mongodb複雜查詢示例

時間  2019-12-14
標籤 mongodb 複雜 查詢 示例 欄目 MongoDB 简体版
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以oracle的測試schema爲例，emp和dept表，原始數據以下sql

SQL> select * from emp;

     EMPNO ENAME      JOB              MGR HIREDATE              SAL       COMM     DEPTNO
---------- ---------- --------- ---------- -------------- ---------- ---------- ----------
      7369 SMITH      CLERK           7902 17-12月-80            800                    20
      7499 ALLEN      SALESMAN        7698 20-2月 -81           1600        300         30
      7521 WARD       SALESMAN        7698 22-2月 -81           1250        500         30
      7566 JONES      MANAGER         7839 02-4月 -81           2975                    20
      7654 MARTIN     SALESMAN        7698 28-9月 -81           1250       1400         30
      7698 BLAKE      MANAGER         7839 01-5月 -81           2850                    30
      7782 CLARK      MANAGER         7839 09-6月 -81           2450                    10
      7839 KING       PRESIDENT            17-11月-81           5000                    10
      7844 TURNER     SALESMAN        7698 08-9月 -81           1500          0         30
      7900 JAMES      CLERK           7698 03-12月-81            950                    30
      7902 FORD       ANALYST         7566 03-12月-81           3000                    20
      7934 MILLER     CLERK           7782 23-1月 -82           1300                    10

已選擇12行。

SQL> select * from dept;

    DEPTNO DNAME          LOC
---------- -------------- -------------
        10 ACCOUNTING     NEW YORK
        20 RESEARCH       DALLAS
        30 SALES          CHICAGO
        40 OPERATIONS     BOSTON

完成以下查詢，mongodb

a 列出每一個部門的名稱及其人數 
b 列出工資最高的頭三名員工姓名及其工資 
c 列出工資比上司高的員工姓名 
d 列出平均工資高於公司總平均工資的部門名稱

首先，須要創建mongodb集合，一開始，我採用徹底模擬emp和dept表的方式，發現不少狀況下都須要join，而mongodb並不支持join，作起來比較迂迴。因而決定，把全部數據都放在同一個集合（emp_dept）內，新加元素TABLE用來表示是來源於哪一個table的數據。如下是初始化emp_dept集合的ruby代碼ruby

#!/usr/bin/env ruby
# 20140307, init_emp_dept.rb
###
###


require "rubygems"
require "mongo"

mongoconn = Mongo::MongoClient.new("localhost", 27017)
db = mongoconn.db("test")

# 7369 SMITH      CLERK           7902 17-12月-80            800                    20
# 7499 ALLEN      SALESMAN        7698 20-2月 -81           1600        300         30
# 7521 WARD       SALESMAN        7698 22-2月 -81           1250        500         30
# 7566 JONES      MANAGER         7839 02-4月 -81           2975                    20
# 7654 MARTIN     SALESMAN        7698 28-9月 -81           1250       1400         30
# 7698 BLAKE      MANAGER         7839 01-5月 -81           2850                    30
# 7782 CLARK      MANAGER         7839 09-6月 -81           2450                    10
# 7839 KING       PRESIDENT            17-11月-81           5000                    10
# 7844 TURNER     SALESMAN        7698 08-9月 -81           1500          0         30
# 7900 JAMES      CLERK           7698 03-12月-81            950                    30
# 7902 FORD       ANALYST         7566 03-12月-81           3000                    20
# 7934 MILLER     CLERK           7782 23-1月 -82           1300                    10

coll = db.collection("emp_dept")
coll.insert({ TABLE:"EMP", EMPNO:7369, ENAME:"SMITH", JOB:"CLERK", MGR:7902, HIREDATE:"19801217", SAL:800, DEPTNO:20 })
coll.insert({ TABLE:"EMP", EMPNO:7499, ENAME:"ALLEN", JOB:"SALESMAN", MGR:7698, HIREDATE:"19810220", SAL:1600, COMM:300, DEPTNO:30 })
coll.insert({ TABLE:"EMP", EMPNO:7521, ENAME:"WARD", JOB:"SALESMAN", MGR:7698, HIREDATE:"19810222", SAL:1250, COMM:500, DEPTNO:30 })
coll.insert({ TABLE:"EMP", EMPNO:7566, ENAME:"JONES", JOB:"MANAGER", MGR:7839, HIREDATE:"19810402", SAL:2975, DEPTNO:20 })
coll.insert({ TABLE:"EMP", EMPNO:7654, ENAME:"MARTIN", JOB:"SALESMAN", MGR:7698, HIREDATE:"19810928", SAL:1250, COMM:1400, DEPTNO:30 })
coll.insert({ TABLE:"EMP", EMPNO:7698, ENAME:"BLAKE", JOB:"MANAGER", MGR:7839, HIREDATE:"19810501", SAL:2850, DEPTNO:30 })
coll.insert({ TABLE:"EMP", EMPNO:7782, ENAME:"CLARK", JOB:"MANAGER", MGR:7839, HIREDATE:"19810609", SAL:2450, DEPTNO:10 })
coll.insert({ TABLE:"EMP", EMPNO:7839, ENAME:"KING", JOB:"PRESIDENT", HIREDATE:"19811117", SAL:5000, DEPTNO:10 })
coll.insert({ TABLE:"EMP", EMPNO:7844, ENAME:"TURNER", JOB:"SALESMAN", MGR:7698, HIREDATE:"19810908", SAL:1500, COMM:0, DEPTNO:30 })
coll.insert({ TABLE:"EMP", EMPNO:7900, ENAME:"JAMES", JOB:"CLERK", MGR:7698, HIREDATE:"19811203", SAL:950, DEPTNO:30 })
coll.insert({ TABLE:"EMP", EMPNO:7902, ENAME:"FORD", JOB:"ANALYST", MGR:7566, HIREDATE:"19811203", SAL:3000, DEPTNO:20 })
coll.insert({ TABLE:"EMP", EMPNO:7934, ENAME:"MILLER", JOB:"CLERK", MGR:7782, HIREDATE:"19820123", SAL:1300, DEPTNO:10 })

# coll.find.each { |doc| puts doc.inspect }

# 10 ACCOUNTING     NEW YORK
# 20 RESEARCH       DALLAS
# 30 SALES          CHICAGO
# 40 OPERATIONS     BOSTON

coll.insert({ TABLE:"DEPT", DEPTNO:10, DNAME:"ACCOUNTING", LOC:"NEW YORK"})
coll.insert({ TABLE:"DEPT", DEPTNO:20, DNAME:"RESEARCH", LOC:"DALLAS"})
coll.insert({ TABLE:"DEPT", DEPTNO:30, DNAME:"SALES", LOC:"CHICAGO"})
coll.insert({ TABLE:"DEPT", DEPTNO:40, DNAME:"OPERATIONS", LOC:"BOSTON"})


coll.find.each { |doc| puts doc.inspect }

# db.drop_collection("emp_dept")

貼一下輸出，便於看清楚集合的結構oracle

{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000001'), "TABLE"=>"EMP", "EMPNO"=>7369, "ENAME"=>"SMITH", "JOB"=>"CLERK", "MGR"=>7902, "HIREDATE"=>"19801217", "SAL"=>800, "DEPTNO"=>20}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000002'), "TABLE"=>"EMP", "EMPNO"=>7499, "ENAME"=>"ALLEN", "JOB"=>"SALESMAN", "MGR"=>7698, "HIREDATE"=>"19810220", "SAL"=>1600, "COMM"=>300, "DEPTNO"=>30}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000003'), "TABLE"=>"EMP", "EMPNO"=>7521, "ENAME"=>"WARD", "JOB"=>"SALESMAN", "MGR"=>7698, "HIREDATE"=>"19810222", "SAL"=>1250, "COMM"=>500, "DEPTNO"=>30}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000004'), "TABLE"=>"EMP", "EMPNO"=>7566, "ENAME"=>"JONES", "JOB"=>"MANAGER", "MGR"=>7839, "HIREDATE"=>"19810402", "SAL"=>2975, "DEPTNO"=>20}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000005'), "TABLE"=>"EMP", "EMPNO"=>7654, "ENAME"=>"MARTIN", "JOB"=>"SALESMAN", "MGR"=>7698, "HIREDATE"=>"19810928", "SAL"=>1250, "COMM"=>1400, "DEPTNO"=>30}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000006'), "TABLE"=>"EMP", "EMPNO"=>7698, "ENAME"=>"BLAKE", "JOB"=>"MANAGER", "MGR"=>7839, "HIREDATE"=>"19810501", "SAL"=>2850, "DEPTNO"=>30}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000007'), "TABLE"=>"EMP", "EMPNO"=>7782, "ENAME"=>"CLARK", "JOB"=>"MANAGER", "MGR"=>7839, "HIREDATE"=>"19810609", "SAL"=>2450, "DEPTNO"=>10}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000008'), "TABLE"=>"EMP", "EMPNO"=>7839, "ENAME"=>"KING", "JOB"=>"PRESIDENT", "HIREDATE"=>"19811117", "SAL"=>5000, "DEPTNO"=>10}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000009'), "TABLE"=>"EMP", "EMPNO"=>7844, "ENAME"=>"TURNER", "JOB"=>"SALESMAN", "MGR"=>7698, "HIREDATE"=>"19810908", "SAL"=>1500, "COMM"=>0, "DEPTNO"=>30}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af00000a'), "TABLE"=>"EMP", "EMPNO"=>7900, "ENAME"=>"JAMES", "JOB"=>"CLERK", "MGR"=>7698, "HIREDATE"=>"19811203", "SAL"=>950, "DEPTNO"=>30}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af00000b'), "TABLE"=>"EMP", "EMPNO"=>7902, "ENAME"=>"FORD", "JOB"=>"ANALYST", "MGR"=>7566, "HIREDATE"=>"19811203", "SAL"=>3000, "DEPTNO"=>20}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af00000c'), "TABLE"=>"EMP", "EMPNO"=>7934, "ENAME"=>"MILLER", "JOB"=>"CLERK", "MGR"=>7782, "HIREDATE"=>"19820123", "SAL"=>1300, "DEPTNO"=>10}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af00000d'), "TABLE"=>"DEPT", "DEPTNO"=>10, "DNAME"=>"ACCOUNTING", "LOC"=>"NEW YORK"}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af00000e'), "TABLE"=>"DEPT", "DEPTNO"=>20, "DNAME"=>"RESEARCH", "LOC"=>"DALLAS"}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af00000f'), "TABLE"=>"DEPT", "DEPTNO"=>30, "DNAME"=>"SALES", "LOC"=>"CHICAGO"}
{"_id"=>BSON::ObjectId('531c2a59cf7c7c45af000010'), "TABLE"=>"DEPT", "DEPTNO"=>40, "DNAME"=>"OPERATIONS", "LOC"=>"BOSTON"}

對於問題a，mongodb的查詢以下測試

/* 20140309
 * a 列出每一個部門的名稱及其人數
 */
db.emp_dept.group({key: {"DEPTNO":true},
　　initial: {"DNAME":"", "csum":0},
　　reduce: function(obj, prev){
   　　 if (obj.TABLE == "DEPT"){
        　　prev.DNAME = prev.DNAME + obj.DNAME
    　　} else if (obj.TABLE == "EMP"){
        　　prev.csum ++;
    　　}
}})

輸出以下ui

[
    {
        "DEPTNO" : 20,
        "DNAME" : "RESEARCH",
        "csum" : 3
    },
    {
        "DEPTNO" : 30,
        "DNAME" : "SALES",
        "csum" : 6
    },
    {
        "DEPTNO" : 10,
        "DNAME" : "ACCOUNTING",
        "csum" : 3
    },
    {
        "DEPTNO" : 40,
        "DNAME" : "OPERATIONS",
        "csum" : 0
    }
]

問題b，比較簡單，代碼以下this

/* 20140309
 * b 列出工資最高的頭三名員工姓名及其工資
 */
db.emp_dept.find({"TABLE":"EMP"},{"ENAME":1,"SAL":1,"_id":0}).sort({"SAL":-1}).limit(3)

輸出以下spa

{ "ENAME" : "KING", "SAL" : 5000 }
{ "ENAME" : "FORD", "SAL" : 3000 }
{ "ENAME" : "JONES", "SAL" : 2975 }

問題c，簡單一點的解法能夠這麼作code

/* 20140309
 * c 列出工資比上司高的員工姓名
 */
// solution 1
db.emp_dept.find({"$where": function(){
    if (this["TABLE"] == "EMP") {
        if (this["MGR"] != null){
            var mgr = db.emp_dept.findOne({"TABLE":"EMP", "EMPNO":this["MGR"]})
            if (this["SAL"] > mgr["SAL"]){
                return true;
            }else {
                return false;
            }
        }
    }
}}, {"EMPNO":1, "ENAME":1, "_id":0})

輸出以下blog

{ "EMPNO" : 7902, "ENAME" : "FORD" }

若是但願可以在查出這些員工的同時，也能列出其上司和其上司的工資，可能就須要用map-reduce，若是用map-reduce的話，寫法就有不少種了，我是這樣寫的

/* 20140309
 * c 列出工資比上司高的員工姓名
 */
// solution 2
get_mgr_sal = function() { 
    if (this["TABLE"] == "EMP") {
        if (this["MGR"] != null){
            var mgr = db.emp_dept.findOne({"TABLE":"EMP", "EMPNO":this["MGR"]});
            if (this["SAL"] > mgr["SAL"]){
                emit({"EMPNO":this["EMPNO"], "ENAME":this["ENAME"]}, {"SAL" : this["SAL"], 
                    "MGR_EMPNO":mgr["EMPNO"], "MGR_NAME":mgr["ENAME"], "MGR_SAL":mgr["SAL"]}); 
            }
        }
    }
}; 

get_mgr_sal = function() { 
    if (this["TABLE"] == "EMP") {
        if (this["MGR"] != null){
            var mgr = db.emp_dept.findOne({"TABLE":"EMP", "EMPNO":this["MGR"]})
            emit({"EMPNO":this["EMPNO"], "ENAME":this["ENAME"]}, {"SAL" : this["SAL"], 
                "MGR_EMPNO":mgr["EMPNO"], "MGR_NAME":mgr["ENAME"], "MGR_SAL":mgr["SAL"]}); 
        }
    }
}; 

higher_than_mgr = function(key, emits) { 
    return emits;
};

filter_result = function(key, reduced){
    if (reduced["SAL"] > reduced["MGR_SAL"]){
        return reduced;
    }
}

db.runCommand({"mapreduce" : "emp_dept", "map" : get_mgr_sal, "reduce" : higher_than_mgr,
    "out":"result", "finalize":filter_result})

db.result.find({"value":{"$ne":null}})

輸出以下

{ "_id" : { "EMPNO" : 7902, "ENAME" : "FORD" }, "value" : { "SAL" : 3000, "MGR_EMPNO" : 7566, "MGR_NAME" : "JONES", "MGR_SAL" : 2975 } }

雖然已經有告終果，可是我感受map-reduce這麼用，感受還不是很舒服，但也沒有想出更好的寫法。

問題d，我只想出了用map-reduce的解法

/* 20140309
 * d 列出平均工資高於公司總平均工資的部門名稱
 */
map_avg_sal_dept = function() {
    if (this["TABLE"] == "EMP"){
        var dept = db.emp_dept.findOne({"TABLE":"DEPT", "DEPTNO":this["DEPTNO"]});
        var key = {"DEPTNO":dept["DEPTNO"], "DNAME":dept["DNAME"]};
        var value = {"SAL_DEPT":this["SAL"], "DEPT_COUNT":1};
        emit(key, value)
    }
}

red_avg_sal_dept = function(key, value) {
    var all_sal_dept = 0.0;
    var all_dept_count = 0;
    for (var i in value){
        all_sal_dept += value[i]["SAL_DEPT"];
        all_dept_count += value[i]["DEPT_COUNT"];
    }
    return {"AVG_SAL_DEPT":all_sal_dept/all_dept_count};
}

get_avg_sal = function(){
    var all_sal = 0.0;
    var all_count = 0;
    db.emp_dept.find({"TABLE":"EMP"}).forEach(
        function (e) {
            all_sal += e["SAL"];
            all_count ++;
        });
    return all_sal/all_count;
}

filter_avg_sal_dept = function(key, reduced) {
    if (reduced["AVG_SAL_DEPT"] > avg_sal){
        return reduced;
    }
}

db.runCommand({"mapreduce" : "emp_dept", "map" : map_avg_sal_dept, "reduce" : red_avg_sal_dept,
    "out":"result", "scope":{avg_sal:get_avg_sal()}, "finalize":filter_avg_sal_dept})

db.result.find({"value":{"$ne":null}})

輸出以下

{ "_id" : { "DEPTNO" : 10, "DNAME" : "ACCOUNTING" }, "value" : { "AVG_SAL_DEPT" : 2916.6666666666665 } }
{ "_id" : { "DEPTNO" : 20, "DNAME" : "RESEARCH" }, "value" : { "AVG_SAL_DEPT" : 2258.3333333333335 } }

整體看起來，用mongodb處理結構化數據，仍是顯得不那麼直觀，解法也顯得很迂迴。固然，若是適應了的話，寫這些代碼可能會變快，可是比起SQL對數據的抽象程度和友好性來講，mongodb仍是差了很多，若是能有mongodb的類sql接口，那就會更好了。

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