6. {
"_id" : ObjectId("4dcd3ebc9278000000005158"),
"timestamp" : ISODate("2011-05-13T14:22:46.777Z"),
"binary" : BinData(0,""),
"string" : "abc",
"number" : 3,
"subobj" : {"subA": 1, "subB": 2 },
"array" : [1, 2, 3],
"dbref" : [_id1, _id2, _id3]
padding
}

7. { db.coll.find({"string": "abc"});
db.coll.find({ "string" : /^a.*$/i });
"_id" : ObjectId("4dcd3ebc9278000000005158"),
"timestamp" : ISODate("2011-05-13T14:22:46.777Z"),
db.coll.find({"subobj.subA": 1});
db.coll.find({"subobj.subB": {$exists: true} });
"binary" : BinData(0,""),
"string" : "abc", db.coll.find({"number": 3});
db.coll.find({"number": {$gt: 1}});
"number" : 3,
"subobj" : {"subA": 1, "subB": 2 },
"array" : [1, 2, 3],
db.coll.find({"array": {$all:[1, 2]} });
"dbref" : [_id1, _id2, _id3]
db.coll.find({"array": {$in:[2, 4, 6]} });
padding
}

8. {
"_id" : ObjectId("4dcd3ebc9278000000005158"),
"timestamp" : ISODate("2011-05-13T14:22:46.777Z"),
{ $set : {"string": "def"} }
"binary" : BinData(0,""), { $inc : {"number": 1} }
"string" : "def",
{ $pull : {"subobj": {"subB": 2 } } }
"number" : 4,
"subobj" : {"subA": 1, "subB": 2 },
"array" : [1, 2, 3, 4, 5, 6],
"dbref"$addToSet : { "array" : { $each : [ 4 , 5 , 6 ] } } }
{ : [_id1, _id2, _id3]
"newkey" : "In-place"
} { $set : {"newkey": "In-place"} }

16. ScientificPython

21. def mapper(key, value):
for word in value.split(): yield word,1
def reducer(key, values):
yield key,sum(values)
if __name__ == "__main__":
import dumbo
dumbo.run(mapper, reducer)
dumbo start wordcount.py
-hadoop /path/to/hadoop
-input wc_input.txt
-output wc_output

35. [2011-07-01 12:01:48,447]

40. db.collection.insert(
{hour:0,
userId:”1234”,
actionType:”login”,}
);

42. m = function(){
this.tags.forEach{
function(z) {
emit(z, {count: 1});
}
};
};
r = function(key, values) {
var total=0;
for (i=0, i<values.length, i++)
total += values[i].count;
return { count : total };
}
res=db.things.mapReduce(m,!r);
# finalize

49. Examples
Conclusions and Future Work
Party Solutions
Motivation
Architecture
Examples
Conclusions and Future Work
ummary of Features
Hadoop-based: same limitations as Streaming (Dumbo) and
Streaming Jython Pydoop
Jython (Happy), except for ease of use
C/C++ Ext Yes No Yes
Other implementations: good if you have your own cluster
Standard Lib Full Partial Full
Hadoop is the most widespread implementation
MR API No* Full Partial
Java-like FW No Yes Yes
HDFS No
Leo, Zanetti
Yes Yes
Pydoop: a Python MapReduce and HDFS API for Hadoop
(*) you can only write the map and reduce parts as executable scripts.

50. Motivation
Architecture
Examples
Conclusions and Future Work
Hadoop Pipes
Communication with Java
framework via persistent
sockets
The C++ app provides a
factory used by the framework
to create MR components
Providing Mapper and
Reducer is mandatory
Leo, Zanetti Pydoop: a Python MapReduce and HDFS API for Hadoop

51. Motivation
Architecture
Examples
Conclusions and Future Work
Integration of Pydoop with C++
Integration with Pipes:
Method calls flow from the
framework through the C++ and the
Pydoop API, ultimately reaching
user-defined methods
Results are wrapped by Boost and
returned to the framework
Integration with HDFS:
Function calls initiated by Pydoop
Results wrapped and returned as
Python objects to the app

55. gawk '
BEGIN{ reducenum='$REDUCE_NUM'; }
{ userid=$7; key=$8; }
key ~ /a{GetLoginBonus}/ { incrby(userid,key,$9,a); next;}
key ~ /a{SideJob}/ { incrby(userid,key,$11,a); next;}
key ~ /a{CleanMyShop}/ { hincr(userid,key,$9,a); next; }
key ~ /(GetAvatarPart|ChangeP|ChangeWakuwakuP|ChangeKonergy)/
{ incrbydiff(userid,key,$9,a); next; }
...‘ $IN
# for reducer1 (such as “userid % reducenum == 0”)
# command userid key value
MULTI
HINCRBY 1111 a{ChangeGreed} 3
HINCRBY 1111 a{GianEvent} 7
HINCRBY 1111 a{TeamChallenge} 5
HINCRBY 2222 a{Battle} 3
HINCRBY 2222 a{ChangeMoney} 3
...
EXEC