Merge branch 'main' into merge/mainto3.0
This commit is contained in:
Shengliang Guan
commit
9c15eb7b3b
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@ -173,6 +173,36 @@ Metric details:
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|
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There are also line charts for the above categories.
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|
||||
### Automatic import of preconfigured alert rules
|
||||
|
||||
After summarizing user experience, 14 commonly used alert rules are sorted out. These alert rules can monitor key indicators of the TDengine cluster and report alerts, such as abnormal and exceeded indicators.
|
||||
Starting from TDengine-Server 3.3.4.3 (TDengine-datasource 3.6.3), TDengine Datasource supports automatic import of preconfigured alert rules. You can import 14 alert rules to Grafana (version 11 or later) with one click.
|
||||
In the TDengine-datasource setting interface, turn on the "Load Tengine Alert" switch, click the "Save & test" button, the plugin will automatically load the mentioned 14 alert rules. The rules will be placed in the Grafana alerts directory. If not required, turn off the "Load TDengine Alert" switch, and click the button next to "Clear TDengine Alert" to clear all the alert rules imported into this data source.
|
||||
|
||||
After importing, click on "Alert rules" on the left side of the Grafana interface to view all current alert rules. By configuring contact points, users can receive alert notifications.
|
||||
|
||||
The specific configuration of the 14 alert rules is as follows:
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||||
|
||||
| alert rule| Rule threshold| Behavior when no data | Data scanning interval |Duration | SQL |
|
||||
| ------ | --------- | ---------------- | ----------- |------- |----------------------|
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||||
|CPU load of dnode node|average > 80%|Trigger alert|5 minutes|5 minutes |`select now(), dnode_id, last(cpu_system) as cup_use from log.taosd_dnodes_info where _ts >= (now- 5m) and _ts < now partition by dnode_id having first(_ts) > 0 `|
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||||
|Memory of dnode node |average > 60%|Trigger alert|5 minutes|5 minutes|`select now(), dnode_id, last(mem_engine) / last(mem_total) * 100 as taosd from log.taosd_dnodes_info where _ts >= (now- 5m) and _ts <now partition by dnode_id`|
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||||
|Disk capacity occupancy of dnode nodes | > 80%|Trigger alert|5 minutes|5 minutes|`select now(), dnode_id, data_dir_level, data_dir_name, last(used) / last(total) * 100 as used from log.taosd_dnodes_data_dirs where _ts >= (now - 5m) and _ts < now partition by dnode_id, data_dir_level, data_dir_name`|
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||||
|Authorization expires |< 60天|Trigger alert|1 day|0 0 seconds|`select now(), cluster_id, last(grants_expire_time) / 86400 as expire_time from log.taosd_cluster_info where _ts >= (now - 24h) and _ts < now partition by cluster_id having first(_ts) > 0 `|
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|The used measurement points has reached the authorized number|>= 90%|Trigger alert|1 day|0 seconds|`select now(), cluster_id, CASE WHEN max(grants_timeseries_total) > 0.0 THEN max(grants_timeseries_used) /max(grants_timeseries_total) * 100.0 ELSE 0.0 END AS result from log.taosd_cluster_info where _ts >= (now - 30s) and _ts < now partition by cluster_id having timetruncate(first(_ts), 1m) > 0`|
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|Number of concurrent query requests | > 100|Do not trigger alert|1 minute|0 seconds|`select now() as ts, count(*) as slow_count from performance_schema.perf_queries`|
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|Maximum time for slow query execution (no time window) |> 300秒|Do not trigger alert|1 minute|0 seconds|`select now() as ts, count(*) as slow_count from performance_schema.perf_queries where exec_usec>300000000`|
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|dnode offline |total != alive|Trigger alert|30 seconds|0 seconds|`select now(), cluster_id, last(dnodes_total) - last(dnodes_alive) as dnode_offline from log.taosd_cluster_info where _ts >= (now -30s) and _ts < now partition by cluster_id having first(_ts) > 0`|
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|vnode offline |total != alive|Trigger alert|30 seconds|0 seconds|`select now(), cluster_id, last(vnodes_total) - last(vnodes_alive) as vnode_offline from log.taosd_cluster_info where _ts >= (now - 30s) and _ts < now partition by cluster_id having first(_ts) > 0 `|
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|Number of data deletion requests |> 0|Do not trigger alert|30 seconds|0 seconds|``select now(), count(`count`) as `delete_count` from log.taos_sql_req where sql_type = 'delete' and _ts >= (now -30s) and _ts < now``|
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|Adapter RESTful request fail |> 5|Do not trigger alert|30 seconds|0 seconds|``select now(), sum(`fail`) as `Failed` from log.adapter_requests where req_type=0 and ts >= (now -30s) and ts < now``|
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|Adapter WebSocket request fail |> 5|Do not trigger alert|30 seconds|0 seconds|``select now(), sum(`fail`) as `Failed` from log.adapter_requests where req_type=1 and ts >= (now -30s) and ts < now``|
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|Dnode data reporting is missing |< 3|Trigger alert|180 seconds|0 seconds|`select now(), cluster_id, count(*) as dnode_report from log.taosd_cluster_info where _ts >= (now -180s) and _ts < now partition by cluster_id having timetruncate(first(_ts), 1h) > 0`|
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||||
|Restart dnode |max(update_time) > last(update_time)|Trigger alert|90 seconds|0 seconds|`select now(), dnode_id, max(uptime) - last(uptime) as dnode_restart from log.taosd_dnodes_info where _ts >= (now - 90s) and _ts < now partition by dnode_id`|
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||||
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TDengine users can modify and improve these alert rules according to their own business needs. In Grafana 7.5 and below versions, the Dashboard and Alert rules functions are combined, while in subsequent new versions, the two functions are separated. To be compatible with Grafana7.5 and below versions, an Alert Used Only panel has been added to the TDinsight panel, which is only required for Grafana7.5 and below versions.
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||||
|
||||
|
||||
## Upgrade
|
||||
|
||||
The following three methods can be used for upgrading:
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||||
|
|
|
|||
|
|
@ -287,3 +287,13 @@ This feature is an optional configuration item, which is enabled by default in t
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|||
You can disable this parameter at any time by modifying telemetryReporting to 0 in taos.cfg, then restarting the database service.
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||||
Code located at: [https://github.com/taosdata/TDengine/blob/62e609c558deb764a37d1a01ba84bc35115a85a4/source/dnode/mnode/impl/src/mndTelem.c](https://github.com/taosdata/TDengine/blob/62e609c558deb764a37d1a01ba84bc35115a85a4/source/dnode/mnode/impl/src/mndTelem.c).
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||||
Additionally, for the highly secure enterprise version, TDengine Enterprise, this parameter will not be operational.
|
||||
|
||||
### 31 What should I do if I encounter 'Sync leader is unreachable' when connecting to the cluster for the first time?
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||||
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||||
Reporting this error indicates that the first connection to the cluster was successful, but the IP address accessed for the first time was not the leader of mnode. An error occurred when the client attempted to establish a connection with the leader. The client searches for the leader node through EP, which specifies the fqdn and port number. There are two common reasons for this error:
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||||
|
||||
- The ports of other dnodes in the cluster are not open
|
||||
- The client's hosts file is not configured correctly
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||||
|
||||
Therefore, first, check whether all ports on the server and cluster (default 6030 for native connections and 6041 for HTTP connections) are open; Next, check if the client's hosts file has configured the fqdn and IP information for all dnodes in the cluster.
|
||||
If the issue still cannot be resolved, it is necessary to contact Taos technical personnel for support.
|
||||
|
|
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|||
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|
@ -4,11 +4,10 @@ sidebar_label: "安装部署"
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|||
---
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||||
|
||||
### 环境准备
|
||||
使用 TDgpt 的高级时序数据分析功能需要在 TDengine 集群中安装部署 AI node（Anode）。Anode 可以运行在 Linux/Windows/MacOS 等平台上，同时需要 3.10 或以上版本的 Python 环境支持。
|
||||
使用 TDgpt 的高级时序数据分析功能需要在 TDengine 集群中安装部署 AI node（Anode）。Anode 运行在 Linux 平台上，并需要 3.10 或以上版本的 Python 环境支持。
|
||||
> 部署 Anode 需要 TDengine Enterprise 3.3.4.3 及以后版本，请首先确认搭配 Anode 使用的 TDengine 能够支持 Anode。
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||||
|
||||
### 安装及卸载
|
||||
不同操作系统上安装及部署 Anode 有一些差异，主要是卸载操作、安装路径、服务启停等方面。本文以 Linux 系统为例，说明安装部署的流程。
|
||||
使用 Linux 环境下的安装包 TDengine-enterprise-anode-1.x.x.tar.gz 可进行 Anode 的安装部署工作，命令如下：
|
||||
|
||||
```bash
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||||
|
|
@ -37,7 +36,7 @@ systemctl status taosanoded
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|||
|/usr/local/taos/taosanode/bin|可执行文件目录|
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||||
|/usr/local/taos/taosanode/resource|资源文件目录，链接到文件夹 /var/lib/taos/taosanode/resource/|
|
||||
|/usr/local/taos/taosanode/lib|库文件目录|
|
||||
|/var/lib/taos/taosanode/model/|模型文件目录，链接到文件夹 /var/lib/taos/taosanode/model|
|
||||
|/usr/local/taos/taosanode/model/|模型文件目录，链接到文件夹 /var/lib/taos/taosanode/model|
|
||||
|/var/log/taos/taosanode/|日志文件目录|
|
||||
|/etc/taos/taosanode.ini|配置文件|
|
||||
|
||||
|
|
@ -64,7 +63,7 @@ pidfile = /usr/local/taos/taosanode/taosanode.pid
|
|||
# conflict with systemctl, so do NOT uncomment this
|
||||
# daemonize = /var/log/taos/taosanode/taosanode.log
|
||||
|
||||
# log directory
|
||||
# uWSGI log files
|
||||
logto = /var/log/taos/taosanode/taosanode.log
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||||
|
||||
# wWSGI monitor port
|
||||
|
|
@ -74,7 +73,7 @@ stats = 127.0.0.1:8387
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|||
virtualenv = /usr/local/taos/taosanode/venv/
|
||||
|
||||
[taosanode]
|
||||
# default app log file
|
||||
# default taosanode log file
|
||||
app-log = /var/log/taos/taosanode/taosanode.app.log
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||||
|
||||
# model storage directory
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ import wndata from './pic/white-noise-data.png'
|
|||
<img src={activity} width="560" alt="预处理流程" />
|
||||
|
||||
TDgpt 首先对输入数据进行白噪声检查（White Noise Data check）, 检查通过以后针对预测分析，还要进行输入（历史）数据的重采样和时间戳对齐处理（异常检测跳过数据重采样和时间戳对齐步骤）。
|
||||
预处理完成以后，再进行预测或异常检测操作。预处理过程部署于预测或异常检测处理逻辑的一部分。
|
||||
预处理完成以后，再进行预测或异常检测操作。预处理过程不属于预测或异常检测处理逻辑的一部分。
|
||||
|
||||
### 白噪声检查
|
||||
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ description: 预测算法
|
|||
|
||||
```bash
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||||
taos> select * from foo;
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||||
ts | k |
|
||||
ts | i32 |
|
||||
========================================
|
||||
2020-01-01 00:00:12.681 | 13 |
|
||||
2020-01-01 00:00:13.727 | 14 |
|
||||
|
|
@ -42,7 +42,7 @@ algo=expr1
|
|||
|
||||
```
|
||||
1. `column_expr`：预测的时序数据列。与异常检测相同，只支持数值类型列输入。
|
||||
2. `options`：异常检测函数的参数，使用规则与 anomaly_window 相同。预测支持 `conf`, `every`, `rows`, `start`, `rows` 几个控制参数，其含义如下：
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||||
2. `options`：预测函数的参数。字符串类型，其中使用 K=V 方式调用算法及相关参数。采用逗号分隔的 K=V 字符串表示，其中的字符串不需要使用单引号、双引号、或转义号等符号，不能使用中文及其他宽字符。预测支持 `conf`, `every`, `rows`, `start`, `rows` 几个控制参数，其含义如下：
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||||
|
||||
### 参数说明
|
||||
|
||||
|
|
|
|||
|
|
@ -34,7 +34,8 @@ return {
|
|||
|
||||
```python
|
||||
import numpy as np
|
||||
from service import AbstractForecastService
|
||||
from taosanalytics.service import AbstractForecastService
|
||||
|
||||
|
||||
# 算法实现类名称 需要以下划线 "_" 开始，并以 Service 结束
|
||||
class _MyForecastService(AbstractForecastService):
|
||||
|
|
@ -56,7 +57,7 @@ class _MyForecastService(AbstractForecastService):
|
|||
|
||||
"""这个预测算法固定返回 1 作为预测值，预测值的数量是用户通过 self.fc_rows 指定"""
|
||||
ts_list = [self.start_ts + i * self.time_step for i in range(self.fc_rows)]
|
||||
res.app(ts_list) # 设置预测结果时间戳列
|
||||
res.append(ts_list) # 设置预测结果时间戳列
|
||||
|
||||
"""生成全部为 1 的预测结果 """
|
||||
res_list = [1] * self.fc_rows
|
||||
|
|
@ -72,10 +73,10 @@ class _MyForecastService(AbstractForecastService):
|
|||
"""返回结果"""
|
||||
return {"res": res, "mse": 0}
|
||||
|
||||
|
||||
def set_params(self, params):
|
||||
"""该算法无需任何输入参数，直接重载父类该函数，不处理算法参数设置逻辑"""
|
||||
pass
|
||||
"""该算法无需任何输入参数，直接调用父类函数，不处理算法参数设置逻辑"""
|
||||
return super().set_params(params)
|
||||
|
||||
```
|
||||
|
||||
将该文件保存在 `./taosanalytics/algo/fc/` 目录下，然后重启 taosanode 服务。在 TDengine 命令行接口中执行 `SHOW ANODES FULL` 能够看到新加入的算法。应用就可以通过 SQL 语句调用该预测算法。
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ sidebar_label: "异常检测"
|
|||
|
||||
```python
|
||||
import numpy as np
|
||||
from service import AbstractAnomalyDetectionService
|
||||
from taosanalytics.service import AbstractAnomalyDetectionService
|
||||
|
||||
# 算法实现类名称 需要以下划线 "_" 开始，并以 Service 结束
|
||||
class _MyAnomalyDetectionService(AbstractAnomalyDetectionService):
|
||||
|
|
|
|||
|
|
@ -19,24 +19,25 @@ Anode的主要目录结构如下图所示
|
|||
|
||||
```bash
|
||||
.
|
||||
├── bin
|
||||
├── cfg
|
||||
├── model
|
||||
│ └── ad_autoencoder
|
||||
├── release
|
||||
├── script
|
||||
└── taosanalytics
|
||||
├── algo
|
||||
│ ├── ad
|
||||
│ └── fc
|
||||
├── misc
|
||||
└── test
|
||||
├── lib
|
||||
│ └── taosanalytics
|
||||
│ ├── algo
|
||||
│ │ ├── ad
|
||||
│ │ └── fc
|
||||
│ ├── misc
|
||||
│ └── test
|
||||
├── log -> /var/log/taos/taosanode
|
||||
├── model -> /var/lib/taos/taosanode/model
|
||||
└── venv -> /var/lib/taos/taosanode/venv
|
||||
|
||||
```
|
||||
|
||||
|目录|说明|
|
||||
|---|---|
|
||||
|taosanalytics| 源代码目录，其下包含了算法具体保存目录 algo，放置杂项目录 misc，单元测试和集成测试目录 test。 algo 目录下 ad 保存异常检测算法代码，fc 目录保存预测算法代码|
|
||||
|script|是安装脚本和发布脚本放置目录|
|
||||
|venv| Python 虚拟环境|
|
||||
|model|放置针对数据集完成的训练模型|
|
||||
|cfg|配置文件目录|
|
||||
|
||||
|
|
@ -63,7 +64,8 @@ Anode采用算法自动加载模式，因此只识别符合命名约定的 Pytho
|
|||
|
||||
```SQL
|
||||
--- algo 后面的参数 name 即为类属性 `name`
|
||||
SELECT COUNT(*) FROM foo ANOMALY_WINDOW(col_name, 'algo=name')
|
||||
SELECT COUNT(*)
|
||||
FROM foo ANOMALY_WINDOW(col_name, 'algo=name')
|
||||
```
|
||||
|
||||
## 添加具有模型的分析算法
|
||||
|
|
@ -76,19 +78,10 @@ SELECT COUNT(*) FROM foo ANOMALY_WINDOW(col_name, 'algo=name')
|
|||
|
||||
```bash
|
||||
.
|
||||
├── cfg
|
||||
├── model
|
||||
│ └── ad_autoencoder
|
||||
│ ├── ad_autoencoder_foo.dat
|
||||
│ └── ad_autoencoder_foo.info
|
||||
├── release
|
||||
├── script
|
||||
└── taosanalytics
|
||||
├── algo
|
||||
│ ├── ad
|
||||
│ └── fc
|
||||
├── misc
|
||||
└── test
|
||||
└── model
|
||||
└── ad_autoencoder
|
||||
├── ad_autoencoder_foo.dat
|
||||
└── ad_autoencoder_foo.info
|
||||
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -136,11 +136,11 @@ alter_database_option: {
|
|||
|
||||
1. 如何查看 cachesize?
|
||||
|
||||
通过 select * from information_schema.ins_databases; 可以查看这些 cachesize 的具体值。
|
||||
通过 select * from information_schema.ins_databases; 可以查看这些 cachesize 的具体值（单位为 MB）。。
|
||||
|
||||
2. 如何查看 cacheload?
|
||||
|
||||
通过 show \<db_name>.vgroups; 可以查看 cacheload
|
||||
通过 show \<db_name>.vgroups; 可以查看 cacheload（单位为字节）。
|
||||
|
||||
3. 判断 cachesize 是否够用
|
||||
|
||||
|
|
|
|||
|
|
@ -289,3 +289,12 @@ https://docs.taosdata.com/reference/components/taosd/#%E7%9B%91%E6%8E%A7%E7%9B%B
|
|||
|
||||
因此用户首先要检查服务端，集群的所有端口（原生连接默认6030，http连接默认6041）有无打开；其次是客户端的hosts文件中是否配置了集群所有节点的fqdn与IP信息。
|
||||
如仍无法解决，则需要联系涛思技术人员支持。
|
||||
|
||||
### 32 同一台服务器，数据库的数据目录 dataDir 不变，为什么原有数据库丢失且集群 ID 发生了变化？
|
||||
背景知识：TDengine 服务端进程（taosd）在启动时，若数据目录（dataDir，该目录在配置文件 taos.cfg 中指定）下不存在有效的数据文件子目录（如 mnode、dnode 和 vnode 等），则会自动创建这些目录。在创建新的 mnode 目录的同时，会分配一个新的集群 ID，从而产生一个新的集群。
|
||||
|
||||
原因分析：taosd 的数据目录 dataDir 可以指向多个不同的挂载点。如果这些挂载点未在 fstab 文件中配置自动挂载，服务器重启后，dataDir 将仅作为一个本地磁盘的普通目录存在，而未能按预期指向挂载的磁盘。此时，若 taosd 服务启动，它将在 dataDir 下新建目录，从而产生一个新的集群。
|
||||
|
||||
问题影响：服务器重启后，原有数据库丢失（注：并非真正丢失，只是原有的数据磁盘未挂载，暂时看不到）且集群 ID 发生变化，导致无法访问原有数据库。对于企业版用户，如果已针对集群 ID 进行授权，还会发现集群服务器的机器码未变，但原有的授权已失效。如果未针对该问题进行监控或者未及时发现并进行处理，则用户不会注意到原有数据库已经丢失，从而造成损失，增加运维成本。
|
||||
|
||||
问题解决：应在 fstab 文件中配置 dataDir 目录的自动挂载，确保 dataDir 始终指向预期的挂载点和目录，此时，再重启服务器，会找回原有的数据库和集群。在后续的版本中，我们将开发一个功能，使 taosd 在检测到启动前后 dataDir 发生变化时，在启动阶段退出，同时提供相应的错误提示。
|
||||
|
|
@ -920,7 +920,9 @@ static int32_t mndRetrieveMnodes(SRpcMsg *pReq, SShowObj *pShow, SSDataBlock *pB
|
|||
SColumnInfoData *pColInfo = taosArrayGet(pBlock->pDataBlock, cols++);
|
||||
code = colDataSetVal(pColInfo, numOfRows, (const char *)&pObj->id, false);
|
||||
if (code != 0) {
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, terrstr());
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, tstrerror(code));
|
||||
sdbCancelFetch(pSdb, pShow->pIter);
|
||||
sdbRelease(pSdb, pObj);
|
||||
goto _out;
|
||||
}
|
||||
|
||||
|
|
@ -930,7 +932,9 @@ static int32_t mndRetrieveMnodes(SRpcMsg *pReq, SShowObj *pShow, SSDataBlock *pB
|
|||
pColInfo = taosArrayGet(pBlock->pDataBlock, cols++);
|
||||
code = colDataSetVal(pColInfo, numOfRows, b1, false);
|
||||
if (code != 0) {
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, terrstr());
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, tstrerror(code));
|
||||
sdbCancelFetch(pSdb, pShow->pIter);
|
||||
sdbRelease(pSdb, pObj);
|
||||
goto _out;
|
||||
}
|
||||
|
||||
|
|
@ -950,10 +954,8 @@ static int32_t mndRetrieveMnodes(SRpcMsg *pReq, SShowObj *pShow, SSDataBlock *pB
|
|||
STR_WITH_MAXSIZE_TO_VARSTR(b2, role, pShow->pMeta->pSchemas[cols].bytes);
|
||||
pColInfo = taosArrayGet(pBlock->pDataBlock, cols++);
|
||||
code = colDataSetVal(pColInfo, numOfRows, (const char *)b2, false);
|
||||
if (code != 0) {
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, terrstr());
|
||||
goto _out;
|
||||
}
|
||||
if (code != 0) goto _err;
|
||||
|
||||
const char *status = "ready";
|
||||
if (objStatus == SDB_STATUS_CREATING) status = "creating";
|
||||
if (objStatus == SDB_STATUS_DROPPING) status = "dropping";
|
||||
|
|
@ -962,25 +964,16 @@ static int32_t mndRetrieveMnodes(SRpcMsg *pReq, SShowObj *pShow, SSDataBlock *pB
|
|||
STR_WITH_MAXSIZE_TO_VARSTR(b3, status, pShow->pMeta->pSchemas[cols].bytes);
|
||||
pColInfo = taosArrayGet(pBlock->pDataBlock, cols++);
|
||||
code = colDataSetVal(pColInfo, numOfRows, (const char *)b3, false);
|
||||
if (code != 0) {
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, terrstr());
|
||||
goto _out;
|
||||
}
|
||||
if (code != 0) goto _err;
|
||||
|
||||
pColInfo = taosArrayGet(pBlock->pDataBlock, cols++);
|
||||
code = colDataSetVal(pColInfo, numOfRows, (const char *)&pObj->createdTime, false);
|
||||
if (code != 0) {
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, terrstr());
|
||||
goto _out;
|
||||
}
|
||||
if (code != 0) goto _err;
|
||||
|
||||
int64_t roleTimeMs = (isDnodeOnline) ? pObj->roleTimeMs : 0;
|
||||
pColInfo = taosArrayGet(pBlock->pDataBlock, cols++);
|
||||
code = colDataSetVal(pColInfo, numOfRows, (const char *)&roleTimeMs, false);
|
||||
if (code != 0) {
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, terrstr());
|
||||
goto _out;
|
||||
}
|
||||
if (code != 0) goto _err;
|
||||
|
||||
numOfRows++;
|
||||
sdbRelease(pSdb, pObj);
|
||||
|
|
@ -991,6 +984,13 @@ static int32_t mndRetrieveMnodes(SRpcMsg *pReq, SShowObj *pShow, SSDataBlock *pB
|
|||
_out:
|
||||
sdbRelease(pSdb, pSelfObj);
|
||||
return numOfRows;
|
||||
|
||||
_err:
|
||||
mError("mnode:%d, failed to set col data val since %s", pObj->id, tstrerror(code));
|
||||
sdbCancelFetch(pSdb, pShow->pIter);
|
||||
sdbRelease(pSdb, pObj);
|
||||
sdbRelease(pSdb, pSelfObj);
|
||||
return numOfRows;
|
||||
}
|
||||
|
||||
static void mndCancelGetNextMnode(SMnode *pMnode, void *pIter) {
|
||||
|
|
|
|||
|
|
@ -103,9 +103,11 @@ static int32_t vnodeGetBufPoolToUse(SVnode *pVnode) {
|
|||
}
|
||||
|
||||
code = taosThreadCondTimedWait(&pVnode->poolNotEmpty, &pVnode->mutex, &ts);
|
||||
if (code && code != TSDB_CODE_TIMEOUT_ERROR) {
|
||||
TSDB_CHECK_CODE(code, lino, _exit);
|
||||
// ignore timeout error and retry
|
||||
if (code == TSDB_CODE_TIMEOUT_ERROR) {
|
||||
code = TSDB_CODE_SUCCESS;
|
||||
}
|
||||
TSDB_CHECK_CODE(code, lino, _exit);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -2450,14 +2450,22 @@ static int32_t doSaveCurrentVal(SqlFunctionCtx* pCtx, int32_t rowIndex, int64_t
|
|||
SFirstLastRes* pInfo = GET_ROWCELL_INTERBUF(pResInfo);
|
||||
|
||||
if (IS_VAR_DATA_TYPE(type)) {
|
||||
if (type == TSDB_DATA_TYPE_JSON) {
|
||||
pInfo->bytes = getJsonValueLen(pData);
|
||||
} else {
|
||||
pInfo->bytes = varDataTLen(pData);
|
||||
}
|
||||
}
|
||||
|
||||
(void)memcpy(pInfo->buf, pData, pInfo->bytes);
|
||||
if (pkData != NULL) {
|
||||
if (IS_VAR_DATA_TYPE(pInfo->pkType)) {
|
||||
if (pInfo->pkType == TSDB_DATA_TYPE_JSON) {
|
||||
pInfo->pkBytes = getJsonValueLen(pkData);
|
||||
} else {
|
||||
pInfo->pkBytes = varDataTLen(pkData);
|
||||
}
|
||||
}
|
||||
(void)memcpy(pInfo->buf + pInfo->bytes, pkData, pInfo->pkBytes);
|
||||
pInfo->pkData = pInfo->buf + pInfo->bytes;
|
||||
}
|
||||
|
|
@ -2985,8 +2993,12 @@ static int32_t doSaveLastrow(SqlFunctionCtx* pCtx, char* pData, int32_t rowIndex
|
|||
pInfo->isNull = false;
|
||||
|
||||
if (IS_VAR_DATA_TYPE(pInputCol->info.type)) {
|
||||
if (pInputCol->info.type == TSDB_DATA_TYPE_JSON) {
|
||||
pInfo->bytes = getJsonValueLen(pData);
|
||||
} else {
|
||||
pInfo->bytes = varDataTLen(pData);
|
||||
}
|
||||
}
|
||||
|
||||
(void)memcpy(pInfo->buf, pData, pInfo->bytes);
|
||||
}
|
||||
|
|
@ -2994,8 +3006,12 @@ static int32_t doSaveLastrow(SqlFunctionCtx* pCtx, char* pData, int32_t rowIndex
|
|||
if (pCtx->hasPrimaryKey && !colDataIsNull_s(pkCol, rowIndex)) {
|
||||
char* pkData = colDataGetData(pkCol, rowIndex);
|
||||
if (IS_VAR_DATA_TYPE(pInfo->pkType)) {
|
||||
if (pInfo->pkType == TSDB_DATA_TYPE_JSON) {
|
||||
pInfo->pkBytes = getJsonValueLen(pkData);
|
||||
} else {
|
||||
pInfo->pkBytes = varDataTLen(pkData);
|
||||
}
|
||||
}
|
||||
(void)memcpy(pInfo->buf + pInfo->bytes, pkData, pInfo->pkBytes);
|
||||
pInfo->pkData = pInfo->buf + pInfo->bytes;
|
||||
}
|
||||
|
|
@ -5874,7 +5890,11 @@ void modeFunctionCleanupExt(SqlFunctionCtx* pCtx) {
|
|||
|
||||
static int32_t saveModeTupleData(SqlFunctionCtx* pCtx, char* data, SModeInfo* pInfo, STuplePos* pPos) {
|
||||
if (IS_VAR_DATA_TYPE(pInfo->colType)) {
|
||||
if (pInfo->colType == TSDB_DATA_TYPE_JSON) {
|
||||
(void)memcpy(pInfo->buf, data, getJsonValueLen(data));
|
||||
} else {
|
||||
(void)memcpy(pInfo->buf, data, varDataTLen(data));
|
||||
}
|
||||
} else {
|
||||
(void)memcpy(pInfo->buf, data, pInfo->colBytes);
|
||||
}
|
||||
|
|
@ -5884,7 +5904,16 @@ static int32_t saveModeTupleData(SqlFunctionCtx* pCtx, char* data, SModeInfo* pI
|
|||
|
||||
static int32_t doModeAdd(SModeInfo* pInfo, int32_t rowIndex, SqlFunctionCtx* pCtx, char* data) {
|
||||
int32_t code = TSDB_CODE_SUCCESS;
|
||||
int32_t hashKeyBytes = IS_STR_DATA_TYPE(pInfo->colType) ? varDataTLen(data) : pInfo->colBytes;
|
||||
int32_t hashKeyBytes;
|
||||
if (IS_VAR_DATA_TYPE(pInfo->colType)) {
|
||||
if (pInfo->colType == TSDB_DATA_TYPE_JSON) {
|
||||
hashKeyBytes = getJsonValueLen(data);
|
||||
} else {
|
||||
hashKeyBytes = varDataTLen(data);
|
||||
}
|
||||
} else {
|
||||
hashKeyBytes = pInfo->colBytes;
|
||||
}
|
||||
|
||||
SModeItem* pHashItem = (SModeItem*)taosHashGet(pInfo->pHash, data, hashKeyBytes);
|
||||
if (pHashItem == NULL) {
|
||||
|
|
@ -6769,14 +6798,32 @@ static void doSaveRateInfo(SRateInfo* pRateInfo, bool isFirst, int64_t ts, char*
|
|||
pRateInfo->firstValue = v;
|
||||
pRateInfo->firstKey = ts;
|
||||
if (pRateInfo->firstPk) {
|
||||
int32_t pkBytes = IS_VAR_DATA_TYPE(pRateInfo->pkType) ? varDataTLen(pk) : pRateInfo->pkBytes;
|
||||
int32_t pkBytes;
|
||||
if (IS_VAR_DATA_TYPE(pRateInfo->pkType)) {
|
||||
if (pRateInfo->pkType == TSDB_DATA_TYPE_JSON) {
|
||||
pkBytes = getJsonValueLen(pk);
|
||||
} else {
|
||||
pkBytes = varDataTLen(pk);
|
||||
}
|
||||
} else {
|
||||
pkBytes = pRateInfo->pkBytes;
|
||||
}
|
||||
(void)memcpy(pRateInfo->firstPk, pk, pkBytes);
|
||||
}
|
||||
} else {
|
||||
pRateInfo->lastValue = v;
|
||||
pRateInfo->lastKey = ts;
|
||||
if (pRateInfo->lastPk) {
|
||||
int32_t pkBytes = IS_VAR_DATA_TYPE(pRateInfo->pkType) ? varDataTLen(pk) : pRateInfo->pkBytes;
|
||||
int32_t pkBytes;
|
||||
if (IS_VAR_DATA_TYPE(pRateInfo->pkType)) {
|
||||
if (pRateInfo->pkType == TSDB_DATA_TYPE_JSON) {
|
||||
pkBytes = getJsonValueLen(pk);
|
||||
} else {
|
||||
pkBytes = varDataTLen(pk);
|
||||
}
|
||||
} else {
|
||||
pkBytes = pRateInfo->pkBytes;
|
||||
}
|
||||
(void)memcpy(pRateInfo->lastPk, pk, pkBytes);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -4633,11 +4633,12 @@ int32_t compareHashTableImpl(SHashObj* p1, SHashObj* p2, SArray* diff) {
|
|||
while (pIter) {
|
||||
char* name = taosHashGetKey(pIter, &len);
|
||||
if (!isBkdDataMeta(name, len) && !taosHashGet(p1, name, len)) {
|
||||
char* fname = taosMemoryCalloc(1, len + 1);
|
||||
int32_t cap = len + 1;
|
||||
char* fname = taosMemoryCalloc(1, cap);
|
||||
if (fname == NULL) {
|
||||
return terrno;
|
||||
}
|
||||
tstrncpy(fname, name, strlen(name));
|
||||
tstrncpy(fname, name, cap);
|
||||
if (taosArrayPush(diff, &fname) == NULL) {
|
||||
taosMemoryFree(fname);
|
||||
return terrno;
|
||||
|
|
@ -4825,13 +4826,14 @@ int32_t dbChkpGetDelta(SDbChkp* p, int64_t chkpId, SArray* list) {
|
|||
size_t len = 0;
|
||||
char* name = taosHashGetKey(pIter, &len);
|
||||
if (name != NULL && !isBkdDataMeta(name, len)) {
|
||||
char* fname = taosMemoryCalloc(1, len + 1);
|
||||
int32_t cap = len + 1;
|
||||
char* fname = taosMemoryCalloc(1, cap);
|
||||
if (fname == NULL) {
|
||||
TAOS_UNUSED(taosThreadRwlockUnlock(&p->rwLock));
|
||||
return terrno;
|
||||
}
|
||||
|
||||
tstrncpy(fname, name, strlen(name));
|
||||
tstrncpy(fname, name, cap);
|
||||
if (taosArrayPush(p->pAdd, &fname) == NULL) {
|
||||
taosMemoryFree(fname);
|
||||
TAOS_UNUSED(taosThreadRwlockUnlock(&p->rwLock));
|
||||
|
|
|
|||
|
|
@ -0,0 +1,106 @@
|
|||
###################################################################
|
||||
# Copyright (c) 2016 by TAOS Technologies, Inc.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This file is proprietary and confidential to TAOS Technologies.
|
||||
# No part of this file may be reproduced, stored, transmitted,
|
||||
# disclosed or used in any form or by any means other than as
|
||||
# expressly provided by the written permission from Jianhui Tao
|
||||
#
|
||||
###################################################################
|
||||
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from frame import etool
|
||||
from frame.etool import *
|
||||
from frame.log import *
|
||||
from frame.cases import *
|
||||
from frame.sql import *
|
||||
from frame.caseBase import *
|
||||
from frame.common import *
|
||||
|
||||
class TDTestCase(TBase):
|
||||
updatecfgDict = {
|
||||
"keepColumnName": "1",
|
||||
"ttlChangeOnWrite": "1",
|
||||
"querySmaOptimize": "1",
|
||||
"slowLogScope": "none",
|
||||
"queryBufferSize": 10240
|
||||
}
|
||||
|
||||
def insert_data(self):
|
||||
tdLog.info(f"insert data.")
|
||||
tdSql.execute("drop database if exists ts_5763;")
|
||||
tdSql.execute("create database ts_5763;")
|
||||
tdSql.execute("use ts_5763;")
|
||||
tdSql.execute("select database();")
|
||||
tdSql.execute("CREATE STABLE metrics (ts TIMESTAMP, v DOUBLE) TAGS (labels JSON)")
|
||||
tdSql.execute("""CREATE TABLE `metrics_0` USING `metrics` (`labels`) TAGS ('{"ident":"192.168.56.167"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_1` USING `metrics` (`labels`) TAGS ('{"ident":"192.168.56.168"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_2` USING `metrics` (`labels`) TAGS ('{"ident":"192.168.56.169"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_3` USING `metrics` (`labels`) TAGS ('{"ident":"192.168.56.170"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_5` USING `metrics` (`labels`) TAGS ('{"asset_name":"中国政务网"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_6` USING `metrics` (`labels`) TAGS ('{"asset_name":"地大物博阿拉丁快解放啦上课交电费"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_7` USING `metrics` (`labels`) TAGS ('{"asset_name":"no1241-上的六块腹肌阿斯利康的肌肤轮廓设计大方"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_8` USING `metrics` (`labels`) TAGS ('{"asset_name":"no1241-上的六块腹肌阿斯利康的肌肤轮廓设计大方","ident":"192.168.0.1"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_9` USING `metrics` (`labels`) TAGS ('{"asset_name":"no1241-上的六块腹肌阿斯利康的肌肤轮廓设计大方","ident":"192.168.0.1"}');""")
|
||||
tdSql.execute("""CREATE TABLE `metrics_10` USING `metrics` (`labels`) TAGS ('{"asset_name":"上的咖啡机no1241-上的六块腹肌阿斯利康的肌肤轮廓设计大方","ident":"192.168.0.1"}');""")
|
||||
|
||||
tdSql.execute("insert into metrics_0 values ('2024-12-12 16:34:39.326',1)")
|
||||
tdSql.execute("insert into metrics_0 values ('2024-12-12 16:34:40.891',2)")
|
||||
tdSql.execute("insert into metrics_0 values ('2024-12-12 16:34:41.986',3)")
|
||||
tdSql.execute("insert into metrics_0 values ('2024-12-12 16:34:42.992',4)")
|
||||
tdSql.execute("insert into metrics_0 values ('2024-12-12 16:34:46.927',5)")
|
||||
tdSql.execute("insert into metrics_0 values ('2024-12-12 16:34:48.473',6)")
|
||||
tdSql.execute("insert into metrics_1 select * from metrics_0")
|
||||
tdSql.execute("insert into metrics_2 select * from metrics_0")
|
||||
tdSql.execute("insert into metrics_3 select * from metrics_0")
|
||||
tdSql.execute("insert into metrics_5 select * from metrics_0")
|
||||
tdSql.execute("insert into metrics_6 select * from metrics_0")
|
||||
tdSql.execute("insert into metrics_7 select * from metrics_0")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:36.459',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:37.388',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:37.622',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:37.852',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:38.081',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:38.307',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:38.535',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:38.792',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:39.035',1)")
|
||||
tdSql.execute("insert into metrics_8 values ('2024-12-12 19:05:39.240',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:29.270',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:30.508',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:31.035',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:31.523',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:31.760',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:32.001',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:32.228',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:32.453',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:32.690',1)")
|
||||
tdSql.execute("insert into metrics_9 values ('2024-12-12 19:05:32.906',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:14.538',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:15.114',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:15.613',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:15.853',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:16.054',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:16.295',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:16.514',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:16.731',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:16.958',1)")
|
||||
tdSql.execute("insert into metrics_10 values ('2024-12-12 19:06:17.176',1)")
|
||||
|
||||
for i in range(1, 10):
|
||||
tdSql.query("select _wstart,first(v)-last(v), first(labels->'asset_name'),first(labels->'ident'),mode(labels->'asset_name'),mode(labels->'ident'),last(labels->'asset_name'),last(labels->'ident') from ts_5763.metrics interval(1s)")
|
||||
tdSql.checkRows(18)
|
||||
|
||||
def run(self):
|
||||
tdLog.debug(f"start to excute {__file__}")
|
||||
|
||||
self.insert_data()
|
||||
|
||||
|
||||
tdLog.success(f"{__file__} successfully executed")
|
||||
|
||||
|
||||
tdCases.addLinux(__file__, TDTestCase())
|
||||
tdCases.addWindows(__file__, TDTestCase())
|
||||
|
|
@ -15,6 +15,7 @@
|
|||
,,y,army,./pytest.sh python3 ./test.py -f cluster/snapshot.py -N 3 -L 3 -D 2
|
||||
,,y,army,./pytest.sh python3 ./test.py -f query/function/test_func_elapsed.py
|
||||
,,y,army,./pytest.sh python3 ./test.py -f query/function/test_function.py
|
||||
,,y,army,./pytest.sh python3 ./test.py -f query/function/test_selection_function_with_json.py
|
||||
,,y,army,./pytest.sh python3 ./test.py -f query/function/test_percentile.py
|
||||
,,y,army,./pytest.sh python3 ./test.py -f query/function/test_resinfo.py
|
||||
,,y,army,./pytest.sh python3 ./test.py -f query/function/test_interp.py
|
||||
|
|
|
|||
Loading…
Reference in New Issue