我们需要在grafana中实现如图所示的日志监控,首先你自己得把elastic search的数据源已经引入到grafana中
我们先看下已经实现的这个监控的json文件内容：
java
{
"uid": "app-logs",
"title": "应用日志 (Elasticsearch)",
"tags": ["smartcs", "logs"],
"timezone": "browser",
"schemaVersion": 39,
"refresh": "30s",
"time": { "from": "now-1h", "to": "now" },
"panels": [
{
"id": 1,
"title": "日志量趋势 (按级别)",
"type": "timeseries",
"gridPos": { "x": 0, "y": 0, "w": 12, "h": 8 },
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"fieldConfig": {
"defaults": {
"color": { "mode": "palette-classic" },
"custom": { "drawStyle": "bars", "stacking": { "mode": "normal" }, "fillOpacity": 80 }
},
"overrides": []
},
"targets": [
{
"refId": "A",
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"bucketAggs": [
{
"id": "3",
"type": "terms",
"field": "level.keyword",
"settings": { "size": "10", "order": "desc", "orderBy": "_count" }
},
{
"id": "2",
"type": "date_histogram",
"field": "@timestamp",
"settings": { "interval": "auto" }
}
],
"metrics": [
{ "id": "1", "type": "count" }
],
"query": "*",
"timeField": "@timestamp"
}
]
},
{
"id": 2,
"title": "ERROR 日志量",
"type": "stat",
"gridPos": { "x": 12, "y": 0, "w": 6, "h": 4 },
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"fieldConfig": {
"defaults": {
"thresholds": { "steps": [{ "color": "#705da0", "value": null }, { "color": "#eab839", "value": 5 }, { "color": "#bf1b00", "value": 20 }] },
"color": { "mode": "thresholds" }
},
"overrides": []
},
"options": {
"reduceOptions": { "calcs": ["sum"] }
},
"targets": [
{
"refId": "A",
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"bucketAggs": [
{ "id": "2", "type": "date_histogram", "field": "@timestamp", "settings": { "interval": "auto" } }
],
"metrics": [
{ "id": "1", "type": "count" }
],
"query": "level:ERROR",
"timeField": "@timestamp"
}
]
},
{
"id": 3,
"title": "日志总量",
"type": "stat",
"gridPos": { "x": 18, "y": 0, "w": 6, "h": 4 },
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"fieldConfig": {
"defaults": {
"thresholds": { "steps": [{ "color": "#705da0", "value": null }] },
"color": { "mode": "thresholds" }
},
"overrides": []
},
"options": {
"reduceOptions": { "calcs": ["sum"] }
},
"targets": [
{
"refId": "A",
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"bucketAggs": [
{ "id": "2", "type": "date_histogram", "field": "@timestamp", "settings": { "interval": "auto" } }
],
"metrics": [
{ "id": "1", "type": "count" }
],
"query": "*",
"timeField": "@timestamp"
}
]
},
{
"id": 4,
"title": "Top10 Logger 分布",
"type": "piechart",
"gridPos": { "x": 12, "y": 4, "w": 12, "h": 8 },
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"fieldConfig": {
"defaults": { "color": { "mode": "palette-classic" } },
"overrides": []
},
"options": {
"pieType": "donut",
"legend": { "displayMode": "table", "placement": "right", "values": ["value", "percent"] }
},
"targets": [
{
"refId": "A",
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"bucketAggs": [
{
"id": "2",
"type": "terms",
"field": "logger.keyword",
"settings": { "size": "10", "order": "desc", "orderBy": "_count" }
}
],
"metrics": [
{ "id": "1", "type": "count" }
],
"query": "*",
"timeField": "@timestamp"
}
]
},
{
"id": 5,
"title": "日志流 (最近100条)",
"type": "logs",
"gridPos": { "x": 0, "y": 12, "w": 24, "h": 12 },
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"options": {
"showTime": true,
"showLabels": true,
"showCommonLabels": false,
"wrapLogMessage": true,
"prettifyLogMessage": false,
"enableLogDetails": true,
"sortOrder": "Descending",
"dedupStrategy": "none"
},
"targets": [
{
"refId": "A",
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"bucketAggs": [],
"metrics": [
{ "id": "1", "type": "raw_data", "settings": { "size": "100" } }
],
"query": "*",
"timeField": "@timestamp"
}
]
},
{
"id": 6,
"title": "ERROR 日志详情",
"type": "logs",
"gridPos": { "x": 0, "y": 24, "w": 24, "h": 10 },
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"options": {
"showTime": true,
"showLabels": true,
"showCommonLabels": false,
"wrapLogMessage": true,
"prettifyLogMessage": false,
"enableLogDetails": true,
"sortOrder": "Descending",
"dedupStrategy": "none"
},
"targets": [
{
"refId": "A",
"datasource": { "type": "elasticsearch", "uid": "ffvsw8nkbherka" },
"bucketAggs": [],
"metrics": [
{ "id": "1", "type": "raw_data", "settings": { "size": "50" } }
],
"query": "level:ERROR",
"timeField": "@timestamp"
}
]
}
]
}一、现在我们先看日志量趋势panel的实现：
1.新建dashboard
2.选择panel新建一个面板
3.在面板里面选择configure visualization
4.进入后选择数据源--->All visualization--->Query type选择metric
5.配置数据按照日志级别进行统计
7.进行其他参数的设置
8.修改完成后点击右上侧的save按钮，根据最后的提示完成就可以了，效果如下：
需要注意的是，如下图所示的两个时间设置，分别代表采集的数据是离当前的时间差和多久刷新一次
二、实现ERROR日志量的panel
1.新增一个panel以及选择数据源为elasticsearch,操作步骤如上
其中第三步是用来做计算操作的，把获取出来的数据做相应的操作，我目前这里是同求和的操作
2.改变颜色通过如下图片
3.现在的效果如下图:
三、实现日志总量
1.选择方式都和ERROR日志量差不多，唯一的区别如图
2.现在的总的效果如下图
在编辑状态下可以改变panel大小和位置
四、实现top10 logger分布
1.数据源的选择方式和前面一样，这次选择的panel类型是pie chart，如下图
2.最终效果如下：
五、实现前100日志查询
1.数据源一样的配置elastic search，panel选择为logs，具体配置如下：
2.最新效果如下:
六、实现ERROR日志详情
1.同样配置elastic search数据源，panel选择logs，具体如下:
3.最后的效果如下:
