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
andauthor_email
committer_name
andcommitter_email
authored_date
andcommitted_date
Key Features:
- No
x-total
orx-total-pages
headers for performance reasons GitLab2 - Supports pagination with
page
andper_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 nameauthor.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 branchesgrep -E -o
: Extract email addresses using regexawk '{print tolower($0)}'
: Convert to lowercasesort | uniq
: Remove duplicatesgrep -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 hourx-ratelimit-remaining
: Remaining requestsx-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
- Obtain explicit consent before using extracted emails
- Respect robots.txt and terms of service
- Implement proper data protection measures
- Provide clear opt-out mechanisms
- Limit data retention to necessary periods
- 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
andauthor_email
committer_name
andcommitter_email
authored_date
andcommitted_date
Key Features:
- No
x-total
orx-total-pages
headers for performance reasons GitLab2 - Supports pagination with
page
andper_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 nameauthor.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 branchesgrep -E -o
: Extract email addresses using regexawk '{print tolower($0)}'
: Convert to lowercasesort | uniq
: Remove duplicatesgrep -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 hourx-ratelimit-remaining
: Remaining requestsx-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
- Obtain explicit consent before using extracted emails
- Respect robots.txt and terms of service
- Implement proper data protection measures
- Provide clear opt-out mechanisms
- Limit data retention to necessary periods
- 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.