本文主要介绍MongoDB的导入导出操作。
一、导入数据
MongoDB支持csv格式的数据导入。本节除了导入MongoDB CookBook书本提供的测试数据，还介绍了其他格式的csv文件数据导入。
[root@hdp04 ~]# wget https://raw.githubusercontent.com/kishorek/India-Codes/master/csv/pincodes.csv
[root@hdp04 ~]# head pincodes.csv
"PostOfficeName","Pincode","DistrictsName","City","State"
"Aberdeen Bazar","744104","A&N Islands","Port Blair","Andaman Nicobar"
"Bakultala","744208","A&N Islands","Port Blair","Andaman Nicobar"
"Bambooflat","744107","A&N Islands","Port Blair","Andaman Nicobar"
"Betapur","744201","A&N Islands","Port Blair","Andaman Nicobar"
[root@hdp04 ~]# mongoimport --type csv -d admin -c postalCodes --headerline --drop pincodes.csv
2018-06-06T15:34:41.922+0800 connected to: localhost
2018-06-06T15:34:41.922+0800 dropping: admin.postalCodes
2018-06-06T15:34:42.981+0800 imported 39734 documents
> db.postalCodes.count()
39734
如果csv文件中存在空字段的内容，则需在导入的时候指定每个字段名称，如下：
[root@hdp04 ~]# tail temp.csv
"zy-2-3106915","candon",,"2","zy14031238480","Pp2d8+VJOoPOUP2UVMBS4g==","520000","520300",,"1",,,,
"zy-2-3106931","jason",,"2","zy14031238484","+3xWTli/pbRaswx0XxALPg==","520000","520300",,"1",,,,
"zy-2-3106958","mako",,"2","zy14031238434","j8+Fz3EkRH+3JtWKNmScmA==","520000","520300",,"1",,,,
"zy-2-3731038","lanqi",,"2","zy160300038106","6N1phcF07vGhDv7Vof3CPA==","520000","520300",,"1",,,,
"zy-2-3175835","wgjk",,"2","zy14031352290","wxU1xIQbTAmWoR+q9LrLcw==","520000","520300",,"1",,,,
[root@hdp04 ~]# mongoimport --db admin --collection temp --type csv --fields ID,NAME,FILED1,USERTYPE,PHONE,FILED2,FILED3,FILED4,PHONE1,TYPE,FILED5,FILED6,FILED7,FILED8 --file temp.csv
2018-06-10T18:25:32.739+0800 connected to: localhost
2018-06-10T18:25:35.734+0800 [#.......................] admin.temp 8.93MB/177MB (5.0%)
2018-06-10T18:25:38.736+0800 [##......................] admin.temp 17.6MB/177MB (9.9%)
2018-06-10T18:25:41.733+0800 [###.....................] admin.temp 26.2MB/177MB (14.8%)
2018-06-10T18:25:44.733+0800 [####....................] admin.temp 34.8MB/177MB (19.7%)
2018-06-10T18:25:47.733+0800 [#####...................] admin.temp 43.4MB/177MB (24.5%)
2018-06-10T18:25:50.734+0800 [#######.................] admin.temp 53.4MB/177MB (30.1%)
2018-06-10T18:25:53.733+0800 [########................] admin.temp 62.9MB/177MB (35.5%)
2018-06-10T18:25:56.736+0800 [##########..............] admin.temp 75.5MB/177MB (42.6%)
2018-06-10T18:25:59.734+0800 [###########.............] admin.temp 84.2MB/177MB (47.6%)
2018-06-10T18:26:02.734+0800 [############............] admin.temp 93.0MB/177MB (52.5%)
2018-06-10T18:26:05.733+0800 [#############...........] admin.temp 102MB/177MB (57.4%)
2018-06-10T18:26:08.733+0800 [##############..........] admin.temp 110MB/177MB (62.2%)
2018-06-10T18:26:11.734+0800 [################........] admin.temp 119MB/177MB (67.2%)
2018-06-10T18:26:14.733+0800 [#################.......] admin.temp 128MB/177MB (72.3%)
2018-06-10T18:26:17.733+0800 [##################......] admin.temp 137MB/177MB (77.6%)
2018-06-10T18:26:20.733+0800 [###################.....] admin.temp 146MB/177MB (82.7%)
2018-06-10T18:26:23.733+0800 [#####################...] admin.temp 156MB/177MB (87.9%)
2018-06-10T18:26:26.733+0800 [######################..] admin.temp 164MB/177MB (92.9%)
2018-06-10T18:26:29.734+0800 [#######################.] admin.temp 175MB/177MB (99.1%)
2018-06-10T18:26:30.197+0800 [########################] admin.temp 177MB/177MB (100.0%)
2018-06-10T18:26:30.197+0800 imported 3347522 documents
> db.temp.count()
3347522
二、简单查询
- 统计行数
> db.postalCodes.count()
39734
2.查询一条记录
> db.postalCodes.findOne()
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c95b"),
"PostOfficeName" : "Aberdeen Bazar",
"Pincode" : 744104,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
3.查找多行记录
> db.postalCodes.find().pretty()
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c95b"),
"PostOfficeName" : "Aberdeen Bazar",
"Pincode" : 744104,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c95c"),
"PostOfficeName" : "Campbell Bay",
"Pincode" : 744302,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c95d"),
"PostOfficeName" : "Betapur",
"Pincode" : 744201,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c95e"),
"PostOfficeName" : "Car Nicobar",
"Pincode" : 744301,
"DistrictsName" : "Car Nicobar",
"City" : "Car Nicobar",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c95f"),
"PostOfficeName" : "Diglipur",
"Pincode" : 744202,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c960"),
"PostOfficeName" : "Haddo",
"Pincode" : 744102,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c961"),
"PostOfficeName" : "Hut Bay",
"Pincode" : 744207,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c962"),
"PostOfficeName" : "Havelock",
"Pincode" : 744211,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c963"),
"PostOfficeName" : "Bambooflat",
"Pincode" : 744107,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c964"),
"PostOfficeName" : "Kapanga",
"Pincode" : 744304,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c965"),
"PostOfficeName" : "Long Island",
"Pincode" : 744203,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c966"),
"PostOfficeName" : "Marine Jetty",
"Pincode" : 744101,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c967"),
"PostOfficeName" : "Mayabunder",
"Pincode" : 744204,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c968"),
"PostOfficeName" : "N S Building",
"Pincode" : 744101,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c969"),
"PostOfficeName" : "Nancowrie",
"Pincode" : 744303,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c96a"),
"PostOfficeName" : "Neel Island",
"Pincode" : 744104,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c96b"),
"PostOfficeName" : "New Secretariate Bldg",
"Pincode" : 744105,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c96c"),
"PostOfficeName" : "Bakultala",
"Pincode" : 744208,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c96d"),
"PostOfficeName" : "Oralkatcha",
"Pincode" : 744210,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
{
"_id" : ObjectId("5b178e91f02ce24e2dd4c96e"),
"PostOfficeName" : "Port Blair",
"Pincode" : 744101,
"DistrictsName" : "A&N Islands",
"City" : "Port Blair",
"State" : "Andaman Nicobar"
}
Type "it" for more
上面的命令查找所有记录，默认只显示前20行，如果要查看更多的记录，键入“it”继续查看(只显示20条记录)。
三、导出数据
MongoDB的mongoexport默认是导出collection的所有内容，如下：
[root@hdp04 ~]# mongoexport -d admin -c postalCodes -o dd.csv
2018-06-11T11:40:19.660+0800 connected to: localhost
2018-06-11T11:40:20.658+0800 [#########...............] admin.postalCodes 16000/39734 (40.3%)
2018-06-11T11:40:21.658+0800 [###################.....] admin.postalCodes 32000/39734 (80.5%)
2018-06-11T11:40:21.873+0800 [########################] admin.postalCodes 39734/39734 (100.0%)
2018-06-11T11:40:21.873+0800 exported 39734 records
如果要导出指定的字段内容，需要加上--fields参数，如下：
[root@hdp04 ~]# mongoexport -d admin -c postalCodes -o ee.csv --fields 'PostOfficeName,Pincode,DistrictsName'
2018-06-11T11:41:33.805+0800 connected to: localhost
2018-06-11T11:41:34.803+0800 [#########...............] admin.postalCodes 16000/39734 (40.3%)
2018-06-11T11:41:35.495+0800 [########################] admin.postalCodes 39734/39734 (100.0%)
2018-06-11T11:41:35.495+0800 exported 39734 records
