聚合用于分析查詢結果集的統計指標，我們以觀看日志分析為例，介紹各種常用的ElasticSearch聚合操作。

目錄：

• 查詢用戶觀看視頻數和觀看時長
• 聚合分頁器
• 查詢視頻uv
  • 單個視頻uv
  • 批量查詢視頻uv
• Having查詢
  • 根據 count 進行過濾
  • 根據其它指標進行過濾

首先展示一下我們要分析的文檔結構:

{"video_id": 1289643545120062253, // 視頻id"video_uid": 3931482202390368051, // 視頻發布者id"uid": 47381776787453866, // 觀看用戶id"time": 1533891263224, // 時間發生時間"watch_duration": 30 // 觀看時長 }

每個文檔記錄了一個觀看事件，我們通過聚合分析用戶的觀看行為。

ElasticSearch引入了兩個相關概念：

• 桶(Buckets): 滿足特定條件的文檔的集合
• 指標(Metrics): 桶中文檔的統計值，如特定字段的平均值

查詢用戶觀看視頻數和觀看時長

首先用sql語句描述這個查詢:

SELECT uid, count(*) as view_count FROM view_log WHERE time >= #{since} AND time <= #{to} GROUP BY uid;

ES 查詢:

GET /view_log/_search {"size" : 0,"query": {"range": {"time": {"gte": 0, // since"lte": 0 // to}}},"aggs": {"agg": { // agg為聚合的名稱"terms": { // 聚合的條件為 uid 相同"field": "uid"}}} }

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},{"key": 31489302390368051,"doc_count": 2717}]} }

result.aggregations.agg.buckets列表中包含了查詢的結果。

因為我們按照terms:uid進行聚合，每個bucket為uid相同的文檔集合，key字段即為uid。

doc_count 字段表明bucket中文檔的數目即sql語句中的count(*) as view_count。

我們可以為查詢添加額外的統計指標, sql描述:

SELECT uid, count(*) as view_count, avg(watch_duration) as avg_duration FROM view_log WHERE time >= #{since} AND time <= #{to} GROUP BY uid;

ES 查詢:

GET /view_log/_search {"size" : 0,"query": {"range": {"time": {"gte": 0, // since"lte": 0 // to}}},"aggs": {"agg": { // agg為聚合的名稱"terms": { // 聚合的條件為 uid 相同"field": "uid"},"aggs": { // 添加統計指標(Metrics)"avg_duration": { "avg": { // 統計 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}}]} }

avg_duration.value 表示 watch_duration 的平均值即該用戶的平均觀看時長。

聚合分頁器

在實際應用中用戶的數量非常驚人, 不可能通過一次查詢得到全部結果因此我們需要分頁器分批取回:

GET /view_log/_search {"size" : 0,"query": {"range": {"time": {"gte": 0, // since"lte": 0 // to}}},"aggs": {"agg": { "terms": { "field": "uid","size": 10000, // bucket 的最大個數"include": { // 將聚合結果分為10頁，序號為[0,9], 取第一頁"partition": 0,"num_partitions": 10 }},"aggs": { "avg_duration": { "avg": { "field": "watch_duration" }}}}} }

上述查詢與上節的查詢幾乎完全相同，只是在aggs.agg.terms字段中添加了include字段進行分頁。

查詢視頻uv

單個視頻uv

uv是指觀看一個視頻的用戶數(unique visit)，與此相對沒有按照用戶去重的觀看數稱為pv(page visit)。

用SQL語句來描述:

SELECT video_id, count(*) as pv, count(distinct uid) as uv FROM view_log WHERE video_id = #{video_id};

ElasticSearch可以方便的進行count(distinct)查詢:

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}} }

批量查詢視頻uv

ElasticSearch也可以批量查詢count(distinct), 先用SQL進行描述:

SELECT video_id, count(*) as pv, count(distinct uid) as uv FROM view_log GROUP BY video_id;

查詢:

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, // 視頻id"doc_count": 427, // 視頻觀看次數 pv"uv": {"value": 124 // 觀看視頻的用戶數 uv}},{"key": 72446898144,"doc_count": 744,"uv": {"value":233}}]}} }

Having查詢

SQL可以使用HAVING語句根據聚合結果進行過濾，ElasticSearch可以使用pipeline aggregations達到此效果不過語法較為繁瑣。

根據 count 進行過濾

使用SQL查詢觀看超過200次的視頻:

SELECT video_id, count(*) as view_count FROM view_log GROUP BY video_id HAVING count(*) > 200; GET /view_log/_search {"size": 0,"aggs": {"view_count": {"terms": {"field": "video_id"},"aggs": {"having": {"bucket_selector": {"buckets_path": { // 選擇 view_count 聚合的 doc_count 進行過濾"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實現類似HAVING查詢的關鍵在于使用bucket_selector選擇聚合結果進行過濾。

根據其它指標進行過濾

接下來我們嘗試查詢平均觀看時長大于5分鐘的視頻, 用SQL描述該查詢:

SELECT video_id FROM view_log GROUP BY video_id HAVING 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}}]}} }

轉載于:https://www.cnblogs.com/Finley/p/9499534.html

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