TDengine 3.0 Stream Processing And Data Subscription - Tdengine Database

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Batch inserted data will first perform Lift and Combine operations on the vnode where the data was inserted
For aggregation across multiple vnodes, the aggregation stream task is deployed on a randomly selected vnode, combining the state of the first level of aggregation again
Execute lower on demand depending on the trigger mode
Two-level incremental aggregation reduces the amount of data transfer and spreads CPU-intensive computation across nodes
Disordered processing
In order to achieve correctness in various scenarios such as disorder, the streaming computation in TDengine 3.0 adopts a processing model based on event time, and Watermark is the upper bound for disorder tolerance, so to understand the processing of disorder data, we first need to understand Watermark.

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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However, there are times when applications still need to fetch data with low latency, such as when implementing monitoring alarms and anomaly detection, in which case the streams need to actually reach the application, and TDengine provides data subscription capabilities to meet this need.

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