1. **Twitter**: Twitter has published a small code snippet related to reducing the memory consumption of the Garbage Collector in Ruby on Rails, helping to improve the performance of their service. They identified a problem where large memory allocations were impacting performance and proposed adjustments to the code to fix this. ```ruby # Adjusting the GC to free memory less frequently GC::Profiler.enable GC::INTERNAL_CONSTANTS[:DEFAULT_MAX_HEAP_SIZE] = 1024 * 1024 * 128 GC.start(full_mark: false, immediate_sweep: false) ``` **Problem solved**: Reduced excessive memory usage in processes that were causing system slowdowns. 2. **GitHub**: In a technical post, GitHub detailed a race condition problem in their notification system, where messages were not being sent in the correct order. GitHub shared a small code snippet in Ruby, showing how they solved this using locks and additional checks. ```ruby class Notification def send_notification return unless acquire_lock # Process and send notification process_and_send release_lock end private def acquire_lock # Implement a lock to prevent race conditions Redis.current.setnx("lock:notification", Time.now) end def release_lock Redis.current.del("lock:notification") end end ``` **Problem solved**: Sending notifications out of order due to race conditions. 3. **LinkedIn**: LinkedIn published an article discussing a latency problem in their news feed services. They shared a code snippet about how they implemented a caching strategy using Redis to reduce response time by prioritizing the delivery of fresh and relevant data. ```py import redis cache = redis.StrictRedis(host='localhost', port=6379, db=0) def get_news_feed(user_id): # Try to get the feed from Redis cache cache_key = f"feed:{user_id}" feed = cache.get(cache_key) if feed is None: # If not in cache, fetch from database feed = fetch_feed_from_db(user_id) cache.set(cache_key, feed, ex=60*5) # Store feed for 5 minutes return feed ``` **Problem solved**: Reduced latency in the delivery of the news feed. 4. **Airbnb**: Airbnb's engineering team has addressed a slow rendering problem in its interactive maps. They published a JavaScript code snippet showing how they made optimizations to Leaflet.js, a map component, using data virtualization techniques to improve performance. ```js const map = L.map('map').setView([51.505, -0.09], 13); const tileLayer = L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', { maxZoom: 19 }).addTo(map); // Function to load markers efficiently function loadMarkers(bounds) { fetchMarkers(bounds).then(markers => { markers.forEach(marker => { L.marker([marker.lat, marker.lng]).addTo(map); }); }); } map.on('moveend', () => { const bounds = map.getBounds(); loadMarkers(bounds); // Load markers only within visible bounds }); ``` **Problem solved**: Improved performance when rendering interactive maps with lots of data. 5. Dropbox: Dropbox faced a duplicate upload problem on their web interface. They shared a small snippet of code that implements a hash check to ensure that duplicate files are not sent to the server, which helped save users bandwidth and time. ```py import hashlib def file_upload(file_data): # Generate a hash of the file to check for duplicates file_hash = hashlib.md5(file_data).hexdigest() if is_file_duplicate(file_hash): return "File has already been uploaded." # Proceed with the upload save_file_to_storage(file_data) store_file_hash(file_hash) return "Upload complete." def is_file_duplicate(file_hash): # Check if the file hash already exists in the database return db.exists(f"file_hash:{file_hash}") ``` **Problem solved**: Avoid duplicate file uploads, saving bandwidth and time. I bring this content here to help those who are starting out in programming to be aware that even large companies need to correct small pieces of code to improve the performance of their services.