查询特定渠道分享数量最大的30个文章的uuid：
{ "size": 0, "query": { "bool": { "must": [ { "terms": { "ul_actType": [ 1030 ], "boost": 1 } }, { "terms": { "ul_distChannel": [ 2000, 1000, 1001, 1019 ], "boost": 1 } } ], "disable_coord": false, "adjust_pure_negative": true, "boost": 1 } }, "explain": false, "aggregations": { "aggs_targetId": { "terms": { "field": "ul_targetId", "size": 30, "min_doc_count": 1, "shard_min_doc_count": 0, "show_term_doc_count_error": false, "order": [ { "_count": "desc" }, { "_term": "asc" } ] } } } }
查询特定渠道30篇文章的阅读数,推荐数,点赞数,分享数：
{ "size": 0, "query": { "bool": { "must": [ { "terms": { "ul_targetId": [ "94a803d3e883f96fc236c136de7fca2a", "dedff564f1e67d86fda44fb8dce6cbc5", "1b5ae5c0cfe26374af883e87d2b2f7c0", "3c5c5b3cc698aae419cad8d6eb02cbf2", "a7886af3f7d46dce4e5396dce3ed2b97", "35140f8d3f96bd48b3f6724c712150b0", "84801260669eec369d18225c6e63b01b", "10dc5b9bb733049a028b4ada6e7a4b84", "3587229434114e2fc3b3380643239f21", "93956578579740bc8b812eec57631f71", "9b9e248c892e3127ddcf3d5a440ab7a6", "4a9eb484d0595f210fbc49fdb1fb1eb5", "42f47d23cc154a4a6baddd5192f74f9c", "9b640b026fd85fe3eaa738dca483ff73", "2085b778ae6627cb236ae3cd6ba08acf", "3a0e3bab19b8d1902e1359a7d330f675", "4a10a6f8ddd78ae47bf3ff79bbc4a929", "83f179c72918e0ac424c6f08cb628922", "c642a8b80a5f72b426c1c30547e34ae3", "7d2994d2b17c331a4d14eb16362b41b7", "eaff7777a93fe1cfd05ce83dac3a07c4", "f239bd18ddb31a9b38305a3fa8135583", "ba3f535eb515c2f5948c902aa78e6733", "3fd4a262c96d17c2df22ede68c013fc8", "7bc7e72fdfb2f89200a975d8bec3323e", "a56fd1fc1cde98355cafb321bf6b7fa5", "1a21061052d3c72c5bff7c658fc9e656", "2cd32bc12d56168dc7143a756697a831", "70881bbbed12c5930af37de3d4c8cb6f", "8c66080ac50992e25a0a3b9f06eba89e" ], "boost": 1 } }, { "terms": { "ul_actType": [ 2000, 1000, 1010, 1030 ], "boost": 1 } }, { "terms": { "ul_distChannel": [ 2000, 1000, 1001, 1019 ], "boost": 1 } } ], "disable_coord": false, "adjust_pure_negative": true, "boost": 1 } }, "explain": false, "aggregations": { "aggs_targetId": { "terms": { "field": "ul_targetId", "size": 30, "min_doc_count": 1, "shard_min_doc_count": 0, "show_term_doc_count_error": false, "order": [ { "_count": "desc" }, { "_term": "asc" } ] }, "aggregations": { "aggs_actType": { "terms": { "field": "ul_actType", "size": 4, "min_doc_count": 1, "shard_min_doc_count": 0, "show_term_doc_count_error": false, "order": [ { "_count": "desc" }, { "_term": "asc" } ] } } } } } }
聚合例子:
{ "query": { "bool": { "must": [ { "terms": { "ul_actType": [ 1001 ] } }, { "terms": { "ul_actFrom": [ 1 ] } }, { "wildcard": { "ul_langMode": "*1*" } }, { "range": { "ul_addTime": { "lt": 1494432000000, "gte": 1494345600000 } } } ], "must_not": [ { "wildcard": { "ul_userId": "temp*" } }, { "term": { "ul_version.keyword": -1 } } ] } }, "size": 0, "aggregations": { "aggs_targetId": { "terms": { "field": "ul_targetId", "size": 50, "min_doc_count": 1000, "order": [ { "_count": "desc" }, { "_term": "asc" } ] }, "aggs": { "rare_targetId": { "bucket_selector": { "buckets_path": { "doc_count": "_count" }, "script": { "inline": "params.doc_count < 3000" } } } } } } }
多字段聚合例子:(2字段)
{ "query": { "bool": { "must": [ { "range": { "image_count": { "from": "0", "to": null, "include_lower": false, "include_upper": true, "boost": 1 } } }, { "term": { "atype": { "value": "0", "boost": 1 } } }, { "term": { "flag_pool": { "value": "0", "boost": 1 } } }, { "term": { "flag_ql": { "value": "0", "boost": 1 } } }, { "term": { "flag_off": { "value": "0", "boost": 1 } } }, { "terms": { "domain": [ "goldenmob.com" ], "boost": 1 } }, { "range": { "add_time": { "from": "0", "to": "2017-06-15", "include_lower": true, "include_upper": true, "boost": 1 } } } ], "disable_coord": false, "adjust_pure_negative": true, "boost": 1 } }, "size": 0, "aggregations": { "agg_author_count": { "terms": { "field": "author" }, "aggregations": { "agg_cate": { "terms": { "field": "app_category" } } } } } }
多字段聚合例子:(3字段)
{ "query": { "bool": { "must": [ { "range": { "image_count": { "from": "0", "to": null, "include_lower": false, "include_upper": true, "boost": 1 } } }, { "term": { "atype": { "value": "0", "boost": 1 } } }, { "term": { "flag_pool": { "value": "0", "boost": 1 } } }, { "term": { "flag_ql": { "value": "0", "boost": 1 } } }, { "term": { "flag_off": { "value": "0", "boost": 1 } } }, { "terms": { "domain": [ "goldenmob.com" ], "boost": 1 } }, { "range": { "add_time": { "from": "0", "to": "2017-06-15", "include_lower": true, "include_upper": true, "boost": 1 } } } ], "disable_coord": false, "adjust_pure_negative": true, "boost": 1 } }, "size": 0, "aggs": { "agg_author_count": { "terms": { "field": "author" }, "aggs": { "agg_cate": { "terms": { "field": "app_category" }, "aggs": { "agg_lang": { "terms": { "field": "lang" } } } } } } } }
注意:这里如果要进行多个字段的聚合,需要注意第二个aggs的位置,是在第一个的terms之后,而不是在第一个agg_author_count之后. aggregations = aggs
JSON转CSV: https://json-csv.com/