TDengine Database 3.0 Stream Processing and Data Subscription

Question

So what do each of these two levels of aggregation mean? We’ll cover this in more detail in the Incremental Computation section. But before that, let’s zoom in and take a look at the exact structure inside Stream Task.

Very strong data transformation capabilities by means of the Power Query feature
Integration with other products in the Microsoft Power Platform
Data security capabilities for sensitive data
Performing time-series data visualization and filtering in Power BI
Developers can connect and send TDengine data to Power BI via a Python script or other methods.

TDengine 3.0 completely abandons the update and delete mechanism of 2.0 and considers the implementation of updates and deletes at the design level, introducing version numbers, turning timing data into points on a two-dimensional graph, with each write request carrying a version number, which is incremented in the order in which the write requests are processed.

TDengine PI Connector expands a PI data historian with a modern time series database
Learn More
The TDengine PI Connector can integrate your PI Systems with our fully managed TDengine Cloud or with an on-premises deployment of TDengine Enterprise Edition. Contact us at business@tdengine.com and we’ll be happy to architect a solution for you that makes the most of your data historian.

SELECT _wstart, HISTOGRAM(pm25, "user_input","[50,100,200,300,350]",0), station FROM weather.pollution PARTITION BY station INTERVAL(1y);
Histogram function for time-series data analytics in TDengine Cloud
Simple Analysis on TDengine Sample Database
Let’s shift our attention to the sample database in TDengine Cloud. As we mentioned, this has synthetic data from smart meters in various cities.

Microsoft Power BI
Power BI is business intelligence and reporting software produced by Microsoft. With Power BI, you can import data and transform it to obtain the visual display that makes it easiest to understand. The main benefits of Power BI are as follows:

USE meters;
Replace the database operating in the current connection with "power", otherwise, before operating on a specific table, you need to use "database name. table name" to specify the name of database to use.

Unlock your data with an open system: Now that your data is in the cloud, you have access to all kinds of modern tools for analytics and visualization. For example, the official TDengine plug-in for Grafana enables new levels of observability and visualization for your data with a range of premade and customizable dashboards. TDengine also provides connectors for a variety of programming languages that make integration with your favorite tools fast and convenient.

Stateful incremental calculation
The stream computation can be divided into many kinds depending on the nature of the function, such as invertible, holistic, etc. Here we only discuss how the computation is implemented for incremental.

In 3.0, the deletion mechanism for chronological data is also completely redone. Compared to 2.0, 3.0 also supports significantly more filter conditions, such as where tag, where timestamp, etc. How is this implemented at the bottom? First of all, it is still based on the version number mechanism.

Inside the Stream Task, the concrete computation is performed by a series of stateful stream operators. When a stream is created, the SQL is parsed into a syntax tree, and the planner splits the syntax tree into multiple pipelines, each of which is a series of concatenated stream operators. As we can see, the biggest difference in the plan level is that the Exchange Operator is removed, all pipelines are made standalone, and data is exchanged between pipelines in push mode. This not only minimizes blocking during stream execution, but also reduces inefficient execution scheduling, since streams no longer need to first schedule the parent Stream Task to pull data from the child Stream Task when an event arrives.

Create a Table
TDengine builds a table independently for each data collection point. Similar to standard relational data, one table has a table name, Schema, but in addition, it can also carry one or more tags. When creating, you need to use the STable as a template and specify the specific value of the tag. Taking the smart meter in Table 1 as an example, the following SQL command can be used to build the table:

You can get the difference between the maximum and minimum values of a column by using the SPREAD function. Note that you can always constrain the time spans by using a WHERE clause on the timestamp field.

Table of Contents
Create a Database
Create a Supertable
Create a Table
Multi-column Model vs Single-column Model
Video Walkthrough
Create a Database
All tables and data in your environment are stored in a database. In tdengine Time Series Database, you generally create a separate database for each group of devices whose data has different characteristics. This way you can configure each database in the optimal manner for the data it contains.

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