TDengine is a high-performance, scalable time-series database with SQL support. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](/develop/cache), [stream processing](/develop/continuous-query), [data subscription](/develop/subscribe) and other functionalities to reduce the complexity and cost of development and operation.
TDengine is a high-performance, scalable time-series database with SQL support. Its code, including its cluster feature is open source under GNU AGPL v3.0. Besides the database engine, it provides [caching](/develop/cache), [stream processing](../develop/continuous-query), [data subscription](../develop/subscribe) and other functionalities to reduce the complexity and cost of development and operation.
This section introduces the major features, competitive advantages, typical use-cases and benchmarks to help you get a high level overview of TDengine.
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@@ -16,9 +16,9 @@ The major features are listed below:
3. Support for [all kinds of queries](/develop/query-data), including aggregation, nested query, downsampling, interpolation and others.
4. Support for [user defined functions](/develop/udf).
5. Support for [caching](/develop/cache). TDengine always saves the last data point in cache, so Redis is not needed in some scenarios.
6. Support for [continuous query](/develop/continuous-query).
7. Support for [data subscription](/develop/subscribe) with the capability to specify filter conditions.
8. Support for [cluster](/cluster/), with the capability of increasing processing power by adding more nodes. High availability is supported by replication.
6. Support for [continuous query](../develop/continuous-query).
7. Support for [data subscription](../develop/subscribe) with the capability to specify filter conditions.
8. Support for [cluster](../cluster/), with the capability of increasing processing power by adding more nodes. High availability is supported by replication.
9. Provides an interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc queries.
10. Provides many ways to [import](/operation/import) and [export](/operation/export) data.
11. Provides [monitoring](/operation/monitor) on running instances of TDengine.
@@ -10,8 +10,6 @@ Between two major release versions, some beta versions may be delivered for user
<PkgList type={0}/>
For the details please refer to [Install and Uninstall](/operation/pkg-install)。
For the details please refer to [Install and Uninstall](../13-operation/01-pkg-install.md).
To see the details of versions, please refer to [Download List](https://tdengine.com/all-downloads) and [Release Notes](https://github.com/taosdata/TDengine/releases).
@@ -10,7 +10,7 @@ import AptGetInstall from "./\_apt_get_install.mdx";
## Quick Install
The full package of TDengine includes the server(taosd), taosAdapter for connecting with third-party systems and providing a RESTful interface, client driver(taosc), command-line program(CLI, taos) and some tools. For the current version, the server taosd and taosAdapter can only be installed and run on Linux systems. In the future taosd and taosAdapter will also be supported on Windows, macOS and other systems. The client driver taosc and TDengine CLI can be installed and run on Windows or Linux. In addition to connectors for multiple languages, TDengine also provides a [RESTful interface](/reference/rest-api) through [taosAdapter](/reference/taosadapter). Prior to version 2.4.0.0, taosAdapter did not exist and the RESTful interface was provided by the built-in HTTP service of taosd.
The full package of TDengine includes the server(taosd), taosAdapter for connecting with third-party systems and providing a RESTful interface, client driver(taosc), command-line program(CLI, taos) and some tools. For the current version, the server taosd and taosAdapter can only be installed and run on Linux systems. In the future taosd and taosAdapter will also be supported on Windows, macOS and other systems. The client driver taosc and TDengine CLI can be installed and run on Windows or Linux. In addition to connectors for multiple languages, TDengine also provides a [RESTful interface](../14-reference/02-rest-api/02-rest-api.mdx) through [taosAdapter](../14-reference/04-taosadapter.md). Prior to version 2.4.0.0, taosAdapter did not exist and the RESTful interface was provided by the built-in HTTP service of taosd.
TDengine supports X64/ARM64/MIPS64/Alpha64 hardware platforms, and will support ARM32, RISC-V and other CPU architectures in the future.
Then you can execute the Linux commands and access TDengine.
For detailed steps, please visit [Experience TDengine via Docker](/train-faq/docker)。
For detailed steps, please visit [Experience TDengine via Docker](../27-train-faq/03-docker.md).
:::info
Starting from 2.4.0.10，besides taosd，TDengine docker image includes: taos，taosAdapter，taosdump，taosBenchmark，TDinsight, scripts and sample code. Once the TDengine container is started，it will start both taosAdapter and taosd automatically to support RESTful interface.
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@@ -98,7 +98,7 @@ To manage the TDengine running instance，or execute ad-hoc queries, TDengine pr
taos
```
If it connects to the TDengine server successfully, it will print out the version and welcome message. If it fails, it will print out the error message, please check [FAQ](/train-faq/faq) for trouble shooting connection issue. TDengine CLI's prompt is：
If it connects to the TDengine server successfully, it will print out the version and welcome message. If it fails, it will print out the error message, please check [FAQ](../27-train-faq/01-faq.md) for trouble shooting connection issue. TDengine CLI's prompt is：
```cmd
taos>
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@@ -120,7 +120,7 @@ select * from t;
QueryOK,2row(s)inset(0.003128s)
```
Besides executing SQL commands, system administrators can check running status, add/drop user accounts and manage the running instances. TDengine CLI with client driver can be installed and run on either Linux or Windows machines. For more details on CLI, please [check here](../reference/taos-shell/).
Besides executing SQL commands, system administrators can check running status, add/drop user accounts and manage the running instances. TDengine CLI with client driver can be installed and run on either Linux or Windows machines. For more details on CLI, please [check here](../14-reference/08-taos-shell.md).
## Experience the blazing fast speed
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@@ -134,7 +134,7 @@ This command will create a super table "meters" under database "test". Under "me
This command will insert 100 million rows into the database quickly. Time to insert depends on the hardware configuration, it only takes a dozen seconds for a regular PC server.
taosBenchmark provides command-line options and a configuration file to customize the scenarios, like number of tables, number of rows per table, number of columns and more. Please execute `taosBenchmark --help` to list them. For details on running taosBenchmark, please check [reference for taosBenchmark](/reference/taosbenchmark)
taosBenchmark provides command-line options and a configuration file to customize the scenarios, like number of tables, number of rows per table, number of columns and more. Please execute `taosBenchmark --help` to list them. For details on running taosBenchmark, please check [reference for taosBenchmark](../14-reference/05-taosbenchmark.md)
@@ -48,7 +48,7 @@ Query OK, 2 row(s) in set (0.001100s)
To meet the requirements of varied use cases, some special functions have been added in TDengine. Some examples are `twa` (Time Weighted Average), `spread` (The difference between the maximum and the minimum), and `last_row` (the last row). Furthermore, continuous query is also supported in TDengine.
For detailed query syntax please refer to [Select](/taos-sql/select).
For detailed query syntax please refer to [Select](../../12-taos-sql/06-select.md).
## Aggregation among Tables
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@@ -81,7 +81,7 @@ taos> SELECT count(*), max(current) FROM meters where groupId = 2 and ts > now -
Query OK, 1 row(s) in set (0.002136s)
```
Join queries are only allowed between subtables of the same STable. In [Select](/taos-sql/select), all query operations are marked as to whether they support STables or not.
Join queries are only allowed between subtables of the same STable. In [Select](../../12-taos-sql/06-select.md), all query operations are marked as to whether they support STables or not.
## Down Sampling and Interpolation
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@@ -128,13 +128,13 @@ In many use cases, it's hard to align the timestamp of the data collected by eac
Interpolation can be performed in TDengine if there is no data in a time range.
For more details please refer to [Aggregate by Window](/taos-sql/interval).
For more details please refer to [Aggregate by Window](../../12-taos-sql/12-interval.md).
## Examples
### Query
In the section describing [Insert](/develop/insert-data/sql-writing), a database named `power` is created and some data are inserted into STable `meters`. Below sample code demonstrates how to query the data in this STable.
In the section describing [Insert](../03-insert-data/01-sql-writing.mdx), a database named `power` is created and some data are inserted into STable `meters`. Below sample code demonstrates how to query the data in this STable.
For more details about these APIs please refer to [C/C++ Connector](/reference/connector/cpp). Their usage will be introduced below using the use case of meters, in which the schema of STable and subtables from the previous section [Continuous Query](/develop/continuous-query) are used. Full sample code can be found [here](https://github.com/taosdata/TDengine/blob/master/examples/c/subscribe.c).
For more details about these APIs please refer to [C/C++ Connector](/reference/connector/cpp). Their usage will be introduced below using the use case of meters, in which the schema of STable and subtables from the previous section [Continuous Query](../continuous-query) are used. Full sample code can be found [here](https://github.com/taosdata/TDengine/blob/master/examples/c/subscribe.c).
If we want to get a notification and take some actions if the current exceeds a threshold, like 10A, from some meters, there are two ways:
JupyterLab is the next generation of the ubiquitous Jupyter Notebook. In this note we show you how to install the TDengine Python connector to connect to TDengine in JupyterLab. You can then insert data and perform queries against the TDengine instance within JupyterLab.
## Install JupyterLab
Installing JupyterLab is very easy. Installation instructions can be found at:
If you don't feel like clicking on the link here are the instructions.
Jupyter's preferred Python package manager is pip, so we show the instructions for pip.
You can also use **conda** or **pipenv** if you are managing Python environments.
````
pip install jupyterlab
````
For **conda** you can run:
````
conda install -c conda-forge jupyterlab
````
For **pipenv** you can run:
````
pipenv install jupyterlab
pipenv shell
````
## Run JupyterLab
You can start JupyterLab from the command line by running:
````
jupyter lab
````
This will automatically launch your default browser and connect to your JupyterLab instance, usually on port 8888.
## Install the TDengine Python connector
You can now install the TDengine Python connector as follows.
Start a new Python kernel in JupyterLab.
If using **conda** run the following:
````
# Install a conda package in the current Jupyter kernel
import sys
!conda install --yes --prefix {sys.prefix} taospy
````
If using **pip** run the following:
````
# Install a pip package in the current Jupyter kernel
import sys
!{sys.executable} -m pip install taospy
````
## Connect to TDengine
You can find detailed examples to use the Python connector, in the TDengine documentation here.
Once you have installed the TDengine Python connector in your JupyterLab kernel, the process of connecting to TDengine is the same as that you would use if you weren't using JupyterLab.
Each TDengine instance, has a database called "log" which has monitoring information about the TDengine instance.
In the "log" database there is a [supertable](https://docs.tdengine.com/taos-sql/stable/) called "disks_info".
The code below is used to fetch data from this table into a pandas DataFrame.
````
import sys
import taos
import pandas
def sqlQuery(conn):
df: pandas.DataFrame = pandas.read_sql("select * from log.disks_info limit 500", conn)
print(df)
return df
conn = taos.connect()
result = sqlQuery(conn)
print(result)
````
TDengine has connectors for various languages including Node.js, Go, PHP and there are kernels for these languages which can be found [here](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels).
@@ -419,11 +419,11 @@ Note that once the installation is complete, do not immediately start the `taosd
To ensure that the system can obtain the necessary information for regular operation. Please set the following vital parameters correctly on the server:
FQDN, firstEp, secondEP, dataDir, logDir, tmpDir, serverPort. For the specific meaning and setting requirements of each parameter, please refer to the document "[TDengine Cluster Installation and Management](/cluster/)"
FQDN, firstEp, secondEP, dataDir, logDir, tmpDir, serverPort. For the specific meaning and setting requirements of each parameter, please refer to the document "[TDengine Cluster Installation and Management](../../cluster/)"
Follow the same steps to set parameters on the other nodes, start the taosd service, and then add Dnodes to the cluster.
Finally, start `taos` and execute the `show dnodes` command. If you can see all the nodes that have joined the cluster, the cluster building process was successfully completed. For specific operation procedures and precautions, please refer to the document "[TDengine Cluster Installation and Management](/cluster/)".
Finally, start `taos` and execute the `show dnodes` command. If you can see all the nodes that have joined the cluster, the cluster building process was successfully completed. For specific operation procedures and precautions, please refer to the document "[TDengine Cluster Installation and Management](../../cluster/)".
