聚合用于分析查询结果集的统计指标，我们以观看日志分析为例，介绍各种常用的ElasticSearch聚合操作。
首先展示一下我们要分析的文档结构:
{
"video_id": 1289643545120062253, // 视频id
"video_uid": 3931482202390368051, // 视频发布者id
"uid": 47381776787453866, // 观看用户id
"time": 1533891263224, // 时间发生时间
"watch_duration": 30 // 观看时长
}每个文档记录了一个观看事件，我们通过聚合分析用户的观看行为。
ElasticSearch引入了两个相关概念：
桶(Buckets): 满足特定条件的文档的集合
指标(Metrics): 桶中文档的统计值，如特定字段的平均值
查询用户观看视频数和观看时长
首先用sql语句描述这个查询:
SELECT uid, count(*) as view_count, avg(watch_duration) as avg_duration
FROM view_logWHERE time >= #{since} AND time <= #{to}
GROUP BY uid;GET /view_log/_search
{
"size" : 0,
"query": {
"range": {
"time": {
"gte": 0, // since
"lte": 0 // to
}
}
},
"aggs": {
"agg": { // agg为聚合的名称
"terms": { // 聚合的条件为 uid 相同
"field": "uid"
},
"aggs": { // 添加统计指标(Metrics)
"avg_duration": {
"avg": { // 统计 watch_duration 的平均值
"field": "watch_duration"
}
}
}
}
}
}response:
{
"took": 10,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 100000,
"max_score": 0,
"hits": []
},
"aggregations": {
"agg": {
"buckets": [
{
"key": 21836334489858688,
"doc_count": 4026,
"avg_duration": {
"value": 12778.882352941177
}
},
{
"key": 31489302390368051,
"doc_count": 2717,
"avg_duration": {
"value": 2652.5714285714284
}
}
]
}
}result.aggregations.agg.buckets列表中包含了查询的结果。
因为我们按照terms:uid进行聚合，每个bucket为uid相同的文档集合，key字段即为uid。
doc_count 字段表明bucket中文档的数目即sql语句中的count(*) as view_count。
avg_duration.value 表示 watch_duration 的平均值即该用户的平均观看时长。
聚合分页器
在实际应用中用户的数量非常惊人, 不可能通过一次查询得到全部结果因此我们需要分页器分批取回:
GET /view_log/_search
{
"size" : 0,
"query": {
"range": {
"time": {
"gte": 0, // since
"lte": 0 // to
}
}
},
"aggs": {
"agg": {
"terms": {
"field": "uid",
"size": 10000, // bucket 的最大个数
"include": { // 将聚合结果分为10页，序号为[0,9], 取第一页
"partition": 0,
"num_partitions": 10
}
},
"aggs": {
"avg_duration": {
"avg": {
"field": "watch_duration"
}
}
}
}
}
}上述查询与上节的查询几乎完全相同，只是在aggs.agg.terms字段中添加了include字段进行分页。
查询视频uv
单个视频uv
uv是指观看一个视频的用户数(user view)，与此相对没有按照用户去重的观看数称为pv(page view)。
用SQL语句来描述:
SELECT video_id, count(*) as pv, count(distinct uid) as uvFROM view_logWHERE video_id = #{video_id};ElasticSearch可以方便的进行count(distinct)查询:
GET /view_log/_search
{
"aggs": {
"uv": {
"cardinality": {
"field": "uid"
}
}
}
}response:
{
"took": 255,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 17579,
"max_score": 0,
"hits": []
},
"aggregations": {
"uv": {
"value": 11
}
}
}批量查询视频uv
ElasticSearch也可以批量查询count(distinct), 先用SQL进行描述:
SELECT video_id, count(*) as pv, count(distinct uid) as uvFROM view_logGROUP BY video_id;
查询:
GET /view_log/_search
{
"size": 0,
"aggs": {
"video": {
"terms": {
"field": "video_id"
},
"aggs": {
"uv": {
"cardinality": {
"field": "uid"
}
}
}
}
}
}response:
{
"took": 313,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 16940,
"max_score": 0,
"hits": []
},
"aggregations": {
"video": {
"buckets": [
{
"key": 25417499722062, // 视频id
"doc_count": 427, // 视频观看次数 pv
"uv": {
"value": 124 // 观看视频的用户数 uv
}
},
{
"key": 72446898144,
"doc_count": 744,
"uv": {
"value":233
}
}
]
}
}
}Having查询
SQL可以使用HAVING语句根据聚合结果进行过滤，ElasticSearch可以使用pipeline aggregations达到此效果不过语法较为繁琐。
根据 count 进行过滤
使用SQL查询观看超过200次的视频:
SELECT video_id, count(*) as view_countFROM view_logGROUP BY video_idHAVING count(*) > 200;
GET /view_log/_search
{
"size": 0,
"aggs": {
"view_count": {
"terms": {
"field": "video_id"
},
"aggs": {
"having": {
"bucket_selector": {
"buckets_path": { // 选择 view_count 聚合的 doc_count 进行过滤
"view_count": "_count"
},
"script": {
"source": "params.view_count > 200"
}
}
}
}
}
}
}response:
{
"took": 83,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 775,
"max_score": 0,
"hits": []
},
"aggregations": {
"view_count": {
"buckets": [
{
"key": 35025417499764062,
"doc_count": 529
},
{
"key": 19913672446898144,
"doc_count": 759
}
]
}
}
}ElasticSearch实现类似HAVING查询的关键在于使用[bucket_selector]选择聚合结果进行过滤。
根据其它指标进行过滤
接下来我们尝试查询平均观看时长大于5分钟的视频, 用SQL描述该查询:
SELECT video_id FROM view_logGROUP BY video_idHAVING avg(watch_duration) > 300;
GET /view_log/_search
{
"size": 0,
"aggs": {
"video": {
"terms": {
"field": "video_id"
},
"aggs": {
"avg_duration": {
"avg": {
"field": "watch_duration"
}
},
"avg_duration_filter": {
"bucket_selector": {
"buckets_path": {
"avg_duration": "avg_duration"
},
"script": {
"source": "params.avg_duration > 200"
}
}
}
}
}
}
}response:
{
"took": 137,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 255,
"max_score": 0,
"hits": []
},
"aggregations": {
"video": {
"buckets": [
{
"key": 5417499764062,
"doc_count": 91576,
"avg_duration": {
"value": 103
}
},
{
"key": 19913672446898144,
"doc_count": 15771,
"avg_duration": {
"value": 197
}
}
]
}
}
}原文出处：https://www.cnblogs.com/Finley/p/9499534.html
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