查看 Manticore 节点的高级信息最简单的方法是在 MySQL 客户端中运行 status。它将显示各种方面的信息，例如：
‹›
- SQL
SQL
📋
⚙
mysql> status
‹›
Response
--------------
mysql Ver 14.14 Distrib 5.7.30, for Linux (x86_64) using EditLine wrapper
Connection id: 378
Current database: Manticore
Current user: Usual
SSL: Not in use
Current pager: stdout
Using outfile: ''
Using delimiter: ;
Server version: 3.4.3 a48c61d6@200702 coroutines git branch coroutines_work_junk...origin/coroutines_work_junk
Protocol version: 10
Connection: 0 via TCP/IP
Server characterset:
Db characterset:
Client characterset: utf8
Conn. characterset: utf8
TCP port: 8306
Uptime: 23 hours 6 sec
Threads: 12 Queue: 3 Clients: 1 Vip clients: 0 Tasks: 5 Queries: 318967 Wall: 7h CPU: 0us
Queue/Th: 0.2 Tasks/Th: 0.4
--------------SHOW STATUS [ LIKE pattern ]SHOW STATUS 是一个 SQL 语句，显示各种有用的性能计数器。IO 和 CPU 计数器仅在 searchd 启动时使用了 --iostats 和 --cpustats 开关（或通过 SET GLOBAL iostats/cpustats=1 启用）时可用。
‹›
- SQL
SQL
📋
⚙
SHOW STATUS;
‹›
Response
+-------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------+
| Counter | Value |
+-------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------+
| uptime | 182 |
| connections | 368 |
| maxed_out | 0 |
| version | 6.3.7 b10359434@24100213 dev (columnar 2.3.1 bd59d08@24093015) (secondary 2.3.1 bd59d08@24093015) (knn 2.3.1 bd59d08@24093015) (buddy v2.3.13) |
| mysql_version | 6.3.7 b10359434@24100213 dev (columnar 2.3.1 bd59d08@24093015) (secondary 2.3.1 bd59d08@24093015) (knn 2.3.1 bd59d08@24093015) |
| command_search | 4 |
| command_excerpt | 0 |
| command_update | 2 |
| command_keywords | 0 |
| command_persist | 0 |
| command_status | 8 |
| command_flushattrs | 0 |
| command_sphinxql | 0 |
| command_ping | 0 |
| command_delete | 3 |
| command_set | 0 |
| command_insert | 3 |
| command_replace | 3 |
| command_commit | 0 |
| command_suggest | 0 |
| command_json | 0 |
| command_callpq | 0 |
| command_cluster | 0 |
| command_getfield | 0 |
| insert_replace_stats_ms_avg | 42.625 35.895 35.895 |
| insert_replace_stats_ms_min | 0.096 0.096 0.096 |
| insert_replace_stats_ms_max | 85.154 85.154 85.154 |
| insert_replace_stats_ms_pct95 | 42.625 42.625 42.625 |
| insert_replace_stats_ms_pct99 | 42.625 42.625 42.625 |
| search_stats_ms_avg | 0.209 0.188 0.188 |
| search_stats_ms_min | 0.205 0.058 0.058 |
| search_stats_ms_max | 0.214 0.278 0.278 |
| search_stats_ms_pct95 | 0.214 0.278 0.278 |
| search_stats_ms_pct99 | 0.214 0.278 0.278 |
| update_stats_ms_avg | 0.024 0.024 0.024 |
| update_stats_ms_min | 0.007 0.007 0.007 |
| update_stats_ms_max | 0.042 0.042 0.042 |
| update_stats_ms_pct95 | 0.042 0.042 0.042 |
| update_stats_ms_pct99 | 0.042 0.042 0.042 |
| agent_connect | 0 |
| agent_tfo | 0 |
| agent_retry | 0 |
| queries | 6 |
| dist_queries | 0 |
| workers_total | 32 |
| workers_active | 3 |
| workers_clients | 1 |
| workers_clients_vip | 0 |
| workers_clients_buddy | 1 |
| work_queue_length | 6 |
| load | 0.03 0.03 0.03 |
| load_primary | 0.00 0.00 0.00 |
| load_secondary | 0.00 0.00 0.00 |
| query_wall | 0.000 |
| query_cpu | OFF |
| dist_wall | 0.000 |
| dist_local | 0.000 |
| dist_wait | 0.000 |
| query_reads | OFF |
| query_readkb | OFF |
| query_readtime | OFF |
| avg_query_wall | 0.000 |
| avg_query_cpu | OFF |
| avg_dist_wall | 0.000 |
| avg_dist_local | 0.000 |
| avg_dist_wait | 0.000 |
| avg_query_reads | OFF |
| avg_query_readkb | OFF |
| avg_query_readtime | OFF |
| qcache_max_bytes | 16777216 |
| qcache_thresh_msec | 3000 |
| qcache_ttl_sec | 60 |
| qcache_cached_queries | 0 |
| qcache_used_bytes | 0 |
| qcache_hits | 0 |
+-------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------+支持可选的 LIKE 子句，允许您仅选择匹配特定模式的变量。模式语法遵循标准 SQL 通配符，其中 % 表示任意数量的任意字符，_ 表示单个字符。
‹›
- qcache
- perf_stats
📋
⚙
SHOW STATUS LIKE 'qcache%';SHOW STATUS LIKE '%stats_ms%';
‹›
Response
+-----------------------+-------+
| Counter | Value |
+-----------------------+-------+
| qcache_max_bytes | 0 |
| qcache_thresh_msec | 3000 |
| qcache_ttl_sec | 60 |
| qcache_cached_queries | 0 |
| qcache_used_bytes | 0 |
| qcache_hits | 0 |
+-----------------------+-------++-------------------------------+-------------------+
| Counter | Value |
+-------------------------------+-------------------+
| insert_replace_stats_ms_avg | N/A 35.895 35.895 |
| insert_replace_stats_ms_min | N/A 0.096 0.096 |
| insert_replace_stats_ms_max | N/A 85.154 85.154 |
| insert_replace_stats_ms_pct95 | N/A 42.625 42.625 |
| insert_replace_stats_ms_pct99 | N/A 42.625 42.625 |
| search_stats_ms_avg | N/A 0.188 0.188 |
| search_stats_ms_min | N/A 0.058 0.058 |
| search_stats_ms_max | N/A 0.278 0.278 |
| search_stats_ms_pct95 | N/A 0.278 0.278 |
| search_stats_ms_pct99 | N/A 0.278 0.278 |
| update_stats_ms_avg | N/A 0.024 0.024 |
| update_stats_ms_min | N/A 0.007 0.007 |
| update_stats_ms_max | N/A 0.042 0.042 |
| update_stats_ms_pct95 | N/A 0.042 0.042 |
| update_stats_ms_pct99 | N/A 0.042 0.042 |
+-------------------------------+-------------------+SHOW STATUS 命令提供了 Manticore 中各种性能指标的详细报告，包括插入/替换、搜索和更新查询的查询时间统计。这些统计数据是在 1、5 和 15 分钟的滑动窗口内计算的，显示查询时间的平均值、最小值、最大值以及 95%/99% 百分位值。
这些指标有助于跟踪特定时间间隔内的性能，使得更容易发现查询响应时间的趋势并找到可能的瓶颈。
以下指标是 SHOW STATUS 输出的一部分：
*_avg：过去 1、5 和 15 分钟内每种查询类型的平均查询时间。*_min：每种查询类型记录的最短查询时间。*_max：每种查询类型记录的最长查询时间。*_pct95：95% 查询完成所需的时间。*_pct99：99% 查询完成所需的时间。
这些统计数据分别针对插入/替换（insert_replace_stats_*）、搜索（search_stats_*）和更新（update_stats_*）查询提供，详细展示了不同操作的性能。
如果在监控间隔内没有执行查询，系统将显示 N/A。
‹›
- perf_stats
perf_stats
📋
⚙
SHOW STATUS LIKE '%stats_ms%';
‹›
Response
+-------------------------------+-------------------+
| Counter | Value |
+-------------------------------+-------------------+
| insert_replace_stats_ms_avg | N/A 35.895 35.895 |
| insert_replace_stats_ms_min | N/A 0.096 0.096 |
| insert_replace_stats_ms_max | N/A 85.154 85.154 |
| insert_replace_stats_ms_pct95 | N/A 42.625 42.625 |
| insert_replace_stats_ms_pct99 | N/A 42.625 42.625 |
| search_stats_ms_avg | N/A 0.188 0.188 |
| search_stats_ms_min | N/A 0.058 0.058 |
| search_stats_ms_max | N/A 0.278 0.278 |
| search_stats_ms_pct95 | N/A 0.278 0.278 |
| search_stats_ms_pct99 | N/A 0.278 0.278 |
| update_stats_ms_avg | N/A 0.024 0.024 |
| update_stats_ms_min | N/A 0.007 0.007 |
| update_stats_ms_max | N/A 0.042 0.042 |
| update_stats_ms_pct95 | N/A 0.042 0.042 |
| update_stats_ms_pct99 | N/A 0.042 0.042 |
+-------------------------------+-------------------+SHOW SETTINGS 是一个 SQL 语句，显示配置文件中的当前设置。设置名称以以下格式表示：'config_section_name'.'setting_name'
结果还包括两个附加值：
configuration_file- 配置文件的路径worker_pid- 正在运行的searchd实例的进程 ID
‹›
- SQL
SQL
📋
⚙
SHOW SETTINGS;
‹›
Response
+--------------------------+-------------------------------------+
| Setting_name | Value |
+--------------------------+-------------------------------------+
| configuration_file | /etc/manticoresearch/manticore.conf |
| worker_pid | 658 |
| searchd.listen | 0.0.0:9312 |
| searchd.listen | 0.0.0:9306:mysql |
| searchd.listen | 0.0.0:9308:http |
| searchd.log | /var/log/manticore/searchd.log |
| searchd.query_log | /var/log/manticore/query.log |
| searchd.pid_file | /run/manticore/searchd.pid |
| searchd.data_dir | /var/lib/manticore |
| searchd.query_log_format | sphinxql |
| searchd.binlog_path | /var/lib/manticore/binlog |
| common.plugin_dir | /usr/local/lib/manticore |
| common.lemmatizer_base | /usr/share/manticore/morph/ |
+--------------------------+-------------------------------------+
13 rows in set (0.00 sec)SHOW AGENT ['agent_or_index'] STATUS [ LIKE pattern ]SHOW AGENT STATUS 显示远程代理或分布式表的统计信息。它包括最后请求时间、最后响应时间、各种错误和成功次数等值。统计信息针对每个代理在最近 1、5 和 15 个间隔内显示，每个间隔由 ha_period_karma 秒组成。
‹›
- SQL
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
📋
⚙
SHOW AGENT STATUS;$client->nodes()->agentstatus();utilsApi.sql('SHOW AGENT STATUS')await utilsApi.sql('SHOW AGENT STATUS')res = await utilsApi.sql("SHOW AGENT STATUS");utilsApi.sql("SHOW AGENT STATUS");utilsApi.Sql("SHOW AGENT STATUS");utils_api.sql("SHOW AGENT STATUS", Some(true)).await;res = await utilsApi.sql("SHOW AGENT STATUS");res := apiClient.UtilsAPI.Sql(context.Background()).Body("SHOW AGENT STATUS").Execute()
‹›
Response
+------------------------------------+----------------------------+
| Variable_name | Value |
+------------------------------------+----------------------------+
| status_period_seconds | 60 |
| status_stored_periods | 15 |
| ag_0_hostname | 192.168.0.202:6713 |
| ag_0_references | 2 |
| ag_0_lastquery | 0.41 |
| ag_0_lastanswer | 0.19 |
| ag_0_lastperiodmsec | 222 |
| ag_0_pingtripmsec | 10.521 |
| ag_0_errorsarow | 0 |
| ag_0_1periods_query_timeouts | 0 |
| ag_0_1periods_connect_timeouts | 0 |
| ag_0_1periods_connect_failures | 0 |
| ag_0_1periods_network_errors | 0 |
| ag_0_1periods_wrong_replies | 0 |
| ag_0_1periods_unexpected_closings | 0 |
| ag_0_1periods_warnings | 0 |
| ag_0_1periods_succeeded_queries | 27 |
| ag_0_1periods_msecsperquery | 232.31 |
| ag_0_5periods_query_timeouts | 0 |
| ag_0_5periods_connect_timeouts | 0 |
| ag_0_5periods_connect_failures | 0 |
| ag_0_5periods_network_errors | 0 |
| ag_0_5periods_wrong_replies | 0 |
| ag_0_5periods_unexpected_closings | 0 |
| ag_0_5periods_warnings | 0 |
| ag_0_5periods_succeeded_queries | 146 |
| ag_0_5periods_msecsperquery | 231.83 |
| ag_1_hostname | 192.168.0.202:6714 |
| ag_1_references | 2 |
| ag_1_lastquery | 0.41 |
| ag_1_lastanswer | 0.19 |
| ag_1_lastperiodmsec | 220 |
| ag_1_pingtripmsec | 10.004 |
| ag_1_errorsarow | 0 |
| ag_1_1periods_query_timeouts | 0 |
| ag_1_1periods_connect_timeouts | 0 |
| ag_1_1periods_connect_failures | 0 |
| ag_1_1periods_network_errors | 0 |
| ag_1_1periods_wrong_replies | 0 |
| ag_1_1periods_unexpected_closings | 0 |
| ag_1_1periods_warnings | 0 |
| ag_1_1periods_succeeded_queries | 27 |
| ag_1_1periods_msecsperquery | 231.24 |
| ag_1_5periods_query_timeouts | 0 |
| ag_1_5periods_connect_timeouts | 0 |
| ag_1_5periods_connect_failures | 0 |
| ag_1_5periods_network_errors | 0 |
| ag_1_5periods_wrong_replies | 0 |
| ag_1_5periods_unexpected_closings | 0 |
| ag_1_5periods_warnings | 0 |
| ag_1_5periods_succeeded_queries | 146 |
| ag_1_5periods_msecsperquery | 230.85 |
+------------------------------------+----------------------------+
50 rows in set (0.01 sec)Array(
[status_period_seconds] => 60
[status_stored_periods] => 15
[ag_0_hostname] => 192.168.0.202:6713
[ag_0_references] => 2
[ag_0_lastquery] => 0.41
[ag_0_lastanswer] => 0.19
[ag_0_lastperiodmsec] => 222
[ag_0_errorsarow] => 0
[ag_0_1periods_query_timeouts] => 0
[ag_0_1periods_connect_timeouts] => 0
[ag_0_1periods_connect_failures] => 0
[ag_0_1periods_network_errors] => 0
[ag_0_1periods_wrong_replies] => 0
[ag_0_1periods_unexpected_closings] => 0
[ag_0_1periods_warnings] => 0
[ag_0_1periods_succeeded_queries] => 27
[ag_0_1periods_msecsperquery] => 232.31
[ag_0_5periods_query_timeouts] => 0
[ag_0_5periods_connect_timeouts] => 0
[ag_0_5periods_connect_failures] => 0
[ag_0_5periods_network_errors] => 0
[ag_0_5periods_wrong_replies] => 0
[ag_0_5periods_unexpected_closings] => 0
[ag_0_5periods_warnings] => 0
[ag_0_5periods_succeeded_queries] => 146
[ag_0_5periods_msecsperquery] => 231.83
[ag_1_hostname 192.168.0.202:6714
[ag_1_references] => 2
[ag_1_lastquery] => 0.41
[ag_1_lastanswer] => 0.19
[ag_1_lastperiodmsec] => 220
[ag_1_errorsarow] => 0
[ag_1_1periods_query_timeouts] => 0
[ag_1_1periods_connect_timeouts] => 0
[ag_1_1periods_connect_failures] => 0
[ag_1_1periods_network_errors] => 0
[ag_1_1periods_wrong_replies] => 0
[ag_1_1periods_unexpected_closings] => 0
[ag_1_1periods_warnings] => 0
[ag_1_1periods_succeeded_queries] => 27
[ag_1_1periods_msecsperquery] => 231.24
[ag_1_5periods_query_timeouts] => 0
[ag_1_5periods_connect_timeouts] => 0
[ag_1_5periods_connect_failures] => 0
[ag_1_5periods_network_errors] => 0
[ag_1_5periods_wrong_replies] => 0
[ag_1_5periods_unexpected_closings
[ag_1_5periods_warnings] => 0
[ag_1_5periods_succeeded_queries] => 146
[ag_1_5periods_msecsperquery] => 230.85
){u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'status_period_seconds', u'Value': u'60'},
{u'Key': u'status_stored_periods', u'Value': u'15'},
{u'Key': u'ag_0_hostname', u'Value': u'192.168.0.202:6713'},
{u'Key': u'ag_0_references', u'Value': u'2'},
{u'Key': u'ag_0_lastquery', u'Value': u'0.41'},
{u'Key': u'ag_0_lastanswer', u'Value': u'0.19'},
{u'Key': u'ag_0_lastperiodmsec', u'Value': u'222'},
{u'Key': u'ag_0_errorsarow', u'Value': u'0'},
{u'Key': u'ag_0_1periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_1periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_1periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_0_1periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_0_1periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_0_1periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'ag_0_1periods_warnings', u'Value': u'0'},
{u'Key': u'ag_0_1periods_succeeded_queries', u'Value': u'27'},
{u'Key': u'ag_0_1periods_msecsperquery', u'Value': u'232.31'},
{u'Key': u'ag_0_5periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_5periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_5periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_0_5periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_0_5periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_0_5periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'ag_0_5periods_warnings', u'Value': u'0'},
{u'Key': u'ag_0_5periods_succeeded_queries', u'Value': u'146'},
{u'Key': u'ag_0_5periods_msecsperquery', u'Value': u'231.83'},
{u'Key': u'ag_1_hostname 192.168.0.202:6714'},
{u'Key': u'ag_1_references', u'Value': u'2'},
{u'Key': u'ag_1_lastquery', u'Value': u'0.41'},
{u'Key': u'ag_1_lastanswer', u'Value': u'0.19'},
{u'Key': u'ag_1_lastperiodmsec', u'Value': u'220'},
{u'Key': u'ag_1_errorsarow', u'Value': u'0'},
{u'Key': u'ag_1_1periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_1periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_1periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_1_1periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_1_1periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_1_1periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'ag_1_1periods_warnings', u'Value': u'0'},
{u'Key': u'ag_1_1periods_succeeded_queries', u'Value': u'27'},
{u'Key': u'ag_1_1periods_msecsperquery', u'Value': u'231.24'},
{u'Key': u'ag_1_5periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_5periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_5periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_1_5periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_1_5periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_1_5periods_warnings', u'Value': u'0'},
{u'Key': u'ag_1_5periods_succeeded_queries', u'Value': u'146'},
{u'Key': u'ag_1_5periods_msecsperquery', u'Value': u'230.85'}],
u'error': u'',
u'total': 0,
u'warning': u''}{u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'status_period_seconds', u'Value': u'60'},
{u'Key': u'status_stored_periods', u'Value': u'15'},
{u'Key': u'ag_0_hostname', u'Value': u'192.168.0.202:6713'},
{u'Key': u'ag_0_references', u'Value': u'2'},
{u'Key': u'ag_0_lastquery', u'Value': u'0.41'},
{u'Key': u'ag_0_lastanswer', u'Value': u'0.19'},
{u'Key': u'ag_0_lastperiodmsec', u'Value': u'222'},
{u'Key': u'ag_0_errorsarow', u'Value': u'0'},
{u'Key': u'ag_0_1periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_1periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_1periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_0_1periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_0_1periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_0_1periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'ag_0_1periods_warnings', u'Value': u'0'},
{u'Key': u'ag_0_1periods_succeeded_queries', u'Value': u'27'},
{u'Key': u'ag_0_1periods_msecsperquery', u'Value': u'232.31'},
{u'Key': u'ag_0_5periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_5periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_0_5periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_0_5periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_0_5periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_0_5periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'ag_0_5periods_warnings', u'Value': u'0'},
{u'Key': u'ag_0_5periods_succeeded_queries', u'Value': u'146'},
{u'Key': u'ag_0_5periods_msecsperquery', u'Value': u'231.83'},
{u'Key': u'ag_1_hostname 192.168.0.202:6714'},
{u'Key': u'ag_1_references', u'Value': u'2'},
{u'Key': u'ag_1_lastquery', u'Value': u'0.41'},
{u'Key': u'ag_1_lastanswer', u'Value': u'0.19'},
{u'Key': u'ag_1_lastperiodmsec', u'Value': u'220'},
{u'Key': u'ag_1_errorsarow', u'Value': u'0'},
{u'Key': u'ag_1_1periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_1periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_1periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_1_1periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_1_1periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_1_1periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'ag_1_1periods_warnings', u'Value': u'0'},
{u'Key': u'ag_1_1periods_succeeded_queries', u'Value': u'27'},
{u'Key': u'ag_1_1periods_msecsperquery', u'Value': u'231.24'},
{u'Key': u'ag_1_5periods_query_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_5periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'ag_1_5periods_connect_failures', u'Value': u'0'},
{u'Key': u'ag_1_5periods_network_errors', u'Value': u'0'},
{u'Key': u'ag_1_5periods_wrong_replies', u'Value': u'0'},
{u'Key': u'ag_1_5periods_warnings', u'Value': u'0'},
{u'Key': u'ag_1_5periods_succeeded_queries', u'Value': u'146'},
{u'Key': u'ag_1_5periods_msecsperquery', u'Value': u'230.85'}],
u'error': u'',
u'total': 0,
u'warning': u''}{
"columns":
[{
"Key":
{
"type": "string"
}
},
{
"Value":
{
"type": "string"
}
}],
"data":
[
{"Key": "status_period_seconds", "Value": "60"},
{"Key": "status_stored_periods", "Value": "15"},
{"Key": "ag_0_hostname", "Value": "192.168.0.202:6713"},
{"Key": "ag_0_references", "Value": "2"},
{"Key": "ag_0_lastquery", "Value": "0.41"},
{"Key": "ag_0_lastanswer", "Value": "0.19"},
{"Key": "ag_0_lastperiodmsec", "Value": "222"},
{"Key": "ag_0_errorsarow", "Value": "0"},
{"Key": "ag_0_1periods_query_timeouts", "Value": "0"},
{"Key": "ag_0_1periods_connect_timeouts", "Value": "0"},
{"Key": "ag_0_1periods_connect_failures", "Value": "0"},
{"Key": "ag_0_1periods_network_errors", "Value": "0"},
{"Key": "ag_0_1periods_wrong_replies", "Value": "0"},
{"Key": "ag_0_1periods_unexpected_closings", "Value": "0"},
{"Key": "ag_0_1periods_warnings", "Value": "0"},
{"Key": "ag_0_1periods_succeeded_queries", "Value": "27"},
{"Key": "ag_0_1periods_msecsperquery", "Value": "232.31"},
{"Key": "ag_0_5periods_query_timeouts", "Value": "0"},
{"Key": "ag_0_5periods_connect_timeouts", "Value": "0"},
{"Key": "ag_0_5periods_connect_failures", "Value": "0"},
{"Key": "ag_0_5periods_network_errors", "Value": "0"},
{"Key": "ag_0_5periods_wrong_replies", "Value": "0"},
{"Key": "ag_0_5periods_unexpected_closings", "Value": "0"},
{"Key": "ag_0_5periods_warnings", "Value": "0"},
{"Key": "ag_0_5periods_succeeded_queries", "Value": "146"},
{"Key": "ag_0_5periods_msecsperquery", "Value": "231.83"},
{"Key": "ag_1_hostname 192.168.0.202:6714"},
{"Key": "ag_1_references", "Value": "2"},
{"Key": "ag_1_lastquery", "Value": "0.41"},
{"Key": "ag_1_lastanswer", "Value": "0.19"},
{"Key": "ag_1_lastperiodmsec", "Value": "220"},
{"Key": "ag_1_errorsarow", "Value": "0"},
{"Key": "ag_1_1periods_query_timeouts", "Value": "0"},
{"Key": "ag_1_1periods_connect_timeouts", "Value": "0"},
{"Key": "ag_1_1periods_connect_failures", "Value": "0"},
{"Key": "ag_1_1periods_network_errors", "Value": "0"},
{"Key": "ag_1_1periods_wrong_replies", "Value": "0"},
{"Key": "ag_1_1periods_unexpected_closings", "Value": "0"},
{"Key": "ag_1_1periods_warnings", "Value": "0"},
{"Key": "ag_1_1periods_succeeded_queries", "Value": "27"},
{"Key": "ag_1_1periods_msecsperquery", "Value": "231.24"},
{"Key": "ag_1_5periods_query_timeouts", "Value": "0"},
{"Key": "ag_1_5periods_connect_timeouts", "Value": "0"},
{"Key": "ag_1_5periods_connect_failures", "Value": "0"},
{"Key": "ag_1_5periods_network_errors", "Value": "0"},
{"Key": "ag_1_5periods_wrong_replies", "Value": "0"},
{"Key": "ag_1_5periods_warnings", "Value": "0"},
{"Key": "ag_1_5periods_succeeded_queries", "Value": "146"},
{"Key": "ag_1_5periods_msecsperquery", "Value": "230.85"}
],
"error": "",
"total": 0,
"warning": ""
}{columns=[{ Key : { type=string }},
{ Value : { type=string }}],
data : [
{ Key=status_period_seconds , Value=60 },
{ Key=status_stored_periods , Value=15 },
{ Key=ag_0_hostname , Value=192.168.0.202:6713 },
{ Key=ag_0_references , Value=2 },
{ Key=ag_0_lastquery , Value=0.41 },
{ Key=ag_0_lastanswer , Value=0.19 },
{ Key=ag_0_lastperiodmsec , Value=222 },
{ Key=ag_0_errorsarow , Value=0 },
{ Key=ag_0_1periods_query_timeouts , Value=0 },
{ Key=ag_0_1periods_connect_timeouts , Value=0 },
{ Key=ag_0_1periods_connect_failures , Value=0 },
{ Key=ag_0_1periods_network_errors , Value=0 },
{ Key=ag_0_1periods_wrong_replies , Value=0 },
{ Key=ag_0_1periods_unexpected_closings , Value=0 },
{ Key=ag_0_1periods_warnings , Value=0 },
{ Key=ag_0_1periods_succeeded_queries , Value=27 },
{ Key=ag_0_1periods_msecsperquery , Value=232.31 },
{ Key=ag_0_5periods_query_timeouts , Value=0 },
{ Key=ag_0_5periods_connect_timeouts , Value=0 },
{ Key=ag_0_5periods_connect_failures , Value=0 },
{ Key=ag_0_5periods_network_errors , Value=0 },
{ Key=ag_0_5periods_wrong_replies , Value=0 },
{ Key=ag_0_5periods_unexpected_closings , Value=0 },
{ Key=ag_0_5periods_warnings , Value=0 },
{ Key=ag_0_5periods_succeeded_queries , Value=146 },
{ Key=ag_0_5periods_msecsperquery , Value=231.83 },
{ Key=ag_1_hostname 192.168.0.202:6714 },
{ Key=ag_1_references , Value=2 },
{ Key=ag_1_lastquery , Value=0.41 },
{ Key=ag_1_lastanswer , Value=0.19 },
{ Key=ag_1_lastperiodmsec , Value=220 },
{ Key=ag_1_errorsarow , Value=0 },
{ Key=ag_1_1periods_query_timeouts , Value=0 },
{ Key=ag_1_1periods_connect_timeouts , Value=0 },
{ Key=ag_1_1periods_connect_failures , Value=0 },
{ Key=ag_1_1periods_network_errors , Value=0 },
{ Key=ag_1_1periods_wrong_replies , Value=0 },
{ Key=ag_1_1periods_unexpected_closings , Value=0 },
{ Key=ag_1_1periods_warnings , Value=0 },
{ Key=ag_1_1periods_succeeded_queries , Value=27 },
{ Key=ag_1_1periods_msecsperquery , Value=231.24 },
{ Key=ag_1_5periods_query_timeouts , Value=0 },
{ Key=ag_1_5periods_connect_timeouts , Value=0 },
{ Key=ag_1_5periods_connect_failures , Value=0 },
{ Key=ag_1_5periods_network_errors , Value=0 },
{ Key=ag_1_5periods_wrong_replies , Value=0 },
{ Key=ag_1_5periods_warnings , Value=0 },
{ Key=ag_1_5periods_succeeded_queries , Value=146 },
{ Key=ag_1_5periods_msecsperquery , Value=230.85 }],
error= ,
total=0,
warning= }{columns=[{ Key : { type=string }},
{ Value : { type=string }}],
data : [
{ Key=status_period_seconds , Value=60 },
{ Key=status_stored_periods , Value=15 },
{ Key=ag_0_hostname , Value=192.168.0.202:6713 },
{ Key=ag_0_references , Value=2 },
{ Key=ag_0_lastquery , Value=0.41 },
{ Key=ag_0_lastanswer , Value=0.19 },
{ Key=ag_0_lastperiodmsec , Value=222 },
{ Key=ag_0_errorsarow , Value=0 },
{ Key=ag_0_1periods_query_timeouts , Value=0 },
{ Key=ag_0_1periods_connect_timeouts , Value=0 },
{ Key=ag_0_1periods_connect_failures , Value=0 },
{ Key=ag_0_1periods_network_errors , Value=0 },
{ Key=ag_0_1periods_wrong_replies , Value=0 },
{ Key=ag_0_1periods_unexpected_closings , Value=0 },
{ Key=ag_0_1periods_warnings , Value=0 },
{ Key=ag_0_1periods_succeeded_queries , Value=27 },
{ Key=ag_0_1periods_msecsperquery , Value=232.31 },
{ Key=ag_0_5periods_query_timeouts , Value=0 },
{ Key=ag_0_5periods_connect_timeouts , Value=0 },
{ Key=ag_0_5periods_connect_failures , Value=0 },
{ Key=ag_0_5periods_network_errors , Value=0 },
{ Key=ag_0_5periods_wrong_replies , Value=0 },
{ Key=ag_0_5periods_unexpected_closings , Value=0 },
{ Key=ag_0_5periods_warnings , Value=0 },
{ Key=ag_0_5periods_succeeded_queries , Value=146 },
{ Key=ag_0_5periods_msecsperquery , Value=231.83 },
{ Key=ag_1_hostname 192.168.0.202:6714 },
{ Key=ag_1_references , Value=2 },
{ Key=ag_1_lastquery , Value=0.41 },
{ Key=ag_1_lastanswer , Value=0.19 },
{ Key=ag_1_lastperiodmsec , Value=220 },
{ Key=ag_1_errorsarow , Value=0 },
{ Key=ag_1_1periods_query_timeouts , Value=0 },
{ Key=ag_1_1periods_connect_timeouts , Value=0 },
{ Key=ag_1_1periods_connect_failures , Value=0 },
{ Key=ag_1_1periods_network_errors , Value=0 },
{ Key=ag_1_1periods_wrong_replies , Value=0 },
{ Key=ag_1_1periods_unexpected_closings , Value=0 },
{ Key=ag_1_1periods_warnings , Value=0 },
{ Key=ag_1_1periods_succeeded_queries , Value=27 },
{ Key=ag_1_1periods_msecsperquery , Value=231.24 },
{ Key=ag_1_5periods_query_timeouts , Value=0 },
{ Key=ag_1_5periods_connect_timeouts , Value=0 },
{ Key=ag_1_5periods_connect_failures , Value=0 },
{ Key=ag_1_5periods_network_errors , Value=0 },
{ Key=ag_1_5periods_wrong_replies , Value=0 },
{ Key=ag_1_5periods_warnings , Value=0 },
{ Key=ag_1_5periods_succeeded_queries , Value=146 },
{ Key=ag_1_5periods_msecsperquery , Value=230.85 }],
error="" ,
total=0,
warning="" }{columns=[{ Key : { type=string }},
{ Value : { type=string }}],
data : [
{ Key=status_period_seconds , Value=60 },
{ Key=status_stored_periods , Value=15 },
{ Key=ag_0_hostname , Value=192.168.0.202:6713 },
{ Key=ag_0_references , Value=2 },
{ Key=ag_0_lastquery , Value=0.41 },
{ Key=ag_0_lastanswer , Value=0.19 },
{ Key=ag_0_lastperiodmsec , Value=222 },
{ Key=ag_0_errorsarow , Value=0 },
{ Key=ag_0_1periods_query_timeouts , Value=0 },
{ Key=ag_0_1periods_connect_timeouts , Value=0 },
{ Key=ag_0_1periods_connect_failures , Value=0 },
{ Key=ag_0_1periods_network_errors , Value=0 },
{ Key=ag_0_1periods_wrong_replies , Value=0 },
{ Key=ag_0_1periods_unexpected_closings , Value=0 },
{ Key=ag_0_1periods_warnings , Value=0 },
{ Key=ag_0_1periods_succeeded_queries , Value=27 },
{ Key=ag_0_1periods_msecsperquery , Value=232.31 },
{ Key=ag_0_5periods_query_timeouts , Value=0 },
{ Key=ag_0_5periods_connect_timeouts , Value=0 },
{ Key=ag_0_5periods_connect_failures , Value=0 },
{ Key=ag_0_5periods_network_errors , Value=0 },
{ Key=ag_0_5periods_wrong_replies , Value=0 },
{ Key=ag_0_5periods_unexpected_closings , Value=0 },
{ Key=ag_0_5periods_warnings , Value=0 },
{ Key=ag_0_5periods_succeeded_queries , Value=146 },
{ Key=ag_0_5periods_msecsperquery , Value=231.83 },
{ Key=ag_1_hostname 192.168.0.202:6714 },
{ Key=ag_1_references , Value=2 },
{ Key=ag_1_lastquery , Value=0.41 },
{ Key=ag_1_lastanswer , Value=0.19 },
{ Key=ag_1_lastperiodmsec , Value=220 },
{ Key=ag_1_errorsarow , Value=0 },
{ Key=ag_1_1periods_query_timeouts , Value=0 },
{ Key=ag_1_1periods_connect_timeouts , Value=0 },
{ Key=ag_1_1periods_connect_failures , Value=0 },
{ Key=ag_1_1periods_network_errors , Value=0 },
{ Key=ag_1_1periods_wrong_replies , Value=0 },
{ Key=ag_1_1periods_unexpected_closings , Value=0 },
{ Key=ag_1_1periods_warnings , Value=0 },
{ Key=ag_1_1periods_succeeded_queries , Value=27 },
{ Key=ag_1_1periods_msecsperquery , Value=231.24 },
{ Key=ag_1_5periods_query_timeouts , Value=0 },
{ Key=ag_1_5periods_connect_timeouts , Value=0 },
{ Key=ag_1_5periods_connect_failures , Value=0 },
{ Key=ag_1_5periods_network_errors , Value=0 },
{ Key=ag_1_5periods_wrong_replies , Value=0 },
{ Key=ag_1_5periods_warnings , Value=0 },
{ Key=ag_1_5periods_succeeded_queries , Value=146 },
{ Key=ag_1_5periods_msecsperquery , Value=230.85 }],
error="" ,
total=0,
warning="" }{
"columns":
[{
"Key":
{
"type": "string"
}
},
{
"Value":
{
"type": "string"
}
}],
"data":
[
{"Key": "status_period_seconds", "Value": "60"},
{"Key": "status_stored_periods", "Value": "15"},
{"Key": "ag_0_hostname", "Value": "192.168.0.202:6713"},
{"Key": "ag_0_references", "Value": "2"},
{"Key": "ag_0_lastquery", "Value": "0.41"},
{"Key": "ag_0_lastanswer", "Value": "0.19"},
{"Key": "ag_0_lastperiodmsec", "Value": "222"},
{"Key": "ag_0_errorsarow", "Value": "0"},
{"Key": "ag_0_1periods_query_timeouts", "Value": "0"},
{"Key": "ag_0_1periods_connect_timeouts", "Value": "0"},
{"Key": "ag_0_1periods_connect_failures", "Value": "0"},
{"Key": "ag_0_1periods_network_errors", "Value": "0"},
{"Key": "ag_0_1periods_wrong_replies", "Value": "0"},
{"Key": "ag_0_1periods_unexpected_closings", "Value": "0"},
{"Key": "ag_0_1periods_warnings", "Value": "0"},
{"Key": "ag_0_1periods_succeeded_queries", "Value": "27"},
{"Key": "ag_0_1periods_msecsperquery", "Value": "232.31"},
{"Key": "ag_0_5periods_query_timeouts", "Value": "0"},
{"Key": "ag_0_5periods_connect_timeouts", "Value": "0"},
{"Key": "ag_0_5periods_connect_failures", "Value": "0"},
{"Key": "ag_0_5periods_network_errors", "Value": "0"},
{"Key": "ag_0_5periods_wrong_replies", "Value": "0"},
{"Key": "ag_0_5periods_unexpected_closings", "Value": "0"},
{"Key": "ag_0_5periods_warnings", "Value": "0"},
{"Key": "ag_0_5periods_succeeded_queries", "Value": "146"},
{"Key": "ag_0_5periods_msecsperquery", "Value": "231.83"},
{"Key": "ag_1_hostname 192.168.0.202:6714"},
{"Key": "ag_1_references", "Value": "2"},
{"Key": "ag_1_lastquery", "Value": "0.41"},
{"Key": "ag_1_lastanswer", "Value": "0.19"},
{"Key": "ag_1_lastperiodmsec", "Value": "220"},
{"Key": "ag_1_errorsarow", "Value": "0"},
{"Key": "ag_1_1periods_query_timeouts", "Value": "0"},
{"Key": "ag_1_1periods_connect_timeouts", "Value": "0"},
{"Key": "ag_1_1periods_connect_failures", "Value": "0"},
{"Key": "ag_1_1periods_network_errors", "Value": "0"},
{"Key": "ag_1_1periods_wrong_replies", "Value": "0"},
{"Key": "ag_1_1periods_unexpected_closings", "Value": "0"},
{"Key": "ag_1_1periods_warnings", "Value": "0"},
{"Key": "ag_1_1periods_succeeded_queries", "Value": "27"},
{"Key": "ag_1_1periods_msecsperquery", "Value": "231.24"},
{"Key": "ag_1_5periods_query_timeouts", "Value": "0"},
{"Key": "ag_1_5periods_connect_timeouts", "Value": "0"},
{"Key": "ag_1_5periods_connect_failures", "Value": "0"},
{"Key": "ag_1_5periods_network_errors", "Value": "0"},
{"Key": "ag_1_5periods_wrong_replies", "Value": "0"},
{"Key": "ag_1_5periods_warnings", "Value": "0"},
{"Key": "ag_1_5periods_succeeded_queries", "Value": "146"},
{"Key": "ag_1_5periods_msecsperquery", "Value": "230.85"}
],
"error": "",
"total": 0,
"warning": ""
}支持可选的 LIKE 子句，语法与 SHOW STATUS 中相同。
‹›
- SQL
- PHP
- Python
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- javascript
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- Go
📋
⚙
SHOW AGENT STATUS LIKE '%5period%msec%';$client->nodes()->agentstatus(
['body'=>
['pattern'=>'%5period%msec%']
]
);utilsApi.sql('SHOW AGENT STATUS LIKE \'%5period%msec%\'')await utilsApi.sql('SHOW AGENT STATUS LIKE \'%5period%msec%\'')res = await utilsApi.sql("SHOW AGENT STATUS LIKE \"%5period%msec%\"");utilsApi.sql("SHOW AGENT STATUS LIKE \"%5period%msec%\"");utilsApi.Sql("SHOW AGENT STATUS LIKE \"%5period%msec%\"");utils_api.sql("SHOW AGENT STATUS LIKE \"%5period%msec%\"", Some(true)).await;res = await utilsApi.sql("SHOW AGENT STATUS LIKE \"%5period%msec%\"");apiClient.UtilsAPI.Sql(context.Background()).Body("SHOW AGENT STATUS LIKE \"%5period%msec%\"").Execute()
‹›
Response
+-----------------------------+--------+
| Key | Value |
+-----------------------------+--------+
| ag_0_5periods_msecsperquery | 234.72 |
| ag_1_5periods_msecsperquery | 233.73 |
| ag_2_5periods_msecsperquery | 343.81 |
+-----------------------------+--------+
3 rows in set (0.00 sec)Array(
[ag_0_5periods_msecsperquery] => 234.72
[ag_1_5periods_msecsperquery] => 233.73
[ag_2_5periods_msecsperquery] => 343.81
){u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'ag_0_5periods_msecsperquery', u'Value': u'234.72'},
{u'Key': u'ag_1_5periods_msecsperquery', u'Value': u'233.73'},
{u'Key': u'ag_2_5periods_msecsperquery', u'Value': u'343.81'}],
u'error': u'',
u'total': 0,
u'warning': u''}{u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'ag_0_5periods_msecsperquery', u'Value': u'234.72'},
{u'Key': u'ag_1_5periods_msecsperquery', u'Value': u'233.73'},
{u'Key': u'ag_2_5periods_msecsperquery', u'Value': u'343.81'}],
u'error': u'',
u'total': 0,
u'warning': u''}{"columns": [{"Key": {"type": "string"}},
{"Value": {"type": "string"}}],
"data": [
{"Key": "ag_0_5periods_msecsperquery", "Value": "234.72"},
{"Key": "ag_1_5periods_msecsperquery", "Value": "233.73"},
{"Key": "ag_2_5periods_msecsperquery", "Value": "343.81"}],
"error": "",
"total": 0,
"warning": ""}{columns: [{Key={type=string}},
{Value={type=string}}],
data: [
{Key=ag_0_5periods_msecsperquery, Value=234.72},
{Key=ag_1_5periods_msecsperquery, Value=233.73},
{Key=ag_2_5periods_msecsperquery, Value=343.81}],
error: ,
total: 0,
warning: }{columns: [{Key={type=string}},
{Value={type=string}}],
data: [
{Key=ag_0_5periods_msecsperquery, Value=234.72},
{Key=ag_1_5periods_msecsperquery, Value=233.73},
{Key=ag_2_5periods_msecsperquery, Value=343.81}],
error: "",
total: 0,
warning: ""}{columns: [{Key={type=string}},
{Value={type=string}}],
data: [
{Key=ag_0_5periods_msecsperquery, Value=234.72},
{Key=ag_1_5periods_msecsperquery, Value=233.73},
{Key=ag_2_5periods_msecsperquery, Value=343.81}],
error: "",
total: 0,
warning: ""}{
"columns":
[{
"Key": {"type": "string"}
},
{
"Value": {"type": "string"}
}],
"data":
[
{"Key": "ag_0_5periods_msecsperquery", "Value": "234.72"},
{"Key": "ag_1_5periods_msecsperquery", "Value": "233.73"},
{"Key": "ag_2_5periods_msecsperquery", "Value": "343.81"}
],
"error": "",
"total": 0,
"warning": ""
}{
"columns":
[{
"Key": {"type": "string"}
},
{
"Value": {"type": "string"}
}],
"data":
[
{"Key": "ag_0_5periods_msecsperquery", "Value": "234.72"},
{"Key": "ag_1_5periods_msecsperquery", "Value": "233.73"},
{"Key": "ag_2_5periods_msecsperquery", "Value": "343.81"}
],
"error": "",
"total": 0,
"warning": ""
}You can specify a particular agent by its address. In this case, only that agent's data will be displayed. Additionally, the agent_ prefix will be used instead of ag_N_:
‹›
- SQL
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- Python-asyncio
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- Go
📋
⚙
SHOW AGENT '192.168.0.202:6714' STATUS LIKE '%15periods%';$client->nodes()->agentstatus(
['body'=>
['agent'=>'192.168.0.202:6714'],
['pattern'=>'%5period%msec%']
]
);utilsApi.sql('SHOW AGENT \'192.168.0.202:6714\' STATUS LIKE \'%15periods%\'')await utilsApi.sql('SHOW AGENT \'192.168.0.202:6714\' STATUS LIKE \'%15periods%\'')res = await utilsApi.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");utilsApi.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");utilsApi.Sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");utils_api.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"", Some(true)).await;res = await utilsApi.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");apiClient.UtilsAPI.Sql(context.Background()).Body("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\").Execute()
‹›
Response
+-------------------------------------+--------+
| Variable_name | Value |
+-------------------------------------+--------+
| agent_15periods_query_timeouts | 0 |
| agent_15periods_connect_timeouts | 0 |
| agent_15periods_connect_failures | 0 |
| agent_15periods_network_errors | 0 |
| agent_15periods_wrong_replies | 0 |
| agent_15periods_unexpected_closings | 0 |
| agent_15periods_warnings | 0 |
| agent_15periods_succeeded_queries | 439 |
| agent_15periods_msecsperquery | 231.73 |
+-------------------------------------+--------+
9 rows in set (0.00 sec)Array(
[agent_15periods_query_timeouts] => 0
[agent_15periods_connect_timeouts] => 0
[agent_15periods_connect_failures] => 0
[agent_15periods_network_errors] => 0
[agent_15periods_wrong_replies] => 0
[agent_15periods_unexpected_closings] => 0
[agent_15periods_warnings] => 0
[agent_15periods_succeeded_queries] => 439
[agent_15periods_msecsperquery] => 231.73
){u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'agent_15periods_query_timeouts', u'Value': u'0'},
{u'Key': u'agent_15periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'agent_15periods_connect_failures', u'Value': u'0'},
{u'Key': u'agent_15periods_network_errors', u'Value': u'0'},
{u'Key': u'agent_15periods_connect_failures', u'Value': u'0'},
{u'Key': u'agent_15periods_wrong_replies', u'Value': u'0'},
{u'Key': u'agent_15periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'agent_15periods_warnings', u'Value': u'0'},
{u'Key': u'agent_15periods_succeeded_queries', u'Value': u'439'},
{u'Key': u'agent_15periods_msecsperquery', u'Value': u'233.73'},
],
u'error': u'',
u'total': 0,
u'warning': u''}{u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'agent_15periods_query_timeouts', u'Value': u'0'},
{u'Key': u'agent_15periods_connect_timeouts', u'Value': u'0'},
{u'Key': u'agent_15periods_connect_failures', u'Value': u'0'},
{u'Key': u'agent_15periods_network_errors', u'Value': u'0'},
{u'Key': u'agent_15periods_connect_failures', u'Value': u'0'},
{u'Key': u'agent_15periods_wrong_replies', u'Value': u'0'},
{u'Key': u'agent_15periods_unexpected_closings', u'Value': u'0'},
{u'Key': u'agent_15periods_warnings', u'Value': u'0'},
{u'Key': u'agent_15periods_succeeded_queries', u'Value': u'439'},
{u'Key': u'agent_15periods_msecsperquery', u'Value': u'233.73'},
],
u'error': u'',
u'total': 0,
u'warning': u''}{"columns": [{"Key": {"type": "string"}},
{"Value": {"type": "string"}}],
"data": [
{"Key": "agent_15periods_query_timeouts", "Value": "0"},
{"Key": "agent_15periods_connect_timeouts", "Value": "0"},
{"Key": "agent_15periods_connect_failures", "Value": "0"},
{"Key": "agent_15periods_network_errors", "Value": "0"},
{"Key": "agent_15periods_connect_failures", "Value": "0"},
{"Key": "agent_15periods_wrong_replies", "Value": "0"},
{"Key": "agent_15periods_unexpected_closings", "Value": "0"},
{"Key": "agent_15periods_warnings", "Value": "0"},
{"Key": "agent_15periods_succeeded_queries", "Value": "439"},
{"Key": "agent_15periods_msecsperquery", "Value": "233.73"},
],
"error": "",
"total": 0,
"warning": ""}{columns=[{Key={type=string}},
{Value={type=string}}],
data=[
{Key=agent_15periods_query_timeouts, Value=0},
{Key=agent_15periods_connect_timeouts, Value=0},
{Key=agent_15periods_connect_failures, Value=0},
{Key=agent_15periods_network_errors, Value=0},
{Key=agent_15periods_connect_failures, Value=0},
{Key=agent_15periods_wrong_replies, Value=0},
{Key=agent_15periods_unexpected_closings, Value=0},
{Key=agent_15periods_warnings, Value=0},
{Key=agent_15periods_succeeded_queries, Value=439},
{Key=agent_15periods_msecsperquery, Value=233.73},
],
error=,
total=0,
warning=}{columns=[{Key={type=string}},
{Value={type=string}}],
data=[
{Key=agent_15periods_query_timeouts, Value=0},
{Key=agent_15periods_connect_timeouts, Value=0},
{Key=agent_15periods_connect_failures, Value=0},
{Key=agent_15periods_network_errors, Value=0},
{Key=agent_15periods_connect_failures, Value=0},
{Key=agent_15periods_wrong_replies, Value=0},
{Key=agent_15periods_unexpected_closings, Value=0},
{Key=agent_15periods_warnings, Value=0},
{Key=agent_15periods_succeeded_queries, Value=439},
{Key=agent_15periods_msecsperquery, Value=233.73},
],
error="",
total=0,
warning=""}{columns=[{Key={type=string}},
{Value={type=string}}],
data=[
{Key=agent_15periods_query_timeouts, Value=0},
{Key=agent_15periods_connect_timeouts, Value=0},
{Key=agent_15periods_connect_failures, Value=0},
{Key=agent_15periods_network_errors, Value=0},
{Key=agent_15periods_connect_failures, Value=0},
{Key=agent_15periods_wrong_replies, Value=0},
{Key=agent_15periods_unexpected_closings, Value=0},
{Key=agent_15periods_warnings, Value=0},
{Key=agent_15periods_succeeded_queries, Value=439},
{Key=agent_15periods_msecsperquery, Value=233.73},
],
error="",
total=0,
warning=""}{
"columns":
[{
{"Key": {"type": "string"}
},
{"Value": {"type": "string"}
}],
"data":
[
{"Key": "agent_15periods_query_timeouts", "Value": "0"},
{"Key": "agent_15periods_connect_timeouts", "Value": "0"},
{"Key": "agent_15periods_connect_failures", "Value": "0"},
{"Key": "agent_15periods_network_errors", "Value": "0"},
{"Key": "agent_15periods_connect_failures", "Value": "0"},
{"Key": "agent_15periods_wrong_replies", "Value": "0"},
{"Key": "agent_15periods_unexpected_closings", "Value": "0"},
{"Key": "agent_15periods_warnings", "Value": "0"},
{"Key": "agent_15periods_succeeded_queries", "Value": "439"},
{"Key": "agent_15periods_msecsperquery", "Value": "233.73"},
],
"error": "",
"total": 0,
"warning": ""
}{
"columns":
[{
{"Key": {"type": "string"}
},
{"Value": {"type": "string"}
}],
"data":
[
{"Key": "agent_15periods_query_timeouts", "Value": "0"},
{"Key": "agent_15periods_connect_timeouts", "Value": "0"},
{"Key": "agent_15periods_connect_failures", "Value": "0"},
{"Key": "agent_15periods_network_errors", "Value": "0"},
{"Key": "agent_15periods_connect_failures", "Value": "0"},
{"Key": "agent_15periods_wrong_replies", "Value": "0"},
{"Key": "agent_15periods_unexpected_closings", "Value": "0"},
{"Key": "agent_15periods_warnings", "Value": "0"},
{"Key": "agent_15periods_succeeded_queries", "Value": "439"},
{"Key": "agent_15periods_msecsperquery", "Value": "233.73"},
],
"error": "",
"total": 0,
"warning": ""
}Finally, you can check the status of the agents in a specific distributed table using the SHOW AGENT table_name STATUS statement. This statement displays the table's HA status (i.e., whether or not it uses agent mirrors at all) and provides information on the mirrors, including: address, blackhole and persistent flags, and the mirror selection probability used when one of the weighted probability strategies is in effect.
‹›
- SQL
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
📋
⚙
SHOW AGENT dist_index STATUS;$client->nodes()->agentstatus(
['body'=>
['agent'=>'dist_index']
]
);utilsApi.sql('SHOW AGENT \'192.168.0.202:6714\' STATUS LIKE \'%15periods%\'')await utilsApi.sql('SHOW AGENT \'192.168.0.202:6714\' STATUS LIKE \'%15periods%\'')res = await utilsApi.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");utilsApi.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");utilsApi.Sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");utils_api.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"", Some(true)).await;res = await utilsApi.sql("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"");apiClient.UtilsAPI.Sql(context.Background()).Body("SHOW AGENT \"192.168.0.202:6714\" STATUS LIKE \"%15periods%\"").Execute()
‹›
Response
+--------------------------------------+--------------------------------+
| Variable_name | Value |
+--------------------------------------+--------------------------------+
| dstindex_1_is_ha | 1 |
| dstindex_1mirror1_id | 192.168.0.202:6713:loc |
| dstindex_1mirror1_probability_weight | 0.372864 |
| dstindex_1mirror1_is_blackhole | 0 |
| dstindex_1mirror1_is_persistent | 0 |
| dstindex_1mirror2_id | 192.168.0.202:6714:loc |
| dstindex_1mirror2_probability_weight | 0.374635 |
| dstindex_1mirror2_is_blackhole | 0 |
| dstindex_1mirror2_is_persistent | 0 |
| dstindex_1mirror3_id | dev1.manticoresearch.com:6714:loc |
| dstindex_1mirror3_probability_weight | 0.252501 |
| dstindex_1mirror3_is_blackhole | 0 |
| dstindex_1mirror3_is_persistent | 0 |
+--------------------------------------+--------------------------------+
13 rows in set (0.00 sec)Array(
[dstindex_1_is_ha] => 1
[dstindex_1mirror1_id] => 192.168.0.202:6713:loc
[dstindex_1mirror1_probability_weight] => 0.372864
[dstindex_1mirror1_is_blackhole] => 0
[dstindex_1mirror1_is_persistent] => 0
[dstindex_1mirror2_id] => 192.168.0.202:6714:loc
[dstindex_1mirror2_probability_weight] => 0.374635
[dstindex_1mirror2_is_blackhole] => 0
[dstindex_1mirror2_is_persistent] => 0
[dstindex_1mirror3_id] => dev1.manticoresearch.com:6714:loc
[dstindex_1mirror3_probability_weight] => 0.252501
[dstindex_1mirror3_is_blackhole] => 0
[dstindex_1mirror3_is_persistent] => 0
){u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'dstindex_1_is_ha', u'Value': u'1'},
{u'Key': u'dstindex_1mirror1_id', u'Value': u'192.168.0.202:6713:loc'},
{u'Key': u'dstindex_1mirror1_probability_weight', u'Value': u'0.372864'},
{u'Key': u'dstindex_1mirror1_is_blackhole', u'Value': u'0'},
{u'Key': u'dstindex_1mirror1_is_persistent', u'Value': u'0'},
{u'Key': u'dstindex_1mirror2_id', u'Value': u'192.168.0.202:6714:loc'},
{u'Key': u'dstindex_1mirror2_probability_weight', u'Value': u'0.374635'},
{u'Key': u'dstindex_1mirror2_is_blackhole', u'Value': u'0'},
{u'Key': u'dstindex_1mirror2_is_persistent', u'Value': u'439'},
{u'Key': u'dstindex_1mirror3_id', u'Value': u'dev1.manticoresearch.com:6714:loc'},
{u'Key': u'dstindex_1mirror3_probability_weight', u'Value': u' 0.252501'},
{u'Key': u'dstindex_1mirror3_is_blackhole', u'Value': u'0'},
{u'Key': u'dstindex_1mirror3_is_persistent', u'Value': u'439'}
],
u'error': u'',
u'total': 0,
u'warning': u''}{u'columns': [{u'Key': {u'type': u'string'}},
{u'Value': {u'type': u'string'}}],
u'data': [
{u'Key': u'dstindex_1_is_ha', u'Value': u'1'},
{u'Key': u'dstindex_1mirror1_id', u'Value': u'192.168.0.202:6713:loc'},
{u'Key': u'dstindex_1mirror1_probability_weight', u'Value': u'0.372864'},
{u'Key': u'dstindex_1mirror1_is_blackhole', u'Value': u'0'},
{u'Key': u'dstindex_1mirror1_is_persistent', u'Value': u'0'},
{u'Key': u'dstindex_1mirror2_id', u'Value': u'192.168.0.202:6714:loc'},
{u'Key': u'dstindex_1mirror2_probability_weight', u'Value': u'0.374635'},
{u'Key': u'dstindex_1mirror2_is_blackhole', u'Value': u'0'},
{u'Key': u'dstindex_1mirror2_is_persistent', u'Value': u'439'},
{u'Key': u'dstindex_1mirror3_id', u'Value': u'dev1.manticoresearch.com:6714:loc'},
{u'Key': u'dstindex_1mirror3_probability_weight', u'Value': u' 0.252501'},
{u'Key': u'dstindex_1mirror3_is_blackhole', u'Value': u'0'},
{u'Key': u'dstindex_1mirror3_is_persistent', u'Value': u'439'}
],
u'error': u'',
u'total': 0,
u'warning': u''}{"columns": [{"Key": {"type": "string"}},
{"Value": {"type": "string"}}],
"data": [
{"Key": "dstindex_1_is_ha", "Value": "1"},
{"Key": "dstindex_1mirror1_id", "Value": "192.168.0.202:6713:loc"},
{"Key": "dstindex_1mirror1_probability_weight", "Value": "0.372864"},
{"Key": "dstindex_1mirror1_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror1_is_persistent", "Value": "0"},
{"Key": "dstindex_1mirror2_id", "Value": "192.168.0.202:6714:loc"},
{"Key": "dstindex_1mirror2_probability_weight", "Value": "0.374635"},
{"Key": "dstindex_1mirror2_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror2_is_persistent", "Value": "439"},
{"Key": "dstindex_1mirror3_id", "Value": "dev1.manticoresearch.com:6714:loc"},
{"Key": "dstindex_1mirror3_probability_weight", "Value": " 0.252501"},
{"Key": "dstindex_1mirror3_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror3_is_persistent", "Value": "439"}
],
"error": "",
"total": 0,
"warning": ""}{columns=[{Key={type=string}},
{Value={type=string}}],
data=[
{Key=dstindex_1_is_ha, Value=1},
{Key=dstindex_1mirror1_id, Value=192.168.0.202:6713:loc},
{Key=dstindex_1mirror1_probability_weight, Value=0.372864},
{Key=dstindex_1mirror1_is_blackhole, Value=0},
{Key=dstindex_1mirror1_is_persistent, Value=0},
{Key=dstindex_1mirror2_id, Value=192.168.0.202:6714:loc},
{Key=dstindex_1mirror2_probability_weight, Value=0.374635},
{Key=dstindex_1mirror2_is_blackhole, Value=0},
{Key=dstindex_1mirror2_is_persistent, Value=439},
{Key=dstindex_1mirror3_id, Value=dev1.manticoresearch.com:6714:loc},
{Key=dstindex_1mirror3_probability_weight, Value= 0.252501},
{Key=dstindex_1mirror3_is_blackhole, Value=0},
{Key=dstindex_1mirror3_is_persistent, Value=439}
],
error=,
total=0,
warning=}{columns=[{Key={type=string}},
{Value={type=string}}],
data=[
{Key=dstindex_1_is_ha, Value=1},
{Key=dstindex_1mirror1_id, Value=192.168.0.202:6713:loc},
{Key=dstindex_1mirror1_probability_weight, Value=0.372864},
{Key=dstindex_1mirror1_is_blackhole, Value=0},
{Key=dstindex_1mirror1_is_persistent, Value=0},
{Key=dstindex_1mirror2_id, Value=192.168.0.202:6714:loc},
{Key=dstindex_1mirror2_probability_weight, Value=0.374635},
{Key=dstindex_1mirror2_is_blackhole, Value=0},
{Key=dstindex_1mirror2_is_persistent, Value=439},
{Key=dstindex_1mirror3_id, Value=dev1.manticoresearch.com:6714:loc},
{Key=dstindex_1mirror3_probability_weight, Value= 0.252501},
{Key=dstindex_1mirror3_is_blackhole, Value=0},
{Key=dstindex_1mirror3_is_persistent, Value=439}
],
error="",
total=0,
warning=""}{columns=[{Key={type=string}},
{Value={type=string}}],
data=[
{Key=dstindex_1_is_ha, Value=1},
{Key=dstindex_1mirror1_id, Value=192.168.0.202:6713:loc},
{Key=dstindex_1mirror1_probability_weight, Value=0.372864},
{Key=dstindex_1mirror1_is_blackhole, Value=0},
{Key=dstindex_1mirror1_is_persistent, Value=0},
{Key=dstindex_1mirror2_id, Value=192.168.0.202:6714:loc},
{Key=dstindex_1mirror2_probability_weight, Value=0.374635},
{Key=dstindex_1mirror2_is_blackhole, Value=0},
{Key=dstindex_1mirror2_is_persistent, Value=439},
{Key=dstindex_1mirror3_id, Value=dev1.manticoresearch.com:6714:loc},
{Key=dstindex_1mirror3_probability_weight, Value= 0.252501},
{Key=dstindex_1mirror3_is_blackhole, Value=0},
{Key=dstindex_1mirror3_is_persistent, Value=439}
],
error="",
total=0,
warning=""}{
"columns":
[{
"Key": {"type": "string"}},
{"Value": {"type": "string"}
}],
"data":
[
{"Key": "dstindex_1_is_ha", "Value": "1"},
{"Key": "dstindex_1mirror1_id", "Value": "192.168.0.202:6713:loc"},
{"Key": "dstindex_1mirror1_probability_weight", "Value": "0.372864"},
{"Key": "dstindex_1mirror1_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror1_is_persistent", "Value": "0"},
{"Key": "dstindex_1mirror2_id", "Value": "192.168.0.202:6714:loc"},
{"Key": "dstindex_1mirror2_probability_weight", "Value": "0.374635"},
{"Key": "dstindex_1mirror2_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror2_is_persistent", "Value": "439"},
{"Key": "dstindex_1mirror3_id", "Value": "dev1.manticoresearch.com:6714:loc"},
{"Key": "dstindex_1mirror3_probability_weight", "Value": " 0.252501"},
{"Key": "dstindex_1mirror3_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror3_is_persistent", "Value": "439"}
],
"error": "",
"total": 0,
"warning": ""
}{
"columns":
[{
"Key": {"type": "string"}},
{"Value": {"type": "string"}
}],
"data":
[
{"Key": "dstindex_1_is_ha", "Value": "1"},
{"Key": "dstindex_1mirror1_id", "Value": "192.168.0.202:6713:loc"},
{"Key": "dstindex_1mirror1_probability_weight", "Value": "0.372864"},
{"Key": "dstindex_1mirror1_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror1_is_persistent", "Value": "0"},
{"Key": "dstindex_1mirror2_id", "Value": "192.168.0.202:6714:loc"},
{"Key": "dstindex_1mirror2_probability_weight", "Value": "0.374635"},
{"Key": "dstindex_1mirror2_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror2_is_persistent", "Value": "439"},
{"Key": "dstindex_1mirror3_id", "Value": "dev1.manticoresearch.com:6714:loc"},
{"Key": "dstindex_1mirror3_probability_weight", "Value": " 0.252501"},
{"Key": "dstindex_1mirror3_is_blackhole", "Value": "0"},
{"Key": "dstindex_1mirror3_is_persistent", "Value": "439"}
],
"error": "",
"total": 0,
"warning": ""
}Manticore Search has a built-in Prometheus exporter. To request metrics, make sure the HTTP port is exposed and simply call the /metrics endpoint.
Note: The exporter requires Buddy to be enabled.
‹›
- http
http
📋
⚙
curl -s 0:9308/metrics
‹›
Response
# HELP manticore_uptime_seconds Time in seconds since start
# TYPE manticore_uptime_seconds counter
manticore_uptime_seconds 25
# HELP manticore_connections_count Connections count since start
# TYPE manticore_connections_count gauge
manticore_connections_count 55
# HELP manticore_maxed_out_error_count Count of maxed_out errors since start
# TYPE manticore_maxed_out_error_count counter
manticore_maxed_out_error_count 0
# HELP manticore_version Manticore Search version
# TYPE manticore_version gauge
manticore_version {version="0.0.0 c88e811b2@25060409 (columnar 5.0.1 59c7092@25060304) (secondary 5.0.1 59c7092@25060304) (knn 5.0.1 59c7092@25060304) (embeddings 1.0.0) (buddy v3.28.6-7-g14ee10)"} 1
# HELP manticore_mysql_version Manticore Search version
# TYPE manticore_mysql_version gauge
manticore_mysql_version {version="0.0.0 c88e811b2@25060409 (columnar 5.0.1 59c7092@25060304) (secondary 5.0.1 59c7092@25060304) (knn 5.0.1 59c7092@25060304) (embeddings 1.0.0)"} 1
# HELP manticore_command_search_count Count of search queries since start
# TYPE manticore_command_search_count counter
manticore_command_search_count 1
......Last modified: September 27, 2025
SHOW META [ LIKE pattern ]SHOW META 是一个 SQL 语句，用于显示有关处理查询的附加元信息，包括查询时间、关键词统计以及使用的辅助索引信息。语法如下：
包含的项目有：
total：实际检索并发送给客户端的匹配数。该值通常受 LIMIT/size 搜索选项的限制。total_found：- 索引中查询的匹配项估计总数。如果需要确切的匹配数量，请使用
SELECT COUNT(*)，而不要依赖此值。 - 对于带有
GROUP BY的查询，total_found表示组的数量，而非单个匹配。 - 当使用带有
GROUP BY的HAVING时，total_found反映应用HAVING过滤后的组数。这支持带有HAVING子句的正确分页。 - 对于 GROUP N BY 查询，不论
N的值，total_found仍然表示组的数量。
- 索引中查询的匹配项估计总数。如果需要确切的匹配数量，请使用
total_relation：指示total_found的值是准确的还是估计的。- 如果 Manticore 无法确定精确的
total_found值，该字段将显示total_relation: gte，意味着实际匹配数量 大于或等于 报告的total_found。 - 如果
total_found是精确值，则显示total_relation: eq。
- 如果 Manticore 无法确定精确的
time：处理搜索查询所用的时间（以秒为单位）。keyword[N]：搜索查询中第 n 个使用的关键词。关键词可以是通配符，例如abc*。docs[N]：包含搜索查询中第 n 个关键词的文档（或记录）总数。如果关键词是通配符，则该值表示所有展开子关键词文档数的总和，可能超过实际匹配的文档数。hits[N]：第 n 个关键词在所有文档中出现的总次数（或命中次数）。index：所用索引的信息（例如辅助索引）。
‹›
- SQL
SQL
📋
⚙
SELECT id, story_author FROM hn_small WHERE MATCH('one|two|three') and comment_ranking > 2 limit 5;
show meta;
‹›
Response
+---------+--------------+
| id | story_author |
+---------+--------------+
| 151171 | anewkid |
| 302758 | bks |
| 805806 | drRoflol |
| 1099245 | tnorthcutt |
| 303252 | whiten |
+---------+--------------+
5 rows in set (0.00 sec)
+----------------+---------------------------------------+
| Variable_name | Value |
+----------------+---------------------------------------+
| total | 5 |
| total_found | 2308 |
| total_relation | eq |
| time | 0.001 |
| keyword[0] | one |
| docs[0] | 224387 |
| hits[0] | 310327 |
| keyword[1] | three |
| docs[1] | 18181 |
| hits[1] | 21102 |
| keyword[2] | two |
| docs[2] | 63251 |
| hits[2] | 75961 |
| index | comment_ranking:SecondaryIndex (100%) |
+----------------+---------------------------------------+
14 rows in set (0.00 sec)SHOW META 可以显示 I/O 和 CPU 计数器，但只有在 searchd 启动时使用了 --iostats 和 --cpustats 开关时，这些信息才可用。
‹›
- SQL
SQL
📋
⚙
SELECT id,channel_id FROM records WHERE MATCH('one|two|three') limit 5;
SHOW META;
‹›
Response
+--------+--------------+
| id | story_author |
+--------+--------------+
| 300263 | throwaway37 |
| 713503 | mahmud |
| 716804 | mahmud |
| 776906 | jimbokun |
| 753332 | foxhop |
+--------+--------------+
5 rows in set (0.01 sec)
+-----------------------+--------+
| Variable_name | Value |
+-----------------------+--------+
| total | 5 |
| total_found | 266385 |
| total_relation | eq |
| time | 0.011 |
| cpu_time | 18.004 |
| agents_cpu_time | 0.000 |
| io_read_time | 0.000 |
| io_read_ops | 0 |
| io_read_kbytes | 0.0 |
| io_write_time | 0.000 |
| io_write_ops | 0 |
| io_write_kbytes | 0.0 |
| agent_io_read_time | 0.000 |
| agent_io_read_ops | 0 |
| agent_io_read_kbytes | 0.0 |
| agent_io_write_time | 0.000 |
| agent_io_write_ops | 0 |
| agent_io_write_kbytes | 0.0 |
| keyword[0] | one |
| docs[0] | 224387 |
| hits[0] | 310327 |
| keyword[1] | three |
| docs[1] | 18181 |
| hits[1] | 21102 |
| keyword[2] | two |
| docs[2] | 63251 |
| hits[2] | 75961 |
+-----------------------+--------+
27 rows in set (0.00 sec)额外的值如 predicted_time、dist_predicted_time、local_fetched_docs、local_fetched_hits、local_fetched_skips 以及其对应的 dist_fetched_* 等仅在 searchd 配置了 预测时间成本，且查询的 OPTION 子句中包含 predicted_time 时才可用。
‹›
- SQL
SQL
📋
⚙
SELECT id,story_author FROM hn_small WHERE MATCH('one|two|three') limit 5 option max_predicted_time=100;
SHOW META;
‹›
Response
+--------+--------------+
| id | story_author |
+--------+--------------+
| 300263 | throwaway37 |
| 713503 | mahmud |
| 716804 | mahmud |
| 776906 | jimbokun |
| 753332 | foxhop |
+--------+--------------+
5 rows in set (0.01 sec)
mysql> show meta;
+---------------------+--------+
| Variable_name | Value |
+---------------------+--------+
| total | 5 |
| total_found | 266385 |
| total_relation | eq |
| time | 0.012 |
| local_fetched_docs | 307212 |
| local_fetched_hits | 407390 |
| local_fetched_skips | 24 |
| predicted_time | 56 |
| keyword[0] | one |
| docs[0] | 224387 |
| hits[0] | 310327 |
| keyword[1] | three |
| docs[1] | 18181 |
| hits[1] | 21102 |
| keyword[2] | two |
| docs[2] | 63251 |
| hits[2] | 75961 |
+---------------------+--------+
17 rows in set (0.00 sec)SHOW META 必须紧接查询之后在同一会话中执行。由于某些 MySQL 连接器/库使用连接池，单独执行 SHOW META 可能导致意外结果，如获取到其它查询的元数据。针对这种情况（且一般推荐），请使用包含查询和 SHOW META 的多语句查询。有些连接器/库支持在同一方法内多查询执行，另一些可能需要专用多查询方法或在连接设置时配置特殊选项。
‹›
- SQL
SQL
📋
⚙
SELECT id,story_author FROM hn_small WHERE MATCH('one|two|three') LIMIT 5; SHOW META;
‹›
Response
+--------+--------------+
| id | story_author |
+--------+--------------+
| 300263 | throwaway37 |
| 713503 | mahmud |
| 716804 | mahmud |
| 776906 | jimbokun |
| 753332 | foxhop |
+--------+--------------+
5 rows in set (0.01 sec)
+----------------+--------+
| Variable_name | Value |
+----------------+--------+
| total | 5 |
| total_found | 266385 |
| total_relation | eq |
| time | 0.011 |
| keyword[0] | one |
| docs[0] | 224387 |
| hits[0] | 310327 |
| keyword[1] | three |
| docs[1] | 18181 |
| hits[1] | 21102 |
| keyword[2] | two |
| docs[2] | 63251 |
| hits[2] | 75961 |
+----------------+--------+
13 rows in set (0.00 sec)也可以使用可选的 LIKE 子句，只选出匹配特定模式的变量。模式语法遵循标准 SQL 通配符，% 表示任意数量的任意字符，_ 表示单个字符。
‹›
- SQL
SQL
📋
⚙
SHOW META LIKE 'total%';
‹›
Response
+----------------+--------+
| Variable_name | Value |
+----------------+--------+
| total | 5 |
| total_found | 266385 |
| total_relation | eq |
+----------------+--------+
3 rows in set (0.00 sec)使用 分面搜索 时，可在 SHOW META 输出中查看 multiplier 字段，了解执行了多少个优化分组的查询。
‹›
- SQL
SQL
📋
⚙
SELECT * FROM facetdemo FACET brand_id FACET price FACET categories;
SHOW META LIKE 'multiplier';
‹›
Response
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
...
+----------+----------+
| brand_id | count(*) |
+----------+----------+
| 1 | 1013 |
...
+-------+----------+
| price | count(*) |
+-------+----------+
| 306 | 7 |
...
+------------+----------+
| categories | count(*) |
+------------+----------+
| 10 | 2436 |
...
+---------------+-------+
| Variable_name | Value |
+---------------+-------+
| multiplier | 4 |
+---------------+-------+
1 row in set (0.00 sec)当 基于成本的查询优化器 选择用 DocidIndex、ColumnarScan 或 SecondaryIndex 代替普通过滤时，SHOW META 命令会反映这一点。
index 变量显示执行查询时使用的辅助索引名称和类型。百分比表示在 RT 表中使用辅助索引的磁盘块数量或在普通表中使用的伪分片数量。
‹›
- SQL
SQL
📋
⚙
SELECT count(*) FROM taxi1 WHERE tip_amount = 5;
SHOW META;
‹›
Response
+----------------+----------------------------------+
| Variable_name | Value |
+----------------+----------------------------------+
| total | 1 |
| total_found | 1 |
| total_relation | eq |
| time | 0.016 |
| index | tip_amount:SecondaryIndex (100%) |
+----------------+----------------------------------+
5 rows in set (0.00 sec)SHOW META 可在执行 CALL PQ 语句后使用，此时会提供不同的输出。
CALL PQ 之后的 SHOW META 包含：
total- 匹配文档所花费的总时间queries_matched- 匹配文档的存储查询数document_matches- 匹配表中存储查询的文档数total_queries_stored- 表中存储查询的总数term_only_queries- 表中含词项的查询数；其余查询使用扩展查询语法。
‹›
- SQL
SQL
📋
⚙
CALL PQ ('pq', ('{"title":"angry", "gid":3 }')); SHOW META;
‹›
Response
+------+
| id |
+------+
| 2 |
+------+
1 row in set (0.00 sec)
+-----------------------+-----------+
| Variable name | Value |
+-----------------------+-----------+
| total | 0.000 sec |
| queries_matched | 1 |
| queries_failed | 0 |
| document_matched | 1 |
| total_queries_stored | 2 |
| term_only_queries | 2 |
| fast_rejected_queries | 1 |
+-----------------------+-----------+
7 rows in set (0.00 sec)使用带有 verbose 选项的 CALL PQ 可提供更详细的输出。
它包括以下附加条目：
Setup- 匹配过程初始设置所花费的时间，例如解析文档和设置选项Queries failed- 失败的查询数量Fast rejected queries- 未完全评估但通过过滤器或其他条件快速匹配并拒绝的查询数量Time per query- 每个查询的详细时间Time of matched queries- 与任何文档匹配的查询所花费的总时间
‹›
- SQL
SQL
📋
⚙
CALL PQ ('pq', ('{"title":"angry", "gid":3 }'), 1 as verbose); SHOW META;
‹›
Response
+------+
| id |
+------+
| 2 |
+------+
1 row in set (0.00 sec)
+-------------------------+-----------+
| Variable name | Value |
+-------------------------+-----------+
| total | 0.000 sec |
| setup | 0.000 sec |
| queries_matched | 1 |
| queries_failed | 0 |
| document_matched | 1 |
| total_queries_stored | 2 |
| term_only_queries | 2 |
| fast_rejected_queries | 1 |
| time_per_query | 69 |
| time_of_matched_queries | 69 |
+-------------------------+-----------+
10 rows in set (0.00 sec)Last modified: November 17, 2025
SHOW THREADS [ OPTION columns=width[,format=sphinxql][,format=all] ]SHOW THREADS 是一个 SQL 语句，用于显示所有线程及其当前活动的信息。
结果表包含以下列：
TID：内核分配给线程的 IDName：线程名称，也可在top、htop、ps及其他进程查看工具中看到Proto：连接协议；可能的值包括sphinx、mysql、http、ssl、compressed、replication，或组合（例如http,ssl或compressed,mysql）State：线程状态；可能的值有handshake、net_read、net_write、query、net_idleConnection from：客户端的ip:portConnID：连接 ID（从 0 开始）This/prev job time：当线程忙碌时 - 当前任务运行的时间；当线程空闲时 - 上一个任务的持续时间 + 后缀prevJobs done：该线程完成的任务数量Thread status：idling或workingInfo：查询信息，如果查询针对分布式表或实时表，可能包含多个查询
‹›
- SQL
- JSON
- PHP
- Python
- Python-asyncio
- javascript
- Java
- C#
- Rust
- TypeScript
- Go
📋
⚙
SHOW THREADS;POST /cli -d "SHOW THREADS"require_once __DIR__ . '/vendor/autoload.php';
$config = ['host'=>'127.0.0.1','port'=>9308];
$client = new \Manticoresearch\Client($config);
print_r($client->nodes()->threads());import manticoresearch
config = manticoresearch.Configuration(
host = "http://127.0.0.1:9308"
)
client = manticoresearch.ApiClient(config)
utilsApi = manticoresearch.UtilsApi(client)
print(utilsApi.sql('SHOW THREADS'))import manticoresearch
config = manticoresearch.Configuration(
host = "http://127.0.0.1:9308"
)
client = manticoresearch.ApiClient(config)
utilsApi = manticoresearch.UtilsApi(client)
res = await utilsApi.sql('SHOW THREADS')
print(res)var Manticoresearch = require('manticoresearch');
var utilsApi = new Manticoresearch.UtilsApi();
async function showThreads() {
res = await utilsApi.sql('SHOW THREADS');
console.log(JSON.stringify(res, null, 4));
}
showThreads();utilsApi.sql("SHOW THREADS");utilsApi.Sql("SHOW THREADS");utils_api.sql("SHOW THREADS", Some(true)).await;res = await utilsApi.sql('SHOW THREADS');apiClient.UtilsAPI.Sql(context.Background()).Body("SHOW THREADS").Execute()
‹›
Response
*************************** 1. row ***************************
TID: 83
Name: work_1
Proto: mysql
State: query
Connection from: 172.17.0.1:43300
ConnID: 8
This/prev job time: 630us
CPU activity: 94%
Jobs done: 2490
Thread status: working
Info: SHOW THREADS
*************************** 2. row ***************************
TID: 84
Name: work_2
Proto: mysql
State: query
Connection from: 172.17.0.1:43301
ConnID: 9
This/prev job time: 689us
CPU activity: 89%
Jobs done: 1830
Thread status: working
Info: show threads+--------+---------+-------+-------+-----------------+--------+-----------------------+-----------+---------------+--------------+
| TID | Name | Proto | State | Connection from | ConnID | This/prev job time, s | Jobs done | Thread status | Info |
+--------+---------+-------+-------+-----------------+--------+-----------------------+-----------+---------------+--------------+
| 501494 | work_23 | http | query | 127.0.0.1:41300 | 1473 | 249us | 1681 | working | show_threads |
+--------+---------+-------+-------+-----------------+--------+-----------------------+-----------+---------------+--------------+Array
(
[0] => Array
(
[TID] => 506960
[Name] => work_8
[Proto] => http
[State] => query
[Connection from] => 127.0.0.1:38072
[ConnID] => 17
[This/prev job time, s] => 231us
[CPU activity] => 94%
[Jobs done] => 8
[Thread status] => working
[Info] => show_threads
)
)[{'columns': [{'TID': {'type': 'long'}}, {'Name': {'type': 'string'}}, {'Proto': {'type': 'string'}}, {'State': {'type': 'string'}}, {'Connection from': {'type': 'string'}}, {'ConnID': {'type': 'long long'}}, {'This/prev job time, s': {'type': 'string'}}, {'CPU activity': {'type': 'string'}}, {'Jobs done': {'type': 'long'}}, {'Thread status': {'type': 'string'}}, {'Info': {'type': 'string'}}], 'data': [{'TID': 506958, 'Name': 'work_6', 'Proto': 'http', 'State': 'query', 'Connection from': '127.0.0.1:38600', 'ConnID': 834, 'This/prev job time, s': '206us', 'CPU activity': '92%', 'Jobs done': 943, 'Thread status': 'working', 'Info': 'show_threads'}], 'total': 1, 'error': '', 'warning': ''}][{'columns': [{'TID': {'type': 'long'}}, {'Name': {'type': 'string'}}, {'Proto': {'type': 'string'}}, {'State': {'type': 'string'}}, {'Connection from': {'type': 'string'}}, {'ConnID': {'type': 'long long'}}, {'This/prev job time, s': {'type': 'string'}}, {'CPU activity': {'type': 'string'}}, {'Jobs done': {'type': 'long'}}, {'Thread status': {'type': 'string'}}, {'Info': {'type': 'string'}}], 'data': [{'TID': 506958, 'Name': 'work_6', 'Proto': 'http', 'State': 'query', 'Connection from': '127.0.0.1:38600', 'ConnID': 834, 'This/prev job time, s': '206us', 'CPU activity': '92%', 'Jobs done': 943, 'Thread status': 'working', 'Info': 'show_threads'}], 'total': 1, 'error': '', 'warning': ''}][
{
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"CPU activity": "92%",
"Jobs done": 3328,
"Thread status": "working",
"Info": "show_threads"
}
],
"total": 1,
"error": "",
"warning": ""
}
]{
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TID={
type=string
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},
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}
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data=[
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TID=82,
Name=work_0,
Proto=http,
State=query,
Connection from=172.17.0.1:60550,
ConnID=163,
This/prev job time=105us,
CPU activity=45%,
Jobs done=849,
Thread status=working,
Info=show_threads
}
],
total=0,
error=,
warning=
}{
columns=[
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data=[
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TID=83,
Name=work_1,
Proto=http,
State=query,
Connection from=172.17.0.1:41410,
ConnID=6,
This/prev job time=689us,
Jobs done=159,
Thread status=working,
Info=show_threads
}
],
total=0,
error="",
warning=""
}{
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Proto=http,
State=query,
Connection from=172.17.0.1:41410,
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This/prev job time=689us,
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}
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}[
{
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},
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},
{
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],
"data": [
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"TID": 506964,
"Name": "work_12",
"Proto": "http",
"State": "query",
"Connection from": "127.0.0.1:36656",
"ConnID": 2884,
"This/prev job time, s": "236us",
"CPU activity": "92%",
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"Thread status": "working",
"Info": "show_threads"
}
],
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"warning": ""
}
][
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"data": [
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"TID": 506964,
"Name": "work_12",
"Proto": "http",
"State": "query",
"Connection from": "127.0.0.1:36656",
"ConnID": 2884,
"This/prev job time, s": "236us",
"CPU activity": "92%",
"Jobs done": 3328,
"Thread status": "working",
"Info": "show_threads"
}
],
"total": 1,
"error": "",
"warning": ""
}
]Info 列显示：
- 通过 Manticore SQL 接口执行的查询的原始文本
- 通过内部 Manticore 二进制协议执行的查询的完整文本语法、注释和数据大小（例如，来自远程 Manticore 实例）
您可以通过指定 columns=N 选项限制 Info 列的最大宽度。
默认情况下，查询以其原始格式显示。但当使用 format=sphinxql 选项时，无论执行协议如何，查询都会以 SQL 格式显示。
使用 format=all 将显示所有线程，而不使用此选项时，空闲和系统线程会被隐藏（例如，忙于执行 OPTIMIZE 的线程）。
‹›
- SQL
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- Python-asyncio
- javascript
- Java
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- Rust
- TypeScript
- Go
📋
⚙
SHOW THREADS OPTION columns=30\GPOST /cli -d "SHOW THREADS OPTION columns=30"$client->nodes()->threads(['body'=>['columns'=>30]]);utilsApi.sql('SHOW THREADS OPTION columns=30')await utilsApi.sql('SHOW THREADS OPTION columns=30')res = await utilsApi.sql('SHOW THREADS OPTION columns=30');utilsApi.sql("SHOW THREADS OPTION columns=30");utilsApi.Sql("SHOW THREADS OPTION columns=30");utils_api.sql("SHOW THREADS OPTION columns=30", Some(true)).await;res = await utilsApi.sql('SHOW THREADS OPTION columns=30');apiClient.UtilsAPI.Sql(context.Background()).Body("SHOW THREADS OPTION columns=30").Execute()Last modified: August 28, 2025
SHOW QUERIES注意：
SHOW QUERIES需要 Manticore Buddy。如果它不起作用，请确保已安装 Buddy。
SHOW QUERIES 返回所有当前正在运行的查询的信息。输出是一个具有以下结构的表：
‹›
- SQL
SQL
📋
⚙
mysql> SHOW QUERIES;
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Response
+------+--------------+---------+----------+-----------------+
| id | query | time | protocol | host |
+------+--------------+---------+----------+-----------------+
| 111 | select | 5ms ago | http | 127.0.0.1:58986 |
| 96 | SHOW QUERIES | 255us | mysql | 127.0.0.1:33616 |
+------+--------------+---------+----------+-----------------+
2 rows in set (0.61 sec)如果您想从线程本身的角度获取信息，请参阅 SHOW THREADS。
Last modified: August 28, 2025