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From Gautam Bajaj <gautam1...@gmail.com>
Subject Re: Using Neo4j with Apache Spark
Date Fri, 13 Mar 2015 07:21:23 GMT
I have been trying to do the same, but where exactly do you suggest
creating the static object? As creating it inside for each RDD will
ultimately result in same problem and not creating it inside will result in
serializability issue.

On Fri, Mar 13, 2015 at 11:47 AM, Tathagata Das <tdas@databricks.com> wrote:

> Well, that's why I had also suggested using a pool of the GraphDBService
> objects :)
> Also present in the programming guide link I had given.
>
> TD
>
>
> On Thu, Mar 12, 2015 at 7:38 PM, Gautam Bajaj <gautam1237@gmail.com>
> wrote:
>
>> Thanks a ton! That worked.
>>
>> However, this may have performance issue. As for each partition, I'd need
>> to restart the server, that was the basic reason I was creating graphDb
>> object outside this loop.
>>
>> On Fri, Mar 13, 2015 at 5:34 AM, Tathagata Das <tdas@databricks.com>
>> wrote:
>>
>>> (Putting user@spark back in the to list)
>>>
>>> In the gist, you are creating graphDB object way outside the
>>> RDD.foreachPartition. I said last time, create the graphDB object inside
>>> the RDD.foreachPartition. You are creating it outside DStream.foreachRDD,
>>> and then using it from inside the rdd.foreachPartition. That is bringing
>>> the graphDB object in the task closure, and hence the system is trying to
>>> serialize the graphDB object when its serializing the closure. If you
>>> create the graphDB object inside the RDD.foreachPartition, then the closure
>>> will not refer to any prior graphDB object and therefore not serialize
>>> anything.
>>>
>>> On Thu, Mar 12, 2015 at 3:46 AM, Gautam Bajaj <gautam1237@gmail.com>
>>> wrote:
>>>
>>>> Here: https://gist.github.com/d34th4ck3r/0c99d1e9fa288e0cc8ab
>>>>
>>>> I'll add the flag and send you stack trace, I have meetings now.
>>>>
>>>> On Thu, Mar 12, 2015 at 6:28 PM, Tathagata Das <tdas@databricks.com>
>>>> wrote:
>>>>
>>>>> Could you show us that version of the code?
>>>>>
>>>>> Also helps to turn on java flag of extended debug info. That will show
>>>>> the lineage of objects leading to the nonserilaizable one.
>>>>> On Mar 12, 2015 1:32 AM, "Gautam Bajaj" <gautam1237@gmail.com>
wrote:
>>>>>
>>>>>> I tried that too. It result in same serializability issue.
>>>>>>
>>>>>> GraphDatabaseSerive that I'm using is : GraphDatabaseFactory() :
>>>>>> http://neo4j.com/api_docs/2.0.0/org/neo4j/graphdb/factory/GraphDatabaseFactory.html
>>>>>>
>>>>>> On Thu, Mar 12, 2015 at 5:21 PM, Tathagata Das <tdas@databricks.com>
>>>>>> wrote:
>>>>>>
>>>>>>> What is GraphDatabaseService object that you are using? Instead
of
>>>>>>> creating them on the driver (outside foreachRDD), can you create
them
>>>>>>> inside the RDD.foreach?
>>>>>>>
>>>>>>> In general, the right pattern for doing this in the programming
guide
>>>>>>>
>>>>>>> http://spark.apache.org/docs/latest/streaming-programming-guide.html#design-patterns-for-using-foreachrdd
>>>>>>>
>>>>>>> So you should be doing (sorry for writing in scala)
>>>>>>>
>>>>>>> dstream.foreachRDD ((rdd: RDD, time: Time) => {
>>>>>>>     rdd.foreachPartition(iterator =>
>>>>>>>         // Create GraphDatabaseService object, or fetch it from
a
>>>>>>> pool of GraphDatabaseService objects
>>>>>>>         // Use it to send the whole partition to Neo4j
>>>>>>>         // Destroy the object or release it to the pool
>>>>>>> })
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Mar 12, 2015 at 1:15 AM, Gautam Bajaj <gautam1237@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Neo4j is running externally. It has nothing to do with Spark
>>>>>>>> processes.
>>>>>>>>
>>>>>>>> Basically, the problem is, I'm unable to figure out a way
to store
>>>>>>>> output of Spark on the database. As Spark Streaming requires
Neo4j Core
>>>>>>>> Java API to be serializable as well.
>>>>>>>>
>>>>>>>> The answer points out to using REST API but their performance
is
>>>>>>>> really poor when compared to Core Java API :
>>>>>>>> http://www.rene-pickhardt.de/get-the-full-neo4j-power-by-using-the-core-java-api-for-traversing-your-graph-data-base-instead-of-cypher-query-language/
>>>>>>>>
>>>>>>>> On Thu, Mar 12, 2015 at 5:09 PM, Tathagata Das <tdas@databricks.com
>>>>>>>> > wrote:
>>>>>>>>
>>>>>>>>> Well the answers you got there are correct as well.
>>>>>>>>> Unfortunately I am not familiar with Neo4j enough to
comment any
>>>>>>>>> more. Is the Neo4j graph database running externally
(outside Spark
>>>>>>>>> cluster), or within the driver process, or on all the
executors? Can you
>>>>>>>>> clarify that?
>>>>>>>>>
>>>>>>>>> TD
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Thu, Mar 12, 2015 at 12:58 AM, Gautam Bajaj <
>>>>>>>>> gautam1237@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>>> Alright, I have also asked this question in StackOverflow:
>>>>>>>>>> http://stackoverflow.com/questions/28896898/using-neo4j-with-apache-spark
>>>>>>>>>>
>>>>>>>>>> The code there is pretty neat.
>>>>>>>>>>
>>>>>>>>>> On Thu, Mar 12, 2015 at 4:55 PM, Tathagata Das <
>>>>>>>>>> tdas@databricks.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> I am not sure if you realized but the code snipper
it pretty
>>>>>>>>>>> mangled up in the email we received. It might
be a good idea to put the
>>>>>>>>>>> code in pastebin or gist, much much easier for
everyone to read.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Thu, Mar 12, 2015 at 12:48 AM, d34th4ck3r
<
>>>>>>>>>>> gautam1237@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> I'm trying to use Neo4j with Apache Spark
Streaming but I am
>>>>>>>>>>>> finding
>>>>>>>>>>>> serializability as an issue.
>>>>>>>>>>>>
>>>>>>>>>>>> Basically, I want Apache Spark to parse and
bundle my data in
>>>>>>>>>>>> real time.
>>>>>>>>>>>> After, the data has been bundled it should
be stored in the
>>>>>>>>>>>> database, Neo4j.
>>>>>>>>>>>> However, I am getting this error:
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.SparkException: Task not
serializable
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
>>>>>>>>>>>>     at
>>>>>>>>>>>> org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
>>>>>>>>>>>>     at
>>>>>>>>>>>> org.apache.spark.SparkContext.clean(SparkContext.scala:1264)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.api.java.JavaRDDLike$class.foreach(JavaRDDLike.scala:297)
>>>>>>>>>>>>     at
>>>>>>>>>>>> org.apache.spark.api.java.JavaPairRDD.foreach(JavaPairRDD.scala:45)
>>>>>>>>>>>>     at twoGrams.Main$4.call(Main.java:102)
>>>>>>>>>>>>     at twoGrams.Main$4.call(Main.java:1)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:282)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.streaming.api.java.JavaDStreamLike$$anonfun$foreachRDD$2.apply(JavaDStreamLike.scala:282)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:41)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)
>>>>>>>>>>>>     at scala.util.Try$.apply(Try.scala:161)
>>>>>>>>>>>>     at
>>>>>>>>>>>> org.apache.spark.streaming.scheduler.Job.run(Job.scala:32)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:172)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>>>>>>>>>>>>     at java.lang.Thread.run(Thread.java:745)
>>>>>>>>>>>> Caused by: java.io.NotSerializableException:
>>>>>>>>>>>> org.neo4j.kernel.EmbeddedGraphDatabase
>>>>>>>>>>>>     at
>>>>>>>>>>>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1183)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
>>>>>>>>>>>>     at
>>>>>>>>>>>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
>>>>>>>>>>>>     at
>>>>>>>>>>>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431)
>>>>>>>>>>>>     at
>>>>>>>>>>>> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
>>>>>>>>>>>>     at
>>>>>>>>>>>> java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:73)
>>>>>>>>>>>>     at
>>>>>>>>>>>>
>>>>>>>>>>>> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:164)
>>>>>>>>>>>>     ... 17 more
>>>>>>>>>>>> Here is my code:
>>>>>>>>>>>>
>>>>>>>>>>>> output a stream of type: JavaPairDStream<String,
>>>>>>>>>>>> ArrayList&lt;String>>
>>>>>>>>>>>>
>>>>>>>>>>>> output.foreachRDD(
>>>>>>>>>>>>                 new
>>>>>>>>>>>>
>>>>>>>>>>>> Function2<JavaPairRDD&lt;String,ArrayList&lt;String>>,Time,Void>(){
>>>>>>>>>>>>
>>>>>>>>>>>>                     @Override
>>>>>>>>>>>>                     public Void call(
>>>>>>>>>>>>                             JavaPairRDD<String,
>>>>>>>>>>>> ArrayList&lt;String>> arg0,
>>>>>>>>>>>>                             Time arg1) throws
Exception {
>>>>>>>>>>>>                         // TODO Auto-generated
method stub
>>>>>>>>>>>>
>>>>>>>>>>>>                         arg0.foreach(
>>>>>>>>>>>>                                 new
>>>>>>>>>>>> VoidFunction<Tuple2&lt;String,ArrayList&lt;String>>>(){
>>>>>>>>>>>>
>>>>>>>>>>>>                                     @Override
>>>>>>>>>>>>                                     public
void call(
>>>>>>>>>>>>                                         
   Tuple2<String,
>>>>>>>>>>>> ArrayList&lt;String>> arg0)
>>>>>>>>>>>>                                         
   throws Exception {
>>>>>>>>>>>>                                         //
TODO Auto-generated
>>>>>>>>>>>> method stub
>>>>>>>>>>>>                                         try(
Transaction tx =
>>>>>>>>>>>> graphDB.beginTx()){
>>>>>>>>>>>>
>>>>>>>>>>>> if(Neo4jOperations.getHMacFromValue(graphDB,
arg0._1)!=null)
>>>>>>>>>>>>
>>>>>>>>>>>> System.out.println("Alread
>>>>>>>>>>>> in Database:" + arg0._1);
>>>>>>>>>>>>                                         
   else{
>>>>>>>>>>>>
>>>>>>>>>>>> Neo4jOperations.createHMac(graphDB, arg0._1);
>>>>>>>>>>>>                                         
   }
>>>>>>>>>>>>                                         
   tx.success();
>>>>>>>>>>>>                                         }
>>>>>>>>>>>>                                     }
>>>>>>>>>>>>
>>>>>>>>>>>>                         });
>>>>>>>>>>>>                         return null;
>>>>>>>>>>>>                     }
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>                 });
>>>>>>>>>>>> Neo4jOperations Class:
>>>>>>>>>>>>
>>>>>>>>>>>> public class Neo4jOperations{
>>>>>>>>>>>>
>>>>>>>>>>>> public static Node getHMacFromValue(GraphDatabaseService
>>>>>>>>>>>> graphDB,String
>>>>>>>>>>>> value){
>>>>>>>>>>>>         try(ResourceIterator<Node>
>>>>>>>>>>>>
>>>>>>>>>>>> HMacs=graphDB.findNodesByLabelAndProperty(DynamicLabel.label("HMac"),
>>>>>>>>>>>> "value", value).iterator()){
>>>>>>>>>>>>             return HMacs.next();
>>>>>>>>>>>>         }
>>>>>>>>>>>>     }
>>>>>>>>>>>>
>>>>>>>>>>>>     public static void createHMac(GraphDatabaseService
>>>>>>>>>>>> graphDB,String
>>>>>>>>>>>> value){
>>>>>>>>>>>>         Node
>>>>>>>>>>>> HMac=graphDB.createNode(DynamicLabel.label("HMac"));
>>>>>>>>>>>>         HMac.setProperty("value", value);
>>>>>>>>>>>>         HMac.setProperty("time", new
>>>>>>>>>>>>
>>>>>>>>>>>> SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime()));
>>>>>>>>>>>>     }
>>>>>>>>>>>> }
>>>>>>>>>>>> I know that I have to Serialize the class
Neo4jOperations, but
>>>>>>>>>>>> I'm able to
>>>>>>>>>>>> figure out how. Or is there any other way
to achieve this?
>>>>>>>>>>>>
>>>>>>>>>>>> Also, how can I store output of Spark Streaming
in a database ?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> --
>>>>>>>>>>>> View this message in context:
>>>>>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Using-Neo4j-with-Apache-Spark-tp22012.html
>>>>>>>>>>>> Sent from the Apache Spark User List mailing
list archive at
>>>>>>>>>>>> Nabble.com.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> ---------------------------------------------------------------------
>>>>>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>>>>>>>>>>> For additional commands, e-mail: user-help@spark.apache.org
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> --
>>>>>>>>>> Gautam
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> Gautam
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Gautam
>>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Gautam
>>>>
>>>
>>>
>>
>>
>> --
>> Gautam
>>
>
>


-- 
Gautam

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