𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐭 𝐔𝐛𝐞𝐫: 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲𝐢𝐧𝐠 𝐭𝐡𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱
Did you know that even a basic feature like counting a driver's completed jobs at Uber involves complex scalability solutions? Here’s how Uber engineers tackle such challenges at scale.
For Uber, counting jobs isn't just a number—it's an operation that must be executed amidst handling millions of transactions daily. The scale demands robust, scalable, and responsive solutions.
Uber uses Apache Pinot, an advanced OLAP datastore, Uber manages both real-time and historical data efficiently. However, scaling this system meant overcoming several obstacles:
𝐂𝐚𝐩𝐚𝐜𝐢𝐭𝐲 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Estimating storage and compute power required innovative approaches due to unpredictable load patterns.
𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐢𝐧𝐠 𝐐𝐮𝐞𝐫𝐲 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Techniques like Inverted Indices and Bloom Filters were implemented to enhance search efficiency and reduce load.
𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐒𝐩𝐢𝐤𝐞𝐬: Jitter was introduced in request timings to manage and smooth out peak loads, ensuring system stability.
This goes beyond just job counting. It’s about building resilient systems that enhance user and partner experiences at scale.
This is a perfect example of the behind-the-scenes engineering feats that make simple features work seamlessly for millions of users
Source: https://lnkd.in/gBWJSM4m
𝐅𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭, 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐚𝐧𝐝 𝐟𝐨𝐥𝐥𝐨𝐰! Go Careers
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