elasticsearch系列六:聚合分析(聚合分析简介、指标聚合、桶聚合)
2018-06-22 23:21
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一、聚合分析简介
1. ES聚合分析是什么?
聚合分析是数据库中重要的功能特性,完成对一个查询的数据集中数据的聚合计算,如:找出某字段(或计算表达式的结果)的最大值、最小值,计算和、平均值等。ES作为搜索引擎兼数据库,同样提供了强大的聚合分析能力。对一个数据集求最大、最小、和、平均值等指标的聚合,在ES中称为指标聚合 metric
而关系型数据库中除了有聚合函数外,还可以对查询出的数据进行分组group by,再在组上进行指标聚合。在 ES 中group by 称为分桶,桶聚合 bucketing
ES中还提供了矩阵聚合(matrix)、管道聚合(pipleline),但还在完善中。
2. ES聚合分析查询的写法
在查询请求体中以aggregations节点按如下语法定义聚合分析:"aggregations" : { "<aggregation_name>" : { <!--聚合的名字 --> "<aggregation_type>" : { <!--聚合的类型 --> <aggregation_body> <!--聚合体:对哪些字段进行聚合 --> } [,"meta" : { [<meta_data_body>] } ]? <!--元 --> [,"aggregations" : { [<sub_aggregation>]+ } ]? <!--在聚合里面在定义子聚合 --> } [,"<aggregation_name_2>" : { ... } ]*<!--聚合的名字 --> }
说明:
aggregations 也可简写为 aggs
3. 聚合分析的值来源
聚合计算的值可以取字段的值,也可是脚本计算的结果。二、指标聚合
1. max min sum avg
示例1:查询所有客户中余额的最大值POST /bank/_search? { "size": 0, "aggs": { "masssbalance": { "max": { "field": "balance" } } } }
结果1:
{ "took": 2080, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "masssbalance": { "value": 49989 } } }
示例2:查询年龄为24岁的客户中的余额最大值
POST /bank/_search? { "size": 2, "query": { "match": { "age": 24 } }, "sort": [ { "balance": { "order": "desc" } } ], "aggs": { "max_balance": { "max": { "field": "balance" } } } }
结果2:
{ "took": 5, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 42, "max_score": null, "hits": [ { "_index": "bank", "_type": "_doc", "_id": "697", "_score": null, "_source": { "account_number": 697, "balance": 48745, "firstname": "Mallory", "lastname": "Emerson", "age": 24, "gender": "F", "address": "318 Dunne Court", "employer": "Exoplode", "email": "malloryemerson@exoplode.com", "city": "Montura", "state": "LA" }, "sort": [ 48745 ] }, { "_index": "bank", "_type": "_doc", "_id": "917", "_score": null, "_source": { "account_number": 917, "balance": 47782, "firstname": "Parks", "lastname": "Hurst", "age": 24, "gender": "M", "address": "933 Cozine Avenue", "employer": "Pyramis", "email": "parkshurst@pyramis.com", "city": "Lindcove", "state": "GA" }, "sort": [ 47782 ] } ] }, "aggregations": { "max_balance": { "value": 48745 } } }
示例3:值来源于脚本,查询所有客户的平均年龄是多少,并对平均年龄加10
POST /bank/_search?size=0 { "aggs": { "avg_age": { "avg": { "script": { "source": "doc.age.value" } } }, "avg_age10": { "avg": { "script": { "source": "doc.age.value + 10" } } } } }
结果3:
{ "took": 86, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "avg_age": { "value": 30.171 }, "avg_age10": { "value": 40.171 } } }
示例4:指定field,在脚本中用_value 取字段的值
POST /bank/_search?size=0 { "aggs": { "sum_balance": { "sum": { "field": "balance", "script": { "source": "_value * 1.03" } } } } }
结果4:
{ "took": 165, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "sum_balance": { "value": 26486282.11 } } }
示例5:为没有值字段指定值。如未指定,缺失该字段值的文档将被忽略。
POST /bank/_search?size=0 { "aggs": { "avg_age": { "avg": { "field": "age", "missing": 18 } } } }
2. 文档计数 count
示例1:统计银行索引bank下年龄为24的文档数量POST /bank/_doc/_count { "query": { "match": { "age" : 24 } } }
结果1:
{ "count": 42, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 } }
3. Value count 统计某字段有值的文档数
示例1:POST /bank/_search?size=0 { "aggs": { "age_count": { "value_count": { "field": "age" } } } }
结果1:
{ "took": 2022, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_count": { "value": 1000 } } }
4. cardinality 值去重计数
示例1:POST /bank/_search?size=0 { "aggs": { "age_count": { "cardinality": { "field": "age" } }, "state_count": { "cardinality": { "field": "state.keyword" } } } }
说明:state的使用它的keyword版
结果1:
{ "took": 2074, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "state_count": { "value": 51 }, "age_count": { "value": 21 } } }
5. stats 统计 count max min avg sum 5个值
示例1:POST /bank/_search?size=0 { "aggs": { "age_stats": { "stats": { "field": "age" } } } }
结果1:
{ "took": 7, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_stats": { "count": 1000, "min": 20, "max": 40, "avg": 30.171, "sum": 30171 } } }
6. Extended stats
高级统计,比stats多4个统计结果: 平方和、方差、标准差、平均值加/减两个标准差的区间示例1:
POST /bank/_search?size=0 { "aggs": { "age_stats": { "extended_stats": { "field": "age" } } } }
结果1:
{ "took": 7, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_stats": { "count": 1000, "min": 20, "max": 40, "avg": 30.171, "sum": 30171, "sum_of_squares": 946393, "variance": 36.10375899999996, "std_deviation": 6.008640362012022, "std_deviation_bounds": { "upper": 42.18828072402404, "lower": 18.153719275975956 } } } }
7. Percentiles 占比百分位对应的值统计
对指定字段(脚本)的值按从小到大累计每个值对应的文档数的占比(占所有命中文档数的百分比),返回指定占比比例对应的值。默认返回[ 1, 5, 25, 50, 75, 95, 99 ]分位上的值。如下中间的结果,可以理解为:占比为50%的文档的age值 <= 31,或反过来:age<=31的文档数占总命中文档数的50%示例1:
POST /bank/_search?size=0 { "aggs": { "age_percents": { "percentiles": { "field": "age" } } } }
结果1:
{ "took": 87, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_percents": { "values": { "1.0": 20, "5.0": 21, "25.0": 25, "50.0": 31, "75.0": 35.00000000000001, "95.0": 39, "99.0": 40 } } } }
结果说明:
占比为50%的文档的age值 <= 31,或反过来:age<=31的文档数占总命中文档数的50%
示例2:指定分位值
POST /bank/_search?size=0 { "aggs": { "age_percents": { "percentiles": { "field": "age", "percents" : [95, 99, 99.9] } } } }
结果2:
{ "took": 8, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_percents": { "values": { "95.0": 39, "99.0": 40, "99.9": 40 } } } }
8. Percentiles rank 统计值小于等于指定值的文档占比
示例1:统计年龄小于25和30的文档的占比,和第7项相反POST /bank/_search?size=0 { "aggs": { "gge_perc_rank": { "percentile_ranks": { "field": "age", "values": [ 25, 30 ] } } } }
结果2:
{ "took": 8, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "gge_perc_rank": { "values": { "25.0": 26.1, "30.0": 49.2 } } } }
结果说明:年龄小于25的文档占比为26.1%,年龄小于30的文档占比为49.2%,
9. Geo Bounds aggregation 求文档集中的地理位置坐标点的范围
参考官网链接:https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geobounds-aggregation.html
10. Geo Centroid aggregation 求地理位置中心点坐标值
参考官网链接:https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics-geocentroid-aggregation.html
三、桶聚合
{ "took": 2059, "timed_out": false, "_shards": { "total": 58, "successful": 58, "skipped": 0, "failed": 0 }, "hits": { "total": 1015, "max_score": 1, "hits": [ { "_index": "bank", "_type": "_doc", "_id": "25", "_score": 1, "_source": { "account_number": 25, "balance": 40540, "firstname": "Virginia", "lastname": "Ayala", "age": 39, "gender": "F", "address": "171 Putnam Avenue", "employer": "Filodyne", "email": "virginiaayala@filodyne.com", "city": "Nicholson", "state": "PA" } }, { "_index": "bank", "_type": "_doc", "_id": "44", "_score": 1, "_source": { "account_number": 44, "balance": 34487, "firstname": "Aurelia", "lastname": "Harding", "age": 37, "gender": "M", "address": "502 Baycliff Terrace", "employer": "Orbalix", "email": "aureliaharding@orbalix.com", "city": "Yardville", "state": "DE" } }, { "_index": "bank", "_type": "_doc", "_id": "99", "_score": 1, "_source": { "account_number": 99, "balance": 47159, "firstname": "Ratliff", "lastname": "Heath", "age": 39, "gender": "F", "address": "806 Rockwell Place", "employer": "Zappix", "email": "ratliffheath@zappix.com", "city": "Shaft", "state": "ND" } }, { "_index": "bank", "_type": "_doc", "_id": "119", "_score": 1, "_source": { "account_number": 119, "balance": 49222, "firstname": "Laverne", "lastname": "Johnson", "age": 28, "gender": "F", "address": "302 Howard Place", "employer": "Senmei", "email": "lavernejohnson@senmei.com", "city": "Herlong", "state": "DC" } }, { "_index": "bank", "_type": "_doc", "_id": "126", "_score": 1, "_source": { "account_number": 126, "balance": 3607, "firstname": "Effie", "lastname": "Gates", "age": 39, "gender": "F", "address": "620 National Drive", "employer": "Digitalus", "email": "effiegates@digitalus.com", "city": "Blodgett", "state": "MD" } }, { "_index": "bank", "_type": "_doc", "_id": "145", "_score": 1, "_source": { "account_number": 145, "balance": 47406, "firstname": "Rowena", "lastname": "Wilkinson", "age": 32, "gender": "M", "address": "891 Elton Street", "employer": "Asimiline", "email": "rowenawilkinson@asimiline.com", "city": "Ripley", "state": "NH" } }, { "_index": "bank", "_type": "_doc", "_id": "183", "_score": 1, "_source": { "account_number": 183, "balance": 14223, "firstname": "Hudson", "lastname": "English", "age": 26, "gender": "F", "address": "823 Herkimer Place", "employer": "Xinware", "email": "hudsonenglish@xinware.com", "city": "Robbins", "state": "ND" } }, { "_index": "bank", "_type": "_doc", "_id": "190", "_score": 1, "_source": { "account_number": 190, "balance": 3150, "firstname": "Blake", "lastname": "Davidson", "age": 30, "gender": "F", "address": "636 Diamond Street", "employer": "Quantasis", "email": "blakedavidson@quantasis.com", "city": "Crumpler", "state": "KY" } }, { "_index": "bank", "_type": "_doc", "_id": "208", "_score": 1, "_source": { "account_number": 208, "balance": 40760, "firstname": "Garcia", "lastname": "Hess", "age": 26, "gender": "F", "address": "810 Nostrand Avenue", "employer": "Quiltigen", "email": "garciahess@quiltigen.com", "city": "Brooktrails", "state": "GA" } }, { "_index": "bank", "_type": "_doc", "_id": "222", "_score": 1, "_source": { "account_number": 222, "balance": 14764, "firstname": "Rachelle", "lastname": "Rice", "age": 36, "gender": "M", "address": "333 Narrows Avenue", "employer": "Enaut", "email": "rachellerice@enaut.com", "city": "Wright", "state": "AZ" } } ] }, "aggregations": { "tags": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "N/A", "doc_count": 1014 }, { "key": "red", "doc_count": 1 } ] } } }
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2. filter Aggregation 对满足过滤查询的文档进行聚合计算
在查询命中的文档中选取符合过滤条件的文档进行聚合,先过滤再聚合示例1:
POST /bank/_search?size=0 { "aggs": { "age_terms": { "filter": {"match":{"gender":"F"}}, "aggs": { "avg_age": { "avg": { "field": "age" } } } } } }
结果1:
{ "took": 163, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_terms": { "doc_count": 493, "avg_age": { "value": 30.3184584178499 } } } }
3. Filters Aggregation 多个过滤组聚合计算
示例1:准备数据:
PUT /logs/_doc/_bulk?refresh {"index":{"_id":1}} {"body":"warning: page could not be rendered"} {"index":{"_id":2}} {"body":"authentication error"} {"index":{"_id":3}} {"body":"warning: connection timed out"}
获取组合过滤后聚合的结果:
GET logs/_search { "size": 0, "aggs": { "messages": { "filters": { "filters": { "errors": { "match": { "body": "error" } }, "warnings": { "match": { "body": "warning" } } } } } } }
上面的结果:
{ "took": 18, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 3, "max_score": 0, "hits": [] }, "aggregations": { "messages": { "buckets": { "errors": { "doc_count": 1 }, "warnings": { "doc_count": 2 } } } } }
示例2:为其他值组指定key
GET logs/_search { "size": 0, "aggs": { "messages": { "filters": { "other_bucket_key": "other_messages", "filters": { "errors": { "match": { "body": "error" } }, "warnings": { "match": { "body": "warning" } } } } } } }
结果2:
{ "took": 5, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 3, "max_score": 0, "hits": [] }, "aggregations": { "messages": { "buckets": { "errors": { "doc_count": 1 }, "warnings": { "doc_count": 2 }, "other_messages": { "doc_count": 0 } } } } }
4. Range Aggregation 范围分组聚合
示例1:POST /bank/_search?size=0 { "aggs": { "age_range": { "range": { "field": "age", "ranges": [ { "to": 25 }, { "from": 25, "to": 35 }, { "from": 35 } ] }, "aggs": { "bmax": { "max": { "field": "balance" } } } } } }
结果1:
{ "took": 7, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_range": { "buckets": [ { "key": "*-25.0", "to": 25, "doc_count": 225, "bmax": { "value": 49587 } }, { "key": "25.0-35.0", "from": 25, "to": 35, "doc_count": 485, "bmax": { "value": 49795 } }, { "key": "35.0-*", "from": 35, "doc_count": 290, "bmax": { "value": 49989 } } ] } } }
示例2:为组指定key
POST /bank/_search?size=0 { "aggs": { "age_range": { "range": { "field": "age", "keyed": true, "ranges": [ { "to": 25, "key": "Ld" }, { "from": 25, "to": 35, "key": "Md" }, { "from": 35, "key": "Od" } ] } } } }
结果2:
{ "took": 2, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "age_range": { "buckets": { "Ld": { "to": 25, "doc_count": 225 }, "Md": { "from": 25, "to": 35, "doc_count": 485 }, "Od": { "from": 35, "doc_count": 290 } } } } }
5. Date Range Aggregation 时间范围分组聚合
示例1:POST /bank/_search?size=0 { "aggs": { "range": { "date_range": { "field": "date", "format": "MM-yyy", "ranges": [ { "to": "now-10M/M" }, { "from": "now-10M/M" } ] } } } }
结果1:
{ "took": 115, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "range": { "buckets": [ { "key": "*-2017-08-01T00:00:00.000Z", "to": 1501545600000, "to_as_string": "2017-08-01T00:00:00.000Z", "doc_count": 0 }, { "key": "2017-08-01T00:00:00.000Z-*", "from": 1501545600000, "from_as_string": "2017-08-01T00:00:00.000Z", "doc_count": 0 } ] } } }
6. Date Histogram Aggregation 时间直方图(柱状)聚合
就是按天、月、年等进行聚合统计。可按 year (1y), quarter (1q), month (1M), week (1w), day (1d), hour (1h), minute (1m), second (1s) 间隔聚合或指定的时间间隔聚合。示例1:
POST /bank/_search?size=0 { "aggs": { "sales_over_time": { "date_histogram": { "field": "date", "interval": "month" } } } }
结果1:
{ "took": 9, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 1000, "max_score": 0, "hits": [] }, "aggregations": { "sales_over_time": { "buckets": [] } } }
7. Missing Aggregation 缺失值的桶聚合
POST /bank/_search?size=0 { "aggs" : { "account_without_a_age" : { "missing" : { "field" : "age" } } } }
8. Geo Distance Aggregation 地理距离分区聚合
参考官网链接:https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-geodistance-aggregation.html
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