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DTSTAMP:20250626T234540Z
LOCATION:B309
DTSTART;TZID=America/New_York:20241117T122500
DTEND;TZID=America/New_York:20241117T123000
UID:submissions.supercomputing.org_SC24_sess736_ws_pdswwip115@linklings.co
 m
SUMMARY:Are Streaming Engines and Vector Databases Integrated Well?
DESCRIPTION:Yeonwoo Jeong and Sungyong Park (Sogang University, South Kore
 a)\n\nRecently, streaming processing engines (SPEs) support plugin-based c
 onnector to streamline online indexing and online query serving. Users can
  seamlessly integrate the online indexing/serving stack with a few code mo
 difications. SPEs transform in-flight massive datasets to high-dimensional
  vector embeddings and delegates to store the embeddings on the vector dat
 abase. In addition, it queries to the vector database to find similar data
 . However, loose coupling of the streaming engine and vector database does
  not recognize the internal operations of each engine, which can lead to p
 erformance bottlenecks in online data indexing and query serving scenarios
 . Through a preliminary experiment, we observed a high tail latency of que
 ry serving when data indexing is overlapped. Based on the results, we sugg
 est the potential performance bottleneck cases that cause a high tail late
 ncy of query serving and present a future work that can mitigate this prob
 lem.\n\nTag: Data Movement and Memory, I/O, Storage, Archive\n\nRegistrati
 on Category: Workshop Reg Pass\n\nSession Chairs: Suren Byna (The Ohio Sta
 te University, Lawrence Berkeley National Laboratory (LBNL)); Dean Hildebr
 and (Google LLC); Anthony Kougkas (Illinois Institute of Technology, Argon
 ne National Laboratory (ANL)); Jay Lofstead (Sandia National Laboratories,
  University of New Mexico); and Bing Xie (Microsoft Corporation)\n\n
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