正如 Mongodb 手册中提到的，“db.collection.group() 方法不适用于分片集群。在分片环境中使用聚合框架或 map-reduce。” 但是今天，我惊讶地发现它可以在 Java 驱动程序中运行。

在我的测试中，分片集合称为“垃圾邮件”，其中包含 4,001,633 个文档。它分为 7 个分片。

集合中的每个文档都有这样的格式。

shard1:PRIMARY> db.spams.findOne()
{
    "IP" : "113.162.134.245",
    "_id" : ObjectId("4ebe8c84466e8b1a56000028"),
    "attach" : [ ],
    "bot" : "Lethic",
    "charset" : "iso-8859-1",
    "city" : "",
    "classA" : "113",
    "classB" : "113.162",
    "classC" : "113.162.134",
    "content_type" : [ ],
    "country" : "Vietnam",
    "cte" : "7bit",
    "date" : ISODate("2011-11-11T00:07:12Z"),
    "day" : "2011-11-11",
    "from_domain_a" : "domain157939.com",
    "geo" : "VN",
    "host" : "",
    "lang" : "unknown",
    "lat" : 16,
    "long" : 106,
    "sequenceID" : "user648",
    "size" : 1060,
    "smtp-mail-from_a" : "barriefrancisco@domain157939.com",
    "smtp-rcpt-to_a" : "jaunn@domain555065.com",
    "subject_ta" : "nxsy8",
    "uri" : [ ],
    "uri_domain" : [ ],
    "x_p0f_detail" : "2000 SP4, XP SP1+",
    "x_p0f_genre" : "Windows",
    "x_p0f_signature" : "65535:105:1:48:M1402,N,N,S:."
}

我想做的是在“2012-01-01T00:00:00Z”之后找到“日期”的文档，并按字段“classA”对它们进行分组，并计算每组中的数量。 所以我写了如下Java代码:

final private String mongoUrl = "172.16.10.61:30000";
final private String databaseName = "test";
final private String collecName = "spams";
private DBCollection collection = null;
private DB db;

public void init(){
    Mongo mongo = null;
    try {
        mongo = new Mongo(new DBAddress(mongoUrl));
    } catch (MongoException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    } catch (UnknownHostException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    db = mongo.getDB(databaseName);
    db.requestStart();
    collection = db.getCollection(collecName);
}

public void group_range_normal(boolean printResult){
    BasicDBObject key = new BasicDBObject("classA", true);
    BasicDBObject initial = new BasicDBObject("cou", 0);

    DateFormat formatter = new SimpleDateFormat("yyyy-MM-dd'T'hh:mm:ss'Z'");
    try {
        Date fromDate = formatter.parse("2012-01-01T00:00:00Z");

        BasicDBObject cond = new BasicDBObject();
        cond.put("date", new BasicDBObject("$gt", fromDate));

        String reduce = "function(obj,pre){pre.cou++}";
        Long runBefore = Calendar.getInstance().getTime().getTime();
        BasicDBList returnList = (BasicDBList) collection.group(key, cond, initial, reduce);
        Long runAfter = Calendar.getInstance().getTime().getTime();
        DBObject errors = db.getLastError();
        if(printResult){
            for (Object o : returnList) {
                System.out.println(o.toString());
            }
        }

        System.out.println("[Group Range Normal]: " + (runAfter - runBefore) + " ms.");
    } catch (ParseException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
}

结果看起来不错:

  ------Group Range Normal------
{ "classA" : "72" , "cou" : 3.0}
{ "classA" : "85" , "cou" : 21.0}
{ "classA" : "115" , "cou" : 23.0}
{ "classA" : "217" , "cou" : 25.0}
{ "classA" : "46" , "cou" : 31.0}
{ "classA" : "117" , "cou" : 58.0}
{ "classA" : "122" , "cou" : 20.0}
{ "classA" : "195" , "cou" : 7.0}
{ "classA" : "190" , "cou" : 44.0}
{ "classA" : "94" , "cou" : 83.0}
{ "classA" : "87" , "cou" : 22.0}
{ "classA" : "95" , "cou" : 53.0}
{ "classA" : "178" , "cou" : 108.0}
{ "classA" : "219" , "cou" : 37.0}
{ "classA" : "76" , "cou" : 1.0}
{ "classA" : "101" , "cou" : 1.0}
{ "classA" : "111" , "cou" : 20.0}
{ "classA" : "194" , "cou" : 34.0}
{ "classA" : "93" , "cou" : 31.0}
{ "classA" : "98" , "cou" : 2.0}
{ "classA" : "180" , "cou" : 45.0}
{ "classA" : "211" , "cou" : 17.0}
{ "classA" : "92" , "cou" : 31.0}
{ "classA" : "177" , "cou" : 21.0}
{ "classA" : "189" , "cou" : 23.0}
{ "classA" : "89" , "cou" : 44.0}
{ "classA" : "78" , "cou" : 12.0}
{ "classA" : "77" , "cou" : 18.0}
{ "classA" : "125" , "cou" : 22.0}
{ "classA" : "200" , "cou" : 16.0}
{ "classA" : "74" , "cou" : 4.0}
{ "classA" : "58" , "cou" : 21.0}
{ "classA" : "80" , "cou" : 12.0}
{ "classA" : "79" , "cou" : 14.0}
{ "classA" : "186" , "cou" : 24.0}
{ "classA" : "105" , "cou" : 2.0}
{ "classA" : "41" , "cou" : 11.0}
{ "classA" : "213" , "cou" : 8.0}
{ "classA" : "220" , "cou" : 10.0}
{ "classA" : "201" , "cou" : 17.0}
{ "classA" : "176" , "cou" : 7.0}
{ "classA" : "112" , "cou" : 46.0}
{ "classA" : "118" , "cou" : 38.0}
{ "classA" : "124" , "cou" : 11.0}
{ "classA" : "82" , "cou" : 19.0}
{ "classA" : "59" , "cou" : 24.0}
{ "classA" : "120" , "cou" : 14.0}
{ "classA" : "114" , "cou" : 17.0}
{ "classA" : "182" , "cou" : 33.0}
{ "classA" : "39" , "cou" : 7.0}
{ "classA" : "90" , "cou" : 7.0}
{ "classA" : "109" , "cou" : 48.0}
{ "classA" : "81" , "cou" : 13.0}
{ "classA" : "27" , "cou" : 16.0}
{ "classA" : "84" , "cou" : 27.0}
{ "classA" : "187" , "cou" : 14.0}
{ "classA" : "91" , "cou" : 25.0}
{ "classA" : "203" , "cou" : 7.0}
{ "classA" : "168" , "cou" : 1.0}
{ "classA" : "123" , "cou" : 25.0}
{ "classA" : "62" , "cou" : 5.0}
{ "classA" : "67" , "cou" : 4.0}
{ "classA" : "2" , "cou" : 48.0}
{ "classA" : "113" , "cou" : 44.0}
{ "classA" : "221" , "cou" : 5.0}
{ "classA" : "121" , "cou" : 26.0}
{ "classA" : "188" , "cou" : 35.0}
{ "classA" : "83" , "cou" : 17.0}
{ "classA" : "119" , "cou" : 21.0}
{ "classA" : "61" , "cou" : 17.0}
{ "classA" : "218" , "cou" : 9.0}
{ "classA" : "49" , "cou" : 15.0}
{ "classA" : "173" , "cou" : 2.0}
{ "classA" : "14" , "cou" : 6.0}
{ "classA" : "159" , "cou" : 4.0}
{ "classA" : "1" , "cou" : 6.0}
{ "classA" : "151" , "cou" : 4.0}
{ "classA" : "181" , "cou" : 2.0}
{ "classA" : "116" , "cou" : 14.0}
{ "classA" : "202" , "cou" : 17.0}
{ "classA" : "42" , "cou" : 2.0}
{ "classA" : "171" , "cou" : 6.0}
{ "classA" : "222" , "cou" : 6.0}
{ "classA" : "209" , "cou" : 1.0}
{ "classA" : "210" , "cou" : 5.0}
{ "classA" : "175" , "cou" : 8.0}
{ "classA" : "71" , "cou" : 3.0}
{ "classA" : "212" , "cou" : 11.0}
{ "classA" : "24" , "cou" : 6.0}
{ "classA" : "110" , "cou" : 18.0}
{ "classA" : "31" , "cou" : 9.0}
{ "classA" : "139" , "cou" : 1.0}
{ "classA" : "196" , "cou" : 2.0}
{ "classA" : "183" , "cou" : 11.0}
{ "classA" : "193" , "cou" : 3.0}
{ "classA" : "207" , "cou" : 5.0}
{ "classA" : "108" , "cou" : 1.0}
{ "classA" : "75" , "cou" : 1.0}
{ "classA" : "106" , "cou" : 3.0}
{ "classA" : "86" , "cou" : 9.0}
{ "classA" : "96" , "cou" : 1.0}
{ "classA" : "174" , "cou" : 2.0}
{ "classA" : "158" , "cou" : 2.0}
{ "classA" : "197" , "cou" : 4.0}
{ "classA" : "141" , "cou" : 6.0}
{ "classA" : "65" , "cou" : 1.0}
{ "classA" : "223" , "cou" : 1.0}
{ "classA" : "184" , "cou" : 2.0}
{ "classA" : "37" , "cou" : 2.0}
{ "classA" : "88" , "cou" : 10.0}
{ "classA" : "149" , "cou" : 1.0}
{ "classA" : "130" , "cou" : 1.0}
{ "classA" : "99" , "cou" : 1.0}
{ "classA" : "208" , "cou" : 1.0}
[Group Range Normal Result]: { "serverUsed" : "/127.0.0.1:27017/172-16-10-61:30000" , "n" : 0 , "lastOp" : { "$ts" : 0 , "$inc" : 0} , "connectionId" : 17496 , "err" :  null  , "ok" : 1.0}
[Group Range Normal Time]: 85 ms.

java驱动版本为mongo-2.9.1.jar。 MongoDB 是 2.2.2 版本。

我也尝试过使用 shell，但它提醒我组不适用于分片集群。

是不是表示db.collection.group()可以在分片集群下工作吗？

最佳答案

的确，MongoDb 提到了这一点

The db.collection.group() method does not work with sharded clusters.

那是必须的

Use the aggregation framework or map-reduce in sharded environments.

但您没有使用 db.collection.group()，mongo shell 中使用的 javascript 函数。您正在使用 java 驱动程序。事实上，Javadocs对于驱动程序，建议 DBCollection.group(...) 使用聚合框架。这就是它起作用的原因。

关于java - MongoDB java 驱动程序可以在分片环境下执行 db.collection.group()，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/14506889/