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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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At execution time, the streaming framework distributes the data in the Output Queue to the downstream Stream Task and notifies the stream’s execution scheduler to dispatch the idle stream threads to trigger the computation.
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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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Stream engine implementation
After explaining what TDengine’s stream engine is and the relationship between data subscriptions and streams, let’s take a look at how TDengine’s stream processing is implemented. Of course, there are a lot of details and a lot of content, so due to time constraints, I will only pick the 3 most important parts, which I think are the most interesting parts to explain, that is, "event-driven", "incremental computation" and "Chaotic Processing".
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