diff --git a/TOC.md b/TOC.md index 233378c78ee06..c19785d416126 100644 --- a/TOC.md +++ b/TOC.md @@ -284,6 +284,9 @@ - [`cluster_load`](/reference/system-databases/cluster-load.md) - [`cluster_systeminfo`](/reference/system-databases/cluster-systeminfo.md) - [`cluster_log`](/reference/system-databases/cluster-log.md) + - [`metrics_schema`](/reference/system-databases/metrics-schema.md) + - [`metrics_tables`](/reference/system-databases/metrics-tables.md) + - [`metrics_summary`](/reference/system-databases/metrics-summary.md) - [Errors Codes](/reference/error-codes.md) - [Supported Client Drivers](/reference/supported-clients.md) + Garbage Collection (GC) diff --git a/reference/system-databases/metrics-schema.md b/reference/system-databases/metrics-schema.md new file mode 100644 index 0000000000000..9efbf7b6b5451 --- /dev/null +++ b/reference/system-databases/metrics-schema.md @@ -0,0 +1,179 @@ +--- +title: Metrics Schema +summary: Learn the `METRICS_SCHEMA` schema. +category: reference +--- + +# Metrics Schema + +To dynamically observe and compare cluster conditions of different time ranges, the SQL diagnosis system introduces cluster monitoring system tables. All monitoring tables are in the metrics schema, and you can query the monitoring information using SQL statements in this schema. The data of the three monitoring-related summary tables ([`metrics_summary`](/reference/system-databases/metrics-summary.md), [`metrics_summary_by_label`](/reference/system-databases/metrics-summary.md), and `inspection_result`) are all obtained by querying the monitoring tables in the metrics schema. Currently, many system tables are added, so you can query the information of these tables using the [`information_schema.metrics_tables`](/reference/system-databases/metrics-tables.md) table. + +## Overview + +The following example uses the `tidb_query_duration` table to introduce the usage and working principles of the monitoring table. The working principles of other monitoring tables are similar. + +Query the information related to the `tidb_query_duration` table on `information_schema.metrics_tables`: + +{{< copyable "sql" >}} + +```sql +select * from information_schema.metrics_tables where table_name='tidb_query_duration'; +``` + +```sql ++---------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------+----------+----------------------------------------------+ +| TABLE_NAME | PROMQL | LABELS | QUANTILE | COMMENT | ++---------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------+----------+----------------------------------------------+ +| tidb_query_duration | histogram_quantile($QUANTILE, sum(rate(tidb_server_handle_query_duration_seconds_bucket{$LABEL_CONDITIONS}[$RANGE_DURATION])) by (le,sql_type,instance)) | instance,sql_type | 0.9 | The quantile of TiDB query durations(second) | ++---------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------+----------+----------------------------------------------+ +``` + +Field description: + +* `TABLE_NAME`: Corresponds to the table name in the metrics schema. In this example, the table name is `tidb_query_duration`. +* `PROMQL`: The working principle of the monitoring table is to map SQL statements to `PromQL` and convert Prometheus results into SQL query results. This field is the expression template of `PromQL`. When getting the data of the monitoring table, the query conditions are used to rewrite the variables in this template to generate the final query expression. +* `LABELS`: The label for the monitoring item. `tidb_query_duration` has two labels: `instance` and `sql_type`. +* `QUANTILE`: The percentile. For monitoring data of the histogram type, a default percentile is specified. If the value of this field is `0`, it means that the monitoring item corresponding to the monitoring table is not a histogram. +* `COMMENT`: Explanations for the monitoring table. You can see that the `tidb_query_duration` table is used to query the percentile time of the TiDB query execution, such as the query time of P999/P99/P90. The unit is second. + +To query the schema of the `tidb_query_duration` table, execute the following statement: + +{{< copyable "sql" >}} + +```sql +show create table metrics_schema.tidb_query_duration; +``` + +```sql ++---------------------+--------------------------------------------------------------------------------------------------------------------+ +| Table | Create Table | ++---------------------+--------------------------------------------------------------------------------------------------------------------+ +| tidb_query_duration | CREATE TABLE `tidb_query_duration` ( | +| | `time` datetime unsigned DEFAULT CURRENT_TIMESTAMP, | +| | `instance` varchar(512) DEFAULT NULL, | +| | `sql_type` varchar(512) DEFAULT NULL, | +| | `quantile` double unsigned DEFAULT '0.9', | +| | `value` double unsigned DEFAULT NULL | +| | ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin COMMENT='The quantile of TiDB query durations(second)' | ++---------------------+--------------------------------------------------------------------------------------------------------------------+ +``` + +* `time`: The time of the monitoring item. +* `instance` and `sql_type`: The labels of the `tidb_query_duration` monitoring item. `instance` means the monitoring address. `sql_type` means the type of the executed SQL statement. +* `quantile`: The percentile. The monitoring item of the histogram type has this column, which indicates the percentile time of the query. For example, `quantile = 0.9` means to query the time of P90. +* `value`: The value of the monitoring item. + +The following statement queries the P99 time within the range of [`2020-03-25 23:40:00`, `2020-03-25 23:42:00`]. + +{{< copyable "sql" >}} + +```sql +select * from metrics_schema.tidb_query_duration where value is not null and time>='2020-03-25 23:40:00' and time <= '2020-03-25 23:42:00' and quantile=0.99; +``` + +```sql ++---------------------+-------------------+----------+----------+----------------+ +| time | instance | sql_type | quantile | value | ++---------------------+-------------------+----------+----------+----------------+ +| 2020-03-25 23:40:00 | 172.16.5.40:10089 | Insert | 0.99 | 0.509929485256 | +| 2020-03-25 23:41:00 | 172.16.5.40:10089 | Insert | 0.99 | 0.494690793986 | +| 2020-03-25 23:42:00 | 172.16.5.40:10089 | Insert | 0.99 | 0.493460506934 | +| 2020-03-25 23:40:00 | 172.16.5.40:10089 | Select | 0.99 | 0.152058493415 | +| 2020-03-25 23:41:00 | 172.16.5.40:10089 | Select | 0.99 | 0.152193879678 | +| 2020-03-25 23:42:00 | 172.16.5.40:10089 | Select | 0.99 | 0.140498483232 | +| 2020-03-25 23:40:00 | 172.16.5.40:10089 | internal | 0.99 | 0.47104 | +| 2020-03-25 23:41:00 | 172.16.5.40:10089 | internal | 0.99 | 0.11776 | +| 2020-03-25 23:42:00 | 172.16.5.40:10089 | internal | 0.99 | 0.11776 | ++---------------------+-------------------+----------+----------+----------------+ +``` + +The first row of the query result above means that at the time of 2020-03-25 23:40:00, on the TiDB instance `172.16.5.40:10089`, the P99 execution time of the `Insert` type statement is 0.509929485256 seconds. The meanings of other rows are similar. Other values of the `sql_type` column are described as follows: + +* `Select`: The `select` type statement is executed. +* `internal`: The internal SQL statement of TiDB, which is used to update the statistical information and get the global variables. + +To view the execution plan of the statement above, execute the following statement: + +{{< copyable "sql" >}} + +```sql +desc select * from metrics_schema.tidb_query_duration where value is not null and time>='2020-03-25 23:40:00' and time <= '2020-03-25 23:42:00' and quantile=0.99; +``` + +```sql ++------------------+----------+------+---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| id | estRows | task | access object | operator info | ++------------------+----------+------+---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +| Selection_5 | 8000.00 | root | | not(isnull(Column#5)) | +| └─MemTableScan_6 | 10000.00 | root | table:tidb_query_duration | PromQL:histogram_quantile(0.99, sum(rate(tidb_server_handle_query_duration_seconds_bucket{}[60s])) by (le,sql_type,instance)), start_time:2020-03-25 23:40:00, end_time:2020-03-25 23:42:00, step:1m0s | ++------------------+----------+------+---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ +``` + +From the result above, you can see that `PromQL`, `start_time`, `end_time`, and `step` are in the execution plan. During the execution process, TiDB calls the `query_range` HTTP API of Prometheus to query the monitoring data. + +You might find that in the range of [`2020-03-25 23:40:00`, `2020-03-25 23:42:00`], each label only has three time values. In the execution plan, the value of `step` is 1 minute, which is determined by the following two variables: + +* `tidb_metric_query_step`: The query resolution step width. To get the `query_range` data from Prometheus, you need to specify `start_time`, `end_time`, and `step`. `step` uses the value of this variable. +* `tidb_metric_query_range_duration`: When the monitoring data is queried, the value of the `$ RANGE_DURATION` field in `PROMQL` is replaced with the value of this variable. The default value is 60 seconds. + +To view the values of monitoring items with different granularities, you can modify the two session variables above before querying the monitoring table. For example: + +1. Modify the values of the two session variables and set the time granularity to 30 seconds. + + > **Note:** + > + > The minimum granularity supported by Prometheus is 30 seconds. + + {{< copyable "sql" >}} + + ```sql + set @@tidb_metric_query_step=30; + set @@tidb_metric_query_range_duration=30; + ``` + +2. Query the `tidb_query_duration` monitoring item as follows. From the result, you can see that within the 3-minute time range, each label has 6 time values, and the interval between each value is 30 seconds. + + {{< copyable "sql" >}} + + ```sql + select * from metrics_schema.tidb_query_duration where value is not null and time>='2020-03-25 23:40:00' and time <= '2020-03-25 23:42:00' and quantile=0.99; + ``` + + ```sql + +---------------------+-------------------+----------+----------+-----------------+ + | time | instance | sql_type | quantile | value | + +---------------------+-------------------+----------+----------+-----------------+ + | 2020-03-25 23:40:00 | 172.16.5.40:10089 | Insert | 0.99 | 0.483285651924 | + | 2020-03-25 23:40:30 | 172.16.5.40:10089 | Insert | 0.99 | 0.484151462113 | + | 2020-03-25 23:41:00 | 172.16.5.40:10089 | Insert | 0.99 | 0.504576 | + | 2020-03-25 23:41:30 | 172.16.5.40:10089 | Insert | 0.99 | 0.493577384561 | + | 2020-03-25 23:42:00 | 172.16.5.40:10089 | Insert | 0.99 | 0.49482474311 | + | 2020-03-25 23:40:00 | 172.16.5.40:10089 | Select | 0.99 | 0.189253402185 | + | 2020-03-25 23:40:30 | 172.16.5.40:10089 | Select | 0.99 | 0.184224951851 | + | 2020-03-25 23:41:00 | 172.16.5.40:10089 | Select | 0.99 | 0.151673410553 | + | 2020-03-25 23:41:30 | 172.16.5.40:10089 | Select | 0.99 | 0.127953838989 | + | 2020-03-25 23:42:00 | 172.16.5.40:10089 | Select | 0.99 | 0.127455434547 | + | 2020-03-25 23:40:00 | 172.16.5.40:10089 | internal | 0.99 | 0.0624 | + | 2020-03-25 23:40:30 | 172.16.5.40:10089 | internal | 0.99 | 0.12416 | + | 2020-03-25 23:41:00 | 172.16.5.40:10089 | internal | 0.99 | 0.0304 | + | 2020-03-25 23:41:30 | 172.16.5.40:10089 | internal | 0.99 | 0.06272 | + | 2020-03-25 23:42:00 | 172.16.5.40:10089 | internal | 0.99 | 0.0629333333333 | + +---------------------+-------------------+----------+----------+-----------------+ + ``` + +3. View the execution plan. From the result, you can also see that the values of `PromQL` and `step` in the execution plan have been changed to 30 seconds. + + {{< copyable "sql" >}} + + ```sql + desc select * from metrics_schema.tidb_query_duration where value is not null and time>='2020-03-25 23:40:00' and time <= '2020-03-25 23:42:00' and quantile=0.99; + ``` + + ```sql + +------------------+----------+------+---------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + | id | estRows | task | access object | operator info | + +------------------+----------+------+---------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + | Selection_5 | 8000.00 | root | | not(isnull(Column#5)) | + | └─MemTableScan_6 | 10000.00 | root | table:tidb_query_duration | PromQL:histogram_quantile(0.99, sum(rate(tidb_server_handle_query_duration_seconds_bucket{}[30s])) by (le,sql_type,instance)), start_time:2020-03-25 23:40:00, end_time:2020-03-25 23:42:00, step:30s | + +------------------+----------+------+---------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ + ``` diff --git a/reference/system-databases/metrics-summary.md b/reference/system-databases/metrics-summary.md new file mode 100644 index 0000000000000..c18362e063fee --- /dev/null +++ b/reference/system-databases/metrics-summary.md @@ -0,0 +1,189 @@ +--- +title: METRICS_SUMMARY +summary: Learn the `METRICS_SUMMARY` system table. +category: reference +--- + +# METRICS_SUMMARY + +The TiDB cluster has many monitoring metrics. To make it easy to detect abnormal monitoring metrics, TiDB 4.0 introduces the following two monitoring summary tables: + +* `information_schema.metrics_summary` +* `information_schema.metrics_summary_by_label` + +The two tables summarize all monitoring data for you to check each monitoring metric efficiently. Compared with `information_schema.metrics_summary`, the `information_schema.metrics_summary_by_label` table has an additional `label` column and performs differentiated statistics according to different labels. + +{{< copyable "sql" >}} + +```sql +desc metrics_summary; +``` + +```sql ++--------------+-----------------------+------+------+---------+-------+ +| Field | Type | Null | Key | Default | Extra | ++--------------+-----------------------+------+------+---------+-------+ +| METRICS_NAME | varchar(64) | YES | | NULL | | +| QUANTILE | double unsigned | YES | | NULL | | +| SUM_VALUE | double(22,6) unsigned | YES | | NULL | | +| AVG_VALUE | double(22,6) unsigned | YES | | NULL | | +| MIN_VALUE | double(22,6) unsigned | YES | | NULL | | +| MAX_VALUE | double(22,6) unsigned | YES | | NULL | | +| COMMENT | varchar(256) | YES | | NULL | | ++--------------+-----------------------+------+------+---------+-------+ +``` + +Field description: + +* `METRICS_NAME`: The monitoring table name. +* `QUANTILE`: The percentile. You can specify `QUANTILE` using SQL statements. For example: + * `select * from metrics_summary where quantile=0.99` specifies viewing the data of the 0.99 percentile. + * `select * from metrics_summary where quantile in (0.80, 0.90, 0.99, 0.999)` specifies viewing the data of the 0.8, 0.90, 0.99, 0.999 percentiles at the same time. +* `SUM_VALUE`, `AVG_VALUE`, `MIN_VALUE`, and `MAX_VALUE` respectively mean the sum, the average value, the minimum value, and the maximum value. +* `COMMENT`: The comment for the corresponding monitoring table. + +For example: + +To query the three groups of monitoring items with the highest average time consumption in the TiDB cluster within the time range of `'2020-03-08 13:23:00', '2020-03-08 13: 33: 00'`, you can directly query the `information_schema.metrics_summary` table and use the `/*+ time_range() */` hint to specify the time range. The SQL statement is as follows: + +{{< copyable "sql" >}} + +```sql +select /*+ time_range('2020-03-08 13:23:00','2020-03-08 13:33:00') */ * +from information_schema.`METRICS_SUMMARY` +where metrics_name like 'tidb%duration' + and avg_value > 0 + and quantile = 0.99 +order by avg_value desc +limit 3\G +``` + +```sql +***************************[ 1. row ]*************************** +METRICS_NAME | tidb_get_token_duration +QUANTILE | 0.99 +SUM_VALUE | 8.972509 +AVG_VALUE | 0.996945 +MIN_VALUE | 0.996515 +MAX_VALUE | 0.997458 +COMMENT | The quantile of Duration (us) for getting token, it should be small until concurrency limit is reached(second) +***************************[ 2. row ]*************************** +METRICS_NAME | tidb_query_duration +QUANTILE | 0.99 +SUM_VALUE | 0.269079 +AVG_VALUE | 0.007272 +MIN_VALUE | 0.000667 +MAX_VALUE | 0.01554 +COMMENT | The quantile of TiDB query durations(second) +***************************[ 3. row ]*************************** +METRICS_NAME | tidb_kv_request_duration +QUANTILE | 0.99 +SUM_VALUE | 0.170232 +AVG_VALUE | 0.004601 +MIN_VALUE | 0.000975 +MAX_VALUE | 0.013 +COMMENT | The quantile of kv requests durations by store +``` + +Similarly, the following example queries the `metrics_summary_by_label` monitoring summary table: + +{{< copyable "sql" >}} + +```sql +select /*+ time_range('2020-03-08 13:23:00','2020-03-08 13:33:00') */ * +from information_schema.`METRICS_SUMMARY_BY_LABEL` +where metrics_name like 'tidb%duration' + and avg_value > 0 + and quantile = 0.99 +order by avg_value desc +limit 10\G +``` + +```sql +***************************[ 1. row ]*************************** +INSTANCE | 172.16.5.40:10089 +METRICS_NAME | tidb_get_token_duration +LABEL | +QUANTILE | 0.99 +SUM_VALUE | 8.972509 +AVG_VALUE | 0.996945 +MIN_VALUE | 0.996515 +MAX_VALUE | 0.997458 +COMMENT | The quantile of Duration (us) for getting token, it should be small until concurrency limit is reached(second) +***************************[ 2. row ]*************************** +INSTANCE | 172.16.5.40:10089 +METRICS_NAME | tidb_query_duration +LABEL | Select +QUANTILE | 0.99 +SUM_VALUE | 0.072083 +AVG_VALUE | 0.008009 +MIN_VALUE | 0.007905 +MAX_VALUE | 0.008241 +COMMENT | The quantile of TiDB query durations(second) +***************************[ 3. row ]*************************** +INSTANCE | 172.16.5.40:10089 +METRICS_NAME | tidb_query_duration +LABEL | Rollback +QUANTILE | 0.99 +SUM_VALUE | 0.072083 +AVG_VALUE | 0.008009 +MIN_VALUE | 0.007905 +MAX_VALUE | 0.008241 +COMMENT | The quantile of TiDB query durations(second) +``` + +The second and third rows of the query results above indicate that the `Select` and `Rollback` statements on `tidb_query_duration` have a long average execution time. + +In addition to the example above, you can use the monitoring summary table to quickly find the module with the largest change from the monitoring data by comparing the full link monitoring items of the two time periods, and quickly locate the bottleneck. The following example compares all monitoring items in two periods (where t1 is the baseline) and sorts these items according to the greatest difference: + +* Period t1：`("2020-03-03 17:08:00", "2020-03-03 17:11:00")` +* Period t2：`("2020-03-03 17:18:00", "2020-03-03 17:21:00")` + +The monitoring items of the two time periods are joined according to `METRICS_NAME` and sorted according to the difference value. `TIME_RANGE` is the hint that specifies the query time. + +{{< copyable "sql" >}} + +```sql +SELECT GREATEST(t1.avg_value,t2.avg_value)/LEAST(t1.avg_value, + t2.avg_value) AS ratio, + t1.metrics_name, + t1.avg_value as t1_avg_value, + t2.avg_value as t2_avg_value, + t2.comment +FROM + (SELECT /*+ time_range("2020-03-03 17:08:00", "2020-03-03 17:11:00")*/ * + FROM information_schema.metrics_summary ) t1 +JOIN + (SELECT /*+ time_range("2020-03-03 17:18:00", "2020-03-03 17:21:00")*/ * + FROM information_schema.metrics_summary ) t2 + ON t1.metrics_name = t2.metrics_name +ORDER BY ratio DESC limit 10; +``` + +```sql ++----------------+------------------------------------------+----------------+------------------+---------------------------------------------------------------------------------------------+ +| ratio | metrics_name | t1_avg_value | t2_avg_value | comment | ++----------------+------------------------------------------+----------------+------------------+---------------------------------------------------------------------------------------------+ +| 5865.59537065 | tidb_slow_query_cop_process_total_time | 0.016333 | 95.804724 | The total time of TiDB slow query statistics with slow query total cop process time(second) | +| 3648.74109023 | tidb_distsql_partial_scan_key_total_num | 10865.666667 | 39646004.4394 | The total num of distsql partial scan key numbers | +| 267.002351165 | tidb_slow_query_cop_wait_total_time | 0.003333 | 0.890008 | The total time of TiDB slow query statistics with slow query total cop wait time(second) | +| 192.43267836 | tikv_cop_total_response_total_size | 2515333.66667 | 484032394.445 | | +| 192.43267836 | tikv_cop_total_response_size_per_seconds | 41922.227778 | 8067206.57408 | | +| 152.780296296 | tidb_distsql_scan_key_total_num | 5304.333333 | 810397.618317 | The total num of distsql scan numbers | +| 126.042290167 | tidb_distsql_execution_total_time | 0.421622 | 53.142143 | The total time of distsql execution(second) | +| 105.164020657 | tikv_cop_scan_details | 134.450733 | 14139.379665 | | +| 105.164020657 | tikv_cop_scan_details_total | 8067.043981 | 848362.77991 | | +| 101.635495394 | tikv_cop_scan_keys_num | 1070.875 | 108838.91113 | | ++----------------+------------------------------------------+----------------+------------------+---------------------------------------------------------------------------------------------+ +``` + +From the query result above, you can get the following information: + +* `tib_slow_query_cop_process_total_time` (the time consumption of `cop process` in TiDB slow queries) in the period t2 is 5,865 times higher than that in period t1. +* `tidb_distsql_partial_scan_key_total_num` (the number of keys to scan requested by TiDB’s `distsql`) in period t2 is 3,648 times higher than that in period t1. During period t2, `tidb_slow_query_cop_wait_total_time` (the waiting time of Coprocessor requesting to queue up in the TiDB slow query) is 267 times higher than that in period t1. +* `tikv_cop_total_response_size` (the size of the TiKV Coprocessor request result) in period t2 is 192 times higher than that in period t1. +* `tikv_cop_scan_details` in period t2 (the scan requested by the TiKV Coprocessor) is 105 times higher than that in period t1. + +From the result above, you can see that the Coprocessor requests in period t2 are much more than those in period t1. This causes TiKV Coprocessor to be overloaded, and the `cop task` has to wait. It might be that some large queries appear in period t2 that bring more load. + +In fact, during the entire time period from t1 to t2, the `go-ycsb` pressure test is running. Then 20 `tpch` queries are running during period t2. So it is the `tpch` queries that cause many Coprocessor requests. diff --git a/reference/system-databases/metrics-tables.md b/reference/system-databases/metrics-tables.md new file mode 100644 index 0000000000000..622039ac4e068 --- /dev/null +++ b/reference/system-databases/metrics-tables.md @@ -0,0 +1,35 @@ +--- +title: METRICS_TABLES +summary: Learn the `METRICS_TABLES` system table. +category: reference +--- + +# METRICS_TABLES + +The `METRICS_TABLES` table provides information of all monitoring tables in the [metrics schema](/reference/system-databases/metrics-schema.md). + +{{< copyable "sql" >}} + +```sql +desc metrics_tables; +``` + +```sql ++------------+-----------------+------+------+---------+-------+ +| Field | Type | Null | Key | Default | Extra | ++------------+-----------------+------+------+---------+-------+ +| TABLE_NAME | varchar(64) | YES | | NULL | | +| PROMQL | varchar(64) | YES | | NULL | | +| LABELS | varchar(64) | YES | | NULL | | +| QUANTILE | double unsigned | YES | | NULL | | +| COMMENT | varchar(256) | YES | | NULL | | ++------------+-----------------+------+------+---------+-------+ +``` + +Field description: + +* `TABLE_NAME`: Corresponds to the table name in `metrics_schema`. +* `PROMQL`: The working principle of the monitoring table is to map SQL statements to `PromQL` and convert Prometheus results into SQL query results. This field is the expression template of `PromQL`. When getting the data of the monitoring table, the query conditions are used to rewrite the variables in this template to generate the final query expression. +* `LABELS`: The label for the monitoring item. Each label corresponds to a column in the monitoring table. If the SQL statement contains the filter of the corresponding column, the corresponding `PromQL` changes accordingly. +* `QUANTILE`: The percentile. For monitoring data of the histogram type, a default percentile is specified. If the value of this field is `0`, it means that the monitoring item corresponding to the monitoring table is not a histogram. +* `COMMENT`: The comment about the monitoring table. diff --git a/reference/system-databases/sql-diagnosis.md b/reference/system-databases/sql-diagnosis.md index ad97e99453c05..e08ae1b9b25b7 100644 --- a/reference/system-databases/sql-diagnosis.md +++ b/reference/system-databases/sql-diagnosis.md @@ -40,14 +40,14 @@ On the system tables earlier than TiDB v4.0, you can only view the current node. ## Cluster monitoring tables -To dynamically observe and compare cluster conditions in different time periods, the SQL diagnosis system introduces cluster monitoring system tables. All monitoring tables are in `metrics_schema`, and you can query the monitoring information SQL statements. Using this method, you can perform correlated queries on all monitoring information of the entire cluster and compare the results of different time periods to quickly identify performance bottlenecks. +To dynamically observe and compare cluster conditions in different time periods, the SQL diagnosis system introduces cluster monitoring system tables. All monitoring tables are in `metrics_schema`, and you can query the monitoring information using SQL statements. Using this method, you can perform correlated queries on all monitoring information of the entire cluster and compare the results of different time periods to quickly identify performance bottlenecks. -+ `information_schema.metrics_tables`: Because many system tables exist now, you can query meta-information of these monitoring tables on the `information_schema.metrics_tables` table. ++ [`information_schema.metrics_tables`](/reference/system-databases/metrics-tables.md)): Because many system tables exist now, you can query meta-information of these monitoring tables on the `information_schema.metrics_tables` table. Because the TiDB cluster has many monitoring metrics, TiDB provides the following monitoring summary tables in v4.0: -+ The monitoring summary table `information_schema.metrics_summary` summarizes all monitoring data to for you to check each monitoring metric with higher efficiency. -+ The monitoring summary table `information_schema.metrics_summary_by_label` also summarizes all monitoring data, but this table performs differentiated statistics according to different labels. ++ The monitoring summary table [`information_schema.metrics_summary`](/reference/system-databases/metrics-summary.md) summarizes all monitoring data to for you to check each monitoring metric with higher efficiency. ++ The monitoring summary table [`information_schema.metrics_summary_by_label`](/reference/system-databases/metrics-summary.md)) also summarizes all monitoring data, but this table performs differentiated statistics according to different labels. ## Automatic diagnosis