spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Tathagata Das <t...@databricks.com>
Subject Re: Using Neo4j with Apache Spark
Date Thu, 12 Mar 2015 20:34:03 GMT
(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
>

Mime
View raw message