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More comprehensive SQL support: Join / Fill / Group by / Subquery etc.
Better stream state management so that users no longer need to care about Watermark in AT ONCE mode
More flexible partitioning mechanism: partition by column / expressions
Multi-aggregation nodes; independently deployed, store-and-calculate separate streaming computing nodes: SNODE
Configurable checkpoint
Benchmark refinement, end-to-end latency metrics, P99 latency metrics
While these follow-on efforts are listed, it is really the users and community developers of TDengine who will determine the development of the streaming compute processing engine. We hope that everyone will actually use TDengine 3.0’s streaming engine and contribute code to it in the open source community, and we will listen to more real-time feedback from our customers and the community.

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Event-driven
As we have already mentioned, one of the biggest differences between stream computing and continuous query is that stream computing can support event-driven, i.e., every arrival of data triggers the computation, a feature that allows us to compute scalar functions for data cleaning and preprocessing; and to provide AT_ONCE trigger mode for window aggregation, eliminating the need to wait for windows to close, thus supports both session and state windows. The bearer of event-driven execution is Stream Task, so let’s look at how Stream Task is deployed.

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