Brief Announcement: A Key-Value Map For Massive Real-Time Analytics

PODC(2016)

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摘要
ABSTRACTModern big data processing platforms employ huge in-memory key-value (KV-) maps. Their applications simultaneously drive high-rate data ingestion and large-scale analytics. These two scenarios expect KV-map implementations that scale well with both real-time updates and massive atomic scans triggered by range queries. However, today's state-of-the art concurrent KV-maps fall short of satisfying these requirements -- they either provide only limited or non-atomic scans, or severely hamper updates when scans are ongoing. We present KiWi, the first atomic KV-map to efficiently support simultaneous massive data retrieval and real-time access. The key to achieving this is treating scans as first class citizens, whereas most existing concurrent KV-maps do not provide atomic scans, and some others add them to existing maps without rethinking the design anew.
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