To handle such spike, these marketplace uses distributed database designed to serve online transaction processing. But it is again limited to individual machine capacity of database storage engines to handle a spike in transactions.
One solution is effective use of caching and shared storage design to improve its scalability and apply machine learning methods to predict spike in transactions to emulate the workload and analyzing QPS (queries per second) performance in the performance testing.
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