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Timestamped State Sharing for Stream Analytics
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DOI：10.1109/TPDS.2021.3073253.png)
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Abstract
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State access in existing distributed stream processing systems is restricted locally within each operator. However, in advanced stream analytics such as online learning and dynamic graph analytics, enabling state sharing across different operators makes application development easier and stream processing more efficient. In addition, when stream records are timestamped, proper time semantics should be defined for both state updates and fetches. We propose a new state abstraction to address the limitations of existing systems and develop a distributed stream processing system, Nova, with native support for timestamped state sharing. We validate the expressiveness and efficiency of Nova with extensive experiments.
Keywords:
Semantics
Pattern matching
Throughput
Sparks
Real-time systems
Industries
Storms
State sharing
distributed stream processing
online learning
dynamic graph analytics
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