Merge remote-tracking branch 'origin/3.0' into feature/3_liaohj

docs-cn/12-taos-sql/07-function.md

@@ -261,6 +261,66 @@ taos> select hyperloglog(dbig) from shll;
 Query OK, 1 row(s) in set (0.008388s)
 ```
 
+### HISTOGRAM
+
+```
+SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_name [WHERE clause];
+```
+
+**功能说明**：统计数据按照用户指定区间的分布。
+
+**返回结果类型**：如归一化参数 normalized 设置为 1，返回结果为双精度浮点类型 DOUBLE，否则为长整形 INT64。
+
+**应用字段**：数值型字段。
+
+**支持的版本**：2.6.0.0 及以后的版本。
+
+**适用于**: 表和超级表。
+
+**说明**：
+1. bin_type 用户指定的分桶类型, 有效输入类型为"user_input“, ”linear_bin", "log_bin"。
+2. bin_description 描述如何生成分桶区间，针对三种桶类型，分别为以下描述格式(均为 JSON 格式字符串)：       
+    - "user_input": "[1, 3, 5, 7]" 
+       用户指定 bin 的具体数值。
+       
+    - "linear_bin": "{"start": 0.0, "width": 5.0, "count": 5, "infinity": true}"
+       "start" 表示数据起始点，"width" 表示每次 bin 偏移量, "count" 为 bin 的总数，"infinity" 表示是否添加（-inf, inf）作为区间起点跟终点，
+       生成区间为[-inf, 0.0, 5.0, 10.0, 15.0, 20.0, +inf]。
+ 
+    - "log_bin": "{"start":1.0, "factor": 2.0, "count": 5, "infinity": true}"
+       "start" 表示数据起始点，"factor" 表示按指数递增的因子，"count" 为 bin 的总数，"infinity" 表示是否添加（-inf, inf）作为区间起点跟终点，
+       生成区间为[-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf]。
+3. normalized 是否将返回结果归一化到 0~1 之间 。有效输入为 0 和 1。
+
+**示例**：
+
+```mysql
+taos> SELECT HISTOGRAM(voltage, "user_input", "[1,3,5,7]", 1) FROM meters;
+         histogram(voltage, "user_input", "[1,3,5,7]", 1) |
+     =======================================================
+     {"lower_bin":1, "upper_bin":3, "count":0.333333}     |
+     {"lower_bin":3, "upper_bin":5, "count":0.333333}     |
+     {"lower_bin":5, "upper_bin":7, "count":0.333333}     |
+     Query OK, 3 row(s) in set (0.004273s)
+     
+taos> SELECT HISTOGRAM(voltage, 'linear_bin', '{"start": 1, "width": 3, "count": 3, "infinity": false}', 0) FROM meters;
+         histogram(voltage, 'linear_bin', '{"start": 1, "width": 3, " |
+     ===================================================================
+     {"lower_bin":1, "upper_bin":4, "count":3}                        |
+     {"lower_bin":4, "upper_bin":7, "count":3}                        |
+     {"lower_bin":7, "upper_bin":10, "count":3}                       |
+     Query OK, 3 row(s) in set (0.004887s)
+    
+taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3, "infinity": true}', 0) FROM meters;
+     histogram(voltage, 'log_bin', '{"start": 1, "factor": 3, "count" |
+     ===================================================================
+     {"lower_bin":-inf, "upper_bin":1, "count":3}                     |
+     {"lower_bin":1, "upper_bin":3, "count":2}                        |
+     {"lower_bin":3, "upper_bin":9, "count":6}                        |
+     {"lower_bin":9, "upper_bin":27, "count":3}                       |
+     {"lower_bin":27, "upper_bin":inf, "count":1}                     |
+```
+
 ## 选择函数
 
 在使用所有的选择函数的时候，可以同时指定输出 ts 列或标签列（包括 tbname），这样就可以方便地知道被选出的值是源于哪个数据行的。

docs-en/12-taos-sql/07-function.md

@@ -259,6 +259,74 @@ taos> select hyperloglog(dbig) from shll;
 Query OK, 1 row(s) in set (0.008388s)
 ```
 
+### HISTOGRAM
+
+```
+SELECT HISTOGRAM(field_name，bin_type, bin_description, normalized) FROM tb_name [WHERE clause];
+```
+
+**Description**：Returns count of data points in user-specified ranges.
+
+**Return value type**：Double or INT64, depends on normalized parameter settings.
+
+**Applicable column type**：Numerical types.
+
+**Applicable versions**：Since version 2.6.0.0.
+
+**Applicable table types**: table, STable
+
+**Explanations**：
+
+1. bin_type: parameter to indicate the bucket type, valid inputs are: "user_input", "linear_bin", "log_bin"。
+2. bin_description: parameter to describe how to generate buckets，can be in the following JSON formats for each bin_type respectively: 
+
+    - "user_input": "[1, 3, 5, 7]":  User specified bin values.
+
+    - "linear_bin": "{"start": 0.0, "width": 5.0, "count": 5, "infinity": true}"
+       "start" - bin starting point.
+       "width" - bin offset.
+       "count" - number of bins generated.
+       "infinity" - whether to add（-inf, inf）as start/end point in generated set of bins.
+       The above "linear_bin" descriptor generates a set of bins: [-inf, 0.0, 5.0, 10.0, 15.0, 20.0, +inf].
+
+    - "log_bin": "{"start":1.0, "factor": 2.0, "count": 5, "infinity": true}"
+       "start" - bin starting point.
+       "factor" - exponential factor of bin offset.
+       "count" - number of bins generated.
+       "infinity" - whether to add（-inf, inf）as start/end point in generated range of bins.
+       The above "log_bin" descriptor generates a set of bins:[-inf, 1.0, 2.0, 4.0, 8.0, 16.0, +inf].
+
+3. normalized: setting to 1/0 to turn on/off result normalization.
+
+**Example**：
+
+```mysql
+taos> SELECT HISTOGRAM(voltage, "user_input", "[1,3,5,7]", 1) FROM meters;
+     histogram(voltage, "user_input", "[1,3,5,7]", 1) |
+     =======================================================
+     {"lower_bin":1, "upper_bin":3, "count":0.333333}     |
+     {"lower_bin":3, "upper_bin":5, "count":0.333333}     |
+     {"lower_bin":5, "upper_bin":7, "count":0.333333}     |
+     Query OK, 3 row(s) in set (0.004273s)
+
+taos> SELECT HISTOGRAM(voltage, 'linear_bin', '{"start": 1, "width": 3, "count": 3, "infinity": false}', 0) FROM meters;
+         histogram(voltage, 'linear_bin', '{"start": 1, "width": 3, " |
+     ===================================================================
+     {"lower_bin":1, "upper_bin":4, "count":3}                        |
+     {"lower_bin":4, "upper_bin":7, "count":3}                        |
+     {"lower_bin":7, "upper_bin":10, "count":3}                       |
+     Query OK, 3 row(s) in set (0.004887s)
+
+taos> SELECT HISTOGRAM(voltage, 'log_bin', '{"start": 1, "factor": 3, "count": 3, "infinity": true}', 0) FROM meters;
+   histogram(voltage, 'log_bin', '{"start": 1, "factor": 3, "count" |
+   ===================================================================
+   {"lower_bin":-inf, "upper_bin":1, "count":3}                     |
+   {"lower_bin":1, "upper_bin":3, "count":2}                        |
+   {"lower_bin":3, "upper_bin":9, "count":6}                        |
+   {"lower_bin":9, "upper_bin":27, "count":3}                       |
+   {"lower_bin":27, "upper_bin":inf, "count":1}                     |
+```
+
 ## Selection Functions
 
 When any select function is used, timestamp column or tag columns including `tbname` can be specified to show that the selected value are from which rows.

docs-en/14-reference/05-taosbenchmark.md

@@ -7,7 +7,7 @@ description: "taosBenchmark (once called taosdemo ) is a tool for testing the pe
 
 ## Introduction
 
-taosBenchmark (formerly taosdemo ) is a tool for testing the performance of TDengine products. taosBenchmark can test the performance of TDengine's insert, query, and subscription functions and simulate large amounts of data generated by many devices. taosBenchmark can flexibly control the number and type of databases, supertables, tag columns, number and type of data columns, and sub-tables, and types of databases, super tables, the number and types of data columns, the number of sub-tables, the amount of data per sub-table, the time interval for inserting data, the number of working threads, whether and how to insert disordered data, and so on. The installer provides taosdemo as a soft link to taosBenchmark for compatibility with past users.
+taosBenchmark (formerly taosdemo ) is a tool for testing the performance of TDengine products. taosBenchmark can test the performance of TDengine's insert, query, and subscription functions and simulate large amounts of data generated by many devices. taosBenchmark can flexibly control the number and type of databases, supertables, tag columns, number and type of data columns, and sub-tables, and types of databases, super tables, the number and types of data columns, the number of sub-tables, the amount of data per sub-table, the time interval for inserting data, the number of working threads, whether and how to insert disordered data, and so on. The installer provides taosdemo as a soft link to taosBenchmark for compatibility and for the convenience of past users.
 
 ## Installation
 
@@ -21,7 +21,7 @@ There are two ways to install taosBenchmark:
 
 ### Configuration and running methods
 
-taosBenchmark supports two configuration methods: [Command-line arguments](#Command-line arguments in detailed) and [JSON configuration file](#Configuration file arguments in detailed). These two methods are mutually exclusive, and with only one command-line parameter, users can use `-f <json file>` to specify a configuration file when using a configuration file. When running taosBenchmark with command-line arguments and controlling its behavior, users should use other parameters for configuration rather than `-f` parameter. In addition, taosBenchmark offers a special way of running without parameters.
+taosBenchmark supports two configuration methods: [Command-line arguments](#Command-line arguments in detailed) and [JSON configuration file](#Configuration file arguments in detailed). These two methods are mutually exclusive. Users can use `-f <json file>` to specify a configuration file. When running taosBenchmark with command-line arguments to control its behavior, users should use other parameters for configuration, but not the `-f` parameter. In addition, taosBenchmark offers a special way of running without parameters.
 
 taosBenchmark supports complete performance testing of TDengine. taosBenchmark supports the TDengine functions in three categories: write, query, and subscribe. These three functions are mutually exclusive, and users can select only one of them each time taosBenchmark runs. It is important to note that the type of functionality to be tested is not configurable when using the command-line configuration method, which can only test writing performance. To test the query and subscription performance of the TDengine, you must use the configuration file method and specify the function type to test via the parameter `filetype` in the configuration file.
 
@@ -35,7 +35,7 @@ Execute the following commands to quickly experience taosBenchmark's default con
 taosBenchmark
 ```
 
-When run without parameters, taosBenchmark connects to the TDengine cluster specified in `/etc/taos` by default and creates a database named test in TDengine, a super table named `meters` under the test database, and 10,000 tables under the super table with 10,000 records written to each table. Note that if there is already a test database, this table is not used. Note that if there is already a test database, this command will delete it first and create a new test database.
+When run without parameters, taosBenchmark connects to the TDengine cluster specified in `/etc/taos` by default and creates a database named `test`, a super table named `meters` under the test database, and 10,000 tables under the super table with 10,000 records written to each table. Note that if there is already a database named "test" this command will delete it first and create a new database.
 
 ### Run with command-line configuration parameters
 
@@ -45,7 +45,7 @@ The `-f <json file>` argument cannot be used when running taosBenchmark with com
 taosBenchmark -I stmt -n 200 -t 100
 ```
 
-The above command, `taosBenchmark` will create a database named `test`, create a super table `meters` in it, create 100 sub-tables in the super table and insert 200 records for each sub-table using parameter binding.
+Using the above command, `taosBenchmark` will create a database named `test`, create a super table `meters` in it, create 100 sub-tables in the super table and insert 200 records for each sub-table using parameter binding.
 
 ### Run with the configuration file
 
@@ -95,10 +95,10 @@ taosBenchmark -f <json file>
 ## Command-line argument in detailed
 
 - **-f/--file <json file\>** :
-  specify the configuration file to use. This file includes All parameters. And users should not use this parameter with other parameters on the command-line. There is no default value.
+  specify the configuration file to use. This file includes All parameters. Users should not use this parameter with other parameters on the command-line. There is no default value.
 
 - **-c/--config-dir <dir\>** :
-  specify the directory where the TDengine cluster configuration file. the default path is `/etc/taos`.
+  specify the directory where the TDengine cluster configuration file. The default path is `/etc/taos`.
 
 - **-h/--host <host\>** :
   Specify the FQDN of the TDengine server to connect to. The default value is localhost.
@@ -272,13 +272,13 @@ The parameters for creating super tables are configured in `super_tables` in the
 
 - **child_table_prefix** : The prefix of the child table name, mandatory configuration item, no default value.
 
-- **escape_character**: specify the super table and child table names containing escape characters. By default is "no". The value can be "yes" or "no".
+- **escape_character**: specify the super table and child table names containing escape characters. The value can be "yes" or "no". The default is "no".
 
 - **auto_create_table**: only when insert_mode is taosc, rest, stmt, and childtable_exists is "no". "yes" means taosBenchmark will automatically create non-existent tables when inserting data; "no" means that taosBenchmark will create all tables before inserting.
 
-- **batch_create_tbl_num** : the number of tables per batch when creating sub-tables, default is 10. Note: the actual number of batches may not be the same as this value when the executed SQL statement is larger than the maximum length supported, it will be automatically truncated and re-executed to continue creating.
+- **batch_create_tbl_num** : the number of tables per batch when creating sub-tables, default is 10. Note: the actual number of batches may not be the same as this value. If the executed SQL statement is larger than the maximum length supported, it will be automatically truncated and re-executed to continue creating.
 
-- **data_source**: specify the source of data-generating. Default is taosBenchmark randomly generated. Users can configure it as "rand" and "sample". When "sample" is used, taosBenchmark will use the data in the file specified by the `sample_file` parameter.
+- **data_source**: specify the source of data-generation. Default is taosBenchmark randomly generated. Users can configure it as "rand" and "sample". When "sample" is used, taosBenchmark will use the data in the file specified by the `sample_file` parameter.
 
 - **insert_mode**: insertion mode with options taosc, rest, stmt, sml, sml-rest, corresponding to normal write, restful interface write, parameter binding interface write, schemaless interface write, restful schemaless interface write (provided by taosAdapter). The default value is taosc.
 
@@ -300,15 +300,15 @@ The parameters for creating super tables are configured in `super_tables` in the
 
 - **partial_col_num**: If this value is a positive number n, only the first n columns are written to, only if insert_mode is taosc and rest, or all columns if n is 0.
 
-- **disorder_ratio** : Specifies the percentage probability of disordered data in the value range [0,50]. The default is 0, which means there is no disorder data.
+- **disorder_ratio** : Specifies the percentage probability of disordered (i.e. out-of-order) data in the value range [0,50]. The default is 0, which means there is no disorder data.
 
-- **disorder_range** : Specifies the timestamp fallback range for the disordered data. The generated disorder timestamp is the timestamp that should be used in the non-disorder case minus a random value in this range. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0.
+- **disorder_range** : Specifies the timestamp fallback range for the disordered  data. The disordered timestamp is generated by subtracting a random value in this range, from the timestamp that would be used in the non-disorder case. Valid only if the percentage of disordered data specified by `-O/--disorder` is greater than 0.
 
-- **timestamp_step**: The timestamp step for inserting data in each child table, in units consistent with the `precision` of the database, the default value is 1.
+- **timestamp_step**: The timestamp step for inserting data in each child table, in units consistent with the `precision` of the database. For e.g. if the `precision` is milliseconds, the timestamp step will be in milliseconds. The default value is 1.
 
 - **start_timestamp** : The timestamp start value of each sub-table, the default value is now.
 
-- **sample_format**: The type of the sample data file, now only "csv" is supported.
+- **sample_format**: The type of the sample data file; for now only "csv" is supported.
 
 - **sample_file**: Specify a CSV format file as the data source. It only works when data_source is a sample. If the number of rows in the CSV file is less than or equal to prepared_rand, then taosBenchmark will read the CSV file data cyclically until it is the same as prepared_rand; otherwise, taosBenchmark will read only the rows with the number of prepared_rand. The final number of rows of data generated is the smaller of the two.
 
@@ -341,7 +341,7 @@ The configuration parameters for specifying super table tag columns and data col
 
 - **create_table_thread_count** : The number of threads to build the table, default is 8.
 
-- **connection_pool_size** : The number of pre-established connections to the TDengine server. If not configured, it is the same number of threads specified.
+- **connection_pool_size** : The number of pre-established connections to the TDengine server. If not configured, it is the same as number of threads specified.
 
 - **result_file** : The path to the result output file, the default value is . /output.txt.
 

docs-en/14-reference/06-taosdump.md

 ---
 title: taosdump
-description: "taosdump is a tool application that supports backing up data from a running TDengine cluster and restoring the backed up data to the same or another running TDengine cluster."
+description: "taosdump is a tool that supports backing up data from a running TDengine cluster and restoring the backed up data to the same, or another running TDengine cluster."
 ---
 
 ## Introduction
 
-taosdump is a tool application that supports backing up data from a running TDengine cluster and restoring the backed up data to the same or another running TDengine cluster.
+taosdump is a tool that supports backing up data from a running TDengine cluster and restoring the backed up data to the same, or another running TDengine cluster.
 
 taosdump can back up a database, a super table, or a normal table as a logical data unit or backup data records in the database, super tables, and normal tables. When using taosdump, you can specify the directory path for data backup. If you do not specify a directory, taosdump will back up the data to the current directory by default.
 
-Suppose the specified location already has data files. In that case, taosdump will prompt the user and exit immediately to avoid data overwriting which means that the same path can only be used for one backup.
-Please be careful if you see a prompt for this.
+If the specified location already has data files, taosdump will prompt the user and exit immediately to avoid data overwriting. This means that the same path can only be used for one backup.
+
+Please be careful if you see a prompt for this and please ensure that you follow best practices and relevant SOPs for data integrity, backup and data security.
 
 Users should not use taosdump to back up raw data, environment settings, hardware information, server configuration, or cluster topology. taosdump uses [Apache AVRO](https://avro.apache.org/) as the data file format to store backup data.
 
@@ -30,7 +31,7 @@ There are two ways to install taosdump:
 2. backup multiple specified databases: use `-D db1,db2,... ` parameters;
 3. back up some super or normal tables in the specified database: use `-dbname stbname1 stbname2 tbname1 tbname2 ... ` parameters. Note that the first parameter of this input sequence is the database name, and only one database is supported. The second and subsequent parameters are the names of super or normal tables in that database, separated by spaces.
 4. back up the system log database: TDengine clusters usually contain a system database named `log`. The data in this database is the data that TDengine runs itself, and the taosdump will not back up the log database by default. If users need to back up the log database, users can use the `-a` or `-allow-sys` command-line parameter. 
-5. Loose mode backup: taosdump version 1.4.1 onwards provides `-n` and `-L` parameters for backing up data without using escape characters and "loose" mode, which can reduce the number of backups if table names, column names, tag names do not use This can reduce the backup data time and backup data footprint if table names, column names, and tag names do not use `escape character`. If you are unsure about using `-n` and `-L` conditions, please use the default parameters for "strict" mode backup. See the [official documentation](/taos-sql/escape) for a description of escaped characters.
+5. Loose mode backup: taosdump version 1.4.1 onwards provides `-n` and `-L` parameters for backing up data without using escape characters and "loose" mode, which can reduce the number of backups if table names, column names, tag names do not use escape characters. This can also reduce the backup data time and backup data footprint. If you are unsure about using `-n` and `-L` conditions, please use the default parameters for "strict" mode backup. See the [official documentation](/taos-sql/escape) for a description of escaped characters.
 
 :::tip
 - taosdump versions after 1.4.1 provide the `-I` argument for parsing Avro file schema and data. If users specify `-s` then only taosdump will parse schema.
@@ -58,7 +59,7 @@ Usage: taosdump [OPTION...] dbname [tbname ...]
   or:  taosdump [OPTION...] -i inpath
   or:  taosdump [OPTION...] -o outpath
 
-  -h, --host=HOST            Server host dumping data from. Default is
+  -h, --host=HOST            Server host from which to dump data. Default is
                              localhost.
   -p, --password             User password to connect to server. Default is
                              taosdata.
@@ -71,10 +72,10 @@ Usage: taosdump [OPTION...] dbname [tbname ...]
   -r, --resultFile=RESULTFILE   DumpOut/In Result file path and name.
   -a, --allow-sys            Allow to dump system database
   -A, --all-databases        Dump all databases.
-  -D, --databases=DATABASES  Dump inputted databases. Use comma to separate
-                             databases' name.
+  -D, --databases=DATABASES  Dump listed databases. Use comma to separate
+                             database names.
   -N, --without-property     Dump database without its properties.
-  -s, --schemaonly           Only dump tables' schema.
+  -s, --schemaonly           Only dump table schemas.
   -y, --answer-yes           Input yes for prompt. It will skip data file
                              checking!
   -d, --avro-codec=snappy    Choose an avro codec among null, deflate, snappy,
@@ -97,7 +98,7 @@ Usage: taosdump [OPTION...] dbname [tbname ...]
                              and try. The workable value is related to the
                              length of the row and type of table schema.
   -I, --inspect              inspect avro file content and print on screen
-  -L, --loose-mode           Using loose mode if the table name and column name
+  -L, --loose-mode           Use loose mode if the table name and column name
                              use letter and number only. Default is NOT.
   -n, --no-escape            No escape char '`'. Default is using it.
   -T, --thread-num=THREAD_NUM   Number of thread for dump in file. Default is

docs-en/14-reference/07-tdinsight/index.md

@@ -5,11 +5,11 @@ sidebar_label: TDinsight
 
 TDinsight is a solution for monitoring TDengine using the builtin native monitoring database and [Grafana].
 
-After TDengine starts, it will automatically create a monitoring database `log`. TDengine will automatically write many metrics in specific intervals into the `log` database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, etc., and also vnode, dnode, and mnode status, and exception alerts. Developers monitoring TDengine cluster operation status in real-time can be very convinient. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel through `TDinsight.sh` installation script.
+After TDengine starts, it will automatically create a monitoring database `log`. TDengine will automatically write many metrics in specific intervals into the `log` database. The metrics may include the server's CPU, memory, hard disk space, network bandwidth, number of requests, disk read/write speed, slow queries, other information like important system operations (user login, database creation, database deletion, etc.), and error alarms. With [Grafana] and [TDengine Data Source Plugin](https://github.com/taosdata/grafanaplugin/releases), TDinsight can visualize cluster status, node information, insertion and query requests, resource usage, vnode, dnode, and mnode status, exception alerts and many other metrics. This is very convenient for developers who want to monitor TDengine cluster status in real-time. This article will guide users to install the Grafana server, automatically install the TDengine data source plug-in, and deploy the TDinsight visualization panel using the `TDinsight.sh` installation script.
 
 ## System Requirements
 
-To deploy TDinsight, a single-node TDengine server or a multi-nodes TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 2.3.3.0 and above, with the `log` database enabled (`monitor = 1`).
+To deploy TDinsight, a single-node TDengine server or a multi-node TDengine cluster and a [Grafana] server are required. This dashboard requires TDengine 2.3.3.0 and above, with the `log` database enabled (`monitor = 1`).
 
 ## Installing Grafana
 
@@ -17,7 +17,7 @@ We recommend using the latest [Grafana] version 7 or 8 here. You can install Gra
 
 ### Installing Grafana on Debian or Ubuntu
 
-For Debian or Ubuntu operating systems, we recommend the Grafana image repository and Use the following command to install from scratch.
+For Debian or Ubuntu operating systems, we recommend the Grafana image repository and using the following command to install from scratch.
 
 ```bash
 sudo apt-get install -y apt-transport-https
@@ -71,7 +71,7 @@ chmod +x TDinsight.sh
 ./TDinsight.sh
 ```
 
-This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://grafana.com/grafana/dashboards/15167) with configurable parameters from the command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications.
+This script will automatically download the latest [Grafana TDengine data source plugin](https://github.com/taosdata/grafanaplugin/releases/latest) and [TDinsight dashboard](https://grafana.com/grafana/dashboards/15167) with configurable parameters for command-line options to the [Grafana Provisioning](https://grafana.com/docs/grafana/latest/administration/provisioning/) configuration file to automate deployment and updates, etc. With the alert setting options provided by this script, you can also get built-in support for AliCloud SMS alert notifications.
 
 Assume you use TDengine and Grafana's default services on the same host. Run `. /TDinsight.sh` and open the Grafana browser window to see the TDinsight dashboard.
 

docs-en/14-reference/11-docker/index.md

@@ -13,7 +13,7 @@ The TDengine image starts with the HTTP service activated by default, using the
 docker run -d --name tdengine -p 6041:6041 tdengine/tdengine
 ```
 
-The above command starts a container named "tdengine" and maps the HTTP service end 6041 to the host port 6041. You can verify that the HTTP service provided in this container is available using the following command.
+The above command starts a container named "tdengine" and maps the HTTP service port 6041 to the host port 6041. You can verify that the HTTP service provided in this container is available using the following command.
 
 ```shell
 curl -u root:taosdata -d "show databases" localhost:6041/rest/sql
@@ -34,7 +34,7 @@ taos> show databases;
 Query OK, 1 row(s) in set (0.002843s)
 ```
 
-The TDengine server running in the container uses the container's hostname to establish a connection. Using TDengine CLI or various connectors (such as JDBC-JNI) to access the TDengine inside the container from outside the container is more complicated. So the above is the simplest way to access the TDengine service in the container and is suitable for some simple scenarios. Please refer to the next section if you want to access the TDengine service in the container from containerized using TDengine CLI or various connectors in some complex scenarios.
+The TDengine server running in the container uses the container's hostname to establish a connection. Using TDengine CLI or various connectors (such as JDBC-JNI) to access the TDengine inside the container from outside the container is more complicated. So the above is the simplest way to access the TDengine service in the container and is suitable for some simple scenarios. Please refer to the next section if you want to access the TDengine service in the container from outside the container using TDengine CLI or various connectors for complex scenarios.
 
 ## Start TDengine on the host network
 
@@ -42,7 +42,7 @@ The TDengine server running in the container uses the container's hostname to es
 docker run -d --name tdengine --network host tdengine/tdengine
 ```
 
-The above command starts TDengine on the host network and uses the host's FQDN to establish a connection instead of the container's hostname. It works too, like using `systemctl` to start TDengine on the host. If the TDengine client is already installed on the host, you can access it directly with the following command.
+The above command starts TDengine on the host network and uses the host's FQDN to establish a connection instead of the container's hostname. It is the equivalent of using `systemctl` to start TDengine on the host. If the TDengine client is already installed on the host, you can access it directly with the following command.
 
 ```shell
 $ taos
@@ -382,7 +382,7 @@ password:             taosdata
     Suppose you want to deploy multiple taosAdapters to improve throughput and provide high availability. In that case, the recommended configuration method uses a reverse proxy such as Nginx to offer a unified access entry. For specific configuration methods, please refer to the official documentation of Nginx. Here is an example:
 
    ```docker
-   ersion: "3"
+   version: "3"
 
    networks:
      inter:

docs-en/14-reference/12-config/index.md

@@ -78,7 +78,7 @@ taos --dump-config
 | Note          | REST service is provided by `taosd` before 2.4.0.0 but by `taosAdapter` after 2.4.0.0, the default port of REST service is 6041 |
 
 :::note
-TDengine uses continuous 13 ports, both TCP and UDP, from the port specified by `serverPort`. These ports need to be kept open if firewall is enabled. Below table describes the ports used by TDengine in details.
+TDengine uses 13 continuous ports, both TCP and UDP, starting with the port specified by `serverPort`. You should ensure, in your firewall rules, that these ports are kept open. Below table describes the ports used by TDengine in details.
 
 :::
 
@@ -197,7 +197,7 @@ TDengine uses continuous 13 ports, both TCP and UDP, from the port specified by
 | Default Value | TimeZone configured in the host |
 
 :::info
-To handle the data insertion and data query from multiple timezones, Unix Timestamp is used and stored TDengine. The timestamp generated from any timezones at same time is same in Unix timestamp. To make sure the time on client side can be converted to Unix timestamp correctly, the timezone must be set properly.
+To handle the data insertion and data query from multiple timezones, Unix Timestamp is used and stored in TDengine. The timestamp generated from any timezones at same time is same in Unix timestamp. To make sure the time on client side can be converted to Unix timestamp correctly, the timezone must be set properly.
 
 On Linux system, TDengine clients automatically obtain timezone from the host. Alternatively, the timezone can be configured explicitly in configuration file `taos.cfg` like below.
 
@@ -209,7 +209,7 @@ timezone Asia/Shanghai
 
 The above examples are all proper configuration for the timezone of UTC+8. On Windows system, however, `timezone Asia/Shanghai` is not supported, it must be set as `timezone UTC-8`.
 
-The setting for timezone impacts the strings not in Unix timestamp, keywords or functions related to date/time, for example
+The setting for timezone impacts strings that are not in Unix timestamp format and keywords or functions related to date/time. For example:
 
 ```sql
 SELECT count(*) FROM table_name WHERE TS<'2019-04-11 12:01:08';
@@ -227,7 +227,7 @@ If the timezone is UTC, it's equal to
 SELECT count(*) FROM table_name WHERE TS<1554984068000;
 ```
 
-To avoid the problems of using time strings, Unix timestamp can be used directly. Furthermore, time strings with timezone can be used in SQL statement, for example "2013-04-12T15:52:01.123+08:00" in RFC3339 format or "2013-04-12T15:52:01.123+0800" in ISO-8601 format, they are not influenced by timezone setting when converted to Unix timestamp.
+To avoid the problems of using time strings, Unix timestamp can be used directly. Furthermore, time strings with timezone can be used in SQL statements. For example "2013-04-12T15:52:01.123+08:00" in RFC3339 format or "2013-04-12T15:52:01.123+0800" in ISO-8601 format are not influenced by timezone setting when converted to Unix timestamp.
 
 :::
 
@@ -244,7 +244,7 @@ A specific type "nchar" is provided in TDengine to store non-ASCII characters su
 
 The characters input on the client side are encoded using the default system encoding, which is UTF-8 on Linux, or GB18030 or GBK on some systems in Chinese, POSIX in docker, CP936 on Windows in Chinese. The encoding of the operating system in use must be set correctly so that the characters in nchar type can be converted to UCS4-LE.
 
-The locale definition standard on Linux is: <Language\>\_<Region\>.<charset\>, for example, in "zh_CN.UTF-8", "zh" means Chinese, "CN" means China mainland, "UTF-8" means charset. On Linux andMac OSX, the charset can be set by locale in the system. On Windows system another configuration parameter `charset` must be used to configure charset because the locale used on Windows is not POSIX standard. Of course, `charset` can also be used on Linux to specify the charset.
+The locale definition standard on Linux is: <Language\>\_<Region\>.<charset\>, for example, in "zh_CN.UTF-8", "zh" means Chinese, "CN" means China mainland, "UTF-8" means charset. On Linux and Mac OSX, the charset can be set by locale in the system. On Windows system another configuration parameter `charset` must be used to configure charset because the locale used on Windows is not POSIX standard. Of course, `charset` can also be used on Linux to specify the charset.
 
 :::
 
@@ -263,7 +263,7 @@ On Linux, if `charset` is not set in `taos.cfg`, when `taos` is started, the cha
 locale zh_CN.UTF-8
 ```
 
-Besides, on Linux system, if the charset contained in `locale` is not consistent with that set by `charset`, the one who comes later in the configuration file is used.
+On a Linux system, if the charset contained in `locale` is not consistent with that set by `charset`, the later setting in the configuration file takes precedence.
 
 ```title="Effective charset is GBK"
 locale zh_CN.UTF-8
@@ -778,7 +778,7 @@ To prevent system resource from being exhausted by multiple concurrent streams,
 ## HTTP Parameters
 
 :::note
-HTTP server had been provided by `taosd` prior to version 2.4.0.0, now is provided by `taosAdapter` after version 2.4.0.0.
+HTTP service was provided by `taosd` prior to version 2.4.0.0 and is provided by `taosAdapter` after version 2.4.0.0.
 The parameters described in this section are only application in versions prior to 2.4.0.0. If you are using any version from 2.4.0.0, please refer to [taosAdapter](/reference/taosadapter/).
 
 :::

docs-en/14-reference/13-schemaless/13-schemaless.md

 ---
 title: Schemaless Writing
-description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data as it is written to the interface."
+description: "The Schemaless write method eliminates the need to create super tables/sub tables in advance and automatically creates the storage structure corresponding to the data, as it is written to the interface."
 ---
 
-In IoT applications, many data items are often collected for intelligent control, business analysis, device monitoring, etc. Due to the version upgrades of the application logic, or the hardware adjustment of the devices themselves, the data collection items may change frequently. To facilitate the data logging work in such cases, TDengine starting from version 2.2.0.0 provides a series of interfaces to the schemaless writing method, which eliminate the need to create super tables and subtables in advance by automatically creating the storage structure corresponding to the data as the data is written to the interface. And when necessary, schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly.
+In IoT applications, data is collected for many purposes such as intelligent control, business analysis, device monitoring and so on. Due to changes in business or functional requirements or changes in device hardware, the application logic and even the data collected may change. To provide the flexibility needed in such cases and in a rapidly changing IoT landscape, TDengine starting from version 2.2.0.0, provides a series of interfaces for the schemaless writing method. These interfaces eliminate the need to create super tables and subtables in advance by automatically creating the storage structure corresponding to the data as the data is written to the interface. When necessary, schemaless writing will automatically add the required columns to ensure that the data written by the user is stored correctly.
 
-The schemaless writing method creates super tables and their corresponding subtables completely indistinguishable from the super tables and subtables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and lack readability.
+The schemaless writing method creates super tables and their corresponding subtables. These are completely indistinguishable from the super tables and subtables created directly via SQL. You can write data directly to them via SQL statements. Note that the names of tables created by schemaless writing are based on fixed mapping rules for tag values, so they are not explicitly ideographic and they lack readability.
 
 ## Schemaless Writing Line Protocol
 
@@ -76,8 +76,7 @@ If the subtable obtained by the parse line protocol does not exist, Schemaless c
 8. Errors encountered throughout the processing will interrupt the writing process and return an error code.
 
 :::tip
-All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed
-48k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
+All processing logic of schemaless will still follow TDengine's underlying restrictions on data structures, such as the total length of each row of data cannot exceed 48k bytes. See [TAOS SQL Boundary Limits](/taos-sql/limit) for specific constraints in this area.
 :::
 
 ## Time resolution recognition
@@ -87,7 +86,7 @@ Three specified modes are supported in the schemaless writing process, as follow
 | **Serial** | **Value** | **Description** |
 | -------- | ------------------- | ------------------------------- |
 | 1 | SML_LINE_PROTOCOL | InfluxDB Line Protocol |
-| 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol | | 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol
+| 2 | SML_TELNET_PROTOCOL | OpenTSDB Text Line Protocol |
 | 3 | SML_JSON_PROTOCOL | JSON protocol format |
 
 In the SML_LINE_PROTOCOL parsing mode, the user is required to specify the time resolution of the input timestamp. The available time resolutions are shown in the following table.
@@ -106,8 +105,11 @@ In SML_TELNET_PROTOCOL and SML_JSON_PROTOCOL modes, the time precision is determ
 
 ## Data schema mapping rules
 
-This section describes how data for line protocols are mapped to data with a schema. The data measurement in each line protocol is mapped to
-The tag name in tag_set is the name of the tag in the data schema, and the name in field_set is the column's name. The following data is used as an example to illustrate the mapping rules.
+This section describes how data for line protocols are mapped to data with a schema. The data measurement in each line protocol is mapped as follows:
+- The tag name in tag_set is the name of the tag in the data schema
+- The name in field_set is the column's name. 
+
+The following data is used as an example to illustrate the mapping rules.
 
 ```json
 st,t1=3,t2=4,t3=t3 c1=3i64,c3="passit",c2=false,c4=4f64 1626006833639000000
@@ -139,7 +141,7 @@ st,t1=3,t2=4,t3=t3 c1=3i64,c5="pass" 1626006833639000000
 st,t1=3,t2=4,t3=t3 c1=3i64,c5="passit" 1626006833640000000
 ```
 
-The first line of the line protocol parsing will declare column c5 is a BINARY(4) field, the second line data write will extract column c5 is still a BINARY column. Still, its width is 6, then you need to increase the width of the BINARY field to be able to accommodate the new string.
+The first line of the line protocol parsing will declare column c5 is a BINARY(4) field. The second line data write will parse column c5 as a BINARY column. But in the second line, c5's width is 6 so you need to increase the width of the BINARY field to be able to accommodate the new string.
 
 ```json
 st,t1=3,t2=4,t3=t3 c1=3i64 1626006833639000000

docs-en/14-reference/_tcollector.mdx

@@ -17,7 +17,7 @@ password = "taosdata"
 ...
 ```
 
-The taosAdapter writes to the database with the default name `tcollector`. You can also modify the taosAdapter configuration file dbs entry to specify a different name. user and password fill in the actual TDengine configuration values. After changing the configuration file, you need to restart the taosAdapter.
+The taosAdapter writes to the database with the default name `tcollector`. You can also modify the taosAdapter configuration file dbs entry to specify a different name. Fill in the actual user and password for TDengine. After changing the configuration file, you need to restart the taosAdapter.
 
 - You can also enable taosAdapter to receive tcollector data by using the taosAdapter command-line parameters or setting environment variables.
 
@@ -25,7 +25,7 @@ The taosAdapter writes to the database with the default name `tcollector`. You c
 
 To use TCollector, you need to download its [source code](https://github.com/OpenTSDB/tcollector). Its configuration items are in its source code. Note: TCollector differs significantly from version to version, so here is an example of the latest code for the current master branch (git commit: 37ae920).
 
-Modify the contents of the `collectors/etc/config.py` and `tcollector.py` files. Change the address of the OpenTSDB host to the domain name or IP address of the server where taosAdapter is deployed, and change the port to the port that taosAdapter supports TCollector on (default is 6049).
+Modify the contents of the `collectors/etc/config.py` and `tcollector.py` files. Change the address of the OpenTSDB host to the domain name or IP address of the server where taosAdapter is deployed, and change the port to the port on which taosAdapter supports TCollector (default is 6049).
 
 Example of git diff output of source code changes.
 

docs-en/20-third-party/01-grafana.mdx

@@ -3,13 +3,13 @@ sidebar_label: Grafana
 title: Grafana
 ---
 
-TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a DashBoard.
+TDengine can be quickly integrated with the open-source data visualization system [Grafana](https://www.grafana.com/) to build a data monitoring and alerting system. The whole process does not require any code development. And you can visualize the contents of the data tables in TDengine on a dashboard.
 
 You can learn more about using the TDengine plugin on [GitHub](https://github.com/taosdata/grafanaplugin/blob/master/README.md).
 
 ## Prerequisites
 
-In order for Grafana to add the TDengine data source successfully, the following preparations are required:
+In order for Grafana to add the TDengine data source successfully, the following preparation is required:
 1. The TDengine cluster is deployed and functioning properly
 2. taosAdapter is installed and running properly. Please refer to the taosAdapter manual for details.
 
@@ -36,7 +36,7 @@ GF_VERSION=3.1.4
 wget https://github.com/taosdata/grafanaplugin/releases/download/v$GF_VERSION/tdengine-datasource-$GF_VERSION.zip
 ```
 
-Take CentOS 7.2 for example, extract the plugin package to /var/lib/grafana/plugins directory, and restart grafana.
+In CentOS 7.2 for example, extract the plugin package to /var/lib/grafana/plugins directory, and restart grafana.
 
 ```bash
 sudo unzip tdengine-datasource-$GF_VERSION.zip -d /var/lib/grafana/plugins/
@@ -76,13 +76,13 @@ Enter the datasource configuration page, and follow the default prompts to modif
 - User: TDengine user name.
 - Password: TDengine user password.
 
-Click `Save & Test` to test. Follows are a success.
+Click `Save & Test` to test. You should see a success message if the test worked.
 
 ![TDengine Database TDinsight plugin add database 4](./grafana/add_datasource4.webp)
 
 ### Create Dashboard
 
-Go back to the main interface to create the Dashboard, click Add Query to enter the panel query page:
+Go back to the main interface to create a dashboard and click Add Query to enter the panel query page:
 
 ![TDengine Database TDinsight plugin create dashboard 1](./grafana/create_dashboard1.webp)
 

docs-en/20-third-party/03-telegraf.md

@@ -5,7 +5,7 @@ title: Telegraf writing
 
 import Telegraf from "../14-reference/_telegraf.mdx"
 
-Telegraf is a viral metrics collection open-source software. Telegraf can collect the operation information of various components without writing any scripts to collect regularly, reducing the difficulty of data acquisition.
+Telegraf is a viral, open-source, metrics collection software. Telegraf can collect the operation information of various components without having to write any scripts to collect regularly, reducing the difficulty of data acquisition.
 
 Telegraf's data can be written to TDengine by simply adding the output configuration of Telegraf to the URL corresponding to taosAdapter and modifying several configuration items. The presence of Telegraf data in TDengine can take advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data.
 

docs-en/20-third-party/06-statsd.md

@@ -7,7 +7,7 @@ import StatsD from "../14-reference/_statsd.mdx"
 
 StatsD is a simple daemon for aggregating application metrics, which has evolved rapidly in recent years into a unified protocol for collecting application performance metrics.
 
-You can write StatsD data to TDengine by simply modifying in the configuration file of StatsD with the domain name (or IP address) of the server running taosAdapter and the corresponding port. It can take full advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data.
+You can write StatsD data to TDengine by simply modifying the configuration file of StatsD with the domain name (or IP address) of the server running taosAdapter and the corresponding port. It can take full advantage of TDengine's efficient storage query performance and clustering capabilities for time-series data.
 
 ## Prerequisites
 

docs-en/20-third-party/07-icinga2.md

@@ -5,7 +5,7 @@ title: icinga2 writing
 
 import Icinga2 from "../14-reference/_icinga2.mdx"
 
-icinga2 is an open-source software monitoring host and network initially developed from the Nagios network monitoring application. Currently, icinga2 is distributed under the GNU GPL v2 license.
+icinga2 is an open-source, host and network monitoring software initially developed from the Nagios network monitoring application. Currently, icinga2 is distributed under the GNU GPL v2 license.
 
 You can write the data collected by icinga2 to TDengine by simply modifying the icinga2 configuration to point to the taosAdapter server and the corresponding port, taking advantage of TDengine's efficient storage and query performance and clustering capabilities for time-series data.
 

docs-en/20-third-party/09-emq-broker.md

@@ -3,7 +3,7 @@ sidebar_label: EMQX Broker
 title: EMQX Broker writing
 ---
 
-MQTT is a popular IoT data transfer protocol, [EMQX](https://github.com/emqx/emqx) is an open-source MQTT Broker software, you can write MQTT data directly to TDengine without any code, you only need to use "rules" in EMQX Dashboard to create a simple configuration. EMQX supports saving data to TDengine by sending it to web services and provides a native TDengine driver for direct saving in the Enterprise Edition. Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use it.).
+MQTT is a popular IoT data transfer protocol. [EMQX](https://github.com/emqx/emqx) is an open-source MQTT Broker software. You can write MQTT data directly to TDengine without any code. You only need to setup "rules" in EMQX Dashboard to create a simple configuration. EMQX supports saving data to TDengine by sending data to a web service and provides a native TDengine driver for direct saving in the Enterprise Edition. Please refer to the [EMQX official documentation](https://www.emqx.io/docs/en/v4.4/rule/rule-engine.html) for details on how to use it.).
 
 ## Prerequisites
 

docs-en/25-application/01-telegraf.md

@@ -5,16 +5,16 @@ title: Quickly Build IT DevOps Visualization System with TDengine + Telegraf + G
 
 ## Background
 
-TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telematics, Industrial Internet, IT DevOps, etc. by TAOSData. Since it opened its source code in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
+TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telemetry, Industrial Internet, IT DevOps and other applications. Since it was open-sourced in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
 
 IT DevOps metric data usually are time sensitive, for example:
 
 - System resource metrics: CPU, memory, IO, bandwidth, etc.
 - Software system metrics: health status, number of connections, number of requests, number of timeouts, number of errors, response time, service type, and other business-related metrics.
 
-Current mainstream IT DevOps system usually include a data collection module, a data persistent module, and a visualization module; Telegraf and Grafana are one of the most popular data collection modules and visualization modules, respectively. The data persistent module is available in a wide range of options, with OpenTSDB or InfluxDB being the most popular. TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance.
+Current mainstream IT DevOps system usually include a data collection module, a data persistent module, and a visualization module; Telegraf and Grafana are one of the most popular data collection modules and visualization modules, respectively. The data persistence module is available in a wide range of options, with OpenTSDB or InfluxDB being the most popular. TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance.
 
-This article introduces how to quickly build a TDengine + Telegraf + Grafana based IT DevOps visualization system without writing even a single line of code and by simply modifying a few lines of configuration files. The architecture is as follows.
+This article introduces how to quickly build a TDengine + Telegraf + Grafana based IT DevOps visualization system without writing even a single line of code and by simply modifying a few lines in configuration files. The architecture is as follows.
 
 ![TDengine Database IT-DevOps-Solutions-Telegraf](./IT-DevOps-Solutions-Telegraf.webp)
 
@@ -79,5 +79,5 @@ Click on the plus icon on the left and select `Import` to get the data from `htt
 
 ## Wrap-up
 
-The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and the powerful ecological software adaptation capability, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes.
+The above demonstrates how to quickly build a IT DevOps visualization system. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system in just a few minutes.
 Please refer to the official documentation and product implementation cases for other features.

docs-en/25-application/02-collectd.md

@@ -5,17 +5,17 @@ title: Quickly build an IT DevOps visualization system using TDengine + collectd
 
 ## Background
 
-TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telematics, Industrial Internet, IT DevOps, etc. by TAOSData. Since it opened its source code in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
+TDengine is a big data platform designed and optimized for IoT (Internet of Things), Vehicle Telemetry, Industrial Internet, IT DevOps and other applications. Since it was open-sourced in July 2019, it has won the favor of a large number of time-series data developers with its innovative data modeling design, convenient installation, easy-to-use programming interface, and powerful data writing and query performance.
 
 IT DevOps metric data usually are time sensitive, for example:
 
 - System resource metrics: CPU, memory, IO, bandwidth, etc.
 - Software system metrics: health status, number of connections, number of requests, number of timeouts, number of errors, response time, service type, and other business-related metrics.
 
-The current mainstream IT DevOps visualization system usually contains a data collection module, a data persistent module, and a visual display module. collectd/StatsD, as an old-fashion open source data collection tool, has a wide user base. However, collectd/StatsD has limited functionality, and often needs to be combined with Telegraf, Grafana, and a time-series database to build a complete monitoring system.
+The current mainstream IT DevOps visualization system usually contains a data collection module, a data persistence module, and a visual display module. collectd/StatsD, as an old-fashion open source data collection tool, has a wide user base. However, collectd/StatsD has limited functionality, and often needs to be combined with Telegraf, Grafana, and a time-series database to build a complete monitoring system.
 The new version of TDengine supports multiple data protocols and can accept data from collectd and StatsD directly, and provides Grafana dashboard for graphical display.
 
-This article introduces how to quickly build an IT DevOps visualization system based on TDengine + collectd / StatsD + Grafana without writing even a single line of code but by simply modifying a few lines of configuration files. The architecture is shown in the following figure.
+This article introduces how to quickly build an IT DevOps visualization system based on TDengine + collectd / StatsD + Grafana without writing even a single line of code but by simply modifying a few lines in configuration files. The architecture is shown in the following figure.
 
 ![TDengine Database IT-DevOps-Solutions-Collectd-StatsD](./IT-DevOps-Solutions-Collectd-StatsD.webp)
 
@@ -99,6 +99,6 @@ Download the dashboard json from `https://github.com/taosdata/grafanaplugin/blob
 
 ## Wrap-up
 
-TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing function in TDengine version 2.4.0.0 and the powerful ecological software adaptation capability, users can build an efficient and easy-to-use IT DevOps visualization system or adapt to an existing system in just a few minutes.
+TDengine, as an emerging time-series big data platform, has the advantages of high performance, high reliability, easy management and easy maintenance. Thanks to the new schemaless protocol parsing feature in TDengine version 2.4.0.0 and ability to integrate easily with a large software ecosystem, users can build an efficient and easy-to-use IT DevOps visualization system, or adapt an existing system, in just a few minutes.
 
 For TDengine's powerful data writing and querying performance and other features, please refer to the official documentation and successful product implementation cases.

include/util/tdef.h

@@ -368,7 +368,11 @@ typedef enum ELogicConditionType {
 #define PRIMARYKEY_TIMESTAMP_COL_ID    1
 #define COL_REACH_END(colId, maxColId) ((colId) > (maxColId))
 
+#ifdef WINDOWS
+#define TSDB_MAX_RPC_THREADS 4  // windows pipe only support 4 connections.
+#else
 #define TSDB_MAX_RPC_THREADS 5
+#endif
 
 #define TSDB_QUERY_TYPE_NON_TYPE      0x00u  // none type
 #define TSDB_QUERY_TYPE_FREE_RESOURCE 0x01u  // free qhandle at vnode

source/dnode/mnode/impl/inc/mndDef.h

@@ -473,7 +473,7 @@ typedef struct {
   char*          ast;
   char*          physicalPlan;
   SSchemaWrapper schema;
-  int32_t        refConsumerCnt;
+  // int32_t        refConsumerCnt;
 } SMqTopicObj;
 
 typedef struct {

source/dnode/mnode/impl/src/mndConsumer.c

@@ -414,6 +414,7 @@ static int32_t mndProcessSubscribeReq(SRpcMsg *pMsg) {
       goto SUBSCRIBE_OVER;
     }
 
+#if 0
     // ref topic to prevent drop
     // TODO make topic complete
     SMqTopicObj topicObj = {0};
@@ -422,6 +423,7 @@ static int32_t mndProcessSubscribeReq(SRpcMsg *pMsg) {
     mInfo("subscribe topic %s by consumer %ld cgroup %s, refcnt %d", pTopic->name, consumerId, cgroup,
           topicObj.refConsumerCnt);
     if (mndSetTopicCommitLogs(pMnode, pTrans, &topicObj) != 0) goto SUBSCRIBE_OVER;
+#endif
 
     mndReleaseTopic(pMnode, pTopic);
   }

source/dnode/mnode/impl/src/mndDb.c

@@ -1044,9 +1044,9 @@ static int32_t mndDropDb(SMnode *pMnode, SRpcMsg *pReq, SDbObj *pDb) {
 
   if (mndSetDropDbRedoLogs(pMnode, pTrans, pDb) != 0) goto _OVER;
   if (mndSetDropDbCommitLogs(pMnode, pTrans, pDb) != 0) goto _OVER;
-  /*if (mndDropOffsetByDB(pMnode, pTrans, pDb) != 0) goto _OVER;*/
-  /*if (mndDropSubByDB(pMnode, pTrans, pDb) != 0) goto _OVER;*/
-  /*if (mndDropTopicByDB(pMnode, pTrans, pDb) != 0) goto _OVER;*/
+  if (mndDropOffsetByDB(pMnode, pTrans, pDb) != 0) goto _OVER;
+  if (mndDropSubByDB(pMnode, pTrans, pDb) != 0) goto _OVER;
+  if (mndDropTopicByDB(pMnode, pTrans, pDb) != 0) goto _OVER;
   if (mndSetDropDbRedoActions(pMnode, pTrans, pDb) != 0) goto _OVER;
 
   SUserObj *pUser = mndAcquireUser(pMnode, pDb->createUser);

source/dnode/mnode/impl/src/mndOffset.c

@@ -21,6 +21,7 @@
 #include "mndMnode.h"
 #include "mndShow.h"
 #include "mndStb.h"
+#include "mndTopic.h"
 #include "mndTrans.h"
 #include "mndUser.h"
 #include "mndVgroup.h"
@@ -188,7 +189,15 @@ static int32_t mndProcessCommitOffsetReq(SRpcMsg *pMsg) {
     bool          create = false;
     SMqOffsetObj *pOffsetObj = mndAcquireOffset(pMnode, key);
     if (pOffsetObj == NULL) {
+      SMqTopicObj *pTopic = mndAcquireTopic(pMnode, pOffset->topicName);
+      if (pTopic == NULL) {
+        terrno = TSDB_CODE_MND_TOPIC_NOT_EXIST;
+        mError("submit offset to topic %s failed since  %s", pOffset->topicName, terrstr());
+        continue;
+      }
       pOffsetObj = taosMemoryMalloc(sizeof(SMqOffsetObj));
+      pOffsetObj->dbUid = pTopic->dbUid;
+      mndReleaseTopic(pMnode, pTopic);
       memcpy(pOffsetObj->key, key, TSDB_PARTITION_KEY_LEN);
       create = true;
     }

source/dnode/mnode/impl/src/mndScheduler.c

@@ -286,7 +286,7 @@ int32_t mndScheduleStream(SMnode* pMnode, STrans* pTrans, SStreamObj* pStream) {
   pStream->tasks = taosArrayInit(totLevel, sizeof(void*));
 
   bool hasExtraSink = false;
-  if (totLevel == 2) {
+  if (totLevel == 2 || strcmp(pStream->sourceDb, pStream->targetDb) != 0) {
     SArray* taskOneLevel = taosArrayInit(0, sizeof(void*));
     taosArrayPush(pStream->tasks, &taskOneLevel);
     // add extra sink
@@ -407,7 +407,7 @@ int32_t mndScheduleStream(SMnode* pMnode, STrans* pTrans, SStreamObj* pStream) {
 
           /*pTask->dispatchMsgType = TDMT_VND_TASK_WRITE_EXEC;*/
           pTask->dispatchMsgType = TDMT_VND_TASK_DISPATCH;
-          SDbObj* pDb = mndAcquireDb(pMnode, pStream->sourceDb);
+          SDbObj* pDb = mndAcquireDb(pMnode, pStream->targetDb);
           ASSERT(pDb);
           if (mndExtractDbInfo(pMnode, pDb, &pTask->shuffleDispatcher.dbInfo, NULL) < 0) {
             sdbRelease(pSdb, pDb);

source/dnode/mnode/impl/src/mndStream.c

@@ -393,6 +393,15 @@ static int32_t mndCreateStream(SMnode *pMnode, SRpcMsg *pReq, SCMCreateStreamReq
   streamObj.trigger = pCreate->triggerType;
   streamObj.waterMark = pCreate->watermark;
 
+  if (streamObj.targetSTbName[0]) {
+    pDb = mndAcquireDbByStb(pMnode, streamObj.targetSTbName);
+    if (pDb == NULL) {
+      terrno = TSDB_CODE_MND_DB_NOT_SELECTED;
+      return -1;
+    }
+    tstrncpy(streamObj.targetDb, pDb->name, TSDB_DB_FNAME_LEN);
+  }
+
   STrans *pTrans = mndTransCreate(pMnode, TRN_POLICY_ROLLBACK, TRN_TYPE_CREATE_STREAM, pReq);
   if (pTrans == NULL) {
     mError("stream:%s, failed to create since %s", pCreate->name, terrstr());

source/dnode/mnode/impl/src/mndSubscribe.c

@@ -157,6 +157,7 @@ static int32_t mndPersistSubChangeVgReq(SMnode *pMnode, STrans *pTrans, const SM
   int32_t vgId = pRebVg->pVgEp->vgId;
   SVgObj *pVgObj = mndAcquireVgroup(pMnode, vgId);
   if (pVgObj == NULL) {
+    ASSERT(0);
     taosMemoryFree(buf);
     return -1;
   }
@@ -451,6 +452,7 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
     taosArrayPush(pConsumerNew->rebNewTopics, &topic);
     mndReleaseConsumer(pMnode, pConsumerOld);
     if (mndSetConsumerCommitLogs(pMnode, pTrans, pConsumerNew) != 0) {
+      ASSERT(0);
       goto REB_FAIL;
     }
   }
@@ -469,9 +471,11 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
     taosArrayPush(pConsumerNew->rebRemovedTopics, &topic);
     mndReleaseConsumer(pMnode, pConsumerOld);
     if (mndSetConsumerCommitLogs(pMnode, pTrans, pConsumerNew) != 0) {
+      ASSERT(0);
       goto REB_FAIL;
     }
   }
+#if 0
   if (consumerNum) {
     char topic[TSDB_TOPIC_FNAME_LEN];
     char cgroup[TSDB_CGROUP_LEN];
@@ -486,9 +490,13 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
       pTopic->refConsumerCnt = topicObj.refConsumerCnt;
       mInfo("subscribe topic %s unref %d consumer cgroup %s, refcnt %d", pTopic->name, consumerNum, cgroup,
             topicObj.refConsumerCnt);
-      if (mndSetTopicCommitLogs(pMnode, pTrans, &topicObj) != 0) goto REB_FAIL;
+      if (mndSetTopicCommitLogs(pMnode, pTrans, &topicObj) != 0) {
+        ASSERT(0);
+        goto REB_FAIL;
+      }
     }
   }
+#endif
 
   // 4. TODO commit log: modification log
 
@@ -496,7 +504,10 @@ static int32_t mndPersistRebResult(SMnode *pMnode, SRpcMsg *pMsg, const SMqRebOu
   mndTransSetCb(pTrans, TRANS_START_FUNC_MQ_REB, TRANS_STOP_FUNC_TEST_MQ_REB, NULL, 0);
 
   // 6. execution
-  if (mndTransPrepare(pMnode, pTrans) != 0) goto REB_FAIL;
+  if (mndTransPrepare(pMnode, pTrans) != 0) {
+    ASSERT(0);
+    goto REB_FAIL;
+  }
 
   mndTransDrop(pTrans);
   return 0;

source/dnode/mnode/impl/src/mndTopic.c

@@ -15,6 +15,7 @@
 
 #include "mndTopic.h"
 #include "mndAuth.h"
+#include "mndConsumer.h"
 #include "mndDb.h"
 #include "mndDnode.h"
 #include "mndMnode.h"
@@ -121,7 +122,7 @@ SSdbRaw *mndTopicActionEncode(SMqTopicObj *pTopic) {
     SDB_SET_BINARY(pRaw, dataPos, swBuf, schemaLen, TOPIC_ENCODE_OVER);
   }
 
-  SDB_SET_INT32(pRaw, dataPos, pTopic->refConsumerCnt, TOPIC_ENCODE_OVER);
+  /*SDB_SET_INT32(pRaw, dataPos, pTopic->refConsumerCnt, TOPIC_ENCODE_OVER);*/
 
   SDB_SET_RESERVE(pRaw, dataPos, MND_TOPIC_RESERVE_SIZE, TOPIC_ENCODE_OVER);
   SDB_SET_DATALEN(pRaw, dataPos, TOPIC_ENCODE_OVER);
@@ -221,7 +222,7 @@ SSdbRow *mndTopicActionDecode(SSdbRaw *pRaw) {
     pTopic->schema.pSchema = NULL;
   }
 
-  SDB_GET_INT32(pRaw, dataPos, &pTopic->refConsumerCnt, TOPIC_DECODE_OVER);
+  /*SDB_GET_INT32(pRaw, dataPos, &pTopic->refConsumerCnt, TOPIC_DECODE_OVER);*/
 
   SDB_GET_RESERVE(pRaw, dataPos, MND_TOPIC_RESERVE_SIZE, TOPIC_DECODE_OVER);
 
@@ -253,7 +254,7 @@ static int32_t mndTopicActionUpdate(SSdb *pSdb, SMqTopicObj *pOldTopic, SMqTopic
   atomic_exchange_64(&pOldTopic->updateTime, pNewTopic->updateTime);
   atomic_exchange_32(&pOldTopic->version, pNewTopic->version);
 
-  atomic_store_32(&pOldTopic->refConsumerCnt, pNewTopic->refConsumerCnt);
+  /*atomic_store_32(&pOldTopic->refConsumerCnt, pNewTopic->refConsumerCnt);*/
 
   /*taosWLockLatch(&pOldTopic->lock);*/
 
@@ -327,7 +328,7 @@ static int32_t mndCreateTopic(SMnode *pMnode, SRpcMsg *pReq, SCMCreateTopicReq *
   topicObj.version = 1;
   topicObj.sql = strdup(pCreate->sql);
   topicObj.sqlLen = strlen(pCreate->sql) + 1;
-  topicObj.refConsumerCnt = 0;
+  /*topicObj.refConsumerCnt = 0;*/
 
   if (pCreate->ast && pCreate->ast[0]) {
     topicObj.ast = strdup(pCreate->ast);
@@ -492,8 +493,8 @@ static int32_t mndDropTopic(SMnode *pMnode, STrans *pTrans, SRpcMsg *pReq, SMqTo
 }
 
 static int32_t mndProcessDropTopicReq(SRpcMsg *pReq) {
-  SMnode *pMnode = pReq->info.node;
-  /*SSdb          *pSdb = pMnode->pSdb;*/
+  SMnode        *pMnode = pReq->info.node;
+  SSdb          *pSdb = pMnode->pSdb;
   SMDropTopicReq dropReq = {0};
 
   if (tDeserializeSMDropTopicReq(pReq->pCont, pReq->contLen, &dropReq) != 0) {
@@ -513,12 +514,36 @@ static int32_t mndProcessDropTopicReq(SRpcMsg *pReq) {
     }
   }
 
+  void           *pIter = NULL;
+  SMqConsumerObj *pConsumer;
+  while (1) {
+    pIter = sdbFetch(pSdb, SDB_CONSUMER, pIter, (void **)&pConsumer);
+    if (pIter == NULL) break;
+
+    if (pConsumer->status == MQ_CONSUMER_STATUS__LOST_REBD) continue;
+    int32_t sz = taosArrayGetSize(pConsumer->assignedTopics);
+    for (int32_t i = 0; i < sz; i++) {
+      char *name = taosArrayGetP(pConsumer->assignedTopics, i);
+      if (strcmp(name, pTopic->name) == 0) {
+        mndReleaseConsumer(pMnode, pConsumer);
+        mndReleaseTopic(pMnode, pTopic);
+        terrno = TSDB_CODE_MND_TOPIC_SUBSCRIBED;
+        mError("topic:%s, failed to drop since subscribed by consumer %ld from cgroup %s", dropReq.name,
+               pConsumer->consumerId, pConsumer->cgroup);
+        return -1;
+      }
+    }
+    sdbRelease(pSdb, pConsumer);
+  }
+
+#if 0
   if (pTopic->refConsumerCnt != 0) {
     mndReleaseTopic(pMnode, pTopic);
     terrno = TSDB_CODE_MND_TOPIC_SUBSCRIBED;
     mError("topic:%s, failed to drop since %s", dropReq.name, terrstr());
     return -1;
   }
+#endif
 
   STrans *pTrans = mndTransCreate(pMnode, TRN_POLICY_ROLLBACK, TRN_TYPE_DROP_TOPIC, pReq);
   if (pTrans == NULL) {

source/libs/function/src/builtins.c

@@ -702,7 +702,7 @@ static int32_t translateFirstLast(SFunctionNode* pFunc, char* pErrBuf, int32_t l
 
 static int32_t translateUnique(SFunctionNode* pFunc, char* pErrBuf, int32_t len) {
   if (1 != LIST_LENGTH(pFunc->pParameterList)) {
-    return TSDB_CODE_SUCCESS;
+    return invaildFuncParaNumErrMsg(pErrBuf, len, pFunc->functionName);
   }
 
   SNode* pPara = nodesListGetNode(pFunc->pParameterList, 0);

source/libs/transport/test/CMakeLists.txt

@@ -111,12 +111,10 @@ target_link_libraries (pushServer
 )
 
 
-if(NOT TD_WINDOWS)
-  add_test(
-    NAME transUT 
-    COMMAND transUT 
-  )
-endif(NOT TD_WINDOWS)
+add_test(
+  NAME transUT 
+  COMMAND transUT 
+)
 add_test(
   NAME transUtilUt 
   COMMAND transportTest

tests/script/general/alter/table.sim

@@ -252,6 +252,7 @@ endi
 
 print ======== step8
 sql alter table tb add column h binary(10)
+sql select * from tb
 sql describe tb
 if $data00 != ts then
   return -1
@@ -304,7 +305,7 @@ endi
 if $data80 != h then
   return -1
 endi
-if $data81 != BINARY then
+if $data81 != VARCHAR then
   return -1
 endi
 if $data82 != 10 then
@@ -371,7 +372,7 @@ endi
 if $data80 != h then
   return -1
 endi
-if $data81 != BINARY then
+if $data81 != VARCHAR then
   return -1
 endi
 if $data82 != 10 then
@@ -447,7 +448,7 @@ endi
 if $data70 != h then
   return -1
 endi
-if $data71 != BINARY then
+if $data71 != VARCHAR then
   return -1
 endi
 if $data72 != 10 then
@@ -496,7 +497,7 @@ endi
 if $data60 != h then
   return -1
 endi
-if $data61 != BINARY then
+if $data61 != VARCHAR then
   return -1
 endi
 if $data62 != 10 then
@@ -539,7 +540,7 @@ endi
 if $data50 != h then
   return -1
 endi
-if $data51 != BINARY then
+if $data51 != VARCHAR then
   return -1
 endi
 if $data52 != 10 then
@@ -576,7 +577,7 @@ endi
 if $data40 != h then
   return -1
 endi
-if $data41 != BINARY then
+if $data41 != VARCHAR then
   return -1
 endi
 if $data42 != 10 then
@@ -607,7 +608,7 @@ endi
 if $data30 != h then
   return -1
 endi
-if $data31 != BINARY then
+if $data31 != VARCHAR then
   return -1
 endi
 if $data32 != 10 then
@@ -632,7 +633,7 @@ endi
 if $data20 != h then
   return -1
 endi
-if $data21 != BINARY then
+if $data21 != VARCHAR then
   return -1
 endi
 if $data22 != 10 then

tests/script/jenkins/basic.txt

@@ -104,6 +104,10 @@
 ./test.sh -f tsim/stable/tag_modify.sim
 ./test.sh -f tsim/stable/tag_rename.sim
 ./test.sh -f tsim/stable/alter_comment.sim
+./test.sh -f tsim/stable/alter_count.sim
+./test.sh -f tsim/stable/alter_insert1.sim
+./test.sh -f tsim/stable/alter_insert2.sim
+./test.sh -f tsim/stable/alter_import.sim
 
 # --- for multi process mode
 ./test.sh -f tsim/user/basic1.sim -m

tests/script/general/alter/import.sim → tests/script/tsim/stable/alter_import.sim

@@ -29,14 +29,14 @@ if $data00 != 3 then
 endi
 
 print ========= step3
-sql import into tb values(now-23d, -23, 0)
-sql import into tb values(now-21d, -21, 0)
+sql insert into tb values(now-23d, -23, 0)
+sql insert into tb values(now-21d, -21, 0)
 sql select count(b) from tb
 if $data00 != 5 then
   return -1
 endi
 
-sql import into tb values(now-29d, -29, 0)
+sql insert into tb values(now-29d, -29, 0)
 sql select count(b) from tb
 if $data00 != 6 then
   return -1

tests/script/general/alter/metrics.sim → tests/script/tsim/stable/alter_metrics.sim

@@ -347,7 +347,7 @@ endi
 if $data80 != h then
   return -1
 endi
-if $data81 != BINARY then
+if $data81 != VARCHAR then
   return -1
 endi
 if $data82 != 10 then
@@ -363,9 +363,8 @@ endi
 print ======== step9
 print ======== step10
 system sh/exec.sh -n dnode1 -s stop -x SIGINT
-sleep 3000
 system sh/exec.sh -n dnode1 -s start
-sleep 3000
+sql connect
 
 sql use d2
 sql describe tb
@@ -420,7 +419,7 @@ endi
 if $data80 != h then
   return -1
 endi
-if $data81 != BINARY then
+if $data81 != VARCHAR then
   return -1
 endi
 if $data82 != 10 then
@@ -502,7 +501,7 @@ endi
 if $data70 != h then
   return -1
 endi
-if $data71 != BINARY then
+if $data71 != VARCHAR then
   return -1
 endi
 if $data72 != 10 then
@@ -557,7 +556,7 @@ endi
 if $data60 != h then
   return -1
 endi
-if $data61 != BINARY then
+if $data61 != VARCHAR then
   return -1
 endi
 if $data62 != 10 then
@@ -606,7 +605,7 @@ endi
 if $data50 != h then
   return -1
 endi
-if $data51 != BINARY then
+if $data51 != VARCHAR then
   return -1
 endi
 if $data52 != 10 then
@@ -649,7 +648,7 @@ endi
 if $data40 != h then
   return -1
 endi
-if $data41 != BINARY then
+if $data41 != VARCHAR then
   return -1
 endi
 if $data42 != 10 then
@@ -686,7 +685,7 @@ endi
 if $data30 != h then
   return -1
 endi
-if $data31 != BINARY then
+if $data31 != VARCHAR then
   return -1
 endi
 if $data32 != 10 then
@@ -717,7 +716,7 @@ endi
 if $data20 != h then
   return -1
 endi
-if $data21 != BINARY then
+if $data21 != VARCHAR then
   return -1
 endi
 if $data22 != 10 then
@@ -758,7 +757,7 @@ endi
 print ======= over
 sql drop database d2
 sql show databases
-if $rows != 0 then 
+if $rows != 2 then 
   return -1
 endi
 

tests/script/tsim/stable/column_modify.sim

@@ -79,28 +79,31 @@ system sh/exec.sh -n dnode1 -s stop -x SIGINT
 system sh/exec.sh -n dnode1 -s start
 
 sql connect
-sql select * from db.ctb
+
+sql select * from db.stb
+print $data[0][0] $data[0][1] $data[0][2] $data[0][3] $data[0][4] $data[0][5] $data[0][6]
+print $data[1][0] $data[1][1] $data[1][2] $data[1][3] $data[1][4] $data[1][5] $data[1][6]
 
 if $rows != 2 then
   return -1
 endi
-#if $data[0][1] != 1 then
-#  return -1
-#endi
-#if $data[0][2] != 1234 then
-#  return -1
-#endi  
-#if $data[0][3] != 101 then
-#  return -1
-#endi
-#if $data[1][1] != 1 then
-#  return -1
-#endi
-#if $data[1][2] != 12345 then
-#  return -1
-#endi
-#if $data[1][3] != 101 then
-#  return -1
-#endi
+if $data[0][1] != 1 then
+  return -1
+endi
+if $data[0][2] != 1234 then
+  return -1
+endi  
+if $data[0][3] != 101 then
+  return -1
+endi
+if $data[1][1] != 1 then
+  return -1
+endi
+if $data[1][2] != 12345 then
+  return -1
+endi
+if $data[1][3] != 101 then
+  return -1
+endi
 
 system sh/exec.sh -n dnode1 -s stop -x SIGINT
\ No newline at end of file

tests/script/tsim/stable/disk.sim

@@ -49,10 +49,9 @@ if $data00 != $totalNum then
   return -1
 endi
 
-sleep 1000
 system sh/exec.sh -n dnode1 -s stop -x SIGINT
-sleep 1000
 system sh/exec.sh -n dnode1 -s start
+sql connect
 
 sql use $db
 sql show vgroups

tests/script/tsim/stable/metrics.sim

@@ -93,9 +93,6 @@ $i = 2
 $tb = $tbPrefix . $i
 sql insert into $tb values (now + 1m , 1 )
 
-print sleep 2000
-sleep 2000
-
 print =============== step6
 
 # sql select * from $mt

tests/system-test/7-tmq/subscribeDb1.py

@@ -382,6 +382,7 @@ class TDTestCase:
             tdLog.info("act consume rows: %d, expect consume rows: %d"%(totalConsumeRows, expectrowcnt))
             tdLog.exit("tmq consume rows error!")
 
+        time.sleep(15)
         tdSql.query("drop topic %s"%topicName1)
 
         tdLog.printNoPrefix("======== test case 10 end ...... ")
@@ -453,6 +454,7 @@ class TDTestCase:
             tdLog.info("act consume rows: %d, expect consume rows: %d"%(totalConsumeRows, expectrowcnt))
             tdLog.exit("tmq consume rows error!")
 
+        time.sleep(15)
         tdSql.query("drop topic %s"%topicName1)
 
         tdLog.printNoPrefix("======== test case 11 end ...... ")