揭秘ClickHouse高效指标监控：五大工具助力数据无忧
ClickHouse是一款高性能的列式数据库管理系统，广泛应用于实时数据分析、在线分析处理（OLAP）等领域。为了保证其稳定性和高效性，对ClickHouse的指标进行监控至关重要。本文将详细介绍五大工具，帮助您实现对ClickHouse的高效指标监控。
一、ClickHouse内置监控工具
ClickHouse自身提供了一些监控工具，如下：
1. system.query_log表
system.query_log表记录了所有执行的查询及其相关信息，包括执行时间、执行状态、错误信息等。通过分析这个表，可以了解查询的性能和效率。
SELECT * FROM system.query_log WHERE timestamp >= now() - INTERVAL 1 DAY ORDER BY query_start_time DESC; 2. system.tables表
system.tables表提供了关于表结构的详细信息，如表的引擎、分区、压缩方式等。通过分析这个表，可以了解表的空间占用、压缩情况等信息。
SELECT * FROM system.tables; 二、第三方监控工具
除了ClickHouse内置的工具外，还有一些第三方工具可以用来监控ClickHouse的性能：
1. Prometheus + Grafana
Prometheus是一款开源的监控和报警工具，Grafana是一款可视化仪表盘工具。通过Prometheus可以收集ClickHouse的性能指标，并通过Grafana展示这些指标。
1.1 安装Prometheus和Grafana
# 安装Prometheus wget https://github.com/prometheus/prometheus/releases/download/v2.34.0/prometheus-2.34.0.linux-amd64.tar.gz tar -xzvf prometheus-2.34.0.linux-amd64.tar.gz # 安装Grafana wget https://dl.grafana.com/oss/release/grafana-7.4.3.linux-amd64.tar.gz tar -xzvf grafana-7.4.3.linux-amd64.tar.gz 1.2 配置Prometheus
在Prometheus的配置文件（prometheus.yml）中添加以下内容：
scrape_configs: - job_name: 'clickhouse' static_configs: - targets: ['localhost:8123'] 1.3 配置Grafana
在Grafana的data/dashboards目录下创建一个名为clickhouse.json的文件，内容如下：
{ "annotations": { "list": [ { "name": "alert", "type": "color", "drawStyle": "solid", "fill": "hollow", "valueMap": { "alert": "red", "ok": "green" }, "updateDelay": 1, "query": "alert" } ] }, "editable": true, "gnetics": false, "graphOptions": { "dataSources": [ { "name": "prometheus", "pluginId": "prometheus", "type": " datasource" } ] }, "graphTooltip": { "enable": true }, "id": 1, "iteration": 1586800604, "layout": { "title": { "text": "ClickHouse监控" }, "titleSize": "h2" }, " panels": [ { "datasource": "prometheus", "gridPos": { "h": 7, "w": 12, "x": 0, "y": 0 }, "type": "graph", "title": "ClickHouse查询性能", "yaxes": [ { "format": "short", "logBase": 1, "max": null, "min": null, "showMaxMin": false, "showOverlappingTrace": true, "unit": "none" }, { "format": "short", "logBase": 1, "max": null, "min": null, "showMaxMin": false, "showOverlappingTrace": true, "unit": "none" } ], "targets": [ { "expr": "sum(rate(query_duration_seconds_count{db="default", query="SELECT * FROM system.tables"}))", "legendFormat": "表查询次数", "metric": "表查询次数", "refId": "A" }, { "expr": "sum(rate(query_duration_seconds_sum{db="default", query="SELECT * FROM system.tables"}))", "legendFormat": "表查询时间", "metric": "表查询时间", "refId": "B" } ] } ], "refresh": 10, "schemaVersion": 26, "tagList": [], "tags": {}, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh_intervals": [ "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timeZone": "browser", "version": 1 } 2. ClickHouse-JMX-Export
ClickHouse-JMX-Export是一个Java库，用于将ClickHouse的JMX指标导出到Prometheus。通过这个库，可以将ClickHouse的JMX指标转换为Prometheus指标，方便使用Prometheus进行监控。
2.1 安装ClickHouse-JMX-Export
git clone https://github.com/yandex/clickhouse-jmx-exporter.git cd clickhouse-jmx-exporter mvn clean install 2.2 配置ClickHouse
在ClickHouse的配置文件（clickhouse-server.xml）中添加以下内容：
<exporters> <jmx> <url>service:jmx:rmi:///jndi/rmi://localhost:1099/jmxrmi</url> </jmx> </exporters> 2.3 运行ClickHouse-JMX-Export
java -jar target/clickhouse-jmx-exporter-0.1.0-SNAPSHOT.jar 3. ClickHouse-Metrics
ClickHouse-Metrics是一个Go语言的库，用于收集ClickHouse的性能指标。通过这个库，可以将ClickHouse的指标发送到Prometheus或InfluxDB等监控系统。
3.1 安装ClickHouse-Metrics
go get -u github.com/ClickHouse/clickhouse-go/v2/metrics 3.2 使用ClickHouse-Metrics
package main import ( "context" "log" "net/http" "os" "time" "github.com/ClickHouse/clickhouse-go/v2" "github.com/prometheus/client_golang/prometheus" "github.com/prometheus/client_golang/prometheus/promhttp" ) var ( queriesTotal = prometheus.NewCounterVec(prometheus.CounterOpts{ Name: "clickhouse_queries_total", Help: "Total number of queries.", }, []string{"db", "query_type"}) queriesDuration = prometheus.NewHistogramVec(prometheus.HistogramOpts{ Name: "clickhouse_queries_duration_seconds", Help: "Duration of queries in seconds.", }, []string{"db", "query_type"}) ) func init() { prometheus.MustRegister(queriesTotal) prometheus.MustRegister(queriesDuration) db, err := clickhouse.Open(&clickhouse.Options{ Addr: []string{"localhost:8123"}, }) if err != nil { log.Fatal(err) } defer db.Close() ctx, cancel := context.WithCancel(context.Background()) defer cancel() go func() { for { select { case <-ctx.Done(): return default: // 采集指标 var rows []struct { QueryType string Db string Duration float64 } err := db.QueryRowCtx(ctx, "SELECT query_type, db, duration FROM system.query_log"). Scan(&rows) if err != nil { log.Fatal(err) } for _, row := range rows { queriesTotal.WithLabelValues(row.Db, row.QueryType).Inc() queriesDuration.WithLabelValues(row.Db, row.QueryType).Observe(row.Duration) } } } }() http.Handle("/metrics", promhttp.Handler()) log.Fatal(http.ListenAndServe(":9115", nil)) } func main() {} 4. Grafana-ClickHouse-App
Grafana-ClickHouse-App是一个Grafana插件，可以将Grafana与ClickHouse连接起来。通过这个插件，可以直接在Grafana中查询ClickHouse数据，并进行可视化。
4.1 安装Grafana-ClickHouse-App
grafana-cli plugins install grafana-clickhouse-app 4.2 配置Grafana
在Grafana中启用Grafana-ClickHouse-App插件。
三、总结
通过对ClickHouse进行监控，可以帮助我们了解其性能和稳定性，及时发现并解决潜在问题。本文介绍的五大工具可以帮助您实现对ClickHouse的高效指标监控，确保数据无忧。
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