MongoDB-JAVA-Driver 3.2版本常用代码全整理(3)

转载，原文连接：http://blog.csdn.net/autfish/article/details/51379379

MongoDB的3.x版本Java驱动相对2.x做了全新的设计，类库和使用方法上有很大区别。例如用Document替换BasicDBObject、通过Builders类构建Bson替代直接输入$命令等，本文整理了基于3.2版本的常用增删改查操作的使用方法。为了避免冗长的篇幅，分为增删改、查询、聚合、地理索引等几部分。

聚合用于统计文档个数、求和、最大最小值、求平均值等，功能和函数名称和SQL中的count、distinct、group等关键字非常类似，此外，还可以通过JavaScript编写MapReduce实现复杂的计算(性能损耗也会非常严重)。

首先来看3.x驱动中的聚合方法的声明：

AggregateIterable<TDocument> aggregate(List<? extends Bson> pipeline)

参数类型是一个Bson的列表，而参数名称是pipeline，其构建方式正如其名，是以多个Bson建立起一条管道，前一个Bson的输出将作为后一个Bson的输入，例如：

mc.aggregate(Arrays.asList(match(eq("owner", "tom")), group("$author", sum("totalWords", "$words"))));

首先用$match查找出owner=tom的文档，并将结果集传递给$group并对字数求和。

下面来看更多命令用法，用于演示的类的基本代码如下

import static com.mongodb.client.model.Accumulators.*;
import static com.mongodb.client.model.Aggregates.*;
import static com.mongodb.client.model.Filters.eq;

import java.text.ParseException;
import java.util.Arrays;

import org.bson.Document;

import com.mongodb.Block;
import com.mongodb.MongoClient;
import com.mongodb.client.AggregateIterable;
import com.mongodb.client.MongoCollection;
import com.mongodb.client.MongoDatabase;

public class AggregatesExamples {

    public static void main(String[] args) throws ParseException {
        //根据实际环境修改ip和端口
        MongoClient mongoClient = new MongoClient("localhost", 27017);
        MongoDatabase database = mongoClient.getDatabase("lesson");

        AggregatesExamples client = new AggregatesExamples(database);
        client.show();
        mongoClient.close();
    }

    private MongoDatabase database;
    public AggregatesExamples(MongoDatabase database) {
        this.database = database;
    }

    public void show() {
        MongoCollection<Document> mc = database.getCollection("blog");
        //每次执行前清空集合以方便重复运行
        mc.drop();

        //插入用于测试的文档
        Document doc1 = new Document("title", "good day").append("owner", "tom").append("words", 300)
                .append("comments", Arrays.asList(new Document("author", "joe").append("score", 3).append("comment", "good"), new Document("author", "white").append("score", 1).append("comment", "oh no")));
        Document doc2 = new Document("title", "good").append("owner", "john").append("words", 400)
                .append("comments", Arrays.asList(new Document("author", "william").append("score", 4).append("comment", "good"), new Document("author", "white").append("score", 6).append("comment", "very good")));
        Document doc3 = new Document("title", "good night").append("owner", "mike").append("words", 200)
                .append("tag", Arrays.asList(1, 2, 3, 4));
        Document doc4 = new Document("title", "happiness").append("owner", "tom").append("words", 1480)
                .append("tag", Arrays.asList(2, 3, 4));
        Document doc5 = new Document("title", "a good thing").append("owner", "tom").append("words", 180)
                .append("tag", Arrays.asList(1, 2, 3, 4, 5));
        mc.insertMany(Arrays.asList(doc1, doc2, doc3, doc4, doc5));

        AggregateIterable<Document> iterable = mc.aggregate(Arrays.asList(match(eq("owner", "tom")),
                group("$author", sum("totalWords", "$words"))));
        printResult("", iterable);

        //TODO: 将在这里填充更多聚合示例
    }

    //打印聚合结果
    public void printResult(String doing, AggregateIterable<Document> iterable) {
        System.out.println(doing);
        iterable.forEach(new Block<Document>() {
            public void apply(final Document document) {
                System.out.println(document);
            }
        });
        System.out.println("------------------------------------------------------");
        System.out.println();
    }
}

如上面代码所示，将把所有的聚合操作集中在show()方法中演示，并且在执行后打印结果集以观察执行结果。下面用常用的聚合代码填充show()方法

 注意需要静态导入:

import static com.mongodb.client.model.Accumulators.*;
import static com.mongodb.client.model.Aggregates.*;

// $match 确定复合条件的文档, 可组合多个条件
iterable = mc.aggregate(Arrays.asList(match(and(eq("owner", "tom"), gt("words", 300)))));
printResult("$match only", iterable);

// $sum求和 $avg平均值 $max最大值 $min最小值
iterable = mc.aggregate(Arrays.asList(
        match(in("owner", "tom", "john", "mike")),
        group("$owner", sum("totalWords", "$words"),
                avg("averageWords", "$words"),
                max("maxWords", "$words"), min("minWords", "$words"))));
printResult("$sum $avg $max $min", iterable);

// $out 把聚合结果输出到集合
mc.aggregate(Arrays.asList(
        match(in("owner", "tom", "john", "mike")),
        group("$owner", sum("totalWords", "$words"),
                avg("averageWords", "$words"),
                max("maxWords", "$words"), min("minWords", "$words")),
        out("wordsCount")));
iterable = database.getCollection("wordsCount").aggregate(
        Arrays.asList(sample(3)));
printResult("$out", iterable);

// 随机取3个文档, 仅返回title和owner字段
iterable = mc.aggregate(Arrays.asList(sample(3),
        project(fields(include("title", "owner"), excludeId()))));
printResult("sample(3)", iterable);

// 从第2个文档开始取2个文档, 仅返回title和owner字段
iterable = mc.aggregate(Arrays.asList(skip(1), limit(2),
        project(fields(include("title", "owner"), excludeId()))));
printResult("skip(1), limit(2)", iterable);

// $lookup 和另一个集合关联
database.getCollection("scores").drop();
database.getCollection("scores").insertMany(
        Arrays.asList(
                new Document("writer", "tom").append("score", 100),
                new Document("writer", "joe").append("score", 95),
                new Document("writer", "john").append("score", 80)));
iterable = mc.aggregate(Arrays.asList(lookup("scores", "owner",
        "writer", "joinedOutput")));
printResult("lookup", iterable);

// 拆分comments为单个文档
iterable = mc.aggregate(Arrays.asList(match(size("comments", 2)),
        project(fields(include("comments"), excludeId())),
        unwind("$comments")));
printResult("unwind comments", iterable);

System.out.println("distinct");
DistinctIterable<String> di = mc.distinct("owner", String.class);
di.forEach(new Block<String>() {
    public void apply(final String str) {
        System.out.println(str);
    }
});
System.out.println("------------------------------------------------------");
System.out.println();

System.out.println("count");
long count = mc.count(Filters.eq("owner", "tom"));
System.out.println("count=" + count);
System.out.println("------------------------------------------------------");
System.out.println();

System.out.println("mapreduce");
String map = "function() { var category; "
        + "if ( this.words >= 280 ) category = 'Long blogs'; "
        + "else category = 'Short blogs'; "
        + "emit(category, {title: this.title});}";

String reduce = "function(key, values) { var cnt = 0; "
        + "values.forEach(function(doc) { cnt += 1; }); "
        + "return {count: cnt};} ";
MapReduceIterable<Document> mi = mc.mapReduce(map, reduce);
mi.forEach(new Block<Document>() {
    public void apply(final Document str) {
        System.out.println(str);
    }
});
System.out.println("------------------------------------------------------");
System.out.println();

(完)