The data model employed by TDengine is similar to a relational database, you need to create databases and tables. Design the data model based on your own application scenarios and you should design the STable (abbreviation for super table) schema to fit your data. This chapter will explain the big picture without getting into syntax details.
The data model employed by TDengine is similar to that of a relational database. You have to create databases and tables. You must design the data model based on your own business and application requirements. You should design the STable (an abbreviation for super table) schema to fit your data. This chapter will explain the big picture without getting into syntactical details.
## Create Database
The characteristics of data from different data collection points may be different, such as collection frequency, days to keep, number of replicas, data block size, whether it's allowed to update data, etc. For TDengine to operate with the best performance, it's strongly suggested to put the data with different characteristics into different databases because different storage policies can be set for each database. When creating a database, there are a lot of parameters that can be configured, such as the days to keep data, the number of replicas, the number of memory blocks, time precision, the minimum and maximum number of rows in each data block, compress or not, the time range of the data in single data file, etc. Below is an example of the SQL statement for creating a database.
The [characteristics of time-series data](https://www.taosdata.com/blog/2019/07/09/86.html) from different data collection points may be different. Characteristics include collection frequency, retention policy and others which determine how you create and configure the database. For e.g. days to keep, number of replicas, data block size, whether data updates are allowed and other configurable parameters would be determined by the characteristics of your data and your business requirements. For TDengine to operate with the best performance, we strongly recommend that you create and configure different databases for data with different characteristics. This allows you, for example, to set up different storage and retention policies. When creating a database, there are a lot of parameters that can be configured such as, the days to keep data, the number of replicas, the number of memory blocks, time precision, the minimum and maximum number of rows in each data block, whether compression is enabled, the time range of the data in single data file and so on. Below is an example of the SQL statement to create a database.
```sql
CREATE DATABASE power KEEP 365 DAYS 10 BLOCKS 6 UPDATE 1;
```
In the above SQL statement, a database named "power" will be created, the data in it will be kept for 365 days, which means the data older than 365 days will be deleted automatically, a new data file will be created every 10 days, the number of memory blocks is 6, data is allowed to be updated. For more details please refer to [Database](/taos-sql/database).
In the above SQL statement:
- a database named "power" will be created
- the data in it will be kept for 365 days, which means that data older than 365 days will be deleted automatically
- a new data file will be created every 10 days
- the number of memory blocks is 6
- data is allowed to be updated
After creating a database, the current database in use can be switched using SQL command `USE`, for example below SQL statement switches the current database to `power`. Without the current database specified, table name must be preceded with the corresponding database name.
For more details please refer to [Database](/taos-sql/database).
After creating a database, the current database in use can be switched using SQL command `USE`. For example the SQL statement below switches the current database to `power`. Without the current database specified, table name must be preceded with the corresponding database name.
```sql
USE power;
...
...
@@ -30,7 +37,7 @@ USE power;
## Create STable
In a time-series application, there may be multiple kinds of data collection points. For example, in the electrical power system there are meters, transformers, bus bars, switches, etc. For easy and efficient aggregation of multiple tables, one STable needs to be created for each kind of data collection point. For example, for the meters in [table 1](/tdinternal/arch#model_table1), the below SQL statement can be used to create the super table.
In a time-series application, there may be multiple kinds of data collection points. For example, in the electrical power system there are meters, transformers, bus bars, switches, etc. For easy and efficient aggregation of multiple tables, one STable needs to be created for each kind of data collection point. For example, for the meters in [table 1](/tdinternal/arch#model_table1), the SQL statement below can be used to create the super table.
```sql
CREATE STable meters (ts timestamp, current float, voltage int, phase float) TAGS (location binary(64), groupId int);
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@@ -41,15 +48,15 @@ If you are using versions prior to 2.0.15, the `STable` keyword needs to be repl
:::
Similar to creating a regular table, when creating a STable, the name and schema need to be provided. In the STable schema, the first column must be timestamp (like ts in the example), and the other columns (like current, voltage and phase in the example) are the data collected. The column type can be integer, float, double, string ,etc. Besides, the schema for tags need to be provided, like location and groupId in the example. The tag type can be integer, float, string, etc. The static properties of a data collection point can be defined as tags, like the location, device type, device group ID, manager ID, etc. Tags in the schema can be added, removed or updated. Please refer to [STable](/taos-sql/stable) for more details.
Similar to creating a regular table, when creating a STable, the name and schema need to be provided. In the STable schema, the first column must always be a timestamp (like ts in the example), and the other columns (like current, voltage and phase in the example) are the data collected. The remaining columns can [contain data of type](/taos-sql/data-type/) integer, float, double, string etc. In addition, the schema for tags, like location and groupId in the example, must be provided. The tag type can be integer, float, string, etc. Tags are essentially the static properties of a data collection point. For example, properties like the location, device type, device group ID, manager ID are tags. Tags in the schema can be added, removed or updated. Please refer to [STable](/taos-sql/stable) for more details.
For each kind of data collection point, a corresponding STable must be created. There may be many STables in an application. For electrical power system, we need to create a STable respectively for meters, transformers, busbars, switches. There may be multiple kinds of data collection points on a single device, for example there may be one data collection point for electrical data like current and voltage and another point for environmental data like temperature, humidity and wind direction, multiple STables are required for such kind of device.
For each kind of data collection point, a corresponding STable must be created. There may be many STables in an application. For electrical power system, we need to create a STable respectively for meters, transformers, busbars, switches. There may be multiple kinds of data collection points on a single device, for example there may be one data collection point for electrical data like current and voltage and another data collection point for environmental data like temperature, humidity and wind direction. Multiple STables are required for these kinds of devices.
At most 4096 (or 1024 prior to version 2.1.7.0) columns are allowed in a STable. If there are more than 4096 of metrics to be collected for a data collection point, multiple STables are required. There can be multiple databases in a system, while one or more STables can exist in a database.
## Create Table
A specific table needs to be created for each data collection point. Similar to RDBMS, table name and schema are required to create a table. Beside, one or more tags can be created for each table. To create a table, a STable needs to be used as template and the values need to be specified for the tags. For example, for the meters in [Table 1](/tdinternal/arch#model_table1), the table can be created using below SQL statement.
A specific table needs to be created for each data collection point. Similar to RDBMS, table name and schema are required to create a table. Additionally, one or more tags can be created for each table. To create a table, a STable needs to be used as template and the values need to be specified for the tags. For example, for the meters in [Table 1](/tdinternal/arch#model_table1), the table can be created using below SQL statement.
```sql
CREATE TABLE d1001 USING meters TAGS ("California.SanFrancisco", 2);
In the above SQL statement, "d1001" is the table name, "meters" is the STable name, followed by the value of tag "Location" and the value of tag "groupId", which are "California.SanFrancisco" and "2" respectively in the example. The tag values can be updated after the table is created. Please refer to [Tables](/taos-sql/table) for details.
In TDengine system, it's recommended to create a table for a data collection point via STable. A table created via STable is called subtable in some parts of the TDengine documentation. All SQL commands applied on regular tables can be applied on subtables.
In the TDengine system, it's recommended to create a table for a data collection point via STable. A table created via STable is called subtable in some parts of the TDengine documentation. All SQL commands applied on regular tables can be applied on subtables.
:::warning
It's not recommended to create a table in a database while using a STable from another database as template.
:::tip
It's suggested to use the global unique ID of a data collection point as the table name, for example the device serial number. If there isn't such a unique ID, multiple IDs that are not global unique can be combined to form a global unique ID. It's not recommended to use a global unique ID as tag value.
It's suggested to use the globally unique ID of a data collection point as the table name. For example the device serial number could be used as a unique ID. If a unique ID doesn't exist, multiple IDs that are not globally unique can be combined to form a globally unique ID. It's not recommended to use a globally unique ID as tag value.
## Create Table Automatically
In some circumstances, it's unknown whether the table already exists when inserting rows. The table can be created automatically using the SQL statement below, and nothing will happen if the table already exist.
In some circumstances, it's unknown whether the table already exists when inserting rows. The table can be created automatically using the SQL statement below, and nothing will happen if the table already exists.
```sql
INSERT INTO d1001 USING meters TAGS ("California.SanFrancisco", 2) VALUES (now, 10.2, 219, 0.32);
...
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@@ -79,6 +86,8 @@ For more details please refer to [Create Table Automatically](/taos-sql/insert#a
## Single Column vs Multiple Column
A multiple columns data model is supported in TDengine. As long as multiple metrics are collected by the same data collection point at the same time, i.e. the timestamp are identical, these metrics can be put in a single STable as columns. However, there is another kind of design, i.e. single column data model, a table is created for each metric, which means a STable is required for each kind of metric. For example, 3 STables are required for current, voltage and phase.
A multiple columns data model is supported in TDengine. As long as multiple metrics are collected by the same data collection point at the same time, i.e. the timestamps are identical, these metrics can be put in a single STable as columns.
However, there is another kind of design, i.e. single column data model in which a table is created for each metric. This means that a STable is required for each kind of metric. For example in a single column model, 3 STables would be required for current, voltage and phase.
It's recommended to use a multiple column data model as much as possible because it's better in the performance of inserting or querying rows. In some cases, however, the metrics to be collected vary frequently and correspondingly the STable schema needs to be changed frequently too. In such case, it's more convenient to use single column data model.
It's recommended to use a multiple column data model as much as possible because insert and query performance is higher. In some cases, however, the collected metrics may vary frequently and so the corresponding STable schema needs to be changed frequently too. In such cases, it's more convenient to use single column data model.
- All the data in `tag_set` will be converted to ncahr type automatically .
- Each data in `field_set` must be self-description for its data type. For example 1.2f32 means a value 1.2 of float type, it will be treated as double without the "f" type suffix.
- All the data in `tag_set` will be converted to nchar type automatically .
- Each data in `field_set` must be self-descriptive for its data type. For example 1.2f32 means a value 1.2 of float type. Without the "f" type suffix, it will be treated as type double.
- Multiple kinds of precision can be used for the `timestamp` field. Time precision can be from nanosecond (ns) to hour (h).
@@ -15,7 +15,7 @@ import CTelnet from "./_c_opts_telnet.mdx";
## Introduction
A single line of text is used in OpenTSDB line protocol to represent one row of data. OpenTSDB employs single column data model, so one line can only contain a single data column. There can be multiple tags. Each line contains 4 parts as below:
A single line of text is used in OpenTSDB line protocol to represent one row of data. OpenTSDB employs a single column data model, so each line can only contain a single data column. There can be multiple tags. Each line contains 4 parts as below:
@@ -20,7 +20,7 @@ import CAsync from "./_c_async.mdx";
## Introduction
SQL is used by TDengine as the query language. Application programs can send SQL statements to TDengine through REST API or connectors. TDengine CLI `taos` can also be used to execute SQL Ad-Hoc queries. Here is the list of major query functionalities supported by TDengine：
SQL is used by TDengine as its query language. Application programs can send SQL statements to TDengine through REST API or connectors. TDengine's CLI `taos` can also be used to execute ad hoc SQL queries. Here is the list of major query functionalities supported by TDengine：
- Query on single column or multiple columns
- Filter on tags or data columns：>, <, =, <\>, like
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@@ -31,7 +31,7 @@ SQL is used by TDengine as the query language. Application programs can send SQL
For example, the SQL statement below can be executed in TDengine CLI `taos` to select the rows whose voltage column is bigger than 215 and limit the output to only 2 rows.
For example, the SQL statement below can be executed in TDengine CLI `taos` to select records with voltage greater than 215 and limit the output to only 2 rows.
```sql
select * from d1001 where voltage > 215 order by ts desc limit 2;
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@@ -46,46 +46,46 @@ taos> select * from d1001 where voltage > 215 order by ts desc limit 2;
Query OK, 2 row(s) in set (0.001100s)
```
To meet the requirements of many use cases, some special functions have been added in TDengine, for example `twa` (Time Weighted Average), `spared` (The difference between the maximum and the minimum), and `last_row` (the last row). Furthermore, continuous query is also supported in TDengine.
To meet the requirements of varied use cases, some special functions have been added in TDengine. Some examples are `twa` (Time Weighted Average), `spread` (The difference between the maximum and the minimum), and `last_row` (the last row). Furthermore, continuous query is also supported in TDengine.
For detailed query syntax please refer to [Select](/taos-sql/select).
## Aggregation among Tables
In many use cases, there are always multiple kinds of data collection points. A new concept, called STable (abbreviated for super table), is used in TDengine to represent a kind of data collection point, and a subtable is used to represent a specific data collection point. Tags are used by TDengine to represent the static properties of data collection points. A specific data collection point has its own values for static properties. By specifying filter conditions on tags, aggregation can be performed efficiently among all the subtables created via the same STable, i.e. same kind of data collection points. Aggregate functions applicable for tables can be used directly on STables, the syntax is exactly the same.
In most use cases, there are always multiple kinds of data collection points. A new concept, called STable (abbreviation for super table), is used in TDengine to represent one type of data collection point, and a subtable is used to represent a specific data collection point of that type. Tags are used by TDengine to represent the static properties of data collection points. A specific data collection point has its own values for static properties. By specifying filter conditions on tags, aggregation can be performed efficiently among all the subtables created via the same STable, i.e. same type of data collection points. Aggregate functions applicable for tables can be used directly on STables; the syntax is exactly the same.
In summary, for a STable, its subtables can be aggregated by a simple query on the STable, it's a kind of join operation. But tables belong to different STables can not be aggregated.
In summary, records across subtables can be aggregated by a simple query on their STable. It is like a join operation. However, tables belonging to different STables can not be aggregated.
### Example 1
In TDengine CLI `taos`, use below SQL to get the average voltage of all the meters in California grouped by location.
In TDengine CLI `taos`, use the SQL below to get the average voltage of all the meters in California grouped by location.
```
taos> SELECT AVG(voltage) FROM meters GROUP BY location;
In TDengine CLI `taos`, use below SQL to get the number of rows and the maximum current in the past 24 hours from meters whose groupId is 2.
In TDengine CLI `taos`, use the SQL below to get the number of rows and the maximum current in the past 24 hours from meters whose groupId is 2.
```
taos> SELECT count(*), max(current) FROM meters where groupId = 2 and ts > now - 24h;
cunt(*) | max(current) |
count(*) | max(current) |
==================================
5 | 13.4 |
Query OK, 1 row(s) in set (0.002136s)
```
Join queries are only allowed between the subtables of the same STable. In [Select](/taos-sql/select), all query operations are marked as to whether they supports STables or not.
Join queries are only allowed between subtables of the same STable. In [Select](/taos-sql/select), all query operations are marked as to whether they support STables or not.
## Down Sampling and Interpolation
In IoT use cases, down sampling is widely used to aggregate the data by time range. The `INTERVAL` keyword in TDengine can be used to simplify the query by time window. For example, the SQL statement below can be used to get the sum of current every 10 seconds from meters table d1001.
In IoT use cases, down sampling is widely used to aggregate data by time range. The `INTERVAL` keyword in TDengine can be used to simplify the query by time window. For example, the SQL statement below can be used to get the sum of current every 10 seconds from meters table d1001.
```
taos> SELECT sum(current) FROM d1001 INTERVAL(10s);
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@@ -169,7 +169,7 @@ In the section describing [Insert](/develop/insert-data/sql-writing), a database
### Asynchronous Query
Besides synchronous queries, an asynchronous query API is also provided by TDengine to insert or query data more efficiently. With a similar hardware and software environment, the async API is 2~4 times faster than sync APIs. Async API works in non-blocking mode, which means an operation can be returned without finishing so that the calling thread can switch to other works to improve the performance of the whole application system. Async APIs perform especially better in the case of poor networks.
Besides synchronous queries, an asynchronous query API is also provided by TDengine to insert or query data more efficiently. With a similar hardware and software environment, the async API is 2~4 times faster than sync APIs. Async API works in non-blocking mode, which means an operation can be returned without finishing so that the calling thread can switch to other work to improve the performance of the whole application system. Async APIs perform especially better in the case of poor networks.
Please note that async query can only be used with a native connection.
description: "Continuous query is a query that's executed automatically according to predefined frequency to provide aggregate query capability by time window, it's actually a simplified time driven stream computing."
description: "Continuous query is a query that's executed automatically at a predefined frequency to provide aggregate query capability by time window. It is essentially simplified, time driven, stream computing."
title: "Continuous Query"
---
Continuous query is a query that's executed automatically according to a predefined frequency to provide aggregate query capability by time window, it's actually a simplified time driven stream computing. Continuous query can be performed on a table or STable in TDengine. The result of continuous query can be pushed to clients or written back to TDengine. Each query is executed on a time window, which moves forward with time. The size of time window and the forward sliding time need to be specified with parameter `INTERVAL` and `SLIDING` respectively.
A continuous query is a query that's executed automatically at a predefined frequency to provide aggregate query capability by time window. It is essentially simplified, time driven, stream computing. A continuous query can be performed on a table or STable in TDengine. The results of a continuous query can be pushed to clients or written back to TDengine. Each query is executed on a time window, which moves forward with time. The size of time window and the forward sliding time need to be specified with parameter `INTERVAL` and `SLIDING` respectively.
Continuous query in TDengine is time driven, and can be defined using TAOS SQL directly without any extra operations. With continuous query, the result can be generated according to a time window to achieve down sampling of the original data. Once a continuous query is defined using TAOS SQL, the query is automatically executed at the end of each time window and the result is pushed back to clients or written to TDengine.
A continuous query in TDengine is time driven, and can be defined using TAOS SQL directly without any extra operations. With a continuous query, the result can be generated based on a time window to achieve down sampling of the original data. Once a continuous query is defined using TAOS SQL, the query is automatically executed at the end of each time window and the result is pushed back to clients or written to TDengine.
There are some differences between continuous query in TDengine and time window computation in stream computing：
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@@ -35,7 +35,7 @@ In this section the use case of meters will be used to introduce how to use cont
```sql
create table meters (ts timestamp, current float, voltage int, phase float) tags (location binary(64), groupId int);
create table D1001 using meters tags ("California.SanFrancisco", 2);
create table D1002 using meters tags ("California.LoSangeles", 2);
create table D1002 using meters tags ("California.LosAngeles", 2);
```
The SQL statement below retrieves the average voltage for a one minute time window, with each time window moving forward by 30 seconds.
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@@ -68,7 +68,7 @@ taos> select * from avg_vol;
2020-07-29 13:39:00.000 | 223.0800000 |
```
Please note that the minimum allowed time window is 10 milliseconds, and no upper limit.
Please note that the minimum allowed time window is 10 milliseconds, and there is no upper limit.
It's possible to specify the start and end time of a continuous query. If the start time is not specified, the timestamp of the first row will be considered as the start time; if the end time is not specified, the continuous query will be performed indefinitely, otherwise it will be terminated once the end time is reached. For example, the continuous query in the SQL statement below will be started from now and terminated one hour later.
description: "Lightweight service for data subscription and pushing, the time series data inserted into TDengine continuously can be pushed automatically to the subscribing clients."
description: "Lightweight service for data subscription and publishing. Time series data inserted into TDengine continuously can be pushed automatically to subscribing clients."
title: Data Subscription
---
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...
@@ -16,9 +16,9 @@ import CDemo from "./_sub_c.mdx";
## Introduction
Due to the nature of time series data, data inserting in TDengine is similar to data publishing in message queues. Data is stored in ascending order of timestamp inside TDengine, so each table in TDengine can essentially be considered as a message queue.
Due to the nature of time series data, data insertion into TDengine is similar to data publishing in message queues. Data is stored in ascending order of timestamp inside TDengine, and so each table in TDengine can essentially be considered as a message queue.
A lightweight service for data subscription and pushing is built in TDengine. With the API provided by TDengine, client programs can use `select` statements to subscribe to data from one or more tables. The subscription and state maintenance is performed on the client side, the client programs poll the server to check whether there is new data, and if so the new data will be pushed back to the client side. If the client program is restarted, where to start for retrieving new data is up to the client side.
A lightweight service for data subscription and publishing is built into TDengine. With the API provided by TDengine, client programs can use `select` statements to subscribe to data from one or more tables. The subscription and state maintenance is performed on the client side. The client programs poll the server to check whether there is new data, and if so the new data will be pushed back to the client side. If the client program is restarted, where to start retrieving new data is up to the client side.
There are 3 major APIs related to subscription provided in the TDengine client driver.
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@@ -32,7 +32,7 @@ For more details about these APIs please refer to [C/C++ Connector](/reference/c
If we want to get a notification and take some actions if the current exceeds a threshold, like 10A, from some meters, there are two ways:
The first way is to query on each sub table and record the last timestamp matching the criteria, then after some time query on the data later than recorded timestamp and repeat this process. The SQL statements for this way are as below.
The first way is to query each sub table and record the last timestamp matching the criteria. Then after some time, query the data later than the recorded timestamp, and repeat this process. The SQL statements for this way are as below.
```sql
select * from D1001 where ts > {last_timestamp1} and current > 10;
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@@ -50,7 +50,7 @@ select * from meters where ts > {last_timestamp} and current > 10;
However, this presents a new problem in how to choose `last_timestamp`. First, the timestamp when the data is generated is different from the timestamp when the data is inserted into the database, sometimes the difference between them may be very big. Second, the time when the data from different meters arrives at the database may be different too. If the timestamp of the "slowest" meter is used as `last_timestamp` in the query, the data from other meters may be selected repeatedly; but if the timestamp of the "fastest" meter is used as `last_timestamp`, some data from other meters may be missed.
All the problems mentioned above can be resolved thoroughly using subscription provided by TDengine.
All the problems mentioned above can be resolved easily using the subscription functionality provided by TDengine.
The first step is to create subscription using `taos_subscribe`.
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@@ -65,31 +65,33 @@ if (async) {
}
```
The subscription in TDengine can be either synchronous or asynchronous. In the above sample code, the value of variable `async` is determined from the CLI input, then it's used to create either an async or sync subscription. Sync subscription means the client program needs to invoke `taos_consume` to retrieve data, and async subscription means another thread created by `taos_subscribe` internally invokes `taos_consume` to retrieve data and pass the data to `subscribe_callback` for processing, `subscribe_callback` is a call back function provided by the client program and it's suggested not to do time consuming operation in the call back function.
The subscription in TDengine can be either synchronous or asynchronous. In the above sample code, the value of variable `async` is determined from the CLI input, then it's used to create either an async or sync subscription. Sync subscription means the client program needs to invoke `taos_consume` to retrieve data, and async subscription means another thread created by `taos_subscribe` internally invokes `taos_consume` to retrieve data and pass the data to `subscribe_callback` for processing. `subscribe_callback` is a callback function provided by the client program. You should not perform time consuming operations in the callback function.
The parameter `taos` is an established connection. There is nothing special in sync subscription mode. In async subscription, it should be exclusively by current thread, otherwise unpredictable error may occur.
The parameter `taos` is an established connection. Nothing special needs to be done for thread safety for synchronous subscription. For asynchronous subscription, the taos_subscribe function should be called exclusively by the current thread, to avoid unpredictable errors.
The parameter `sql` is a `select` statement in which `where` clause can be used to specify filter conditions. In our example, the data whose current exceeds 10A needs to be subscribed like below SQL statement:
The parameter `sql` is a `select` statement in which the `where` clause can be used to specify filter conditions. In our example, we can subscribe to the records in which the current exceeds 10A, with the following SQL statement:
```sql
select * from meters where current > 10;
```
Please note that, all the data will be processed because no start time is specified. If only the data from one day ago needs to be processed, a time related condition can be added:
Please note that, all the data will be processed because no start time is specified. If we only want to process data for the past day, a time related condition can be added:
```sql
select * from meters where ts > now - 1d and current > 10;
```
The parameter `topic` is the name of the subscription, it needs to be guaranteed unique in the client program, but it's not necessary to be globally unique because subscription is implemented in the APIs on the client side.
The parameter `topic` is the name of the subscription. The client application must guarantee that the name is unique. However, it doesn't have to be globally unique because subscription is implemented in the APIs on the client side.
If the subscription named as `topic` doesn't exist, the parameter `restart` will be ignored. If the subscription named as `topic` has been created before by the client program, when the client program is restarted with the subscription named `topic`, parameter `restart` is used to determine whether to retrieve data from the beginning or from the last point where the subscription was broken. If the value of `restart` is **true** (i.e. a non-zero value), the data will be retrieved from beginning, or if it is **false** (i.e. zero), the data already consumed before will not be processed again.
If the subscription named as `topic` doesn't exist, the parameter `restart` will be ignored. If the subscription named as `topic` has been created before by the client program, when the client program is restarted with the subscription named `topic`, parameter `restart` is used to determine whether to retrieve data from the beginning or from the last point where the subscription was broken.
The last parameter of `taos_subscribe` is the polling interval in unit of millisecond. In sync mode, if the time difference between two continuous invocations to `taos_consume` is smaller than the interval specified by `taos_subscribe`, `taos_consume` will be blocked until the interval is reached. In async mode, this interval is the minimum interval between two invocations to the call back function.
If the value of `restart` is **true** (i.e. a non-zero value), data will be retrieved from the beginning. If it is **false** (i.e. zero), the data already consumed before will not be processed again.
The last parameter of `taos_subscribe` is the polling interval in units of millisecond. In sync mode, if the time difference between two continuous invocations to `taos_consume` is smaller than the interval specified by `taos_subscribe`, `taos_consume` will be blocked until the interval is reached. In async mode, this interval is the minimum interval between two invocations to the call back function.
The second to last parameter of `taos_subscribe` is used to pass arguments to the call back function. `taos_subscribe` doesn't process this parameter and simply passes it to the call back function. This parameter is simply ignored in sync mode.
After a subscription is created, its data can be consumed and processed, below is the sample code of how to consume data in sync mode, in the else part if `if (async)`.
After a subscription is created, its data can be consumed and processed. Shown below is the sample code to consume data in sync mode, in the else condition of `if (async)`.
```c
if (async) {
...
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@@ -106,7 +108,7 @@ if (async) {
}
```
In the above sample code, there is an infinite loop, each time carriage return is entered `taos_consume` is invoked, the return value of `taos_consume` is the selected result set, exactly as the input of `taos_use_result`, in the above sample `print_result` is used instead to simplify the sample. Below is the implementation of `print_result`.
In the above sample code in the else condition, there is an infinite loop. Each time carriage return is entered `taos_consume` is invoked. The return value of `taos_consume` is the selected result set. In the above sample, `print_result` is used to simplify the printing of the result set. It is similar to `taos_use_result`. Below is the implementation of `print_result`.
```c
void print_result(TAOS_RES* res, int blockFetch) {
...
...
@@ -133,9 +135,9 @@ void print_result(TAOS_RES* res, int blockFetch) {
}
```
In the above code `taos_print_row` is used to process the data consumed. All the matching rows will be printed.
In the above code `taos_print_row` is used to process the data consumed. All matching rows are printed.
In async mode, the data consuming is simpler as below.
In async mode, consuming data is simpler as shown below.
```c
void subscribe_callback(TAOS_SUB* tsub, TAOS_RES *res, void* param, int code) {
...
...
@@ -175,7 +177,7 @@ Then, this row of data will be shown by the example program on the first termina
## Examples
Below example program demonstrates how to subscribe the data rows whose current exceeds 10A using connectors.
The example program below demonstrates how to subscribe, using connectors, to data rows in which current exceeds 10A.
### Prepare Data
...
...
@@ -250,7 +252,7 @@ taos> use power;
taos> insert into d1001 values(now, 12.4, 220, 1);
```
Because the current in inserted row exceeds 10A, it will be consumed by the example program.
Because the current in the inserted row exceeds 10A, it will be consumed by the example program.
The cache management policy in TDengine is First-In-First-Out (FIFO), which is also known as insert driven cache management policy and different from read driven cache management, i.e. Least-Recent-Used (LRU). It simply stores the latest data in cache and flushes the oldest data in cache to disk when the cache usage reaches a threshold. In IoT use cases, the most cared about data is the latest data, i.e. current state. The cache policy in TDengine is based the nature of IoT data.
The cache management policy in TDengine is First-In-First-Out (FIFO). FIFO is also known as insert driven cache management policy and it is different from read driven cache management, which is more commonly known as Least-Recently-Used (LRU). FIFO simply stores the latest data in cache and flushes the oldest data in cache to disk, when the cache usage reaches a threshold. In IoT use cases, it is the current state i.e. the latest or most recent data that is important. The cache policy in TDengine, like much of the design and architecture of TDengine, is based on the nature of IoT data.
Caching the latest data provides the capability of retrieving data in milliseconds. With this capability, TDengine can be configured properly to be used as caching system without deploying another separate caching system to simplify the system architecture and minimize the operation cost. The cache will be emptied after TDengine is restarted, TDengine doesn't reload data from disk into cache like a real key-value caching system.
Caching the latest data provides the capability of retrieving data in milliseconds. With this capability, TDengine can be configured properly to be used as a caching system without deploying another separate caching system. This simplifies the system architecture and minimizes operational costs. The cache is emptied after TDengine is restarted. TDengine does not reload data from disk into cache, like a key-value caching system.
The memory space used by TDengine cache is fixed in size, according to the configuration based on application requirement and system resources. Independent memory pool is allocated for and managed by each vnode (virtual node) in TDengine, there is no sharing of memory pools between vnodes. All the tables belonging to a vnode share all the cache memory of the vnode.
The memory space used by the TDengine cache is fixed in size and configurable. It should be allocated based on application requirements and system resources. An independent memory pool is allocated for and managed by each vnode (virtual node) in TDengine. There is no sharing of memory pools between vnodes. All the tables belonging to a vnode share all the cache memory of the vnode.
Memory pool is divided into blocks and data is stored in row format in memory and each block follows FIFO policy. The size of each block is determined by configuration parameter `cache`, the number of blocks for each vnode is determined by `blocks`. For each vnode, the total cache size is `cache * blocks`. A cache block needs to ensure that each table can store at least dozens of records to be efficient.
The memory pool is divided into blocks and data is stored in row format in memory and each block follows FIFO policy. The size of each block is determined by configuration parameter `cache` and the number of blocks for each vnode is determined by the parameter `blocks`. For each vnode, the total cache size is `cache * blocks`. A cache block needs to ensure that each table can store at least dozens of records, to be efficient.
`last_row` function can be used to retrieve the last row of a table or a STable to quickly show the current state of devices on monitoring screen. For example the below SQL statement retrieves the latest voltage of all meters in San Francisco of California.
`last_row` function can be used to retrieve the last row of a table or a STable to quickly show the current state of devices on monitoring screen. For example the below SQL statement retrieves the latest voltage of all meters in San Francisco, California.
The FQDN of all hosts needs to be setup properly, all the FQDNs need to be configured in the /etc/hosts of each host. It must be confirmed that each FQDN can be accessed (by ping, for example) from any other hosts.
The FQDN of all hosts must be setup properly. For e.g. FQDNs may have to be configured in the /etc/hosts file on each host. You must confirm that each FQDN can be accessed from any other host. For e.g. you can do this by using the `ping` command.
On each host the command `hostname -f` can be executed to get the hostname. `ping` command can be executed on each host to check whether any other host is accessible from it. If any host is not accessible, the network configuration, like /etc/hosts or DNS configuration, need to be checked and revised to make any two hosts accessible to each other.
To get the hostname on any host, the command `hostname -f` can be executed. `ping <FQDN>` command can be executed on each host to check whether any other host is accessible from it. If any host is not accessible, the network configuration, like /etc/hosts or DNS configuration, needs to be checked and revised, to make any two hosts accessible to each other.
:::note
- The host where the client program runs also needs to be configured properly for FQDN, to make sure all hosts for client or server can be accessed from any other. In other words, the hosts where the client is running are also considered as a part of the cluster.
-It's suggested to disable the firewall for all hosts in the cluster. At least TCP/UDP for port 6030~6042 need to be open if a firewall is enabled.
-Please ensure that your firewall rules do not block TCP/UDP on ports 6030-6042 on all hosts in the cluster.
:::
### Step 2
If any previous version of TDengine has been installed and configured on any host, the installation needs to be removed and the data needs to be cleaned up. For details about uninstalling please refer to [Install and Uninstall](/operation/pkg-install). To clean up the data, please use `rm -rf /var/lib/taos/\*` assuming the `dataDir` is configured as `/var/lib/taos`.
If any previous version of TDengine has been installed and configured on any host, the installation needs to be removed and the data needs to be cleaned up. For details about uninstalling please refer to [Install and Uninstall](/operation/pkg-install). To clean up the data, please use `rm -rf /var/lib/taos/\*` assuming the `dataDir` is configured as `/var/lib/taos`.
:::note
As a best practice, before cleaning up any data files or directories, please ensure that your data has been backed up correctly, if required by your data integrity, backup, security, or other standard operating protocols (SOP).
:::
### Step 3
Now it's time to install TDengine on all hosts without starting `taosd`, the versions on all hosts should be same. If it's prompted to input the existing TDengine cluster, simply press carriage return to ignore it. `install.sh -e no` can also be used to disable this prompt. For details please refer to [Install and Uninstall](/operation/pkg-install).
Now it's time to install TDengine on all hosts but without starting `taosd`. Note that the versions on all hosts should be same. If you are prompted to input the existing TDengine cluster, simply press carriage return to ignore the prompt. `install.sh -e no` can also be used to disable this prompt. For details please refer to [Install and Uninstall](/operation/pkg-install).
### Step 4
Now each physical node (referred to as `dnode` hereinafter, it's abbreviation for "data node") of TDengine needs to be configured properly. Please note that one dnode doesn't stand for one host, multiple TDengine nodes can be started on single host as long as they are configured properly without conflicting. More specifically each instance of the configuration file `taos.cfg` stands for a dnode. Assuming the first dnode of TDengine cluster is "h1.taosdata.com:6030", its `taos.cfg` is configured as following.
Now each physical node (referred to, hereinafter, as `dnode` which is an abbreviation for "data node") of TDengine needs to be configured properly. Please note that one dnode doesn't stand for one host. Multiple TDengine dnodes can be started on a single host as long as they are configured properly without conflicting. More specifically each instance of the configuration file `taos.cfg` stands for a dnode. Assuming the first dnode of TDengine cluster is "h1.taosdata.com:6030", its `taos.cfg` is configured as following.
```c
// firstEp is the end point to connect to when any dnode starts
...
...
@@ -67,9 +73,11 @@ Prior to version 2.0.19.0, besides the above parameters, `locale` and `charset`
## Start Cluster
In the following example we assume that first dnode has FQDN h1.taosdata.com and the second dnode has FQDN h2.taosdata.com.
### Start The First DNODE
The first dnode can be started following the instructions in [Get Started](/get-started/), for example h1.taosdata.com. Then TDengine CLI `taos` can be launched to execute command `show dnodes`, the output is as following for example:
The first dnode can be started following the instructions in [Get Started](/get-started/). Then TDengine CLI `taos` can be launched to execute command `show dnodes`, the output is as following for example:
```
Welcome to the TDengine shell from Linux, Client Version:2.0.0.0
...
...
@@ -80,27 +88,41 @@ Copyright (c) 2017 by TAOS Data, Inc. All rights reserved.
taos> show dnodes;
id | end_point | vnodes | cores | status | role | create_time |
From the above output, it is shown that the end point of the started dnode is "h1.taos.com:6030", which is the `firstEp` of the cluster.
From the above output, it is shown that the end point of the started dnode is "h1.taosdata.com:6030", which is the `firstEp` of the cluster.
### Start Other DNODEs
There are a few steps necessary to add other dnodes in the cluster.
First, start `taosd` as instructed in [Get Started](/get-started/), assuming it's for the second dnode. Before starting `taosd`, please making sure the configuration is correct, especially `firstEp`, `FQDN` and `serverPort`, `firstEp` must be same as the dnode shown in the section "Start First DNODE", i.e. "h1.taosdata.com" in this example.
Let's assume we are starting the second dnode with FQDN, h2.taosdata.com. First we make sure the configuration is correct.
```c
// firstEp is the end point to connect to when any dnode starts
firstEph1.taosdata.com:6030
// must be configured to the FQDN of the host where the dnode is launched
fqdnh2.taosdata.com
// the port used by the dnode, default is 6030
serverPort6030
```
Second, we can start `taosd` as instructed in [Get Started](/get-started/).
Then, on the first dnode, use TDengine CLI `taos` to execute below command to add the end point of the dnode in the cluster. In the command "fqdn:port" should be quoted using double quotes.
Then, on the first dnode i.e. h1.taosdata.com in our example, use TDengine CLI `taos` to execute the following command to add the end point of the dnode in the cluster. In the command "fqdn:port" should be quoted using double quotes.
```sql
CREATEDNODE"h2.taos.com:6030";
```
Then on the first dnode, execute `show dnodes` in `taos` to show whether the second dnode has been added in the cluster successfully or not.
Then on the first dnode h1.taosdata.com, execute `show dnodes` in `taos` to show whether the second dnode has been added in the cluster successfully or not.
TDengine uses **WAL**, i.e. Write Ahead Log, to achieve fault tolerance and high reliability.
When a data block is received by TDengine, the original data block is firstly written into WAL. The log in WAL will be deleted only after the data has been written into data files in the database. Data can be recovered from WAL in case the server is stopped abnormally due to any reason and then restarted.
When a data block is received by TDengine, the original data block is first written into WAL. The log in WAL will be deleted only after the data has been written into data files in the database. Data can be recovered from WAL in case the server is stopped abnormally due to any reason and then restarted.
There are 2 configuration parameters related to WAL:
- walLevel：0：wal is disabled; 1：wal is enabled without fsync; 2：wal is enabled with fsync.
- fsync：only valid when walLevel is set to 2, it specified the interval of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written.
- walLevel：
- 0：wal is disabled;
- 1：wal is enabled without fsync;
- 2：wal is enabled with fsync.
- fsync：only valid when walLevel is set to 2, it specifies the interval of invoking fsync. If set to 0, it means fsync is invoked immediately once WAL is written.
To achieve absolutely no data loss, walLevel needs to be set to 2 and fsync needs to be set to 1. The penalty is the performance of data ingestion downgrades. However, if the concurrent threads of data insertion on the client side can reach a big enough number, for example 50, the data ingestion performance would be still good enough, our verification shows that the drop is only 30% compared to fsync is set to 3,000 milliseconds.
...
...
@@ -20,10 +23,10 @@ To achieve absolutely no data loss, walLevel needs to be set to 2 and fsync need
TDengine uses replications to provide high availability and disaster recovery capability.
TDengine cluster is managed by mnode. To make sure the high availability of mnode, multiple replicas can be configured by system parameter `numOfMnodes`. The data replication between mnode replicas is in synchronous way to guarantee the metadata consistency.
TDengine cluster is managed by mnode. To make sure the high availability of mnode, multiple replicas can be configured by the system parameter `numOfMnodes`. The data replication between mnode replicas is performed in a synchronous way to guarantee the metadata consistency.
The number of replicas for time series data in TDengine is associated with each database, there can be a lot of databases in a cluster while each database can be configured with a different number of replicas. When creating a database, parameter `replica` is used to configure the number of replications. To achieve high availability, `replica` needs to be higher than 1.
The number of replicas for the time series data in TDengine is associated with each database, there can be a lot of databases in a cluster while each database can be configured with a different number of replicas. When creating a database, parameter `replica` is used to configure the number of replications. To achieve high availability, `replica` needs to be higher than 1.
The number of dnodes in a TDengine cluster must NOT be lower than the number of replicas for any database, otherwise it would fail when trying to create table.
The number of dnodes in a TDengine cluster must NOT be lower than the number of replicas for any database, otherwise it would fail when trying to create a table.
As long as the dnodes of a TDengine cluster are deployed on different physical machines and replica number is set to bigger than 1, high availability can be achieved without any other assistance. If dnodes of TDengine cluster are deployed in geographically different data centers, disaster recovery can be achieved too.
As long as the dnodes of a TDengine cluster are deployed on different physical machines and the replica number is set to bigger than 1, high availability can be achieved without any other assistance. If dnodes of TDengine cluster are deployed in geographically different data centers, disaster recovery can be achieved too.
There are multiple ways of importing data provided byTDengine: import with script, import from data file, import using `taosdump`.
There are multiple ways of importing data provided byTDengine: import with script, import from data file, import using `taosdump`.
## Import Using Script
TDengine CLI `taos` supports `source <filename>` command for executing the SQL statements in the file in batch. The SQL statements for creating databases, creating tables, and inserting rows can be written in single file with one statement on each line, then the file can be executed using`source` command in TDengine CLI `taos` to execute the SQL statements in order and in batch. In the script file, any line beginning with "#" is treated as comments and ignored silently.
TDengine CLI `taos` supports `source <filename>` command for executing the SQL statements in the file in batch. The SQL statements for creating databases, creating tables, and inserting rows can be written in a single file with one statement on each line, then the file can be executed using the`source` command in TDengine CLI `taos` to execute the SQL statements in order and in batch. In the script file, any line beginning with "#" is treated as comments and ignored silently.
## Import from Data File
In TDengine CLI, data can be imported from a CSV file into an existing table. The data in single CSV must belong to same table and must be consistent with the schema of that table. The SQL statement is as below:
In TDengine CLI, data can be imported from a CSV file into an existing table. The data in a single CSV must belong to the same table and must be consistent with the schema of that table. The SQL statement is as below:
```sql
insertintotb1file'path/data.csv';
```
:::note
If there is description in the first line of a CSV file, please remove it before importing. If there is no value for a column, please use `NULL` without quotes.
If there is a description in the first line of the CSV file, please remove it before importing. If there is no value for a column, please use `NULL` without quotes.
:::
For example, there is a subtable d1001 whose schema is as below:
For example, there is a subtable d1001 whose schema is as below:
```sql
taos>DESCRIBEd1001
...
...
@@ -49,7 +49,7 @@ The format of the CSV file to be imported, data.csv, is as below:
'2018-10-12 06:38:05.000',18.30000,219,0.31000
```
Then, below SQL statement can be used to import data from file "data.csv", assuming the file is located under the home directory of current Linux user.
Then, the below SQL statement can be used to import data from file "data.csv", assuming the file is located under the home directory of the current Linux user.
A convenient tool for importing and exporting data is provided by TDengine, `taosdump`, which can used to export data from one TDengine cluster and import into another one. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump).
A convenient tool for importing and exporting data is provided by TDengine, `taosdump`, which can be used to export data from one TDengine cluster and import into another one. For the details of using `taosdump` please refer to [Tool for exporting and importing data: taosdump](/reference/taosdump).
System operator can use TDengine CLI to show the connections, ongoing queries, stream computing, and can close connection or stop ongoing query task or stream computing.
A system operator can use TDengine CLI to show the connections, ongoing queries, stream computing, and can close connection or stop ongoing query task or stream computing.
## Show Connections
...
...
@@ -51,4 +51,4 @@ The first column of the output is stream ID, which is composed of the connection
KILLSTREAM<stream-id>;
```
The the above SQL command, `stream-id` is from the first column of the output of `SHOW STREAMS`.
The above SQL command, `stream-id` is from the first column of the output of `SHOW STREAMS`.
After TDengine is started, a database named `log` for monitoring is created automatically. The information about CPU, memory, disk, bandwidth, number of requests, disk I/O speed, slow query is written into `log` database on the basis of a predefined interval. Besides, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into `log` database too. System operator can view the data in `log` database from TDengine CLI or from a web console.
After TDengine is started, a database named `log` for monitoring is created automatically. The information about CPU, memory, disk, bandwidth, number of requests, disk I/O speed, slow query is written into `log` database on the basis of a predefined interval. Additionally, some important system operations, like logon, create user, drop database, and alerts and warnings generated in TDengine are written into the `log` database too. A system operator can view the data in `log` database from TDengine CLI or from a web console.
Collection of the monitoring information is enabled by default, but can be disabled by parameter `monitor` in configuration file.
The collection of the monitoring information is enabled by default, but can be disabled by parameter `monitor` in the configuration file.
## TDinsight
TDinsight is a total solution which uses the monitor database `log` mentioned previously and Grafana to monitor a TDengine cluster.
TDinsight is a complete solution which uses the monitor database `log` mentioned previously and Grafana to monitor a TDengine cluster.
From version 2.3.3.0, more monitoring data has been added in the `log` database. Please refer to [TDinsight Grafana Dashboard](https://grafana.com/grafana/dashboards/15167) to learn more details about using TDinsight to monitor TDengine.
A script `TDinsight.sh` is provided to deploy TDinsight in automatic way.
A script `TDinsight.sh` is provided to deploy TDinsight automatically.
- The AliCloud SMS alert built in TDengine data source plugin can be enabled with parameter `-s`, the parameters of this way are as follows:
- The AliCloud SMS alert built in TDengine data source plugin can be enabled with parameter `-s`, the parameters of enabling this plugin are listed below:
-`-I`: AliCloud SMS Key ID
-`-K`: AliCloud SMS Key Secret
...
...
@@ -47,7 +47,7 @@ There are two ways to setup Grafana alert notification.
-`-T`: Input parameters in JSON format for the SMS notification template, for example`{"alarm_level":"%s","time":"%s","name":"%s","content":"%s"}`
-`-B`: List of mobile numbers to be notified
Below is an example of the full command using this way.
Below is an example of the full command using the AliCloud SMS alert.
```bash
sudo ./TDinsight.sh -a http://localhost:6041 -u root -p taosdata -s\
...
...
@@ -55,6 +55,6 @@ There are two ways to setup Grafana alert notification.
@@ -10,13 +10,13 @@ The diagnostic for network connection can be executed between Linux and Linux or
Diagnostic steps：
1. If the port range to be diagnosed are being occupied by a `taosd` server process, please firstly stop `taosd.
2. On the server side, execute command `taos -n server -P <port> -l <pktlen>` to monitor the port range starting from the port specified by `-P` parameter with the role of "server.
3. On the client side, execute command `taos -n client -h <fqdnofserver> -P <port> -l <pktlen>` to send testing package to the specified server and port.
1. If the port range to be diagnosed are being occupied by a `taosd` server process, please first stop `taosd.
2. On the server side, execute command `taos -n server -P <port> -l <pktlen>` to monitor the port range starting from the port specified by `-P` parameter with the role of "server".
3. On the client side, execute command `taos -n client -h <fqdnofserver> -P <port> -l <pktlen>` to send a testing package to the specified server and port.
-l <pktlen\>： The size of the testing package, in bytes. The value range is [11, 64,000] and default value is 1,000. Please be noted that the package length must be same in the above 2 commands executed on server side and client side respectively.
-l <pktlen\>： The size of the testing package, in bytes. The value range is [11, 64,000] and default value is 1,000. Please note that the package length must be same in the above 2 commands executed on server side and client side respectively.
Output of the server side is as below for example:
Output of the server side for the example is below:
```bash
# taos -n server -P 6000
...
...
@@ -47,7 +47,7 @@ Output of the server side is as below for example:
12/21 14:50:22.721261 0x7f53427ec700 UTL UDP: send:1000 bytes to 172.27.0.8 at 6011
```
Output of the client side is as below for example:
Output of the client side for the example is below:
```bash
# taos -n client -h 172.27.0.7 -P 6000
...
...
@@ -65,13 +65,13 @@ Output of the client side is as below for example:
12/21 14:50:22.721274 0x7fc95d859200 UTL successed to test UDP port:6011
```
The output needs to be checked carefully for the system operator to find out root cause and solve the problem.
The output needs to be checked carefully for the system operator to find out the root cause and solve the problem.
## Startup Status and RPC Diagnostic
`taos -n startup -h <fqdnofserver>` can be used to check the startup status of a `taosd` process. This is a comman task for a system operator to do to determine whether `taosd` has been started successfully, especially in case of cluster.
`taos -n rpc -h <fqdnofserver>` can be used to check whether the port of a started `taosd` can be accessed or not. If `taosd` process doesn't respond or work abnormally, this command can be used to initiate a rpc communication with the specified fqdn to determine whether it's network problem or `taosd` is abnormal.
`taos -n rpc -h <fqdnofserver>` can be used to check whether the port of a started `taosd` can be accessed or not. If `taosd` process doesn't respond or is working abnormally, this command can be used to initiate a rpc communication with the specified fqdn to determine whether it's a network problem or `taosd` is abnormal.
The above commands can be executed on Linux Shell to check whether the port for sync works well and whether the sync module of the server side works well. Besides, `-P 6042` is used to check whether the arbitrator is configured properly and works well.
The above commands can be executed on Linux Shell to check whether the port for sync is working well and whether the sync module on the server side is working well. Additionally, `-P 6042` is used to check whether the arbitrator is configured properly and is working well.
## Network Speed Diagnostic
...
...
@@ -88,12 +88,12 @@ The above commands can be executed on Linux Shell to check whether the port for
From version 2.2.0.0, the above command can be executed on Linux Shell to test the network speed, it sends uncompressed package to a running `taosd` server process or a simulated server process started by `taos -n server` to test the network speed. Parameters can be used when testing network speed are as below:
-n：When set to "speed", it means testing network speed
-h：The FQDN or IP of the server process to be connected to; if not set, the FQDN configured in `taos.cfg` is used
-P：The port of the server process to connect to, the default value is 6030
-N：The number of packages that will be sent in the test, range is [1,10000], default value is 100
-l：The size of each package in bytes, range is [1024, 1024 \* 1024 \* 1024], default value is 1024
-S：The type of network packages to send, can be either TCP or UDP, default value is
-n：When set to "speed", it means testing network speed.
-h：The FQDN or IP of the server process to be connected to; if not set, the FQDN configured in `taos.cfg` is used.
-P：The port of the server process to connect to, the default value is 6030.
-N：The number of packages that will be sent in the test, range is [1,10000], default value is 100.
-l：The size of each package in bytes, range is [1024, 1024 \* 1024 \* 1024], default value is 1024.
-S：The type of network packages to send, can be either TCP or UDP, default value is TCP.
## FQDN Resolution Diagnostic
...
...
@@ -101,22 +101,22 @@ From version 2.2.0.0, the above command can be executed on Linux Shell to test t
From version 2.2.0.0, the above command can be executed on Linux Shell to test the resolution speed of FQDN. It can be used to try to resolve a FQDN to an IP address and record the time spent in this process. The parameters that can be used for this purpose are as below:
-n：When set to "fqdn", it means testing the speed of resolving FQDN
-h：The FQDN to be resolved. If not set, the `FQDN` parameter in `taos.cfg` is used by default.
-n：When set to "fqdn", it means testing the speed of resolving FQDN.
-h：The FQDN to be resolved. If not set, the `FQDN` parameter in `taos.cfg` is used by default.
## Server Log
The parameter `debugFlag` is used to control the log level of `taosd` server process. The default value is 131, for debug purpose it needs to be escalated to 135 or 143.
The parameter `debugFlag` is used to control the log level of the `taosd` server process. The default value is 131, for debug purpose it needs to be escalated to 135 or 143.
Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is huge volume of data insertion and data query requests. If all the logs are stored together, some important information may be missed very easily, so on server side important information is stored at different place from other logs.
Once this parameter is set to 135 or 143, the log file grows very quickly especially when there is a huge volume of data insertion and data query requests. If all the logs are stored together, some important information may be missed very easily, so on server side important information is stored at different place from other logs.
- The log at level of INFO, WARNING and ERROR is stored in `taosinfo` so that it is easy to find important information
- The log at level of DEBUG (135) and TRACE (143) and other information not handled by `taosinfo` are stored in `taosdlog`
## Client Log
An independent log file, named as "taoslog+<seq num\>" is generated for each client program, i.e. a client process. The default value of `debugFlag` is also 131 and only log at level of INFO/ERROR/WARNING is recorded, it and needs to be changed to 135 or 143 so that log at DEBUG or TRACE level can be recorded for debugging purpose.
An independent log file, named as "taoslog+<seq num\>" is generated for each client program, i.e. a client process. The default value of `debugFlag` is also 131 and only logs at level of INFO/ERROR/WARNING are recorded, for debugging purposes it needs to be changed to 135 or 143 so that logs at DEBUG or TRACE level can be recorded.
The maximum length of a single log file is controlled by parameter `numOfLogLines` and only 2 log files are kept for each `taosd` server process.
log file is written in async way to minimize the workload on disk, bu the penalty is that a few log lines may be lost in some extreme conditions.
Log files are written in an async way to minimize the workload on disk, but the trade off for performance is that a few log lines may be lost in some extreme conditions.
