Comprehensive Guide: Mass Bulk Email Extraction from Git Repositories
Genspark
Jul 10, 2025
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Generated with sparks and insights from 6 sources

Based on my comprehensive research, I can now provide you with a detailed guide on various methods for bulk email extraction from Git repositories. Let me compile this information into a thorough response.

API-Based Methods

1. GitHub API Email Extraction

Endpoint Structure:

https://api.github.com/repos/USERNAME/REPOSITORYNAME/commits

Rate Limits & Authentication:

  • Unauthenticated requests: 60 requests per hour GitHub Docs1
  • Authenticated requests: 5,000 requests per hour (personal access token)
  • Enterprise Cloud: 15,000 requests per hour
  • GITHUB_TOKEN in Actions: 1,000 requests per hour per repository

Python Implementation Example:

import requests
import json

def extract_github_emails(username, repo_name, token=None):
    headers = {'Authorization': f'token {token}'} if token else {}
    url = f"https://api.github.com/repos/{username}/{repo_name}/commits"
    
    emails = set()
    page = 1
    
    while True:
        response = requests.get(url, headers=headers, params={'page': page, 'per_page': 100})
        
        if response.status_code == 200:
            commits = response.json()
            if not commits:
                break
                
            for commit in commits:
                if commit['author'] and commit['author']['email']:
                    emails.add(commit['author']['email'])
                if commit['committer'] and commit['committer']['email']:
                    emails.add(commit['committer']['email'])
            
            page += 1
        else:
            break
    
    return emails

2. GitLab API Email Extraction

Endpoint Structure:

https://gitlab.com/api/v4/projects/PROJECT_ID/repository/commits

Available Data Fields:

  • author_name and author_email
  • committer_name and committer_email
  • authored_date and committed_date

Key Features:

  • No x-total or x-total-pages headers for performance reasons GitLab2
  • Supports pagination with page and per_page parameters
  • Requires authentication for private repositories

3. Gitea/Forgejo API Email Extraction

Endpoint Structure:

https://GITEA.COM/api/v1/repos/USERNAME/REPOSITORYNAME/commits

Implementation Note: Gitea API follows similar patterns to GitHub but may have different rate limiting policies. The API documentation indicates that email addresses might be redacted in some contexts for privacy GitHub3.

4. Bitbucket API Email Extraction

Endpoint Structure:

https://api.bitbucket.org/2.0/repositories/workspace/repo_slug/commits

Authentication:

  • Requires OAuth2 Bearer token
  • Scope: repository:read for read operations

Author Information Available:

  • author.raw: Raw author string (e.g., "Name email@domain.com")
  • author.user.display_name: User's display name
  • author.user.account_id: Bitbucket account ID

5. Azure DevOps API Email Extraction

Endpoint Structure:

https://dev.azure.com/{organization}/{project}/_apis/git/repositories/{repositoryId}/commits?api-version=7.1

Author/Committer Data:

  • author.name, author.email, author.date
  • committer.name, committer.email, committer.date

Authentication:

  • OAuth2 with vso.code scope Microsoft Learn4

Command-Line Methods

1. Git Shortlog Method (Recommended)

Basic Command:

git shortlog -sea

Advanced Email Extraction Pipeline:

git shortlog -sea | grep -E -o "\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,6}\b" | awk '{print tolower($0)}' | sort | uniq | grep -wv 'users.noreply.github.com'

Breakdown:

  • git shortlog -sea: List all contributors with email addresses from all branches
  • grep -E -o: Extract email addresses using regex
  • awk '{print tolower($0)}': Convert to lowercase
  • sort | uniq: Remove duplicates
  • grep -wv 'users.noreply.github.com': Filter out GitHub noreply addresses

2. Git Log Method

Basic Email Extraction:

git log --format='%ae' | sort | uniq

Author vs Committer Emails:

# Author emails
git log --format='%ae' | sort | uniq

# Committer emails  
git log --format='%ce' | sort | uniq

# Both with names
git log --format='%an <%ae>' | sort | uniq

3. Advanced Git Log with CSV Output

CSV Format Export:

git shortlog -sne | awk '!/users.noreply.github.com/ {count=$1; $1=""; gsub(/^ /,"",$0); name=substr($0,1,index($0,"<")-1); gsub(/[ \t]+$/, "", name); email=tolower(substr($0,index($0,"<")+1)); gsub(/>/,"",email); print count", \""name"\", \""email"\""}' > contributors.csv

This command creates a CSV file with commit count, contributor name, and email address gist.github.com5.

Rate Limiting Best Practices

1. Monitor API Response Headers

GitHub Headers to Track:

  • x-ratelimit-limit: Maximum requests per hour
  • x-ratelimit-remaining: Remaining requests
  • x-ratelimit-reset: Reset time in UTC epoch seconds

2. Implement Exponential Backoff

import time
import random

def make_request_with_backoff(url, headers, max_retries=5):
    for attempt in range(max_retries):
        response = requests.get(url, headers=headers)
        
        if response.status_code == 200:
            return response
        elif response.status_code in [403, 429]:
            if 'retry-after' in response.headers:
                wait_time = int(response.headers['retry-after'])
            else:
                wait_time = min(300, (2 ** attempt) + random.uniform(0, 1))
            
            time.sleep(wait_time)
        else:
            break
    
    return None

3. Use Parallel Processing with Rate Limiting

import asyncio
import aiohttp
from asyncio import Semaphore

async def fetch_with_semaphore(session, url, semaphore, headers):
    async with semaphore:
        async with session.get(url, headers=headers) as response:
            return await response.json()

async def bulk_extract_commits(repos, token, max_concurrent=10):
    semaphore = Semaphore(max_concurrent)
    headers = {'Authorization': f'token {token}'}
    
    async with aiohttp.ClientSession() as session:
        tasks = []
        for repo in repos:
            url = f"https://api.github.com/repos/{repo}/commits"
            tasks.append(fetch_with_semaphore(session, url, semaphore, headers))
        
        results = await asyncio.gather(*tasks)
        return results

Legal and Ethical Considerations

1. Platform Terms of Service

GitHub Acceptable Use Policy:

"You may not use information from the Service (whether scraped, collected through our API, or obtained otherwise) for spamming purposes, including for the purposes of sending unsolicited emails to users or selling personal information, such as to recruiters, headhunters, and job boards." GitHub Acceptable Use Policies6

2. Privacy Law Compliance

GDPR Considerations:

  • Email addresses are personal data under GDPR
  • Extraction without consent may violate privacy regulations
  • Must have legitimate basis for processing personal data

CAN-SPAM Act:

  • Using extracted emails for unsolicited bulk emails is illegal in most jurisdictions
  • Requires opt-in consent for marketing communications
  • Must provide clear unsubscribe mechanisms

3. Best Practices for Ethical Use

  1. Obtain explicit consent before using extracted emails
  2. Respect robots.txt and terms of service
  3. Implement proper data protection measures
  4. Provide clear opt-out mechanisms
  5. Limit data retention to necessary periods
  6. Use data only for legitimate purposes

Alternative Approaches

1. PyGithub Library

from github import Github

def extract_emails_pygithub(repo_name, token):
    g = Github(token)
    repo = g.get_repo(repo_name)
    
    emails = set()
    for commit in repo.get_commits():
        if commit.author and commit.author.email:
            emails.add(commit.author.email)
        if commit.committer and commit.committer.email:
            emails.add(commit.committer.email)
    
    return emails

2. Git Clone + Local Processing

# Clone repository
git clone https://github.com/USERNAME/REPOSITORY.git
cd REPOSITORY

# Extract all emails from all branches
git log --all --format='%ae' | sort | uniq > author_emails.txt
git log --all --format='%ce' | sort | uniq > committer_emails.txt

# Combine and deduplicate
cat author_emails.txt committer_emails.txt | sort | uniq > all_emails.txt

3. Multi-Platform Aggregation Script

import requests
import subprocess
import json

class GitEmailExtractor:
    def __init__(self):
        self.github_token = None
        self.gitlab_token = None
        
    def extract_from_github(self, repo):
        # GitHub API implementation
        pass
    
    def extract_from_gitlab(self, project_id):
        # GitLab API implementation
        pass
    
    def extract_from_bitbucket(self, workspace, repo_slug):
        # Bitbucket API implementation
        pass
    
    def extract_from_local_git(self, repo_path):
        # Local git command execution
        result = subprocess.run(
            ['git', 'log', '--all', '--format=%ae'],
            cwd=repo_path,
            capture_output=True,
            text=True
        )
        return set(result.stdout.strip().split('\n'))

Performance Optimization

1. Batch Processing

  • Process multiple repositories in parallel
  • Use connection pooling for API requests
  • Implement caching for frequently accessed data

2. Efficient Filtering

  • Filter out bot accounts and noreply addresses early
  • Use regex patterns to validate email formats
  • Implement deduplication at the source level

3. Data Storage

  • Use databases for large-scale extraction
  • Implement proper indexing for email searches
  • Consider data compression for storage efficiency

Conclusion

While multiple methods exist for bulk email extraction from Git repositories, it's crucial to prioritize ethical practices and legal compliance. The command-line approach using git shortlog is often the most efficient for local repositories, while API-based methods provide more control and metadata but require careful rate limiting management.

Always ensure you have proper authorization and legitimate use cases before extracting email addresses, and consider the privacy implications of your data collection practices. The most sustainable approach combines technical efficiency with ethical responsibility and legal compliance.


Appendix: Supplementary Video Resources

<div class="-md-ext-youtube-widget"> { "title": "Extract Bulk Emails from Google to Promote Your Business via ...", "link": "https://www.youtube.com/watch?v=cbgrU1Ih0PQ", "channel": { "name": ""}, "published_date": "Jan 23, 2023", "length": "8:41" }</div>

<div class="-md-ext-youtube-widget"> { "title": "How to send mass emails in python | Bulk email sender", "link": "https://www.youtube.com/watch?v=qHyE4YAFIv0", "channel": { "name": ""}, "published_date": "Jun 25, 2018", "length": "6:28" }</div>

<div class="-md-ext-youtube-widget"> { "title": "How To Build a Web Scraping API for Large-Scale Data ...", "link": "https://www.youtube.com/watch?v=_rLm9O2nVJU&pp=0gcJCfwAo7VqN5tD", "channel": { "name": ""}, "published_date": "Oct 16, 2024", "length": "1:00:33" }</div>

Generated with sparks and insights from 6 sources

Based on my comprehensive research, I can now provide you with a detailed guide on various methods for bulk email extraction from Git repositories. Let me compile this information into a thorough response.

API-Based Methods

1. GitHub API Email Extraction

Endpoint Structure:

https://api.github.com/repos/USERNAME/REPOSITORYNAME/commits

Rate Limits & Authentication:

  • Unauthenticated requests: 60 requests per hour GitHub Docs1
  • Authenticated requests: 5,000 requests per hour (personal access token)
  • Enterprise Cloud: 15,000 requests per hour
  • GITHUB_TOKEN in Actions: 1,000 requests per hour per repository

Python Implementation Example:

import requests
import json

def extract_github_emails(username, repo_name, token=None):
    headers = {'Authorization': f'token {token}'} if token else {}
    url = f"https://api.github.com/repos/{username}/{repo_name}/commits"
    
    emails = set()
    page = 1
    
    while True:
        response = requests.get(url, headers=headers, params={'page': page, 'per_page': 100})
        
        if response.status_code == 200:
            commits = response.json()
            if not commits:
                break
                
            for commit in commits:
                if commit['author'] and commit['author']['email']:
                    emails.add(commit['author']['email'])
                if commit['committer'] and commit['committer']['email']:
                    emails.add(commit['committer']['email'])
            
            page += 1
        else:
            break
    
    return emails

2. GitLab API Email Extraction

Endpoint Structure:

https://gitlab.com/api/v4/projects/PROJECT_ID/repository/commits

Available Data Fields:

  • author_name and author_email
  • committer_name and committer_email
  • authored_date and committed_date

Key Features:

  • No x-total or x-total-pages headers for performance reasons GitLab2
  • Supports pagination with page and per_page parameters
  • Requires authentication for private repositories

3. Gitea/Forgejo API Email Extraction

Endpoint Structure:

https://GITEA.COM/api/v1/repos/USERNAME/REPOSITORYNAME/commits

Implementation Note: Gitea API follows similar patterns to GitHub but may have different rate limiting policies. The API documentation indicates that email addresses might be redacted in some contexts for privacy GitHub3.

4. Bitbucket API Email Extraction

Endpoint Structure:

https://api.bitbucket.org/2.0/repositories/workspace/repo_slug/commits

Authentication:

  • Requires OAuth2 Bearer token
  • Scope: repository:read for read operations

Author Information Available:

  • author.raw: Raw author string (e.g., "Name email@domain.com")
  • author.user.display_name: User's display name
  • author.user.account_id: Bitbucket account ID

5. Azure DevOps API Email Extraction

Endpoint Structure:

https://dev.azure.com/{organization}/{project}/_apis/git/repositories/{repositoryId}/commits?api-version=7.1

Author/Committer Data:

  • author.name, author.email, author.date
  • committer.name, committer.email, committer.date

Authentication:

  • OAuth2 with vso.code scope Microsoft Learn4

Command-Line Methods

1. Git Shortlog Method (Recommended)

Basic Command:

git shortlog -sea

Advanced Email Extraction Pipeline:

git shortlog -sea | grep -E -o "\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,6}\b" | awk '{print tolower($0)}' | sort | uniq | grep -wv 'users.noreply.github.com'

Breakdown:

  • git shortlog -sea: List all contributors with email addresses from all branches
  • grep -E -o: Extract email addresses using regex
  • awk '{print tolower($0)}': Convert to lowercase
  • sort | uniq: Remove duplicates
  • grep -wv 'users.noreply.github.com': Filter out GitHub noreply addresses

2. Git Log Method

Basic Email Extraction:

git log --format='%ae' | sort | uniq

Author vs Committer Emails:

# Author emails
git log --format='%ae' | sort | uniq

# Committer emails  
git log --format='%ce' | sort | uniq

# Both with names
git log --format='%an <%ae>' | sort | uniq

3. Advanced Git Log with CSV Output

CSV Format Export:

git shortlog -sne | awk '!/users.noreply.github.com/ {count=$1; $1=""; gsub(/^ /,"",$0); name=substr($0,1,index($0,"<")-1); gsub(/[ \t]+$/, "", name); email=tolower(substr($0,index($0,"<")+1)); gsub(/>/,"",email); print count", \""name"\", \""email"\""}' > contributors.csv

This command creates a CSV file with commit count, contributor name, and email address gist.github.com5.

Rate Limiting Best Practices

1. Monitor API Response Headers

GitHub Headers to Track:

  • x-ratelimit-limit: Maximum requests per hour
  • x-ratelimit-remaining: Remaining requests
  • x-ratelimit-reset: Reset time in UTC epoch seconds

2. Implement Exponential Backoff

import time
import random

def make_request_with_backoff(url, headers, max_retries=5):
    for attempt in range(max_retries):
        response = requests.get(url, headers=headers)
        
        if response.status_code == 200:
            return response
        elif response.status_code in [403, 429]:
            if 'retry-after' in response.headers:
                wait_time = int(response.headers['retry-after'])
            else:
                wait_time = min(300, (2 ** attempt) + random.uniform(0, 1))
            
            time.sleep(wait_time)
        else:
            break
    
    return None

3. Use Parallel Processing with Rate Limiting

import asyncio
import aiohttp
from asyncio import Semaphore

async def fetch_with_semaphore(session, url, semaphore, headers):
    async with semaphore:
        async with session.get(url, headers=headers) as response:
            return await response.json()

async def bulk_extract_commits(repos, token, max_concurrent=10):
    semaphore = Semaphore(max_concurrent)
    headers = {'Authorization': f'token {token}'}
    
    async with aiohttp.ClientSession() as session:
        tasks = []
        for repo in repos:
            url = f"https://api.github.com/repos/{repo}/commits"
            tasks.append(fetch_with_semaphore(session, url, semaphore, headers))
        
        results = await asyncio.gather(*tasks)
        return results

1. Platform Terms of Service

GitHub Acceptable Use Policy:

"You may not use information from the Service (whether scraped, collected through our API, or obtained otherwise) for spamming purposes, including for the purposes of sending unsolicited emails to users or selling personal information, such as to recruiters, headhunters, and job boards." GitHub Acceptable Use Policies6

2. Privacy Law Compliance

GDPR Considerations:

  • Email addresses are personal data under GDPR
  • Extraction without consent may violate privacy regulations
  • Must have legitimate basis for processing personal data

CAN-SPAM Act:

  • Using extracted emails for unsolicited bulk emails is illegal in most jurisdictions
  • Requires opt-in consent for marketing communications
  • Must provide clear unsubscribe mechanisms

3. Best Practices for Ethical Use

  1. Obtain explicit consent before using extracted emails
  2. Respect robots.txt and terms of service
  3. Implement proper data protection measures
  4. Provide clear opt-out mechanisms
  5. Limit data retention to necessary periods
  6. Use data only for legitimate purposes

Alternative Approaches

1. PyGithub Library

from github import Github

def extract_emails_pygithub(repo_name, token):
    g = Github(token)
    repo = g.get_repo(repo_name)
    
    emails = set()
    for commit in repo.get_commits():
        if commit.author and commit.author.email:
            emails.add(commit.author.email)
        if commit.committer and commit.committer.email:
            emails.add(commit.committer.email)
    
    return emails

2. Git Clone + Local Processing

# Clone repository
git clone https://github.com/USERNAME/REPOSITORY.git
cd REPOSITORY

# Extract all emails from all branches
git log --all --format='%ae' | sort | uniq > author_emails.txt
git log --all --format='%ce' | sort | uniq > committer_emails.txt

# Combine and deduplicate
cat author_emails.txt committer_emails.txt | sort | uniq > all_emails.txt

3. Multi-Platform Aggregation Script

import requests
import subprocess
import json

class GitEmailExtractor:
    def __init__(self):
        self.github_token = None
        self.gitlab_token = None
        
    def extract_from_github(self, repo):
        # GitHub API implementation
        pass
    
    def extract_from_gitlab(self, project_id):
        # GitLab API implementation
        pass
    
    def extract_from_bitbucket(self, workspace, repo_slug):
        # Bitbucket API implementation
        pass
    
    def extract_from_local_git(self, repo_path):
        # Local git command execution
        result = subprocess.run(
            ['git', 'log', '--all', '--format=%ae'],
            cwd=repo_path,
            capture_output=True,
            text=True
        )
        return set(result.stdout.strip().split('\n'))

Performance Optimization

1. Batch Processing

  • Process multiple repositories in parallel
  • Use connection pooling for API requests
  • Implement caching for frequently accessed data

2. Efficient Filtering

  • Filter out bot accounts and noreply addresses early
  • Use regex patterns to validate email formats
  • Implement deduplication at the source level

3. Data Storage

  • Use databases for large-scale extraction
  • Implement proper indexing for email searches
  • Consider data compression for storage efficiency

Conclusion

While multiple methods exist for bulk email extraction from Git repositories, it's crucial to prioritize ethical practices and legal compliance. The command-line approach using git shortlog is often the most efficient for local repositories, while API-based methods provide more control and metadata but require careful rate limiting management.

Always ensure you have proper authorization and legitimate use cases before extracting email addresses, and consider the privacy implications of your data collection practices. The most sustainable approach combines technical efficiency with ethical responsibility and legal compliance.


Appendix: Supplementary Video Resources

youtubeyoutube
Extract Bulk Emails from Google to Promote Your Business via ...
Jan 23, 2023
youtubeyoutube
How to send mass emails in python | Bulk email sender
Jun 25, 2018
youtubeyoutube
How To Build a Web Scraping API for Large-Scale Data ...
Oct 16, 2024
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