download：BAT大牛亲授 基于ElasticSearch的搜房网实战
Elasticsearch 统计代码例子
aggs
avg 均匀数
最近15分钟的均匀访问时间，upstream_time_ms是每次访问时间，单位毫秒
{
"query": {
"filtered": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-15m",
"lt": "now"
}
}
}
}},
"aggs": {
"execute_time": {
"avg": {
"field": "upstream_time_ms"
}
}}
}
//当然你也能够直接将过滤器写在aggs里面
{
"size": 0,
"aggs": {
"filtered_aggs": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-15m",
"lt": "now"
}
}
},
"aggs": {
"execute_time": {
"avg": {
"field": "upstream_time_ms"
}
}
}
}}
}
cardinality 基数，比方计算uv
你可能留意到了size:0，假如你只需求统计数据，不要数据自身，就设置它，这不是我投机倒把，官方文档也是这么干的。
{
"size": 0,
"aggs": {
"filtered_aggs": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-15m",
"lt": "now"
}
}
},
"aggs": {
"ipv": {
"cardinality": {
"field": "ip"
}
}
}
}}
}
percentiles 基于百分比统计
最近15分钟，99.9的恳求的执行时间不超越几
{
"size": 0,
"query": {
"filtered": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-15m",
"lt": "now"
}
}
}
}},
"aggs": {
"execute_time": {
"percentiles": {
"field": "upstream_time_ms",
"percents": [
90,
95,
99.9
]
}
}}
}
//返回值，0.1%的恳求超越了159ms
{
"took": 620,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0},
"hits": {
"total": 679400,
"max_score": 0,
"hits": []},
"aggregations": {
"execute_time": {
"values": {
"90.0": 24.727003484320534,
"95.0": 72.6200981699678,
"99.9": 159.01065773524886 //99.9的数据落在159以内，是系统计算出来159
}
}}
}
percentile_ranks 指定一个范围，有几数据落在这里
{
"size": 0,
"query": {
"filtered": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-15m",
"lt": "now"
}
}
}
}},
"aggs": {
"execute_time": {
"percentile_ranks": {
"field": "upstream_time_ms",
"values": [
50,
160
]
}
}}
}
//返回值
{
"took": 666,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0},
"hits": {
"total": 681014,
"max_score": 0,
"hits": []},
"aggregations": {
"execute_time": {
"values": {
"50.0": 94.14716385885366,
"160.0": 99.91130872493076 //99.9的数据落在了160以内，这次，160是我指定的，系统计算出99.9
}
}}
}
统计最近15分钟，不同的链接恳求时间大小
{
"size": 0,
"query": {
"filtered": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-15m",
"lt": "now"
}
}
}
}},
"aggs": {
"execute_time": {
"terms": {
"field": "uri"
},
"aggs": {
"avg_time": {
"avg": {
"field": "upstream_time_ms"
}
}
}
}}
}
//返回,看起来url1 比 url2慢一点(avg_time)，不过url1的恳求量比拟大 （doc_count）
{
"took": 1655,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0},
"hits": {
"total": 710802,
"max_score": 0,
"hits": []},
"aggregations": {
"execute_time": {
"doc_count_error_upper_bound": 10,
"sum_other_doc_count": 347175,
"buckets": [
{
"key": "/url1",
"doc_count": 362688,
"avg_time": {
"value": 6.601660380271749
}
},
{
"key": "/url2",
"doc_count": 939,
"avg_time": {
"value": 5.313099041533547
}
}
]
}}
}
找出url响应最慢的前2名
{
"size": 0,
"query": {
"filtered": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-15m",
"lt": "now"
}
}
}
}},
"aggs": {
"execute_time": {
"terms": {
"size": 2,
"field": "uri",
"order": {
"avg_time": "desc"
}
},
"aggs": {
"avg_time": {
"avg": {
"field": "upstream_time_ms"
}
}
}
}}
}
//返回值
{
"took": 1622,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0},
"hits": {
"total": 748712,
"max_score": 0,
"hits": []},
"aggregations": {
"execute_time": {
"doc_count_error_upper_bound": -1,
"sum_other_doc_count": 748710,
"buckets": [
{
"key": "url_shit",
"doc_count": 123,
"avg_time": {
"value": 8884
}
},
{
"key": "url_shit2",
"doc_count": 456,
"avg_time": {
"value": 8588
}
}
]
}}
}
value_count 文档数量
相当于
select count(*) from table group by uri，为了到达这个目的，只需求把上文中,avg 换成value_count。不过avg的时分，结果中的doc_count其实到达了同样效果。
怎样取数据画个图？比方：最近2分钟，每20秒的时间窗口中，均匀响应时间是几
{
"size": 0,
"query": {
"filtered": {
"filter": {
"range": {
"@timestamp": {
"gt": "now-2m",
"lt": "now"
}
}
}
}},
"aggs": {
"execute_time": {
"date_histogram": {
"field": "@timestamp",
"interval": "20s"
},
"aggs": {
"avg_time": {
"avg": {
"field": "upstream_time_ms"
}
}
}
}}
}
pv 分时统计图（每小时一统计）
周期大小对性能影响不大
{
"size":0,
"fields":false,
"aggs": {
"execute_time": {
"date_histogram": {
"field": "@timestamp",
"interval": "1h"
}
}}
}