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Posted to commits@iotdb.apache.org by ta...@apache.org on 2022/12/13 02:11:39 UTC
[iotdb] branch master updated: Update metric doc (#8419)
This is an automated email from the ASF dual-hosted git repository.
tanxinyu pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/iotdb.git
The following commit(s) were added to refs/heads/master by this push:
new e487d0706b Update metric doc (#8419)
e487d0706b is described below
commit e487d0706ba652faa15e91cb49c327092eed8ab1
Author: ZhangHongYin <46...@users.noreply.github.com>
AuthorDate: Tue Dec 13 10:11:33 2022 +0800
Update metric doc (#8419)
---
docs/UserGuide/Monitor-Alert/Metric-Tool.md | 115 +++++++++++++------------
docs/zh/UserGuide/Monitor-Alert/Metric-Tool.md | 111 ++++++++++++------------
2 files changed, 114 insertions(+), 112 deletions(-)
diff --git a/docs/UserGuide/Monitor-Alert/Metric-Tool.md b/docs/UserGuide/Monitor-Alert/Metric-Tool.md
index 8bf78c2797..0c3b75ade0 100644
--- a/docs/UserGuide/Monitor-Alert/Metric-Tool.md
+++ b/docs/UserGuide/Monitor-Alert/Metric-Tool.md
@@ -21,7 +21,7 @@
Along with IoTDB running, we hope to observe the status of IoTDB, so as to troubleshoot system problems or discover potential system risks in time. A series of metrics that can **reflect the operating status of the system** are system monitoring metrics.
-# 1. When to use metric framework?
+## 1. When to use metric framework?
Belows are some typical application scenarios
@@ -44,49 +44,50 @@ Belows are some typical application scenarios
We could use the count of error logs、the alive status of nodes in cluster, etc, to determine whether the system is running abnormally.
-# 2. Who will use metric framework?
+## 2. Who will use metric framework?
Any person cares about the system's status, including but not limited to RD, QA, SRE, DBA, can use the metrics to work more efficiently.
-# 3. What is metrics?
+## 3. What is metrics?
-## 3.1. Key Concept
+### 3.1. Key Concept
In IoTDB's metric module, each metrics is uniquely identified by `Metric Name` and `Tags`.
-- `Metric Name`: The name of metric's category, for example, ```logback_events``` indicates log events。
-- `Tags`: **Metric classification**, in the form of Key-Value pairs, each metric can have 0 or more categories, common Key-Value pairs:
- - `name = xxx`: The name of the metric. For example, for the monitoring item`entry_seconds_count`, the meaning of name is the name of the monitored interface.
- - `status = xxx`: The status of the metric is subdivided. For example, the monitoring item of the monitoring task can use this parameter to separate the running task and the stopped task.
- - `user = xxx`: The metric is related to a specific user, such as the total number of writes by the root user.
- - Customize for the situation: For example, there is a ```level``` classification under logback_events, which is used to indicate the number of logs under a specific level
+- `Metric Name`: Metric type name, such as `logback_events` means log events.
+- `Tags`: indicator classification, in the form of Key-Value pairs, each indicator can have 0 or more categories, common Key-Value pairs:
+ - `name = xxx`: The name of the monitored object, which is the description of **business logic**. For example, for a monitoring item of type `Metric Name = entry_seconds_count`, the meaning of name refers to the monitored business interface.
+ - `type = xxx`: Monitoring indicator type subdivision, which is a description of **monitoring indicator** itself. For example, for monitoring items of type `Metric Name = point`, the meaning of type refers to the specific type of monitoring points.
+ - `status = xxx`: The status of the monitored object is a description of **business logic**. For example, for monitoring items of type `Metric Name = Task`, this parameter can be used to distinguish the status of the monitored object.
+ - `user = xxx`: The relevant user of the monitored object is a description of **business logic**. For example, count the total points written by the `root` user.
+ - Customize according to the specific situation: For example, there is a level classification under logback_events_total, which is used to indicate the number of logs under a specific level.
- `Metric Level`: The level of metric managing level, The default startup level is `Core` level, the recommended startup level is `Important level`, and the audit strictness is `Core > Important > Normal > All`
- `Core`: Core metrics of the system, used by the **operation and maintenance personnel**, which is related to the **performance, stability, and security** of the system, such as the status of the instance, the load of the system, etc.
- `Important`: Important metrics of the module, which is used by **operation and maintenance and testers**, and is directly related to **the running status of each module**, such as the number of merged files, execution status, etc.
- `Normal`: Normal metrics of the module, used by **developers** to facilitate **locating the module** when problems occur, such as specific key operation situations in the merger.
- `All`: All metrics of the module, used by **module developers**, often used when the problem is reproduced, so as to solve the problem quickly.
-### 3.1.1. External data format for metrics
+### 3.2. External data format for metrics
- IoTDB provides metrics in JMX, Prometheus and IoTDB formats:
- For JMX, metrics can be obtained through ```org.apache.iotdb.metrics```.
- For Prometheus, the value of the metrics can be obtained through the externally exposed port
- External exposure in IoTDB mode: metrics can be obtained by executing IoTDB queries
-# 4. The detail of metrics
+## 4. The detail of metrics
Currently, IoTDB provides metrics for some main modules externally, and with the development of new functions and system optimization or refactoring, metrics will be added and updated synchronously.
If you want to add your own metrics data in IoTDB, please see the [IoTDB Metric Framework] (https://github.com/apache/iotdb/tree/master/metrics) document.
-## 4.1. Core level metrics
+### 4.1. Core level metrics
Core-level metrics are enabled by default during system operation. The addition of each Core-level metrics needs to be carefully evaluated. The current Core-level metrics are as follows:
-### 4.1.1. Cluster
+#### 4.1.1. Cluster
| Metric | Tags | Type | Description |
| ----------- | ----------------------------------------------- | --------- | --------------------------------------------------- |
| config_node | name="total",status="Registered/Online/Unknown" | AutoGauge | The number of registered/online/unknown confignodes |
| data_node | name="total",status="Registered/Online/Unknown" | AutoGauge | The number of registered/online/unknown datanodes |
-### 4.1.2. IoTDB process
+#### 4.1.2. IoTDB process
| Metric | Tags | Type | Description |
| ----------------- | -------------- | --------- | ------------------------------------------------------ |
| process_cpu_load | name="process" | AutoGauge | The current CPU usage of IoTDB process, Unit: % |
@@ -95,7 +96,7 @@ Core-level metrics are enabled by default during system operation. The addition
| process_total_mem | name="memory" | AutoGauge | The current requested memory for IoTDB process |
| process_free_mem | name="memory" | AutoGauge | The free available memory of IoTDB process |
-### 4.1.3. System
+#### 4.1.3. System
| Metric | Tags | Type | Description |
| ------------------------------ | ------------- | --------- | ---------------------------------------------------------- |
| sys_cpu_load | name="system" | AutoGauge | The current CPU usage of system, Unit: % |
@@ -108,15 +109,15 @@ Core-level metrics are enabled by default during system operation. The addition
| sys_disk_total_space | name="disk" | AutoGauge | The total disk space |
| sys_disk_free_space | name="disk" | AutoGauge | The available disk space |
-## 4.2. Important level metrics
+### 4.2. Important level metrics
-### 4.2.1. Cluster
+#### 4.2.1. Cluster
| Metric | Tags | Type | Description |
| ------------------------- | ------------------------------------------------- | ----- | ------------------------------------------------ |
| cluster_node_leader_count | name="{{ip}}:{{port}}" | Gauge | The count of consensus group leader on each node |
| cluster_node_status | name="{{ip}}:{{port}}",type="ConfigNode/DataNode" | Gauge | The current node status, 0=Unkonwn 1=online |
-### 4.2.2. Node
+#### 4.2.2. Node
| Metric | Tags | Type | Description |
| -------- | ------------------------------------------ | --------- | ------------------------------------------------------------- |
| quantity | name="database" | AutoGauge | The number of database |
@@ -127,7 +128,7 @@ Core-level metrics are enabled by default during system operation. The addition
| region | name="{{ip}}:{{port}}",type="SchemaRegion" | Gauge | The number of SchemaRegion in PartitionTable of specific node |
| region | name="{{ip}}:{{port}}",type="DataRegion" | Gauge | The number of DataRegion in PartitionTable of specific node |
-### 4.2.3. IoTConsensus
+#### 4.2.3. IoTConsensus
| Metric | Tags | Type | Description |
| ------------ | -------------------------------------------------------------------------------------------- | --------- | --------------------------------------------------------------------- |
| mutli_leader | name="logDispatcher-{{IP}}:{{Port}}", region="{{region}}", type="currentSyncIndex" | AutoGauge | The sync index of synchronization thread in replica group |
@@ -142,7 +143,7 @@ Core-level metrics are enabled by default during system operation. The addition
| stage | name="iot_consensus", region="{{region}}", type="constructBatch" | Histogram | The time consumed to construct batch in synchronization thread |
| stage | name="iot_consensus", region="{{region}}", type="syncLogTimePerRequest" | Histogram | The time consumed to sync log in asynchronous callback process |
-### 4.2.4. Cache
+#### 4.2.4. Cache
| Metric | Tags | Type | Description |
| --------- | ---------------------------------- | --------- | ------------------------------------------------------------------------ |
| cache_hit | name="chunk" | AutoGauge | The cache hit ratio of ChunkCache, Unit: % |
@@ -156,7 +157,7 @@ Core-level metrics are enabled by default during system operation. The addition
| cache | name="DataPartition", type="hit" | Counter | The hit number of DataPartition Cache |
| cache | name="DataPartition", type="all" | Counter | The access number of SDataPartition Cache |
-### 4.2.5. Interface
+#### 4.2.5. Interface
| Metric | Tags | Type | Description |
| --------------------- | ---------------------------------- | --------- | -------------------------------------------------------------- |
| operation | name = "{{name}}" | Histogram | The time consumed of operations in client |
@@ -170,14 +171,14 @@ Core-level metrics are enabled by default during system operation. The addition
| thrift_active_threads | name="MPPDataExchangeRPC-Service" | AutoGauge | The number of thrift active internal connections in MPP |
| thrift_active_threads | name="ClientRPC-Service" | AutoGauge | The number of thrift active connections of client |
-### 4.2.6. Memory
+#### 4.2.6. Memory
| Metric | Tags | Type | Description |
| ------ | ----------------------------- | --------- | ------------------------------------------------------------------ |
| mem | name="database_{{name}}" | AutoGauge | The memory usage of DataRegion in DataNode, Unit: byte |
| mem | name="chunkMetaData_{{name}}" | AutoGauge | The memory usage of chunkMetaData when writting TsFile, Unit: byte |
| mem | name="IoTConsensus" | AutoGauge | The memory usage of IoTConsensus, Unit: byte |
-### 4.2.7. Task
+#### 4.2.7. Task
| Metric | Tags | Type | Description |
| --------- | ------------------------------------------------- | --------- | ------------------------------------- |
| queue | name="compaction_inner", status="running/waiting" | Gauge | The number of inner compaction tasks |
@@ -187,7 +188,7 @@ Core-level metrics are enabled by default during system operation. The addition
| queue | name="Sub_RawQuery",status="running/waiting" | AutoGauge | The number of Sub_RawQuery |
-### 4.2.8. Compaction
+#### 4.2.8. Compaction
| Metric | Tags | Type | Description |
| --------------------- | --------------------------------------------------- | ------- | -------------------------------------- |
| data_written | name="compaction", type="aligned/not-aligned/total" | Counter | The written size of compaction |
@@ -196,7 +197,7 @@ Core-level metrics are enabled by default during system operation. The addition
| compaction_task_count | name = "inner_compaction", type="unsequence" | Counter | The number of inner sequence compction |
| compaction_task_count | name = "cross_compaction", type="cross" | Counter | The number of corss compction |
-### 4.2.9. File
+#### 4.2.9. File
| Metric | Tags | Type | Description |
| ---------- | ------------ | --------- | ----------------------------------------- |
| file_size | name="wal" | AutoGauge | The size of WAL file, Unit: byte |
@@ -206,7 +207,7 @@ Core-level metrics are enabled by default during system operation. The addition
| file_count | name="seq" | AutoGauge | The count of sequence TsFile |
| file_count | name="unseq" | AutoGauge | The count of unsequence TsFile |
-### 4.2.10. IoTDB Process
+#### 4.2.10. IoTDB Process
| Metric | Tags | Type | Description |
| --------------------- | -------------- | --------- | ------------------------------------------- |
| process_used_mem | name="memory" | AutoGauge | The used memory of IoTDB process |
@@ -214,12 +215,12 @@ Core-level metrics are enabled by default during system operation. The addition
| process_threads_count | name="process" | AutoGauge | The number of thread of IoTDB process |
| process_status | name="process" | AutoGauge | The status of IoTDB process, 1=live, 0=dead |
-### 4.2.11. Log
+#### 4.2.11. Log
| Metric | Tags | Type | Description |
| -------------- | ----------------------------------- | ------- | ------------------------ |
| logback_events | level="trace/debug/info/warn/error" | Counter | The number of log events |
-### 4.2.12. JVM Thread
+#### 4.2.12. JVM Thread
| Metric | Tags | Type | Description |
| -------------------------- | ------------------------------------------------------------- | --------- | ---------------------------------------- |
@@ -228,7 +229,7 @@ Core-level metrics are enabled by default during system operation. The addition
| jvm_threads_peak_threads | | AutoGauge | The number of peak thread |
| jvm_threads_states_threads | state="runnable/blocked/waiting/timed-waiting/new/terminated" | AutoGauge | The number of thread in different states |
-### 4.2.13. JVM GC
+#### 4.2.13. JVM GC
| Metric | Tags | Type | Description |
| ----------------------------- | ----------------------------------------------------- | --------- | --------------------------------------------------------------------------- |
| jvm_gc_pause | action="end of major GC/end of minor GC",cause="xxxx" | Timer | The number and time consumed of Young GC/Full Gc caused by different reason |
@@ -240,7 +241,7 @@ Core-level metrics are enabled by default during system operation. The addition
| jvm_gc_memory_promoted_bytes | | Counter | The accumulative value of positive memory growth of old memory |
| jvm_gc_memory_allocated_bytes | | Counter | The accumulative value of positive memory growth of allocated memory |
-### 4.2.14. JVM Memory
+#### 4.2.14. JVM Memory
| Metric | Tags | Type | Description |
| ------------------------------- | ------------------------------- | --------- | --------------------------- |
| jvm_buffer_memory_used_bytes | id="direct/mapped" | AutoGauge | The used size of buffer |
@@ -250,41 +251,41 @@ Core-level metrics are enabled by default during system operation. The addition
| jvm_memory_max_bytes | {area="heap/nonheap",id="xxx",} | AutoGauge | The max memory of JVM |
| jvm_memory_used_bytes | {area="heap/nonheap",id="xxx",} | AutoGauge | The used memory of JVM |
-### 4.2.15. JVM Class
+#### 4.2.15. JVM Class
| Metric | Tags | Type | Description |
| ---------------------------- | ---- | --------- | ---------------------------- |
| jvm_classes_unloaded_classes | | AutoGauge | The number of unloaded class |
| jvm_classes_loaded_classes | | AutoGauge | The number of loaded class |
-### 4.2.16. JVM Compilation
+#### 4.2.16. JVM Compilation
| Metric | Tags | Type | Description |
| ----------------------- | --------------------------------------------- | --------- | -------------------------------- |
| jvm_compilation_time_ms | {compiler="HotSpot 64-Bit Tiered Compilers",} | AutoGauge | The time consumed in compilation |
-## 4.3. Normal level Metrics
+### 4.3. Normal level Metrics
-### 4.3.1. Cluster
+#### 4.3.1. Cluster
| Metric | Tags | Type | Description |
| ------ | -------------------------------------------------------------- | --------- | ------------------------------------------------------------------ |
| region | name="{{DatabaseName}}",type="SchemaRegion/DataRegion" | AutoGauge | The number of DataRegion/SchemaRegion of database in specific node |
| slot | name="{{DatabaseName}}",type="schemaSlotNumber/dataSlotNumber" | AutoGauge | The number of DataSlot/SchemaSlot of database in specific node |
-## 4.4. All Metric
+### 4.4. All Metric
Currently there is no All level metrics, and it will continue to be added in the future.
-# 5. How to get these metrics？
+## 5. How to get these metrics？
The relevant configuration of the metric module is in `conf/iotdb-{datanode/confignode}.properties`, and all configuration items support hot loading through the `load configuration` command.
-## 5.1. JMX
+### 5.1. JMX
For metrics exposed externally using JMX, you can view them through Jconsole. After entering the Jconsole monitoring page, you will first see an overview of various running conditions of IoTDB. Here you can see heap memory information, thread information, class information, and the server's CPU usage.
-### 5.1.1. Obtain metric data
+#### 5.1.1. Obtain metric data
After connecting to JMX, you can find the "MBean" named "org.apache.iotdb.metrics" through the "MBeans" tab, and you can view the specific values of all monitoring metrics in the sidebar.
<img style="width:100%; max-width:800px; max-height:600px; margin-left:auto; margin-right:auto; display:block;" alt="metric-jmx" src="https://user-images.githubusercontent.com/46039728/204018765-6fda9391-ebcf-4c80-98c5-26f34bd74df0.png">
-### 5.1.2. Get other relevant data
+#### 5.1.2. Get other relevant data
After connecting to JMX, you can find the "MBean" named "org.apache.iotdb.service" through the "MBeans" tab, as shown in the image below, to understand the basic status of the service
<img style="width:100%; max-width:800px; max-height:600px; margin-left:auto; margin-right:auto; display:block;" src="https://user-images.githubusercontent.com/46039728/149951720-707f1ee8-32ee-4fde-9252-048caebd232e.png"> <br>
@@ -293,9 +294,9 @@ In order to improve query performance, IOTDB caches ChunkMetaData and TsFileMeta
<img style="width:100%; max-width:800px; max-height:600px; margin-left:auto; margin-right:auto; display:block;" src="https://user-images.githubusercontent.com/19167280/112426760-73e3da80-8d73-11eb-9a8f-9232d1f2033b.png">
-## 5.2. Prometheus
+### 5.2. Prometheus
-### 5.2.1. The mapping from metric type to prometheus forma
+#### 5.2.1. The mapping from metric type to prometheus forma
> For metrics whose Metric Name is name and Tags are K1=V1, ..., Kn=Vn, the mapping is as follows, where value is a specific value
| Metric Type | Mapping |
@@ -306,7 +307,7 @@ In order to improve query performance, IOTDB caches ChunkMetaData and TsFileMeta
| Rate | name_total{k1="V1", ..., Kn="Vn"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="m1"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="m5"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="m15"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="mean"} value |
| Timer | name_seconds_max{k1="V1", ..., Kn="Vn"} value <br> name_seconds_sum{k1="V1", ..., Kn="Vn"} value <br> name_seconds_count{k1="V1", ..., Kn="Vn"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.0"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.25"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.5"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.75"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="1.0"} value |
-### 5.2.2. Config File
+#### 5.2.2. Config File
1) Taking DataNode as an example, modify the iotdb-datanode.properties configuration file as follows:
```properties
@@ -331,7 +332,7 @@ file_count{name="seq",} 2.0
...
```
-### 5.2.3. Prometheus + Grafana
+#### 5.2.3. Prometheus + Grafana
As shown above, IoTDB exposes monitoring metrics data in the standard Prometheus format to the outside world. Prometheus can be used to collect and store monitoring indicators, and Grafana can be used to visualize monitoring indicators.
@@ -372,19 +373,19 @@ The following documents may help you have a good journey with Prometheus and Gra
[Grafana query metrics from Prometheus](https://prometheus.io/docs/visualization/grafana/#grafana-support-for-prometheus)
-### 5.2.4. Apache IoTDB Dashboard
+#### 5.2.4. Apache IoTDB Dashboard
We provide the Apache IoTDB Dashboard, and the rendering shown in Grafana is as follows:
-#### 5.2.4.1. How to get Apache IoTDB Dashboard
+##### 5.2.4.1. How to get Apache IoTDB Dashboard
1. You can obtain the json files of Dashboards corresponding to different iotdb versions in the grafana-metrics-example folder.
2. You can visit [Grafana Dashboard official website](https://grafana.com/grafana/dashboards/), search for `Apache IoTDB Dashboard` and use
When creating Grafana, you can select the json file you just downloaded to `Import` and select the corresponding target data source for Apache IoTDB Dashboard.
-#### 5.2.4.2. Apache IoTDB ConfigNode Dashboard Instructions
+##### 5.2.4.2. Apache IoTDB ConfigNode Dashboard Instructions
> Except for the metrics specified specially, the following metrics are guaranteed to be available in the monitoring framework at the Important levels.
- `Overview`: system overview
@@ -415,7 +416,7 @@ When creating Grafana, you can select the json file you just downloaded to `Impo
- `The Time Consumed of Compliation Per Minute`: average compilation time per minute
- `The Number Of Class`: The number of classes loaded and unloaded by the JVM
-#### 5.2.4.3. Apache IoTDB DataNode Dashboard Instructions
+##### 5.2.4.3. Apache IoTDB DataNode Dashboard Instructions
> Except for the metrics specified specially, the following metrics are guaranteed to be available in the monitoring framework at the Important levels.
- `Overview`: system overview
@@ -469,18 +470,18 @@ When creating Grafana, you can select the json file you just downloaded to `Impo
- `The Time Consumed of Compliation Per Minute`: average compilation time per minute
- `The Number Of Class`: The number of classes loaded and unloaded by the JVM
-## 5.3. IoTDB
+### 5.3. IoTDB
-### 5.3.1. IoTDB mapping relationship of metrics
-> For metrics whose Metric Name is name and Tags are K1=V1, ..., Kn=Vn, the mapping is as follows, taking root.__system.metric as an example by default
+#### 5.3.1. IoTDB mapping relationship of metrics
+> For metrics whose Metric Name is name and Tags are K1=V1, ..., Kn=Vn, the mapping is as follows, taking root.__system.metric.`ip:port` as an example by default
| Metric Type | Mapping [...]
| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- [...]
-| Counter | root.__system.metric.name.`K1=V1`...`Kn=Vn`.value [...]
-| AutoGauge、Gauge | root.__system.metric.name.`K1=V1`...`Kn=Vn`.value [...]
-| Histogram | root.__system.metric.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p75 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p100 [...]
-| Rate | root.__system.metric.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.m1 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.m5 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.m15 [...]
-| Timer | root.__system.metric.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p75 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p [...]
+| Counter | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.value [...]
+| AutoGauge、Gauge | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.value [...]
+| Histogram | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p75 <br> root.__system.metric.`ip:port [...]
+| Rate | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.m1 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.m5 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.m15 [...]
+| Timer | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.`ip:por [...]
-### 5.3.2. Obtain metrics
+#### 5.3.2. Obtain metrics
According to the above mapping relationship, related IoTDB query statements can be formed to obtain metrics
\ No newline at end of file
diff --git a/docs/zh/UserGuide/Monitor-Alert/Metric-Tool.md b/docs/zh/UserGuide/Monitor-Alert/Metric-Tool.md
index c48f48dadb..045b13a8d6 100644
--- a/docs/zh/UserGuide/Monitor-Alert/Metric-Tool.md
+++ b/docs/zh/UserGuide/Monitor-Alert/Metric-Tool.md
@@ -21,7 +21,7 @@
在 IoTDB 的运行过程中，我们希望对 IoTDB 的状态进行观测，以便于排查系统问题或者及时发现系统潜在的风险，能够**反映系统运行状态的一系列指标**就是系统监控指标。
-# 1. 什么场景下会使用到监控?
+## 1. 什么场景下会使用到监控?
那么什么时候会用到监控框架呢？下面列举一些常见的场景。
@@ -44,50 +44,51 @@
此时我们可能需要通过错误日志的数量、集群节点的状态等指标来判断系统是否在正常运行。
-# 2. 什么人需要使用监控?
+## 2. 什么人需要使用监控?
所有关注系统状态的人员都可以使用，包括但不限于研发、测试、运维、DBA等等
-# 3. 什么是监控指标？
+## 3. 什么是监控指标？
-## 3.1. 监控指标名词解释
+### 3.1. 监控指标名词解释
在 IoTDB 的监控模块，每个监控指标被 `Metric Name` 和 `Tags` 唯一标识。
-- `Metric Name`：**指标类型名称**，比如logback_events表示日志事件。
-- `Tags`：**指标分类**，形式为Key-Value对，每个指标下面可以有0到多个分类，常见的Key-Value对：
- - `name = xxx`：被监控项的名称，比如对`entry_seconds_count`这个监控项，name 的含义是被监控的接口名称。
- - `status = xxx`：被监控项的状态细分，比如监控 Task 的监控项可以通过该参数，将运行的 Task 和停止的 Task 分开。
- - `user = xxx`：被监控项和某个特定用户相关，比如统计root用户的写入总次数。
- - 根据具体情况自定义：比如logback_events_total下有一个```level```的分类，用来表示特定级别下的日志数量
+- `Metric Name`：指标类型名称，比如`logback_events`表示日志事件。
+- `Tags`：指标分类，形式为Key-Value对，每个指标下面可以有0到多个分类，常见的Key-Value对：
+ - `name = xxx`：被监控对象的名称，是对**业务逻辑**的说明。比如对于`Metric Name = entry_seconds_count`类型的监控项，name的含义是指被监控的业务接口。
+ - `type = xxx`：监控指标类型细分，是对**监控指标**本身的说明。比如对于`Metric Name = point`类型的监控项，type的含义是指监控具体是什么类型的点数。
+ - `status = xxx`：被监控对象的状态，是对**业务逻辑**的说明。比如对于`Metric Name = Task`类型的监控项可以通过该参数，从而区分被监控对象的状态。
+ - `user = xxx`：被监控对象的相关用户，是对**业务逻辑**的说明。比如统计`root`用户的写入总点数。
+ - 根据具体情况自定义：比如logback_events_total下有一个level的分类，用来表示特定级别下的日志数量。
- `Metric Level`：**指标管理级别**，默认启动级别为`Core`级别，建议启动级别为`Important级别`，审核严格程度`Core > Important > Normal > All`
- `Core`：系统的核心指标，供**系统内核和运维人员**使用，关乎系统的**性能、稳定性、安全性**，比如实例的状况，系统的负载等。
- `Important`：模块的重要指标，供**运维和测试人员**使用，直接关乎**每个模块的运行状态**，比如合并文件个数、执行情况等。
- `Normal`：模块的一般指标，供**开发人员**使用，方便在出现问题时**定位模块**，比如合并中的特定关键操作情况。
- `All`：模块的全部指标，供**模块开发人员**使用，往往在复现问题的时候使用，从而快速解决问题。
-## 3.2. 监控指标对外获取数据格式
+### 3.2. 监控指标对外获取数据格式
- IoTDB 对外提供 JMX、 Prometheus 和 IoTDB 格式的监控指标：
- 对于 JMX ，可以通过```org.apache.iotdb.metrics```获取系统监控指标指标。
- 对于 Prometheus ，可以通过对外暴露的端口获取监控指标的值
- 对于 IoTDB 方式对外暴露：可以通过执行 IoTDB 的查询来获取监控指标
-# 4. 监控指标有哪些？
+## 4. 监控指标有哪些？
目前，IoTDB 对外提供一些主要模块的监控指标，并且随着新功能的开发以及系统优化或者重构，监控指标也会同步添加和更新。如果想自己在 IoTDB 中添加更多系统监控指标埋点，可以参考[IoTDB Metrics Framework](https://github.com/apache/iotdb/tree/master/metrics)使用说明。
-## 4.1. Core 级别监控指标
+### 4.1. Core 级别监控指标
Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别的监控指标的添加都需要经过谨慎的评估，目前 Core 级别的监控指标如下所述：
-### 4.1.1. 集群运行状态
+#### 4.1.1. 集群运行状态
| Metric | Tags | Type | Description |
| ----------- | ----------------------------------------------- | --------- | -------------------------------------- |
| config_node | name="total",status="Registered/Online/Unknown" | AutoGauge | 已注册/在线/离线 confignode 的节点数量 |
| data_node | name="total",status="Registered/Online/Unknown" | AutoGauge | 已注册/在线/离线 datanode 的节点数量 |
-### 4.1.2. IoTDB 进程运行状态
+#### 4.1.2. IoTDB 进程运行状态
| Metric | Tags | Type | Description |
| ----------------- | -------------- | --------- | ----------------------------------- |
| process_cpu_load | name="process" | AutoGauge | IoTDB 进程的 CPU 占用率，单位为% |
@@ -96,7 +97,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| process_total_mem | name="memory" | AutoGauge | IoTDB 进程当前已申请内存 |
| process_free_mem | name="memory" | AutoGauge | IoTDB 进程当前剩余可用内存 |
-### 4.1.3. 系统运行状态
+#### 4.1.3. 系统运行状态
| Metric | Tags | Type | Description |
| ------------------------------ | ------------- | --------- | ---------------------------------------- |
| sys_cpu_load | name="system" | AutoGauge | 系统的 CPU 占用率，单位为% |
@@ -109,17 +110,17 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| sys_disk_total_space | name="disk" | AutoGauge | 系统磁盘总大小 |
| sys_disk_free_space | name="disk" | AutoGauge | 系统磁盘可用大小 |
-## 4.2. Important 级别监控指标
+### 4.2. Important 级别监控指标
目前 Important 级别的监控指标如下所述：
-### 4.2.1. 集群运行状态
+#### 4.2.1. 集群运行状态
| Metric | Tags | Type | Description |
| ------------------------- | ------------------------------------------------- | ----- | ------------------------------ |
| cluster_node_leader_count | name="{{ip}}:{{port}}" | Gauge | 节点上共识组Leader的数量 |
| cluster_node_status | name="{{ip}}:{{port}}",type="ConfigNode/DataNode" | Gauge | 节点的状态，0=Unkonwn 1=online |
-### 4.2.2. 节点统计
+#### 4.2.2. 节点统计
| Metric | Tags | Type | Description |
| -------- | ------------------------------------------ | --------- | ------------------------------------ |
| quantity | name="database" | AutoGauge | 系统数据库数量 |
@@ -130,7 +131,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| region | name="{{ip}}:{{port}}",type="SchemaRegion" | Gauge | 分区表中对应节点上 DataRegion 总数量 |
| region | name="{{ip}}:{{port}}",type="DataRegion" | Gauge | 分区表中对应节点上 DataRegion 总数量 |
-### 4.2.3. IoT共识协议统计
+#### 4.2.3. IoT共识协议统计
| Metric | Tags | Type | Description |
| ------------- | -------------------------------------------------------------------------------------------- | --------- | -------------------------------- |
| iot_consensus | name="logDispatcher-{{IP}}:{{Port}}", region="{{region}}", type="currentSyncIndex" | AutoGauge | 副本组同步线程的当前同步进度 |
@@ -145,7 +146,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| stage | name="iot_consensus", region="{{region}}", type="constructBatch" | Histogram | 同步线程构造 Batch 耗时 |
| stage | name="iot_consensus", region="{{region}}", type="syncLogTimePerRequest" | Histogram | 异步回调流程同步日志耗时 |
-### 4.2.4. 缓存统计
+#### 4.2.4. 缓存统计
| Metric | Tags | Type | Description |
| --------- | ---------------------------------- | --------- | ------------------------------------------------------- |
| cache_hit | name="chunk" | AutoGauge | ChunkCache的命中率，单位为% |
@@ -159,7 +160,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| cache | name="DataPartition", type="hit" | Counter | DataPartition Cache 的命中次数 |
| cache | name="DataPartition", type="all" | Counter | DataPartition Cache 的访问次数 |
-### 4.2.5. 接口层统计
+#### 4.2.5. 接口层统计
| Metric | Tags | Type | Description |
| --------------------- | ---------------------------------- | --------- | ----------------------------------- |
| operation | name = "{{name}}" | Histogram | 客户端执行的操作的耗时情况 |
@@ -173,14 +174,14 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| thrift_active_threads | name="MPPDataExchangeRPC-Service" | AutoGauge | MPP 框架的内部活跃 Thrift 连接数 |
| thrift_active_threads | name="ClientRPC-Service" | AutoGauge | Client 建立的活跃 Thrift 连接数 |
-### 4.2.6. 内存统计
+#### 4.2.6. 内存统计
| Metric | Tags | Type | Description |
| ------ | ----------------------------- | --------- | ------------------------------------------------- |
| mem | name="database_{{name}}" | AutoGauge | DataNode内对应DataRegion的内存占用，单位为byte |
| mem | name="chunkMetaData_{{name}}" | AutoGauge | 写入TsFile时的ChunkMetaData的内存占用，单位为byte |
| mem | name="IoTConsensus" | AutoGauge | IoT共识协议的内存占用，单位为byte |
-### 4.2.7. 任务统计
+#### 4.2.7. 任务统计
| Metric | Tags | Type | Description |
| --------- | ------------------------------------------------- | --------- | ------------------ |
| queue | name="compaction_inner", status="running/waiting" | Gauge | 空间内合并任务数 |
@@ -189,7 +190,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| queue | name="flush",status="running/waiting" | AutoGauge | 刷盘任务数 |
| queue | name="Sub_RawQuery",status="running/waiting" | AutoGauge | Sub_RawQuery任务数 |
-### 4.2.8. 合并统计
+#### 4.2.8. 合并统计
| Metric | Tags | Type | Description |
| --------------------- | --------------------------------------------------- | ------- | ------------------ |
| data_written | name="compaction", type="aligned/not-aligned/total" | Counter | 合并时写入量 |
@@ -198,7 +199,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| compaction_task_count | name = "inner_compaction", type="unsequence" | Counter | 乱序空间内合并次数 |
| compaction_task_count | name = "cross_compaction", type="cross" | Counter | 跨空间合并次数 |
-### 4.2.9. 文件统计信息
+#### 4.2.9. 文件统计信息
| Metric | Tags | Type | Description |
| ---------- | ------------ | --------- | ---------------------------- |
| file_size | name="wal" | AutoGauge | 写前日志总大小，单位为byte |
@@ -208,7 +209,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| file_count | name="seq" | AutoGauge | 顺序TsFile文件个数 |
| file_count | name="unseq" | AutoGauge | 乱序TsFile文件个数 |
-### 4.2.10. IoTDB 进程统计
+#### 4.2.10. IoTDB 进程统计
| Metric | Tags | Type | Description |
| --------------------- | -------------- | --------- | ------------------------------------ |
| process_used_mem | name="memory" | AutoGauge | IoTDB 进程当前使用内存 |
@@ -216,12 +217,12 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| process_threads_count | name="process" | AutoGauge | IoTDB 进程当前线程数 |
| process_status | name="process" | AutoGauge | IoTDB 进程存活状态，1为存活，0为终止 |
-### 4.2.11. IoTDB 日志统计
+#### 4.2.11. IoTDB 日志统计
| Metric | Tags | Type | Description |
| -------------- | ----------------------------------- | ------- | ------------------ |
| logback_events | level="trace/debug/info/warn/error" | Counter | 不同类型的日志个数 |
-### 4.2.12. JVM 线程统计
+#### 4.2.12. JVM 线程统计
| Metric | Tags | Type | Description |
| -------------------------- | ------------------------------------------------------------- | --------- | ------------------------ |
@@ -230,7 +231,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| jvm_threads_peak_threads | | AutoGauge | 峰值线程数 |
| jvm_threads_states_threads | state="runnable/blocked/waiting/timed-waiting/new/terminated" | AutoGauge | 当前处于各种状态的线程数 |
-### 4.2.13. JVM GC 统计
+#### 4.2.13. JVM GC 统计
| Metric | Tags | Type | Description |
| ----------------------------- | ----------------------------------------------------- | --------- | -------------------------------------- |
| jvm_gc_pause | action="end of major GC/end of minor GC",cause="xxxx" | Timer | 不同原因的Young GC/Full GC的次数与耗时 |
@@ -242,7 +243,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| jvm_gc_memory_promoted_bytes | | Counter | 老年代内存正向增长累计值 |
| jvm_gc_memory_allocated_bytes | | Counter | GC分配内存正向增长累计值 |
-### 4.2.14. JVM 内存统计
+#### 4.2.14. JVM 内存统计
| Metric | Tags | Type | Description |
| ------------------------------- | ------------------------------- | --------- | -------------------- |
| jvm_buffer_memory_used_bytes | id="direct/mapped" | AutoGauge | 已经使用的缓冲区大小 |
@@ -252,34 +253,34 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| jvm_memory_max_bytes | {area="heap/nonheap",id="xxx",} | AutoGauge | 最大内存 |
| jvm_memory_used_bytes | {area="heap/nonheap",id="xxx",} | AutoGauge | 已使用内存大小 |
-### 4.2.15. JVM 类加载统计
+#### 4.2.15. JVM 类加载统计
| Metric | Tags | Type | Description |
| ---------------------------- | ---- | --------- | ------------------- |
| jvm_classes_unloaded_classes | | AutoGauge | 累计卸载的class数量 |
| jvm_classes_loaded_classes | | AutoGauge | 累计加载的class数量 |
-### 4.2.16. JVM 编译时间统计
+#### 4.2.16. JVM 编译时间统计
| Metric | Tags | Type | Description |
| ----------------------- | --------------------------------------------- | --------- | ------------------ |
| jvm_compilation_time_ms | {compiler="HotSpot 64-Bit Tiered Compilers",} | AutoGauge | 耗费在编译上的时间 |
-## 4.3. All 级别监控指标
+### 4.3. All 级别监控指标
目前还没有All级别的监控指标，后续会持续添加。
-# 5. 怎样获取这些系统监控？
+## 5. 怎样获取这些系统监控？
- 监控模块的相关配置均在`conf/iotdb-{datanode/confignode}.properties`中，所有配置项支持通过`load configuration`命令热加载。
-## 5.1. 使用 JMX 方式
+### 5.1. 使用 JMX 方式
对于使用 JMX 对外暴露的指标，可以通过 Jconsole 来进行查看。在进入 Jconsole 监控页面后，首先会看到 IoTDB 的各类运行情况的概览。在这里，您可以看到堆内存信息、线程信息、类信息以及服务器的 CPU 使用情况。
-### 5.1.1. 获取监控指标数据
+#### 5.1.1. 获取监控指标数据
连接到 JMX 后，您可以通过 "MBeans" 标签找到名为 "org.apache.iotdb.metrics" 的 "MBean"，可以在侧边栏中查看所有监控指标的具体值。
<img style="width:100%; max-width:800px; max-height:600px; margin-left:auto; margin-right:auto; display:block;" alt="metric-jmx" src="https://user-images.githubusercontent.com/46039728/204018765-6fda9391-ebcf-4c80-98c5-26f34bd74df0.png">
-### 5.1.2. 获取其他相关数据
+#### 5.1.2. 获取其他相关数据
连接到 JMX 后，您可以通过 "MBeans" 标签找到名为 "org.apache.iotdb.service" 的 "MBean"，如下图所示，了解服务的基本状态
<img style="width:100%; max-width:800px; max-height:600px; margin-left:auto; margin-right:auto; display:block;" src="https://user-images.githubusercontent.com/46039728/149951720-707f1ee8-32ee-4fde-9252-048caebd232e.png"> <br>
@@ -288,9 +289,9 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
<img style="width:100%; max-width:800px; max-height:600px; margin-left:auto; margin-right:auto; display:block;" src="https://user-images.githubusercontent.com/19167280/112426760-73e3da80-8d73-11eb-9a8f-9232d1f2033b.png">
-## 5.2. 使用 Prometheus 方式
+### 5.2. 使用 Prometheus 方式
-### 5.2.1. 监控指标的 Prometheus 映射关系
+#### 5.2.1. 监控指标的 Prometheus 映射关系
> 对于 Metric Name 为 name, Tags 为 K1=V1, ..., Kn=Vn 的监控指标有如下映射，其中 value 为具体值
| 监控指标类型 | 映射关系 |
@@ -301,7 +302,7 @@ Core 级别的监控指标在系统运行中默认开启，每一个 Core 级别
| Rate | name_total{k1="V1", ..., Kn="Vn"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="m1"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="m5"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="m15"} value <br> name_total{k1="V1", ..., Kn="Vn", rate="mean"} value |
| Timer | name_seconds_max{k1="V1", ..., Kn="Vn"} value <br> name_seconds_sum{k1="V1", ..., Kn="Vn"} value <br> name_seconds_count{k1="V1", ..., Kn="Vn"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.0"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.25"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.5"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="0.75"} value <br> name_seconds{k1="V1", ..., Kn="Vn", quantile="1.0"} value |
-### 5.2.2. 修改配置文件
+#### 5.2.2. 修改配置文件
1) 以 DataNode 为例，修改 iotdb-datanode.properties 配置文件如下：
```properties
@@ -324,7 +325,7 @@ file_count{name="seq",} 2.0
...
```
-### 5.2.3. Prometheus + Grafana
+#### 5.2.3. Prometheus + Grafana
如上所示，IoTDB 对外暴露出标准的 Prometheus 格式的监控指标数据，可以使用 Prometheus 采集并存储监控指标，使用 Grafana 可视化监控指标。
@@ -365,19 +366,19 @@ static_configs:
[Grafana从Prometheus查询数据并绘图的文档](https://prometheus.io/docs/visualization/grafana/#grafana-support-for-prometheus)
-### 5.2.4. Apache IoTDB Dashboard
+#### 5.2.4. Apache IoTDB Dashboard
我们提供了Apache IoTDB Dashboard，在Grafana中显示的效果图如下所示：
-#### 5.2.4.1. 如何获取 Apache IoTDB Dashboard？
+##### 5.2.4.1. 如何获取 Apache IoTDB Dashboard？
1. 您可以在 grafana-metrics-example 文件夹下获取到对应不同iotdb版本的Dashboard的json文件。
2. 您可以访问[Grafana Dashboard官网](https://grafana.com/grafana/dashboards/)搜索`Apache IoTDB Dashboard`并使用
在创建Grafana时，您可以选择Import刚刚下载的json文件，并为Apache IoTDB Dashboard选择对应目标数据源。
-#### 5.2.4.2. Apache IoTDB ConfigNode Dashboard 说明
+##### 5.2.4.2. Apache IoTDB ConfigNode Dashboard 说明
> 除特殊说明的监控项以外，以下监控项均保证在Important级别的监控框架中可用。
- `Overview`：系统概述
@@ -408,7 +409,7 @@ static_configs:
- `The Time Consumed of Compliation Per Minute`：平均每分钟编译耗时
- `The Number Of Class`：JVM 加载和卸载的类数量
-#### 5.2.4.3. Apache IoTDB DataNode Dashboard 说明
+##### 5.2.4.3. Apache IoTDB DataNode Dashboard 说明
> 除特殊说明的监控项以外，以下监控项均保证在Important级别的监控框架中可用。
- `Overview`：系统概述
@@ -462,18 +463,18 @@ static_configs:
- `The Time Consumed of Compliation Per Minute`：平均每分钟编译耗时
- `The Number Of Class`：JVM 加载和卸载的类数量
-## 5.3. 使用 IoTDB 方式
+### 5.3. 使用 IoTDB 方式
-### 5.3.1. 监控指标的 IoTDB 映射关系
-> 对于 Metric Name 为 name, Tags 为 K1=V1, ..., Kn=Vn 的监控指标有如下映射，以默认写到 root.__system.metric 为例
+#### 5.3.1. 监控指标的 IoTDB 映射关系
+> 对于 Metric Name 为 name, Tags 为 K1=V1, ..., Kn=Vn 的监控指标有如下映射，以默认写到 root.__system.metric.`ip:port` 为例
| 监控指标类型 | 映射关系 [...]
| ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- [...]
-| Counter | root.__system.metric.name.`K1=V1`...`Kn=Vn`.value [...]
-| AutoGauge、Gauge | root.__system.metric.name.`K1=V1`...`Kn=Vn`.value [...]
-| Histogram | root.__system.metric.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p75 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p100 [...]
-| Rate | root.__system.metric.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.m1 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.m5 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.m15 [...]
-| Timer | root.__system.metric.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p75 <br> root.__system.metric.name.`K1=V1`...`Kn=Vn`.p [...]
+| Counter | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.value [...]
+| AutoGauge、Gauge | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.value [...]
+| Histogram | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p75 <br> root.__system.metric.`ip:port [...]
+| Rate | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.m1 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.m5 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.m15 [...]
+| Timer | root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.count <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.max <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.mean <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.sum <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p0 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p25 <br> root.__system.metric.`ip:port`.name.`K1=V1`...`Kn=Vn`.p50 <br> root.__system.metric.`ip:por [...]
-### 5.3.2. 获取监控指标
+#### 5.3.2. 获取监控指标
根据如上的映射关系，可以构成相关的 IoTDB 查询语句获取监控指标
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