The document discusses MongoDB aggregation techniques including count, distinct, group, and mapReduce. It provides examples of how to use each technique including counting documents, finding distinct values, grouping documents by a field, and using mapReduce to parallelize operations. MapReduce jobs in MongoDB are currently single threaded but sharding or external parallelization can be used to improve performance.

1. MongoDB Ch 6. Aggregation 아키텍트를 꿈꾸는 사람들cafe.naver.com/architect1 현수명 soomong.net #soomong

2. Count Distinct Group MapReduce

3. Count > db.foo.find() { "_id" : ObjectId(“4e2…"), "bar" : "baz" } { "_id" : ObjectId(“4e2…"), "a" : "abc" } { "_id" : ObjectId(“4e2…"), "ohho" : "hohohohoho" } { "_id" : ObjectId(“4e2…"), "123456" : "gooood" } > db.foo.count() 4

4. Count > db.foo.insert({"x":2}) > db.foo.count() 5

5. Count+ > db.foo.count function (x) { return this.find(x).count(); > db.foo.count = 0 0 > db.foo.count() Mon Jul 25 00:13:19 TypeError: db.foo.count is not a function (shell):1

6. Distinct > db.foo.find() { "_id" : ObjectId(“4e2…"), "bar" : "baz" } { "_id" : ObjectId(“4e2…"), "a" : "abc" } { "_id" : ObjectId(“4e2…"), "ohho" : "hohohohoho" } { "_id" : ObjectId(“4e2…"), "123456" : "gooood" } > db.runCommand({"distinct":"foo","key":"ohho"}) { "values" : [ "hohohohoho" ], "stats" : { "n" : 5, "nscanned" : 5, "nscannedObjects" : 5, "timems" : 1 }, "ok" : 1 }

7. Distinct+ > db.runCommand function (obj) { if (typeofobj == "string") { var n = {}; n[obj] = 1; obj = n; } return this.getCollection("$cmd").findOne(obj); } > db.foo.runCommand function (cmd, params) { if (typeofcmd == "object") { return this._db._dbCommand(cmd); } var c = {}; c[cmd] = this.getName(); if (params) { Object.extend(c, params); } return this._db._dbCommand(c); }

8. Group > db.stocks.find() { "_id" : ObjectId(“…"), "day" : "2011/07/25", "time" : “2011/07/25 01:00:00 GMT-400", "price" : 4.23 } { "_id" : ObjectId(“…"), "day" : "2011/07/26", "time" : “2011/07/26 11:05:00 GMT-400", "price" : 4.27 } { "_id" : ObjectId(“…"), "day" : "2011/07/25", "time" : “2011/07/25 03:47:00 GMT-400", "price" : 4.1 } { "_id" : ObjectId(“…"), "day" : "2011/07/28", "time" : “2011/07/28 05:27:38 GMT-400", "price" : 4.3 } { "_id" : ObjectId(“…"), "day" : "2011/07/26", "time" : “2011/07/26 06:34:50 GMT-400", "price" : 4.01 } {"time" : “2011/07/25 03:47:00 GMT-400", "price" : 4.1 } {"time" : “2011/07/26 11:05:00 GMT-400", "price" : 4.27 } {"time" : “2011/07/28 05:27:38 GMT-400", "price" : 4.3 }

9. Group > db.runCommand({"group" : { ... "ns" : "stocks", ... "key" : "day", ... "initial" : {"time" : 0}, ... "$reduce" : function(doc, prev) { ... if (doc.time > prev.time) { ... prev.price = doc.price; ... prev.time = doc.time; ... } ... }}}) {"time" : “2011/07/25 03:47:00 GMT-400", "price" : 4.1 } {"time" : “2011/07/26 11:05:00 GMT-400", "price" : 4.27 } {"time" : “2011/07/28 05:27:38 GMT-400", "price" : 4.3 }

10. Group "ns" : Collection "key" : 묶고싶은 Key "initial" : reduce 연산의 초기값 " $ , $reduce" : reduce 연산 "condition" : 조건 "finalize" : 마지막에 한번만 수행 "$keyf" : 키를 함수로

11. MapReduce 문제를 분할 각 청크를 다른 서버로 각 서버가 처리후 결과 모으기 장점 쉽게 병렬화 단점 복잡타

12. Map 컬렉션내에 각 키가 몇번씩 나타나는지 세어보자 map = function () { for (var key in this) { emit(key, {count:1}); }

13. Reduce reduce = function (key, emits) { total = 0; for (vari in emits) { total += emits[i].count; } return {count:total};

14. Execution mr = db.foo.mapReduce (map,reduce,{out:"mongoDBmapReduce"}) { "result" : "mongoDBmapReduce", "timeMillis" : 11, "counts" : { "input" : 4, "emit" : 8, "output" : 5 }, "ok" : 1, } input : 맵함수로 보내진 문서수 emit : 맵 함수에서 호출한 emit 수 Output : 결과 컬렉션에 생성된 문서수

16. MapReduce Parallelism As of right now, MapReduce jobs on a single mongod process are single threaded. This is due to a design limitationin current JavaScript engines. We are looking into alternatives to solve this issue, but for now if you want to parallelize your MapReduce jobs, you will need to either use shardingor do the aggregation client-side in your code.

17. Reference MongoDB완벽 가이드 -한빛미디어 http://www.mongodb.org/ http://www.coolinfographics.com/

18. 감사합니다