Coinbase reveals how it uses ML to manage high traffic
Coinbase reveals how it uses ML to manage high traffic
Coinbase has effectively leveraged machine learning (ML) to address the challenges posed by unpredictable user activity on its platform, particularly during periods of high volatility in cryptocurrency markets. These spikes make it essential for the platform to scale its resources dynamically.
The nature of crypto markets means that traffic on Coinbase can surge unexpectedly. Traditional methods of scaling databases in response to traffic spikes often proved inadequate, as they were too slow to react to the sudden changes. Recognizing this, Coinbase has developed an automated scaling solution powered by ML, according to the exchange's article.
The new system predicts traffic surges before they happen, allowing the platform to scale its resources proactively rather than reactively to ensure seamless operation.
Database scaling approaches and challenges.
While many web services can scale horizontally (by adding more machines), this method poses specific challenges for databases. Horizontal scaling involves adding new nodes or replicas, but this process is slow due to the time it takes to recover data from them.
Given many limitations, Coinbase's solution needed to trigger scaling before traffic spikes, not after. Therefore, Coinbase turned to ML for a predictive approach.
Machine learning model for predictive scaling.
The core of Coinbase's solution is a classification model that predicts whether traffic will exceed a certain threshold within the next few hours. This model integrates data on current platform load and external factors like cryptocurrency price fluctuations, which are known to correlate with traffic spikes. The model's calculations indicate how much traffic the infrastructure should be able to handle at any given time.
Implementation and benefits.
The scale target is monitored by an Auto-Scaler module, which adjusts the database capacity as needed. If the predicted traffic exceeds the current scale target, the system scales up preemptively. If the traffic remains low, the system scales down to conserve resources.
This approach not only ensures that the platform can handle traffic spikes without downtime but also optimizes infrastructure costs by avoiding unnecessary over-provisioning.
The new solution positions Coinbase to better manage its resources while maintaining a robust and responsive service for users.
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