【README】

1.本文總結了 dsl 與 sql的對比寫法；

2.es采用 7.2.1 版本；

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【1】創建es索引

1）新建一個數據庫事務執行日志索引

put localhost:9200/txlog

{ "mappings" :{ "properties":{"APPNAME":{"type":"keyword"} , "ITFNAME":{"type":"keyword"}, "DBNAME":{"type":"keyword"}, "EXE_COST":{"type":"integer"}, "EXE_TIMES":{"type":"integer"} , "CREATE_TIME":{"type":"date", "format":"yyyy-MM-dd HH:mm:ss.SSS||yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||epoch_millis"} }} }

字段含義：

序號	字段名	描述
1	APPNAME	應用名
2	ITFNAME	接口名
3	DBNAME	數據庫名
4	EXE_COST	執行耗時
5	EXE_TIMES	??????? 執行次數
6	CREATE_TIME	創建時間

字段類型為 keyword，即不分詞，支持精確查找，不支持模糊查找；

補充： 如要分詞，使用 text，即支持模糊查找；

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【2】插入10條數據

采用 bulk 批量插入api；如下：

post localhost:9200/txlog/_doc/_bulk

{"index":{}} {"APPNAME":"trcd1", "ITFNAME":"TRLOGQRY11", "DBNAME":"TRDB11", "EXE_COST":"11", "EXE_TIMES":"2", "CREATE_TIME":"2022-04-13 20:09:00"} {"index":{}} {"APPNAME":"trcd1", "ITFNAME":"TRLOGQRY11", "DBNAME":"TRDB11", "EXE_COST":"12", "EXE_TIMES":"3", "CREATE_TIME":"2022-04-14 20:09:00"} {"index":{}} {"APPNAME":"trcd1", "ITFNAME":"TRLOGQRY11", "DBNAME":"TRDB11", "EXE_COST":"13", "EXE_TIMES":"4", "CREATE_TIME":"2022-04-15 20:09:00"} {"index":{}} {"APPNAME":"trcd1", "ITFNAME":"TRLOGQRY11", "DBNAME":"TRDB11", "EXE_COST":"14", "EXE_TIMES":"5", "CREATE_TIME":"2022-04-16 20:09:00"} {"index":{}} {"APPNAME":"trcd2", "ITFNAME":"TRLOGQRY21", "DBNAME":"TRDB21", "EXE_COST":"15", "EXE_TIMES":"6", "CREATE_TIME":"2022-04-17 20:09:00"} {"index":{}} {"APPNAME":"trcd2", "ITFNAME":"TRLOGQRY21", "DBNAME":"TRDB21", "EXE_COST":"16", "EXE_TIMES":"7", "CREATE_TIME":"2022-04-17 20:09:00"} {"index":{}} {"APPNAME":"trcd2", "ITFNAME":"TRLOGQRY22", "DBNAME":"TRDB22", "EXE_COST":"17", "EXE_TIMES":"8", "CREATE_TIME":"2022-04-19 20:09:00"} {"index":{}} {"APPNAME":"trcd2", "ITFNAME":"TRLOGQRY23", "DBNAME":"TRDB01", "EXE_COST":"18", "EXE_TIMES":"9", "CREATE_TIME":"2022-04-20 20:09:00"} {"index":{}} {"APPNAME":"trcd2", "ITFNAME":"TRLOGQRY23", "DBNAME":"TRDB01", "EXE_COST":"19", "EXE_TIMES":"12", "CREATE_TIME":"2022-04-21 20:09:00"} {"index":{}} {"APPNAME":"trcd2", "ITFNAME":"TRLOGQRY23", "DBNAME":"TRDB01", "EXE_COST":"20", "EXE_TIMES":"22", "CREATE_TIME":"2022-04-22 20:09:00"}

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【3】dsl與sql對比

【3.0】普通查詢

sql：

select col1, col2 from table1 where col = '' ... limit 10

dsl如下：

{"_source":["APPNAME", "ITFNAME", "DBNAME", "EXE_COST"],"query":{"bool":{"filter":{"bool":{"must":[{"range":{"CREATE_TIME":{"gte":"2022-04-13 00:00:00", "lte":"2022-04-19 00:00:00"}}}]}}}}, "size":10 }

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【3.1】 范圍查詢

sql：

where create_time > '' and create_time < ''

dsl如下：把 bool 封裝在filter里面，不會計算分數，提高查詢性能；

{"query":{"bool":{"filter":{"bool":{"must":[{"range":{"CREATE_TIME":{"gte":"2022-04-13 00:00:00", "lte":"2022-04-15 00:00:00"}}}]}}}} }

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【3.2】聚合查詢

1）根據appname 分組 求均值，求和；

sql如下：
?

select sum(exe_times) as sum_times, avg(exe_cost) as avg_cost from table where create_time > '#' and create_time < '#' group by appname

dsl如下：（先分組，后求均值，求和）

{"query":{"bool":{"filter":{"bool":{"must":[{"range":{"CREATE_TIME":{"gte":"2022-04-13 00:00:00", "lte":"2022-04-19 00:00:00"}}}]}}}}, "size":0, "aggs":{"group_by_app":{"terms":{"field":"APPNAME"}, "aggs":{"sum_times":{"sum":{"field":"EXE_TIMES"}}, "avg_cost":{"avg":{"field":"EXE_COST"}}}}} }

查詢結果：

"aggregations": {"group_by_app": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "trcd1","doc_count": 4,"avg_cost": {"value": 12.5},"sum_times": {"value": 14.0}},{"key": "trcd2","doc_count": 2,"avg_cost": {"value": 15.5},"sum_times": {"value": 13.0}}]}}

2）根據多個字段分組，如 應用名， 數據庫名

sql：

select * from (select sum(exe_times) as sum_times, avg(exe_cost) as avg_cost from table where create_time > '#' and create_time < '#'group by appname,dbname ) a order by sum_times desc limit 3

dsl：使用 painless 腳本 根據多個字段分組；

{"query":{"bool":{"filter":{"bool":{"must":[{"range":{"CREATE_TIME":{"gte":"2022-04-13 00:00:00", "lte":"2022-04-19 00:00:00"}}}]}}}}, "size":0, "aggs":{"group_by_app":{"terms":{"script":{"source":"doc['APPNAME'] + '#SPLIT#' + doc['DBNAME']", "lang":"painless"}, "size":"3", "min_doc_count":"1", "order":{"sum_times":"desc"}}, "aggs":{"sum_times":{"sum":{"field":"EXE_TIMES"}}, "avg_cost":{"avg":{"field":"EXE_COST"}}}}} }

步驟：

按照 appname， dbname 分組；

聚合操作：求和，求均值；

按照 sum_times 倒排 聚合結果；

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