es6 dsl与sql对比

Posted 2022-04-17 PacosonSWJTU

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【README】

1.本文总结了 dsl 与 sql的对比写法；

2.es采用 7.2.1 版本；

【1】创建es索引

1）新建一个数据库事务执行日志索引

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"

字段含义：

序号	字段名	描述
1	APPNAME	应用名
2	ITFNAME	接口名
3	DBNAME	数据库名
4	EXE_COST	执行耗时
5	EXE_TIMES	执行次数
6	CREATE_TIME	创建时间

字段类型为 keyword，即不分词，支持精确查找，不支持模糊查找；

补充：如要分词，使用 text，即支持模糊查找；

【2】插入10条数据

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"

【3】dsl与sql对比

【3.0】普通查询

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

【3.1】范围查询

sql：

where  create_time > '' and create_time < ''

dsl如下：把 bool 封装在filter里面，不会计算分数，提高查询性能；


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

【3.2】聚合查询

1）根据appname 分组求均值，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：（先分组，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"

查询结果：

"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）根据多个字段分组，如应用名，数据库名

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 脚本根据多个字段分组；


    "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"

步骤：