How Instagram Optimized Video Storage to Prevent Server Crash
Instagram faced the challenge of storing billions of videos and preventing server crashes. By optimizing video storage through innovative compression techniques, the platform reduced computing power usage by 94%, ensuring seamless performance for its 2 billion users.
The Challenge of Storing Billions of Videos
Instagram, with its 2 billion monthly active users, faced a daunting challenge: storing an overwhelming number of videos and photos uploaded every second. The platform projected that within 12 months, their servers would crash due to insufficient computing capacity for video uploads. This situation demanded an urgent and innovative solution.
The Strategy: Optimize Rather Than Expand
Instagram’s approach was to optimize existing servers instead of investing in new data centers, which would be a costly affair. The focus shifted to finding ways to save computing power during video uploads.
The Process: Understanding Video Compression
Instagram’s servers generate multiple versions of each uploaded video to accommodate different viewing conditions, such as poor internet connections or thumbnail views. This process involves two types of video codecs: H264 and AV1. H264, a widely used codec since 2003, compresses videos by analyzing and removing redundant frame information. AV1, a newer and more efficient codec, offers 30% smaller file sizes with better quality and is open source and royalty-free.
The Discovery: A Significant Oversight
Upon examining the video upload process, Instagram engineers discovered that the settings for the two minimum functionality encodings (basic adaptive bitrate and progressive encodings) were very similar. This led to a creative idea: repackaging the progressive encodings into a file structure that can handle higher bit rates. This would avoid doing the same work twice, saving time.
The Solution: A Clever Repackaging Technique
By repackaging the progressive encodings, Instagram significantly reduced the time and resources needed for video processing. The new method took only 0.36 seconds, compared to the previous 86 seconds for a 23-second 720p video. This approach allowed for more efficient use of resources, especially for the advanced AV1 encoding.
The Impact: Massive Savings and Lessons Learned
This optimization led to a 94% reduction in computing power. It highlighted the importance of continuous innovation, even in areas that seem “good enough.” The solution also demonstrated that overlooking certain aspects of complex systems is not necessarily a sign of bad engineering but rather an opportunity for significant improvements.
Conclusion
Instagram’s case study serves as an inspiring example of how a thoughtful approach to problem-solving and a willingness to re-examine existing processes can lead to substantial improvements in efficiency and performance. This story is a testament to the power of software engineering and its potential to drive innovation in the face of insurmountable challenges.