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cmake/taostools_CMakeLists.txt.in
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# taos-tools
ExternalProject_Add(taos-tools
GIT_REPOSITORY https://github.com/taosdata/taos-tools.git
GIT_TAG d237772
GIT_TAG e8bfca6
SOURCE_DIR "${TD_SOURCE_DIR}/tools/taos-tools"
BINARY_DIR ""
#BUILD_IN_SOURCE TRUE
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docs/en/01-index.md
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---
TDengine is an open source, cloud native time-series database 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. This document is the TDengine user manual. It introduces the basic, as well as novel concepts, in TDengine, and also talks in detail about installation, features, SQL, APIs, operation, maintenance, kernel design and other topics. It’s written mainly for architects, developers and system administrators.
TDengine is an [open source](https://tdengine.com/tdengine/open-source-time-series-database/), [cloud native](https://tdengine.com/tdengine/cloud-native-time-series-database/) time-series database 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. This document is the TDengine user manual. It introduces the basic, as well as novel concepts, in TDengine, and also talks in detail about installation, features, SQL, APIs, operation, maintenance, kernel design and other topics. It’s written mainly for architects, developers and system administrators.
To get an overview of TDengine, such as a feature list, benchmarks, and competitive advantages, please browse through the [Introduction](./intro) section.
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docs/en/02-intro/index.md
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toc_max_heading_level: 2
---
TDengine is an open source, high-performance, cloud native time-series database optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. 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/stream), [data subscription](../develop/tmq) and other functionalities to reduce the system complexity and cost of development and operation.
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. 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/stream), [data subscription](/develop/tmq) and other functionalities to reduce the system 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.
......@@ -11,21 +11,33 @@ This section introduces the major features, competitive advantages, typical use-
The major features are listed below:
1. While TDengine supports [using SQL to insert](/develop/insert-data/sql-writing), it also supports [Schemaless writing](/reference/schemaless/) just like NoSQL databases. TDengine also supports standard protocols like [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) among others.
2. TDengine supports seamless integration with third-party data collection agents like [Telegraf](/third-party/telegraf)，[Prometheus](/third-party/prometheus)，[StatsD](/third-party/statsd)，[collectd](/third-party/collectd)，[icinga2](/third-party/icinga2), [TCollector](/third-party/tcollector), [EMQX](/third-party/emq-broker), [HiveMQ](/third-party/hive-mq-broker). These agents can write data into TDengine with simple configuration and without a single line of code.
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/stream).
7. Support for [data subscription](../develop/tmq with the capability to specify filter conditions.
8. Support for [cluster](../deployment/), 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.
12. Provides [connectors](/reference/connector/) for [C/C++](/reference/connector/cpp), [Java](/reference/connector/java), [Python](/reference/connector/python), [Go](/reference/connector/go), [Rust](/reference/connector/rust), [Node.js](/reference/connector/node) and other programming languages.
13. Provides a [REST API](/reference/rest-api/).
14. Supports seamless integration with [Grafana](/third-party/grafana) for visualization.
15. Supports seamless integration with Google Data Studio.
1. Insert data
* supports [using SQL to insert](/develop/insert-data/sql-writing).
* supports [schemaless writing](/reference/schemaless/) just like NoSQL databases. It also supports standard protocols like [InfluxDB LINE](/develop/insert-data/influxdb-line)，[OpenTSDB Telnet](/develop/insert-data/opentsdb-telnet), [OpenTSDB JSON ](/develop/insert-data/opentsdb-json) among others.
* supports seamless integration with third-party tools like [Telegraf](/third-party/telegraf/), [Prometheus](/third-party/prometheus/), [collectd](/third-party/collectd/), [StatsD](/third-party/statsd/), [TCollector](/third-party/tcollector/) and [icinga2/](/third-party/icinga2/), they can write data into TDengine with simple configuration and without a single line of code.
2. Query data
* supports standard [SQL](/taos-sql/), including nested query.
* supports [time series specific functions](/taos-sql/function/#time-series-extensions) and [time series specific queries](/taos-sql/distinguished), like downsampling, interpolation, cumulated sum, time weighted average, state window, session window and many others.
* supports [user defined functions](/taos-sql/udf).
3. [Caching](/develop/cache/): TDengine always saves the last data point in cache, so Redis is not needed for time-series data processing.
4. [Stream Processing](/develop/stream/): not only is the continuous query is supported, but TDengine also supports even driven stream processing, so Flink or spark is not needed for time-series daata processing.
5. [Data Dubscription](/develop/tmq/): application can subscribe a table or a set of tables. API is the same as Kafka, but you can specify filter conditions.
6. Visualization
* supports seamless integration with [Grafana](/third-party/grafana/) for visualization.
* supports seamless integration with Google Data Studio.
7. Cluster
* supports [cluster](/deployment/) with the capability of increasing processing power by adding more nodes.
* supports [deployment on Kubernetes](/deployment/k8s/)
* supports high availability via data replication.
8. Administration
* provides [monitoring](/operation/monitor) on running instances of TDengine.
* provides many ways to [import](/operation/import) and [export](/operation/export) data.
9. Tools
* provides an interactive [command-line interface](/reference/taos-shell) for management, maintenance and ad-hoc queries.
* provides a tool [taosBenchmark](/reference/taosbenchmark/) for testing the performance of TDengine.
10. Programming
* provides [connectors](/reference/connector/) for [C/C++](/reference/connector/cpp), [Java](/reference/connector/java), [Python](/reference/connector/python), [Go](/reference/connector/go), [Rust](/reference/connector/rust), [Node.js](/reference/connector/node) and other programming languages.
* provides a [REST API](/reference/rest-api/).
For more details on features, please read through the entire documentation.
......@@ -33,17 +45,18 @@ For more details on features, please read through the entire documentation.
By making full use of [characteristics of time series data](https://tdengine.com/tsdb/characteristics-of-time-series-data/), TDengine differentiates itself from other time series databases, with the following advantages.
- **High-Performance**: 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.
- **[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.
- **Simplified 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.
- **[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**: 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.
- **[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**: 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[
](https://tdengine.com/tdengine/easy-time-series-data-platform/) 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**: 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.
- **[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.
- **Open Source**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered 18.8k stars on GitHub. There is an active developer community, and over 139k running instances worldwide.
- **[Open Source](https://tdengine.com/tdengine/open-source-time-series-database/)**: TDengine’s core modules, including cluster feature, are all available under open source licenses. It has gathered over 19k stars on GitHub. There is an active developer community, and over 140k running instances worldwide.
With TDengine, the total cost of ownership of your time-series data platform can be greatly reduced. 1: With its superior performance, the computing and storage resources are reduced significantly；2: With SQL support, it can be seamlessly integrated with many third party tools, and learning costs/migration costs are reduced significantly；3: With its simplified solution and nearly zero management, the operation and maintenance costs are reduced significantly.
......@@ -96,14 +109,13 @@ As a high-performance, scalable and SQL supported time-series database, TDengine
| **System Maintenance Requirements** | **Not Applicable** | **Might Be Applicable** | **Very Applicable** | **Description** |
| ------------------------------------------------- | ------------------ | ----------------------- | ------------------- | ------------------------------------------------------------ |
| Native high-reliability | | | √ | TDengine has a very robust, reliable and easily configurable system architecture to simplify routine operation. Human errors and accidents are eliminated to the greatest extent, with a streamlined experience for operators. |
| Minimize learning and maintenance costs | | | √ | In addition to being easily configurable, standard SQL support and the Taos shell for ad hoc queries makes maintenance simpler, allows reuse and reduces learning costs.|
| Minimize learning and maintenance costs | | | √ | In addition to being easily configurable, standard SQL support and the TDengine CLI for ad hoc queries makes maintenance simpler, allows reuse and reduces learning costs.|
| Abundant talent supply | √ | | | Given the above, and given the extensive training and professional services provided by TDengine, it is easy to migrate from existing solutions or create a new and lasting solution based on TDengine.|
## Comparison with other databases
- [Writing Performance Comparison of TDengine and InfluxDB ](https://tdengine.com/2022/02/23/4975.html)
- [Query Performance Comparison of TDengine and InfluxDB](https://tdengine.com/2022/02/24/5120.html)
- [TDengine vs InfluxDB、OpenTSDB、Cassandra、MySQL、ClickHouse](https://www.tdengine.com/downloads/TDengine_Testing_Report_en.pdf)
- [TDengine vs OpenTSDB](https://tdengine.com/2019/09/12/710.html)
- [TDengine vs Cassandra](https://tdengine.com/2019/09/12/708.html)
- [TDengine vs InfluxDB](https://tdengine.com/2019/09/12/706.html)
docs/en/04-concept/index.md
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## Metric
Metric refers to the physical quantity collected by sensors, equipment or other types of data collection devices, such as current, voltage, temperature, pressure, GPS position, etc., which change with time, and the data type can be integer, float, Boolean, or strings. As time goes by, the amount of collected metric data stored increases.
Metric refers to the physical quantity collected by sensors, equipment or other types of data collection devices, such as current, voltage, temperature, pressure, GPS position, etc., which change with time, and the data type can be integer, float, Boolean, or strings. As time goes by, the amount of collected metric data stored increases. In the smart meters example, current, voltage and phase are the metrics.
## Label/Tag
Label/Tag refers to the static properties of sensors, equipment or other types of data collection devices, which do not change with time, such as device model, color, fixed location of the device, etc. The data type can be any type. Although static, TDengine allows users to add, delete or update tag values at any time. Unlike the collected metric data, the amount of tag data stored does not change over time.
Label/Tag refers to the static properties of sensors, equipment or other types of data collection devices, which do not change with time, such as device model, color, fixed location of the device, etc. The data type can be any type. Although static, TDengine allows users to add, delete or update tag values at any time. Unlike the collected metric data, the amount of tag data stored does not change over time. In the meters example, `location` and `groupid` are the tags.
## Data Collection Point
Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same time stamp. For some complex equipment, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car. So in this example the car would have three data collection points.
Data Collection Point (DCP) refers to hardware or software that collects metrics based on preset time periods or triggered by events. A data collection point can collect one or multiple metrics, but these metrics are collected at the same time and have the same time stamp. For some complex equipment, there are often multiple data collection points, and the sampling rate of each collection point may be different, and fully independent. For example, for a car, there could be a data collection point to collect GPS position metrics, a data collection point to collect engine status metrics, and a data collection point to collect the environment metrics inside the car. So in this example the car would have three data collection points. In the smart meters example, d1001, d1002, d1003, and d1004 are the data collection points.
## Table
......@@ -137,7 +137,7 @@ The design of one table for one data collection point will require a huge number
STable is a template for a type of data collection point. A STable contains a set of data collection points (tables) that have the same schema or data structure, but with different static attributes (tags). To describe a STable, in addition to defining the table structure of the metrics, it is also necessary to define the schema of its tags. The data type of tags can be int, float, string, and there can be multiple tags, which can be added, deleted, or modified afterward. If the whole system has N different types of data collection points, N STables need to be established.
In the design of TDengine, **a table is used to represent a specific data collection point, and STable is used to represent a set of data collection points of the same type**.
In the design of TDengine, **a table is used to represent a specific data collection point, and STable is used to represent a set of data collection points of the same type**. In the smart meters example, we can create a super table named `meters`.
## Subtable
......@@ -156,7 +156,9 @@ The relationship between a STable and the subtables created based on this STable
Queries can be executed on both a table (subtable) and a STable. For a query on a STable, TDengine will treat the data in all its subtables as a whole data set for processing. TDengine will first find the subtables that meet the tag filter conditions, then scan the time-series data of these subtables to perform aggregation operation, which reduces the number of data sets to be scanned which in turn greatly improves the performance of data aggregation across multiple DCPs. In essence, querying a supertable is a very efficient aggregate query on multiple DCPs of the same type.
In TDengine, it is recommended to use a subtable instead of a regular table for a DCP.
In TDengine, it is recommended to use a subtable instead of a regular table for a DCP. In the smart meters example, we can create subtables like d1001, d1002, d1003, and d1004 under super table meters.
To better understand the data model using metri, tags, super table and subtable, please refer to the diagram below which demonstrates the data model of the smart meters example. ![Meters Data Model Diagram](./supertable.webp)
## Database
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docs/en/05-get-started/01-docker.md
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Note: For information about installing docker, see the [official documentation](https://docs.docker.com/get-docker/).
## Open the TDengine CLI
On the container, run the following command to open the TDengine CLI:
```
$ taos
taos>
```
## Insert Data into TDengine
You can use the `taosBenchmark` tool included with TDengine to write test data into your deployment.
......@@ -53,45 +42,57 @@ To do so, run the following command:
```
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to ten and a `location` tag of either `California.SanFrancisco` or `California.SanDiego`.
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `Campbell`, `Cupertino`, `Los Angeles`, `Mountain View`, `Palo Alto`, `San Diego`, `San Francisco`, `San Jose`, `Santa Clara` or `Sunnyvale`.
The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required depends on the hardware specifications of the local system.
You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](/reference/taosbenchmark).
## Open the TDengine CLI
On the container, run the following command to open the TDengine CLI:
```
$ taos
taos>
```
## Query Data in TDengine
After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance. For example:
Query the number of rows in the `meters` supertable:
From the TDengine CLI query the number of rows in the `meters` supertable:
```sql
taos> select count(*) from test.meters;
select count(*) from test.meters;
```
Query the average, maximum, and minimum values of all 100 million rows of data:
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters;
select avg(current), max(voltage), min(phase) from test.meters;
```
Query the number of rows whose `location` tag is `California.SanFrancisco`:
Query the number of rows whose `location` tag is `San Francisco`:
```sql
taos> select count(*) from test.meters where location="San Francisco";
select count(*) from test.meters where location="San Francisco";
```
Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
```
Query the average, maximum, and minimum values for table `d10` in 10 second intervals:
Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
```sql
taos> select avg(current), max(voltage), min(phase) from test.d10 interval(10s);
select first(ts), avg(current), max(voltage), min(phase) from test.d10 interval(1s);
```
In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be _wstart which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
## Additional Information
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docs/en/05-get-started/03-package.md
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</TabItem>
<TabItem label="Windows" value="windows">
1. Download the Windows installation package.
<PkgListV3 type={3}/>
2. Run the downloaded package to install TDengine.
</TabItem>
<TabItem value="apt-get" label="apt-get">
You can use `apt-get` to install TDengine from the official package repository.
......@@ -102,6 +95,15 @@ sudo apt-get install tdengine
:::tip
This installation method is supported only for Debian and Ubuntu.
::::
</TabItem>
<TabItem label="Windows" value="windows">
Note: TDengine only supports Windows Server 2016/2019 and windows 10/11 system versions on the windows platform.
1. Download the Windows installation package.
<PkgListV3 type={3}/>
2. Run the downloaded package to install TDengine.
</TabItem>
</Tabs>
......@@ -172,6 +174,20 @@ After the installation is complete, run `C:\TDengine\taosd.exe` to start TDengin
</TabItem>
</Tabs>
## Test data insert performance
After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
```bash
taosBenchmark
```
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to 10 and a `location` tag of either `Campbell`, `Cupertino`, `Los Angeles`, `Mountain View`, `Palo Alto`, `San Diego`, `San Francisco`, `San Jose`, `Santa Clara` or `Sunnyvale`.
The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in less than a minute.
You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
## Command Line Interface
You can use the TDengine CLI to monitor your TDengine deployment and execute ad hoc queries. To open the CLI, run the following command:
......@@ -203,51 +219,38 @@ Query OK, 2 row(s) in set (0.003128s)
```
You can also can monitor the deployment status, add and remove user accounts, and manage running instances. You can run the TDengine CLI on either Linux or Windows machines. For more information, see [TDengine CLI](../../reference/taos-shell/).
## Test data insert performance
After your TDengine Server is running normally, you can run the taosBenchmark utility to test its performance:
```bash
taosBenchmark
```
This command creates the `meters` supertable in the `test` database. In the `meters` supertable, it then creates 10,000 subtables named `d0` to `d9999`. Each table has 10,000 rows and each row has four columns: `ts`, `current`, `voltage`, and `phase`. The timestamps of the data in these columns range from 2017-07-14 10:40:00 000 to 2017-07-14 10:40:09 999. Each table is randomly assigned a `groupId` tag from 1 to ten and a `location` tag of either `California.SanFrancisco` or `California.LosAngeles`.
The `taosBenchmark` command creates a deployment with 100 million data points that you can use for testing purposes. The time required to create the deployment depends on your hardware. On most modern servers, the deployment is created in less than a minute.
You can customize the test deployment that taosBenchmark creates by specifying command-line parameters. For information about command-line parameters, run the `taosBenchmark --help` command. For more information about taosBenchmark, see [taosBenchmark](../../reference/taosbenchmark).
## Test data query performance
After using taosBenchmark to create your test deployment, you can run queries in the TDengine CLI to test its performance:
Query the number of rows in the `meters` supertable:
From the TDengine CLI query the number of rows in the `meters` supertable:
```sql
taos> select count(*) from test.meters;
select count(*) from test.meters;
```
Query the average, maximum, and minimum values of all 100 million rows of data:
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters;
select avg(current), max(voltage), min(phase) from test.meters;
```
Query the number of rows whose `location` tag is `California.SanFrancisco`:
Query the number of rows whose `location` tag is `San Francisco`:
```sql
taos> select count(*) from test.meters where location="California.SanFrancisco";
select count(*) from test.meters where location="San Francisco";
```
Query the average, maximum, and minimum values of all rows whose `groupId` tag is `10`:
```sql
taos> select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
select avg(current), max(voltage), min(phase) from test.meters where groupId=10;
```
Query the average, maximum, and minimum values for table `d10` in 10 second intervals:
Query the average, maximum, and minimum values for table `d10` in 1 second intervals:
```sql
taos> select avg(current), max(voltage), min(phase) from test.d10 interval(10s);
select first(ts), avg(current), max(voltage), min(phase) from test.d10 interval(1s);
```
In the query above you are selecting the first timestamp (ts) in the interval, another way of selecting this would be _wstart which will give the start of the time window. For more information about windowed queries, see [Time-Series Extensions](../../taos-sql/distinguished/).
docs/en/07-develop/03-insert-data/05-high-volume.md 0 → 100644
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docs/en/07-develop/07-tmq.mdx
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......@@ -16,7 +16,7 @@ import CDemo from "./_sub_c.mdx";
TDengine provides data subscription and consumption interfaces similar to message queue products. These interfaces make it easier for applications to obtain data written to TDengine either in real time and to process data in the order that events occurred. This simplifies your time-series data processing systems and reduces your costs because it is no longer necessary to deploy a message queue product such as Kafka.
To use TDengine data subscription, you define topics like in Kafka. However, a topic in TDengine is based on query conditions for an existing supertable, standard table, or subtable - in other words, a SELECT statement. You can use SQL to filter data by tag, table name, column, or expression and then perform a scalar function or user-defined function on the data. Aggregate functions are not supported. This gives TDengine data subscription more flexibility than similar products. The granularity of data can be controlled on demand by applications, while filtering and preprocessing are handled by TDengine instead of the application layer. This implementation reduces the amount of data transmitted and the complexity of applications.
To use TDengine data subscription, you define topics like in Kafka. However, a topic in TDengine is based on query conditions for an existing supertable, table, or subtable - in other words, a SELECT statement. You can use SQL to filter data by tag, table name, column, or expression and then perform a scalar function or user-defined function on the data. Aggregate functions are not supported. This gives TDengine data subscription more flexibility than similar products. The granularity of data can be controlled on demand by applications, while filtering and preprocessing are handled by TDengine instead of the application layer. This implementation reduces the amount of data transmitted and the complexity of applications.
By subscribing to a topic, a consumer can obtain the latest data in that topic in real time. Multiple consumers can be formed into a consumer group that consumes messages together. Consumer groups enable faster speed through multi-threaded, distributed data consumption. Note that consumers in different groups that are subscribed to the same topic do not consume messages together. A single consumer can subscribe to multiple topics. If the data in a supertable is sharded across multiple vnodes, consumer groups can consume it much more efficiently than single consumers. TDengine also includes an acknowledgement mechanism that ensures at-least-once delivery in complicated environments where machines may crash or restart.
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docs/en/07-develop/08-cache.md
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......@@ -20,11 +20,11 @@ In theory, larger cache sizes are always better. However, at a certain point, it
## Read Cache
When you create a database, you can configure whether the latest data from every subtable is cached. To do so, set the *cachelast* parameter as follows:
- 0: Caching is disabled.
- 1: The latest row of data in each subtable is cached. This option significantly improves the performance of the `LAST_ROW` function
- 2: The latest non-null value in each column of each subtable is cached. This option significantly improves the performance of the `LAST` function in normal situations, such as WHERE, ORDER BY, GROUP BY, and INTERVAL statements.
- 3: Rows and columns are both cached. This option is equivalent to simultaneously enabling options 1 and 2.
When you create a database, you can configure whether the latest data from every subtable is cached. To do so, set the *cachemodel* parameter as follows:
- none: Caching is disabled.
- last_row: The latest row of data in each subtable is cached. This option significantly improves the performance of the `LAST_ROW` function
- last_value: The latest non-null value in each column of each subtable is cached. This option significantly improves the performance of the `LAST` function in normal situations, such as WHERE, ORDER BY, GROUP BY, and INTERVAL statements.
- both: Rows and columns are both cached. This option is equivalent to simultaneously enabling option last_row and last_value.
## Metadata Cache
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docs/en/10-deployment/01-deploy.md
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......@@ -114,7 +114,9 @@ The above process can be repeated to add more dnodes in the cluster.
Any node that is in the cluster and online can be the firstEp of new nodes.
Nodes use the firstEp parameter only when joining a cluster for the first time. After a node has joined the cluster, it stores the latest mnode in its end point list and no longer makes use of firstEp.
However, firstEp is used by clients that connect to the cluster. For example, if you run `taos shell` without arguments, it connects to the firstEp by default.
However, firstEp is used by clients that connect to the cluster. For example, if you run TDengine CLI `taos` without arguments, it connects to the firstEp by default.
Two dnodes that are launched without a firstEp value operate independently of each other. It is not possible to add one dnode to the other dnode and form a cluster. It is also not possible to form two independent clusters into a new cluster.
:::
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docs/en/10-deployment/03-k8s.md
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......@@ -9,6 +9,7 @@ TDengine is a cloud-native time-series database that can be deployed on Kubernet
Before deploying TDengine on Kubernetes, perform the following:
* Current steps are compatible with Kubernetes v1.5 and later version.
* Install and configure minikube, kubectl, and helm.
* Install and deploy Kubernetes and ensure that it can be accessed and used normally. Update any container registries or other services as necessary.
......@@ -100,7 +101,7 @@ spec:
# Must set if you want a cluster.
- name: TAOS_FIRST_EP
value: "$(STS_NAME)-0.$(SERVICE_NAME).$(STS_NAMESPACE).svc.cluster.local:$(TAOS_SERVER_PORT)"
# TAOS_FQND should always be setted in k8s env.
# TAOS_FQDN should always be set in k8s env.
- name: TAOS_FQDN
value: "$(POD_NAME).$(SERVICE_NAME).$(STS_NAMESPACE).svc.cluster.local"
volumeMounts:
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docs/en/10-deployment/05-helm.md
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......@@ -170,71 +170,21 @@ taoscfg:
# number of replications, for cluster only
TAOS_REPLICA: "1"
# number of days per DB file
# TAOS_DAYS: "10"
# number of days to keep DB file, default is 10 years.
#TAOS_KEEP: "3650"
# cache block size (Mbyte)
#TAOS_CACHE: "16"
# number of cache blocks per vnode
#TAOS_BLOCKS: "6"
# minimum rows of records in file block
#TAOS_MIN_ROWS: "100"
# maximum rows of records in file block
#TAOS_MAX_ROWS: "4096"
#
# TAOS_NUM_OF_THREADS_PER_CORE: number of threads per CPU core
#TAOS_NUM_OF_THREADS_PER_CORE: "1.0"
# TAOS_NUM_OF_RPC_THREADS: number of threads for RPC
#TAOS_NUM_OF_RPC_THREADS: "2"
#
# TAOS_NUM_OF_COMMIT_THREADS: number of threads to commit cache data
#TAOS_NUM_OF_COMMIT_THREADS: "4"
#
# TAOS_RATIO_OF_QUERY_CORES:
# the proportion of total CPU cores available for query processing
# 2.0: the query threads will be set to double of the CPU cores.
# 1.0: all CPU cores are available for query processing [default].
# 0.5: only half of the CPU cores are available for query.
# 0.0: only one core available.
#TAOS_RATIO_OF_QUERY_CORES: "1.0"
#
# TAOS_KEEP_COLUMN_NAME:
# the last_row/first/last aggregator will not change the original column name in the result fields
#TAOS_KEEP_COLUMN_NAME: "0"
# enable/disable backuping vnode directory when removing vnode
#TAOS_VNODE_BAK: "1"
# enable/disable installation / usage report
#TAOS_TELEMETRY_REPORTING: "1"
# enable/disable load balancing
#TAOS_BALANCE: "1"
# max timer control blocks
#TAOS_MAX_TMR_CTRL: "512"
# time interval of system monitor, seconds
#TAOS_MONITOR_INTERVAL: "30"
# number of seconds allowed for a dnode to be offline, for cluster only
#TAOS_OFFLINE_THRESHOLD: "8640000"
# RPC re-try timer, millisecond
#TAOS_RPC_TIMER: "1000"
# RPC maximum time for ack, seconds.
#TAOS_RPC_MAX_TIME: "600"
# time interval of dnode status reporting to mnode, seconds, for cluster only
#TAOS_STATUS_INTERVAL: "1"
......@@ -245,37 +195,7 @@ taoscfg:
#TAOS_MIN_SLIDING_TIME: "10"
# minimum time window, milli-second
#TAOS_MIN_INTERVAL_TIME: "10"
# maximum delay before launching a stream computation, milli-second
#TAOS_MAX_STREAM_COMP_DELAY: "20000"
# maximum delay before launching a stream computation for the first time, milli-second
#TAOS_MAX_FIRST_STREAM_COMP_DELAY: "10000"
# retry delay when a stream computation fails, milli-second
#TAOS_RETRY_STREAM_COMP_DELAY: "10"
# the delayed time for launching a stream computation, from 0.1(default, 10% of whole computing time window) to 0.9
#TAOS_STREAM_COMP_DELAY_RATIO: "0.1"
# max number of vgroups per db, 0 means configured automatically
#TAOS_MAX_VGROUPS_PER_DB: "0"
# max number of tables per vnode
#TAOS_MAX_TABLES_PER_VNODE: "1000000"
# the number of acknowledgments required for successful data writing
#TAOS_QUORUM: "1"
# enable/disable compression
#TAOS_COMP: "2"
# write ahead log (WAL) level, 0: no wal; 1: write wal, but no fysnc; 2: write wal, and call fsync
#TAOS_WAL_LEVEL: "1"
# if walLevel is set to 2, the cycle of fsync being executed, if set to 0, fsync is called right away
#TAOS_FSYNC: "3000"
#TAOS_MIN_INTERVAL_TIME: "1"
# the compressed rpc message, option:
# -1 (no compression)
......@@ -283,17 +203,8 @@ taoscfg:
# > 0 (rpc message body which larger than this value will be compressed)
#TAOS_COMPRESS_MSG_SIZE: "-1"
# max length of an SQL
#TAOS_MAX_SQL_LENGTH: "1048576"
# the maximum number of records allowed for super table time sorting
#TAOS_MAX_NUM_OF_ORDERED_RES: "100000"
# max number of connections allowed in dnode
#TAOS_MAX_SHELL_CONNS: "5000"
# max number of connections allowed in client
#TAOS_MAX_CONNECTIONS: "5000"
#TAOS_MAX_SHELL_CONNS: "50000"
# stop writing logs when the disk size of the log folder is less than this value
#TAOS_MINIMAL_LOG_DIR_G_B: "0.1"
......@@ -313,21 +224,8 @@ taoscfg:
# enable/disable system monitor
#TAOS_MONITOR: "1"
# enable/disable recording the SQL statements via restful interface
#TAOS_HTTP_ENABLE_RECORD_SQL: "0"
# number of threads used to process http requests
#TAOS_HTTP_MAX_THREADS: "2"
# maximum number of rows returned by the restful interface
#TAOS_RESTFUL_ROW_LIMIT: "10240"
# The following parameter is used to limit the maximum number of lines in log files.
# max number of lines per log filters
# numOfLogLines 10000000
# enable/disable async log
#TAOS_ASYNC_LOG: "0"
#TAOS_ASYNC_LOG: "1"
#
# time of keeping log files, days
......@@ -344,25 +242,8 @@ taoscfg:
# debug flag for all log type, take effect when non-zero value\
#TAOS_DEBUG_FLAG: "143"
# enable/disable recording the SQL in taos client
#TAOS_ENABLE_RECORD_SQL: "0"
# generate core file when service crash
#TAOS_ENABLE_CORE_FILE: "1"
# maximum display width of binary and nchar fields in the shell. The parts exceeding this limit will be hidden
#TAOS_MAX_BINARY_DISPLAY_WIDTH: "30"
# enable/disable stream (continuous query)
#TAOS_STREAM: "1"
# in retrieve blocking model, only in 50% query threads will be used in query processing in dnode
#TAOS_RETRIEVE_BLOCKING_MODEL: "0"
# the maximum allowed query buffer size in MB during query processing for each data node
# -1 no limit (default)
# 0 no query allowed, queries are disabled
#TAOS_QUERY_BUFFER_SIZE: "-1"
```
## Scaling Out
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docs/en/12-taos-sql/01-data-type.md
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......@@ -11,7 +11,7 @@ When using TDengine to store and query data, the most important part of the data
- The format must be `YYYY-MM-DD HH:mm:ss.MS`, the default time precision is millisecond (ms), for example `2017-08-12 18:25:58.128`
- Internal function `now` can be used to get the current timestamp on the client side
- The current timestamp of the client side is applied when `now` is used to insert data
- Epoch Time：timestamp can also be a long integer number, which means the number of seconds, milliseconds or nanoseconds, depending on the time precision, from 1970-01-01 00:00:00.000 (UTC/GMT)
- Epoch Time：timestamp can also be a long integer number, which means the number of seconds, milliseconds or nanoseconds, depending on the time precision, from UTC 1970-01-01 00:00:00.
- Add/subtract operations can be carried out on timestamps. For example `now-2h` means 2 hours prior to the time at which query is executed. The units of time in operations can be b(nanosecond), u(microsecond), a(millisecond), s(second), m(minute), h(hour), d(day), or w(week). So `select * from t1 where ts > now-2w and ts <= now-1w` means the data between two weeks ago and one week ago. The time unit can also be n (calendar month) or y (calendar year) when specifying the time window for down sampling operations.
Time precision in TDengine can be set by the `PRECISION` parameter when executing `CREATE DATABASE`. The default time precision is millisecond. In the statement below, the precision is set to nanonseconds.
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docs/en/12-taos-sql/03-table.md
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......@@ -57,7 +57,7 @@ table_option: {
3. MAX_DELAY: specifies the maximum latency for pushing computation results. The default value is 15 minutes or the value of the INTERVAL parameter, whichever is smaller. Enter a value between 0 and 15 minutes in milliseconds, seconds, or minutes. You can enter multiple values separated by commas (,). Note: Retain the default value if possible. Configuring a small MAX_DELAY may cause results to be frequently pushed, affecting storage and query performance. This parameter applies only to supertables and takes effect only when the RETENTIONS parameter has been specified for the database.
4. ROLLUP: specifies aggregate functions to roll up. Rolling up a function provides downsampled results based on multiple axes. This parameter applies only to supertables and takes effect only when the RETENTIONS parameter has been specified for the database. You can specify only one function to roll up. The rollup takes effect on all columns except TS. Enter one of the following values: avg, sum, min, max, last, or first.
5. SMA: specifies functions on which to enable small materialized aggregates (SMA). SMA is user-defined precomputation of aggregates based on data blocks. Enter one of the following values: max, min, or sum This parameter can be used with supertables and standard tables.
6. TTL: specifies the time to live (TTL) for the table. If the period specified by the TTL parameter elapses without any data being written to the table, TDengine will automatically delete the table. Note: The system may not delete the table at the exact moment that the TTL expires. Enter a value in days. The default value is 0. Note: The TTL parameter has a higher priority than the KEEP parameter. If a table is marked for deletion because the TTL has expired, it will be deleted even if the time specified by the KEEP parameter has not elapsed. This parameter can be used with standard tables and subtables.
6. TTL: specifies the time to live (TTL) for the table. If TTL is specified when creatinga table, after the time period for which the table has been existing is over TTL, TDengine will automatically delete the table. Please be noted that the system may not delete the table at the exact moment that the TTL expires but guarantee there is such a system and finally the table will be deleted. The unit of TTL is in days. The default value is 0, i.e. never expire.
## Create Subtables
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docs/en/12-taos-sql/06-select.md
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......@@ -171,8 +171,8 @@ The \_QSTART and \_QEND pseudocolumns contain the beginning and end of the time
The \_QSTART and \_QEND pseudocolumns cannot be used in a WHERE clause.
**\_WSTART, \_WEND, and \_DURATION**
\_WSTART, \_WEND, and \_WDURATION pseudocolumns
**\_WSTART, \_WEND, and \_WDURATION**
The \_WSTART, \_WEND, and \_WDURATION pseudocolumns indicate the beginning, end, and duration of a window.
These pseudocolumns can be used only in time window-based aggregations and must occur after the aggregation clause.
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docs/en/12-taos-sql/10-function.md
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......@@ -846,7 +846,7 @@ SELECT FIRST(field_name) FROM { tb_name | stb_name } [WHERE clause];
### INTERP
```sql
SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [ RANGE(timestamp1,timestamp2) ] [EVERY(interval)] [FILL ({ VALUE | PREV | NULL | LINEAR | NEXT})];
SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] RANGE(timestamp1,timestamp2) EVERY(interval) FILL({ VALUE | PREV | NULL | LINEAR | NEXT});
```
**Description**: The value that matches the specified timestamp range is returned, if existing; or an interpolation value is returned.
......@@ -861,11 +861,10 @@ SELECT INTERP(field_name) FROM { tb_name | stb_name } [WHERE where_condition] [
- `INTERP` is used to get the value that matches the specified time slice from a column. If no such value exists an interpolation value will be returned based on `FILL` parameter.
- The input data of `INTERP` is the value of the specified column and a `where` clause can be used to filter the original data. If no `where` condition is specified then all original data is the input.
- The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1<=timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified. If `RANGE` is not specified, then the timestamp of the first row that matches the filter condition is treated as timestamp1, the timestamp of the last row that matches the filter condition is treated as timestamp2.
- The number of rows in the result set of `INTERP` is determined by the parameter `EVERY`. Starting from timestamp1, one interpolation is performed for every time interval specified `EVERY` parameter. If `EVERY` parameter is not used, the time windows will be considered as no ending timestamp, i.e. there is only one time window from timestamp1.
- Interpolation is performed based on `FILL` parameter. No interpolation is performed if `FILL` is not used, that means either the original data that matches is returned or nothing is returned.
- `INTERP` can only be used to interpolate in single timeline. So it must be used with `group by tbname` when it's used on a STable. It can't be used with `GROUP BY` when it's used in the inner query of a nested query.
- The result of `INTERP` is not influenced by `ORDER BY TIMESTAMP`, which impacts the output order only..
- The output time range of `INTERP` is specified by `RANGE(timestamp1,timestamp2)` parameter, with timestamp1<=timestamp2. timestamp1 is the starting point of the output time range and must be specified. timestamp2 is the ending point of the output time range and must be specified.
- The number of rows in the result set of `INTERP` is determined by the parameter `EVERY`. Starting from timestamp1, one interpolation is performed for every time interval specified `EVERY` parameter.
- Interpolation is performed based on `FILL` parameter.
- `INTERP` can only be used to interpolate in single timeline. So it must be used with `partition by tbname` when it's used on a STable.
### LAST
......@@ -1140,7 +1139,7 @@ SELECT STATECOUNT(field_name, oper, val) FROM { tb_name | stb_name } [WHERE clau
**Applicable parameter values**:
- oper : Can be one of `LT` (lower than), `GT` (greater than), `LE` (lower than or equal to), `GE` (greater than or equal to), `NE` (not equal to), `EQ` (equal to), the value is case insensitive
- oper : Can be one of `'LT'` (lower than), `'GT'` (greater than), `'LE'` (lower than or equal to), `'GE'` (greater than or equal to), `'NE'` (not equal to), `'EQ'` (equal to), the value is case insensitive, the value must be in quotes.
- val ： Numeric types
**Return value type**: Integer
......@@ -1167,7 +1166,7 @@ SELECT stateDuration(field_name, oper, val, unit) FROM { tb_name | stb_name } [W
**Applicable parameter values**:
- oper : Can be one of `LT` (lower than), `GT` (greater than), `LE` (lower than or equal to), `GE` (greater than or equal to), `NE` (not equal to), `EQ` (equal to), the value is case insensitive
- oper : Can be one of `'LT'` (lower than), `'GT'` (greater than), `'LE'` (lower than or equal to), `'GE'` (greater than or equal to), `'NE'` (not equal to), `'EQ'` (equal to), the value is case insensitive, the value must be in quotes.
- val ： Numeric types
- unit: The unit of time interval. Enter one of the following options: 1b (nanoseconds), 1u (microseconds), 1a (milliseconds), 1s (seconds), 1m (minutes), 1h (hours), 1d (days), or 1w (weeks) If you do not enter a unit of time, the precision of the current database is used by default.
......@@ -1232,7 +1231,7 @@ SELECT SERVER_VERSION();
### SERVER_STATUS
```sql
SELECT SERVER_VERSION();
SELECT SERVER_STATUS();
```
**Description**: The server status.
docs/en/12-taos-sql/12-distinguished.md
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......@@ -58,6 +58,15 @@ The following restrictions apply:
- The window clause cannot be used with a GROUP BY clause.
- `WHERE` clause can be used to specify the starting and ending time and other filter conditions
### Window Pseudocolumns
**\_WSTART, \_WEND, and \_WDURATION**
The \_WSTART, \_WEND, and \_WDURATION pseudocolumns indicate the beginning, end, and duration of a window.
These pseudocolumns occur after the aggregation clause.
### FILL Clause
`FILL` clause is used to specify how to fill when there is data missing in any window, including:
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docs/en/12-taos-sql/19-limit.md
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......@@ -30,7 +30,7 @@ The following characters cannot occur in a password: single quotation marks ('),
- Maximum number of columns is 4096. There must be at least 2 columns, and the first column must be timestamp.
- The maximum length of a tag name is 64 bytes
- Maximum number of tags is 128. There must be at least 1 tag. The total length of tag values cannot exceed 16 KB.
- Maximum length of single SQL statement is 1 MB (1048576 bytes). It can be configured in the parameter `maxSQLLength` in the client side, the applicable range is [65480, 1048576].
- Maximum length of single SQL statement is 1 MB (1048576 bytes).
- At most 4096 columns can be returned by `SELECT`. Functions in the query statement constitute columns. An error is returned if the limit is exceeded.
- Maximum numbers of databases, STables, tables are dependent only on the system resources.
- The number of replicas can only be 1 or 3.
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docs/en/12-taos-sql/24-show.md
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......@@ -3,7 +3,7 @@ sidebar_label: SHOW Statement
title: SHOW Statement for Metadata
---
In addition to running SELECT statements on INFORMATION_SCHEMA, you can also use SHOW to obtain system metadata, information, and status.
`SHOW` command can be used to get brief system information. To get details about metatadata, information, and status in the system, please use `select` to query the tables in database `INFORMATION_SCHEMA`.
## SHOW ACCOUNTS
......@@ -194,7 +194,7 @@ Shows information about streams in the system.
SHOW SUBSCRIPTIONS;
```
Shows all subscriptions in the current database.
Shows all subscriptions in the system.
## SHOW TABLES
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docs/en/13-operation/01-pkg-install.md
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......@@ -15,9 +15,41 @@ About details of installing TDenine, please refer to [Installation Guide](../../
## Uninstall
<Tabs>
<TabItem label="apt-get uninstall" value="aptremove">
<TabItem label="Uninstall apt-get" value="aptremove">
Apt-get package of TDengine can be uninstalled as below:
```bash
$ sudo apt-get remove tdengine
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages will be REMOVED:
tdengine
0 upgraded, 0 newly installed, 1 to remove and 18 not upgraded.
After this operation, 68.3 MB disk space will be freed.
Do you want to continue? [Y/n] y
(Reading database ... 135625 files and directories currently installed.)
Removing tdengine (3.0.0.0) ...
TDengine is removed successfully!
```
Apt-get package of taosTools can be uninstalled as below:
TBD
```
$ sudo apt remove taostools
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages will be REMOVED:
taostools
0 upgraded, 0 newly installed, 1 to remove and 0 not upgraded.
After this operation, 68.3 MB disk space will be freed.
Do you want to continue? [Y/n]
(Reading database ... 147973 files and directories currently installed.)
Removing taostools (2.1.2) ...
```
</TabItem>
<TabItem label="Uninstall Deb" value="debuninst">
......@@ -32,6 +64,14 @@ TDengine is removed successfully!
```
Deb package of taosTools can be uninstalled as below:
```
$ sudo dpkg -r taostools
(Reading database ... 147973 files and directories currently installed.)
Removing taostools (2.1.2) ...
```
</TabItem>
<TabItem label="Uninstall RPM" value="rpmuninst">
......@@ -43,6 +83,13 @@ $ sudo rpm -e tdengine
TDengine is removed successfully!
```
RPM package of taosTools can be uninstalled as below:
```
sudo rpm -e taostools
taosToole is removed successfully!
```
</TabItem>
<TabItem label="Uninstall tar.gz" value="taruninst">
......@@ -54,6 +101,15 @@ $ rmtaos
TDengine is removed successfully!
```
tar.gz package of taosTools can be uninstalled as below:
```
$ rmtaostools
Start to uninstall taos tools ...
taos tools is uninstalled successfully!
```
</TabItem>
<TabItem label="Windows uninstall" value="windows">
Run C:\TDengine\unins000.exe to uninstall TDengine on a Windows system.
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docs/en/13-operation/03-tolerance.md
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......@@ -27,4 +27,4 @@ The number of dnodes in a TDengine cluster must NOT be lower than the number of
As long as the dnodes of a TDengine cluster are deployed on different physical machines and the replica number is higher than 1, high availability can be achieved without any other assistance. For disaster recovery, dnodes of a TDengine cluster should be deployed in geographically different data centers.
Alternatively, you can use taosX to synchronize the data from one TDengine cluster to another cluster in a remote location. For more information, see [taosX](../../reference/taosX).
Alternatively, you can use taosX to synchronize the data from one TDengine cluster to another cluster in a remote location. However, taosX is only available in TDengine enterprise version, for more information please contact tdengine.com.
docs/en/14-reference/02-rest-api/02-rest-api.mdx
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......@@ -10,7 +10,7 @@ One difference from the native connector is that the REST interface is stateless
## Installation
The REST interface does not rely on any TDengine native library, so the client application does not need to install any TDengine libraries. The client application's development language only needs to support the HTTP protocol.
The REST interface does not rely on any TDengine native library, so the client application does not need to install any TDengine libraries. The client application's development language only needs to support the HTTP protocol. The REST interface is provided by [taosAdapter](../taosadapter), to use REST interface you need to make sure `taosAdapter` is running properly.
## Verification
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---
sidebar_position: 1
sidebar_label: C/C++
title: C/C++ Connector
---
......
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---
toc_max_heading_level: 4
sidebar_position: 2
sidebar_label: Java
title: TDengine Java Connector
description: The TDengine Java Connector is implemented on the standard JDBC API and provides native and REST connectors.
......
docs/en/14-reference/03-connector/go.mdx → docs/en/14-reference/03-connector/05-go.mdx
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---
toc_max_heading_level: 4
sidebar_position: 4
sidebar_label: Go
title: TDengine Go Connector
---
......
docs/en/14-reference/03-connector/rust.mdx → docs/en/14-reference/03-connector/06-rust.mdx
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---
toc_max_heading_level: 4
sidebar_position: 5
sidebar_label: Rust
title: TDengine Rust Connector
---
......
docs/en/14-reference/03-connector/python.mdx → docs/en/14-reference/03-connector/07-python.mdx
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---
sidebar_position: 3
sidebar_label: Python
title: TDengine Python Connector
description: "taospy is the official Python connector for TDengine. taospy provides a rich API that makes it easy for Python applications to use TDengine. tasopy wraps both the native and REST interfaces of TDengine, corresponding to the two submodules of tasopy: taos and taosrest. In addition to wrapping the native and REST interfaces, taospy also provides a programming interface that conforms to the Python Data Access Specification (PEP 249), making it easy to integrate taospy with many third-party tools, such as SQLAlchemy and pandas."
......@@ -150,10 +149,19 @@ If the test is successful, it will output the server version information, e.g.
```json
{
"status": "succ",
"head": ["server_version()"],
"column_meta": [["server_version()", 8, 8]],
"data": [["2.4.0.16"]],
"code": 0,
"column_meta": [
[
"server_version()",
"VARCHAR",
7
]
],
"data": [
[
"3.0.0.0"
]
],
"rows": 1
}
```
......
docs/en/14-reference/03-connector/node.mdx → docs/en/14-reference/03-connector/08-node.mdx
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---
toc_max_heading_level: 4
sidebar_position: 6
sidebar_label: Node.js
title: TDengine Node.js Connector
---
......
docs/en/14-reference/03-connector/csharp.mdx → docs/en/14-reference/03-connector/09-csharp.mdx
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---
toc_max_heading_level: 4
sidebar_position: 7
sidebar_label: C#
title: C# Connector
---
......
docs/en/14-reference/04-taosadapter.md
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......@@ -329,4 +329,4 @@ In TDengine server 2.2.x.x or earlier, the TDengine server process (taosd) conta
| 3 | telegrafUseFieldNum | See the taosAdapter telegraf configuration method | |
| 4 | restfulRowLimit | restfulRowLimit | Embedded httpd outputs 10240 rows of data by default, the maximum allowed is 102400. taosAdapter also provides restfulRowLimit but it is not limited by default. You can configure it according to the actual scenario.
| 5 | httpDebugFlag | Not applicable | httpdDebugFlag does not work for taosAdapter |
| 6 | httpDBNameMandatory | N/A | taosAdapter requires the database name to be specified in the URL |
\ No newline at end of file
| 6 | httpDBNameMandatory | N/A | taosAdapter requires the database name to be specified in the URL |
docs/en/14-reference/11-docker/index.md
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......@@ -72,7 +72,7 @@ Next, ensure the hostname "tdengine" is resolvable in `/etc/hosts`.
echo 127.0.0.1 tdengine |sudo tee -a /etc/hosts
```
Finally, the TDengine service can be accessed from the taos shell or any connector with "tdengine" as the server address.
Finally, the TDengine service can be accessed from the TDengine CLI or any connector with "tdengine" as the server address.
```shell
taos -h tdengine -P 6030
......@@ -115,7 +115,7 @@ If you want to start your application in a container, you need to add the corres
```docker
FROM ubuntu:20.04
RUN apt-get update && apt-get install -y wget
ENV TDENGINE_VERSION=2.4.0.0
ENV TDENGINE_VERSION=3.0.0.0
RUN wget -c https://www.taosdata.com/assets-download/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
&& tar xvf TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
&& cd TDengine-client-${TDENGINE_VERSION} \
......@@ -216,7 +216,7 @@ Here is the full Dockerfile:
```docker
FROM golang:1.17.6-buster as builder
ENV TDENGINE_VERSION=2.4.0.0
ENV TDENGINE_VERSION=3.0.0.0
RUN wget -c https://www.taosdata.com/assets-download/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
&& tar xvf TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
&& cd TDengine-client-${TDENGINE_VERSION} \
......@@ -232,7 +232,7 @@ RUN go build
FROM ubuntu:20.04
RUN apt-get update && apt-get install -y wget
ENV TDENGINE_VERSION=2.4.0.0
ENV TDENGINE_VERSION=3.0.0.0
RUN wget -c https://www.taosdata.com/assets-download/TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
&& tar xvf TDengine-client-${TDENGINE_VERSION}-Linux-x64.tar.gz \
&& cd TDengine-client-${TDENGINE_VERSION} \
......@@ -320,7 +320,7 @@ password: taosdata
2. Start the cluster
```shell
$ VERSION=2.4.0.0 docker-compose up -d
$ VERSION=3.0.0.0 docker-compose up -d
Creating network "test_default" with the default driver
Creating volume "test_taosdata-td1" with default driver
Creating volume "test_taoslog-td1" with default driver
......@@ -457,7 +457,7 @@ If you want to deploy a container-based TDengine cluster on multiple hosts, you
The docker-compose file can refer to the previous section. Here is the command to start TDengine with docker swarm:
```shell
$ VERSION=2.4.0 docker stack deploy -c docker-compose.yml taos
$ VERSION=3.0.0.0 docker stack deploy -c docker-compose.yml taos
Creating network taos_inter
Creating network taos_api
Creating service taos_arbitrator
......@@ -473,20 +473,20 @@ Checking status:
$ docker stack ps taos
ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS
79ni8temw59n taos_nginx.1 nginx:latest TM1701 Running Running about a minute ago
3e94u72msiyg taos_adapter.1 tdengine/tdengine:2.4.0 TM1702 Running Running 56 seconds ago
100amjkwzsc6 taos_td-2.1 tdengine/tdengine:2.4.0 TM1703 Running Running about a minute ago
pkjehr2vvaaa taos_td-1.1 tdengine/tdengine:2.4.0 TM1704 Running Running 2 minutes ago
tpzvgpsr1qkt taos_arbitrator.1 tdengine/tdengine:2.4.0 TM1705 Running Running 2 minutes ago
rvss3g5yg6fa taos_adapter.2 tdengine/tdengine:2.4.0 TM1706 Running Running 56 seconds ago
i2augxamfllf taos_adapter.3 tdengine/tdengine:2.4.0 TM1707 Running Running 56 seconds ago
lmjyhzccpvpg taos_adapter.4 tdengine/tdengine:2.4.0 TM1708 Running Running 56 seconds ago
3e94u72msiyg taos_adapter.1 tdengine/tdengine:3.0.0.0 TM1702 Running Running 56 seconds ago
100amjkwzsc6 taos_td-2.1 tdengine/tdengine:3.0.0.0 TM1703 Running Running about a minute ago
pkjehr2vvaaa taos_td-1.1 tdengine/tdengine:3.0.0.0 TM1704 Running Running 2 minutes ago
tpzvgpsr1qkt taos_arbitrator.1 tdengine/tdengine:3.0.0.0 TM1705 Running Running 2 minutes ago
rvss3g5yg6fa taos_adapter.2 tdengine/tdengine:3.0.0.0 TM1706 Running Running 56 seconds ago
i2augxamfllf taos_adapter.3 tdengine/tdengine:3.0.0.0 TM1707 Running Running 56 seconds ago
lmjyhzccpvpg taos_adapter.4 tdengine/tdengine:3.0.0.0 TM1708 Running Running 56 seconds ago
$ docker service ls
ID NAME MODE REPLICAS IMAGE PORTS
561t4lu6nfw6 taos_adapter replicated 4/4 tdengine/tdengine:2.4.0
3hk5ct3q90sm taos_arbitrator replicated 1/1 tdengine/tdengine:2.4.0
561t4lu6nfw6 taos_adapter replicated 4/4 tdengine/tdengine:3.0.0.0
3hk5ct3q90sm taos_arbitrator replicated 1/1 tdengine/tdengine:3.0.0.0
d8qr52envqzu taos_nginx replicated 1/1 nginx:latest *:6041->6041/tcp, *:6044->6044/udp
2isssfvjk747 taos_td-1 replicated 1/1 tdengine/tdengine:2.4.0
9pzw7u02ichv taos_td-2 replicated 1/1 tdengine/tdengine:2.4.0
2isssfvjk747 taos_td-1 replicated 1/1 tdengine/tdengine:3.0.0.0
9pzw7u02ichv taos_td-2 replicated 1/1 tdengine/tdengine:3.0.0.0
```
From the above output, you can see two dnodes, two taosAdapters, and one Nginx reverse proxy service.
......@@ -502,5 +502,5 @@ verify: Service converged
$ docker service ls -f name=taos_adapter
ID NAME MODE REPLICAS IMAGE PORTS
561t4lu6nfw6 taos_adapter replicated 1/1 tdengine/tdengine:2.4.0
561t4lu6nfw6 taos_adapter replicated 1/1 tdengine/tdengine:3.0.0.0
```
docs/en/14-reference/12-config/index.md
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......@@ -82,13 +82,12 @@ The parameters described in this document by the effect that they have on the sy
| Protocol | Default Port | Description | How to configure |
| :------- | :----------- | :----------------------------------------------- | :--------------------------------------------------------------------------------------------- |
| TCP | 6030 | Communication between client and server. In a multi-node cluster, communication between nodes. serverPort |
| TCP | 6041 | REST connection between client and server | refer to [taosAdapter](../taosadapter/) |
| TCP | 6041 | REST connection between client and server | Prior to 2.4.0.0: serverPort+11; After 2.4.0.0 refer to [taosAdapter](/reference/taosadapter/) |
| TCP | 6043 | Service Port of TaosKeeper | The parameter of TaosKeeper |
| TCP | 6044 | Data access port for StatsD | Configurable through taosAdapter parameters.
| UDP | 6045 | Data access for statsd | Configurable through taosAdapter parameters.
| TCP | 6060 | Port of Monitoring Service in Enterprise version | |
### maxShellConns
| Attribute | Description |
......@@ -345,7 +344,7 @@ The charset that takes effect is UTF-8.
| Attribute | Description |
| -------- | --------------------------------- |
| Applicable | Server and Client |
| Meaning | The interval for taos shell to send heartbeat to mnode |
| Meaning | The interval for TDengine CLI to send heartbeat to mnode |
| Unit | second |
| Value Range | 1-120 |
| Default Value | 3 |
......
docs/en/25-application/01-telegraf.md
浏览文件 @ f0b51ac0
......@@ -34,7 +34,7 @@ Please refer to the [official documentation](https://grafana.com/grafana/downloa
### TDengine
Download the latest TDengine-server 2.4.0.x or above from the [Downloads](http://taosdata.com/cn/all-downloads/) page on the TAOSData website and install it.
Download the latest TDengine-server from the [Downloads](http://tdengine.com/en/all-downloads/) page on the TAOSData website and install it.
## Data Connection Setup
......@@ -79,5 +79,5 @@ Click on the plus icon on the left and select `Import` to get the data from `htt
## Wrap-up
The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes.
The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the schemaless protocol parsing feature in TDengine and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes.
Please refer to the official documentation and product implementation cases for other features.
docs/en/25-application/02-collectd.md
浏览文件 @ f0b51ac0
......@@ -37,7 +37,7 @@ Please refer to the [official documentation](https://grafana.com/grafana/downloa
### Install TDengine
Download the latest TDengine-server 2.4.0.x or above from the [Downloads](http://taosdata.com/cn/all-downloads/) page on the TAOSData website and install it.
Download the latest TDengine-server from the [Downloads](http://tdengine.com/en/all-downloads/) page on the TAOSData website and install it.
## Data Connection Setup
......@@ -99,6 +99,6 @@ Download the dashboard json from `https://github.com/taosdata/grafanaplugin/blob
## Wrap-up
TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system, or adapt an existing system, in just a few minutes.
TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing feature in TDengine and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system, or adapt an existing system, in just a few minutes.
For TDengine's powerful data writing and querying performance and other features, please refer to the official documentation and successful product implementation cases.
docs/examples/java/src/main/java/com/taos/example/RestInsertExample.java
浏览文件 @ f0b51ac0
......@@ -16,14 +16,14 @@ public class RestInsertExample {
private static List<String> getRawData() {
return Arrays.asList(
"d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,California.SanFrancisco,2",
"d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,California.SanFrancisco,2",
"d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,California.SanFrancisco,2",
"d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,California.SanFrancisco,3",
"d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,California.LosAngeles,2",
"d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,California.LosAngeles,2",
"d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,California.LosAngeles,3",
"d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,California.LosAngeles,3"
"d1001,2018-10-03 14:38:05.000,10.30000,219,0.31000,'California.SanFrancisco',2",
"d1001,2018-10-03 14:38:15.000,12.60000,218,0.33000,'California.SanFrancisco',2",
"d1001,2018-10-03 14:38:16.800,12.30000,221,0.31000,'California.SanFrancisco',2",
"d1002,2018-10-03 14:38:16.650,10.30000,218,0.25000,'California.SanFrancisco',3",
"d1003,2018-10-03 14:38:05.500,11.80000,221,0.28000,'California.LosAngeles',2",
"d1003,2018-10-03 14:38:16.600,13.40000,223,0.29000,'California.LosAngeles',2",
"d1004,2018-10-03 14:38:05.000,10.80000,223,0.29000,'California.LosAngeles',3",
"d1004,2018-10-03 14:38:06.500,11.50000,221,0.35000,'California.LosAngeles',3"
);
}
......
docs/examples/java/src/main/java/com/taos/example/SubscribeDemo.java
浏览文件 @ f0b51ac0
......@@ -57,7 +57,7 @@ public class SubscribeDemo {
properties.setProperty(TMQConstants.ENABLE_AUTO_COMMIT, "true");
properties.setProperty(TMQConstants.GROUP_ID, "test");
properties.setProperty(TMQConstants.VALUE_DESERIALIZER,
"com.taosdata.jdbc.MetersDeserializer");
"com.taos.example.MetersDeserializer");
// poll data
try (TaosConsumer<Meters> consumer = new TaosConsumer<>(properties)) {
......
docs/examples/java/src/main/java/com/taos/example/highvolume/DataBaseMonitor.java 0 → 100644
浏览文件 @ f0b51ac0
package com.taos.example.highvolume;
import java.sql.*;
/**
* Prepare target database.
* Count total records in database periodically so that we can estimate the writing speed.
*/
public class DataBaseMonitor {
private Connection conn;
private Statement stmt;
public DataBaseMonitor init() throws SQLException {
if (conn == null) {
String jdbcURL = System.getenv("TDENGINE_JDBC_URL");
conn = DriverManager.getConnection(jdbcURL);
stmt = conn.createStatement();
}
return this;
}
public void close() {
try {
stmt.close();
} catch (SQLException e) {
}
try {
conn.close();
} catch (SQLException e) {
}
}
public void prepareDatabase() throws SQLException {
stmt.execute("DROP DATABASE IF EXISTS test");
stmt.execute("CREATE DATABASE test");
stmt.execute("CREATE STABLE test.meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) TAGS (location BINARY(64), groupId INT)");
}
public Long count() throws SQLException {
if (!stmt.isClosed()) {
ResultSet result = stmt.executeQuery("SELECT count(*) from test.meters");
result.next();
return result.getLong(1);
}
return null;
}
/**
* show test.stables;
*
* name | created_time | columns | tags | tables |
* ============================================================================================
* meters | 2022-07-20 08:39:30.902 | 4 | 2 | 620000 |
*/
public Long getTableCount() throws SQLException {
if (!stmt.isClosed()) {
ResultSet result = stmt.executeQuery("show test.stables");
result.next();
return result.getLong(5);
}
return null;
}
}
\ No newline at end of file
docs/examples/java/src/main/java/com/taos/example/highvolume/FastWriteExample.java 0 → 100644
浏览文件 @ f0b51ac0
package com.taos.example.highvolume;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.sql.*;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.BlockingQueue;
public class FastWriteExample {
final static Logger logger = LoggerFactory.getLogger(FastWriteExample.class);
final static int taskQueueCapacity = 1000000;
final static List<BlockingQueue<String>> taskQueues = new ArrayList<>();
final static List<ReadTask> readTasks = new ArrayList<>();
final static List<WriteTask> writeTasks = new ArrayList<>();
final static DataBaseMonitor databaseMonitor = new DataBaseMonitor();
public static void stopAll() {
logger.info("shutting down");
readTasks.forEach(task -> task.stop());
writeTasks.forEach(task -> task.stop());
databaseMonitor.close();
}
public static void main(String[] args) throws InterruptedException, SQLException {
int readTaskCount = args.length > 0 ? Integer.parseInt(args[0]) : 1;
int writeTaskCount = args.length > 1 ? Integer.parseInt(args[1]) : 3;
int tableCount = args.length > 2 ? Integer.parseInt(args[2]) : 1000;
int maxBatchSize = args.length > 3 ? Integer.parseInt(args[3]) : 3000;
logger.info("readTaskCount={}, writeTaskCount={} tableCount={} maxBatchSize={}",
readTaskCount, writeTaskCount, tableCount, maxBatchSize);
databaseMonitor.init().prepareDatabase();
// Create task queues, whiting tasks and start writing threads.
for (int i = 0; i < writeTaskCount; ++i) {
BlockingQueue<String> queue = new ArrayBlockingQueue<>(taskQueueCapacity);
taskQueues.add(queue);
WriteTask task = new WriteTask(queue, maxBatchSize);
Thread t = new Thread(task);
t.setName("WriteThread-" + i);
t.start();
}
// create reading tasks and start reading threads
int tableCountPerTask = tableCount / readTaskCount;
for (int i = 0; i < readTaskCount; ++i) {
ReadTask task = new ReadTask(i, taskQueues, tableCountPerTask);
Thread t = new Thread(task);
t.setName("ReadThread-" + i);
t.start();
}
Runtime.getRuntime().addShutdownHook(new Thread(FastWriteExample::stopAll));
long lastCount = 0;
while (true) {
Thread.sleep(10000);
long numberOfTable = databaseMonitor.getTableCount();
long count = databaseMonitor.count();
logger.info("numberOfTable={} count={} speed={}", numberOfTable, count, (count - lastCount) / 10);
lastCount = count;
}
}
}
\ No newline at end of file
docs/examples/java/src/main/java/com/taos/example/highvolume/MockDataSource.java 0 → 100644
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package com.taos.example.highvolume;
import java.util.Iterator;
/**
* Generate test data
*/
class MockDataSource implements Iterator {
private String tbNamePrefix;
private int tableCount;
private long maxRowsPerTable = 1000000000L;
// 100 milliseconds between two neighbouring rows.
long startMs = System.currentTimeMillis() - maxRowsPerTable * 100;
private int currentRow = 0;
private int currentTbId = -1;
// mock values
String[] location = {"LosAngeles", "SanDiego", "Hollywood", "Compton", "San Francisco"};
float[] current = {8.8f, 10.7f, 9.9f, 8.9f, 9.4f};
int[] voltage = {119, 116, 111, 113, 118};
float[] phase = {0.32f, 0.34f, 0.33f, 0.329f, 0.141f};
public MockDataSource(String tbNamePrefix, int tableCount) {
this.tbNamePrefix = tbNamePrefix;
this.tableCount = tableCount;
}
@Override
public boolean hasNext() {
currentTbId += 1;
if (currentTbId == tableCount) {
currentTbId = 0;
currentRow += 1;
}
return currentRow < maxRowsPerTable;
}
@Override
public String next() {
long ts = startMs + 100 * currentRow;
int groupId = currentTbId % 5 == 0 ? currentTbId / 5 : currentTbId / 5 + 1;
StringBuilder sb = new StringBuilder(tbNamePrefix + "_" + currentTbId + ","); // tbName
sb.append(ts).append(','); // ts
sb.append(current[currentRow % 5]).append(','); // current
sb.append(voltage[currentRow % 5]).append(','); // voltage
sb.append(phase[currentRow % 5]).append(','); // phase
sb.append(location[currentRow % 5]).append(','); // location
sb.append(groupId); // groupID
return sb.toString();
}
}
\ No newline at end of file
docs/examples/java/src/main/java/com/taos/example/highvolume/ReadTask.java 0 → 100644
浏览文件 @ f0b51ac0
package com.taos.example.highvolume;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.Iterator;
import java.util.List;
import java.util.concurrent.BlockingQueue;
class ReadTask implements Runnable {
private final static Logger logger = LoggerFactory.getLogger(ReadTask.class);
private final int taskId;
private final List<BlockingQueue<String>> taskQueues;
private final int queueCount;
private final int tableCount;
private boolean active = true;
public ReadTask(int readTaskId, List<BlockingQueue<String>> queues, int tableCount) {
this.taskId = readTaskId;
this.taskQueues = queues;
this.queueCount = queues.size();
this.tableCount = tableCount;
}
/**
* Assign data received to different queues.
* Here we use the suffix number in table name.
* You are expected to define your own rule in practice.
*
* @param line record received
* @return which queue to use
*/
public int getQueueId(String line) {
String tbName = line.substring(0, line.indexOf(',')); // For example: tb1_101
String suffixNumber = tbName.split("_")[1];
return Integer.parseInt(suffixNumber) % this.queueCount;
}
@Override
public void run() {
logger.info("started");
Iterator<String> it = new MockDataSource("tb" + this.taskId, tableCount);
try {
while (it.hasNext() && active) {
String line = it.next();
int queueId = getQueueId(line);
taskQueues.get(queueId).put(line);
}
} catch (Exception e) {
logger.error("Read Task Error", e);
}
}
public void stop() {
logger.info("stop");
this.active = false;
}
}
\ No newline at end of file
docs/examples/java/src/main/java/com/taos/example/highvolume/SQLWriter.java 0 → 100644
浏览文件 @ f0b51ac0
package com.taos.example.highvolume;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.sql.*;
import java.util.HashMap;
import java.util.Map;
/**
* A helper class encapsulate the logic of writing using SQL.
* <p>
* The main interfaces are two methods:
* <ol>
* <li>{@link SQLWriter#processLine}, which receive raw lines from WriteTask and group them by table names.</li>
* <li>{@link SQLWriter#flush}, which assemble INSERT statement and execute it.</li>
* </ol>
* <p>
* There is a technical skill worth mentioning: we create table as needed when "table does not exist" error occur instead of creating table automatically using syntax "INSET INTO tb USING stb".
* This ensure that checking table existence is a one-time-only operation.
* </p>
*
* </p>
*/
public class SQLWriter {
final static Logger logger = LoggerFactory.getLogger(SQLWriter.class);
private Connection conn;
private Statement stmt;
/**
* current number of buffered records
*/
private int bufferedCount = 0;
/**
* Maximum number of buffered records.
* Flush action will be triggered if bufferedCount reached this value,
*/
private int maxBatchSize;
/**
* Maximum SQL length.
*/
private int maxSQLLength;
/**
* Map from table name to column values. For example:
* "tb001" -> "(1648432611249,2.1,114,0.09) (1648432611250,2.2,135,0.2)"
*/
private Map<String, String> tbValues = new HashMap<>();
/**
* Map from table name to tag values in the same order as creating stable.
* Used for creating table.
*/
private Map<String, String> tbTags = new HashMap<>();
public SQLWriter(int maxBatchSize) {
this.maxBatchSize = maxBatchSize;
}
/**
* Get Database Connection
*
* @return Connection
* @throws SQLException
*/
private static Connection getConnection() throws SQLException {
String jdbcURL = System.getenv("TDENGINE_JDBC_URL");
return DriverManager.getConnection(jdbcURL);
}
/**
* Create Connection and Statement
*
* @throws SQLException
*/
public void init() throws SQLException {
conn = getConnection();
stmt = conn.createStatement();
stmt.execute("use test");
ResultSet rs = stmt.executeQuery("show variables");
while (rs.next()) {
String configName = rs.getString(1);
if ("maxSQLLength".equals(configName)) {
maxSQLLength = Integer.parseInt(rs.getString(2));
logger.info("maxSQLLength={}", maxSQLLength);
}
}
}
/**
* Convert raw data to SQL fragments, group them by table name and cache them in a HashMap.
* Trigger writing when number of buffered records reached maxBachSize.
*
* @param line raw data get from task queue in format: tbName,ts,current,voltage,phase,location,groupId
*/
public void processLine(String line) throws SQLException {
bufferedCount += 1;
int firstComma = line.indexOf(',');
String tbName = line.substring(0, firstComma);
int lastComma = line.lastIndexOf(',');
int secondLastComma = line.lastIndexOf(',', lastComma - 1);
String value = "(" + line.substring(firstComma + 1, secondLastComma) + ") ";
if (tbValues.containsKey(tbName)) {
tbValues.put(tbName, tbValues.get(tbName) + value);
} else {
tbValues.put(tbName, value);
}
if (!tbTags.containsKey(tbName)) {
String location = line.substring(secondLastComma + 1, lastComma);
String groupId = line.substring(lastComma + 1);
String tagValues = "('" + location + "'," + groupId + ')';
tbTags.put(tbName, tagValues);
}
if (bufferedCount == maxBatchSize) {
flush();
}
}
/**
* Assemble INSERT statement using buffered SQL fragments in Map {@link SQLWriter#tbValues} and execute it.
* In case of "Table does not exit" exception, create all tables in the sql and retry the sql.
*/
public void flush() throws SQLException {
StringBuilder sb = new StringBuilder("INSERT INTO ");
for (Map.Entry<String, String> entry : tbValues.entrySet()) {
String tableName = entry.getKey();
String values = entry.getValue();
String q = tableName + " values " + values + " ";
if (sb.length() + q.length() > maxSQLLength) {
executeSQL(sb.toString());
logger.warn("increase maxSQLLength or decrease maxBatchSize to gain better performance");
sb = new StringBuilder("INSERT INTO ");
}
sb.append(q);
}
executeSQL(sb.toString());
tbValues.clear();
bufferedCount = 0;
}
private void executeSQL(String sql) throws SQLException {
try {
stmt.executeUpdate(sql);
} catch (SQLException e) {
// convert to error code defined in taoserror.h
int errorCode = e.getErrorCode() & 0xffff;
if (errorCode == 0x362 || errorCode == 0x218) {
// Table does not exist
createTables();
executeSQL(sql);
} else {
logger.error("Execute SQL: {}", sql);
throw e;
}
} catch (Throwable throwable) {
logger.error("Execute SQL: {}", sql);
throw throwable;
}
}
/**
* Create tables in batch using syntax:
* <p>
* CREATE TABLE [IF NOT EXISTS] tb_name1 USING stb_name TAGS (tag_value1, ...) [IF NOT EXISTS] tb_name2 USING stb_name TAGS (tag_value2, ...) ...;
* </p>
*/
private void createTables() throws SQLException {
StringBuilder sb = new StringBuilder("CREATE TABLE ");
for (String tbName : tbValues.keySet()) {
String tagValues = tbTags.get(tbName);
sb.append("IF NOT EXISTS ").append(tbName).append(" USING meters TAGS ").append(tagValues).append(" ");
}
String sql = sb.toString();
try {
stmt.executeUpdate(sql);
} catch (Throwable throwable) {
logger.error("Execute SQL: {}", sql);
throw throwable;
}
}
public boolean hasBufferedValues() {
return bufferedCount > 0;
}
public int getBufferedCount() {
return bufferedCount;
}
public void close() {
try {
stmt.close();
} catch (SQLException e) {
}
try {
conn.close();
} catch (SQLException e) {
}
}
}
\ No newline at end of file
docs/examples/java/src/main/java/com/taos/example/highvolume/StmtWriter.java 0 → 100644
浏览文件 @ f0b51ac0
package com.taos.example.highvolume;
public class StmtWriter {
}
docs/examples/java/src/main/java/com/taos/example/highvolume/WriteTask.java 0 → 100644
浏览文件 @ f0b51ac0
package com.taos.example.highvolume;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.concurrent.BlockingQueue;
class WriteTask implements Runnable {
private final static Logger logger = LoggerFactory.getLogger(WriteTask.class);
private final int maxBatchSize;
// the queue from which this writing task get raw data.
private final BlockingQueue<String> queue;
// A flag indicate whether to continue.
private boolean active = true;
public WriteTask(BlockingQueue<String> taskQueue, int maxBatchSize) {
this.queue = taskQueue;
this.maxBatchSize = maxBatchSize;
}
@Override
public void run() {
logger.info("started");
String line = null; // data getting from the queue just now.
SQLWriter writer = new SQLWriter(maxBatchSize);
try {
writer.init();
while (active) {
line = queue.poll();
if (line != null) {
// parse raw data and buffer the data.
writer.processLine(line);
} else if (writer.hasBufferedValues()) {
// write data immediately if no more data in the queue
writer.flush();
} else {
// sleep a while to avoid high CPU usage if no more data in the queue and no buffered records, .
Thread.sleep(100);
}
}
if (writer.hasBufferedValues()) {
writer.flush();
}
} catch (Exception e) {
String msg = String.format("line=%s, bufferedCount=%s", line, writer.getBufferedCount());
logger.error(msg, e);
} finally {
writer.close();
}
}
public void stop() {
logger.info("stop");
this.active = false;
}
}
\ No newline at end of file
docs/examples/java/src/test/java/com/taos/test/TestAll.java
浏览文件 @ f0b51ac0
......@@ -23,16 +23,16 @@ public class TestAll {
String jdbcUrl = "jdbc:TAOS://localhost:6030?user=root&password=taosdata";
try (Connection conn = DriverManager.getConnection(jdbcUrl)) {
try (Statement stmt = conn.createStatement()) {
String sql = "INSERT INTO power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000)\n" +
" power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 15:38:15.000',12.60000,218,0.33000)\n" +
" power.d1001 USING power.meters TAGS(California.SanFrancisco, 2) VALUES('2018-10-03 15:38:16.800',12.30000,221,0.31000)\n" +
" power.d1002 USING power.meters TAGS(California.SanFrancisco, 3) VALUES('2018-10-03 15:38:16.650',10.30000,218,0.25000)\n" +
" power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 15:38:05.500',11.80000,221,0.28000)\n" +
" power.d1003 USING power.meters TAGS(California.LosAngeles, 2) VALUES('2018-10-03 15:38:16.600',13.40000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:05.000',10.80000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:06.000',10.80000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:07.000',10.80000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS(California.LosAngeles, 3) VALUES('2018-10-03 15:38:08.500',11.50000,221,0.35000)";
String sql = "INSERT INTO power.d1001 USING power.meters TAGS('California.SanFrancisco', 2) VALUES('2018-10-03 14:38:05.000',10.30000,219,0.31000)\n" +
" power.d1001 USING power.meters TAGS('California.SanFrancisco', 2) VALUES('2018-10-03 15:38:15.000',12.60000,218,0.33000)\n" +
" power.d1001 USING power.meters TAGS('California.SanFrancisco', 2) VALUES('2018-10-03 15:38:16.800',12.30000,221,0.31000)\n" +
" power.d1002 USING power.meters TAGS('California.SanFrancisco', 3) VALUES('2018-10-03 15:38:16.650',10.30000,218,0.25000)\n" +
" power.d1003 USING power.meters TAGS('California.LosAngeles', 2) VALUES('2018-10-03 15:38:05.500',11.80000,221,0.28000)\n" +
" power.d1003 USING power.meters TAGS('California.LosAngeles', 2) VALUES('2018-10-03 15:38:16.600',13.40000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS('California.LosAngeles', 3) VALUES('2018-10-03 15:38:05.000',10.80000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS('California.LosAngeles', 3) VALUES('2018-10-03 15:38:06.000',10.80000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS('California.LosAngeles', 3) VALUES('2018-10-03 15:38:07.000',10.80000,223,0.29000)\n" +
" power.d1004 USING power.meters TAGS('California.LosAngeles', 3) VALUES('2018-10-03 15:38:08.500',11.50000,221,0.35000)";
stmt.execute(sql);
}
......
docs/examples/python/connect_native_reference.py
浏览文件 @ f0b51ac0
......@@ -11,10 +11,10 @@ conn: taos.TaosConnection = taos.connect(host="localhost",
server_version = conn.server_info
print("server_version", server_version)
client_version = conn.client_info
print("client_version", client_version) # 2.4.0.16
print("client_version", client_version) # 3.0.0.0
conn.close()
# possible output:
# 2.4.0.16
# 2.4.0.16
# 3.0.0.0
# 3.0.0.0
docs/examples/python/fast_write_example.py 0 → 100644
浏览文件 @ f0b51ac0
# install dependencies:
# recommend python >= 3.8
# pip3 install faster-fifo
#
import logging
import math
import sys
import time
import os
from multiprocessing import Process
from faster_fifo import Queue
from mockdatasource import MockDataSource
from queue import Empty
from typing import List
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG, format="%(asctime)s [%(name)s] - %(message)s")
READ_TASK_COUNT = 1
WRITE_TASK_COUNT = 1
TABLE_COUNT = 1000
QUEUE_SIZE = 1000000
MAX_BATCH_SIZE = 3000
read_processes = []
write_processes = []
def get_connection():
"""
If variable TDENGINE_FIRST_EP is provided then it will be used. If not, firstEP in /etc/taos/taos.cfg will be used.
You can also override the default username and password by supply variable TDENGINE_USER and TDENGINE_PASSWORD
"""
import taos
firstEP = os.environ.get("TDENGINE_FIRST_EP")
if firstEP:
host, port = firstEP.split(":")
else:
host, port = None, 0
user = os.environ.get("TDENGINE_USER", "root")
password = os.environ.get("TDENGINE_PASSWORD", "taosdata")
return taos.connect(host=host, port=int(port), user=user, password=password)
# ANCHOR: read
def run_read_task(task_id: int, task_queues: List[Queue]):
table_count_per_task = TABLE_COUNT // READ_TASK_COUNT
data_source = MockDataSource(f"tb{task_id}", table_count_per_task)
try:
for batch in data_source:
for table_id, rows in batch:
# hash data to different queue
i = table_id % len(task_queues)
# block putting forever when the queue is full
task_queues[i].put_many(rows, block=True, timeout=-1)
except KeyboardInterrupt:
pass
# ANCHOR_END: read
# ANCHOR: write
def run_write_task(task_id: int, queue: Queue):
from sql_writer import SQLWriter
log = logging.getLogger(f"WriteTask-{task_id}")
writer = SQLWriter(get_connection)
lines = None
try:
while True:
try:
# get as many as possible
lines = queue.get_many(block=False, max_messages_to_get=MAX_BATCH_SIZE)
writer.process_lines(lines)
except Empty:
time.sleep(0.01)
except KeyboardInterrupt:
pass
except BaseException as e:
log.debug(f"lines={lines}")
raise e
# ANCHOR_END: write
def set_global_config():
argc = len(sys.argv)
if argc > 1:
global READ_TASK_COUNT
READ_TASK_COUNT = int(sys.argv[1])
if argc > 2:
global WRITE_TASK_COUNT
WRITE_TASK_COUNT = int(sys.argv[2])
if argc > 3:
global TABLE_COUNT
TABLE_COUNT = int(sys.argv[3])
if argc > 4:
global QUEUE_SIZE
QUEUE_SIZE = int(sys.argv[4])
if argc > 5:
global MAX_BATCH_SIZE
MAX_BATCH_SIZE = int(sys.argv[5])
# ANCHOR: monitor
def run_monitor_process():
log = logging.getLogger("DataBaseMonitor")
conn = get_connection()
conn.execute("DROP DATABASE IF EXISTS test")
conn.execute("CREATE DATABASE test")
conn.execute("CREATE STABLE test.meters (ts TIMESTAMP, current FLOAT, voltage INT, phase FLOAT) "
"TAGS (location BINARY(64), groupId INT)")
def get_count():
res = conn.query("SELECT count(*) FROM test.meters")
rows = res.fetch_all()
return rows[0][0] if rows else 0
last_count = 0
while True:
time.sleep(10)
count = get_count()
log.info(f"count={count} speed={(count - last_count) / 10}")
last_count = count
# ANCHOR_END: monitor
# ANCHOR: main
def main():
set_global_config()
logging.info(f"READ_TASK_COUNT={READ_TASK_COUNT}, WRITE_TASK_COUNT={WRITE_TASK_COUNT}, "
f"TABLE_COUNT={TABLE_COUNT}, QUEUE_SIZE={QUEUE_SIZE}, MAX_BATCH_SIZE={MAX_BATCH_SIZE}")
monitor_process = Process(target=run_monitor_process)
monitor_process.start()
time.sleep(3) # waiting for database ready.
task_queues: List[Queue] = []
# create task queues
for i in range(WRITE_TASK_COUNT):
queue = Queue(max_size_bytes=QUEUE_SIZE)
task_queues.append(queue)
# create write processes
for i in range(WRITE_TASK_COUNT):
p = Process(target=run_write_task, args=(i, task_queues[i]))
p.start()
logging.debug(f"WriteTask-{i} started with pid {p.pid}")
write_processes.append(p)
# create read processes
for i in range(READ_TASK_COUNT):
queues = assign_queues(i, task_queues)
p = Process(target=run_read_task, args=(i, queues))
p.start()
logging.debug(f"ReadTask-{i} started with pid {p.pid}")
read_processes.append(p)
try:
monitor_process.join()
except KeyboardInterrupt:
monitor_process.terminate()
[p.terminate() for p in read_processes]
[p.terminate() for p in write_processes]
[q.close() for q in task_queues]
def assign_queues(read_task_id, task_queues):
"""
Compute target queues for a specific read task.
"""
ratio = WRITE_TASK_COUNT / READ_TASK_COUNT
from_index = math.floor(read_task_id * ratio)
end_index = math.ceil((read_task_id + 1) * ratio)
return task_queues[from_index:end_index]
if __name__ == '__main__':
main()
# ANCHOR_END: main
docs/examples/python/mockdatasource.py 0 → 100644
浏览文件 @ f0b51ac0
import time
class MockDataSource:
samples = [
"8.8,119,0.32,LosAngeles,0",
"10.7,116,0.34,SanDiego,1",
"9.9,111,0.33,Hollywood,2",
"8.9,113,0.329,Compton,3",
"9.4,118,0.141,San Francisco,4"
]
def __init__(self, tb_name_prefix, table_count):
self.table_name_prefix = tb_name_prefix + "_"
self.table_count = table_count
self.max_rows = 10000000
self.current_ts = round(time.time() * 1000) - self.max_rows * 100
# [(tableId, tableName, values),]
self.data = self._init_data()
def _init_data(self):
lines = self.samples * (self.table_count // 5 + 1)
data = []
for i in range(self.table_count):
table_name = self.table_name_prefix + str(i)
data.append((i, table_name, lines[i])) # tableId, row
return data
def __iter__(self):
self.row = 0
return self
def __next__(self):
"""
next 1000 rows for each table.
return: {tableId:[row,...]}
"""
# generate 1000 timestamps
ts = []
for _ in range(1000):
self.current_ts += 100
ts.append(str(self.current_ts))
# add timestamp to each row
# [(tableId, ["tableName,ts,current,voltage,phase,location,groupId"])]
result = []
for table_id, table_name, values in self.data:
rows = [table_name + ',' + t + ',' + values for t in ts]
result.append((table_id, rows))
return result
docs/examples/python/sql_writer.py 0 → 100644
浏览文件 @ f0b51ac0
import logging
import taos
class SQLWriter:
log = logging.getLogger("SQLWriter")
def __init__(self, get_connection_func):
self._tb_values = {}
self._tb_tags = {}
self._conn = get_connection_func()
self._max_sql_length = self.get_max_sql_length()
self._conn.execute("USE test")
def get_max_sql_length(self):
rows = self._conn.query("SHOW variables").fetch_all()
for r in rows:
name = r[0]
if name == "maxSQLLength":
return int(r[1])
return 1024 * 1024
def process_lines(self, lines: str):
"""
:param lines: [[tbName,ts,current,voltage,phase,location,groupId]]
"""
for line in lines:
ps = line.split(",")
table_name = ps[0]
value = '(' + ",".join(ps[1:-2]) + ') '
if table_name in self._tb_values:
self._tb_values[table_name] += value
else:
self._tb_values[table_name] = value
if table_name not in self._tb_tags:
location = ps[-2]
group_id = ps[-1]
tag_value = f"('{location}',{group_id})"
self._tb_tags[table_name] = tag_value
self.flush()
def flush(self):
"""
Assemble INSERT statement and execute it.
When the sql length grows close to MAX_SQL_LENGTH, the sql will be executed immediately, and a new INSERT statement will be created.
In case of "Table does not exit" exception, tables in the sql will be created and the sql will be re-executed.
"""
sql = "INSERT INTO "
sql_len = len(sql)
buf = []
for tb_name, values in self._tb_values.items():
q = tb_name + " VALUES " + values
if sql_len + len(q) >= self._max_sql_length:
sql += " ".join(buf)
self.execute_sql(sql)
sql = "INSERT INTO "
sql_len = len(sql)
buf = []
buf.append(q)
sql_len += len(q)
sql += " ".join(buf)
self.execute_sql(sql)
self._tb_values.clear()
def execute_sql(self, sql):
try:
self._conn.execute(sql)
except taos.Error as e:
error_code = e.errno & 0xffff
# Table does not exit
if error_code == 9731:
self.create_tables()
else:
self.log.error("Execute SQL: %s", sql)
raise e
except BaseException as baseException:
self.log.error("Execute SQL: %s", sql)
raise baseException
def create_tables(self):
sql = "CREATE TABLE "
for tb in self._tb_values.keys():
tag_values = self._tb_tags[tb]
sql += "IF NOT EXISTS " + tb + " USING meters TAGS " + tag_values + " "
try:
self._conn.execute(sql)
except BaseException as e:
self.log.error("Execute SQL: %s", sql)
raise e
docs/zh/02-intro.md
浏览文件 @ f0b51ac0
---
title: 产品简介
description: 简要介绍 TDengine 的主要功能
toc_max_heading_level: 2
---
......@@ -22,8 +23,8 @@ TDengine的主要功能如下：
9. 提供[命令行程序](../reference/taos-shell)，便于管理集群，检查系统状态，做即席查询
10. 提供多种数据的[导入](../operation/import)、[导出](../operation/export)
11. 支持对[TDengine 集群本身的监控](../operation/monitor)
12. 提供 [C/C++](../reference/connector/cpp), [Java](../reference/connector/java), [Python](../reference/connector/python), [Go](../reference/connector/go), [Rust](../reference/connector/rust), [Node.js](../reference/connector/node) 等多种编程语言的[连接器](../reference/connector/)
13. 支持 [REST 接口](../reference/rest-api/)
12. 提供各种语言的[连接器](../connector): 如 C/C++, Java, Go, Node.JS, Rust, Python, C# 等
13. 支持 [REST 接口](../connector/rest-api/)
14. 支持与[ Grafana 无缝集成](../third-party/grafana)
15. 支持与 Google Data Studio 无缝集成
16. 支持 [Kubernetes 部署](../deployment/k8s)
......
docs/zh/04-concept/index.md
浏览文件 @ f0b51ac0
---
sidebar_label: 基本概念
title: 数据模型和基本概念
description: TDengine 的数据模型和基本概念
---
为了便于解释基本概念，便于撰写示例程序，整个 TDengine 文档以智能电表作为典型时序数据场景。假设每个智能电表采集电流、电压、相位三个量，有多个智能电表，每个电表有位置 location 和分组 group ID 的静态属性. 其采集的数据类似如下的表格：
......@@ -104,15 +106,15 @@ title: 数据模型和基本概念
## 采集量 (Metric)
采集量是指传感器、设备或其他类型采集点采集的物理量，比如电流、电压、温度、压力、GPS 位置等，是随时间变化的，数据类型可以是整型、浮点型、布尔型，也可是字符串。随着时间的推移，存储的采集量的数据量越来越大。
采集量是指传感器、设备或其他类型采集点采集的物理量，比如电流、电压、温度、压力、GPS 位置等，是随时间变化的，数据类型可以是整型、浮点型、布尔型，也可是字符串。随着时间的推移，存储的采集量的数据量越来越大。智能电表示例中的电流、电压、相位就是采集量。
## 标签 (Label/Tag)
标签是指传感器、设备或其他类型采集点的静态属性，不是随时间变化的，比如设备型号、颜色、设备的所在地等，数据类型可以是任何类型。虽然是静态的，但 TDengine 容许用户修改、删除或增加标签值。与采集量不一样的是，随时间的推移，存储的标签的数据量不会有什么变化。
标签是指传感器、设备或其他类型采集点的静态属性，不是随时间变化的，比如设备型号、颜色、设备的所在地等，数据类型可以是任何类型。虽然是静态的，但 TDengine 容许用户修改、删除或增加标签值。与采集量不一样的是，随时间的推移，存储的标签的数据量不会有什么变化。智能电表示例中的location与groupId就是标签。
## 数据采集点 (Data Collection Point)
数据采集点是指按照预设时间周期或受事件触发采集物理量的硬件或软件。一个数据采集点可以采集一个或多个采集量，**但这些采集量都是同一时刻采集的，具有相同的时间戳**。对于复杂的设备，往往有多个数据采集点，每个数据采集点采集的周期都可能不一样，而且完全独立，不同步。比如对于一台汽车，有数据采集点专门采集 GPS 位置，有数据采集点专门采集发动机状态，有数据采集点专门采集车内的环境，这样一台汽车就有三个数据采集点。
数据采集点是指按照预设时间周期或受事件触发采集物理量的硬件或软件。一个数据采集点可以采集一个或多个采集量，**但这些采集量都是同一时刻采集的，具有相同的时间戳**。对于复杂的设备，往往有多个数据采集点，每个数据采集点采集的周期都可能不一样，而且完全独立，不同步。比如对于一台汽车，有数据采集点专门采集 GPS 位置，有数据采集点专门采集发动机状态，有数据采集点专门采集车内的环境，这样一台汽车就有三个数据采集点。智能电表示例中的d1001, d1002, d1003, d1004等就是数据采集点。
## 表 (Table)
......@@ -131,13 +133,14 @@ TDengine 建议用数据采集点的名字（如上表中的 D1001）来做表
对于复杂的设备，比如汽车，它有多个数据采集点，那么就需要为一台汽车建立多张表。
## 超级表 (STable)
由于一个数据采集点一张表，导致表的数量巨增，难以管理，而且应用经常需要做采集点之间的聚合操作，聚合的操作也变得复杂起来。为解决这个问题，TDengine 引入超级表（Super Table，简称为 STable）的概念。
超级表是指某一特定类型的数据采集点的集合。同一类型的数据采集点，其表的结构是完全一样的，但每个表（数据采集点）的静态属性（标签）是不一样的。描述一个超级表（某一特定类型的数据采集点的集合），除需要定义采集量的表结构之外，还需要定义其标签的 schema，标签的数据类型可以是整数、浮点数、字符串，标签可以有多个，可以事后增加、删除或修改。如果整个系统有 N 个不同类型的数据采集点，就需要建立 N 个超级表。
在 TDengine 的设计里，**表用来代表一个具体的数据采集点，超级表用来代表一组相同类型的数据采集点集合**。
在 TDengine 的设计里，**表用来代表一个具体的数据采集点，超级表用来代表一组相同类型的数据采集点集合**。智能电表示例中，我们可以创建一个超级表meters.
## 子表 (Subtable)
......@@ -156,7 +159,9 @@ TDengine 建议用数据采集点的名字（如上表中的 D1001）来做表
查询既可以在表上进行，也可以在超级表上进行。针对超级表的查询，TDengine 将把所有子表中的数据视为一个整体数据集进行处理，会先把满足标签过滤条件的表从超级表中找出来，然后再扫描这些表的时序数据，进行聚合操作，这样需要扫描的数据集会大幅减少，从而显著提高查询的性能。本质上，TDengine 通过对超级表查询的支持，实现了多个同类数据采集点的高效聚合。
TDengine系统建议给一个数据采集点建表，需要通过超级表建表，而不是建普通表。
TDengine系统建议给一个数据采集点建表，需要通过超级表建表，而不是建普通表。在智能电表的示例中，我们可以通过超级表meters创建子表d1001, d1002, d1003, d1004等。
为了更好地理解超级与子表的关系，可以参考下面关于智能电表数据模型的示意图。 ![智能电表数据模型示意图](./supertable.webp)
## 库 (database)
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---
sidebar_label: Docker
title: 通过 Docker 快速体验 TDengine
description: 使用 Docker 快速体验 TDengine 的高效写入和查询
---
本节首先介绍如何通过 Docker 快速体验 TDengine，然后介绍如何在 Docker 环境下体验 TDengine 的写入和查询功能。如果你不熟悉 Docker，请使用[安装包的方式快速体验](../../get-started/package/)。如果您希望为 TDengine 贡献代码或对内部技术实现感兴趣，请参考 [TDengine GitHub 主页](https://github.com/taosdata/TDengine) 下载源码构建和安装.
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---
sidebar_label: 安装包
title: 使用安装包立即开始
description: 使用安装包快速体验 TDengine
---
import Tabs from "@theme/Tabs";
......@@ -9,7 +10,7 @@ import PkgListV3 from "/components/PkgListV3";
您可以[用 Docker 立即体验](../../get-started/docker/) TDengine。如果您希望对 TDengine 贡献代码或对内部实现感兴趣，请参考我们的 [TDengine GitHub 主页](https://github.com/taosdata/TDengine) 下载源码构建和安装.
TDengine 完整的软件包包括服务端（taosd）、用于与第三方系统对接并提供 RESTful 接口的 taosAdapter、应用驱动（taosc）、命令行程序 (CLI，taos) 和一些工具软件。目前 taosAdapter 仅在 Linux 系统上安装和运行，后续将支持 Windows、macOS 等系统。TDengine 除了提供多种语言的连接器之外，还通过 [taosAdapter](../../reference/taosadapter/) 提供 [RESTful 接口](../../reference/rest-api/)。
TDengine 完整的软件包包括服务端（taosd）、用于与第三方系统对接并提供 RESTful 接口的 taosAdapter、应用驱动（taosc）、命令行程序 (CLI，taos) 和一些工具软件。目前 taosAdapter 仅在 Linux 系统上安装和运行，后续将支持 Windows、macOS 等系统。TDengine 除了提供多种语言的连接器之外，还通过 [taosAdapter](../../reference/taosadapter/) 提供 [RESTful 接口](../../connector/rest-api/)。
为方便使用，标准的服务端安装包包含了 taosd、taosAdapter、taosc、taos、taosdump、taosBenchmark、TDinsight 安装脚本和示例代码；如果您只需要用到服务端程序和客户端连接的 C/C++ 语言支持，也可以仅下载 lite 版本的安装包。
......@@ -67,13 +68,6 @@ install.sh 安装脚本在执行过程中，会通过命令行交互界面询问
</TabItem>
<TabItem label="Windows 安装" value="windows">
1. 从列表中下载获得 exe 安装程序；
<PkgListV3 type={3}/>
2. 运行可执行程序来安装 TDengine。
</TabItem>
<TabItem value="apt-get" label="apt-get">
可以使用 apt-get 工具从官方仓库安装。
......@@ -102,11 +96,20 @@ sudo apt-get install tdengine
:::tip
apt-get 方式只适用于 Debian 或 Ubuntu 系统
::::
</TabItem>
<TabItem label="Windows 安装" value="windows">
注意：目前 TDengine 在 Windows 平台上只支持 Windows server 2016/2019 和 Windows 10/11 系统版本。
1. 从列表中下载获得 exe 安装程序；
<PkgListV3 type={3}/>
2. 运行可执行程序来安装 TDengine。
</TabItem>
</Tabs>
:::info
下载其他组件、最新 Beta 版及之前版本的安装包，请点击[发布历史页面](../../releases)
下载其他组件、最新 Beta 版及之前版本的安装包，请点击[发布历史页面](../../releases/tdengine)
:::
:::note
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import PkgList from "/components/PkgList";
TDengine 的安装非常简单，从下载到安装成功仅仅只要几秒钟。
为方便使用，从 2.4.0.10 开始，标准的服务端安装包包含了 taos、taosd、taosAdapter、taosdump、taosBenchmark、TDinsight 安装脚本和示例代码；如果您只需要用到服务端程序和客户端连接的 C/C++ 语言支持，也可以仅下载 lite 版本的安装包。
在安装包格式上，我们提供 tar.gz, rpm 和 deb 格式，为企业客户提供 tar.gz 格式安装包，以方便在特定操作系统上使用。需要注意的是，rpm 和 deb 包不含 taosdump、taosBenchmark 和 TDinsight 安装脚本，这些工具需要通过安装 taosTool 包获得。
发布版本包括稳定版和 Beta 版，Beta 版含有更多新功能。正式上线或测试建议安装稳定版。您可以根据需要选择下载：
<PkgList type={0}/>
具体的安装方法，请参见[安装包的安装和卸载](/operation/pkg-install)。
下载其他组件、最新 Beta 版及之前版本的安装包，请点击[这里](https://www.taosdata.com/all-downloads)
查看 Release Notes, 请点击[这里](https://github.com/taosdata/TDengine/releases)
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......@@ -3,7 +3,7 @@ title: 立即开始
description: '快速设置 TDengine 环境并体验其高效写入和查询'
---
TDengine 完整的软件包包括服务端（taosd）、用于与第三方系统对接并提供 RESTful 接口的 taosAdapter、应用驱动（taosc）、命令行程序 (CLI，taos) 和一些工具软件。TDengine 除了提供多种语言的连接器之外，还通过 [taosAdapter](/reference/taosadapter) 提供 [RESTful 接口](/reference/rest-api)。
TDengine 完整的软件包包括服务端（taosd）、用于与第三方系统对接并提供 RESTful 接口的 taosAdapter、应用驱动（taosc）、命令行程序 (CLI，taos) 和一些工具软件。TDengine 除了提供多种语言的连接器之外，还通过 [taosAdapter](../reference/taosadapter) 提供 [RESTful 接口](../connector/rest-api)。
本章主要介绍如何利用 Docker 或者安装包快速设置 TDengine 环境并体验其高效写入和查询。
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{{#include docs/examples/java/src/main/java/com/taos/example/WSConnectExample.java:main}}
```
更多连接参数配置，参考[Java 连接器](/reference/connector/java)
更多连接参数配置，参考[Java 连接器](../../connector/java)
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---
title: 建立连接
description: "本节介绍如何使用连接器建立与 TDengine 的连接，给出连接器安装、连接的简单说明。"
description: 使用连接器建立与 TDengine 的连接，以及连接器的安装和连接
---
import Tabs from "@theme/Tabs";
......@@ -14,10 +14,10 @@ import ConnCSNative from "./_connect_cs.mdx";
import ConnC from "./_connect_c.mdx";
import ConnR from "./_connect_r.mdx";
import ConnPHP from "./_connect_php.mdx";
import InstallOnWindows from "../../14-reference/03-connector/_linux_install.mdx";
import InstallOnLinux from "../../14-reference/03-connector/_windows_install.mdx";
import VerifyLinux from "../../14-reference/03-connector/_verify_linux.mdx";
import VerifyWindows from "../../14-reference/03-connector/_verify_windows.mdx";
import InstallOnWindows from "../../08-connector/_linux_install.mdx";
import InstallOnLinux from "../../08-connector/_windows_install.mdx";
import VerifyLinux from "../../08-connector/_verify_linux.mdx";
import VerifyWindows from "../../08-connector/_verify_windows.mdx";
TDengine 提供了丰富的应用程序开发接口，为了便于用户快速开发自己的应用，TDengine 支持了多种编程语言的连接器，其中官方连接器包括支持 C/C++、Java、Python、Go、Node.js、C#、Rust、Lua（社区贡献）和 PHP （社区贡献）的连接器。这些连接器支持使用原生接口（taosc）和 REST 接口（部分语言暂不支持）连接 TDengine 集群。社区开发者也贡献了多个非官方连接器，例如 ADO.NET 连接器、Lua 连接器和 PHP 连接器。
......@@ -33,7 +33,7 @@ TDengine 提供了丰富的应用程序开发接口，为了便于用户快速
关键不同点在于：
1. 使用 REST 连接，用户无需安装客户端驱动程序 taosc，具有跨平台易用的优势，但性能要下降 30%左右。
2. 使用原生连接可以体验 TDengine 的全部功能，如[参数绑定接口](/reference/connector/cpp#参数绑定-api)、[订阅](/reference/connector/cpp#订阅和消费-api)等等。
2. 使用原生连接可以体验 TDengine 的全部功能，如[参数绑定接口](../../connector/cpp/#参数绑定-api)、[订阅](../../connector/cpp/#订阅和消费-api)等等。
## 安装客户端驱动 taosc
......@@ -223,7 +223,7 @@ phpize && ./configure && make -j && make install
**手动指定 TDengine 目录：**
```shell
phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/2.4.0.0 && make -j && make install
phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/3.0.0.0 && make -j && make install
```
> `--with-tdengine-dir=` 后跟上 TDengine 目录。
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---
sidebar_label: 数据建模
title: TDengine 数据建模
description: TDengine 中如何建立数据模型
---
TDengine 采用类关系型数据模型，需要建库、建表。因此对于一个具体的应用场景，需要考虑库、超级表和普通表的设计。本节不讨论细致的语法规则，只介绍概念。
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## SQL 写入简介
应用通过连接器执行 INSERT 语句来插入数据，用户还可以通过 TAOS Shell，手动输入 INSERT 语句插入数据。
应用通过连接器执行 INSERT 语句来插入数据，用户还可以通过 TDengine CLI，手动输入 INSERT 语句插入数据。
### 一次写入一条
下面这条 INSERT 就将一条记录写入到表 d1001 中：
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---
sidebar_label: 写入数据
title: 写入数据
description: TDengine 的各种写入方式
---
TDengine 支持多种写入协议，包括 SQL，InfluxDB Line 协议, OpenTSDB Telnet 协议，OpenTSDB JSON 格式协议。数据可以单条插入，也可以批量插入，可以插入一个数据采集点的数据，也可以同时插入多个数据采集点的数据。同时，TDengine 支持多线程插入，支持时间乱序数据插入，也支持历史数据插入。InfluxDB Line 协议、OpenTSDB Telnet 协议和 OpenTSDB JSON 格式协议是 TDengine 支持的三种无模式写入协议。使用无模式方式写入无需提前创建超级表和子表，并且引擎能自适用数据对表结构做调整。
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---
sidebar_label: 查询数据
title: 查询数据
description: "主要查询功能，通过连接器执行同步查询和异步查询"
---
......@@ -51,7 +52,7 @@ Query OK, 2 row(s) in set (0.001100s)
### 示例一
在 TAOS Shell，查找加利福尼亚州所有智能电表采集的电压平均值，并按照 location 分组。
在 TDengine CLI，查找加利福尼亚州所有智能电表采集的电压平均值，并按照 location 分组。
```
taos> SELECT AVG(voltage), location FROM meters GROUP BY location;
......@@ -64,7 +65,7 @@ Query OK, 2 rows in database (0.005995s)
### 示例二
在 TAOS shell, 查找 groupId 为 2 的所有智能电表的记录条数，电流的最大值。
在 TDengine CLI, 查找 groupId 为 2 的所有智能电表的记录条数，电流的最大值。
```
taos> SELECT count(*), max(current) FROM meters where groupId = 2;
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......@@ -64,7 +64,7 @@ DLL_EXPORT void tmq_conf_destroy(tmq_conf_t *conf);
DLL_EXPORT void tmq_conf_set_auto_commit_cb(tmq_conf_t *conf, tmq_commit_cb *cb, void *param);
```
这些 API 的文档请见 [C/C++ Connector](/reference/connector/cpp)，下面介绍一下它们的具体用法（超级表和子表结构请参考“数据建模”一节），完整的示例代码请见下面 C 语言的示例代码。
这些 API 的文档请见 [C/C++ Connector](../../connector/cpp)，下面介绍一下它们的具体用法（超级表和子表结构请参考“数据建模”一节），完整的示例代码请见下面 C 语言的示例代码。
</TabItem>
<TabItem value="java" label="Java">
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......@@ -20,11 +20,11 @@ create database db0 vgroups 100 buffer 16MB
## 读缓存
在创建数据库时可以选择是否缓存该数据库中每个子表的最新数据。由参数 cachelast 设置，分为三种情况：
- 0: 不缓存
- 1: 缓存子表最近一行数据，这将显著改善 last_row 函数的性能
- 2: 缓存子表每一列最近的非 NULL 值，这将显著改善无特殊影响（比如 WHERE, ORDER BY, GROUP BY, INTERVAL）时的 last 函数的性能
- 3: 同时缓存行和列，即等同于上述 cachelast 值为 1 或 2 时的行为同时生效
在创建数据库时可以选择是否缓存该数据库中每个子表的最新数据。由参数 cachemodel 设置，分为四种情况：
- none: 不缓存
- last_row: 缓存子表最近一行数据，这将显著改善 last_row 函数的性能
- last_value: 缓存子表每一列最近的非 NULL 值，这将显著改善无特殊影响（比如 WHERE, ORDER BY, GROUP BY, INTERVAL）时的 last 函数的性能
- both: 同时缓存最近的行和列，即等同于上述 cachemodel 值为 last_row 和 last_value 的行为同时生效
## 元数据缓存
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---
title: 开发指南
sidebar_label: 开发指南
description: 让开发者能够快速上手的指南
---
开发一个应用，如果你准备采用TDengine作为时序数据处理的工具，那么有如下几个事情要做：
......@@ -12,7 +14,7 @@ title: 开发指南
7. 在很多场景下(如车辆管理)，应用需要获取每个数据采集点的最新状态，那么建议你采用TDengine的cache功能，而不用单独部署Redis等缓存软件。
8. 如果你发现TDengine的函数无法满足你的要求，那么你可以使用用户自定义函数来解决问题。
本部分内容就是按照上述的顺序组织的。为便于理解，TDengine为每个功能为每个支持的编程语言都提供了示例代码。如果你希望深入了解SQL的使用，需要查看[SQL手册](/taos-sql/)。如果想更深入地了解各连接器的使用，请阅读[连接器参考指南](/reference/connector/)。如果还希望想将TDengine与第三方系统集成起来，比如Grafana, 请参考[第三方工具](/third-party/)。
本部分内容就是按照上述的顺序组织的。为便于理解，TDengine为每个功能为每个支持的编程语言都提供了示例代码。如果你希望深入了解SQL的使用，需要查看[SQL手册](/taos-sql/)。如果想更深入地了解各连接器的使用，请阅读[连接器参考指南](../connector/)。如果还希望想将TDengine与第三方系统集成起来，比如Grafana, 请参考[第三方工具](../third-party/)。
如果在开发过程中遇到任何问题，请点击每个页面下方的["反馈问题"](https://github.com/taosdata/TDengine/issues/new/choose), 在GitHub上直接递交issue。
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---
title: REST API
sidebar_label: REST API
description: 详细介绍 TDengine 提供的 RESTful API.
---
为支持各种不同类型平台的开发，TDengine 提供符合 REST 设计标准的 API，即 REST API。为最大程度降低学习成本，不同于其他数据库 REST API 的设计方法，TDengine 直接通过 HTTP POST 请求 BODY 中包含的 SQL 语句来操作数据库，仅需要一个 URL。REST 连接器的使用参见 [视频教程](https://www.taosdata.com/blog/2020/11/11/1965.html)。
......@@ -10,7 +12,7 @@ title: REST API
## 安装
RESTful 接口不依赖于任何 TDengine 的库，因此客户端不需要安装任何 TDengine 的库，只要客户端的开发语言支持 HTTP 协议即可。
RESTful 接口不依赖于任何 TDengine 的库，因此客户端不需要安装任何 TDengine 的库，只要客户端的开发语言支持 HTTP 协议即可。TDengine 的 RESTful API 由 [taosAdapter](../../reference/taosadapter) 提供，在使用 RESTful API 之前需要确保 `taosAdapter` 正常运行。
## 验证
......
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---
sidebar_position: 1
sidebar_label: C/C++
title: C/C++ Connector
---
......@@ -22,7 +21,7 @@ TDengine 客户端驱动的动态库位于：
## 支持的平台
请参考[支持的平台列表](/reference/connector#支持的平台)
请参考[支持的平台列表](../#支持的平台)
## 支持的版本
......@@ -30,7 +29,7 @@ TDengine 客户端驱动的版本号与 TDengine 服务端的版本号是一一
## 安装步骤
TDengine 客户端驱动的安装请参考 [安装指南](/reference/connector#安装步骤)
TDengine 客户端驱动的安装请参考 [安装指南](../#安装步骤)
## 建立连接
......
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---
toc_max_heading_level: 4
sidebar_position: 2
sidebar_label: Java
title: TDengine Java Connector
description: TDengine Java 连接器基于标准 JDBC API 实现, 并提供原生连接与 REST连接两种连接器。
......@@ -35,7 +34,7 @@ REST 连接支持所有能运行 Java 的平台。
## 版本支持
请参考[版本支持列表](/reference/connector#版本支持)
请参考[版本支持列表](../#版本支持)
## TDengine DataType 和 Java DataType
......@@ -64,7 +63,7 @@ TDengine 目前支持时间戳、数字、字符、布尔类型，与 Java 对
使用 Java Connector 连接数据库前，需要具备以下条件：
- 已安装 Java 1.8 或以上版本运行时环境和 Maven 3.6 或以上版本
- 已安装 TDengine 客户端驱动（使用原生连接必须安装，使用 REST 连接无需安装），具体步骤请参考[安装客户端驱动](/reference/connector#安装客户端驱动)
- 已安装 TDengine 客户端驱动（使用原生连接必须安装，使用 REST 连接无需安装），具体步骤请参考[安装客户端驱动](../#安装客户端驱动)
### 安装连接器
......@@ -630,7 +629,7 @@ public void setNString(int columnIndex, ArrayList<String> list, int size) throws
### 无模式写入
TDengine 支持无模式写入功能。无模式写入兼容 InfluxDB 的 行协议（Line Protocol）、OpenTSDB 的 telnet 行协议和 OpenTSDB 的 JSON 格式协议。详情请参见[无模式写入](../../schemaless)。
TDengine 支持无模式写入功能。无模式写入兼容 InfluxDB 的 行协议（Line Protocol）、OpenTSDB 的 telnet 行协议和 OpenTSDB 的 JSON 格式协议。详情请参见[无模式写入](../../reference/schemaless/)。
**注意**：
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......@@ -9,11 +9,11 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Preparition from "./_preparition.mdx"
import GoInsert from "../../07-develop/03-insert-data/_go_sql.mdx"
import GoInfluxLine from "../../07-develop/03-insert-data/_go_line.mdx"
import GoOpenTSDBTelnet from "../../07-develop/03-insert-data/_go_opts_telnet.mdx"
import GoOpenTSDBJson from "../../07-develop/03-insert-data/_go_opts_json.mdx"
import GoQuery from "../../07-develop/04-query-data/_go.mdx"
import GoInsert from "../07-develop/03-insert-data/_go_sql.mdx"
import GoInfluxLine from "../07-develop/03-insert-data/_go_line.mdx"
import GoOpenTSDBTelnet from "../07-develop/03-insert-data/_go_opts_telnet.mdx"
import GoOpenTSDBJson from "../07-develop/03-insert-data/_go_opts_json.mdx"
import GoQuery from "../07-develop/04-query-data/_go.mdx"
`driver-go` 是 TDengine 的官方 Go 语言连接器，实现了 Go 语言[ database/sql ](https://golang.org/pkg/database/sql/) 包的接口。Go 开发人员可以通过它开发存取 TDengine 集群数据的应用软件。
......@@ -30,7 +30,7 @@ REST 连接支持所有能运行 Go 的平台。
## 版本支持
请参考[版本支持列表](/reference/connector#版本支持)
请参考[版本支持列表](../#版本支持)
## 支持的功能特性
......@@ -56,7 +56,7 @@ REST 连接支持所有能运行 Go 的平台。
### 安装前准备
* 安装 Go 开发环境（Go 1.14 及以上，GCC 4.8.5 及以上）
* 如果使用原生连接器，请安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](/reference/connector#安装客户端驱动)
* 如果使用原生连接器，请安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](../#安装客户端驱动)
配置好环境变量，检查命令：
......
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---
toc_max_heading_level: 4
sidebar_position: 5
sidebar_label: Rust
title: TDengine Rust Connector
---
......@@ -9,9 +8,9 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Preparition from "./_preparition.mdx"
import RustInsert from "../../07-develop/03-insert-data/_rust_sql.mdx"
import RustBind from "../../07-develop/03-insert-data/_rust_stmt.mdx"
import RustQuery from "../../07-develop/04-query-data/_rust.mdx"
import RustInsert from "../07-develop/03-insert-data/_rust_sql.mdx"
import RustBind from "../07-develop/03-insert-data/_rust_stmt.mdx"
import RustQuery from "../07-develop/04-query-data/_rust.mdx"
[![Crates.io](https://img.shields.io/crates/v/taos)](https://crates.io/crates/taos) ![Crates.io](https://img.shields.io/crates/d/taos) [![docs.rs](https://img.shields.io/docsrs/taos)](https://docs.rs/taos)
......@@ -28,7 +27,7 @@ Websocket 连接支持所有能运行 Rust 的平台。
## 版本支持
请参考[版本支持列表](/reference/connector#版本支持)
请参考[版本支持列表](../#版本支持)
Rust 连接器仍然在快速开发中，1.0 之前无法保证其向后兼容。建议使用 3.0 版本以上的 TDengine，以避免已知问题。
......@@ -37,7 +36,7 @@ Rust 连接器仍然在快速开发中，1.0 之前无法保证其向后兼容
### 安装前准备
* 安装 Rust 开发工具链
* 如果使用原生连接，请安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](/reference/connector#安装客户端驱动)
* 如果使用原生连接，请安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](../#安装客户端驱动)
### 添加 taos 依赖
......
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---
sidebar_position: 3
sidebar_label: Python
title: TDengine Python Connector
description: "taospy 是 TDengine 的官方 Python 连接器。taospy 提供了丰富的 API， 使得 Python 应用可以很方便地使用 TDengine。tasopy 对 TDengine 的原生接口和 REST 接口都进行了封装， 分别对应 tasopy 的两个子模块：tasos 和 taosrest。除了对原生接口和 REST 接口的封装，taospy 还提供了符合 Python 数据访问规范(PEP 249)的编程接口。这使得 taospy 和很多第三方工具集成变得简单，比如 SQLAlchemy 和 pandas"
......@@ -8,7 +7,7 @@ description: "taospy 是 TDengine 的官方 Python 连接器。taospy 提供了
import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
`taospy` 是 TDengine 的官方 Python 连接器。`taospy` 提供了丰富的 API， 使得 Python 应用可以很方便地使用 TDengine。`taospy` 对 TDengine 的[原生接口](/reference/connector/cpp)和 [REST 接口](/reference/rest-api)都进行了封装， 分别对应 `taospy` 包的 `taos` 模块 和 `taosrest` 模块。
`taospy` 是 TDengine 的官方 Python 连接器。`taospy` 提供了丰富的 API， 使得 Python 应用可以很方便地使用 TDengine。`taospy` 对 TDengine 的[原生接口](../cpp)和 [REST 接口](../rest-api)都进行了封装， 分别对应 `taospy` 包的 `taos` 模块 和 `taosrest` 模块。
除了对原生接口和 REST 接口的封装，`taospy` 还提供了符合 [Python 数据访问规范(PEP 249)](https://peps.python.org/pep-0249/) 的编程接口。这使得 `taospy` 和很多第三方工具集成变得简单，比如 [SQLAlchemy](https://www.sqlalchemy.org/) 和 [pandas](https://pandas.pydata.org/)。
使用客户端驱动提供的原生接口直接与服务端建立的连接的方式下文中称为“原生连接”；使用 taosAdapter 提供的 REST 接口与服务端建立的连接的方式下文中称为“REST 连接”。
......@@ -17,7 +16,7 @@ Python 连接器的源码托管在 [GitHub](https://github.com/taosdata/taos-con
## 支持的平台
- 原生连接[支持的平台](/reference/connector/#支持的平台)和 TDengine 客户端支持的平台一致。
- 原生连接[支持的平台](../#支持的平台)和 TDengine 客户端支持的平台一致。
- REST 连接支持所有能运行 Python 的平台。
## 版本选择
......@@ -150,10 +149,19 @@ curl -u root:taosdata http://<FQDN>:<PORT>/rest/sql -d "select server_version()"
```json
{
"status": "succ",
"head": ["server_version()"],
"column_meta": [["server_version()", 8, 8]],
"data": [["2.4.0.16"]],
"code": 0,
"column_meta": [
[
"server_version()",
"VARCHAR",
7
]
],
"data": [
[
"3.0.0.0"
]
],
"rows": 1
}
```
......@@ -266,7 +274,7 @@ TaosCursor 类使用原生连接进行写入、查询操作。在客户端多线
##### RestClient 类的使用
`RestClient` 类是对于 [REST API](/reference/rest-api) 的直接封装。它只包含一个 `sql()` 方法用于执行任意 SQL 语句， 并返回执行结果。
`RestClient` 类是对于 [REST API](../rest-api) 的直接封装。它只包含一个 `sql()` 方法用于执行任意 SQL 语句， 并返回执行结果。
```python title="RestClient 的使用"
{{#include docs/examples/python/rest_client_example.py}}
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---
toc_max_heading_level: 4
sidebar_position: 6
sidebar_label: Node.js
title: TDengine Node.js Connector
---
......@@ -9,11 +8,11 @@ import Tabs from "@theme/Tabs";
import TabItem from "@theme/TabItem";
import Preparition from "./_preparition.mdx";
import NodeInsert from "../../07-develop/03-insert-data/_js_sql.mdx";
import NodeInfluxLine from "../../07-develop/03-insert-data/_js_line.mdx";
import NodeOpenTSDBTelnet from "../../07-develop/03-insert-data/_js_opts_telnet.mdx";
import NodeOpenTSDBJson from "../../07-develop/03-insert-data/_js_opts_json.mdx";
import NodeQuery from "../../07-develop/04-query-data/_js.mdx";
import NodeInsert from "../07-develop/03-insert-data/_js_sql.mdx";
import NodeInfluxLine from "../07-develop/03-insert-data/_js_line.mdx";
import NodeOpenTSDBTelnet from "../07-develop/03-insert-data/_js_opts_telnet.mdx";
import NodeOpenTSDBJson from "../07-develop/03-insert-data/_js_opts_json.mdx";
import NodeQuery from "../07-develop/04-query-data/_js.mdx";
`@tdengine/client` 和 `@tdengine/rest` 是 TDengine 的官方 Node.js 语言连接器。 Node.js 开发人员可以通过它开发可以存取 TDengine 集群数据的应用软件。注意：从 TDengine 3.0 开始 Node.js 原生连接器的包名由 `td2.0-connector` 改名为 `@tdengine/client` 而 rest 连接器的包名由 `td2.0-rest-connector` 改为 `@tdengine/rest`。并且不与 TDengine 2.x 兼容。
......@@ -28,7 +27,7 @@ REST 连接器支持所有能运行 Node.js 的平台。
## 版本支持
请参考[版本支持列表](/reference/connector#版本支持)
请参考[版本支持列表](../#版本支持)
## 支持的功能特性
......@@ -52,7 +51,7 @@ REST 连接器支持所有能运行 Node.js 的平台。
### 安装前准备
- 安装 Node.js 开发环境
- 如果使用 REST 连接器，跳过此步。但如果使用原生连接器，请安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](/reference/connector#安装客户端驱动)。我们使用 [node-gyp](https://github.com/nodejs/node-gyp) 和 TDengine 实例进行交互，还需要根据具体操作系统来安装下文提到的一些依赖工具。
- 如果使用 REST 连接器，跳过此步。但如果使用原生连接器，请安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](../#安装客户端驱动)。我们使用 [node-gyp](https://github.com/nodejs/node-gyp) 和 TDengine 实例进行交互，还需要根据具体操作系统来安装下文提到的一些依赖工具。
<Tabs defaultValue="Linux">
<TabItem value="Linux" label="Linux 系统安装依赖工具">
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---
toc_max_heading_level: 4
sidebar_position: 7
sidebar_label: C#
title: C# Connector
---
......@@ -9,16 +8,16 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Preparition from "./_preparition.mdx"
import CSInsert from "../../07-develop/03-insert-data/_cs_sql.mdx"
import CSInfluxLine from "../../07-develop/03-insert-data/_cs_line.mdx"
import CSOpenTSDBTelnet from "../../07-develop/03-insert-data/_cs_opts_telnet.mdx"
import CSOpenTSDBJson from "../../07-develop/03-insert-data/_cs_opts_json.mdx"
import CSQuery from "../../07-develop/04-query-data/_cs.mdx"
import CSAsyncQuery from "../../07-develop/04-query-data/_cs_async.mdx"
import CSInsert from "../07-develop/03-insert-data/_cs_sql.mdx"
import CSInfluxLine from "../07-develop/03-insert-data/_cs_line.mdx"
import CSOpenTSDBTelnet from "../07-develop/03-insert-data/_cs_opts_telnet.mdx"
import CSOpenTSDBJson from "../07-develop/03-insert-data/_cs_opts_json.mdx"
import CSQuery from "../07-develop/04-query-data/_cs.mdx"
import CSAsyncQuery from "../07-develop/04-query-data/_cs_async.mdx"
`TDengine.Connector` 是 TDengine 提供的 C# 语言连接器。C# 开发人员可以通过它开发存取 TDengine 集群数据的 C# 应用软件。
`TDengine.Connector` 连接器支持通过 TDengine 客户端驱动（taosc）建立与 TDengine 运行实例的连接，提供数据写入、查询、订阅、schemaless 数据写入、参数绑定接口数据写入等功能 `TDengine.Connector` 目前暂未提供 REST 连接方式，用户可以参考 [REST API](/reference/rest-api/) 文档自行编写。
`TDengine.Connector` 连接器支持通过 TDengine 客户端驱动（taosc）建立与 TDengine 运行实例的连接，提供数据写入、查询、订阅、schemaless 数据写入、参数绑定接口数据写入等功能 `TDengine.Connector` 目前暂未提供 REST 连接方式，用户可以参考 [REST API](../rest-api/) 文档自行编写。
本文介绍如何在 Linux 或 Windows 环境中安装 `TDengine.Connector`，并通过 `TDengine.Connector` 连接 TDengine 集群，进行数据写入、查询等基本操作。
......@@ -32,7 +31,7 @@ import CSAsyncQuery from "../../07-develop/04-query-data/_cs_async.mdx"
## 版本支持
请参考[版本支持列表](/reference/connector#版本支持)
请参考[版本支持列表](../#版本支持)
## 支持的功能特性
......@@ -49,7 +48,7 @@ import CSAsyncQuery from "../../07-develop/04-query-data/_cs_async.mdx"
* 安装 [.NET SDK](https://dotnet.microsoft.com/download)
* [Nuget 客户端](https://docs.microsoft.com/en-us/nuget/install-nuget-client-tools) （可选安装）
* 安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](/reference/connector#安装客户端驱动)
* 安装 TDengine 客户端驱动，具体步骤请参考[安装客户端驱动](../#安装客户端驱动)
### 使用 dotnet CLI 安装
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---
sidebar_position: 1
sidebar_label: PHP
title: PHP Connector
---
......@@ -38,7 +37,7 @@ TDengine 客户端驱动的版本号与 TDengine 服务端的版本号是一一
### 安装 TDengine 客户端驱动
TDengine 客户端驱动的安装请参考 [安装指南](/reference/connector#安装步骤)
TDengine 客户端驱动的安装请参考 [安装指南](../#安装步骤)
### 编译安装 php-tdengine
......@@ -61,7 +60,7 @@ phpize && ./configure && make -j && make install
**手动指定 tdengine 目录：**
```shell
phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/2.4.0.0 && make -j && make install
phpize && ./configure --with-tdengine-dir=/usr/local/Cellar/tdengine/3.0.0.0 && make -j && make install
```
> `--with-tdengine-dir=` 后跟上 tdengine 目录。
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---
sidebar_label: 错误码
title: TDengine C/C++ 连接器错误码
description: C/C++ 连接器的错误码列表和详细说明
---
本文中详细列举了在使用 TDengine C/C++ 连接器时客户端可能得到的错误码以及所要采取的相应动作。其它语言的连接器在使用原生连接方式时也会所得到的返回码返回给连接器的调用者。
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......@@ -4,7 +4,7 @@ import PkgListV3 from "/components/PkgListV3";
<PkgListV3 type={1} sys="Linux" />
[所有下载](../../releases)
[所有下载](../../releases/tdengine)
2. 解压缩软件包
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......@@ -4,7 +4,8 @@ import PkgListV3 from "/components/PkgListV3";
<PkgListV3 type={4} sys="Windows" />
[所有下载](../../releases)
[所有下载](../../releases/tdengine)
2. 执行安装程序，按提示选择默认值，完成安装
3. 安装路径
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---
sidebar_label: 连接器
title: 连接器
description: 详细介绍各种语言的连接器及 REST API
---
TDengine 提供了丰富的应用程序开发接口，为了便于用户快速开发自己的应用，TDengine 支持了多种编程语言的连接器，其中官方连接器包括支持 C/C++、Java、Python、Go、Node.js、C# 和 Rust 的连接器。这些连接器支持使用原生接口（taosc）和 REST 接口（部分语言暂不支持）连接 TDengine 集群。社区开发者也贡献了多个非官方连接器，例如 ADO.NET 连接器、Lua 连接器和 PHP 连接器。
......
docs/zh/10-deployment/01-deploy.md
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---
sidebar_label: 手动部署
title: 集群部署和管理
description: 使用命令行工具手动部署 TDengine 集群
---
## 准备工作
......@@ -70,7 +71,7 @@ serverPort 6030
## 启动集群
按照《立即开始》里的步骤，启动第一个数据节点，例如 h1.taosdata.com，然后执行 taos，启动 taos shell，从 shell 里执行命令“SHOW DNODES”，如下所示：
按照《立即开始》里的步骤，启动第一个数据节点，例如 h1.taosdata.com，然后执行 taos，启动 TDengine CLI，在其中执行命令 “SHOW DNODES”，如下所示：
```
taos> show dnodes;
......@@ -114,7 +115,7 @@ SHOW DNODES;
任何已经加入集群在线的数据节点，都可以作为后续待加入节点的 firstEp。
firstEp 这个参数仅仅在该数据节点首次加入集群时有作用，加入集群后，该数据节点会保存最新的 mnode 的 End Point 列表，不再依赖这个参数。
接下来，配置文件中的 firstEp 参数就主要在客户端连接的时候使用了，例如 taos shell 如果不加参数，会默认连接由 firstEp 指定的节点。
接下来，配置文件中的 firstEp 参数就主要在客户端连接的时候使用了，例如 TDengine CLI 如果不加参数，会默认连接由 firstEp 指定的节点。
两个没有配置 firstEp 参数的数据节点 dnode 启动后，会独立运行起来。这个时候，无法将其中一个数据节点加入到另外一个数据节点，形成集群。无法将两个独立的集群合并成为新的集群。
:::
......
docs/zh/10-deployment/03-k8s.md
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---
sidebar_label: Kubernetes
title: 在 Kubernetes 上部署 TDengine 集群
description: 利用 Kubernetes 部署 TDengine 集群的详细指南
---
作为面向云原生架构设计的时序数据库，TDengine 支持 Kubernetes 部署。这里介绍如何使用 YAML 文件一步一步从头创建一个 TDengine 集群，并重点介绍 Kubernetes 环境下 TDengine 的常用操作。
......@@ -9,6 +10,7 @@ title: 在 Kubernetes 上部署 TDengine 集群
要使用 Kubernetes 部署管理 TDengine 集群，需要做好如下准备工作。
* 本文适用 Kubernetes v1.5 以上版本
* 本文和下一章使用 minikube、kubectl 和 helm 等工具进行安装部署，请提前安装好相应软件
* Kubernetes 已经安装部署并能正常访问使用或更新必要的容器仓库或其他服务
......@@ -365,7 +367,7 @@ kubectl scale statefulsets tdengine --replicas=1
```
在 taos shell 中的所有数据库操作将无法成功。
在 TDengine CLI 中的所有数据库操作将无法成功。
```
taos> show dnodes;
......
docs/zh/10-deployment/05-helm.md
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---
sidebar_label: Helm
title: 使用 Helm 部署 TDengine 集群
description: 使用 Helm 部署 TDengine 集群的详细指南
---
Helm 是 Kubernetes 的包管理器，上一节使用 Kubernets 部署 TDengine 集群的操作已经足够简单，但 Helm 依然可以提供更强大的能力。
......@@ -171,70 +172,19 @@ taoscfg:
TAOS_REPLICA: "1"
# number of days per DB file
# TAOS_DAYS: "10"
# number of days to keep DB file, default is 10 years.
#TAOS_KEEP: "3650"
# cache block size (Mbyte)
#TAOS_CACHE: "16"
# number of cache blocks per vnode
#TAOS_BLOCKS: "6"
# minimum rows of records in file block
#TAOS_MIN_ROWS: "100"
# maximum rows of records in file block
#TAOS_MAX_ROWS: "4096"
#
# TAOS_NUM_OF_THREADS_PER_CORE: number of threads per CPU core
#TAOS_NUM_OF_THREADS_PER_CORE: "1.0"
# TAOS_NUM_OF_RPC_THREADS: number of threads for RPC
#TAOS_NUM_OF_RPC_THREADS: "2"
#
# TAOS_NUM_OF_COMMIT_THREADS: number of threads to commit cache data
#TAOS_NUM_OF_COMMIT_THREADS: "4"
#
# TAOS_RATIO_OF_QUERY_CORES:
# the proportion of total CPU cores available for query processing
# 2.0: the query threads will be set to double of the CPU cores.
# 1.0: all CPU cores are available for query processing [default].
# 0.5: only half of the CPU cores are available for query.
# 0.0: only one core available.
#TAOS_RATIO_OF_QUERY_CORES: "1.0"
#
# TAOS_KEEP_COLUMN_NAME:
# the last_row/first/last aggregator will not change the original column name in the result fields
#TAOS_KEEP_COLUMN_NAME: "0"
# enable/disable backuping vnode directory when removing vnode
#TAOS_VNODE_BAK: "1"
# enable/disable installation / usage report
#TAOS_TELEMETRY_REPORTING: "1"
# enable/disable load balancing
#TAOS_BALANCE: "1"
# max timer control blocks
#TAOS_MAX_TMR_CTRL: "512"
# time interval of system monitor, seconds
#TAOS_MONITOR_INTERVAL: "30"
# number of seconds allowed for a dnode to be offline, for cluster only
#TAOS_OFFLINE_THRESHOLD: "8640000"
# RPC re-try timer, millisecond
#TAOS_RPC_TIMER: "1000"
# RPC maximum time for ack, seconds.
#TAOS_RPC_MAX_TIME: "600"
# time interval of dnode status reporting to mnode, seconds, for cluster only
#TAOS_STATUS_INTERVAL: "1"
......@@ -245,37 +195,7 @@ taoscfg:
#TAOS_MIN_SLIDING_TIME: "10"
# minimum time window, milli-second
#TAOS_MIN_INTERVAL_TIME: "10"
# maximum delay before launching a stream computation, milli-second
#TAOS_MAX_STREAM_COMP_DELAY: "20000"
# maximum delay before launching a stream computation for the first time, milli-second
#TAOS_MAX_FIRST_STREAM_COMP_DELAY: "10000"
# retry delay when a stream computation fails, milli-second
#TAOS_RETRY_STREAM_COMP_DELAY: "10"
# the delayed time for launching a stream computation, from 0.1(default, 10% of whole computing time window) to 0.9
#TAOS_STREAM_COMP_DELAY_RATIO: "0.1"
# max number of vgroups per db, 0 means configured automatically
#TAOS_MAX_VGROUPS_PER_DB: "0"
# max number of tables per vnode
#TAOS_MAX_TABLES_PER_VNODE: "1000000"
# the number of acknowledgments required for successful data writing
#TAOS_QUORUM: "1"
# enable/disable compression
#TAOS_COMP: "2"
# write ahead log (WAL) level, 0: no wal; 1: write wal, but no fysnc; 2: write wal, and call fsync
#TAOS_WAL_LEVEL: "1"
# if walLevel is set to 2, the cycle of fsync being executed, if set to 0, fsync is called right away
#TAOS_FSYNC: "3000"
#TAOS_MIN_INTERVAL_TIME: "1"
# the compressed rpc message, option:
# -1 (no compression)
......@@ -283,17 +203,8 @@ taoscfg:
# > 0 (rpc message body which larger than this value will be compressed)
#TAOS_COMPRESS_MSG_SIZE: "-1"
# max length of an SQL
#TAOS_MAX_SQL_LENGTH: "1048576"
# the maximum number of records allowed for super table time sorting
#TAOS_MAX_NUM_OF_ORDERED_RES: "100000"
# max number of connections allowed in dnode
#TAOS_MAX_SHELL_CONNS: "5000"
# max number of connections allowed in client
#TAOS_MAX_CONNECTIONS: "5000"
#TAOS_MAX_SHELL_CONNS: "50000"
# stop writing logs when the disk size of the log folder is less than this value
#TAOS_MINIMAL_LOG_DIR_G_B: "0.1"
......@@ -313,21 +224,8 @@ taoscfg:
# enable/disable system monitor
#TAOS_MONITOR: "1"
# enable/disable recording the SQL statements via restful interface
#TAOS_HTTP_ENABLE_RECORD_SQL: "0"
# number of threads used to process http requests
#TAOS_HTTP_MAX_THREADS: "2"
# maximum number of rows returned by the restful interface
#TAOS_RESTFUL_ROW_LIMIT: "10240"
# The following parameter is used to limit the maximum number of lines in log files.
# max number of lines per log filters
# numOfLogLines 10000000
# enable/disable async log
#TAOS_ASYNC_LOG: "0"
#TAOS_ASYNC_LOG: "1"
#
# time of keeping log files, days
......@@ -344,25 +242,8 @@ taoscfg:
# debug flag for all log type, take effect when non-zero value\
#TAOS_DEBUG_FLAG: "143"
# enable/disable recording the SQL in taos client
#TAOS_ENABLE_RECORD_SQL: "0"
# generate core file when service crash
#TAOS_ENABLE_CORE_FILE: "1"
# maximum display width of binary and nchar fields in the shell. The parts exceeding this limit will be hidden
#TAOS_MAX_BINARY_DISPLAY_WIDTH: "30"
# enable/disable stream (continuous query)
#TAOS_STREAM: "1"
# in retrieve blocking model, only in 50% query threads will be used in query processing in dnode
#TAOS_RETRIEVE_BLOCKING_MODEL: "0"
# the maximum allowed query buffer size in MB during query processing for each data node
# -1 no limit (default)
# 0 no query allowed, queries are disabled
#TAOS_QUERY_BUFFER_SIZE: "-1"
```
## 扩容
......
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---
sidebar_label: 部署集群
title: 部署集群
description: 部署 TDengine 集群的多种方式
---
TDengine 支持集群，提供水平扩展的能力。如果需要获得更高的处理能力，只需要多增加节点即可。TDengine 采用虚拟节点技术，将一个节点虚拟化为多个虚拟节点，以实现负载均衡。同时，TDengine可以将多个节点上的虚拟节点组成虚拟节点组，通过多副本机制，以保证供系统的高可用。TDengine的集群功能完全开源。
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tests/script/tsim/parser/alter1.sim
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tests/script/tsim/query/diff.sim
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tests/script/tsim/query/explain.sim
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tests/script/tsim/query/session.sim
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tests/script/tsim/query/stddev.sim
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tests/script/tsim/query/udf.sim
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tests/system-test/2-query/interp.py
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tools/CMakeLists.txt
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