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TDengine
提交 8abb76f2 编写于 作者: E Elias Soong
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# Connect with other tools
## Telegraf
TDengine is easy to integrate with [Telegraf](https://www.influxdata.com/time-series-platform/telegraf/), an open-source server agent for collecting and sending metrics and events, without more development.
### Install Telegraf
At present, TDengine supports Telegraf newer than version 1.7.4. Users can go to the [download link] and choose the proper package to install on your system.
### Configure Telegraf
Telegraf is configured by changing items in the configuration file */etc/telegraf/telegraf.conf*.
In **output plugins** section，add _[[outputs.http]]_ iterm：
- _url_: http://ip:6020/telegraf/udb, in which _ip_ is the IP address of any node in TDengine cluster. Port 6020 is the RESTful APT port used by TDengine. _udb_ is the name of the database to save data, which needs to create beforehand.
- _method_: "POST"
- _username_: username to login TDengine
- _password_: password to login TDengine
- _data_format_: "json"
- _json_timestamp_units_: "1ms"
In **agent** part：
- hostname: used to distinguish different machines. Need to be unique.
- metric_batch_size: 30，the maximum number of records allowed to write in Telegraf. The larger the value is, the less frequent requests are sent. For TDengine, the value should be less than 50.
Please refer to the [Telegraf docs](https://docs.influxdata.com/telegraf/v1.11/) for more information.
## Grafana
[Grafana] is an open-source system for time-series data display. It is easy to integrate TDengine and Grafana to build a monitor system. Data saved in TDengine can be fetched and shown on the Grafana dashboard.
### Install Grafana
For now, TDengine only supports Grafana newer than version 5.2.4. Users can go to the [Grafana download page] for the proper package to download.
### Configure Grafana
TDengine Grafana plugin is in the _/usr/local/taos/connector/grafana_ directory.
Taking Centos 7.2 as an example, just copy TDengine directory to _/var/lib/grafana/plugins_ directory and restart Grafana.
### Use Grafana
Users can log in the Grafana server (username/password:admin/admin) through localhost:3000 to configure TDengine as the data source. As is shown in the picture below, TDengine as a data source option is shown in the box:
![img](../assets/clip_image001.png)
When choosing TDengine as the data source, the Host in HTTP configuration should be configured as the IP address of any node of a TDengine cluster. The port should be set as 6020. For example, when TDengine and Grafana are on the same machine, it should be configured as _http://localhost:6020.
Besides, users also should set the username and password used to log into TDengine. Then click _Save&Test_ button to save.
![img](../assets/clip_image001-2474914.png)
Then, TDengine as a data source should show in the Grafana data source list.
![img](../assets/clip_image001-2474939.png)
Then, users can create Dashboards in Grafana using TDengine as the data source:
![img](../assets/clip_image001-2474961.png)
Click _Add Query_ button to add a query and input the SQL command you want to run in the _INPUT SQL_ text box. The SQL command should expect a two-row, multi-column result, such as _SELECT count(*) FROM sys.cpu WHERE ts>=from and ts<​to interval(interval)_, in which, _from_, _to_ and _inteval_ are TDengine inner variables representing query time range and time interval.
_ALIAS BY_ field is to set the query alias. Click _GENERATE SQL_ to send the command to TDengine:
![img](../assets/clip_image001-2474987.png)
Please refer to the [Grafana official document] for more information about Grafana.
## Matlab
Matlab can connect to and retrieve data from TDengine by TDengine JDBC Driver.
### MatLab and TDengine JDBC adaptation
Several steps are required to adapt Matlab to TDengine. Taking adapting Matlab2017a on Windows10 as an example:
1. Copy the file _JDBCDriver-1.0.0-dist.jar_ in TDengine package to the directory _${matlab_root}\MATLAB\R2017a\java\jar\toolbox_
2. Copy the file _taos.lib_ in TDengine package to _${matlab_ root _dir}\MATLAB\R2017a\lib\win64_
3. Add the .jar package just copied to the Matlab classpath. Append the line below as the end of the file of _${matlab_ root _dir}\MATLAB\R2017a\toolbox\local\classpath.txt_
​ `$matlabroot/java/jar/toolbox/JDBCDriver-1.0.0-dist.jar`
4. Create a file called _javalibrarypath.txt_ in directory _${user_home}\AppData\Roaming\MathWorks\MATLAB\R2017a\_, and add the _taos.dll_ path in the file. For example, if the file _taos.dll_ is in the directory of _C:\Windows\System32_，then add the following line in file *javalibrarypath.txt*:
​ `C:\Windows\System32`
### TDengine operations in Matlab
After correct configuration, open Matlab:
- build a connection：
`conn = database(‘db’, ‘root’, ‘taosdata’, ‘com.taosdata.jdbc.TSDBDriver’, ‘jdbc:TSDB://127.0.0.1:0/’)`
- Query：
`sql0 = [‘select * from tb’]`
`data = select(conn, sql0);`
- Insert a record:
`sql1 = [‘insert into tb values (now, 1)’]`
`exec(conn, sql1)`
Please refer to the file _examples\Matlab\TDengineDemo.m_ for more information.
## R
Users can use R language to access the TDengine server with the JDBC interface. At first, install JDBC package in R:
```R
install.packages('rJDBC', repos='http://cran.us.r-project.org')
```
Then use _library_ function to load the package:
```R
library('RJDBC')
```
Then load the TDengine JDBC driver:
```R
drv<-JDBC("com.taosdata.jdbc.TSDBDriver","JDBCDriver-1.0.0-dist.jar", identifier.quote="\"")
```
If succeed, no error message will display. Then use the following command to try a database connection:
```R
conn<-dbConnect(drv,"jdbc:TSDB://192.168.0.1:0/?user=root&password=taosdata","root","taosdata")
```
Please replace the IP address in the command above to the correct one. If no error message is shown, then the connection is established successfully. TDengine supports below functions in _RJDBC_ package:
- _dbWriteTable(conn, "test", iris, overwrite=FALSE, append=TRUE)_: write the data in a data frame _iris_ to the table _test_ in the TDengine server. Parameter _overwrite_ must be _false_. _append_ must be _TRUE_ and the schema of the data frame _iris_ should be the same as the table _test_.
- _dbGetQuery(conn, "select count(*) from test")_: run a query command
- _dbSendUpdate(conn, "use db")_: run any non-query command.
- _dbReadTable(conn, "test"_): read all the data in table _test_
- _dbDisconnect(conn)_: close a connection
- _dbRemoveTable(conn, "test")_: remove table _test_
Below functions are **not supported** currently:
- _dbExistsTable(conn, "test")_: if talbe _test_ exists
- _dbListTables(conn)_: list all tables in the connection
[Telegraf]: www.taosdata.com
[download link]: https://portal.influxdata.com/downloads
[Telegraf document]: www.taosdata.com
[Grafana]: https://grafana.com
[Grafana download page]: https://grafana.com/grafana/download
[Grafana official document]: https://grafana.com/docs/
documentation20/webdocs/markdowndocs/Connector.md 已删除 100644 → 0
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# TDengine connectors
TDengine provides many connectors for development, including C/C++, JAVA, Python, RESTful, Go, Node.JS, etc.
NOTE: All APIs which require a SQL string as parameter, including but not limit to `taos_query`, `taos_query_a`, `taos_subscribe` in the C/C++ Connector and their counterparts in other connectors, can ONLY process one SQL statement at a time. If more than one SQL statements are provided, their behaviors are undefined.
## C/C++ API
C/C++ APIs are similar to the MySQL APIs. Applications should include TDengine head file _taos.h_ to use C/C++ APIs by adding the following line in code:
```C
#include <taos.h>
```
Make sure TDengine library _libtaos.so_ is installed and use _-ltaos_ option to link the library when compiling. In most cases, if the return value of an API is integer, it return _0_ for success and other values as an error code for failure; if the return value is pointer, then _NULL_ is used for failure.
### Fundamental API
Fundamentatal APIs prepare runtime environment for other APIs, for example, create a database connection.
- `void taos_init()`
Initialize the runtime environment for TDengine client. The API is not necessary since it is called int _taos_connect_ by default.
- `void taos_cleanup()`
Cleanup runtime environment, client should call this API before exit.
- `int taos_options(TSDB_OPTION option, const void * arg, ...)`
Set client options. The parameter _option_ supports values of _TSDB_OPTION_CONFIGDIR_ (configuration directory), _TSDB_OPTION_SHELL_ACTIVITY_TIMER_, _TSDB_OPTION_LOCALE_ (client locale) and _TSDB_OPTION_TIMEZONE_ (client timezone).
- `char* taos_get_client_info()`
Retrieve version information of client.
- `TAOS *taos_connect(const char *ip, const char *user, const char *pass, const char *db, int port)`
Open a connection to a TDengine server. The parameters are:
* ip: IP address of the server
* user: username
* pass: password
* db: database to use, **NULL** for no database to use after connection. Otherwise, the database should exist before connection or a connection error is reported.
* port: port number to connect
The handle returned by this API should be kept for future use.
- `char *taos_get_server_info(TAOS *taos)`
Retrieve version information of server.
- `int taos_select_db(TAOS *taos, const char *db)`
Set default database to `db`.
- `void taos_close(TAOS *taos)`
Close a connection to a TDengine server by the handle returned by _taos_connect_`
### C/C++ sync API
Sync APIs are those APIs waiting for responses from the server after sending a request. TDengine has the following sync APIs:
- `TAOS_RES* taos_query(TAOS *taos, const char *sql)`
The API used to run a SQL command. The command can be DQL, DML or DDL. The parameter _taos_ is the handle returned by _taos_connect_. Return value _NULL_ means failure.
- `int taos_result_precision(TAOS_RES *res)`
Get the timestamp precision of the result set, return value _0_ means milli-second, _1_ mean micro-second and _2_ means nano-second.
- `TAOS_ROW taos_fetch_row(TAOS_RES *res)`
Fetch a row of return results through _res_.
- `int taos_fetch_block(TAOS_RES *res, TAOS_ROW *rows)`
Fetch multiple rows from the result set, return value is row count.
- `int taos_num_fields(TAOS_RES *res)` and `int taos_field_count(TAOS_RES* res)`
These two APIs are identical, both return the number of fields in the return result.
- `int* taos_fetch_lengths(TAOS_RES *res)`
Get the field lengths of the result set, return value is an array whose length is the field count.
- `int taos_affected_rows(TAOS_RES *res)`
Get affected row count of the executed statement.
- `TAOS_FIELD *taos_fetch_fields(TAOS_RES *res)`
Fetch the description of each field. The description includes the property of data type, field name, and bytes. The API should be used with _taos_num_fields_ to fetch a row of data. The structure of `TAOS_FIELD` is:
```c
typedef struct taosField {
char name[65]; // field name
uint8_t type; // data type
int16_t bytes; // length of the field in bytes
} TAOS_FIELD;
```
- `void taos_stop_query(TAOS_RES *res)`
Stop the execution of a query.
- `void taos_free_result(TAOS_RES *res)`
Free the resources used by a result set. Make sure to call this API after fetching results or memory leak would happen.
- `char *taos_errstr(TAOS_RES *res)`
Return the reason of the last API call failure. The return value is a string.
- `int *taos_errno(TAOS_RES *res)`
Return the error code of the last API call failure. The return value is an integer.
**Note**: The connection to a TDengine server is not multi-thread safe. So a connection can only be used by one thread.
### C/C++ async API
In addition to sync APIs, TDengine also provides async APIs, which are more efficient. Async APIs are returned right away without waiting for a response from the server, allowing the application to continute with other tasks without blocking. So async APIs are more efficient, especially useful when in a poor network.
All async APIs require callback functions. The callback functions have the format:
```C
void fp(void *param, TAOS_RES * res, TYPE param3)
```
The first two parameters of the callback function are the same for all async APIs. The third parameter is different for different APIs. Generally, the first parameter is the handle provided to the API for action. The second parameter is a result handle.
- `void taos_query_a(TAOS *taos, const char *sql, void (*fp)(void *param, TAOS_RES *, int code), void *param);`
The async version of _taos_query_.
* taos: the handle returned by _taos_connect_.
* sql: the SQL command to run.
* fp: user defined callback function. The third parameter of the callback function _code_ is _0_ (for success) or a negative number (for failure, call taos_errstr to get the error as a string). Applications mainly handle the second parameter, the returned result set.
* param: user provided parameter which is required by the callback function.
- `void taos_fetch_rows_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, int numOfRows), void *param);`
The async API to fetch a batch of rows, which should only be used with a _taos_query_a_ call.
* res: result handle returned by _taos_query_a_.
* fp: the callback function. _param_ is a user-defined structure to pass to _fp_. The parameter _numOfRows_ is the number of result rows in the current fetch cycle. In the callback function, applications should call _taos_fetch_row_ to get records from the result handle. After getting a batch of results, applications should continue to call _taos_fetch_rows_a_ API to handle the next batch, until the _numOfRows_ is _0_ (for no more data to fetch) or _-1_ (for failure).
- `void taos_fetch_row_a(TAOS_RES *res, void (*fp)(void *param, TAOS_RES *, TAOS_ROW row), void *param);`
The async API to fetch a result row.
* res: result handle.
* fp: the callback function. _param_ is a user-defined structure to pass to _fp_. The third parameter of the callback function is a single result row, which is different from that of _taos_fetch_rows_a_ API. With this API, it is not necessary to call _taos_fetch_row_ to retrieve each result row, which is handier than _taos_fetch_rows_a_ but less efficient.
Applications may apply operations on multiple tables. However, **it is important to make sure the operations on the same table are serialized**. That means after sending an insert request in a table to the server, no operations on the table are allowed before a response is received.
### C/C++ parameter binding API
TDengine also provides parameter binding APIs, like MySQL, only question mark `?` can be used to represent a parameter in these APIs.
- `TAOS_STMT* taos_stmt_init(TAOS *taos)`
Create a TAOS_STMT to represent the prepared statement for other APIs.
- `int taos_stmt_prepare(TAOS_STMT *stmt, const char *sql, unsigned long length)`
Parse SQL statement _sql_ and bind result to _stmt_ , if _length_ larger than 0, its value is used to determine the length of _sql_, the API auto detects the actual length of _sql_ otherwise.
- `int taos_stmt_bind_param(TAOS_STMT *stmt, TAOS_BIND *bind)`
Bind values to parameters. _bind_ points to an array, the element count and sequence of the array must be identical as the parameters of the SQL statement. The usage of _TAOS_BIND_ is same as _MYSQL_BIND_ in MySQL, its definition is as below:
```c
typedef struct TAOS_BIND {
int buffer_type;
void * buffer;
unsigned long buffer_length; // not used in TDengine
unsigned long *length;
int * is_null;
int is_unsigned; // not used in TDengine
int * error; // not used in TDengine
} TAOS_BIND;
```
- `int taos_stmt_add_batch(TAOS_STMT *stmt)`
Add bound parameters to batch, client can call `taos_stmt_bind_param` again after calling this API. Note this API only support _insert_ / _import_ statements, it returns an error in other cases.
- `int taos_stmt_execute(TAOS_STMT *stmt)`
Execute the prepared statement. This API can only be called once for a statement at present.
- `TAOS_RES* taos_stmt_use_result(TAOS_STMT *stmt)`
Acquire the result set of an executed statement. The usage of the result is same as `taos_use_result`, `taos_free_result` must be called after one you are done with the result set to release resources.
- `int taos_stmt_close(TAOS_STMT *stmt)`
Close the statement, release all resources.
### C/C++ continuous query interface
TDengine provides APIs for continuous query driven by time, which run queries periodically in the background. There are only two APIs:
- `TAOS_STREAM *taos_open_stream(TAOS *taos, const char *sqlstr, void (*fp)(void *param, TAOS_RES * res, TAOS_ROW row), int64_t stime, void *param, void (*callback)(void *));`
The API is used to create a continuous query.
* _taos_: the connection handle returned by _taos_connect_.
* _sqlstr_: the SQL string to run. Only query commands are allowed.
* _fp_: the callback function to run after a query. TDengine passes query result `row`, query state `res` and user provided parameter `param` to this function. In this callback, `taos_num_fields` and `taos_fetch_fields` could be used to fetch field information.
* _param_: a parameter passed to _fp_
* _stime_: the time of the stream starts in the form of epoch milliseconds. If _0_ is given, the start time is set as the current time.
* _callback_: a callback function to run when the continuous query stops automatically.
The API is expected to return a handle for success. Otherwise, a NULL pointer is returned.
- `void taos_close_stream (TAOS_STREAM *tstr)`
Close the continuous query by the handle returned by _taos_open_stream_. Make sure to call this API when the continuous query is not needed anymore.
### C/C++ subscription API
For the time being, TDengine supports subscription on one or multiple tables. It is implemented through periodic pulling from a TDengine server.
* `TAOS_SUB *taos_subscribe(TAOS* taos, int restart, const char* topic, const char *sql, TAOS_SUBSCRIBE_CALLBACK fp, void *param, int interval)`
The API is used to start a subscription session, it returns the subscription object on success and `NULL` in case of failure, the parameters are:
* **taos**: The database connnection, which must be established already.
* **restart**: `Zero` to continue a subscription if it already exits, other value to start from the beginning.
* **topic**: The unique identifier of a subscription.
* **sql**: A sql statement for data query, it can only be a `select` statement, can only query for raw data, and can only query data in ascending order of the timestamp field.
* **fp**: A callback function to receive query result, only used in asynchronization mode and should be `NULL` in synchronization mode, please refer below for its prototype.
* **param**: User provided additional parameter for the callback function.
* **interval**: Pulling interval in millisecond. Under asynchronization mode, API will call the callback function `fp` in this interval, system performance will be impacted if this interval is too short. Under synchronization mode, if the duration between two call to `taos_consume` is less than this interval, the second call blocks until the duration exceed this interval.
* `typedef void (*TAOS_SUBSCRIBE_CALLBACK)(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code)`
Prototype of the callback function, the parameters are:
* tsub: The subscription object.
* res: The query result.
* param: User provided additional parameter when calling `taos_subscribe`.
* code: Error code in case of failures.
* `TAOS_RES *taos_consume(TAOS_SUB *tsub)`
The API used to get the new data from a TDengine server. It should be put in an loop. The parameter `tsub` is the handle returned by `taos_subscribe`. This API should only be called in synchronization mode. If the duration between two call to `taos_consume` is less than pulling interval, the second call blocks until the duration exceed the interval. The API returns the new rows if new data arrives, or empty rowset otherwise, and if there's an error, it returns `NULL`.
* `void taos_unsubscribe(TAOS_SUB *tsub, int keepProgress)`
Stop a subscription session by the handle returned by `taos_subscribe`. If `keepProgress` is **not** zero, the subscription progress information is kept and can be reused in later call to `taos_subscribe`, the information is removed otherwise.
## Java Connector
To Java delevopers, TDengine provides `taos-jdbcdriver` according to the JDBC(3.0) API. Users can find and download it through [Sonatype Repository][1].
Since the native language of TDengine is C, the necessary TDengine library should be checked before using the taos-jdbcdriver:
* libtaos.so (Linux)
After TDengine is installed successfully, the library `libtaos.so` will be automatically copied to the `/usr/lib/`, which is the system's default search path.
* taos.dll (Windows)
After TDengine client is installed, the library `taos.dll` will be automatically copied to the `C:/Windows/System32`, which is the system's default search path.
> Note: Please make sure that [TDengine Windows client][14] has been installed if developing on Windows. Now although TDengine client would be defaultly installed together with TDengine server, it can also be installed [alone][15].
Since TDengine is time-series database, there are still some differences compared with traditional databases in using TDengine JDBC driver:
* TDengine doesn't allow to delete/modify a single record, and thus JDBC driver also has no such method.
* No support for transaction
* No support for union between tables
* No support for nested query，`There is at most one open ResultSet for each Connection. Thus, TSDB JDBC Driver will close current ResultSet if it is not closed and a new query begins`.
## Version list of TAOS-JDBCDriver and required TDengine and JDK
| taos-jdbcdriver | TDengine | JDK |
| --- | --- | --- |
| 2.0.2 | 2.0.0.x or higher | 1.8.x |
| 1.0.3 | 1.6.1.x or higher | 1.8.x |
| 1.0.2 | 1.6.1.x or higher | 1.8.x |
| 1.0.1 | 1.6.1.x or higher | 1.8.x |
## DataType in TDengine and Java
The datatypes in TDengine include timestamp, number, string and boolean, which are converted as follows in Java:
| TDengine | Java |
| --- | --- |
| TIMESTAMP | java.sql.Timestamp |
| INT | java.lang.Integer |
| BIGINT | java.lang.Long |
| FLOAT | java.lang.Float |
| DOUBLE | java.lang.Double |
| SMALLINT, TINYINT |java.lang.Short |
| BOOL | java.lang.Boolean |
| BINARY, NCHAR | java.lang.String |
## How to get TAOS-JDBC Driver
### maven repository
taos-jdbcdriver has been published to [Sonatype Repository][1]:
* [sonatype][8]
* [mvnrepository][9]
* [maven.aliyun][10]
Using the following pom.xml for maven projects
```xml
<dependencies>
<dependency>
<groupId>com.taosdata.jdbc</groupId>
<artifactId>taos-jdbcdriver</artifactId>
<version>2.0.2</version>
</dependency>
</dependencies>
```
### JAR file from the source code
After downloading the [TDengine][3] source code, execute `mvn clean package` in the directory `src/connector/jdbc` and then the corresponding jar file is generated.
## Usage
### get the connection
```java
Class.forName("com.taosdata.jdbc.TSDBDriver");
String jdbcUrl = "jdbc:TAOS://127.0.0.1:6030/log?user=root&password=taosdata";
Connection conn = DriverManager.getConnection(jdbcUrl);
```
> `6030` is the default port and `log` is the default database for system monitor.
A normal JDBC URL looks as follows:
`jdbc:TAOS://{host_ip}:{port}/[database_name]?[user={user}|&password={password}|&charset={charset}|&cfgdir={config_dir}|&locale={locale}|&timezone={timezone}]`
values in `{}` are necessary while values in `[]` are optional。Each option in the above URL denotes:
* user：user name for login, defaultly root。
* password：password for login，defaultly taosdata。
* charset：charset for client，defaultly system charset
* cfgdir：log directory for client, defaultly _/etc/taos/_ on Linux and _C:/TDengine/cfg_ on Windows。
* locale：language for client，defaultly system locale。
* timezone：timezone for client，defaultly system timezone。
The options above can be configures (`ordered by priority`):
1. JDBC URL
As explained above.
2. java.sql.DriverManager.getConnection(String jdbcUrl, Properties connProps)
```java
public Connection getConn() throws Exception{
Class.forName("com.taosdata.jdbc.TSDBDriver");
String jdbcUrl = "jdbc:TAOS://127.0.0.1:0/log?user=root&password=taosdata";
Properties connProps = new Properties();
connProps.setProperty(TSDBDriver.PROPERTY_KEY_USER, "root");
connProps.setProperty(TSDBDriver.PROPERTY_KEY_PASSWORD, "taosdata");
connProps.setProperty(TSDBDriver.PROPERTY_KEY_CONFIG_DIR, "/etc/taos");
connProps.setProperty(TSDBDriver.PROPERTY_KEY_CHARSET, "UTF-8");
connProps.setProperty(TSDBDriver.PROPERTY_KEY_LOCALE, "en_US.UTF-8");
connProps.setProperty(TSDBDriver.PROPERTY_KEY_TIME_ZONE, "UTC-8");
Connection conn = DriverManager.getConnection(jdbcUrl, connProps);
return conn;
}
```
3. Configuration file (taos.cfg)
Default configuration file is _/var/lib/taos/taos.cfg_ On Linux and _C:\TDengine\cfg\taos.cfg_ on Windows
```properties
# client default username
# defaultUser root
# client default password
# defaultPass taosdata
# default system charset
# charset UTF-8
# system locale
# locale en_US.UTF-8
```
> More options can refer to [client configuration][13]
### Create databases and tables
```java
Statement stmt = conn.createStatement();
// create database
stmt.executeUpdate("create database if not exists db");
// use database
stmt.executeUpdate("use db");
// create table
stmt.executeUpdate("create table if not exists tb (ts timestamp, temperature int, humidity float)");
```
> Note: if no step like `use db`, the name of database must be added as prefix like _db.tb_ when operating on tables
### Insert data
```java
// insert data
int affectedRows = stmt.executeUpdate("insert into tb values(now, 23, 10.3) (now + 1s, 20, 9.3)");
System.out.println("insert " + affectedRows + " rows.");
```
> _now_ is the server time.
> _now+1s_ is 1 second later than current server time. The time unit includes: _a_(millisecond), _s_(second), _m_(minute), _h_(hour), _d_(day), _w_(week), _n_(month), _y_(year).
### Query database
```java
// query data
ResultSet resultSet = stmt.executeQuery("select * from tb");
Timestamp ts = null;
int temperature = 0;
float humidity = 0;
while(resultSet.next()){
ts = resultSet.getTimestamp(1);
temperature = resultSet.getInt(2);
humidity = resultSet.getFloat("humidity");
System.out.printf("%s, %d, %s\n", ts, temperature, humidity);
}
```
> query is consistent with relational database. The subscript start with 1 when retrieving return results. It is recommended to use the column name to retrieve results.
### Close all
```java
resultSet.close();
stmt.close();
conn.close();
```
> `please make sure the connection is closed to avoid the error like connection leakage`
## Using connection pool
**HikariCP**
* dependence in pom.xml：
```xml
<dependency>
<groupId>com.zaxxer</groupId>
<artifactId>HikariCP</artifactId>
<version>3.4.1</version>
</dependency>
```
* Examples：
```java
public static void main(String[] args) throws SQLException {
HikariConfig config = new HikariConfig();
config.setJdbcUrl("jdbc:TAOS://127.0.0.1:6030/log");
config.setUsername("root");
config.setPassword("taosdata");
config.setMinimumIdle(3); //minimum number of idle connection
config.setMaximumPoolSize(10); //maximum number of connection in the pool
config.setConnectionTimeout(10000); //maximum wait milliseconds for get connection from pool
config.setIdleTimeout(60000); // max idle time for recycle idle connection
config.setConnectionTestQuery("describe log.dn"); //validation query
config.setValidationTimeout(3000); //validation query timeout
HikariDataSource ds = new HikariDataSource(config); //create datasource
Connection connection = ds.getConnection(); // get connection
Statement statement = connection.createStatement(); // get statement
//query or insert
// ...
connection.close(); // put back to conneciton pool
}
```
> The close() method will not close the connection from HikariDataSource.getConnection(). Instead, the connection is put back to the connection pool.
> More instructions can refer to [User Guide][5]
**Druid**
* dependency in pom.xml：
```xml
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>druid</artifactId>
<version>1.1.20</version>
</dependency>
```
* Examples：
```java
public static void main(String[] args) throws Exception {
Properties properties = new Properties();
properties.put("driverClassName","com.taosdata.jdbc.TSDBDriver");
properties.put("url","jdbc:TAOS://127.0.0.1:6030/log");
properties.put("username","root");
properties.put("password","taosdata");
properties.put("maxActive","10"); //maximum number of connection in the pool
properties.put("initialSize","3");//initial number of connection
properties.put("maxWait","10000");//maximum wait milliseconds for get connection from pool
properties.put("minIdle","3");//minimum number of connection in the pool
properties.put("timeBetweenEvictionRunsMillis","3000");// the interval milliseconds to test connection
properties.put("minEvictableIdleTimeMillis","60000");//the minimum milliseconds to keep idle
properties.put("maxEvictableIdleTimeMillis","90000");//the maximum milliseconds to keep idle
properties.put("validationQuery","describe log.dn"); //validation query
properties.put("testWhileIdle","true"); // test connection while idle
properties.put("testOnBorrow","false"); // don't need while testWhileIdle is true
properties.put("testOnReturn","false"); // don't need while testWhileIdle is true
//create druid datasource
DataSource ds = DruidDataSourceFactory.createDataSource(properties);
Connection connection = ds.getConnection(); // get connection
Statement statement = connection.createStatement(); // get statement
//query or insert
// ...
connection.close(); // put back to conneciton pool
}
```
> More instructions can refer to [User Guide][6]
**Notice**
* TDengine `v1.6.4.1` provides a function `select server_status()` to check heartbeat. It is highly recommended to use this function for `Validation Query`.
As follows，`1` will be returned if `select server_status()` is successfully executed。
```shell
taos> select server_status();
server_status()|
================
1 |
Query OK, 1 row(s) in set (0.000141s)
```
## Python Connector
### Install TDengine Python client
Users can find python client packages in our source code directory _src/connector/python_. There are two directories corresponding two python versions. Please choose the correct package to install. Users can use _pip_ command to install:
```cmd
pip install src/connector/python/python2/
```
or
```
pip install src/connector/python/python3/
```
If _pip_ command is not installed on the system, users can choose to install pip or just copy the _taos_ directory in the python client directory to the application directory to use.
### Python client interfaces
To use TDengine Python client, import TDengine module at first:
```python
import taos
```
Users can get module information from Python help interface or refer to our [python code example](). We list the main classes and methods below:
- _TDengineConnection_ class
Run `help(taos.TDengineConnection)` in python terminal for details.
- _TDengineCursor_ class
Run `help(taos.TDengineCursor)` in python terminal for details.
- connect method
Open a connection. Run `help(taos.connect)` in python terminal for details.
## RESTful Connector
TDengine also provides RESTful API to satisfy developing on different platforms. Unlike other databases, TDengine RESTful API applies operations to the database through the SQL command in the body of HTTP POST request. What users are required to provide is just a URL.
For the time being, TDengine RESTful API uses a _\<TOKEN\>_ generated from username and password for identification. Safer identification methods will be provided in the future.
### HTTP URL encoding
To use TDengine RESTful API, the URL should have the following encoding format:
```
http://<ip>:<PORT>/rest/sql
```
- _ip_: IP address of any node in a TDengine cluster
- _PORT_: TDengine HTTP service port. It is 6020 by default.
For example, the URL encoding _http://192.168.0.1:6020/rest/sql_ used to send HTTP request to a TDengine server with IP address as 192.168.0.1.
It is required to add a token in an HTTP request header for identification.
```
Authorization: Basic <TOKEN>
```
The HTTP request body contains the SQL command to run. If the SQL command contains a table name, it should also provide the database name it belongs to in the form of `<db_name>.<tb_name>`. Otherwise, an error code is returned.
For example, use _curl_ command to send a HTTP request:
```
curl -H 'Authorization: Basic <TOKEN>' -d '<SQL>' <ip>:<PORT>/rest/sql
```
or use
```
curl -u username:password -d '<SQL>' <ip>:<PORT>/rest/sql
```
where `TOKEN` is the encryted string of `{username}:{password}` using the Base64 algorithm, e.g. `root:taosdata` will be encoded as `cm9vdDp0YW9zZGF0YQ==`
### HTTP response
The HTTP resonse is in JSON format as below:
```
{
"status": "succ",
"head": ["column1","column2", …],
"data": [
["2017-12-12 23:44:25.730", 1],
["2017-12-12 22:44:25.728", 4]
],
"rows": 2
}
```
Specifically,
- _status_: the result of the operation, success or failure
- _head_: description of returned result columns
- _data_: the returned data array. If no data is returned, only an _affected_rows_ field is listed
- _rows_: the number of rows returned
### Example
- Use _curl_ command to query all the data in table _t1_ of database _demo_:
`curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'select * from demo.t1' 192.168.0.1:6020/rest/sql`
The return value is like:
```
{
"status": "succ",
"head": ["column1","column2","column3"],
"data": [
["2017-12-12 23:44:25.730", 1, 2.3],
["2017-12-12 22:44:25.728", 4, 5.6]
],
"rows": 2
}
```
- Use HTTP to create a database：
`curl -H 'Authorization: Basic cm9vdDp0YW9zZGF0YQ==' -d 'create database demo' 192.168.0.1:6020/rest/sql`
The return value should be:
```
{
"status": "succ",
"head": ["affected_rows"],
"data": [[1]],
"rows": 1,
}
```
## Go Connector
TDengine provides a GO client package `taosSql`. `taosSql` implements a kind of interface of GO `database/sql/driver`. User can access TDengine by importing the package in their program with the following instructions, detailed usage please refer to `https://github.com/taosdata/driver-go/blob/develop/taosSql/driver_test.go`
```Go
import (
"database/sql"
_ github.com/taosdata/driver-go/taoSql“
)
```
### API
* `sql.Open(DRIVER_NAME string, dataSourceName string) *DB`
Open DB, generally DRIVER_NAME will be used as a constant with default value `taosSql`, dataSourceName is a combined String with format `user:password@/tcp(host:port)/dbname`. If user wants to access TDengine with multiple goroutine concurrently, the better way is to create an sql.Open object in each goroutine to access TDengine.
**Note**: When calling this api, only a few initial work are done, instead the validity check happened during executing `Query` or `Exec`, at this time the connection will be created, and system will check if `user、password、host、port` is valid. Additionaly the most of features are implemented in the taosSql dependency lib `libtaos`, from this view, sql.Open is lightweight.
* `func (db *DB) Exec(query string, args ...interface{}) (Result, error)`
Execute non-Query related SQLs, the execution result is stored with type of Result.
* `func (db *DB) Query(query string, args ...interface{}) (*Rows, error)`
Execute Query related SQLs, the execution result is *Raw, the detailed usage can refer GO interface `database/sql/driver`
## Node.js Connector
TDengine also provides a node.js connector package that is installable through [npm](https://www.npmjs.com/). The package is also in our source code at *src/connector/nodejs/*. The following instructions are also available [here](https://github.com/taosdata/tdengine/tree/master/src/connector/nodejs)
To get started, just type in the following to install the connector through [npm](https://www.npmjs.com/).
```cmd
npm install td-connector
```
It is highly suggested you use npm. If you don't have it installed, you can also just copy the nodejs folder from *src/connector/nodejs/* into your node project folder.
To interact with TDengine, we make use of the [node-gyp](https://github.com/nodejs/node-gyp) library. To install, you will need to install the following depending on platform (the following instructions are quoted from node-gyp)
### On Unix
- `python` (`v2.7` recommended, `v3.x.x` is **not** supported)
- `make`
- A proper C/C++ compiler toolchain, like [GCC](https://gcc.gnu.org)
### On macOS
- `python` (`v2.7` recommended, `v3.x.x` is **not** supported) (already installed on macOS)
- Xcode
- You also need to install the
```
Command Line Tools
```
via Xcode. You can find this under the menu
```
Xcode -> Preferences -> Locations
```
(or by running
```
xcode-select --install
```
in your Terminal)
- This step will install `gcc` and the related toolchain containing `make`
### On Windows
#### Option 1
Install all the required tools and configurations using Microsoft's [windows-build-tools](https://github.com/felixrieseberg/windows-build-tools) using `npm install --global --production windows-build-tools` from an elevated PowerShell or CMD.exe (run as Administrator).
#### Option 2
Install tools and configuration manually:
- Install Visual C++ Build Environment: [Visual Studio Build Tools](https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools) (using "Visual C++ build tools" workload) or [Visual Studio 2017 Community](https://visualstudio.microsoft.com/pl/thank-you-downloading-visual-studio/?sku=Community) (using the "Desktop development with C++" workload)
- Install [Python 2.7](https://www.python.org/downloads/) (`v3.x.x` is not supported), and run `npm config set python python2.7` (or see below for further instructions on specifying the proper Python version and path.)
- Launch cmd, `npm config set msvs_version 2017`
If the above steps didn't work for you, please visit [Microsoft's Node.js Guidelines for Windows](https://github.com/Microsoft/nodejs-guidelines/blob/master/windows-environment.md#compiling-native-addon-modules) for additional tips.
To target native ARM64 Node.js on Windows 10 on ARM, add the components "Visual C++ compilers and libraries for ARM64" and "Visual C++ ATL for ARM64".
### Usage
The following is a short summary of the basic usage of the connector, the full api and documentation can be found [here](http://docs.taosdata.com/node)
#### Connection
To use the connector, first require the library ```td-connector```. Running the function ```taos.connect``` with the connection options passed in as an object will return a TDengine connection object. The required connection option is ```host```, other options if not set, will be the default values as shown below.
A cursor also needs to be initialized in order to interact with TDengine from Node.js.
```javascript
const taos = require('td-connector');
var conn = taos.connect({host:"127.0.0.1", user:"root", password:"taosdata", config:"/etc/taos",port:0})
var cursor = conn.cursor(); // Initializing a new cursor
```
To close a connection, run
```javascript
conn.close();
```
#### Queries
We can now start executing simple queries through the ```cursor.query``` function, which returns a TaosQuery object.
```javascript
var query = cursor.query('show databases;')
```
We can get the results of the queries through the ```query.execute()``` function, which returns a promise that resolves with a TaosResult object, which contains the raw data and additional functionalities such as pretty printing the results.
```javascript
var promise = query.execute();
promise.then(function(result) {
result.pretty(); //logs the results to the console as if you were in the taos shell
});
```
You can also query by binding parameters to a query by filling in the question marks in a string as so. The query will automatically parse what was binded and convert it to the proper format for use with TDengine
```javascript
var query = cursor.query('select * from meterinfo.meters where ts <= ? and areaid = ?;').bind(new Date(), 5);
query.execute().then(function(result) {
result.pretty();
})
```
The TaosQuery object can also be immediately executed upon creation by passing true as the second argument, returning a promise instead of a TaosQuery.
```javascript
var promise = cursor.query('select * from meterinfo.meters where v1 = 30;', true)
promise.then(function(result) {
result.pretty();
})
```
#### Async functionality
Async queries can be performed using the same functions such as `cursor.execute`, `cursor.query`, but now with `_a` appended to them.
Say you want to execute an two async query on two seperate tables, using `cursor.query_a`, you can do that and get a TaosQuery object, which upon executing with the `execute_a` function, returns a promise that resolves with a TaosResult object.
```javascript
var promise1 = cursor.query_a('select count(*), avg(v1), avg(v2) from meter1;').execute_a()
var promise2 = cursor.query_a('select count(*), avg(v1), avg(v2) from meter2;').execute_a();
promise1.then(function(result) {
result.pretty();
})
promise2.then(function(result) {
result.pretty();
})
```
### Example
An example of using the NodeJS connector to create a table with weather data and create and execute queries can be found [here](https://github.com/taosdata/TDengine/tree/master/tests/examples/nodejs/node-example.js) (The preferred method for using the connector)
An example of using the NodeJS connector to achieve the same things but without all the object wrappers that wrap around the data returned to achieve higher functionality can be found [here](https://github.com/taosdata/TDengine/tree/master/tests/examples/nodejs/node-example-raw.js)
[1]: https://search.maven.org/artifact/com.taosdata.jdbc/taos-jdbcdriver
[2]: https://mvnrepository.com/artifact/com.taosdata.jdbc/taos-jdbcdriver
[3]: https://github.com/taosdata/TDengine
[4]: https://www.taosdata.com/blog/2019/12/03/jdbcdriver%e6%89%be%e4%b8%8d%e5%88%b0%e5%8a%a8%e6%80%81%e9%93%be%e6%8e%a5%e5%ba%93/
[5]: https://github.com/brettwooldridge/HikariCP
[6]: https://github.com/alibaba/druid
[7]: https://github.com/taosdata/TDengine/issues
[8]: https://search.maven.org/artifact/com.taosdata.jdbc/taos-jdbcdriver
[9]: https://mvnrepository.com/artifact/com.taosdata.jdbc/taos-jdbcdriver
[10]: https://maven.aliyun.com/mvn/search
[11]: https://github.com/taosdata/TDengine/tree/develop/tests/examples/JDBC/SpringJdbcTemplate
[12]: https://github.com/taosdata/TDengine/tree/develop/tests/examples/JDBC/springbootdemo
[13]: https://www.taosdata.com/cn/documentation20/administrator/#%E5%AE%A2%E6%88%B7%E7%AB%AF%E9%85%8D%E7%BD%AE
[14]: https://www.taosdata.com/cn/documentation20/connector/#Windows
[15]: https://www.taosdata.com/cn/getting-started/#%E5%BF%AB%E9%80%9F%E4%B8%8A%E6%89%8B
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# TaosData Contributor License Agreement
This TaosData Contributor License Agreement (CLA) applies to any contribution you make to any TaosData projects. If you are representing your employing organization to sign this agreement, please warrant that you have the authority to grant the agreement.
## Terms
**"TaosData"**, **"we"**, **"our"** and **"us"** means TaosData, inc.
**"You"** and **"your"** means you or the organization you are on behalf of to sign this agreement.
**"Contribution"** means any original work you, or the organization you represent submit to TaosData for any project in any manner.
## Copyright License
All rights of your Contribution submitted to TaosData in any manner are granted to TaosData and recipients of software distributed by TaosData. You waive any rights that my affect our ownership of the copyright and grant to us a perpetual, worldwide, transferable, non-exclusive, no-charge, royalty-free, irrevocable, and sublicensable license to use, reproduce, prepare derivative works of, publicly display, publicly perform, sublicense, and distribute Contributions and any derivative work created based on a Contribution.
## Patent License
With respect to any patents you own or that you can license without payment to any third party, you grant to us and to any recipient of software distributed by us, a perpetual, worldwide, transferable, non-exclusive, no-charge, royalty-free, irrevocable patent license to make, have make, use, sell, offer to sell, import, and otherwise transfer the Contribution in whole or in part, alone or included in any product under any patent you own, or license from a third party, that is necessarily infringed by the Contribution or by combination of the Contribution with any Work.
## Your Representations and Warranties
You represent and warrant that:
- the Contribution you submit is an original work that you can legally grant the rights set out in this agreement.
- the Contribution you submit and licenses you granted does not and will not, infringe the rights of any third party.
- you are not aware of any pending or threatened claims, suits, actions, or charges pertaining to the contributions. You also warrant to notify TaosData immediately if you become aware of any such actual or potential claims, suits, actions, allegations or charges.
## Support
You are not obligated to support your Contribution except you volunteer to provide support. If you want, you can provide for a fee.
**I agree and accept on behalf of myself and behalf of my organization:**
\ No newline at end of file
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#Documentation
TDengine is a highly efficient platform to store, query, and analyze time-series data. It works like a relational database, but you are strongly suggested to read through the following documentation before you experience it.
##Getting Started
- Quick Start: download, install and experience TDengine in a few seconds
- TDengine Shell: command-line interface to access TDengine server
- Major Features: insert/query, aggregation, cache, pub/sub, continuous query
## Data Model and Architecture
- Data Model: relational database model, but one table for one device with static tags
- Architecture: Management Module, Data Module, Client Module
- Writing Process: records recieved are written to WAL, cache, then ack is sent back to client
- Data Storage: records are sharded in the time range, and stored column by column
##TAOS SQL
- Data Types: support timestamp, int, float, double, binary, nchar, bool, and other types
- Database Management: add, drop, check databases
- Table Management: add, drop, check, alter tables
- Inserting Records: insert one or more records into tables, historical records can be imported
- Data Query: query data with time range and filter conditions, support limit/offset
- SQL Functions: support aggregation, selector, transformation functions
- Downsampling: aggregate data in successive time windows, support interpolation
##STable: Super Table
- What is a Super Table: an innovated way to aggregate tables
- Create a STable: it is like creating a standard table, but with tags defined
- Create a Table via STable: use STable as the template, with tags specified
- Aggregate Tables via STable: group tables together by specifying the tags filter condition
- Create Table Automatically: create tables automatically with a STable as a template
- Management of STables: create/delete/alter super table just like standard tables
- Management of Tags: add/delete/alter tags on super tables or tables
##Advanced Features
- Continuous Query: query executed by TDengine periodically with a sliding window
- Publisher/Subscriber: subscribe to the newly arrived data like a typical messaging system
- Caching: the newly arrived data of each device/table will always be cached
##Connector
- C/C++ Connector: primary method to connect to the server through libtaos client library
- Java Connector: driver for connecting to the server from Java applications using the JDBC API
- Python Connector: driver for connecting to the server from Python applications
- RESTful Connector: a simple way to interact with TDengine via HTTP
- Go Connector: driver for connecting to the server from Go applications
- Node.js Connector: driver for connecting to the server from node applications
##Connections with Other Tools
- Telegraf: pass the collected DevOps metrics to TDengine
- Grafana: query the data saved in TDengine and visualize them
- Matlab: access TDengine server from Matlab via JDBC
- R: access TDengine server from R via JDBC
##Administrator
- Directory and Files: files and directories related with TDengine
- Configuration on Server: customize IP port, cache size, file block size and other settings
- Configuration on Client: customize locale, default user and others
- User Management: add/delete users, change passwords
- Import Data: import data into TDengine from either script or CSV file
- Export Data: export data either from TDengine shell or from tool taosdump
- Management of Connections, Streams, Queries: check or kill the connections, queries
- System Monitor: collect the system metric, and log important operations
##More on System Architecture
- Storage Design: column-based storage with optimization on time-series data
- Query Design: an efficient way to query time-series data
- Technical blogs to delve into the inside of TDengine
## More on IoT Big Data
- [Characteristics of IoT Big Data](https://www.taosdata.com/blog/2019/07/09/characteristics-of-iot-big-data/)
- [Why don’t General Big Data Platforms Fit IoT Scenarios?](https://www.taosdata.com/blog/2019/07/09/why-does-the-general-big-data-platform-not-fit-iot-data-processing/)
- [Why TDengine is the Best Choice for IoT Big Data Processing?](https://www.taosdata.com/blog/2019/07/09/why-tdengine-is-the-best-choice-for-iot-big-data-processing/)
##Tutorials & FAQ
- <a href='https://www.taosdata.com/en/faq'>FAQ</a>: a list of frequently asked questions and answers
- <a href='https://www.taosdata.com/en/blog/?categories=4'>Use cases</a>: a few typical cases to explain how to use TDengine in IoT platform
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#Getting Started
## Quick Start
At the moment, TDengine only runs on Linux. You can set up and install it either from the <a href='#Install-from-Source'>source code</a> or the <a href='#Install-from-Package'>packages</a>. It takes only a few seconds from download to run it successfully.
### Install from Source
Please visit our [github page](https://github.com/taosdata/TDengine) for instructions on installation from the source code.
### Install from Package
Three different packages are provided, please pick up the one you like.
<ul id='packageList'>
<li><a id='tdengine-rpm' style='color:var(--b2)'>TDengine RPM package (1.5M)</a></li>
<li><a id='tdengine-deb' style='color:var(--b2)'>TDengine DEB package (1.7M)</a></li>
<li><a id='tdengine-tar' style='color:var(--b2)'>TDengine Tarball (3.0M)</a></li>
</ul>
For the time being, TDengine only supports installation on Linux systems using [`systemd`](https://en.wikipedia.org/wiki/Systemd) as the service manager. To check if your system has *systemd* package, use the _which systemctl_ command.
```cmd
which systemctl
```
If the `systemd` package is not found, please [install from source code](#Install-from-Source).
### Running TDengine
After installation, start the TDengine service by the `systemctl` command.
```cmd
systemctl start taosd
```
Then check if the server is working now.
```cmd
systemctl status taosd
```
If the service is running successfully, you can play around through TDengine shell `taos`, the command line interface tool located in directory /usr/local/bin/taos
**Note: The _systemctl_ command needs the root privilege. Use _sudo_ if you are not the _root_ user.**
##TDengine Shell
To launch TDengine shell, the command line interface, in a Linux terminal, type:
```cmd
taos
```
The welcome message is printed if the shell connects to TDengine server successfully, otherwise, an error message will be printed (refer to our [FAQ](../faq) page for troubleshooting the connection error). The TDengine shell prompt is:
```cmd
taos>
```
In the TDengine shell, you can create databases, create tables and insert/query data with SQL. Each query command ends with a semicolon. It works like MySQL, for example:
```mysql
create database db;
use db;
create table t (ts timestamp, cdata int);
insert into t values ('2019-07-15 10:00:00', 10);
insert into t values ('2019-07-15 10:01:05', 20);
select * from t;
ts | speed |
===================================
19-07-15 10:00:00.000| 10|
19-07-15 10:01:05.000| 20|
Query OK, 2 row(s) in set (0.001700s)
```
Besides the SQL commands, the system administrator can check system status, add or delete accounts, and manage the servers.
###Shell Command Line Parameters
You can run `taos` command with command line options to fit your needs. Some frequently used options are listed below:
- -c, --config-dir: set the configuration directory. It is _/etc/taos_ by default
- -h, --host: set the IP address of the server it will connect to, Default is localhost
- -s, --commands: set the command to run without entering the shell
- -u, -- user: user name to connect to server. Default is root
- -p, --password: password. Default is 'taosdata'
- -?, --help: get a full list of supported options
Examples:
```cmd
taos -h 192.168.0.1 -s "use db; show tables;"
```
###Run Batch Commands
Inside TDengine shell, you can run batch commands in a file with *source* command.
```
taos> source <filename>;
```
### Tips
- Use up/down arrow key to check the command history
- To change the default password, use "`alter user`" command
- ctrl+c to interrupt any queries
- To clean the cached schema of tables or STables, execute command `RESET QUERY CACHE`
## Major Features
The core functionality of TDengine is the time-series database. To reduce the development and management complexity, and to improve the system efficiency further, TDengine also provides caching, pub/sub messaging system, and stream computing functionalities. It provides a full stack for IoT big data platform. The detailed features are listed below:
- SQL like query language used to insert or explore data
- C/C++, Java(JDBC), Python, Go, RESTful, and Node.JS interfaces for development
- Ad hoc queries/analysis via Python/R/Matlab or TDengine shell
- Continuous queries to support sliding-window based stream computing
- Super table to aggregate multiple time-streams efficiently with flexibility
- Aggregation over a time window on one or multiple time-streams
- Built-in messaging system to support publisher/subscriber model
- Built-in cache for each time stream to make latest data available as fast as light speed
- Transparent handling of historical data and real-time data
- Integrating with Telegraf, Grafana and other tools seamlessly
- A set of tools or configuration to manage TDengine
For enterprise edition, TDengine provides more advanced features below:
- Linear scalability to deliver higher capacity/throughput
- High availability to guarantee the carrier-grade service
- Built-in replication between nodes which may span multiple geographical sites
- Multi-tier storage to make historical data management simpler and cost-effective
- Web-based management tools and other tools to make maintenance simpler
TDengine is specially designed and optimized for time-series data processing in IoT, connected cars, Industrial IoT, IT infrastructure and application monitoring, and other scenarios. Compared with other solutions, it is 10x faster on insert/query speed. With a single-core machine, over 20K requestes can be processed, millions data points can be ingested, and over 10 million data points can be retrieved in a second. Via column-based storage and tuned compression algorithm for different data types, less than 1/10 storage space is required.
## Explore More on TDengine
Please read through the whole <a href='../documentation'>documentation</a> to learn more about TDengine.
documentation20/webdocs/markdowndocs/More on System Architecture-ch.md 已删除 100644 → 0
浏览文件 @ 2e0a381b
# TDengine的技术设计
## 存储设计
TDengine的数据存储主要包含**元数据的存储**和**写入数据的存储**。以下章节详细介绍了TDengine各种数据的存储结构。
### 元数据的存储
TDengine中的元数据信息包括TDengine中的数据库，表，超级表等信息。元数据信息默认存放在 _/var/lib/taos/mgmt/_ 文件夹下。该文件夹的目录结构如下所示：
```
/var/lib/taos/
+--mgmt/
+--db.db
+--meters.db
+--user.db
+--vgroups.db
```
元数据在文件中按顺序排列。文件中的每条记录代表TDengine中的一个元数据机构（数据库、表等）。元数据文件只进行追加操作，即便是元数据的删除，也只是在数据文件中追加一条删除的记录。
### 写入数据的存储
TDengine中写入的数据在硬盘上是按时间维度进行分片的。同一个vnode中的表在同一时间范围内的数据都存放在同一文件组中，如下图中的v0f1804*文件。这一数据分片方式可以大大简化数据在时间维度的查询，提高查询速度。在默认配置下，硬盘上的每个文件存放10天数据。用户可根据需要调整数据库的 _daysPerFile_ 配置项进行配置。 数据在文件中是按块存储的。每个数据块只包含一张表的数据，且数据是按照时间主键递增排列的。数据在数据块中按列存储，这样使得同类型的数据存放在一起，可以大大提高压缩的比例，节省存储空间。TDengine对不同类型的数据采用了不同的压缩算法进行压缩，以达到最优的压缩结果。TDengine使用的压缩算法包括simple8B、delta-of-delta、RLE以及LZ4等。
TDengine的数据文件默认存放在 */var/lib/taos/data/* 下。而 */var/lib/taos/tsdb/* 文件夹下存放了vnode的信息、vnode中表的信息以及数据文件的链接等。其完整目录结构如下所示：
```
/var/lib/taos/
+--tsdb/
| +--vnode0
| +--meterObj.v0
| +--db/
| +--v0f1804.head->/var/lib/taos/data/vnode0/v0f1804.head1
| +--v0f1804.data->/var/lib/taos/data/vnode0/v0f1804.data
| +--v0f1804.last->/var/lib/taos/data/vnode0/v0f1804.last1
| +--v0f1805.head->/var/lib/taos/data/vnode0/v0f1805.head1
| +--v0f1805.data->/var/lib/taos/data/vnode0/v0f1805.data
| +--v0f1805.last->/var/lib/taos/data/vnode0/v0f1805.last1
| :
+--data/
+--vnode0/
+--v0f1804.head1
+--v0f1804.data
+--v0f1804.last1
+--v0f1805.head1
+--v0f1805.data
+--v0f1805.last1
:
```
#### meterObj文件
每个vnode中只存在一个 _meterObj_ 文件。该文件中存储了vnode的基本信息（创建时间，配置信息，vnode的统计信息等）以及该vnode中表的信息。其结构如下所示：
```
<文件开始>
[文件头]
[表记录1偏移量和长度]
[表记录2偏移量和长度]
...
[表记录N偏移量和长度]
[表记录1]
[表记录2]
...
[表记录N]
[表记录]
<文件结尾>
```
其中，文件头大小为512字节，主要存放vnode的基本信息。每条表记录代表属于该vnode中的一张表在硬盘上的表示。
#### head文件
head文件中存放了其对应的data文件中数据块的索引信息。该文件组织形式如下：
```
<文件开始>
[文件头]
[表1偏移量]
[表2偏移量]
...
[表N偏移量]
[表1数据索引]
[表2数据索引]
...
[表N数据索引]
<文件结尾>
```
文件开头的偏移量列表表示对应表的数据索引块的开始位置在文件中的偏移量。每张表的数据索引信息在head文件中都是连续存放的。这也使得TDengine在读取单表数据时，可以将该表所有的数据块索引一次性读入内存，大大提高读取速度。表的数据索引块组织如下：
```
[索引块信息]
[数据块1索引]
[数据块2索引]
...
[数据块N索引]
```
其中，索引块信息中记录了数据块的个数等描述信息。每个数据块索引对应一个在data文件或last文件中的一个单独的数据块。索引信息中记录了数据块存放的文件、数据块起始位置的偏移量、数据块中数据时间主键的范围等。索引块中的数据块索引是按照时间范围顺序排放的，这也就是说，索引块M对应的数据块中的数据时间范围都大于索引块M-1的。这种预先排序的存储方式使得在TDengine在进行按照时间戳进行查询时可以使用折半查找算法，大大提高查询速度。
#### data文件
data文件中存放了真实的数据块。该文件只进行追加操作。其文件组织形式如下：
```
<文件开始>
[文件头]
[数据块1]
[数据块2]
...
[数据块N]
<文件结尾>
```
每个数据块只属于vnode中的一张表，且数据块中的数据按照时间主键排列。数据块中的数据按列组织排放，使得同一类型的数据排放在一起，方便压缩和读取。每个数据块的组织形式如下所示：
```
[列1信息]
[列2信息]
...
[列N信息]
[列1数据]
[列2数据]
...
[列N数据]
```
列信息中包含该列的类型，列的压缩算法，列数据在文件中的偏移量以及长度等。除此之外，列信息中也包含该内存块中该列数据的预计算结果，从而在过滤查询时根据预计算结果判定是否读取数据块，大大提高读取速度。
#### last文件
为了防止数据块的碎片化，提高查询速度和压缩率，TDengine引入了last文件。当要落盘的数据块中的数据条数低于某个阈值时，TDengine会先将该数据块写入到last文件中进行暂时存储。当有新的数据需要落盘时，last文件中的数据会被读取出来与新数据组成新的数据块写入到data文件中。last文件的组织形式与data文件类似。
### TDengine数据存储小结
TDengine通过其创新的架构和存储结构设计，有效提高了计算机资源的使用率。一方面，TDengine的虚拟化使得TDengine的水平扩展及备份非常容易。另一方面，TDengine将表中数据按时间主键排序存储且其列式存储的组织形式都使TDengine在写入、查询以及压缩方面拥有非常大的优势。
## 查询处理
### 概述
TDengine提供了多种多样针对表和超级表的查询处理功能，除了常规的聚合查询之外，还提供针对时序数据的窗口查询、统计聚合等功能。TDengine的查询处理需要客户端、管理节点、数据节点协同完成。 各组件包含的与查询处理相关的功能和模块如下：
客户端（Client App）。客户端包含TAOS SQL的解析（SQL Parser）和查询请求执行器（Query Executor），第二阶段聚合器（Result Merger），连续查询管理器（Continuous Query Manager）等主要功能模块构成。SQL解析器负责对SQL语句进行解析校验，并转化为抽象语法树，查询执行器负责将抽象语法树转化查询执行逻辑，并根据SQL语句查询条件，将其转换为针对管理节点元数据查询和针对数据节点的数据查询两级查询处理。由于TAOS SQL当前不提供复杂的嵌套查询和pipeline查询处理机制，所以不再需要查询计划优化、逻辑查询计划到物理查询计划转换等过程。第二阶段聚合器负责将各数据节点查询返回的独立结果进行二阶段聚合生成最后的结果。连续查询管理器则负责针对用户建立的连续查询进行管理，负责定时拉起查询请求并按需将结果写回TDengine或返回给客户应用。此外，客户端还负责查询失败后重试、取消查询请求、以及维持连接心跳、向管理节点上报查询状态等工作。
管理节点（Management Node）。管理节点保存了整个集群系统的全部数据的元数据信息，向客户端节点提供查询所需的数据的元数据，并根据集群的负载情况切分查询请求。通过超级表包含了通过该超级表创建的所有表的信息，因此查询处理器（Query Executor）负责针对标签（TAG）的查询处理，并将满足标签查询请求的表信息返回给客户端。此外，管理节点还维护集群的查询状态（Query Status Manager）维护，查询状态管理中在内存中临时保存有当前正在执行的全部查询，当客户端使用 *show queries* 命令的时候，将当前系统正在运行的查询信息返回客户端。
数据节点（Data Node）。数据节点保存了数据库中全部数据内容，并通过查询执行器、查询处理调度器、查询任务队列（Query Task Queue）进行查询处理的调度执行，从客户端接收到的查询处理请求都统一放置到处理队列中，查询执行器从队列中获得查询请求，并负责执行。通过查询优化器（Query Optimizer）对于查询进行基本的优化处理，以及通过数据节点的查询执行器（Query Executor）扫描符合条件的数据单元并返回计算结果。等接收客户端发出的查询请求，执行查询处理，并将结果返回。同时数据节点还需要响应来自管理节点的管理信息和命令，例如 *kill query* 命令以后，需要即刻停止执行的查询任务。
<center> <img src="../assets/fig1.png"> </center>
<center>图 1. 系统查询处理架构图(只包含查询相关组件)</center>
### 普通查询处理
客户端、管理节点、数据节点协同完成TDengine的查询处理全流程。我们以一个具体的SQL查询为例，说明TDengine的查询处理流程。SQL语句向超级表*FOO_SUPER_TABLE*查询获取时间范围在2019年1月12日整天，标签TAG_LOC是'beijing'的表所包含的所有记录总数，SQL语句如下：
```sql
SELECT COUNT(*)
FROM FOO_SUPER_TABLE
WHERE TAG_LOC = 'beijing' AND TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00'
```
首先，客户端调用TAOS SQL解析器对SQL语句进行解析及合法性检查，然后生成语法树，并从中提取查询的对象 — 超级表 *FOO_SUPER_TABLE* ，然后解析器向管理节点（Management Node）请求其相应的元数据信息，并将过滤信息（TAG_LOC='beijing'）同时发送到管理节点。
管理节点接收元数据获取的请求，首先找到超级表 *FOO_SUPER_TABLE* 基础信息，然后应用查询条件来过滤通过该超级表创建的全部表，最后满足查询条件（TAG_LOC='beijing'），即 *TAG_LOC* 标签列是 'beijing' 的的通过其查询执行器将满足查询要求的对象（表或超级表）的元数据信息返回给客户端。
客户端获得了 *FOO_SUPER_TABLE* 的元数据信息后，查询执行器根据元数据中的数据分布，分别向保存有相应数据的节点发起查询请求，此时时间戳范围过滤条件（TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00'）需要同时发送给全部的数据节点。
数据节点接收到发自客户端的查询，转化为内部结构并进行优化以后将其放入任务执行队列，等待查询执行器执行。当查询结果获得以后，将查询结果返回客户端。数据节点执行查询的过程均相互独立，完全只依赖于自身的数据和内容进行计算。
当所有查询涉及的数据节点返回结果后，客户端将每个数据节点查询的结果集再次进行聚合（针对本案例，即将所有结果再次进行累加），累加的结果即为最后的查询结果。第二阶段聚合并不是所有的查询都需要。例如，针对数据的列选取操作，实际上是不需要第二阶段聚合。
### REST查询处理
在 C/C++ 、Python接口、 JDBC 接口之外，TDengine 还提供基于 HTTP 协议的 REST 接口。不同于使用应用客户端开发程序进行的开发。当用户使用 REST 接口的时候，所有的查询处理过程都是在服务器端来完成，用户的应用服务不会参与数据库的计算过程，查询处理完成后结果通过 HTTP的 JSON 格式返回给用户。
<center> <img src="../assets/fig2.png"> </center>
<center>图 2. REST查询架构</center>
当用户使用基于HTTP的REST查询接口，HTTP的请求首先与位于数据节点的HTTP连接器（ Connector），建立连接，然后通过REST的签名机制，使用Token来确保请求的可靠性。对于数据节点，HTTP连接器接收到请求后，调用内嵌的客户端程序发起查询请求，内嵌客户端将解析通过HTTP连接器传递过来的SQL语句，解析该SQL语句并按需向管理节点请求元数据信息，然后向本机或集群中其他节点发送查询请求，最后按需聚合计算结果。HTTP连接器接收到请求SQL以后，后续的流程处理与采用应用客户端方式的查询处理完全一致。最后，还需要将查询的结果转换为JSON格式字符串，并通过HTTP 响应返回给客户端。
可以看到，在处理HTTP流程的整个过程中，用户应用不再参与到查询处理的过程中，只负责通过HTTP协议发送SQL请求并接收JSON格式的结果。同时还需要注意的是，每个数据节点均内嵌了一个HTTP连接器和客户端程序，因此请求集群中任何一个数据节点，该数据节点均能够通过HTTP协议返回用户的查询结果。
### 技术特征
由于TDengine采用数据和标签分离存储的模式，能够极大地降低标签数据存储的冗余度。标签数据直接关联到每个表，并采用全内存的结构进行管理和维护标签数据,全内存的结构提供快速的查询处理，千万级别规模的标签数据查询可以在毫秒级别返回。首先针对标签数据的过滤可以有效地降低第二阶段的查询涉及的数据规模。为有效地提升查询处理的性能，针对物联网数据的不可更改的特点，TDengine采用在每个保存的数据块上，都记录下该数据块中数据的最大值、最小值、和等统计数据。如果查询处理涉及整个数据块的全部数据，则直接使用预计算结果，不再读取数据块的内容。由于预计算模块的大小远小于磁盘上存储的具体数据的大小，对于磁盘IO为瓶颈的查询处理，使用预计算结果可以极大地减小读取IO，并加速查询处理的流程。
由于TDengine采用按列存储数据。当从磁盘中读取数据块进行计算的时候，按照查询列信息读取该列数据，并不需要读取其他不相关的数据，可以最小化读取数据。此外，由于采用列存储结构，数据节点针对数据的扫描采用该列数据块进行，可以充分利用CPU L2高速缓存，极大地加速数据扫描的速度。此外，对于某些查询，并不会等全部查询结果生成后再返回结果。例如，列选取查询，当第一批查询结果获得以后，数据节点直接将其返回客户端。同时，在查询处理过程中，系统在数据节点接收到查询请求以后马上返回客户端查询确认信息，并同时拉起查询处理过程，并等待查询执行完成后才返回给用户查询有响应。
## TDengine集群设计
### 1：集群与主要逻辑单元
TDengine是基于硬件、软件系统不可靠、一定会有故障的假设进行设计的，是基于任何单台计算机都无足够能力处理海量数据的假设进行设计的。因此TDengine从研发的第一天起，就按照分布式高可靠架构进行设计，是完全去中心化的，是水平扩展的，这样任何单台或多台服务器宕机或软件错误都不影响系统的服务。通过节点虚拟化并辅以自动化负载均衡技术，TDengine能最大限度地利用异构集群中的计算和存储资源。而且只要数据副本数大于一，无论是硬软件的升级、还是IDC的迁移等都无需停止集群的服务，极大地保证系统的正常运行，并且降低了系统管理员和运维人员的工作量。
下面的示例图上有八个物理节点，每个物理节点被逻辑的划分为多个虚拟节点。下面对系统的基本概念进行介绍。
![assets/nodes.png](../assets/nodes.png)
**物理节点(dnode)**：集群中的一物理服务器或云平台上的一虚拟机。为安全以及通讯效率，一个物理节点可配置两张网卡，或两个IP地址。其中一张网卡用于集群内部通讯，其IP地址为**privateIp**, 另外一张网卡用于与集群外部应用的通讯，其IP地址为**publicIp**。在一些云平台（如阿里云），对外的IP地址是映射过来的，因此publicIp还有一个对应的内部IP地址**internalIp**(与privateIp不同)。对于只有一个IP地址的物理节点，publicIp, privateIp以及internalIp都是同一个地址，没有任何区别。一个dnode上有而且只有一个taosd实例运行。
**虚拟数据节点(vnode)**：在物理节点之上的可独立运行的基础逻辑单元，时序数据写入、存储、查询等操作逻辑都在虚拟节点中进行(图中V)，采集的时序数据就存储在vnode上。一个vnode包含固定数量的表。当创建一张新表时，系统会检查是否需要创建新的vnode。一个物理节点上能创建的vnode的数量取决于物理节点的硬件资源。一个vnode只属于一个DB，但一个DB可以有多个vnode。
**虚拟数据节点组(vgroup)**: 位于不同物理节点的vnode可以组成一个虚拟数据节点组vnode group(如上图dnode0中的V0, dnode1中的V1, dnode6中的V2属于同一个虚拟节点组)。归属于同一个vgroup的虚拟节点采取master/slave的方式进行管理。写只能在master上进行，但采用asynchronous的方式将数据同步到slave，这样确保了一份数据在多个物理节点上有拷贝。如果master节点宕机，其他节点监测到后，将重新选举vgroup里的master, 新的master能继续处理数据请求，从而保证系统运行的可靠性。一个vgroup里虚拟节点个数就是数据的副本数。如果一个DB的副本数为N，系统必须有至少N个物理节点。副本数在创建DB时通过参数replica可以指定，缺省为1。使用TDengine, 数据的安全依靠多副本解决，因此不再需要昂贵的磁盘阵列等存储设备。
**虚拟管理节点(mnode)**：负责所有节点运行状态的监控和维护，以及节点之间的负载均衡（图中M）。同时，虚拟管理节点也负责元数据(包括用户、数据库、表、静态标签等)的存储和管理，因此也称为Meta Node。TDengine集群中可配置多个(最多不超过5个) mnode，它们自动构建成为一个管理节点集群(图中M0, M1, M2)。mnode间采用master/slave的机制进行管理，而且采取强一致方式进行数据同步。mnode集群的创建由系统自动完成，无需人工干预。每个dnode上至多有一个mnode，而且每个dnode都知道整个集群中所有mnode的IP地址。
**taosc**：一个软件模块，是TDengine给应用提供的驱动程序（driver）,内嵌于JDBC、ODBC driver中，或者C语言连接库里。应用都是通过taosc而不是直接来与整个集群进行交互的。这个模块负责获取并缓存元数据；将插入、查询等请求转发到正确的虚拟节点；在把结果返回给应用时，还需要负责最后一级的聚合、排序、过滤等操作。对于JDBC, ODBC, C/C++接口而言，这个模块是在应用所处的计算机上运行，但消耗的资源很小。为支持全分布式的REST接口，taosc在TDengine集群的每个dnode上都有一运行实例。
**对外服务地址**：TDengine集群可以容纳单台、多台甚至几千台物理节点。应用只需要向集群中任何一个物理节点的publicIp发起连接即可。启动CLI应用taos时，选项-h需要提供的就是publicIp。
**master/secondIp**：每一个dnode都需要配置一个masterIp。dnode启动后，将对配置的masterIp发起加入集群的连接请求。masterIp是已经创建的集群中的任何一个节点的privateIp，对于集群中的第一个节点，就是它自己的privateIp。为保证连接成功，每个dnode还可配置secondIp, 该IP地址也是已创建的集群中的任何一个节点的privateIp。如果一个节点连接masterIp失败,它将试图连接secondIp。
dnode启动后，会获知集群的mnode IP列表，并且定时向mnode发送状态信息。
vnode与mnode只是逻辑上的划分，都是执行程序taosd里的不同线程而已，无需安装不同的软件，做任何特殊的配置。最小的系统配置就是一个物理节点,vnode,mnode和taosc都存在而且都正常运行，但单一节点无法保证系统的高可靠。
### 2：一典型的操作流程
为解释vnode, mnode, taosc和应用之间的关系以及各自扮演的角色，下面对写入数据这个典型操作的流程进行剖析。
![Picture1](../assets/Picture2.png)
1. 应用通过JDBC、ODBC或其他API接口发起插入数据的请求。
2. taosc会检查缓存，看是有保存有该表的meta data。如果有，直接到第4步。如果没有，taosc将向mnode发出get meta-data请求。
3. mnode将该表的meta-data返回给taosc。Meta-data包含有该表的schema, 而且还有该表所属的vgroup信息（vnode ID以及所在的dnode的IP地址，如果副本数为N，就有N组vnodeID/IP)。如果taosc迟迟得不到mnode回应，而且存在多个mnode,taosc将向下一个mnode发出请求。
4. taosc向master vnode发起插入请求。
5. vnode插入数据后，给taosc一个应答，表示插入成功。如果taosc迟迟得不到vnode的回应，taosc会认为该节点已经离线。这种情况下，如果被插入的数据库有多个副本，taosc将向vgroup里下一个vnode发出插入请求。
6. taosc通知APP，写入成功。
对于第二和第三步，taosc启动时，并不知道mnode的IP地址，因此会直接向配置的集群对外服务的IP地址发起请求。如果接收到该请求的dnode并没有配置mnode，该dnode会在回复的消息中告知mnode的IP地址列表（如果有多个dnodes，mnode的IP地址可以有多个），这样taosc会重新向新的mnode的IP地址发出获取meta-data的请求。
对于第四和第五步，没有缓存的情况下，taosc无法知道虚拟节点组里谁是master，就假设第一个vnodeID/IP就是master,向它发出请求。如果接收到请求的vnode并不是master,它会在回复中告知谁是master，这样taosc就向建议的master vnode发出请求。一旦得到插入成功的回复，taosc会缓存住master节点的信息。
上述是插入数据的流程，查询、计算的流程也完全一致。taosc把这些复杂的流程全部封装屏蔽了，因此应用无需处理重定向、获取meta data等细节，完全是透明的。
通过taosc缓存机制，只有在第一次对一张表操作时，才需要访问mnode, 因此mnode不会成为系统瓶颈。但因为schema有可能变化，而且vgroup有可能发生改变（比如负载均衡发生），因此taosc需要定时自动刷新缓存。
### 3：数据分区
vnode(虚拟数据节点)保存采集的时序数据，而且查询、计算都在这些节点上进行。为便于负载均衡、数据恢复、支持异构环境，TDengine将一个物理节点根据其计算和存储资源切分为多个vnode。这些vnode的管理是TDengine自动完成的，对应用完全透明。
对于单独一个数据采集点，无论其数据量多大，一个vnode（或vnode group, 如果副本数大于1）有足够的计算资源和存储资源来处理（如果每秒生成一条16字节的记录，一年产生的原始数据不到0.5G），因此TDengine将一张表的所有数据都存放在一个vnode里，而不会让同一个采集点的数据分布到两个或多个dnode上。而且一个vnode可存储多张表的数据，一个vnode可容纳的表的数目由配置参数tables指定，缺省为2000。设计上，一个vnode里所有的表都属于同一个DB。因此一个数据库DB需要的vnode或vgroup的个数等于：数据库表的数目/tables。
创建DB时，系统并不会马上分配资源。但当创建一张表时，系统将看是否有已经分配的vnode, 而且是否有空位，如果有，立即在该有空位的vnode创建表。如果没有，系统将从集群中，根据当前的负载情况，在一个dnode上创建一新的vnode, 然后创建表。如果DB有多个副本，系统不是只创建一个vnode，而是一个vgroup(虚拟数据节点组)。系统对vnode的数目没有任何限制，仅仅受限于物理节点本身的计算和存储资源。
参数tables的设置需要考虑具体场景，创建DB时，可以个性化指定该参数。该参数不宜过大，也不宜过小。过小，极端情况，就是每个数据采集点一个vnode, 这样导致系统数据文件过多。过大，虚拟化带来的优势就会丧失。给定集群计算资源的情况下，整个系统vnode的个数应该是CPU核的数目的两倍以上。
### 4：负载均衡
每个dnode(物理节点)都定时向 mnode(虚拟管理节点)报告其状态（包括硬盘空间、内存大小、CPU、网络、虚拟节点个数等），因此mnode了解整个集群的状态。基于整体状态，当mnode发现某个dnode负载过重，它会将dnode上的一个或多个vnode挪到其他dnode。在挪动过程中，对外服务继续进行，数据插入、查询和计算操作都不受影响。负载均衡操作结束后，应用也无需重启，将自动连接新的vnode。
如果mnode一段时间没有收到dnode的状态报告，mnode会认为这个dnode已经离线。如果离线时间超过一定时长（时长由配置参数offlineThreshold决定），该dnode将被mnode强制剔除出集群。该dnode上的vnodes如果副本数大于一，系统将自动在其他dnode上创建新的副本，以保证数据的副本数。
**Note：**目前集群功能仅仅限于企业版
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# TDengine System Architecture
## Storage Design
TDengine data mainly include **metadata** and **data** that we will introduce in the following sections.
### Metadata Storage
Metadata include the information of databases, tables, etc. Metadata files are saved in _/var/lib/taos/mgmt/_ directory by default. The directory tree is as below:
```
/var/lib/taos/
+--mgmt/
+--db.db
+--meters.db
+--user.db
+--vgroups.db
```
A metadata structure (database, table, etc.) is saved as a record in a metadata file. All metadata files are appended only, and even a drop operation adds a deletion record at the end of the file.
### Data storage
Data in TDengine are sharded according to the time range. Data of tables in the same vnode in a certain time range are saved in the same filegroup, such as files v0f1804*. This sharding strategy can effectively improve data searching speed. By default, a group of files contains data in 10 days, which can be configured by *daysPerFile* in the configuration file or by *DAYS* keyword in *CREATE DATABASE* clause. Data in files are blockwised. A data block only contains one table's data. Records in the same data block are sorted according to the primary timestamp, which helps to improve the compression rate and save storage. The compression algorithms used in TDengine include simple8B, delta-of-delta, RLE, LZ4, etc.
By default, TDengine data are saved in */var/lib/taos/data/* directory. _/var/lib/taos/tsdb/_ directory contains vnode informations and data file linkes.
```
/var/lib/taos/
+--tsdb/
| +--vnode0
| +--meterObj.v0
| +--db/
| +--v0f1804.head->/var/lib/taos/data/vnode0/v0f1804.head1
| +--v0f1804.data->/var/lib/taos/data/vnode0/v0f1804.data
| +--v0f1804.last->/var/lib/taos/data/vnode0/v0f1804.last1
| +--v0f1805.head->/var/lib/taos/data/vnode0/v0f1805.head1
| +--v0f1805.data->/var/lib/taos/data/vnode0/v0f1805.data
| +--v0f1805.last->/var/lib/taos/data/vnode0/v0f1805.last1
| :
+--data/
+--vnode0/
+--v0f1804.head1
+--v0f1804.data
+--v0f1804.last1
+--v0f1805.head1
+--v0f1805.data
+--v0f1805.last1
:
```
#### meterObj file
There are only one meterObj file in a vnode. Informations bout the vnode, such as created time, configuration information, vnode statistic informations are saved in this file. It has the structure like below:
```
<start_of_file>
[file_header]
[table_record1_offset&length]
[table_record2_offset&length]
...
[table_recordN_offset&length]
[table_record1]
[table_record2]
...
[table_recordN]
<end_of_file>
```
The file header takes 512 bytes, which mainly contains informations about the vnode. Each table record is the representation of a table on disk.
#### head file
The _head_ files contain the index of data blocks in the _data_ file. The inner organization is as below:
```
<start_of_file>
[file_header]
[table1_offset]
[table2_offset]
...
[tableN_offset]
[table1_index_block]
[table2_index_block]
...
[tableN_index_block]
<end_of_file>
```
The table offset array in the _head_ file saves the information about the offsets of each table index block. Indices on data blocks in the same table are saved continuously. This also makes it efficient to load data indices on the same table. The data index block has a structure like:
```
[index_block_info]
[block1_index]
[block2_index]
...
[blockN_index]
```
The index block info part contains the information about the index block such as the number of index blocks, etc. Each block index corresponds to a real data block in the _data_ file or _last_ file. Information about the location of the real data block, the primary timestamp range of the data block, etc. are all saved in the block index part. The block indices are sorted in ascending order according to the primary timestamp. So we can apply algorithms such as the binary search on the data to efficiently search blocks according to time.
#### data file
The _data_ files store the real data block. They are append-only. The organization is as:
```
<start_of_file>
[file_header]
[block1]
[block2]
...
[blockN]
<end_of_file>
```
A data block in _data_ files only belongs to a table in the vnode and the records in a data block are sorted in ascending order according to the primary timestamp key. Data blocks are column-oriented. Data in the same column are stored contiguously, which improves reading speed and compression rate because of their similarity. A data block has the following organization:
```
[column1_info]
[column2_info]
...
[columnN_info]
[column1_data]
[column2_data]
...
[columnN_data]
```
The column info part includes information about column types, column compression algorithm, column data offset and length in the _data_ file, etc. Besides, pre-calculated results of the column data in the block are also in the column info part, which helps to improve reading speed by avoiding loading data block necessarily.
#### last file
To avoid storage fragment and to import query speed and compression rate, TDengine introduces an extra file, the _last_ file. When the number of records in a data block is lower than a threshold, TDengine will flush the block to the _last_ file for temporary storage. When new data comes, the data in the _last_ file will be merged with the new data and form a larger data block and written to the _data_ file. The organization of the _last_ file is similar to the _data_ file.
### Summary
The innovation in architecture and storage design of TDengine improves resource usage. On the one hand, the virtualization makes it easy to distribute resources between different vnodes and for future scaling. On the other hand, sorted and column-oriented storage makes TDengine have a great advantage in writing, querying and compression.
## Query Design
#### Introduction
TDengine provides a variety of query functions for both tables and super tables. In addition to regular aggregate queries, it also provides time window based query and statistical aggregation for time series data. TDengine's query processing requires the client app, management node, and data node to work together. The functions and modules involved in query processing included in each component are as follows:
Client (Client App). The client development kit, embed in a client application, consists of TAOS SQL parser and query executor, the second-stage aggregator (Result Merger), continuous query manager and other major functional modules. The SQL parser is responsible for parsing and verifying the SQL statement and converting it into an abstract syntax tree. The query executor is responsible for transforming the abstract syntax tree into the query execution logic and creates the metadata query according to the query condition of the SQL statement. Since TAOS SQL does not currently include complex nested queries and pipeline query processing mechanism, there is no longer need for query plan optimization and physical query plan conversions. The second-stage aggregator is responsible for performing the aggregation of the independent results returned by query involved data nodes at the client side to generate final results. The continuous query manager is dedicated to managing the continuous queries created by users, including issuing fixed-interval query requests and writing the results back to TDengine or returning to the client application as needed. Also, the client is also responsible for retrying after the query fails, canceling the query request, and maintaining the connection heartbeat and reporting the query status to the management node.
Management Node. The management node keeps the metadata of all the data of the entire cluster system, provides the metadata of the data required for the query from the client node, and divides the query request according to the load condition of the cluster. The super table contains information about all the tables created according to the super table, so the query processor (Query Executor) of the management node is responsible for the query processing of the tags of tables and returns the table information satisfying the tag query. Besides, the management node maintains the query status of the cluster in the Query Status Manager component, in which the metadata of all queries that are currently executing are temporarily stored in-memory buffer. When the client issues *show queries* command to management node, current running queries information is returned to the client.
Data Node. The data node, responsible for storing all data of the database, consists of query executor, query processing scheduler, query task queue, and other related components. Once the query requests from the client received, they are put into query task queue and waiting to be processed by query executor. The query executor extracts the query request from the query task queue and invokes the query optimizer to perform the basic optimization for the query execution plan. And then query executor scans the qualified data blocks in both cache and disk to obtain qualified data and return the calculated results. Besides, the data node also needs to respond to management information and commands from the management node. For example, after the *kill query* received from the management node, the query task needs to be stopped immediately.
<center> <img src="../assets/fig1.png"> </center>
<center>Fig 1. System query processing architecture diagram (only query related components)</center>
#### Query Process Design
The client, the management node, and the data node cooperate to complete the entire query processing of TDengine. Let's take a concrete SQL query as an example to illustrate the whole query processing flow. The SQL statement is to query on super table *FOO_SUPER_TABLE* to get the total number of records generated on January 12, 2019, from the table, of which TAG_LOC equals to 'beijing'. The SQL statement is as follows:
```sql
SELECT COUNT(*)
FROM FOO_SUPER_TABLE
WHERE TAG_LOC = 'beijing' AND TS >= '2019-01-12 00:00:00' AND TS < '2019-01-13 00:00:00'
```
First, the client invokes the TAOS SQL parser to parse and validate the SQL statement, then generates a syntax tree, and extracts the object of the query - the super table *FOO_SUPER_TABLE*, and then the parser sends requests with filtering information (TAG_LOC='beijing') to management node to get the corresponding metadata about *FOO_SUPER_TABLE*.
Once the management node receives the request for metadata acquisition, first finds the super table *FOO_SUPER_TABLE* basic information, and then applies the query condition (TAG_LOC='beijing') to filter all the related tables created according to it. And finally, the query executor returns the metadata information that satisfies the query request to the client.
After the client obtains the metadata information of *FOO_SUPER_TABLE*, the query executor initiates a query request with timestamp range filtering condition (TS >= '2019- 01-12 00:00:00' AND TS < '2019-01-13 00:00:00') to all nodes that hold the corresponding data according to the information about data distribution in metadata.
The data node receives the query sent from the client, converts it into an internal structure and puts it into the query task queue to be executed by query executor after optimizing the execution plan. When the query result is obtained, the query result is returned to the client. It should be noted that the data nodes perform the query process independently of each other, and rely solely on their data and content for processing.
When all data nodes involved in the query return results, the client aggregates the result sets from each data node. In this case, all results are accumulated to generate the final query result. The second stage of aggregation is not always required for all queries. For example, a column selection query does not require a second-stage aggregation at all.
#### REST Query Process
In addition to C/C++, Python, and JDBC interface, TDengine also provides a REST interface based on the HTTP protocol, which is different from using the client application programming interface. When the user uses the REST interface, all the query processing is completed on the server-side, and the user's application is not involved in query processing anymore. After the query processing is completed, the result is returned to the client through the HTTP JSON string.
<center> <img src="../assets/fig2.png"> </center>
<center>Fig. 2 REST query architecture</center>
When a client uses an HTTP-based REST query interface, the client first establishes a connection with the HTTP connector at the data node and then uses the token to ensure the reliability of the request through the REST signature mechanism. For the data node, after receiving the request, the HTTP connector invokes the embedded client program to initiate a query processing, and then the embedded client parses the SQL statement from the HTTP connector and requests the management node to get metadata as needed. After that, the embedded client sends query requests to the same data node or other nodes in the cluster and aggregates the calculation results on demand. Finally, you also need to convert the result of the query into a JSON format string and return it to the client via an HTTP response. After the HTTP connector receives the request SQL, the subsequent process processing is completely consistent with the query processing using the client application development kit.
It should be noted that during the entire processing, the client application is no longer involved in, and is only responsible for sending SQL requests through the HTTP protocol and receiving the results in JSON format. Besides, each data node is embedded with an HTTP connector and a client, so any data node in the cluster received requests from a client, the data node can initiate the query and return the result to the client through the HTTP protocol, with transfer the request to other data nodes.
#### Technology
Because TDengine stores data and tags value separately, the tag value is kept in the management node and directly associated with each table instead of records, resulting in a great reduction of the data storage. Therefore, the tag value can be managed by a fully in-memory structure. First, the filtering of the tag data can drastically reduce the data size involved in the second phase of the query. The query processing for the data is performed at the data node. TDengine takes advantage of the immutable characteristics of IoT data by calculating the maximum, minimum, and other statistics of the data in one data block on each saved data block, to effectively improve the performance of query processing. If the query process involves all the data of the entire data block, the pre-computed result is used directly, and the content of the data block is no longer needed. Since the size of disk space required to store the pre-computation result is much smaller than the size of the specific data, the pre-computation result can greatly reduce the disk IO and speed up the query processing.
TDengine employs column-oriented data storage techniques. When the data block is involved to be loaded from the disk for calculation, only the required column is read according to the query condition, and the read overhead can be minimized. The data of one column is stored in a contiguous memory block and therefore can make full use of the CPU L2 cache to greatly speed up the data scanning. Besides, TDengine utilizes the eagerly responding mechanism and returns a partial result before the complete result is acquired. For example, when the first batch of results is obtained, the data node immediately returns it directly to the client in case of a column select query.
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# 超级表STable：多表聚合
TDengine要求每个数据采集点单独建表，这样能极大提高数据的插入/查询性能，但是导致系统中表的数量猛增，让应用对表的维护以及聚合、统计操作难度加大。为降低应用的开发难度，TDengine引入了超级表STable (Super Table)的概念。
## 什么是超级表
STable是同一类型数据采集点的抽象，是同类型采集实例的集合，包含多张数据结构一样的子表。每个STable为其子表定义了表结构和一组标签：表结构即表中记录的数据列及其数据类型；标签名和数据类型由STable定义，标签值记录着每个子表的静态信息，用以对子表进行分组过滤。子表本质上就是普通的表，由一个时间戳主键和若干个数据列组成，每行记录着具体的数据，数据查询操作与普通表完全相同；但子表与普通表的区别在于每个子表从属于一张超级表，并带有一组由STable定义的标签值。每种类型的采集设备可以定义一个STable。数据模型定义表的每列数据的类型，如温度、压力、电压、电流、GPS实时位置等，而标签信息属于Meta Data，如采集设备的序列号、型号、位置等，是静态的，是表的元数据。用户在创建表（数据采集点）时指定STable(采集类型)外，还可以指定标签的值，也可事后增加或修改。
TDengine扩展标准SQL语法用于定义STable，使用关键词tags指定标签信息。语法如下：
```mysql
CREATE TABLE <stable_name> (<field_name> TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …)
```
其中tag_name是标签名，tag_type是标签的数据类型。标签可以使用时间戳之外的其他TDengine支持的数据类型，标签的个数最多为6个，名字不能与系统关键词相同，也不能与其他列名相同。如：
```mysql
create table thermometer (ts timestamp, degree float)
tags (location binary(20), type int)
```
上述SQL创建了一个名为thermometer的STable，带有标签location和标签type。
为某个采集点创建表时，可以指定其所属的STable以及标签的值，语法如下：
```mysql
CREATE TABLE <tb_name> USING <stb_name> TAGS (tag_value1,...)
```
沿用上面温度计的例子，使用超级表thermometer建立单个温度计数据表的语句如下：
```mysql
create table t1 using thermometer tags (‘beijing’, 10)
```
上述SQL以thermometer为模板，创建了名为t1的表，这张表的Schema就是thermometer的Schema，但标签location值为‘beijing’，标签type值为10。
用户可以使用一个STable创建数量无上限的具有不同标签的表，从这个意义上理解，STable就是若干具有相同数据模型，不同标签的表的集合。与普通表一样，用户可以创建、删除、查看超级表STable，大部分适用于普通表的查询操作都可运用到STable上，包括各种聚合和投影选择函数。除此之外，可以设置标签的过滤条件，仅对STbale中部分表进行聚合查询，大大简化应用的开发。
TDengine对表的主键（时间戳）建立索引，暂时不提供针对数据模型中其他采集量（比如温度、压力值）的索引。每个数据采集点会采集若干数据记录，但每个采集点的标签仅仅是一条记录，因此数据标签在存储上没有冗余，且整体数据规模有限。TDengine将标签数据与采集的动态数据完全分离存储，而且针对STable的标签建立了高性能内存索引结构，为标签提供全方位的快速操作支持。用户可按照需求对其进行增删改查（Create，Retrieve，Update，Delete，CRUD）操作。
STable从属于库，一个STable只属于一个库，但一个库可以有一到多个STable, 一个STable可有多个子表。
## 超级表管理
- 创建超级表
```mysql
CREATE TABLE <stable_name> (<field_name> TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …)
```
与创建表的SQL语法相似。但需指定TAGS字段的名称和类型。
说明：
1. TAGS列总长度不能超过512 bytes；
2. TAGS列的数据类型不能是timestamp和nchar类型；
3. TAGS列名不能与其他列名相同;
4. TAGS列名不能为预留关键字.
- 显示已创建的超级表
```mysql
show stables;
```
查看数据库内全部STable，及其相关信息，包括STable的名称、创建时间、列数量、标签（TAG）数量、通过该STable建表的数量。
- 删除超级表
```mysql
DROP TABLE <stable_name>
```
Note: 删除STable不会级联删除通过STable创建的表；相反删除STable时要求通过该STable创建的表都已经被删除。
- 查看属于某STable并满足查询条件的表
```mysql
SELECT TBNAME,[TAG_NAME,…] FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] …)
```
查看属于某STable并满足查询条件的表。说明：TBNAME为关键词，显示通过STable建立的子表表名，查询过程中可以使用针对标签的条件。
```mysql
SELECT COUNT(TBNAME) FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] …)
```
统计属于某个STable并满足查询条件的子表的数量
## 写数据时自动建子表
在某些特殊场景中，用户在写数据时并不确定某个设备的表是否存在，此时可使用自动建表语法来实现写入数据时里用超级表定义的表结构自动创建不存在的子表，若该表已存在则不会建立新表。注意：自动建表语句只能自动建立子表而不能建立超级表，这就要求超级表已经被事先定义好。自动建表语法跟insert/import语法非常相似，唯一区别是语句中增加了超级表和标签信息。具体语法如下：
```mysql
INSERT INTO <tb_name> USING <stb_name> TAGS (<tag1_value>, ...) VALUES (field_value, ...) (field_value, ...) ...;
```
向表tb_name中插入一条或多条记录，如果tb_name这张表不存在，则会用超级表stb_name定义的表结构以及用户指定的标签值(即tag1_value…)来创建名为tb_name新表，并将用户指定的值写入表中。如果tb_name已经存在，则建表过程会被忽略，系统也不会检查tb_name的标签是否与用户指定的标签值一致，也即不会更新已存在表的标签。
```mysql
INSERT INTO <tb1_name> USING <stb1_name> TAGS (<tag1_value1>, ...) VALUES (<field1_value1>, ...) (<field1_value2>, ...) ... <tb_name2> USING <stb_name2> TAGS(<tag1_value2>, ...) VALUES (<field1_value1>, ...) ...;
```
向多张表tb1_name，tb2_name等插入一条或多条记录，并分别指定各自的超级表进行自动建表。
## STable中TAG管理
除了更新标签的值的操作是针对子表进行，其他所有的标签操作（添加标签、删除标签等）均只能作用于STable，不能对单个子表操作。对STable添加标签以后，依托于该STable建立的所有表将自动增加了一个标签，对于数值型的标签，新增加的标签的默认值是0.
- 添加新的标签
```mysql
ALTER TABLE <stable_name> ADD TAG <new_tag_name> <TYPE>
```
为STable增加一个新的标签，并指定新标签的类型。标签总数不能超过6个。
- 删除标签
```mysql
ALTER TABLE <stable_name> DROP TAG <tag_name>
```
删除超级表的一个标签，从超级表删除某个标签后，该超级表下的所有子表也会自动删除该标签。
说明：第一列标签不能删除，至少需要为STable保留一个标签。
- 修改标签名
```mysql
ALTER TABLE <stable_name> CHANGE TAG <old_tag_name> <new_tag_name>
```
修改超级表的标签名，从超级表修改某个标签名后，该超级表下的所有子表也会自动更新该标签名。
- 修改子表的标签值
```mysql
ALTER TABLE <table_name> SET TAG <tag_name>=<new_tag_value>
```
## STable多表聚合
针对所有的通过STable创建的子表进行多表聚合查询，支持按照全部的TAG值进行条件过滤，并可将结果按照TAGS中的值进行聚合，暂不支持针对binary类型的模糊匹配过滤。语法如下：
```mysql
SELECT function<field_name>,…
FROM <stable_name>
WHERE <tag_name> <[=|<=|>=|<>] values..> ([AND|OR] …)
INTERVAL (<interval> [, offset])
GROUP BY <tag_name>, <tag_name>…
ORDER BY <tag_name> <asc|desc>
SLIMIT <group_limit>
SOFFSET <group_offset>
LIMIT <record_limit>
OFFSET <record_offset>
```
**说明**：
超级表聚合查询，TDengine目前支持以下聚合\选择函数：sum、count、avg、first、last、min、max、top、bottom，以及针对全部或部分列的投影操作，使用方式与单表查询的计算过程相同。暂不支持其他类型的聚合计算和四则运算。当前所有的函数及计算过程均不支持嵌套的方式进行执行。
不使用GROUP BY的查询将会对超级表下所有满足筛选条件的表按时间进行聚合，结果输出默认是按照时间戳单调递增输出，用户可以使用ORDER BY _c0 ASC|DESC选择查询结果时间戳的升降排序；使用GROUP BY <tag_name> 的聚合查询会按照tags进行分组，并对每个组内的数据分别进行聚合，输出结果为各个组的聚合结果，组间的排序可以由ORDER BY <tag_name> 语句指定，每个分组内部，时间序列是单调递增的。
使用SLIMIT/SOFFSET语句指定组间分页，即指定结果集中输出的最大组数以及对组起始的位置。使用LIMIT/OFFSET语句指定组内分页，即指定结果集中每个组内最多输出多少条记录以及记录起始的位置。
## STable使用示例
以温度传感器采集时序数据作为例，示范STable的使用。 在这个例子中，对每个温度计都会建立一张表，表名为温度计的ID，温度计读数的时刻记为ts，采集的值记为degree。通过tags给每个采集器打上不同的标签，其中记录温度计的地区和类型，以方便我们后面的查询。所有温度计的采集量都一样，因此我们用STable来定义表结构。
###定义STable表结构并使用它创建子表
创建STable语句如下：
```mysql
CREATE TABLE thermometer (ts timestamp, degree double)
TAGS(location binary(20), type int)
```
假设有北京，天津和上海三个地区的采集器共4个，温度采集器有3种类型，我们就可以对每个采集器建表如下：
```mysql
CREATE TABLE therm1 USING thermometer TAGS (’beijing’, 1);
CREATE TABLE therm2 USING thermometer TAGS (’beijing’, 2);
CREATE TABLE therm3 USING thermometer TAGS (’tianjin’, 1);
CREATE TABLE therm4 USING thermometer TAGS (’shanghai’, 3);
```
其中therm1，therm2，therm3，therm4是超级表thermometer四个具体的子表，也即普通的Table。以therm1为例，它表示采集器therm1的数据，表结构完全由thermometer定义，标签location=”beijing”, type=1表示therm1的地区是北京，类型是第1类的温度计。
###写入数据
注意，写入数据时不能直接对STable操作，而是要对每张子表进行操作。我们分别向四张表therm1，therm2， therm3， therm4写入一条数据，写入语句如下：
```mysql
INSERT INTO therm1 VALUES (’2018-01-01 00:00:00.000’, 20);
INSERT INTO therm2 VALUES (’2018-01-01 00:00:00.000’, 21);
INSERT INTO therm3 VALUES (’2018-01-01 00:00:00.000’, 24);
INSERT INTO therm4 VALUES (’2018-01-01 00:00:00.000’, 23);
```
###按标签聚合查询
查询位于北京(beijing)和天津(tianjing)两个地区的温度传感器采样值的数量count(*)、平均温度avg(degree)、最高温度max(degree)、最低温度min(degree)，并将结果按所处地域(location)和传感器类型(type)进行聚合。
```mysql
SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
FROM thermometer
WHERE location=’beijing’ or location=’tianjing’
GROUP BY location, type
```
###按时间周期聚合查询
查询仅位于北京以外地区的温度传感器最近24小时(24h)采样值的数量count(*)、平均温度avg(degree)、最高温度max(degree)和最低温度min(degree)，将采集结果按照10分钟为周期进行聚合，并将结果按所处地域(location)和传感器类型(type)再次进行聚合。
```mysql
SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
FROM thermometer
WHERE name<>’beijing’ and ts>=now-1d
INTERVAL(10M)
GROUP BY location, type
```
\ No newline at end of file
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# STable: Super Table
"One Table for One Device" design can improve the insert/query performance significantly for a single device. But it has a side effect, the aggregation of multiple tables becomes hard. To reduce the complexity and improve the efficiency, TDengine introduced a new concept: STable (Super Table).
## What is a Super Table
STable is an abstract and a template for a type of device. A STable contains a set of devices (tables) that have the same schema or data structure. Besides the shared schema, a STable has a set of tags, like the model, serial number and so on. Tags are used to record the static attributes for the devices and are used to group a set of devices (tables) for aggregation. Tags are metadata of a table and can be added, deleted or changed.
TDengine does not save tags as a part of the data points collected. Instead, tags are saved as metadata. Each table has a set of tags. To improve query performance, tags are all cached and indexed. One table can only belong to one STable, but one STable may contain many tables.
Like a table, you can create, show, delete and describe STables. Most query operations on tables can be applied to STable too, including the aggregation and selector functions. For queries on a STable, if no tags filter, the operations are applied to all the tables created via this STable. If there is a tag filter, the operations are applied only to a subset of the tables which satisfy the tag filter conditions. It will be very convenient to use tags to put devices into different groups for aggregation.
##Create a STable
Similiar to creating a standard table, syntax is:
```mysql
CREATE TABLE <stable_name> (<field_name> TIMESTAMP, field_name1 field_type,…) TAGS(tag_name tag_type, …)
```
New keyword "tags" is introduced, where tag_name is the tag name, and tag_type is the associated data type.
Note：
1. The bytes of all tags together shall be less than 512
2. Tag's data type can not be time stamp or nchar
3. Tag name shall be different from the field name
4. Tag name shall not be the same as system keywords
5. Maximum number of tags is 6
For example:
```mysql
create table thermometer (ts timestamp, degree float)
tags (location binary(20), type int)
```
The above statement creates a STable thermometer with two tag "location" and "type"
##Create a Table via STable
To create a table for a device, you can use a STable as its template and assign the tag values. The syntax is:
```mysql
CREATE TABLE <tb_name> USING <stb_name> TAGS (tag_value1,...)
```
You can create any number of tables via a STable, and each table may have different tag values. For example, you create five tables via STable thermometer below:
```mysql
create table t1 using thermometer tags (‘beijing’, 10);
create table t2 using thermometer tags (‘beijing’, 20);
create table t3 using thermometer tags (‘shanghai’, 10);
create table t4 using thermometer tags (‘shanghai’, 20);
create table t5 using thermometer tags (‘new york’, 10);
```
## Aggregate Tables via STable
You can group a set of tables together by specifying the tags filter condition, then apply the aggregation operations. The result set can be grouped and ordered based on tag value. Syntax is：
```mysql
SELECT function<field_name>,…
FROM <stable_name>
WHERE <tag_name> <[=|<=|>=|<>] values..> ([AND|OR] …)
INTERVAL (<time range>)
GROUP BY <tag_name>, <tag_name>…
ORDER BY <tag_name> <asc|desc>
SLIMIT <group_limit>
SOFFSET <group_offset>
LIMIT <record_limit>
OFFSET <record_offset>
```
For the time being, STable supports only the following aggregation/selection functions: *sum, count, avg, first, last, min, max, top, bottom*, and the projection operations, the same syntax as a standard table. Arithmetic operations are not supported, embedded queries not either.
*INTERVAL* is used for the aggregation over a time range.
If *GROUP BY* is not used, the aggregation is applied to all the selected tables, and the result set is output in ascending order of the timestamp, but you can use "*ORDER BY _c0 ASC|DESC*" to specify the order you like.
If *GROUP BY <tag_name>* is used, the aggregation is applied to groups based on tags. Each group is aggregated independently. Result set is a group of aggregation results. The group order is decided by *ORDER BY <tag_name>*. Inside each group, the result set is in the ascending order of the time stamp.
*SLIMIT/SOFFSET* are used to limit the number of groups and starting group number.
*LIMIT/OFFSET* are used to limit the number of records in a group and the starting rows.
###Example 1:
Check the average, maximum, and minimum temperatures of Beijing and Shanghai, and group the result set by location and type. The SQL statement shall be:
```mysql
SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
FROM thermometer
WHERE location=’beijing’ or location=’tianjing’
GROUP BY location, type
```
### Example 2:
List the number of records, average, maximum, and minimum temperature every 10 minutes for the past 24 hours for all the thermometers located in Beijing with type 10. The SQL statement shall be:
```mysql
SELECT COUNT(*), AVG(degree), MAX(degree), MIN(degree)
FROM thermometer
WHERE name=’beijing’ and type=10 and ts>=now-1d
INTERVAL(10M)
```
## Create Table Automatically
Insert operation will fail if the table is not created yet. But for STable, TDengine can create the table automatically if the application provides the STable name, table name and tags' value when inserting data points. The syntax is:
```mysql
INSERT INTO <tb_name> USING <stb_name> TAGS (<tag1_value>, ...) VALUES (field_value, ...) (field_value, ...) ... <tb_name2> USING <stb_name2> TAGS(<tag1_value2>, ...) VALUES (<field1_value1>, ...) ...;
```
When inserting data points into table tb_name, the system will check if table tb_name is created or not. If it is already created, the data points will be inserted as usual. But if the table is not created yet, the system will create the table tb_bame using STable stb_name as the template with the tags. Multiple tables can be specified in the SQL statement.
## Management of STables
After you can create a STable, you can describe, delete, change STables. This section lists all the supported operations.
### Show STables in current DB
```mysql
show stables;
```
It lists all STables in current DB, including the name, created time, number of fileds, number of tags, and number of tables which are created via this STable.
### Describe a STable
```mysql
DESCRIBE <stable_name>
```
It lists the STable's schema and tags
### Drop a STable
```mysql
DROP TABLE <stable_name>
```
To delete a STable, all the tables created via this STable shall be deleted first, otherwise, it will fail.
### List the Associated Tables of a STable
```mysql
SELECT TBNAME,[TAG_NAME,…] FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] …)
```
It will list all the tables which satisfy the tag filter conditions. The tables are all created from this specific STable. TBNAME is a new keyword introduced, it is the table name associated with the STable.
```mysql
SELECT COUNT(TBNAME) FROM <stable_name> WHERE <tag_name> <[=|=<|>=|<>] values..> ([AND|OR] …)
```
The above SQL statement will list the number of tables in a STable, which satisfy the filter condition.
## Management of Tags
You can add, delete and change the tags for a STable, and you can change the tag value of a table. The SQL commands are listed below.
###Add a Tag
```mysql
ALTER TABLE <stable_name> ADD TAG <new_tag_name> <TYPE>
```
It adds a new tag to the STable with a data type. The maximum number of tags is 6.
###Drop a Tag
```mysql
ALTER TABLE <stable_name> DROP TAG <tag_name>
```
It drops a tag from a STable. The first tag could not be deleted, and there must be at least one tag.
###Change a Tag's Name
```mysql
ALTER TABLE <stable_name> CHANGE TAG <old_tag_name> <new_tag_name>
```
It changes the name of a tag from old to new.
###Change the Tag's Value
```mysql
ALTER TABLE <table_name> SET TAG <tag_name>=<new_tag_value>
```
It changes a table's tag value to a new one.
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# TAOS SQL
TDengine provides a SQL like query language to insert or query data. You can execute the SQL statements through the TDengine Shell, or through C/C++, Java(JDBC), Python, Restful, Go, and Node.js APIs to interact with the `taosd` service.
Before reading through, please have a look at the conventions used for syntax descriptions here in this documentation.
* Squared brackets ("[]") indicate optional arguments or clauses
* Curly braces ("{}") indicate that one member from a set of choices in the braces must be chosen
* A single verticle line ("|") works a separator for multiple optional args or clauses
* Dots ("…") means repeating for as many times
## Data Types
### Timestamp
The timestamp is the most important data type in TDengine. The first column of each table must be **`TIMESTAMP`** type, but other columns can also be **`TIMESTAMP`** type. The following rules for timestamp:
* String Format: `'YYYY-MM-DD HH:mm:ss.MS'`, which represents the year, month, day, hour, minute and second and milliseconds. For example,`'2017-08-12 18:52:58.128'` is a valid timestamp string. Note: timestamp string must be quoted by either single quote or double quote.
* Epoch Time: a timestamp value can also be a long integer representing milliseconds since the epoch. For example, the values in the above example can be represented as an epoch `1502535178128` in milliseconds. Please note the epoch time doesn't need any quotes.
* Internal Function **`NOW`** : this is the current time of the server
* If timestamp is 0 when inserting a record, timestamp will be set to the current time of the server
* Arithmetic operations can be applied to timestamp. For example: `now-2h` represents a timestamp which is 2 hours ago from the current server time. Units include `a` (milliseconds), `s` (seconds), `m` (minutes), `h` (hours), `d` (days), `w` (weeks), `n` (months), `y` (years). **`NOW`** can be used in either insertions or queries.
Default time precision is millisecond, you can change it to microseocnd by setting parameter enableMicrosecond in [system configuration](../administrator/#Configuration-on-Server). For epoch time, the long integer shall be microseconds since the epoch. For the above string format, MS shall be six digits.
### Data Types
The full list of data types is listed below. For string types of data, we will use ***M*** to indicate the maximum length of that type.
| | Data Type | Bytes | Note |
| ---- | :---------: | :-----: | ------------------------------------------------------------ |
| 1 | TINYINT | 1 | A nullable integer type with a range of [-127, 127]​ |
| 2 | SMALLINT | 2 | A nullable integer type with a range of [-32767, 32767]​ |
| 3 | INT | 4 | A nullable integer type with a range of [-2^31+1, 2^31-1 ] |
| 4 | BIGINT | 8 | A nullable integer type with a range of [-2^59, 2^59 ]​ |
| 5 | FLOAT | 4 | A standard nullable float type with 6 -7 significant digits and a range of [-3.4E38, 3.4E38] |
| 6 | DOUBLE | 8 | A standard nullable double float type with 15-16 significant digits and a range of [-1.7E308, 1.7E308]​ |
| 7 | BOOL | 1 | A nullable boolean type, [**`true`**, **`false`**] |
| 8 | TIMESTAMP | 8 | A nullable timestamp type with the same usage as the primary column timestamp |
| 9 | BINARY(*M*) | *M* | A nullable string type whose length is *M*, error should be threw with exceeded chars, the maximum length of *M* is 16374, but as maximum row size is 16K bytes, the actual upper limit will generally less than 16374. This type of string only supports ASCii encoded chars. |
| 10 | NCHAR(*M*) | 4 * *M* | A nullable string type whose length is *M*, error should be threw with exceeded chars. The **`NCHAR`** type supports Unicode encoded chars. |
All the keywords in a SQL statement are case-insensitive, but strings values are case-sensitive and must be quoted by a pair of `'` or `"`. To quote a `'` or a `"` , you can use the escape character `\`.
## Database Management
- **Create a Database**
```mysql
CREATE DATABASE [IF NOT EXISTS] db_name [KEEP keep]
```
Option: `KEEP` is used for data retention policy. The data records will be removed once keep-days are passed. There are more parameters related to DB storage, please check [system configuration](../administrator/#Configuration-on-Server).
- **Use a Database**
```mysql
USE db_name
```
Use or switch the current database.
- **Drop a Database**
```mysql
DROP DATABASE [IF EXISTS] db_name
```
Remove a database, all the tables inside the DB will be removed too, be careful.
- **List all Databases**
```mysql
SHOW DATABASES
```
## Table Management
- **Create a Table**
```mysql
CREATE TABLE [IF NOT EXISTS] tb_name (timestamp_field_name TIMESTAMP, field1_name data_type1 [, field2_name data_type2 ...])
```
Note:
1) The first column must be a `timestamp`, and the system will set it as the primary key.
2) The maximum number of columns is 1024, the minimum number of columns is 2.
3) The maximum length: database name is 33, table nume is 193, column name is 65.
4) The record size is limited to 16k bytes.
5) The total tag length is limited 16k bytes, the maximum number of tags is 128.
6) For `binary` or `nchar` data types, the length must be specified. For example, binary(20) means a binary data type with 20 bytes.
- **Drop a Table**
```mysql
DROP TABLE [IF EXISTS] tb_name
```
- **List all Tables **
```mysql
SHOW TABLES [LIKE tb_name_wildcar]
```
It shows all tables in the current DB.
Note: Wildcard characters can be used in the table name to filter tables.
Wildcard characters:
1) ’%’ means 0 to any number of characters.
2）’_’ underscore means exactly one character.
- **Print Table Schema**
```mysql
DESCRIBE tb_name
```
- **Add a Column**
```mysql
ALTER TABLE tb_name ADD COLUMN field_name data_type
```
- **Drop a Column**
```mysql
ALTER TABLE tb_name DROP COLUMN field_name
```
If the table is created via [Super Table](), the schema can only be changed via STable. But for tables not created from STable, you can change their schema directly.
**Tips**: You can apply an operation on a table not in the current DB by concatenating DB name with the character '.', then with the table name. For example, 'demo.tb1' means the operation is applied to table `tb1` in DB `demo` even though `demo` is not the currently selected DB.
## STable Management
- **Create a STable**
```mysql
CREATE TABLE [IF NOT EXISTS] stb_name (timestamp_field_name TIMESTAMP, field1_name data_type1 [, field2_name data_type2 ...]) TAGS (tag1_name tag_type1, tag2_name tag_type2 [, tag3_name tag_type3])
```
Create a STable is the same as you create a table, but the tag name and type shoudl be specified
Note:
1) The total tag length should not be exceeded 512 bytes
2) The type of tag cannot be timestamp
3) Tag name should not be the same as other column name or tag name
4) Tag name should not be any of Taos key words.
- **Drop a STable**
```mysql
DROP TABLE [IF EXISTS] tb_name
```
Delete a STable will also delete all the tables created using the STable.
- **List all STables**
```mysql
SHOW STABLES [LIKE tb_name_wildcar]
```
It shows all STables in the current DB.
Note: Wildcard characters can be used in the table name to filter tables.
Wildcard characters:
1) ’%’ means 0 to any number of characters.
2）’_’ underscore means exactly one character.
- **Print STable Schema**
```mysql
DESCRIBE stb_name
```
- **Add a Column**
```mysql
ALTER TABLE stb_name ADD COLUMN field_name data_type
```
- **Drop a Column**
```mysql
ALTER TABLE stb_name DROP COLUMN field_name
```
If the table is created via [Super Table](), the schema can only be changed via STable. But for tables not created from STable, you can change their schema directly.
**Tips**: You can apply an operation on a table not in the current DB by concatenating DB name with the character '.', then with the table name. For example, 'demo.tb1' means the operation is applied to table `tb1` in DB `demo` even though `demo` is not the currently selected DB.
## STable Tag Management
- **Add a tag**
```mysql
ALTER TABLE stb_name ADD TAG new_tag_name tag_type
```
Add a tag and specify tag type for STable. The total number of tags should be no more than 128.
- **Drop a tag**
```mysql
ALTER TABLE stb_name DROP TAG tag_name
```
Delete a tag from STable, it will also delete the tag for the tables created using STable.
- **Modify tag name**
```mysql
ALTER TABLE stb_name CHANGE TAG old_tag_name new_tag_name
```
Modify a tag name for a STable, it will also modify the tag name for the tables created using STable.
- **Change tag value**
```mysql
ALTER TABLE tb_name SET TAG tag_name=new_tag_value
```
**Note**: 'Add a tag', 'Drop a tag', 'Modify tag name' used for STable, 'Change tag value' used for table.
## Inserting Records
- **Insert a Record**
```mysql
INSERT INTO tb_name VALUES (field_value, ...);
```
Insert a data record into table tb_name
- **Insert a Record with Selected Columns**
```mysql
INSERT INTO tb_name (field1_name, ...) VALUES(field1_value, ...)
```
Insert a data record into table tb_name, with data in selected columns. If a column is not selected, the system will put NULL there. First column (time stamp ) cant not be null, it must be inserted.
- **Insert a Batch of Records**
```mysql
INSERT INTO tb_name VALUES (field1_value1, ...) (field1_value2, ...)...;
```
Insert multiple data records into the table
- **Insert a Batch of Records with Selected Columns**
```mysql
INSERT INTO tb_name (field1_name, ...) VALUES(field1_value1, ...) (field1_value2, ...)
```
- **Insert Records into Multiple Tables**
```mysql
INSERT INTO tb1_name VALUES (field1_value1, ...)(field1_value2, ...)...
tb2_name VALUES (field1_value1, ...)(field1_value2, ...)...;
```
Insert data records into table tb1_name and tb2_name
- **Insert Records into Multiple Tables with Selected Columns**
```mysql
INSERT INTO tb1_name (tb1_field1_name, ...) VALUES (field1_value1, ...) (field1_value1, ...)
tb2_name (tb2_field1_name, ...) VALUES(field1_value1, ...) (field1_value2, ...)
```
Note: 1. If the timestamp is 0, the time stamp will be set to the system time on the server.
2. The timestamp of the oldest record allowed to be inserted is relative to the current server time, minus the configured keep value (the number of days the data is retained), and the timestamp of the latest record allowed to be inserted is relative to the current server time, plus the configured days value (the time span in which the data file stores data, in days). Both keep and days can be specified when creating the database. The default values are 3650 days and 10 days, respectively.
**IMPORT**: If you do want to insert a historical data record into a table, use IMPORT command instead of INSERT. IMPORT has the same syntax as INSERT.
## Data Query
###Query Syntax:
```mysql
SELECT select_expr [, select_expr ...]
FROM {tb_name_list}
[WHERE where_condition]
[INTERVAL [interval_offset,] interval_val]
[FILL fill_val]
[SLIDING fill_val]
[GROUP BY col_list]
[ORDER BY col_list { DESC | ASC }]
[HAVING expr_list]
[SLIMIT limit_val [, SOFFSET offset_val]]
[LIMIT limit_val [, OFFSET offset_val]]
[>> export_file]
SELECT function_list FROM tb_name
[WHERE where_condition]
[LIMIT limit [, OFFSET offset]]
[>> export_file]
```
- To query a table, use `*` to select all data from a table; or a specified list of expressions `expr_list` of columns. The SQL expression can contain alias and arithmetic operations between numeric typed columns.
- For the `WHERE` conditions, use logical operations to filter the timestamp column and all numeric columns, and wild cards to filter the two string typed columns.
- Sort the result set by the first column timestamp `_c0` (or directly use the timestamp column name) in either descending or ascending order (by default). "Order by" could not be applied to other columns.
- Use `LIMIT` and `OFFSET` to control the number of rows returned and the starting position of the retrieved rows. LIMIT/OFFSET is applied after "order by" operations.
- Export the retrieved result set into a CSV file using `>>`. The target file's full path should be explicitly specified in the statement.
###Supported Operations of Data Filtering:
| Operation | Note | Applicable Data Types |
| --------- | ----------------------------- | ------------------------------------- |
| > | larger than | **`timestamp`** and all numeric types |
| < | smaller than | **`timestamp`** and all numeric types |
| >= | larger than or equal to | **`timestamp`** and all numeric types |
| <= | smaller than or equal to | **`timestamp`** and all numeric types |
| = | equal to | all types |
| <> | not equal to | all types |
| % | match with any char sequences | **`binary`** **`nchar`** |
| _ | match with a single char | **`binary`** **`nchar`** |
1. For two or more conditions, only AND is supported, OR is not supported yet.
2. For filtering, only a single range is supported. For example, `value>20 and value<30` is a valid condition, but `value<20 AND value<>5` is an invalid condition
### Some Examples
- For the examples below, table tb1 is created via the following statements
```mysql
CREATE TABLE tb1 (ts timestamp, col1 int, col2 float, col3 binary(50))
```
- Query all the records in tb1 in the last hour:
```mysql
SELECT * FROM tb1 WHERE ts >= NOW - 1h
```
- Query all the records in tb1 between 2018-06-01 08:00:00.000 and 2018-06-02 08:00:00.000, and filter out only the records whose col3 value ends with 'nny', and sort the records by their timestamp in a descending order:
```mysql
SELECT * FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' AND ts <= '2018-06-02 08:00:00.000' AND col3 LIKE '%nny' ORDER BY ts DESC
```
- Query the sum of col1 and col2 as alias 'complex_metric', and filter on the timestamp and col2 values. Limit the number of returned rows to 10, and offset the result by 5.
```mysql
SELECT (col1 + col2) AS 'complex_metric' FROM tb1 WHERE ts > '2018-06-01 08:00:00.000' and col2 > 1.2 LIMIT 10 OFFSET 5
```
- Query the number of records in tb1 in the last 10 minutes, whose col2 value is larger than 3.14, and export the result to file `/home/testoutpu.csv`.
```mysql
SELECT COUNT(*) FROM tb1 WHERE ts >= NOW - 10m AND col2 > 3.14 >> /home/testoutpu.csv
```
## SQL Functions
### Aggregation Functions
TDengine supports aggregations over numerical values, they are listed below:
- **COUNT**
```mysql
SELECT COUNT([*|field_name]) FROM tb_name [WHERE clause]
```
Function: return the number of rows.
Return Data Type: `integer`.
Applicable Data Types: all.
Applied to: table/STable.
Note:
1) `*` can be used for all columns, as long as a column has non-NULL values, it will be counted.
2) If it is on a specific column, only rows with non-NULL values will be counted
- **AVG**
```mysql
SELECT AVG(field_name) FROM tb_name [WHERE clause]
```
Function: return the average value of a specific column.
Return Data Type: `double`.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
- **TWA**
```mysql
SELECT TWA(field_name) FROM tb_name WHERE clause
```
Function: return the time-weighted average value of a specific column
Return Data Type: `double`
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`
Applied to: table/STable
- **SUM**
```mysql
SELECT SUM(field_name) FROM tb_name [WHERE clause]
```
Function: return the sum of a specific column.
Return Data Type: `long integer` or `double`.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
- **STDDEV**
```mysql
SELECT STDDEV(field_name) FROM tb_name [WHERE clause]
```
Function: returns the standard deviation of a specific column.
Return Data Type: double.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table.
- **LEASTSQUARES**
```mysql
SELECT LEASTSQUARES(field_name, start_val, step_val) FROM tb_name [WHERE clause]
```
Function: performs a linear fit to the primary timestamp and the specified column.
Return Data Type: return a string of the coefficient and the interception of the fitted line.
Applicable Data Types: all types except timestamp, binary, nchar, bool.
Applied to: table.
Note: The timestmap is taken as the independent variable while the specified column value is taken as the dependent variables.
### Selector Functions
- **MIN**
```mysql
SELECT MIN(field_name) FROM {tb_name | stb_name} [WHERE clause]
```
Function: return the minimum value of a specific column.
Return Data Type: the same data type.
Applicable Data Types: all types except timestamp, binary, nchar, bool.
Applied to: table/STable.
- **MAX**
```mysql
SELECT MAX(field_name) FROM { tb_name | stb_name } [WHERE clause]
```
Function: return the maximum value of a specific column.
Return Data Type: the same data type.
Applicable Data Types: all types except timestamp, binary, nchar, bool.
Applied to: table/STable.
- **FIRST**
```mysql
SELECT FIRST(field_name) FROM { tb_name | stb_name } [WHERE clause]
```
Function: return the first non-NULL value.
Return Data Type: the same data type.
Applicable Data Types: all types.
Applied to: table/STable.
Note: To return all columns, use first(*).
- **LAST**
```mysql
SELECT LAST(field_name) FROM { tb_name | stb_name } [WHERE clause]
```
Function: return the last non-NULL value.
Return Data Type: the same data type.
Applicable Data Types: all types.
Applied to: table/STable.
Note: To return all columns, use last(*).
- **TOP**
```mysql
SELECT TOP(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
```
Function: return the `k` largest values.
Return Data Type: the same data type.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
Note:
1) Valid range of `k`: 1≤*k*≤100
2) The associated `timestamp` will be returned too.
- **BOTTOM**
```mysql
SELECT BOTTOM(field_name, K) FROM { tb_name | stb_name } [WHERE clause]
```
Function: return the `k` smallest values.
Return Data Type: the same data type.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
Note:
1) valid range of `k`: 1≤*k*≤100;
2) The associated `timestamp` will be returned too.
- **PERCENTILE**
```mysql
SELECT PERCENTILE(field_name, P) FROM { tb_name | stb_name } [WHERE clause]
```
Function: the value of the specified column below which `P` percent of the data points fall.
Return Data Type: double.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
Note: The range of `P` is `[0, 100]`. When `P=0` , `PERCENTILE` returns the equal value as `MIN`; when `P=100`, `PERCENTILE` returns the equal value as `MAX`.
- **APERCENTILE**
```mysql
SELECT APERCENTILE(field_name, P) FROM { tb_name | stb_name } [WHERE clause]
```
Function: the value of the specified column below which `P` percent of the data points fall, it returns approximate value of percentile.
Return Data Type: double.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
Note: The range of `P` is `[0, 100]`. When `P=0` , `APERCENTILE` returns the equal value as `MIN`; when `P=100`, `APERCENTILE` returns the equal value as `MAX`. `APERCENTILE` has a much better performance than `PERCENTILE`.
- **LAST_ROW**
```mysql
SELECT LAST_ROW(field_name) FROM { tb_name | stb_name }
```
Function: return the last row.
Return Data Type: the same data type.
Applicable Data Types: all types.
Applied to: table/STable.
Note: different from last, last_row returns the last row even if it has NULL values.
### Transformation Functions
- **DIFF**
```mysql
SELECT DIFF(field_name) FROM tb_name [WHERE clause]
```
Function: return the difference between successive values of the specified column.
Return Data Type: the same data type.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table.
- **SPREAD**
```mysql
SELECT SPREAD(field_name) FROM { tb_name | stb_name } [WHERE clause]
```
Function: return the difference between the maximum and the mimimum value.
Return Data Type: double.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
Note: spread gives the range of data variation in a table/supertable; it is equivalent to `MAX()` - `MIN()`
- **Arithmetic Operations**
```mysql
SELECT field_name [+|-|*|/|%][Value|field_name] FROM { tb_name | stb_name } [WHERE clause]
```
Function: arithmetic operations on the selected columns.
Return Data Type: double.
Applicable Data Types: all types except `timestamp`, `binary`, `nchar`, `bool`.
Applied to: table/STable.
Note: 1) bracket can be used for operation priority; 2) If a column has NULL value, the result is NULL.
## Downsampling
Time-series data are usually sampled by sensors at a very high frequency, but more often we are only interested in the downsampled, aggregated data of each timeline. TDengine provides a convenient way to downsample the highly frequently sampled data points as well as filling the missing data with a variety of interpolation choices.
```mysql
SELECT function_list FROM tb_name
[WHERE where_condition]
INTERVAL (interval)
[FILL ({NONE | VALUE | PREV | NULL | LINEAR})]
SELECT function_list FROM stb_name
[WHERE where_condition]
INTERVAL (interval)
[FILL ({ VALUE | PREV | NULL | LINEAR})]
[GROUP BY tags]
```
The downsampling time window is defined by `interval`, which is at least 10 milliseconds. The query returns a new series of downsampled data that has a series of fixed timestamps with an increment of `interval`.
For the time being, only function count, avg, sum, stddev, leastsquares, percentile, min, max, first, last are supported. Functions that may return multiple rows are not supported.
You can also use `FILL` to interpolate the intervals that don't contain any data.`FILL` currently supports four different interpolation strategies which are listed below:
| Interpolation | Usage |
| --------------------------------- | ------------------------------------------------------------ |
| `FILL(VALUE, val1 [, val2, ...])` | Interpolate with specified constants |
| `FILL(PREV)` | Interpolate with the value at the previous timestamp |
| `FILL(LINEAR)` | Linear interpolation with the non-null values at the previous timestamp and at the next timestamp |
| `FILL(NULL)` | Interpolate with **`NULL`** value |
A few downsampling examples:
- Find the number of data points, the maximum value of `col1` and minimum value of `col2` in a tb1 for every 10 minutes in the last 5 hours:
```mysql
SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL (10m)
```
- Fill the above downsampling results using constant-value interpolation:
```mysql
SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL(10m) FILL(VALUE, 0, 1, -1)
```
Note that the number of constant values in `FILL()` should be equal or fewer than the number of functions in the `SELECT` clause. Exceeding fill constants will be ignored.
- Fill the above downsampling results using `PREV` interpolation:
```mysql
SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL(10m) FILL(PREV)
```
This will interpolate missing data points with the value at the previous timestamp.
- Fill the above downsampling results using `NULL` interpolation:
```mysql
SELECT COUNT(*), MAX(col1), MIN(col2) FROM tb1 WHERE ts > NOW - 5h INTERVAL(10m) FILL(NULL)
```
Fill **`NULL`** to the interpolated data points.
Notes:
1. `FILL` can generate tons of interpolated data points if the interval is small and the queried time range is large. So always remember to specify a time range when using interpolation. For each query with interpolation, the result set can not exceed 10,000,000 records.
2. The result set will always be sorted by time in ascending order.
3. If the query object is a supertable, then all the functions will be applied to all the tables that qualify the `WHERE` conditions. If the `GROUP BY` clause is also applied, the result set will be sorted ascendingly by time in each single group, otherwise, the result set will be sorted ascendingly by time as a whole.
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#Administrator
## Directory and Files
After TDengine is installed, by default, the following directories will be created:
| Directory/File | Description |
| ---------------------- | :------------------------------ |
| /etc/taos/taos.cfg | TDengine configuration file |
| /usr/local/taos/driver | TDengine dynamic link library |
| /var/lib/taos | TDengine default data directory |
| /var/log/taos | TDengine default log directory |
| /usr/local/taos/bin. | TDengine executables |
### Executables
All TDengine executables are located at _/usr/local/taos/bin_ , including:
- `taosd`：TDengine server
- `taos`： TDengine Shell, the command line interface.
- `taosdump`：TDengine data export tool
- `rmtaos`： a script to uninstall TDengine
You can change the data directory and log directory setting through the system configuration file
## Configuration on Server
`taosd` is running on the server side, you can change the system configuration file taos.cfg to customize its behavior. By default, taos.cfg is located at /etc/taos, but you can specify the path to configuration file via the command line parameter -c. For example: `taosd -c /home/user` means the configuration file will be read from directory /home/user.
This section lists only the most important configuration parameters. Please check taos.cfg to find all the configurable parameters. **Note: to make your new configurations work, you have to restart taosd after you change taos.cfg**.
- mgmtShellPort: TCP and UDP port between client and TDengine mgmt (default: 6030). Note: 5 successive UDP ports (6030-6034) starting from this number will be used.
- vnodeShellPort: TCP and UDP port between client and TDengine vnode (default: 6035). Note: 5 successive UDP ports (6035-6039) starting from this number will be used.
- httpPort: TCP port for RESTful service (default: 6020)
- dataDir: data directory, default is /var/lib/taos
- maxUsers: maximum number of users allowed
- maxDbs: maximum number of databases allowed
- maxTables: maximum number of tables allowed
- enableMonitor: turn on/off system monitoring, 0: off, 1: on
- logDir: log directory, default is /var/log/taos
- numOfLogLines: maximum number of lines in the log file
- debugFlag: log level, 131: only error and warnings, 135: all
In different scenarios, data characteristics are different. For example, the retention policy, data sampling period, record size, the number of devices, and data compression may be different. To gain the best performance, you can change the following configurations related to storage:
- days: number of days to cover for a data file
- keep: number of days to keep the data
- rows: number of rows of records in a block in data file.
- comp: compression algorithm, 0: off, 1: standard; 2: maximum compression
- ctime: period (seconds) to flush data to disk
- clog: flag to turn on/off Write Ahead Log, 0: off, 1: on
- tables: maximum number of tables allowed in a vnode
- cache: cache block size (bytes)
- tblocks: maximum number of cache blocks for a table
- abloks: average number of cache blocks for a table
- precision: timestamp precision, us: microsecond ms: millisecond, default is ms
For an application, there may be multiple data scenarios. The best design is to put all data with the same characteristics into one database. One application may have multiple databases, and every database has its own configuration to maximize the system performance. You can specify the above configurations related to storage when you create a database. For example:
```mysql
CREATE DATABASE demo DAYS 10 CACHE 16000 ROWS 2000
```
The above SQL statement will create a database demo, with 10 days for each data file, 16000 bytes for a cache block, and 2000 rows in a file block.
The configuration provided when creating a database will overwrite the configuration in taos.cfg.
## Configuration on Client
*taos* is the TDengine shell and is a client that connects to taosd. TDengine uses the same configuration file taos.cfg for the client, with default location at /etc/taos. You can change it by specifying command line parameter -c when you run taos. For example, *taos -c /home/user*, it will read the configuration file taos.cfg from directory /home/user.
The parameters related to client configuration are listed below:
- masterIP: IP address of TDengine server
- charset: character set, default is the system . For data type nchar, TDengine uses unicode to store the data. Thus, the client needs to tell its character set.
- locale: system language setting
- defaultUser: default login user, default is root
- defaultPass: default password, default is taosdata
For TCP/UDP port, and system debug/log configuration, it is the same as the server side.
For server IP, user name, password, you can always specify them in the command line when you run taos. If they are not specified, they will be read from the taos.cfg
## User Management
System administrator (user root) can add, remove a user, or change the password from the TDengine shell. Commands are listed below:
Create a user, password shall be quoted with the single quote.
```mysql
CREATE USER user_name PASS ‘password’
```
Remove a user
```mysql
DROP USER user_name
```
Change the password for a user
```mysql
ALTER USER user_name PASS ‘password’
```
List all users
```mysql
SHOW USERS
```
## Import Data
Inside the TDengine shell, you can import data into TDengine from either a script or CSV file
**Import from Script**
```
source <filename>
```
Inside the file, you can put all SQL statements there. Each SQL statement has a line. If a line starts with "#", it means comments, it will be skipped. The system will execute the SQL statements line by line automatically until the ends
**Import from CSV**
```mysql
insert into tb1 file 'path/data.csv'
```
CSV file contains records for only one table, and the data structure shall be the same as the defined schema for the table. The header of CSV file shall be removed.
For example, the following is a sub-table d1001:
```mysql
taos> DESCRIBE d1001
Field | Type | Length | Note |
=================================================================================
ts | TIMESTAMP | 8 | |
current | FLOAT | 4 | |
voltage | INT | 4 | |
phase | FLOAT | 4 | |
location | BINARY | 64 | TAG |
groupid | INT | 4 | TAG |
```
The data format in data.csv like this：
```csv
'2018-10-04 06:38:05.000',10.30000,219,0.31000
'2018-10-05 06:38:15.000',12.60000,218,0.33000
'2018-10-06 06:38:16.800',13.30000,221,0.32000
'2018-10-07 06:38:05.000',13.30000,219,0.33000
'2018-10-08 06:38:05.000',14.30000,219,0.34000
'2018-10-09 06:38:05.000',15.30000,219,0.35000
'2018-10-10 06:38:05.000',16.30000,219,0.31000
'2018-10-11 06:38:05.000',17.30000,219,0.32000
'2018-10-12 06:38:05.000',18.30000,219,0.31000
```
then data can be imported into database by this cmd:
```
taos> insert into d1001 file '~/data.csv';
Query OK, 9 row(s) affected (0.004763s)
```
## Export Data
You can export data either from TDengine shell or from tool taosdump.
**Export from TDengine Shell**
```mysql
select * from <tb_name> >> data.csv
```
The above SQL statement will dump the query result set into data.csv file.
**Export Using taosdump**
TDengine provides a data dumping tool taosdump. You can choose to dump a database, a table, all data or data only a time range, even only the metadata. For example:
- Export one or more tables in a DB: taosdump [OPTION…] dbname tbname …
- Export one or more DBs: taosdump [OPTION…] --databases dbname…
- Export all DBs (excluding system DB): taosdump [OPTION…] --all-databases
run *taosdump —help* to get a full list of the options
## Management of Connections, Streams, Queries
The system administrator can check, kill the ongoing connections, streams, or queries.
```
SHOW CONNECTIONS
```
It lists all connections. The first column shows connection-id from the client.
```
KILL CONNECTION <connection-id>
```
It kills the connection, where connection-id is the number of the first column showed by "SHOW CONNECTIONS".
```
SHOW QUERIES
```
It shows the ongoing queries. The first column shows the connection-id:query-no, where connection-id is the connection from the client, and id assigned by the system
```
KILL QUERY <query-id>
```
It kills the query, where query-id is the connection-id:query-no showed by "SHOW QUERIES". You can copy and paste it.
```
SHOW STREAMS
```
It shows the continuous queries. The first column shows the connection-id:stream-no, where connection-id is the connection from the client, and id assigned by the system.
```
KILL STREAM <stream-id>
```
It kills the continuous query, where stream-id is the connection-id:stream-no showed by "SHOW STREAMS". You can copy and paste it.
## System Monitor
TDengine runs a system monitor in the background. Once it is started, it will create a database sys automatically. System monitor collects the metric like CPU, memory, network, disk, number of requests periodically, and writes them into database sys. Also, TDengine will log all important actions, like login, logout, create database, drop database and so on, and write them into database sys.
You can check all the saved monitor information from database sys. By default, system monitor is turned on. But you can turn it off by changing the parameter in the configuration file.
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#Advanced Features
##Continuous Query
Continuous Query is a query executed by TDengine periodically with a sliding window, it is a simplified stream computing driven by timers, not by events. Continuous query can be applied to a table or a STable, and the result set can be passed to the application directly via call back function, or written into a new table in TDengine. The query is always executed on a specified time window (window size is specified by parameter interval), and this window slides forward while time flows (the sliding period is specified by parameter sliding).
Continuous query is defined by TAOS SQL, there is nothing special. One of the best applications is downsampling. Once it is defined, at the end of each cycle, the system will execute the query, pass the result to the application or write it to a database.
If historical data pints are inserted into the stream, the query won't be re-executed, and the result set won't be updated. If the result set is passed to the application, the application needs to keep the status of continuous query, the server won't maintain it. If application re-starts, it needs to decide the time where the stream computing shall be started.
####How to use continuous query
- Pass result set to application
Application shall use API taos_stream (details in connector section) to start the stream computing. Inside the API, the SQL syntax is:
```sql
SELECT aggregation FROM [table_name | stable_name]
INTERVAL(window_size) SLIDING(period)
```
where the new keyword INTERVAL specifies the window size, and SLIDING specifies the sliding period. If parameter sliding is not specified, the sliding period will be the same as window size. The minimum window size is 10ms. The sliding period shall not be larger than the window size. If you set a value larger than the window size, the system will adjust it to window size automatically.
For example:
```sql
SELECT COUNT(*) FROM FOO_TABLE
INTERVAL(1M) SLIDING(30S)
```
The above SQL statement will count the number of records for the past 1-minute window every 30 seconds.
- Save the result into a database
If you want to save the result set of stream computing into a new table, the SQL shall be:
```sql
CREATE TABLE table_name AS
SELECT aggregation from [table_name | stable_name]
INTERVAL(window_size) SLIDING(period)
```
Also, you can set the time range to execute the continuous query. If no range is specified, the continuous query will be executed forever. For example, the following continuous query will be executed from now and will stop in one hour.
```sql
CREATE TABLE QUERY_RES AS
SELECT COUNT(*) FROM FOO_TABLE
WHERE TS > NOW AND TS <= NOW + 1H
INTERVAL(1M) SLIDING(30S)
```
###Manage the Continuous Query
Inside TDengine shell, you can use the command "show streams" to list the ongoing continuous queries, the command "kill stream" to kill a specific continuous query.
If you drop a table generated by the continuous query, the query will be removed too.
##Publisher/Subscriber
Time series data is a sequence of data points over time. Inside a table, the data points are stored in order of timestamp. Also, there is a data retention policy, the data points will be removed once their lifetime is passed. From another view, a table in DTengine is just a standard message queue.
To reduce the development complexity and improve data consistency, TDengine provides the pub/sub functionality. To publish a message, you simply insert a record into a table. Compared with popular messaging tool Kafka, you subscribe to a table or a SQL query statement, instead of a topic. Once new data points arrive, TDengine will notify the application. The process is just like Kafka.
The detailed API will be introduced in the [connectors](https://www.taosdata.com/en/documentation/advanced-features/) section.
##Caching
TDengine allocates a fixed-size buffer in memory, the newly arrived data will be written into the buffer first. Every device or table gets one or more memory blocks. For typical IoT scenarios, the hot data shall always be newly arrived data, they are more important for timely analysis. Based on this observation, TDengine manages the cache blocks in First-In-First-Out strategy. If no enough space in the buffer, the oldest data will be saved into hard disk first, then be overwritten by newly arrived data. TDengine also guarantees every device can keep at least one block of data in the buffer.
By this design, the application can retrieve the latest data from each device super-fast, since they are all available in memory. You can use last or last_row function to return the last data record. If the super table is used, it can be used to return the last data records of all or a subset of devices. For example, to retrieve the latest temperature from thermometers in located Beijing, execute the following SQL
```mysql
select last(*) from thermometers where location=’beijing’
```
By this design, caching tool, like Redis, is not needed in the system. It will reduce the complexity of the system.
TDengine creates one or more virtual nodes(vnode) in each data node. Each vnode contains data for multiple tables and has its own buffer. The buffer of a vnode is fully separated from the buffer of another vnode, not shared. But the tables in a vnode share the same buffer.
System configuration parameter cacheBlockSize configures the cache block size in bytes, and another parameter cacheNumOfBlocks configures the number of cache blocks. The total memory for the buffer of a vnode is $cacheBlockSize \times cacheNumOfBlocks$. Another system parameter numOfBlocksPerMeter configures the maximum number of cache blocks a table can use. When you create a database, you can specify these parameters.
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# Data Model and Architecture
## Data Model
### A Typical IoT Scenario
In a typical IoT scenario, there are many types of devices. Each device is collecting one or multiple metrics. For a specific type of device, the collected data looks like the table below:
| Device ID | Time Stamp | Value 1 | Value 2 | Value 3 | Tag 1 | Tag 2 |
| :-------: | :-----------: | :-----: | :-----: | :-----: | :---: | :---: |
| D1001 | 1538548685000 | 10.3 | 219 | 0.31 | Red | Tesla |
| D1002 | 1538548684000 | 10.2 | 220 | 0.23 | Blue | BMW |
| D1003 | 1538548686500 | 11.5 | 221 | 0.35 | Black | Honda |
| D1004 | 1538548685500 | 13.4 | 223 | 0.29 | Red | Volvo |
| D1001 | 1538548695000 | 12.6 | 218 | 0.33 | Red | Tesla |
| D1004 | 1538548696600 | 11.8 | 221 | 0.28 | Black | Honda |
Each data record has device ID, timestamp, the collected metrics, and static tags associated with the device. Each device generates a data record in a pre-defined timer or triggered by an event. It is a sequence of data points, like a stream.
### Data Characteristics
Being a series of data points over time, data points generated by devices, sensors, servers, or applications have strong common characteristics.
1. metric is always structured data;
2. there are rarely delete/update operations on collected data;
3. there is only one single data source for one device or sensor;
4. ratio of read/write is much lower than typical Internet application;
5. the user pays attention to the trend of data, not the specific value at a specific time;
6. there is always a data retention policy;
7. the data query is always executed in a given time range and a subset of devices;
8. real-time aggregation or analytics is mandatory;
9. traffic is predictable based on the number of devices and sampling frequency;
10. data volume is huge, a system may generate 10 billion data points in a day.
By utilizing the above characteristics, TDengine designs the storage and computing engine in a special and optimized way for time-series data. The system efficiency is improved significantly.
### Relational Database Model
Since time-series data is more likely to be structured data, TDengine adopts the traditional relational database model to process them. You need to create a database, create tables with schema definition, then insert data points and execute queries to explore the data. Standard SQL is used, there is no learning curve.
### One Table for One Device
Due to different network latency, the data points from different devices may arrive at the server out of order. But for the same device, data points will arrive at the server in order if system is designed well. To utilize this special feature, TDengine requires the user to create a table for each device (time-stream). For example, if there are over 10,000 smart meters, 10,000 tables shall be created. For the table above, 4 tables shall be created for device D1001, D1002, D1003 and D1004, to store the data collected.
This strong requirement can guarantee the data points from a device can be saved in a continuous memory/hard disk space block by block. If queries are applied only on one device in a time range, this design will reduce the read latency significantly since a whole block is owned by one single device. Also, write latency can be significantly reduced too, since the data points generated by the same device will arrive in order, the new data point will be simply appended to a block. Cache block size and the rows of records in a file block can be configured to fit the scenarios.
### Best Practices
**Table**: TDengine suggests to use device ID as the table name (like D1001 in the above diagram). Each device may collect one or more metrics (like value1, valu2, valu3 in the diagram). Each metric has a column in the table, the metric name can be used as the column name. The data type for a column can be int, float, double, tinyint, bigint, bool or binary. Sometimes, a device may have multiple metric group, each group have different sampling period, you shall create a table for each group for each device. The first column in the table must be time stamp. TDengine uses time stamp as the index, and won’t build the index on any metrics stored.
**Tags:** to support aggregation over multiple tables efficiently, [STable(Super Table)](../super-table) concept is introduced by TDengine. A STable is used to represent the same type of device. The schema is used to define the collected metrics(like value1, value2, value3 in the diagram), and tags are used to define the static attributes for each table or device(like tag1, tag2 in the diagram). A table is created via STable with a specific tag value. All or a subset of tables in a STable can be aggregated by filtering tag values.
**Database:** different types of devices may generate data points in different patterns and shall be processed differently. For example, sampling frequency, data retention policy, replication number, cache size, record size, the compression algorithm may be different. To make the system more efficient, TDengine suggests creating a different database with unique configurations for different scenarios
**Schemaless vs Schema:** compared with NoSQL database, since a table with schema definition shall be created before the data points can be inserted, flexibilities are not that good, especially when the schema is changed. But in most IoT scenarios, the schema is well defined and is rarely changed, the loss of flexibilities won’t be a big pain to developers or the administrator. TDengine allows the application to change the schema in a second even there is a huge amount of historical data when schema has to be changed.
TDengine does not impose a limitation on the number of tables, [STables](../super-table), or databases. You can create any number of STable or databases to fit the scenarios.
## Architecture
There are two main modules in TDengine server as shown in Picture 1: **Management Module (MGMT)** and **Data Module(DNODE)**. The whole TDengine architecture also includes a **TDengine Client Module**.
<center> <img src="../assets/structure.png"> </center>
<center> Picture 1 TDengine Architecture </center>
### MGMT Module
The MGMT module deals with the storage and querying on metadata, which includes information about users, databases, and tables. Applications will connect to the MGMT module at first when connecting the TDengine server. When creating/dropping databases/tables, The request is sent to the MGMT module at first to create/delete metadata. Then the MGMT module will send requests to the data module to allocate/free resources required. In the case of writing or querying, applications still need to visit MGMT module to get meta data, according to which, then access the DNODE module.
### DNODE Module
The DNODE module is responsible for storing and querying data. For the sake of future scaling and high-efficient resource usage, TDengine applies virtualization on resources it uses. TDengine introduces the concept of virtual node (vnode), which is the unit of storage, resource allocation and data replication (enterprise edition). As is shown in Picture 2, TDengine treats each data node as an aggregation of vnodes.
When a DB is created, the system will allocate a vnode. Each vnode contains multiple tables, but a table belongs to only one vnode. Each DB has one or mode vnodes, but one vnode belongs to only one DB. Each vnode contains all the data in a set of tables. Vnodes have their own cache, directory to store data. Resources between different vnodes are exclusive with each other, no matter cache or file directory. However, resources in the same vnode are shared between all the tables in it. By virtualization, TDengine can distribute resources reasonably to each vnode and improve resource usage and concurrency. The number of vnodes on a dnode is configurable according to its hardware resources.
<center> <img src="../assets/vnode.png"> </center>
<center> Picture 2 TDengine Virtualization </center>
### Client Module
TDengine client module accepts requests (mainly in SQL form) from applications and converts the requests to internal representations and sends to the server side. TDengine supports multiple interfaces, which are all built on top of TDengine client module.
For the communication between client and MGMT module, TCP/UDP is used, the port is set by the parameter mgmtShellPort in system configuration file taos.cfg, default is 6030. For the communication between client and DNODE module, TCP/UDP is used, the port is set by the parameter vnodeShellPort in the system configuration file, default is 6035.
## Writing Process
Picture 3 shows the full writing process of TDengine. TDengine uses [Writing Ahead Log] (WAL) strategy to assure data security and integrity. Data received from the client is written to the commit log at first. When TDengine recovers from crashes caused by power lose or other situations, the commit log is used to recover data. After writting to commit log, data will be wrtten to the corresponding vnode cache, then an acknowledgment is sent to the application. There are two mechanisms that can flush data in cache to disk for persistent storage:
1. **Flush driven by timer**: There is a backend timer which flushes data in cache periodically to disks. The period is configurable via parameter commitTime in system configuration file taos.cfg.
2. **Flush driven by data**: Data in the cache is also flushed to disks when the left buffer size is below a threshold. Flush driven by data can reset the timer of flush driven by the timer.
<center> <img src="../assets/write_process.png"> </center>
<center> Picture 3 TDengine Writting Process </center>
New commit log file will be opened when the committing process begins. When the committing process finishes, the old commit file will be removed.
## Data Storage
TDengine data are saved in _/var/lib/taos_ directory by default. It can be changed to other directories by setting the parameter dataDir in system configuration file taos.cfg.
TDengine's metadata includes the database, table, user, super table and tag information. To reduce the latency, metadata are all buffered in the cache.
Data records saved in tables are sharded according to the time range. Data of tables in the same vnode in a certain time range are saved in the same file group. This sharding strategy can effectively improve data searching speed. By default, one group of files contain data in 10 days, which can be configured by *daysPerFile* in the configuration file or by *DAYS* keyword in *CREATE DATABASE* clause.
Data records are removed automatically once their lifetime is passed. The lifetime is configurable via parameter daysToKeep in the system configuration file. The default value is 3650 days.
Data in files are blockwise. A data block only contains one table's data. Records in the same data block are sorted according to the primary timestamp. To improve the compression ratio, records are stored column by column, and the different compression algorithm is applied based on each column's data type.
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#FAQ
#### 1. How to upgrade TDengine from 1.X versions to 2.X and above versions?
Version 2.X is a complete refactoring of the previous version, and configuration files and data files are incompatible. Be sure to do the following before upgrading:
1. Delete the configuration file, and execute <code>sudo rm -rf /etc/taos/taos</code>
2. Delete the log file, and execute <code>sudo rm -rf /var/log/taos </code>
3. ENSURE THAT YOUR DATAS ARE NO LONGER NEEDED! Delete the data file, and execute <code>sudo rm -rf /var/lib/taos </code>
4. Enjoy the latest stable version of TDengine
5. If the data needs to be migrated or the data file is corrupted, please contact the official technical support team for assistance
#### 2. When encoutered with the error "Unable to establish connection", what can I do?
The client may encounter connection errors. Please follow the steps below for troubleshooting:
1. Make sure that the client and server version Numbers are exactly the same, and that the open source community and Enterprise versions are not mixed.
2. On the server side, execute `systemctl status taosd` to check the status of *taosd* service. If *taosd* is not running, start it and retry connecting.
3. Make sure you have used the correct server IP address to connect to.
4. Ping the server. If no response is received, check your network connection.
5. Check the firewall setting, make sure the TCP/UDP ports from 6030-6039 are enabled.
6. For JDBC, ODBC, Python, Go connections on Linux, make sure the native library *libtaos.so* are located at /usr/local/lib/taos, and /usr/local/lib/taos is in the *LD_LIBRARY_PATH*.
7. For JDBC, ODBC, Python, Go connections on Windows, make sure *driver/c/taos.dll* is in the system search path (or you can copy taos.dll into *C:\Windows\System32*)
8. If the above steps can not help, try the network diagnostic tool *nc* to check if TCP/UDP port works
check UDP port：`nc -vuz {hostIP} {port} `
check TCP port on server: `nc -l {port}`
check TCP port on client: ` nc {hostIP} {port}`
#### 3. Why I get "Invalid SQL" error when a query is syntactically correct?
If you are sure your query has correct syntax, please check the length of the SQL string. Before version 2.0, it shall be less than 64KB.
#### 4. Does TDengine support validation queries?
For the time being, TDengine does not have a specific set of validation queries. However, TDengine comes with a system monitoring database named 'sys', which can usually be used as a validation query object.
#### 5. Can I delete or update a record that has been written into TDengine?
The answer is NO. The design of TDengine is based on the assumption that records are generated by the connected devices, you won't be allowed to change it. But TDengine provides a retention policy, the data records will be removed once their lifetime is passed.
#### 6. What is the most efficient way to write data to TDengine?
TDengine supports several different writing regimes. The most efficient way to write data to TDengine is to use batch inserting. For details on batch insertion syntax, please refer to [Taos SQL](../documentation/taos-sql)
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