使用morphia对基于mongodb应用开发
来源：未知 责任编辑：智问网络 发表时间:2013-10-22 19:28 点击:次
最近初学了下mongoDB，作为比较火的一个NoSQL数据库，确实比较强大，但是这几天学下来更多的感觉到的是学习、使用都很方便。
首先是初学者体验使用方便，直接下载(http://www.mongodb.org/downloads)解压，然后启一下服务便可使用：mongod --dbpath your_db_data_dir，启动以后默认端口27017, 默认http端口28017，可以通过http://localhost: 28017 查看基本信息。当然，如果你还没有下载的想法，可以直接在其官网上尝试Try The Online Shell，就可以使用其来做各种操作，当然online的功能较少。
其次，一改关系数据库的表模型，mongodb是一个以松散的集合形势呈现，这种no shema让我感觉非常方便。从开发人员的角度看，mongodb中的每一个数据对象就是一个JSON，所有的操作(save,update,find etc.)都可以像操作JSON一样，当然mongodb数据是一种叫做BSON格式的，即Binary JSON：http://bsonspec.org/ 。例如：
将文档{ name:”abc”,age:12}插入到users集合：
db.users.insert ({name:”abc”,age:12})
修改文档，增加其emails属性：
db.users.update(
{name:"abc"},
{"$set": {emails:["abc@gmail.com","abc@163.com"]}}
)
查找所有users文档：
db.users.find();
查找age > 20的前5个：
db.users.find({
age: {“$gt”:20}
}).limit(5)
删除：db.users.delete({name:”abc”})
这些基本的操作不一一例举，包括像index的操作，统计函数等，总之，一切都是文档：查询表达式是文档、返回数据是文档、修改数据的新值是文档、index的操作也是文档形式等等。另外，诸如其支持的MAPREDUCE操作，只需在其规范内定义自己的map和reduce函数即可完成简单MR计算；使用GridFS规范来存储大文件。
最后的方便之处就是对很多开发语言的支持，PHP, Java, Python, Ruby, Perl。这里我就使用Morphia来做一个非常简单demo: 假设一千个店铺(store)分布在不同的地方(place)，每个地方都有一个二维的坐标(x,y)用来表示其位置，我们可以很方便的查找到诸如 离***地方最近的***店铺。整个例子分三步：
1) 准备数据
2) 测试数据是否准备好
3) 查找店铺
使用jars: mongo-2.7.0.jar, morphia-0.98.jar
下面是两个model类:
@Entity(value="stores",noClassnameStored=true)
public class Store {
@Id
private ObjectId id;
private String name;
private String desc;
@Embedded
public Place place;
@Override
public String toString() {
return "Store [desc=" + desc + ", id=" + id + ", name=" +
name + ", place=" + place + "]";
}
public Store(){}
public Store(String name, String desc, Place place) {
this.name = name;
this.desc = desc;
this.place = place;
}
//省略getter,setters
}
@Entity(value="stores",noClassnameStored=true)
public class Store {
@Id
private ObjectId id;
private String name;
private String desc;
@Embedded
public Place place;
@Override
public String toString() {
return "Store [desc=" + desc + ", id=" + id + ", name=" +
name + ", place=" + place + "]";
}
public Store(){}
public Store(String name, String desc, Place place) {
this.name = name;
this.desc = desc;
this.place = place;
}
//省略getter,setters
}
@Embedded
public class Place {
private String name = "";
@Indexed(IndexDirection.GEO2D)
private double[] loc = null;
public Place(String name, double[] loc) {
this.name = name;
this.loc = loc;
}
public Place() {
}
@Override
public String toString() {
return "Place [loc=" + Arrays.toString(loc) + ", name=" + name + "]";
}
//省略getter,setters
}
@Embedded
public class Place {
private String name = "";
@Indexed(IndexDirection.GEO2D)
private double[] loc = null;
public Place(String name, double[] loc) {
this.name = name;
this.loc = loc;
}
public Place() {
}
@Override
public String toString() {
return "Place [loc=" + Arrays.toString(loc) + ", name=" + name + "]";
}
//省略getter,setters
}
因为morphia提供了BasicDao，所以这里就准备一个简单的Dao:
package morphia.dao;
import java.util.List;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.dao.BasicDAO;
import com.google.code.morphia.query.Query;
public class StoreDao extends BasicDAO<Store, String> {
public StoreDao(Datastore ds) {
super(ds);
ds.ensureIndexes();
ds.ensureCaps();
}
/**
* 查找离p 最近的5个店铺
* @param p
* @return
*/
public List<Store> findNearPlace(Place p) {
return ds.createQuery(Store.class).field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 查找离p 最近的5个肯德基店
* @param p
* @return
*/
public List<Store> findKFCNearPlace(Place p) {
return ds.createQuery(Store.class).filter("name", "肯德基").field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 删除所有店铺
*/
public void deleteAllStore(){
Query<Store> q = ds.createQuery(Store.class);
ds.delete(q);
}
}
package morphia.dao;
import java.util.List;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.dao.BasicDAO;
import com.google.code.morphia.query.Query;
public class StoreDao extends BasicDAO<Store, String> {
public StoreDao(Datastore ds) {
super(ds);
ds.ensureIndexes();
ds.ensureCaps();
}
/**
* 查找离p 最近的5个店铺
* @param p
* @return
*/
public List<Store> findNearPlace(Place p) {
return ds.createQuery(Store.class).field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 查找离p 最近的5个肯德基店
* @param p
* @return
*/
public List<Store> findKFCNearPlace(Place p) {
return ds.createQuery(Store.class).filter("name", "肯德基").field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 删除所有店铺
*/
public void deleteAllStore(){
Query<Store> q = ds.createQuery(Store.class);
ds.delete(q);
}
}
接下来就可以写测试类了：
package mongo.morphia.test;
import java.util.List;
import morphia.dao.PlaceDao;
import morphia.dao.StoreDao;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.Morphia;
import com.mongodb.Mongo;
public class StoreDaoTest {
public static String[] STORE_TYPE = {"肯德基","麦当劳","必胜客","吉野家","蒸功夫"};
public static StoreDaoTest m = new StoreDaoTest();
static DaoHolder daoHolder = new DaoHolder();
//测试保存，准备一千家店铺的数据
public void testSave(){
long start = System.currentTimeMillis();
for( int i = 0; i < 1000; i++){
double x = Math.round(Math.random() * 10000)/100.0D;
double y = Math.round(Math.random() * 10000)/100.0D;
Place p = new Place("Place_"+x+"_"+y,new double[]{x,y});
Store s = new Store(STORE_TYPE[i%5],STORE_TYPE[i%5]+"@"+p.getName(),p);
daoHolder.storeDao.save(s);
}
System.out.println(System.currentTimeMillis() - start);
}
//测试删除
public void testDeleteAll(){
System.out.println("Before delete the number of stores is: " + daoHolder.storeDao.count());
daoHolder.storeDao.deleteAllStore();
System.out.println("After delete the number of stores is: " + daoHolder.storeDao.count());
}
//根据地理位置查找
public void testFindNearPlace(){
Place p = new Place("somewhere",new double[]{23.5,67.8});
System.out.println("Find 5 stores near "+ p.toString());
List<Store> list = daoHolder.storeDao.findNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
System.out.println("Find 5 KFC stores near "+ p.toString());
list = daoHolder.storeDao.findKFCNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
}
//查找所有store
public void testFindAll(){
long start = System.currentTimeMillis();
List<Store> list = daoHolder.storeDao.find().asList();
for( Store s : list)
System.out.println(s.toString());
System.out.println(System.currentTimeMillis() - start);
}
static class DaoHolder{
PlaceDao placeDao;
StoreDao storeDao;
public DaoHolder(){
try {
Mongo mongo = new Mongo("localhost",27017);
Morphia morphia = new Morphia();
Datastore ds = morphia.createDatastore(mongo, "testDB");
placeDao = new PlaceDao(ds);
storeDao = new StoreDao(ds);
} catch (Exception e) {
e.printStackTrace();
}
}
}
}
package mongo.morphia.test;
import java.util.List;
import morphia.dao.PlaceDao;
import morphia.dao.StoreDao;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.Morphia;
import com.mongodb.Mongo;
public class StoreDaoTest {
public static String[] STORE_TYPE = {"肯德基","麦当劳","必胜客","吉野家","蒸功夫"};
public static StoreDaoTest m = new StoreDaoTest();
static DaoHolder daoHolder = new DaoHolder();
//测试保存，准备一千家店铺的数据
public void testSave(){
long start = System.currentTimeMillis();
for( int i = 0; i < 1000; i++){
double x = Math.round(Math.random() * 10000)/100.0D;
double y = Math.round(Math.random() * 10000)/100.0D;
Place p = new Place("Place_"+x+"_"+y,new double[]{x,y});
Store s = new Store(STORE_TYPE[i%5],STORE_TYPE[i%5]+"@"+p.getName(),p);
daoHolder.storeDao.save(s);
}
System.out.println(System.currentTimeMillis() - start);
}
//测试删除
public void testDeleteAll(){
System.out.println("Before delete the number of stores is: " + daoHolder.storeDao.count());
daoHolder.storeDao.deleteAllStore();
System.out.println("After delete the number of stores is: " + daoHolder.storeDao.count());
}
//根据地理位置查找
public void testFindNearPlace(){
Place p = new Place("somewhere",new double[]{23.5,67.8});
System.out.println("Find 5 stores near "+ p.toString());
List<Store> list = daoHolder.storeDao.findNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
System.out.println("Find 5 KFC stores near "+ p.toString());
list = daoHolder.storeDao.findKFCNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
}
//查找所有store
public void testFindAll(){
long start = System.currentTimeMillis();
List<Store> list = daoHolder.storeDao.find().asList();
for( Store s : list)
System.out.println(s.toString());
System.out.println(System.currentTimeMillis() - start);
}
static class DaoHolder{
PlaceDao placeDao;
StoreDao storeDao;
public DaoHolder(){
try {
Mongo mongo = new Mongo("localhost",27017);
Morphia morphia = new Morphia();
Datastore ds = morphia.createDatastore(mongo, "testDB");
placeDao = new PlaceDao(ds);
storeDao = new StoreDao(ds);
} catch (Exception e) {
e.printStackTrace();
}
}
}
}
首先， 通过调用StoreDaoTest.m.testSave()保存一千家店铺，用来做数据准备。
其次，通过调用StoreDaoTest.m.testFindAll()查看数据是否ok，当然也可以通过shell窗口查看。
现在可以通过StoreDaoTest.m.testFindNearPlace()来查找地方p附近的相关店铺了，在这个方法中，我查了两次，一次是查找离p[loc=[23.5, 67.8], name=somewhere]最近的任意五个店铺，dao中这样写：
ds.createQuery(Store.class).field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
第二次是查找离p[loc=[23.5, 67.8], name=somewhere]最近的五个肯德基店铺，dao中这样写：
ds.createQuery(Store.class).filter("name", "肯德基")
.field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
输出结果：
Find 5 stores near Place [loc=[23.5, 67.8], name=somewhere]
Store [desc=麦当劳@Place_24.42_67.77, id=4ef9cc2cec9dcb16b1b552d2, name=麦当劳, place=Place [loc=[24.42, 67.77], name=Place_24.42_67.77]]
Store [desc=蒸功夫@Place_24.32_70.0, id=4ef9cc2dec9dcb16b1b553b6, name=蒸功夫, place=Place [loc=[24.32, 70.0], name=Place_24.32_70.0]]
Store [desc=必胜客@Place_24.08_64.89, id=4ef9cc2dec9dcb16b1b5544f, name=必胜客, place=Place [loc=[24.08, 64.89], name=Place_24.08_64.89]]
Store [desc=肯德基@Place_21.05_65.88, id=4ef9cc2dec9dcb16b1b5539e, name=肯德基, place=Place [loc=[21.05, 65.88], name=Place_21.05_65.88]]
Store [desc=吉野家@Place_25.78_65.66, id=4ef9cc2cec9dcb16b1b551f3, name=吉野家, place=Place [loc=[25.78, 65.66], name=Place_25.78_65.66]]
Find 5 KFC stores near Place [loc=[23.5, 67.8], name=somewhere]
Store [desc=肯德基@Place_21.05_65.88, id=4ef9cc2dec9dcb16b1b5539e, name=肯德基, place=Place [loc=[21.05, 65.88], name=Place_21.05_65.88]]
Store [desc=肯德基@Place_21.13_74.42, id=4ef9cc2dec9dcb16b1b5550b, name=肯德基, place=Place [loc=[21.13, 74.42], name=Place_21.13_74.42]]
Store [desc=肯德基@Place_20.26_77.73, id=4ef9cc2cec9dcb16b1b55204, name=肯德基, place=Place [loc=[20.26, 77.73], name=Place_20.26_77.73]]
Store [desc=肯德基@Place_32.55_73.14, id=4ef9cc2cec9dcb16b1b552ae, name=肯德基, place=Place [loc=[32.55, 73.14], name=Place_32.55_73.14]]
Store [desc=肯德基@Place_19.01_77.68, id=4ef9cc2cec9dcb16b1b55209, name=肯德基, place=Place [loc=[19.01, 77.68], name=Place_19.01_77.68]]
最后当然也可以通过StoreDaoTest.m.testDeleteAll() 删除所有测试数据。例子很简单，是我这个礼拜学习的一个小结吧，不罗嗦了。当然很多mongodb的操作命令就不记录了，有用到了再查吧，接下来会去学习一下spring data整合mongodb
摘自 wilson
首先是初学者体验使用方便，直接下载(http://www.mongodb.org/downloads)解压，然后启一下服务便可使用：mongod --dbpath your_db_data_dir，启动以后默认端口27017, 默认http端口28017，可以通过http://localhost: 28017 查看基本信息。当然，如果你还没有下载的想法，可以直接在其官网上尝试Try The Online Shell，就可以使用其来做各种操作，当然online的功能较少。
其次，一改关系数据库的表模型，mongodb是一个以松散的集合形势呈现，这种no shema让我感觉非常方便。从开发人员的角度看，mongodb中的每一个数据对象就是一个JSON，所有的操作(save,update,find etc.)都可以像操作JSON一样，当然mongodb数据是一种叫做BSON格式的，即Binary JSON：http://bsonspec.org/ 。例如：
将文档{ name:”abc”,age:12}插入到users集合：
db.users.insert ({name:”abc”,age:12})
修改文档，增加其emails属性：
db.users.update(
{name:"abc"},
{"$set": {emails:["abc@gmail.com","abc@163.com"]}}
)
查找所有users文档：
db.users.find();
查找age > 20的前5个：
db.users.find({
age: {“$gt”:20}
}).limit(5)
删除：db.users.delete({name:”abc”})
这些基本的操作不一一例举，包括像index的操作，统计函数等，总之，一切都是文档：查询表达式是文档、返回数据是文档、修改数据的新值是文档、index的操作也是文档形式等等。另外，诸如其支持的MAPREDUCE操作，只需在其规范内定义自己的map和reduce函数即可完成简单MR计算；使用GridFS规范来存储大文件。
最后的方便之处就是对很多开发语言的支持，PHP, Java, Python, Ruby, Perl。这里我就使用Morphia来做一个非常简单demo: 假设一千个店铺(store)分布在不同的地方(place)，每个地方都有一个二维的坐标(x,y)用来表示其位置，我们可以很方便的查找到诸如 离***地方最近的***店铺。整个例子分三步：
1) 准备数据
2) 测试数据是否准备好
3) 查找店铺
使用jars: mongo-2.7.0.jar, morphia-0.98.jar
下面是两个model类:
@Entity(value="stores",noClassnameStored=true)
public class Store {
@Id
private ObjectId id;
private String name;
private String desc;
@Embedded
public Place place;
@Override
public String toString() {
return "Store [desc=" + desc + ", id=" + id + ", name=" +
name + ", place=" + place + "]";
}
public Store(){}
public Store(String name, String desc, Place place) {
this.name = name;
this.desc = desc;
this.place = place;
}
//省略getter,setters
}
@Entity(value="stores",noClassnameStored=true)
public class Store {
@Id
private ObjectId id;
private String name;
private String desc;
@Embedded
public Place place;
@Override
public String toString() {
return "Store [desc=" + desc + ", id=" + id + ", name=" +
name + ", place=" + place + "]";
}
public Store(){}
public Store(String name, String desc, Place place) {
this.name = name;
this.desc = desc;
this.place = place;
}
//省略getter,setters
}
@Embedded
public class Place {
private String name = "";
@Indexed(IndexDirection.GEO2D)
private double[] loc = null;
public Place(String name, double[] loc) {
this.name = name;
this.loc = loc;
}
public Place() {
}
@Override
public String toString() {
return "Place [loc=" + Arrays.toString(loc) + ", name=" + name + "]";
}
//省略getter,setters
}
@Embedded
public class Place {
private String name = "";
@Indexed(IndexDirection.GEO2D)
private double[] loc = null;
public Place(String name, double[] loc) {
this.name = name;
this.loc = loc;
}
public Place() {
}
@Override
public String toString() {
return "Place [loc=" + Arrays.toString(loc) + ", name=" + name + "]";
}
//省略getter,setters
}
因为morphia提供了BasicDao，所以这里就准备一个简单的Dao:
package morphia.dao;
import java.util.List;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.dao.BasicDAO;
import com.google.code.morphia.query.Query;
public class StoreDao extends BasicDAO<Store, String> {
public StoreDao(Datastore ds) {
super(ds);
ds.ensureIndexes();
ds.ensureCaps();
}
/**
* 查找离p 最近的5个店铺
* @param p
* @return
*/
public List<Store> findNearPlace(Place p) {
return ds.createQuery(Store.class).field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 查找离p 最近的5个肯德基店
* @param p
* @return
*/
public List<Store> findKFCNearPlace(Place p) {
return ds.createQuery(Store.class).filter("name", "肯德基").field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 删除所有店铺
*/
public void deleteAllStore(){
Query<Store> q = ds.createQuery(Store.class);
ds.delete(q);
}
}
package morphia.dao;
import java.util.List;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.dao.BasicDAO;
import com.google.code.morphia.query.Query;
public class StoreDao extends BasicDAO<Store, String> {
public StoreDao(Datastore ds) {
super(ds);
ds.ensureIndexes();
ds.ensureCaps();
}
/**
* 查找离p 最近的5个店铺
* @param p
* @return
*/
public List<Store> findNearPlace(Place p) {
return ds.createQuery(Store.class).field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 查找离p 最近的5个肯德基店
* @param p
* @return
*/
public List<Store> findKFCNearPlace(Place p) {
return ds.createQuery(Store.class).filter("name", "肯德基").field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
}
/**
* 删除所有店铺
*/
public void deleteAllStore(){
Query<Store> q = ds.createQuery(Store.class);
ds.delete(q);
}
}
接下来就可以写测试类了：
package mongo.morphia.test;
import java.util.List;
import morphia.dao.PlaceDao;
import morphia.dao.StoreDao;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.Morphia;
import com.mongodb.Mongo;
public class StoreDaoTest {
public static String[] STORE_TYPE = {"肯德基","麦当劳","必胜客","吉野家","蒸功夫"};
public static StoreDaoTest m = new StoreDaoTest();
static DaoHolder daoHolder = new DaoHolder();
//测试保存，准备一千家店铺的数据
public void testSave(){
long start = System.currentTimeMillis();
for( int i = 0; i < 1000; i++){
double x = Math.round(Math.random() * 10000)/100.0D;
double y = Math.round(Math.random() * 10000)/100.0D;
Place p = new Place("Place_"+x+"_"+y,new double[]{x,y});
Store s = new Store(STORE_TYPE[i%5],STORE_TYPE[i%5]+"@"+p.getName(),p);
daoHolder.storeDao.save(s);
}
System.out.println(System.currentTimeMillis() - start);
}
//测试删除
public void testDeleteAll(){
System.out.println("Before delete the number of stores is: " + daoHolder.storeDao.count());
daoHolder.storeDao.deleteAllStore();
System.out.println("After delete the number of stores is: " + daoHolder.storeDao.count());
}
//根据地理位置查找
public void testFindNearPlace(){
Place p = new Place("somewhere",new double[]{23.5,67.8});
System.out.println("Find 5 stores near "+ p.toString());
List<Store> list = daoHolder.storeDao.findNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
System.out.println("Find 5 KFC stores near "+ p.toString());
list = daoHolder.storeDao.findKFCNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
}
//查找所有store
public void testFindAll(){
long start = System.currentTimeMillis();
List<Store> list = daoHolder.storeDao.find().asList();
for( Store s : list)
System.out.println(s.toString());
System.out.println(System.currentTimeMillis() - start);
}
static class DaoHolder{
PlaceDao placeDao;
StoreDao storeDao;
public DaoHolder(){
try {
Mongo mongo = new Mongo("localhost",27017);
Morphia morphia = new Morphia();
Datastore ds = morphia.createDatastore(mongo, "testDB");
placeDao = new PlaceDao(ds);
storeDao = new StoreDao(ds);
} catch (Exception e) {
e.printStackTrace();
}
}
}
}
package mongo.morphia.test;
import java.util.List;
import morphia.dao.PlaceDao;
import morphia.dao.StoreDao;
import morphia.model.Place;
import morphia.model.Store;
import com.google.code.morphia.Datastore;
import com.google.code.morphia.Morphia;
import com.mongodb.Mongo;
public class StoreDaoTest {
public static String[] STORE_TYPE = {"肯德基","麦当劳","必胜客","吉野家","蒸功夫"};
public static StoreDaoTest m = new StoreDaoTest();
static DaoHolder daoHolder = new DaoHolder();
//测试保存，准备一千家店铺的数据
public void testSave(){
long start = System.currentTimeMillis();
for( int i = 0; i < 1000; i++){
double x = Math.round(Math.random() * 10000)/100.0D;
double y = Math.round(Math.random() * 10000)/100.0D;
Place p = new Place("Place_"+x+"_"+y,new double[]{x,y});
Store s = new Store(STORE_TYPE[i%5],STORE_TYPE[i%5]+"@"+p.getName(),p);
daoHolder.storeDao.save(s);
}
System.out.println(System.currentTimeMillis() - start);
}
//测试删除
public void testDeleteAll(){
System.out.println("Before delete the number of stores is: " + daoHolder.storeDao.count());
daoHolder.storeDao.deleteAllStore();
System.out.println("After delete the number of stores is: " + daoHolder.storeDao.count());
}
//根据地理位置查找
public void testFindNearPlace(){
Place p = new Place("somewhere",new double[]{23.5,67.8});
System.out.println("Find 5 stores near "+ p.toString());
List<Store> list = daoHolder.storeDao.findNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
System.out.println("Find 5 KFC stores near "+ p.toString());
list = daoHolder.storeDao.findKFCNearPlace(p);
for( Store s : list)
System.out.println(s.toString());
}
//查找所有store
public void testFindAll(){
long start = System.currentTimeMillis();
List<Store> list = daoHolder.storeDao.find().asList();
for( Store s : list)
System.out.println(s.toString());
System.out.println(System.currentTimeMillis() - start);
}
static class DaoHolder{
PlaceDao placeDao;
StoreDao storeDao;
public DaoHolder(){
try {
Mongo mongo = new Mongo("localhost",27017);
Morphia morphia = new Morphia();
Datastore ds = morphia.createDatastore(mongo, "testDB");
placeDao = new PlaceDao(ds);
storeDao = new StoreDao(ds);
} catch (Exception e) {
e.printStackTrace();
}
}
}
}
首先， 通过调用StoreDaoTest.m.testSave()保存一千家店铺，用来做数据准备。
其次，通过调用StoreDaoTest.m.testFindAll()查看数据是否ok，当然也可以通过shell窗口查看。
现在可以通过StoreDaoTest.m.testFindNearPlace()来查找地方p附近的相关店铺了，在这个方法中，我查了两次，一次是查找离p[loc=[23.5, 67.8], name=somewhere]最近的任意五个店铺，dao中这样写：
ds.createQuery(Store.class).field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
第二次是查找离p[loc=[23.5, 67.8], name=somewhere]最近的五个肯德基店铺，dao中这样写：
ds.createQuery(Store.class).filter("name", "肯德基")
.field("place.loc").near(p.getLoc()[0], p.getLoc()[1]).limit(5).asList();
输出结果：
Find 5 stores near Place [loc=[23.5, 67.8], name=somewhere]
Store [desc=麦当劳@Place_24.42_67.77, id=4ef9cc2cec9dcb16b1b552d2, name=麦当劳, place=Place [loc=[24.42, 67.77], name=Place_24.42_67.77]]
Store [desc=蒸功夫@Place_24.32_70.0, id=4ef9cc2dec9dcb16b1b553b6, name=蒸功夫, place=Place [loc=[24.32, 70.0], name=Place_24.32_70.0]]
Store [desc=必胜客@Place_24.08_64.89, id=4ef9cc2dec9dcb16b1b5544f, name=必胜客, place=Place [loc=[24.08, 64.89], name=Place_24.08_64.89]]
Store [desc=肯德基@Place_21.05_65.88, id=4ef9cc2dec9dcb16b1b5539e, name=肯德基, place=Place [loc=[21.05, 65.88], name=Place_21.05_65.88]]
Store [desc=吉野家@Place_25.78_65.66, id=4ef9cc2cec9dcb16b1b551f3, name=吉野家, place=Place [loc=[25.78, 65.66], name=Place_25.78_65.66]]
Find 5 KFC stores near Place [loc=[23.5, 67.8], name=somewhere]
Store [desc=肯德基@Place_21.05_65.88, id=4ef9cc2dec9dcb16b1b5539e, name=肯德基, place=Place [loc=[21.05, 65.88], name=Place_21.05_65.88]]
Store [desc=肯德基@Place_21.13_74.42, id=4ef9cc2dec9dcb16b1b5550b, name=肯德基, place=Place [loc=[21.13, 74.42], name=Place_21.13_74.42]]
Store [desc=肯德基@Place_20.26_77.73, id=4ef9cc2cec9dcb16b1b55204, name=肯德基, place=Place [loc=[20.26, 77.73], name=Place_20.26_77.73]]
Store [desc=肯德基@Place_32.55_73.14, id=4ef9cc2cec9dcb16b1b552ae, name=肯德基, place=Place [loc=[32.55, 73.14], name=Place_32.55_73.14]]
Store [desc=肯德基@Place_19.01_77.68, id=4ef9cc2cec9dcb16b1b55209, name=肯德基, place=Place [loc=[19.01, 77.68], name=Place_19.01_77.68]]
最后当然也可以通过StoreDaoTest.m.testDeleteAll() 删除所有测试数据。例子很简单，是我这个礼拜学习的一个小结吧，不罗嗦了。当然很多mongodb的操作命令就不记录了，有用到了再查吧，接下来会去学习一下spring data整合mongodb
摘自 wilson
相关新闻>>
最新推荐更多>>>
- 发表评论
-
- 最新评论 更多>>
