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Instagram Video Downloader Lambda

A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.

Features

  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos

Prerequisites

  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials

Environment Variables

Create a .env file with the following variables:

SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576

Installation

  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt

Project Structure

  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)

Usage

Deploying the Lambda Function

  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda

Triggering Video Downloads

Run the batch processor to send videos to the queue:

python trigger_sqs_batches.py

CloudWatch Metrics

The following metrics are available in CloudWatch under the namespace 'InstagramCollector':

  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages

Error Handling

The system implements comprehensive error handling:

  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

Support

For issues and feature requests, please create an issue in the repository.

Maintained by Oriane XYZ

About

No description, website, or topics provided.

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Instagram Video Downloader Lambda
A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.
Features
  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos
Prerequisites
  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials
Environment Variables
Create a .env file with the following variables:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576
Installation
  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
Project Structure
  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)
Usage
Deploying the Lambda Function
  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda
Triggering Video Downloads
Run the batch processor to send videos to the queue:
python trigger_sqs_batches.py
CloudWatch Metrics
The following metrics are available in CloudWatch under the namespace 'InstagramCollector':
  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages
Error Handling
The system implements comprehensive error handling:
  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Instagram Video Downloader Lambda
A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.
Features
  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos
Prerequisites
  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials
Environment Variables
Create a .env file with the following variables:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576
Installation
  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
Project Structure
  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)
Usage
Deploying the Lambda Function
  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda
Triggering Video Downloads
Run the batch processor to send videos to the queue:
python trigger_sqs_batches.py
CloudWatch Metrics
The following metrics are available in CloudWatch under the namespace 'InstagramCollector':
  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages
Error Handling
The system implements comprehensive error handling:
  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Instagram Video Downloader Lambda
A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.
Features
  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos
Prerequisites
  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials
Environment Variables
Create a .env file with the following variables:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576
Installation
  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
Project Structure
  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)
Usage
Deploying the Lambda Function
  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda
Triggering Video Downloads
Run the batch processor to send videos to the queue:
python trigger_sqs_batches.py
CloudWatch Metrics
The following metrics are available in CloudWatch under the namespace 'InstagramCollector':
  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages
Error Handling
The system implements comprehensive error handling:
  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Instagram Video Downloader Lambda
A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.
Features
  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos
Prerequisites
  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials
Environment Variables
Create a .env file with the following variables:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576
Installation
  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
Project Structure
  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)
Usage
Deploying the Lambda Function
  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda
Triggering Video Downloads
Run the batch processor to send videos to the queue:
python trigger_sqs_batches.py
CloudWatch Metrics
The following metrics are available in CloudWatch under the namespace 'InstagramCollector':
  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages
Error Handling
The system implements comprehensive error handling:
  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Instagram Video Downloader Lambda
A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.
Features
  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos
Prerequisites
  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials
Environment Variables
Create a .env file with the following variables:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576
Installation
  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
Project Structure
  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)
Usage
Deploying the Lambda Function
  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda
Triggering Video Downloads
Run the batch processor to send videos to the queue:
python trigger_sqs_batches.py
CloudWatch Metrics
The following metrics are available in CloudWatch under the namespace 'InstagramCollector':
  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages
Error Handling
The system implements comprehensive error handling:
  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Instagram Video Downloader Lambda
A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.
Features
  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos
Prerequisites
  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials
Environment Variables
Create a .env file with the following variables:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576
Installation
  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
Project Structure
  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)
Usage
Deploying the Lambda Function
  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda
Triggering Video Downloads
Run the batch processor to send videos to the queue:
python trigger_sqs_batches.py
CloudWatch Metrics
The following metrics are available in CloudWatch under the namespace 'InstagramCollector':
  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages
Error Handling
The system implements comprehensive error handling:
  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Instagram Video Downloader Lambda
A serverless AWS Lambda function that downloads Instagram videos and stores them in S3. The project uses Supabase for metadata management and AWS SQS for message queuing.
Features
  • Downloads Instagram videos at specified resolutions
  • Stores videos in AWS S3
  • Uses Supabase for metadata management
  • Implements AWS SQS for message queuing
  • Includes CloudWatch metrics for monitoring
  • Handles retries and error cases
  • Supports batch processing of videos
Prerequisites
  • AWS Account with appropriate permissions
  • Supabase account and project
  • Python 3.10 or higher
  • AWS CLI configured with appropriate credentials
Environment Variables
Create a .env file with the following variables:
SUPABASE_URL=your_supabase_urlSUPABASE_KEY=your_supabase_keyS3_BUCKET=your_s3_bucket_nameMAX_RETRIES=3DOWNLOAD_TIMEOUT=300CHUNK_SIZE=1048576
Installation
  1. Clone the repository:
git clone <repository-url>cd InstagramVideoDownloader-lambda
  1. Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
Project Structure
  • lambda_function.py: Main Lambda function code
  • trigger_sqs_batches.py: Script to trigger SQS message batches
  • scripts/pack_lambda.sh: Script to package Lambda function
  • requirements.txt: Python dependencies
  • .env: Environment variables (not committed to git)
Usage
Deploying the Lambda Function
  1. Package the Lambda function:
./scripts/pack_lambda.sh
  1. Upload the lambda_function.zip to AWS Lambda
Triggering Video Downloads
Run the batch processor to send videos to the queue:
python trigger_sqs_batches.py
CloudWatch Metrics
The following metrics are available in CloudWatch under the namespace 'InstagramCollector':
  • ProcessingStarted: Number of videos processing started
  • ProcessingSuccess: Number of successfully processed videos
  • ProcessingFailed: Number of failed video processing attempts
  • DownloadTime: Time taken to download videos
  • UploadTime: Time taken to upload videos to S3
  • TotalProcessingTime: Total processing time per video
  • SupabaseVideoFound: Number of videos found in Supabase
  • SupabaseVideoNotFound: Number of videos not found in Supabase
  • SupabaseError: Number of Supabase errors
  • DownloadTimeout: Number of download timeouts
  • DownloadError: Number of download errors
  • S3UploadError: Number of S3 upload errors
  • InvalidInput: Number of invalid input messages
Error Handling
The system implements comprehensive error handling:
  • Retries for failed downloads
  • Error logging to CloudWatch
  • Metrics tracking for various failure scenarios
  • Graceful cleanup of temporary files
Contributing
  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request
Support
For issues and feature requests, please create an issue in the repository.
Maintained by Oriane XYZ
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages