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Finding lines of code similar to a code fragment across large knowledge bases in fractions of a second is a new branch of code clone research also known as real-time code clone search. Among the requirements real-time code clone search has to meet are scalability, short response time, scalable incremental corpus updates, and support for type-1, type-2, and type-3 clones. We conducted a set of empirical studies on a large open source code corpus to gain insight about its characteristics. We used these results to design and optimize a multi-level indexing approach using hash table-based and binary search to improve Internet-scale real-time code clone search response time. Finally, we performed an evaluation on an Internet-scale corpus (1.5 million Java files and 266 MLOC). Our approach maintains a response time for 99.9% of clone searches in the microseconds range, while supporting the aforementioned requirements.