MongoDB-JAVA
转载，原文连接：http://blog.csdn.net/autfish/article/details/51379379MongoDB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
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[*]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.*;
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[*]// $match 确定复合条件的文档, 可组合多个条件
[*]iterable = mc.aggregate(Arrays.asList(match(and(eq("owner", "tom"), gt("words", 300)))));
[*]printResult("$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();
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