[](https://bestpractices.coreinfrastructure.org/projects/4201)
English | [简体中文](README-CN.md) | [Lean more about TSDB](https://tdengine.com/tsdb)
English | [简体中文](README-CN.md) | [Learn more about TSDB](https://tdengine.com/tsdb/)
# What is TDengine？
TDengine is an open source, high-performance, cloud native [time-series database](https://tdengine.com/tsdb/) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine differentiates itself from other time-seires databases with the following advantages:
-**[High-Performance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
-**[HighPerformance](https://tdengine.com/tdengine/high-performance-time-series-database/)**: TDengine is the only time-series database to solve the high cardinality issue to support billions of data collection points while out performing other time-series databases for data ingestion, querying and data compression.
-**[Simplified Solution](https://tdengine.com/tdengine/simplified-time-series-data-solution/)**: Through built-in caching, stream processing and data subscription features, TDengine provides a simplified solution for time-series data processing. It reduces system design complexity and operation costs significantly.
-**[Cloud Native](https://tdengine.com/tdengine/cloud-native-time-series-database/)**: Through native distributed design, sharding and partitioning, separation of compute and storage, RAFT, support for kubernetes deployment and full observability, TDengine is a cloud native Time-Series Database and can be deployed on public, private or hybrid clouds.
-**[Ease of Use](https://docs.tdengine.com/get-started/docker/)**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
-**[Ease of Use](https://tdengine.com/tdengine/easy-time-series-data-platform/)**: For administrators, TDengine significantly reduces the effort to deploy and maintain. For developers, it provides a simple interface, simplified solution and seamless integrations for third party tools. For data users, it gives easy data access.
-**[Easy Data Analytics](https://tdengine.com/tdengine/time-series-data-analytics-made-easy/)**: Through super tables, storage and compute separation, data partitioning by time interval, pre-computation and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
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@@ -232,9 +232,9 @@ After building successfully, TDengine can be installed by
sudo make install
```
Users can find more information about directories installed on the system in the [directory and files](https://docs.taosdata.com/reference/directory/) section.
Users can find more information about directories installed on the system in the [directory and files](https://docs.tdengine.com/reference/directory/) section.
Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.taosdata.com/get-started/package/) for it.
Installing from source code will also configure service management for TDengine.Users can also choose to [install from packages](https://docs.tdengine.com/get-started/package/) for it.
To start the service after installation, in a terminal, use:
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@@ -321,7 +321,11 @@ TDengine provides abundant developing tools for users to develop on TDengine. Fo
Please follow the [contribution guidelines](CONTRIBUTING.md) to contribute to the project.
@@ -68,7 +68,7 @@ A query can be performed on some or all columns. Data and tag columns can all be
### Wildcards
You can use an asterisk (\*) as a wildcard character to indicate all columns. For standard tables, the asterisk indicates only data columns. For supertables and subtables, tag columns are also included.
You can use an asterisk (\*) as a wildcard character to indicate all columns. For normal tables or sub-tables, the asterisk indicates only data columns. For supertables, tag columns are also included when using asterisk (\*).
@@ -116,7 +116,7 @@ The parameters are described as follows:
- **protocol**: Specify which connection method to use. For example, `taos+ws://localhost:6041` uses Websocket to establish connections.
- **username/password**: Username and password used to create connections.
- **host/port**: Specifies the server and port to establish a connection. If you do not specify a hostname or port, native connections default to `localhost:6030` and Websocket connections default to `localhost:6041`.
- **database**: Specify the default database to connect to.
- **database**: Specify the default database to connect to. It's optional.
@@ -17,7 +17,7 @@ import CSAsyncQuery from "../../07-develop/04-query-data/_cs_async.mdx"
`TDengine.Connector` is a C# language connector provided by TDengine that allows C# developers to develop C# applications that access TDengine cluster data.
The `TDengine.Connector` connector supports connect to TDengine instances via the TDengine client driver (taosc), providing data writing, querying, subscription, schemaless writing, bind interface, etc. The `TDengine.Connector` currently does not provide a REST connection interface. Developers can write their RESTful application by referring to the [REST API](/reference/rest-api/) documentation.
The `TDengine.Connector` connector supports connect to TDengine instances via the TDengine client driver (taosc), providing data writing, querying, subscription, schemaless writing, bind interface, etc.The `TDengine.Connector` also supports WebSocket and developers can build connection through DSN, which supports data writing, querying, and parameter binding, etc.
This article describes how to install `TDengine.Connector` in a Linux or Windows environment and connect to TDengine clusters via `TDengine.Connector` to perform basic operations such as data writing and querying.
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@@ -35,6 +35,10 @@ Please refer to [version support list](/reference/connector#version-support)
| [CURD](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/Query/Query.cs) | Table creation, data insertion, and query examples with TDengine.Connector |
| [JSON Tag](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/JSONTag) | Writing and querying JSON tag data with TDengine Connector |
| [stmt](https://github.com/taosdata/taos-connector-dotnet/tree/3.0/examples/Stmt) | Parameter binding with TDengine Connector |
| [schemaless](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/schemaless) | Schemaless writes with TDengine Connector |
| [async query](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/AsyncQuery/QueryAsync.cs) | Asynchronous queries with TDengine Connector |
| [TMQ](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/TMQ/TMQ.cs) | Data subscription with TDengine Connector |
| [Subscription](https://github.com/taosdata/taos-connector-dotnet/blob/3.0/examples/TMQ/TMQ.cs) | Subscription example with TDengine Connector |
| [Basic WebSocket Usage](https://github.com/taosdata/taos-connector-dotnet/blob/5a4a7cd0dbcda114447cdc6d0c6dedd8e84a52da/examples/WS/WebSocketSample.cs) | WebSocket basic data in and out with TDengine connector |
| [WebSocket Parameter Binding](https://github.com/taosdata/taos-connector-dotnet/blob/5a4a7cd0dbcda114447cdc6d0c6dedd8e84a52da/examples/WS/WebSocketSTMT.cs) | WebSocket parameter binding example |
res=driver.query(conn,"CREATE TABLE therm1 USING thermometer TAGS ('beijing', 1)")
res=driver.query(conn,"create table therm1 using thermometer tags ('beijing', 1)")
ifres.code~=0then
print(res.error)
return
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@@ -86,7 +134,7 @@ else
print("create table--- pass")
end
res=driver.query(conn,"INSERT INTO therm1 VALUES ('2019-09-01 00:00:00.001', 20),('2019-09-01 00:00:00.002', 21)")
res=driver.query(conn,"insert into therm1 values ('2019-09-01 00:00:00.001', 20),('2019-09-01 00:00:00.002', 21)")
ifres.code~=0then
print(res.error)
...
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@@ -99,14 +147,14 @@ else
end
end
res=driver.query(conn,"SELECT COUNT(*) count, AVG(degree) AS av, MAX(degree), MIN(degree) FROM thermometer WHERE location='beijing' or location='tianjin' GROUP BY location, type")
res=driver.query(conn,"select count(*) as cnt, avg(degree) as av, max(degree), min(degree) from thermometer where location='beijing' or location='tianjin' group by location, type")
ifres.code~=0then
print("select from super table--- failed:"..res.error)
res=driver.query(conn,"create stream stream_avg_degree into avg_degree as select avg(degree) from thermometer interval(5s) sliding(1s)")
functionstream_callback(t)
print("------------------------")
print("continuous query result:")
forkey,valueinpairs(t)do
print("key:"..key..", value:"..value)
end
end
localstream
res=driver.open_stream(conn,"SELECT COUNT(*) as count, AVG(degree) as avg, MAX(degree) as max, MIN(degree) as min FROM thermometer interval(2s) sliding(2s);)",0,stream_callback)
ifres.code~=0then
print("open stream--- failed:"..res.error)
return
else
print("open stream--- pass")
stream=res.stream
end
print("From now on we start continous insert in an definite (infinite if you want) loop.")
print("From now on we start continous insert in an definite loop, pls wait for about 10 seconds and check stream table for result.")
localloop_index=0
whileloop_index<30do
whileloop_index<10do
localt=os.time()*1000
localv=loop_index
localv=math.random(20)
res=driver.query(conn,string.format("INSERT INTO therm1 VALUES (%d, %d)",t,v))
ifres.code~=0then
...
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@@ -162,7 +193,5 @@ while loop_index < 30 do
os.execute("sleep "..1)
loop_index=loop_index+1
end
driver.close_stream(stream)
driver.query(conn,"DROP STREAM IF EXISTS avg_therm_s")
