len(line) > 100 else line
                })
                
                if len(errors) >= max_errors:
                    break
    
    return {
        'valid': len(errors) == 0,
        'total_lines': total_lines,
        'valid_lines': valid_lines,
        'error_count': len(errors),
        'errors': errors,
        'success_rate': (valid_lines / total_lines) * 100 if total_lines > 0 else 0
    }

# Uso
result = validate_jsonl_file('dados.jsonl')
if result['valid']:
    print(f"✅ Arquivo válido: {result['valid_lines']} linhas")
else:
    print(f"❌ {result['error_count']} erros encontrados")
    for error in result['errors'][:5]:  # Mostrar primeiros 5 erros
        print(f"  Linha {error['line']}: {error['error']}")

def infer_schema_from_jsonl(file_path: str, sample_size: int = 1000) -> Dict[str, Any]:
    """
    Infere schema a partir de arquivo JSONL
    """
    fields = {}
    samples_processed = 0
    
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            if samples_processed >= sample_size:
                break
                
            try:
                data = json.loads(line.strip())
                for key, value in data.items():
                    if key not in fields:
                        fields[key] = {
                            'type': None,
                            'nullable': False,
                            'samples': []
                        }
                    
                    # Inferir tipo
                    if value is None:
                        fields[key]['nullable'] = True
                    else:
                        value_type = type(value).__name__
                        if fields[key]['type'] is None:
                            fields[key]['type'] = value_type
                        elif fields[key]['type'] != value_type:
                            fields[key]['type'] = 'mixed'
                    
                    # Coletar amostras
                    if len(fields[key]['samples']) < 5:
                        fields[key]['samples'].append(value)
                
                samples_processed += 1
                
            except json.JSONDecodeError:
                continue
    
    return {
        'inferred_schema': fields,
        'samples_processed': samples_processed
    }

class QueryTemplates:
    """Templates de consultas frequentemente usadas"""
    
    @staticmethod
    def top_values(field: str, limit: int = 10) -> Dict[str, Any]:
        """Top N valores mais frequentes"""
        return {
            "select": [field, "count(*) as frequency"],
            "group_by": [field],
            "order_by": [{"field": "frequency", "direction": "desc"}],
            "limit": limit
        }
    
    @staticmethod
    def daily_aggregation(date_field: str, value_field: str, 
                         start_date: str, end_date: str) -> Dict[str, Any]:
        """Agregação diária de valores"""
        return {
            "select": [
                f"date({date_field}) as date",
                f"count(*) as count",
                f"sum({value_field}) as total",
                f"avg({value_field}) as average"
            ],
            "where": [
                {"field": date_field, "op": ">=", "value": start_date},
                {"field": date_field, "op": "<=", "value": end_date}
            ],
            "group_by": [f"date({date_field})"],
            "order_by": [{"field": "date", "direction": "asc"}]
        }
    
    @staticmethod
    def percentile_analysis(field: str) -> Dict[str, Any]:
        """Análise de percentis"""
        return {
            "select": [
                f"min({field}) as min_value",
                f"max({field}) as max_value",
                f"avg({field}) as avg_value",
                f"quantile(0.25)({field}) as p25",
                f"quantile(0.5)({field}) as p50",
                f"quantile(0.75)({field}) as p75",
                f"quantile(0.95)({field}) as p95"
            ]
        }

# Exemplos de uso
templates = QueryTemplates()

# Top 10 categorias mais frequentes
top_categories = templates.top_values("categoria", 10)

# Vendas diárias do mês
daily_sales = templates.daily_aggregation(
    "data_venda", "valor", "2024-08-01", "2024-08-31"
)

# Análise estatística de preços
price_stats = templates.percentile_analysis("preco")

import asyncio
from datetime import datetime, timedelta

import os
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor, as_completed

class BatchUploader:
    def __init__(self, client: DataSnapClient, max_workers: int = 5):
        self.client = client
        self.max_workers = max_workers
    
    def upload_directory(self, directory_path: str, schema_slug: str, 
                        pattern: str = "*.jsonl") -> Dict[str, Any]:
        """Upload todos os arquivos JSONL de um diretório"""
        directory = Path(directory_path)
        files = list(directory.glob(pattern))
        
        results = {
            'successful': [],
            'failed': [],
            'total': len(files)
        }
        
        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            future_to_file = {
                executor.submit(self._upload_single_file, schema_slug, str(file)): file
                for file in files
            }
            
            for future in as_completed(future_to_file):
                file_path = future_to_file[future]
                try:
                    result = future.result()
                    results['successful'].append({
                        'file': str(file_path),
                        'result': result
                    })
                    print(f"✅ {file_path.name} enviado com sucesso")
                except Exception as e:
                    results['failed'].append({
                        'file': str(file_path),
                        'error': str(e)
                    })
                    print(f"❌ Erro ao enviar {file_path.name}: {e}")
        
        return results
    
    def _upload_single_file(self, schema_slug: str, file_path: str):
        """Upload de um único arquivo com validação"""
        # Validar antes do upload
        validation = validate_jsonl_file(file_path, max_errors=1)
        if not validation['valid']:
            raise ValueError(f"Arquivo inválido: {validation['errors'][0]['error']}")
        
        return self.client.upload_file(schema_slug, file_path)

import os
from dataclasses import dataclass
from typing import Optional

@dataclass
class EnvironmentConfig:
    """Configuração por ambiente"""
    
    # Obrigatórios
    token: str
    
    # Opcionais com defaults
    environment: str = "production"
    timeout: int = 30
    retry_attempts: int = 3
    max_file_size_mb: int = 100
    
    @classmethod
    def from_env(cls) -> 'EnvironmentConfig':
        """Cria configuração a partir de variáveis de ambiente"""
        token = os.getenv('DATASNAP_TOKEN')
        if not token:
            raise ValueError("DATASNAP_TOKEN é obrigatório")
        
        return cls(
            token=token,
            environment=os.getenv('DATASNAP_ENV', 'production'),
            timeout=int(os.getenv('DATASNAP_TIMEOUT', '30')),
            retry_attempts=int(os.getenv('DATASNAP_RETRIES', '3')),
            max_file_size_mb=int(os.getenv('DATASNAP_MAX_SIZE', '100'))
        )
    
    @property
    def base_url(self) -> str:
        """URL base por ambiente"""
        urls = {
            'production': 'https://api.datasnap.cloud'
        }
        return urls.get(self.environment, urls['production'])

# Uso
config = EnvironmentConfig.from_env()
client = DataSnapClient(DataSnapConfig(
    token=config.token,
    base_url=config.base_url,
    timeout=config.timeout,
    retry_attempts=config.retry_attempts
))

version: '3.8'

services:
  datasnap-processor:
    build: .
    environment:
      - DATASNAP_TOKEN=${DATASNAP_TOKEN}
      - DATASNAP_ENV=${DATASNAP_ENV:-production}
      - DATASNAP_TIMEOUT=30
      - DATASNAP_RETRIES=3
    volumes:
      - ./data:/app/data
      - ./logs:/app/logs
    restart: unless-stopped
    
  datasnap-scheduler:
    build: .
    command: python scheduler.py
    environment:
      - DATASNAP_TOKEN=${DATASNAP_TOKEN}
      - SCHEDULE_INTERVAL=${SCHEDULE_INTERVAL:-3600}
    depends_on:
      - datasnap-processor
    restart: unless-stopped

import logging
import json
from datetime import datetime

class DataSnapLogger:
    def __init__(self, name: str = "datasnap", level: str = "INFO"):
        self.logger = logging.getLogger(name)
        self.logger.setLevel(getattr(logging, level))
        
        # Handler para saída estruturada
        handler = logging.StreamHandler()
        handler.setFormatter(logging.Formatter('%(message)s'))
        self.logger.addHandler(handler)
    
    def log_request(self, method: str, url: str, status_code: int, 
                   response_time: float, **kwargs):
        """Log estruturado para requisições"""
        log_data = {
            'timestamp': datetime.utcnow().isoformat(),
            'event': 'api_request',
            'method': method,
            'url': url,
            'status_code': status_code,
            'response_time_ms': round(response_time * 1000, 2),
            **kwargs
        }
        self.logger.info(json.dumps(log_data))
    
    def log_upload(self, schema_slug: str, file_path: str, 
                  file_size: int, success: bool, **kwargs):
        """Log estruturado para uploads"""
        log_data = {
            'timestamp': datetime.utcnow().isoformat(),
            'event': 'file_upload',
            'schema': schema_slug,
            'file_path': file_path,
            'file_size_mb': round(file_size / (1024*1024), 2),
            'success': success,
            **kwargs
        }
        self.logger.info(json.dumps(log_data))

# Uso
logger = DataSnapLogger()
logger.log_upload("vendas", "data.jsonl", 1048576, True, file_id=123)