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Accelerating CRUD with Chrono Dilation for Time-Series Storage Systems

DescriptionIn recent years, we have seen an un-precedented growth of data in our daily lives ranging from health data from an Apple Watch, financial stock price data, volatile crypto-currency data, to diagnostic data of nuclear/rocket simulations. The increase in high-precision, high-sample-rate time-series data is a challenge to existing database technologies. We have developed a novel technique that utilizes sparse-file support to achieve O(1) time complexity in create, read, update, and delete (CRUD) operations while supporting time granularity down to 1-second. We designed and implemented XStore to be lightweight and offer high performance without the need to maintain an index of the time-series data. We have conducted a detailed evaluation between XStore and existing best-of-breed systems such as MongoDB using synthetic data spanning 20 years, with second granularity, totaling over 5 billion datapoints. Through empirical experiments against MongoDB, XStore achieves 2.5X better latency and delivers up to 3X improvement in throughput.

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