Depending on the size of the rdd you could also do a collect broadcast and
then compute the product in a map function over the other rdd. If this is
the same rdd you might also want to cache it. This pattern worked quite
good for me
Le 25 avr. 2014 18:33, "Alex Boisvert" <alex.boisv...@gmail.com> a écrit :
> You might want to try the built-in RDD.cartesian() method.
>
>
> On Thu, Apr 24, 2014 at 9:05 PM, Qin Wei <wei....@dewmobile.net> wrote:
>
>> Hi All,
>>
>> I have a problem with the Item-Based Collaborative Filtering
>> Recommendation
>> Algorithms in spark.
>> The basic flow is as below:
>> (Item1 , (User1 ,
>> Score1))
>> RDD1 ==> (Item2 , (User2 ,
>> Score2))
>> (Item1 , (User2 ,
>> Score3))
>> (Item2 , (User1 ,
>> Score4))
>>
>> RDD1.groupByKey ==> RDD2
>> (Item1, ((User1,
>> Score1),
>> (User2, Score3)))
>> (Item2, ((User1,
>> Score4),
>> (User2, Score2)))
>>
>> The similarity of Vector ((User1, Score1), (User2, Score3)) and
>> ((User1, Score4), (User2, Score2)) is the similarity of Item1 and
>> Item2.
>>
>> In my situation, RDD2 contains 20 million records, my spark programm is
>> extreamly slow, the source code is as below:
>> val conf = new
>> SparkConf().setMaster("spark://211.151.121.184:7077").setAppName("Score
>> Calcu Total").set("spark.executor.memory",
>> "20g").setJars(Seq("/home/deployer/score-calcu-assembly-1.0.jar"))
>> val sc = new SparkContext(conf)
>>
>> val mongoRDD =
>> sc.textFile(args(0).toString,
>> 400)
>> val jsonRDD = mongoRDD.map(arg => new
>> JSONObject(arg))
>>
>> val newRDD = jsonRDD.map(arg => {
>> var score =
>> haha(arg.get("a").asInstanceOf[JSONObject])
>>
>> // set score to 0.5 for testing
>> arg.put("score", 0.5)
>> arg
>> })
>>
>> val resourceScoresRDD = newRDD.map(arg =>
>> (arg.get("rid").toString.toLong, (arg.get("zid").toString,
>> arg.get("score").asInstanceOf[Number].doubleValue))).groupByKey().cache()
>> val resourceScores =
>> resourceScoresRDD.collect()
>> val bcResourceScores =
>> sc.broadcast(resourceScores)
>>
>> val simRDD =
>> resourceScoresRDD.mapPartitions({iter =>
>> val m = bcResourceScores.value
>> for{ (r1, v1) <- iter
>> (r2, v2) <- m
>> if r1 > r2
>> } yield (r1, r2, cosSimilarity(v1,
>> v2))}, true).filter(arg => arg._3 > 0.1)
>>
>> println(simRDD.count)
>>
>> And I saw this in Spark Web UI:
>> <
>> http://apache-spark-user-list.1001560.n3.nabble.com/file/n4807/QQ%E6%88%AA%E5%9B%BE20140424204018.png
>> >
>> <
>> http://apache-spark-user-list.1001560.n3.nabble.com/file/n4807/QQ%E6%88%AA%E5%9B%BE20140424204001.png
>> >
>>
>> My standalone cluster has 3 worker node (16 core and 32G RAM),and the
>> workload of the machine in my cluster is heavy when the spark program is
>> running.
>>
>> Is there any better way to do the algorithm?
>>
>> Thanks!
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/what-is-the-best-way-to-do-cartesian-tp4807.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
>
>
