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Testing the impact of streams on write performance: write latency vs. throughput for the case with and without streams
Test stream result latency at large write throughput
Validate several major scenarios such as scalar functions, individual aggregation per sub-table, and multiple vgroup super-table aggregation, respectively
Roadmap
The next optimizations for the TDengine 3.0 streaming engine will be divided into the following areas.

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With a sliding window, aggregating the streams of all sub-tables in the super table
CREATE STREAM current_stream
TRIGGER AT_ONCE
INTO current_stream_output_stb AS
SELECT
_wstart as start,
_wend as end,
max(current) as max_current
FROM meters
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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.