Raw time-series data is often cleaned and preprocessed before being permanently stored in a database. In a traditional time-series solution, this generally requires the deployment of stream processing systems such as Kafka or Flink. However, the complexity of such systems increases the cost of development and maintenance.
With the stream processing engine built into TDengine, you can process incoming data streams in real time and define stream transformations in SQL. Incoming data is automatically processed, and the results are pushed to specified tables based on triggering rules that you define. This is a lightweight alternative to complex processing engines that returns computation results in milliseconds even in high throughput scenarios.
The stream processing engine includes data filtering, scalar function computation (including user-defined functions), and window aggregation, with support for sliding windows, session windows, and event windows. Stream processing can write data to supertables from other supertables, standard tables, or subtables. When you create a stream, the target supertable is automatically created. New data is then processed and written to that supertable according to the rules defined for the stream. You can use PARTITION BY statements to partition the data by table name or tag. Separate partitions are then written to different subtables within the target supertable.
TDengine stream processing supports the aggregation of supertables that are deployed across multiple vnodes. It can also handle out-of-order writes and includes a watermark mechanism that determines the extent to which out-of-order data is accepted by the system. You can configure whether to drop or reprocess out-of-order data through the **ignore expired** parameter.
For more information, see [Stream Processing](../../taos-sql/stream).
For more information, see [Stream Processing](../../taos-sql/stream).
## Usage Scenario 1
It is common that smart electrical meter systems for businesses generate millions of data points that are widely dispersed and not ordered. The time required to clean and convert this data makes efficient, real-time processing impossible for traditional solutions. This scenario shows how you can configure TDengine stream processing to drop data points over 220 V, find the maximum voltage for 5 second windows, and output this data to a table.
### Create a Database for Raw Data
A database including one supertable and four subtables is created as follows:
In this scenario, the active power and reactive power are determined from the data gathered in the previous scenario. The location and name of each meter are concatenated with a period (.) between them, and the data set is partitioned by meter name and written to a new database.
### Create a Database for Raw Data
The procedure from the previous scenario is used to create the database.
description:Briefly introduce how to replicate data among TDengine cloud services
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TDengine provides full support for data replication. You can replicate data from TDengine cloud service to local TDengine, from local TDengine to TDengine cloud service, or from one cloud service to another one and it doesn't matter which cloud or region the two services reside in.
