Python के साथ ईमेल सत्यापन API: संपूर्ण एकीकरण गाइड

Python की सरलता और शक्तिशाली इकोसिस्टम इसे आपके एप्लिकेशन में ईमेल सत्यापन एकीकृत करने के लिए एक उत्कृष्ट विकल्प बनाती है। चाहे आप Django या Flask के साथ वेब एप्लिकेशन बना रहे हों, pandas के साथ डेटा प्रोसेसिंग कर रहे हों, या स्वचालित वर्कफ़्लो बना रहे हों, पेशेवर ईमेल सत्यापन जोड़ने से यह सुनिश्चित होता है कि आपके ईमेल संचार वास्तविक प्राप्तकर्ताओं तक पहुंचे और आपकी प्रेषक प्रतिष्ठा की रक्षा करे।

यह व्यापक गाइड आपको Python के साथ BillionVerify ईमेल सत्यापन API को एकीकृत करने के बारे में बताती है, बुनियादी एकल ईमेल सत्यापन से लेकर उन्नत बैच प्रोसेसिंग और प्रोडक्शन-रेडी इम्प्लीमेंटेशन तक।

पूर्वापेक्षाएँ और सेटअप

कोड में जाने से पहले, सुनिश्चित करें कि आपके पास उचित वातावरण कॉन्फ़िगर किया गया है। यह ट्यूटोरियल मानता है कि आपके सिस्टम पर Python 3.8 या उच्चतर इंस्टॉल है।

आवश्यक पैकेज इंस्टॉल करना

आवश्यक पैकेज इंस्टॉल करके शुरू करें। हम HTTP संचार के लिए requests लाइब्रेरी का उपयोग करेंगे, हालांकि हम बाद में अतुल्यकालिक ऑपरेशन के लिए aiohttp का भी उपयोग करेंगे।

pip install requests python-dotenv

async समर्थन और उन्नत फीचर्स के लिए:

pip install aiohttp pandas

प्रोजेक्ट संरचना

अपने प्रोजेक्ट को एक स्वच्छ संरचना के साथ व्यवस्थित करें जो चिंताओं को अलग करती है:

email_verification/
├── __init__.py
├── client.py           # Main verification client
├── models.py           # Data models
├── exceptions.py       # Custom exceptions
├── utils.py            # Helper functions
├── batch_processor.py  # Batch verification logic
└── examples/
    ├── basic_usage.py
    ├── flask_integration.py
    └── django_integration.py

एनवायरनमेंट कॉन्फ़िगरेशन

अपने API क्रेडेंशियल को सुरक्षित रूप से स्टोर करने के लिए एक .env फ़ाइल बनाएं:

EMAILVERIFY_API_KEY=your_api_key_here
EMAILVERIFY_API_URL=https://api.billionverify.com/v1

अपने एप्लिकेशन में इन वेरिएबल को लोड करें:

import os
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.getenv('EMAILVERIFY_API_KEY')
API_URL = os.getenv('EMAILVERIFY_API_URL', 'https://api.billionverify.com/v1')

बुनियादी ईमेल सत्यापन

आइए सबसे सरल इम्प्लीमेंटेशन से शुरू करें: Python की requests लाइब्रेरी का उपयोग करके एकल ईमेल पते को सत्यापित करना।

सरल सत्यापन फ़ंक्शन

import requests
from typing import Dict, Any

def verify_email(email: str, api_key: str) -> Dict[str, Any]:
    """
    Verify a single email address using the BillionVerify API.

    Args:
        email: The email address to verify
        api_key: Your BillionVerify API key

    Returns:
        Dictionary containing verification results

    Raises:
        requests.RequestException: If the API request fails
    """
    url = "https://api.billionverify.com/v1/verify"

    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }

    payload = {"email": email}

    response = requests.post(url, json=payload, headers=headers, timeout=30)
    response.raise_for_status()

    return response.json()

# Example usage
if __name__ == "__main__":
    result = verify_email("test@example.com", API_KEY)
    print(f"Email valid: {result.get('is_valid')}")
    print(f"Deliverable: {result.get('is_deliverable')}")

प्रतिक्रिया को समझना

API कई सत्यापन संकेतकों के साथ एक व्यापक प्रतिक्रिया लौटाता है:

{
    "email": "user@example.com",
    "is_valid": True,
    "is_deliverable": True,
    "is_disposable": False,
    "is_role_based": False,
    "is_catch_all": False,
    "is_free_provider": True,
    "risk_score": 15,
    "domain": "example.com",
    "mx_records": ["mx1.example.com", "mx2.example.com"],
    "smtp_check": True,
    "verification_time_ms": 245
}

प्रत्येक फ़ील्ड मूल्यवान जानकारी प्रदान करता है:

• is_valid: क्या ईमेल प्रारूप वाक्यविन्यासिक रूप से सही है
• is_deliverable: क्या मेलबॉक्स मौजूद है और मेल प्राप्त कर सकता है
• is_disposable: अस्थायी या डिस्पोजेबल ईमेल पतों की पहचान करता है
• is_role_based: info@ या support@ जैसे जेनेरिक पतों का पता लगाता है
• is_catch_all: उन डोमेन को इंगित करता है जो सभी पतों को स्वीकार करते हैं
• risk_score: 0 (सबसे कम जोखिम) से 100 (उच्चतम जोखिम) तक संख्यात्मक मूल्यांकन

प्रोडक्शन-रेडी क्लाइंट बनाना

प्रोडक्शन एप्लिकेशन के लिए, आपको एक मजबूत क्लाइंट क्लास की आवश्यकता है जो प्रमाणीकरण, पुनः प्रयास, दर सीमा और त्रुटि हैंडलिंग को सुचारू रूप से संभालता है।

कस्टम अपवाद

सबसे पहले, बेहतर त्रुटि हैंडलिंग के लिए कस्टम अपवाद परिभाषित करें:

# exceptions.py

class EmailVerificationError(Exception):
    """Base exception for email verification errors."""
    pass

class AuthenticationError(EmailVerificationError):
    """Raised when API authentication fails."""
    pass

class RateLimitError(EmailVerificationError):
    """Raised when API rate limit is exceeded."""
    def __init__(self, retry_after: int = 60):
        self.retry_after = retry_after
        super().__init__(f"Rate limit exceeded. Retry after {retry_after} seconds.")

class ValidationError(EmailVerificationError):
    """Raised when email validation fails."""
    pass

class APIError(EmailVerificationError):
    """Raised for general API errors."""
    def __init__(self, status_code: int, message: str):
        self.status_code = status_code
        super().__init__(f"API error {status_code}: {message}")

डेटा मॉडल

टाइप-सेफ रिस्पॉन्स हैंडलिंग के लिए dataclasses या Pydantic का उपयोग करें:

# models.py

from dataclasses import dataclass
from typing import List, Optional

@dataclass
class VerificationResult:
    """Represents the result of an email verification."""
    email: str
    is_valid: bool
    is_deliverable: bool
    is_disposable: bool
    is_role_based: bool
    is_catch_all: bool
    is_free_provider: bool
    risk_score: int
    domain: str
    mx_records: List[str]
    smtp_check: bool
    verification_time_ms: int

    @classmethod
    def from_dict(cls, data: dict) -> 'VerificationResult':
        """Create a VerificationResult from API response dictionary."""
        return cls(
            email=data.get('email', ''),
            is_valid=data.get('is_valid', False),
            is_deliverable=data.get('is_deliverable', False),
            is_disposable=data.get('is_disposable', False),
            is_role_based=data.get('is_role_based', False),
            is_catch_all=data.get('is_catch_all', False),
            is_free_provider=data.get('is_free_provider', False),
            risk_score=data.get('risk_score', 100),
            domain=data.get('domain', ''),
            mx_records=data.get('mx_records', []),
            smtp_check=data.get('smtp_check', False),
            verification_time_ms=data.get('verification_time_ms', 0)
        )

    def is_safe_to_send(self) -> bool:
        """Determine if it's safe to send emails to this address."""
        return (
            self.is_valid and
            self.is_deliverable and
            not self.is_disposable and
            self.risk_score < 50
        )

मुख्य क्लाइंट क्लास

अब पूर्ण-विशेषताओं वाले सत्यापन क्लाइंट को लागू करें:

# client.py

import time
import logging
from typing import Optional, List
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

from .models import VerificationResult
from .exceptions import (
    AuthenticationError,
    RateLimitError,
    ValidationError,
    APIError
)

logger = logging.getLogger(__name__)

class EmailVerificationClient:
    """
    Production-ready client for the BillionVerify email verification API.

    Features:
    - Automatic retry with exponential backoff
    - Rate limit handling
    - Connection pooling
    - Comprehensive error handling
    - Response caching (optional)
    """

    DEFAULT_BASE_URL = "https://api.billionverify.com/v1"
    DEFAULT_TIMEOUT = 30
    MAX_RETRIES = 3

    def __init__(
        self,
        api_key: str,
        base_url: Optional[str] = None,
        timeout: int = DEFAULT_TIMEOUT,
        max_retries: int = MAX_RETRIES
    ):
        """
        Initialize the email verification client.

        Args:
            api_key: Your BillionVerify API key
            base_url: Optional custom API base URL
            timeout: Request timeout in seconds
            max_retries: Maximum number of retry attempts
        """
        if not api_key:
            raise ValueError("API key is required")

        self.api_key = api_key
        self.base_url = base_url or self.DEFAULT_BASE_URL
        self.timeout = timeout
        self.max_retries = max_retries

        # Configure session with retry logic
        self.session = self._create_session()

    def _create_session(self) -> requests.Session:
        """Create a requests session with retry configuration."""
        session = requests.Session()

        # Configure retry strategy
        retry_strategy = Retry(
            total=self.max_retries,
            backoff_factor=1,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["GET", "POST"]
        )

        adapter = HTTPAdapter(
            max_retries=retry_strategy,
            pool_connections=10,
            pool_maxsize=20
        )

        session.mount("http://", adapter)
        session.mount("https://", adapter)

        # Set default headers
        session.headers.update({
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
            "User-Agent": "BillionVerify-Python/1.0"
        })

        return session

    def _handle_response(self, response: requests.Response) -> dict:
        """
        Handle API response and raise appropriate exceptions.

        Args:
            response: The requests Response object

        Returns:
            Parsed JSON response

        Raises:
            AuthenticationError: For 401/403 responses
            RateLimitError: For 429 responses
            APIError: For other error responses
        """
        if response.status_code == 401:
            raise AuthenticationError("Invalid API key")

        if response.status_code == 403:
            raise AuthenticationError("Access forbidden. Check API key permissions.")

        if response.status_code == 429:
            retry_after = int(response.headers.get('Retry-After', 60))
            raise RateLimitError(retry_after)

        if response.status_code == 400:
            error_data = response.json()
            raise ValidationError(error_data.get('message', 'Validation failed'))

        if response.status_code >= 400:
            raise APIError(response.status_code, response.text)

        return response.json()

    def verify(self, email: str) -> VerificationResult:
        """
        Verify a single email address.

        Args:
            email: The email address to verify

        Returns:
            VerificationResult object with verification details
        """
        url = f"{self.base_url}/verify"

        logger.debug(f"Verifying email: {email}")

        response = self.session.post(
            url,
            json={"email": email},
            timeout=self.timeout
        )

        data = self._handle_response(response)
        result = VerificationResult.from_dict(data)

        logger.info(
            f"Verified {email}: valid={result.is_valid}, "
            f"deliverable={result.is_deliverable}, "
            f"risk_score={result.risk_score}"
        )

        return result

    def verify_batch(
        self,
        emails: List[str],
        callback_url: Optional[str] = None
    ) -> str:
        """
        Submit a batch of emails for verification.

        Args:
            emails: List of email addresses to verify
            callback_url: Optional webhook URL for results notification

        Returns:
            Batch ID for tracking the verification job
        """
        url = f"{self.base_url}/verify/batch"

        payload = {"emails": emails}
        if callback_url:
            payload["callback_url"] = callback_url

        response = self.session.post(
            url,
            json=payload,
            timeout=self.timeout
        )

        data = self._handle_response(response)
        batch_id = data.get('batch_id')

        logger.info(f"Submitted batch verification: {batch_id} ({len(emails)} emails)")

        return batch_id

    def get_batch_status(self, batch_id: str) -> dict:
        """
        Get the status of a batch verification job.

        Args:
            batch_id: The batch ID returned from verify_batch

        Returns:
            Dictionary with batch status and progress
        """
        url = f"{self.base_url}/verify/batch/{batch_id}"

        response = self.session.get(url, timeout=self.timeout)
        return self._handle_response(response)

    def get_batch_results(self, batch_id: str) -> List[VerificationResult]:
        """
        Get the results of a completed batch verification.

        Args:
            batch_id: The batch ID returned from verify_batch

        Returns:
            List of VerificationResult objects
        """
        url = f"{self.base_url}/verify/batch/{batch_id}/results"

        response = self.session.get(url, timeout=self.timeout)
        data = self._handle_response(response)

        return [VerificationResult.from_dict(item) for item in data.get('results', [])]

    def close(self):
        """Close the underlying session."""
        self.session.close()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.close()

बैच ईमेल सत्यापन

बड़ी ईमेल सूचियों को प्रोसेस करने के लिए कुशल बैच हैंडलिंग की आवश्यकता होती है। यहां प्रगति ट्रैकिंग और परिणाम प्रबंधन के साथ मजबूत बैच सत्यापन को लागू करने का तरीका बताया गया है।

बैच प्रोसेसर इम्प्लीमेंटेशन

# batch_processor.py

import time
import logging
from typing import List, Callable, Optional
from concurrent.futures import ThreadPoolExecutor, as_completed

from .client import EmailVerificationClient
from .models import VerificationResult
from .exceptions import RateLimitError

logger = logging.getLogger(__name__)

class BatchProcessor:
    """
    Process large email lists with progress tracking and result handling.
    """

    def __init__(
        self,
        client: EmailVerificationClient,
        batch_size: int = 1000,
        max_workers: int = 5,
        progress_callback: Optional[Callable[[int, int], None]] = None
    ):
        """
        Initialize the batch processor.

        Args:
            client: EmailVerificationClient instance
            batch_size: Number of emails per batch submission
            max_workers: Maximum concurrent verification threads
            progress_callback: Optional callback for progress updates
        """
        self.client = client
        self.batch_size = batch_size
        self.max_workers = max_workers
        self.progress_callback = progress_callback

    def process_list(
        self,
        emails: List[str],
        use_async_batch: bool = True
    ) -> List[VerificationResult]:
        """
        Process a list of emails with optimal strategy.

        Args:
            emails: List of email addresses
            use_async_batch: Use async batch API for large lists

        Returns:
            List of verification results
        """
        total = len(emails)
        logger.info(f"Starting verification of {total} emails")

        if total <= 10:
            # Small list: verify individually
            return self._process_sequential(emails)
        elif use_async_batch and total > 100:
            # Large list: use batch API
            return self._process_batch_api(emails)
        else:
            # Medium list: use concurrent individual verification
            return self._process_concurrent(emails)

    def _process_sequential(
        self,
        emails: List[str]
    ) -> List[VerificationResult]:
        """Process emails sequentially."""
        results = []
        total = len(emails)

        for i, email in enumerate(emails):
            try:
                result = self.client.verify(email)
                results.append(result)
            except RateLimitError as e:
                logger.warning(f"Rate limit hit, waiting {e.retry_after}s")
                time.sleep(e.retry_after)
                result = self.client.verify(email)
                results.append(result)
            except Exception as e:
                logger.error(f"Failed to verify {email}: {e}")
                results.append(self._create_error_result(email, str(e)))

            if self.progress_callback:
                self.progress_callback(i + 1, total)

        return results

    def _process_concurrent(
        self,
        emails: List[str]
    ) -> List[VerificationResult]:
        """Process emails concurrently with thread pool."""
        results = []
        total = len(emails)
        completed = 0

        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            future_to_email = {
                executor.submit(self._verify_with_retry, email): email
                for email in emails
            }

            for future in as_completed(future_to_email):
                email = future_to_email[future]
                try:
                    result = future.result()
                    results.append(result)
                except Exception as e:
                    logger.error(f"Failed to verify {email}: {e}")
                    results.append(self._create_error_result(email, str(e)))

                completed += 1
                if self.progress_callback:
                    self.progress_callback(completed, total)

        return results

    def _process_batch_api(
        self,
        emails: List[str]
    ) -> List[VerificationResult]:
        """Process emails using the async batch API."""
        all_results = []
        total = len(emails)
        processed = 0

        # Split into batches
        batches = [
            emails[i:i + self.batch_size]
            for i in range(0, total, self.batch_size)
        ]

        for batch_num, batch in enumerate(batches, 1):
            logger.info(f"Submitting batch {batch_num}/{len(batches)}")

            # Submit batch
            batch_id = self.client.verify_batch(batch)

            # Wait for completion with polling
            results = self._wait_for_batch(batch_id)
            all_results.extend(results)

            processed += len(batch)
            if self.progress_callback:
                self.progress_callback(processed, total)

        return all_results

    def _wait_for_batch(
        self,
        batch_id: str,
        poll_interval: int = 5,
        max_wait: int = 3600
    ) -> List[VerificationResult]:
        """
        Wait for batch verification to complete.

        Args:
            batch_id: The batch ID to wait for
            poll_interval: Seconds between status checks
            max_wait: Maximum seconds to wait

        Returns:
            List of verification results
        """
        start_time = time.time()

        while time.time() - start_time < max_wait:
            status = self.client.get_batch_status(batch_id)

            if status.get('status') == 'completed':
                return self.client.get_batch_results(batch_id)

            if status.get('status') == 'failed':
                raise RuntimeError(f"Batch {batch_id} failed: {status.get('error')}")

            progress = status.get('progress', 0)
            logger.debug(f"Batch {batch_id} progress: {progress}%")

            time.sleep(poll_interval)

        raise TimeoutError(f"Batch {batch_id} did not complete within {max_wait}s")

    def _verify_with_retry(
        self,
        email: str,
        max_retries: int = 3
    ) -> VerificationResult:
        """Verify email with retry logic for rate limits."""
        for attempt in range(max_retries):
            try:
                return self.client.verify(email)
            except RateLimitError as e:
                if attempt < max_retries - 1:
                    time.sleep(e.retry_after)
                else:
                    raise

        raise RuntimeError(f"Failed to verify {email} after {max_retries} attempts")

    @staticmethod
    def _create_error_result(email: str, error: str) -> VerificationResult:
        """Create a result object for failed verification."""
        return VerificationResult(
            email=email,
            is_valid=False,
            is_deliverable=False,
            is_disposable=False,
            is_role_based=False,
            is_catch_all=False,
            is_free_provider=False,
            risk_score=100,
            domain=email.split('@')[-1] if '@' in email else '',
            mx_records=[],
            smtp_check=False,
            verification_time_ms=0
        )

CSV फ़ाइलों और Pandas के साथ काम करना

डेटा प्रोसेसिंग वर्कफ़्लो के लिए, pandas के साथ एकीकृत करें:

import pandas as pd
from typing import Optional

def verify_csv_file(
    client: EmailVerificationClient,
    input_file: str,
    output_file: str,
    email_column: str = 'email',
    batch_size: int = 1000
) -> pd.DataFrame:
    """
    Verify emails from a CSV file and save results.

    Args:
        client: EmailVerificationClient instance
        input_file: Path to input CSV file
        output_file: Path to output CSV file
        email_column: Name of the column containing emails
        batch_size: Processing batch size

    Returns:
        DataFrame with verification results
    """
    # Read input file
    df = pd.read_csv(input_file)

    if email_column not in df.columns:
        raise ValueError(f"Column '{email_column}' not found in CSV")

    emails = df[email_column].dropna().tolist()

    # Process with progress tracking
    processor = BatchProcessor(
        client,
        batch_size=batch_size,
        progress_callback=lambda done, total: print(f"Progress: {done}/{total}")
    )

    results = processor.process_list(emails)

    # Create results DataFrame
    results_df = pd.DataFrame([
        {
            'email': r.email,
            'is_valid': r.is_valid,
            'is_deliverable': r.is_deliverable,
            'is_disposable': r.is_disposable,
            'is_role_based': r.is_role_based,
            'is_catch_all': r.is_catch_all,
            'risk_score': r.risk_score,
            'domain': r.domain
        }
        for r in results
    ])

    # Merge with original data
    merged = df.merge(results_df, left_on=email_column, right_on='email', how='left')

    # Save results
    merged.to_csv(output_file, index=False)

    # Print summary
    print(f"\nVerification Summary:")
    print(f"  Total emails: {len(emails)}")
    print(f"  Valid: {results_df['is_valid'].sum()}")
    print(f"  Deliverable: {results_df['is_deliverable'].sum()}")
    print(f"  Disposable: {results_df['is_disposable'].sum()}")
    print(f"  High risk (score >= 50): {(results_df['risk_score'] >= 50).sum()}")

    return merged

asyncio के साथ अतुल्यकालिक सत्यापन

उच्च-प्रदर्शन एप्लिकेशन के लिए, aiohttp के साथ Python के asyncio का उपयोग करें:

import asyncio
import aiohttp
from typing import List, Optional
from dataclasses import dataclass

class AsyncEmailVerificationClient:
    """
    Asynchronous email verification client using aiohttp.
    """

    def __init__(
        self,
        api_key: str,
        base_url: str = "https://api.billionverify.com/v1",
        concurrency_limit: int = 10
    ):
        self.api_key = api_key
        self.base_url = base_url
        self.semaphore = asyncio.Semaphore(concurrency_limit)
        self._session: Optional[aiohttp.ClientSession] = None

    async def _get_session(self) -> aiohttp.ClientSession:
        """Get or create aiohttp session."""
        if self._session is None or self._session.closed:
            self._session = aiohttp.ClientSession(
                headers={
                    "Authorization": f"Bearer {self.api_key}",
                    "Content-Type": "application/json"
                }
            )
        return self._session

    async def verify(self, email: str) -> dict:
        """Verify a single email asynchronously."""
        async with self.semaphore:
            session = await self._get_session()
            url = f"{self.base_url}/verify"

            async with session.post(url, json={"email": email}) as response:
                if response.status == 429:
                    retry_after = int(response.headers.get('Retry-After', 60))
                    await asyncio.sleep(retry_after)
                    return await self.verify(email)

                response.raise_for_status()
                return await response.json()

    async def verify_many(self, emails: List[str]) -> List[dict]:
        """Verify multiple emails concurrently."""
        tasks = [self.verify(email) for email in emails]
        results = await asyncio.gather(*tasks, return_exceptions=True)

        return [
            r if not isinstance(r, Exception) else {"email": emails[i], "error": str(r)}
            for i, r in enumerate(results)
        ]

    async def close(self):
        """Close the aiohttp session."""
        if self._session and not self._session.closed:
            await self._session.close()

    async def __aenter__(self):
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        await self.close()


# Usage example
async def main():
    emails = [
        "user1@example.com",
        "user2@example.com",
        "user3@example.com"
    ]

    async with AsyncEmailVerificationClient(api_key="your_key") as client:
        results = await client.verify_many(emails)

        for result in results:
            if "error" in result:
                print(f"Error: {result['error']}")
            else:
                print(f"{result['email']}: valid={result['is_valid']}")

# Run the async function
asyncio.run(main())

Flask एकीकरण

Flask वेब एप्लिकेशन में ईमेल सत्यापन एकीकृत करें:

from flask import Flask, request, jsonify
from functools import wraps
import os

from email_verification import EmailVerificationClient, ValidationError

app = Flask(__name__)

# Initialize client once
verification_client = EmailVerificationClient(
    api_key=os.getenv('EMAILVERIFY_API_KEY')
)

def verify_email_param(f):
    """Decorator to verify email parameter in requests."""
    @wraps(f)
    def decorated_function(*args, **kwargs):
        email = request.json.get('email') if request.is_json else request.form.get('email')

        if not email:
            return jsonify({"error": "Email is required"}), 400

        try:
            result = verification_client.verify(email)

            if not result.is_deliverable:
                return jsonify({
                    "error": "Please provide a valid email address",
                    "details": {
                        "is_disposable": result.is_disposable,
                        "risk_score": result.risk_score
                    }
                }), 400

            # Attach result to request for use in route
            request.email_verification = result

        except ValidationError as e:
            return jsonify({"error": str(e)}), 400
        except Exception as e:
            # Log error but don't block user
            app.logger.error(f"Email verification failed: {e}")

        return f(*args, **kwargs)

    return decorated_function


@app.route('/api/register', methods=['POST'])
@verify_email_param
def register():
    """User registration endpoint with email verification."""
    data = request.json

    # Email has been verified by decorator
    email = data.get('email')
    verification = getattr(request, 'email_verification', None)

    # Warn about disposable emails but allow
    warning = None
    if verification and verification.is_disposable:
        warning = "You're using a disposable email. Some features may be limited."

    # Create user (your implementation)
    user = create_user(email=email, **data)

    response = {"success": True, "user_id": user.id}
    if warning:
        response["warning"] = warning

    return jsonify(response), 201


@app.route('/api/verify-email', methods=['POST'])
def verify_email_endpoint():
    """Standalone email verification endpoint."""
    email = request.json.get('email')

    if not email:
        return jsonify({"error": "Email is required"}), 400

    try:
        result = verification_client.verify(email)

        return jsonify({
            "email": result.email,
            "is_valid": result.is_valid,
            "is_deliverable": result.is_deliverable,
            "is_disposable": result.is_disposable,
            "risk_score": result.risk_score,
            "safe_to_send": result.is_safe_to_send()
        })

    except ValidationError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": "Verification service unavailable"}), 503


if __name__ == '__main__':
    app.run(debug=True)

Django एकीकरण

Django एप्लिकेशन के लिए, एक पुन: उपयोग योग्य फ़ॉर्म वैलिडेटर और मिडलवेयर बनाएं:

# validators.py
from django.core.exceptions import ValidationError
from django.conf import settings

from email_verification import EmailVerificationClient

def get_verification_client():
    """Get or create verification client."""
    if not hasattr(get_verification_client, '_client'):
        get_verification_client._client = EmailVerificationClient(
            api_key=settings.EMAILVERIFY_API_KEY
        )
    return get_verification_client._client


def validate_email_deliverable(email: str) -> None:
    """
    Django validator to check email deliverability.

    Usage in forms:
        email = forms.EmailField(validators=[validate_email_deliverable])
    """
    client = get_verification_client()

    try:
        result = client.verify(email)

        if not result.is_valid:
            raise ValidationError("Please enter a valid email address.")

        if not result.is_deliverable:
            raise ValidationError(
                "This email address doesn't appear to exist. "
                "Please check for typos."
            )

        if result.is_disposable:
            raise ValidationError(
                "Please use a permanent email address, "
                "not a disposable one."
            )

    except ValidationError:
        raise
    except Exception as e:
        # Log but don't block on service errors
        import logging
        logging.error(f"Email verification failed: {e}")


# forms.py
from django import forms
from .validators import validate_email_deliverable

class RegistrationForm(forms.Form):
    email = forms.EmailField(
        validators=[validate_email_deliverable],
        help_text="We'll send a confirmation email to this address."
    )
    password = forms.CharField(widget=forms.PasswordInput)

    def clean_email(self):
        email = self.cleaned_data['email']
        # Additional cleaning if needed
        return email.lower().strip()


# middleware.py
from django.http import JsonResponse
from django.conf import settings

class EmailVerificationMiddleware:
    """
    Middleware to verify emails in API requests.

    Add to MIDDLEWARE setting:
        'myapp.middleware.EmailVerificationMiddleware',
    """

    VERIFICATION_PATHS = ['/api/register/', '/api/contact/']

    def __init__(self, get_response):
        self.get_response = get_response

    def __call__(self, request):
        # Check if path needs verification
        if request.path in self.VERIFICATION_PATHS and request.method == 'POST':
            import json

            try:
                data = json.loads(request.body)
                email = data.get('email')

                if email:
                    from .validators import get_verification_client
                    client = get_verification_client()
                    result = client.verify(email)

                    if not result.is_safe_to_send():
                        return JsonResponse({
                            'error': 'Invalid email address',
                            'details': {
                                'is_valid': result.is_valid,
                                'is_deliverable': result.is_deliverable,
                                'is_disposable': result.is_disposable
                            }
                        }, status=400)

                    # Attach to request for views
                    request.email_verification = result

            except (json.JSONDecodeError, Exception):
                pass

        return self.get_response(request)

प्रतिक्रिया कैशिंग

बुद्धिमान कैशिंग के साथ API कॉल को कम करें और प्रदर्शन में सुधार करें:

import hashlib
import json
import time
from typing import Optional
from functools import lru_cache

class CachedEmailVerificationClient(EmailVerificationClient):
    """
    Email verification client with response caching.
    """

    def __init__(
        self,
        api_key: str,
        cache_ttl: int = 86400,  # 24 hours default
        **kwargs
    ):
        super().__init__(api_key, **kwargs)
        self.cache_ttl = cache_ttl
        self._cache = {}

    def _cache_key(self, email: str) -> str:
        """Generate cache key from email."""
        normalized = email.lower().strip()
        return hashlib.md5(normalized.encode()).hexdigest()

    def verify(self, email: str, skip_cache: bool = False) -> VerificationResult:
        """
        Verify email with caching.

        Args:
            email: Email to verify
            skip_cache: Force fresh verification

        Returns:
            VerificationResult from cache or API
        """
        cache_key = self._cache_key(email)

        # Check cache
        if not skip_cache and cache_key in self._cache:
            cached_data, cached_time = self._cache[cache_key]

            if time.time() - cached_time < self.cache_ttl:
                return cached_data

        # Fetch from API
        result = super().verify(email)

        # Cache result
        self._cache[cache_key] = (result, time.time())

        return result

    def clear_cache(self):
        """Clear all cached results."""
        self._cache.clear()

    def remove_from_cache(self, email: str):
        """Remove specific email from cache."""
        cache_key = self._cache_key(email)
        self._cache.pop(cache_key, None)


# Redis-based caching for distributed systems
import redis

class RedisCachedClient(EmailVerificationClient):
    """Email verification client with Redis caching."""

    def __init__(
        self,
        api_key: str,
        redis_url: str = "redis://localhost:6379",
        cache_ttl: int = 86400,
        **kwargs
    ):
        super().__init__(api_key, **kwargs)
        self.redis = redis.from_url(redis_url)
        self.cache_ttl = cache_ttl
        self.cache_prefix = "email_verify:"

    def verify(self, email: str, skip_cache: bool = False) -> VerificationResult:
        """Verify with Redis caching."""
        cache_key = f"{self.cache_prefix}{email.lower().strip()}"

        # Check cache
        if not skip_cache:
            cached = self.redis.get(cache_key)
            if cached:
                data = json.loads(cached)
                return VerificationResult.from_dict(data)

        # Fetch from API
        result = super().verify(email)

        # Cache result
        self.redis.setex(
            cache_key,
            self.cache_ttl,
            json.dumps({
                'email': result.email,
                'is_valid': result.is_valid,
                'is_deliverable': result.is_deliverable,
                'is_disposable': result.is_disposable,
                'is_role_based': result.is_role_based,
                'is_catch_all': result.is_catch_all,
                'is_free_provider': result.is_free_provider,
                'risk_score': result.risk_score,
                'domain': result.domain,
                'mx_records': result.mx_records,
                'smtp_check': result.smtp_check,
                'verification_time_ms': result.verification_time_ms
            })
        )

        return result

अपने एकीकरण का परीक्षण

सुनिश्चित करें कि आपका एकीकरण सही ढंग से काम करता है, व्यापक परीक्षण लिखें:

import pytest
from unittest.mock import Mock, patch

from email_verification import EmailVerificationClient
from email_verification.models import VerificationResult
from email_verification.exceptions import AuthenticationError, RateLimitError


@pytest.fixture
def client():
    """Create test client."""
    return EmailVerificationClient(api_key="test_key")


@pytest.fixture
def mock_response():
    """Create mock API response."""
    return {
        "email": "test@example.com",
        "is_valid": True,
        "is_deliverable": True,
        "is_disposable": False,
        "is_role_based": False,
        "is_catch_all": False,
        "is_free_provider": False,
        "risk_score": 10,
        "domain": "example.com",
        "mx_records": ["mx.example.com"],
        "smtp_check": True,
        "verification_time_ms": 150
    }


class TestEmailVerificationClient:
    """Tests for EmailVerificationClient."""

    def test_verify_valid_email(self, client, mock_response):
        """Test successful email verification."""
        with patch.object(client.session, 'post') as mock_post:
            mock_post.return_value.status_code = 200
            mock_post.return_value.json.return_value = mock_response

            result = client.verify("test@example.com")

            assert result.is_valid is True
            assert result.is_deliverable is True
            assert result.risk_score == 10

    def test_verify_disposable_email(self, client):
        """Test detection of disposable email."""
        mock_data = {
            "email": "temp@mailinator.com",
            "is_valid": True,
            "is_deliverable": True,
            "is_disposable": True,
            "is_role_based": False,
            "is_catch_all": False,
            "is_free_provider": False,
            "risk_score": 80,
            "domain": "mailinator.com",
            "mx_records": [],
            "smtp_check": True,
            "verification_time_ms": 100
        }

        with patch.object(client.session, 'post') as mock_post:
            mock_post.return_value.status_code = 200
            mock_post.return_value.json.return_value = mock_data

            result = client.verify("temp@mailinator.com")

            assert result.is_disposable is True
            assert result.risk_score == 80
            assert result.is_safe_to_send() is False

    def test_authentication_error(self, client):
        """Test handling of authentication errors."""
        with patch.object(client.session, 'post') as mock_post:
            mock_post.return_value.status_code = 401

            with pytest.raises(AuthenticationError):
                client.verify("test@example.com")

    def test_rate_limit_handling(self, client, mock_response):
        """Test rate limit error handling."""
        with patch.object(client.session, 'post') as mock_post:
            mock_post.return_value.status_code = 429
            mock_post.return_value.headers = {'Retry-After': '30'}

            with pytest.raises(RateLimitError) as exc_info:
                client.verify("test@example.com")

            assert exc_info.value.retry_after == 30


class TestVerificationResult:
    """Tests for VerificationResult model."""

    def test_from_dict(self, mock_response):
        """Test creating result from dictionary."""
        result = VerificationResult.from_dict(mock_response)

        assert result.email == "test@example.com"
        assert result.is_valid is True
        assert result.domain == "example.com"

    def test_is_safe_to_send_valid(self, mock_response):
        """Test safe to send for valid email."""
        result = VerificationResult.from_dict(mock_response)
        assert result.is_safe_to_send() is True

    def test_is_safe_to_send_disposable(self, mock_response):
        """Test safe to send blocks disposable."""
        mock_response['is_disposable'] = True
        mock_response['risk_score'] = 80

        result = VerificationResult.from_dict(mock_response)
        assert result.is_safe_to_send() is False

    def test_is_safe_to_send_high_risk(self, mock_response):
        """Test safe to send blocks high risk."""
        mock_response['risk_score'] = 75

        result = VerificationResult.from_dict(mock_response)
        assert result.is_safe_to_send() is False

त्रुटि हैंडलिंग की सर्वोत्तम प्रथाएं

प्रोडक्शन विश्वसनीयता के लिए व्यापक त्रुटि हैंडलिंग लागू करें:

import logging
from typing import Optional, Callable
from functools import wraps

logger = logging.getLogger(__name__)

def with_verification_fallback(
    fallback_value: bool = True,
    log_errors: bool = True
):
    """
    Decorator to handle verification errors gracefully.

    Args:
        fallback_value: Value to return on error
        log_errors: Whether to log errors
    """
    def decorator(func: Callable):
        @wraps(func)
        def wrapper(*args, **kwargs):
            try:
                return func(*args, **kwargs)
            except AuthenticationError:
                logger.critical("Email verification API authentication failed")
                raise  # Re-raise auth errors
            except RateLimitError as e:
                if log_errors:
                    logger.warning(f"Rate limit exceeded, retry after {e.retry_after}s")
                return fallback_value
            except Exception as e:
                if log_errors:
                    logger.error(f"Email verification failed: {e}")
                return fallback_value
        return wrapper
    return decorator


class SafeEmailVerifier:
    """
    Wrapper that provides safe verification with fallbacks.
    """

    def __init__(
        self,
        client: EmailVerificationClient,
        strict_mode: bool = False,
        default_result: Optional[VerificationResult] = None
    ):
        self.client = client
        self.strict_mode = strict_mode
        self.default_result = default_result or self._create_default_result()

    def verify(self, email: str) -> VerificationResult:
        """
        Verify email with graceful error handling.

        In non-strict mode, returns a permissive default on errors.
        In strict mode, propagates errors.
        """
        try:
            return self.client.verify(email)
        except AuthenticationError:
            # Always propagate auth errors
            raise
        except (RateLimitError, Exception) as e:
            logger.error(f"Verification error for {email}: {e}")

            if self.strict_mode:
                raise

            # Return permissive default
            result = self._create_default_result()
            result.email = email
            return result

    def _create_default_result(self) -> VerificationResult:
        """Create a permissive default result."""
        return VerificationResult(
            email="",
            is_valid=True,
            is_deliverable=True,
            is_disposable=False,
            is_role_based=False,
            is_catch_all=False,
            is_free_provider=False,
            risk_score=0,
            domain="",
            mx_records=[],
            smtp_check=True,
            verification_time_ms=0
        )

मॉनिटरिंग और लॉगिंग

प्रोडक्शन डिप्लॉयमेंट के लिए उचित मॉनिटरिंग लागू करें:

import time
import logging
from dataclasses import dataclass, field
from typing import Dict, List
from datetime import datetime, timedelta
from collections import defaultdict

@dataclass
class VerificationMetrics:
    """Track verification metrics for monitoring."""

    total_verifications: int = 0
    successful_verifications: int = 0
    failed_verifications: int = 0
    total_response_time_ms: int = 0
    errors_by_type: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
    results_by_status: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
    hourly_counts: Dict[str, int] = field(default_factory=lambda: defaultdict(int))

    @property
    def success_rate(self) -> float:
        if self.total_verifications == 0:
            return 0.0
        return self.successful_verifications / self.total_verifications * 100

    @property
    def avg_response_time_ms(self) -> float:
        if self.successful_verifications == 0:
            return 0.0
        return self.total_response_time_ms / self.successful_verifications

    def record_success(self, result: VerificationResult):
        """Record a successful verification."""
        self.total_verifications += 1
        self.successful_verifications += 1
        self.total_response_time_ms += result.verification_time_ms

        # Track status distribution
        if result.is_deliverable:
            self.results_by_status['deliverable'] += 1
        if result.is_disposable:
            self.results_by_status['disposable'] += 1
        if result.is_catch_all:
            self.results_by_status['catch_all'] += 1

        # Track hourly usage
        hour_key = datetime.now().strftime('%Y-%m-%d-%H')
        self.hourly_counts[hour_key] += 1

    def record_error(self, error_type: str):
        """Record a verification error."""
        self.total_verifications += 1
        self.failed_verifications += 1
        self.errors_by_type[error_type] += 1

    def get_summary(self) -> dict:
        """Get metrics summary."""
        return {
            'total_verifications': self.total_verifications,
            'success_rate': f"{self.success_rate:.1f}%",
            'avg_response_time_ms': f"{self.avg_response_time_ms:.0f}",
            'errors_by_type': dict(self.errors_by_type),
            'results_distribution': dict(self.results_by_status)
        }


class MonitoredEmailVerificationClient(EmailVerificationClient):
    """Client with built-in monitoring and metrics."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.metrics = VerificationMetrics()
        self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")

    def verify(self, email: str) -> VerificationResult:
        """Verify with metrics tracking."""
        start_time = time.time()

        try:
            result = super().verify(email)
            self.metrics.record_success(result)

            # Log verification details
            self.logger.info(
                "email_verification",
                extra={
                    'email_domain': result.domain,
                    'is_valid': result.is_valid,
                    'is_deliverable': result.is_deliverable,
                    'is_disposable': result.is_disposable,
                    'risk_score': result.risk_score,
                    'response_time_ms': result.verification_time_ms
                }
            )

            return result

        except Exception as e:
            error_type = type(e).__name__
            self.metrics.record_error(error_type)

            self.logger.error(
                "email_verification_error",
                extra={
                    'email_domain': email.split('@')[-1] if '@' in email else 'unknown',
                    'error_type': error_type,
                    'error_message': str(e),
                    'duration_ms': int((time.time() - start_time) * 1000)
                }
            )

            raise

निष्कर्ष

Python के साथ BillionVerify ईमेल सत्यापन API को एकीकृत करना आपको किसी भी एप्लिकेशन में मजबूत ईमेल सत्यापन बनाने में सक्षम बनाता है। इस गाइड में पैटर्न और उदाहरण प्रोडक्शन उपयोग के लिए एक ठोस आधार प्रदान करते हैं।

सफल Python एकीकरण के लिए मुख्य बातें:

1. एक संरचित क्लाइंट क्लास का उपयोग करें विश्वसनीयता के लिए उचित त्रुटि हैंडलिंग, पुनः प्रयास तर्क और कनेक्शन पूलिंग के साथ

2. कैशिंग लागू करें बार-बार सत्यापित पतों के लिए API कॉल को कम करने और प्रतिक्रिया समय में सुधार करने के लिए

3. अपनी मात्रा के आधार पर सही प्रोसेसिंग रणनीति चुनें: छोटी सूचियों के लिए क्रमिक, मध्यम मात्रा के लिए समवर्ती थ्रेड्स, और बड़ी सूचियों के लिए बैच API

4. async/await का लाभ उठाएं उच्च-थ्रूपुट एप्लिकेशन के लिए aiohttp के साथ जिन्हें बहुत सारे ईमेल को जल्दी से सत्यापित करने की आवश्यकता है

5. अपने फ्रेमवर्क के साथ एकीकृत करें डेकोरेटर, वैलिडेटर या मिडलवेयर पैटर्न का उपयोग करके जो Django, Flask या अन्य Python फ्रेमवर्क के साथ स्वाभाविक रूप से फिट होते हैं

6. अपने सत्यापन उपयोग की निगरानी और माप करें पैटर्न को समझने और लागत को अनुकूलित करने के लिए मेट्रिक्स ट्रैकिंग के साथ

7. त्रुटियों को सुचारू रूप से संभालें ऐसे फॉलबैक के साथ जो उपयोगकर्ताओं को ब्लॉक नहीं करते जब सत्यापन सेवा अस्थायी रूप से अनुपलब्ध हो

बुनियादी क्लाइंट इम्प्लीमेंटेशन से शुरू करें और जैसे-जैसे आपकी आवश्यकताएं बढ़ें, क्रमिक रूप से कैशिंग, बैच प्रोसेसिंग और मॉनिटरिंग जैसी सुविधाओं को जोड़ें। मॉड्यूलर डिज़ाइन आपकी विशिष्ट आवश्यकताओं के लिए एकीकरण को कस्टमाइज़ करना आसान बनाता है।

प्रोडक्शन डिप्लॉयमेंट के लिए, हमेशा अपने अपेक्षित ईमेल पैटर्न और मात्रा के साथ अच्छी तरह से परीक्षण करें, डिबगिंग के लिए उचित लॉगिंग लागू करें, और प्रमाणीकरण विफलताओं या असामान्य त्रुटि दरों के लिए अलर्ट सेट अप करें।