Data Format for Running Collaborative Filtering in Spark MLlib

2016-10-03 Thread Baktaawar 
Hi 

I am working on building a recommender system on a learning content data. My
data format is a user-item matrix of views. Similar to the below one 

NS  


353 0   0   0   0   0   0   0   0   0   
0   ... 0   0   0   0   0   0   0   0   
0   0
354 0   0   0   0   0   0   0   0   0   
0   ... 0   0   0   0   0   0   0   0   
0   0
355 0   0   0   0   0   0   0   0   0   
0   ... 0   0   0   0   0   0   0   0   
0   0
356 0   0   0   0   0   0   0   0   0   
0   ... 0   0   0   0   0   0   0   0   
0   0
357 0   0   0   0   0

Where each row is for a user id and each column is all the videos in the
system. The value corresponding to each video column is either 1 if a user
has watched/clicked on the video else 0. 

This is an implicit feedback dataset. 

Now, I am looking at spark.Mllib package and they seem to give an example
where they are saying the dataframe should be of the form [(userid),
(product),(ratings)]. My dataframe is basically user-video where each column
is different videos and value of those columns is the rating(views in this
case). 

I guess this is what the original paper and elsewhere in collaborative
filtering algorithm the data is represented. Am not sure if this format of
data is supported by Spark.mllib or I have to convert it to the one they
have given an example for?. Any idea how to do that from my dataset?



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Re: Spark Java Heap Error

2016-09-13 Thread Baktaawar 
this is the settings I have.

# Example:

# spark.master spark://master:7077

# spark.eventLog.enabled   true

# spark.eventLog.dir   hdfs://namenode:8021/directory

# spark.serializer
org.apache.spark.serializer.KryoSerializer

spark.driver.memory  16g

spark.executor.memory2g

spark.driver.maxResultSize   8g

spark.rdd.compress   false

spark.storage.memoryFraction 0.5


Same problem.
ᐧ

On Tue, Sep 13, 2016 at 10:27 AM, Manish Tripathi 
wrote:

> Data set is not big. It is 56K X 9K . It does have column names as long
> strings.
>
> It fits very easily in Pandas. That is also in memory thing. So I am not
> sure if memory is an issue here. If Pandas can fit it very easily and work
> on it very fast then Spark shouldnt have problems too right?
> ᐧ
>
> On Tue, Sep 13, 2016 at 10:24 AM, neil90 [via Apache Spark User List] <
> ml-node+s1001560n27707...@n3.nabble.com> wrote:
>
>> Im assuming the dataset your dealing with is big hence why you wanted to
>> allocate ur full 16gb of Ram to it.
>>
>> I suggest running the python spark-shell as such "pyspark --driver-memory
>> 16g".
>>
>> Also if you cache your data and it doesn't fully fit in memory you can do
>> df.cache(StorageLevel.MEMORY_AND_DISK).
>>
>> --
>> If you reply to this email, your message will be added to the discussion
>> below:
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>> va-Heap-Error-tp27669p27707.html
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>> 
>> .
>> NAML
>> 
>>
>
>




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Re: Spark Java Heap Error

2016-09-13 Thread Baktaawar 
Data set is not big. It is 56K X 9K . It does have column names as long
strings.

It fits very easily in Pandas. That is also in memory thing. So I am not
sure if memory is an issue here. If Pandas can fit it very easily and work
on it very fast then Spark shouldnt have problems too right?
ᐧ

On Tue, Sep 13, 2016 at 10:24 AM, neil90 [via Apache Spark User List] <
ml-node+s1001560n27707...@n3.nabble.com> wrote:

> Im assuming the dataset your dealing with is big hence why you wanted to
> allocate ur full 16gb of Ram to it.
>
> I suggest running the python spark-shell as such "pyspark --driver-memory
> 16g".
>
> Also if you cache your data and it doesn't fully fit in memory you can do
> df.cache(StorageLevel.MEMORY_AND_DISK).
>
> --
> If you reply to this email, your message will be added to the discussion
> below:
> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-
> Java-Heap-Error-tp27669p27707.html
> To unsubscribe from Spark Java Heap Error, click here
> 
> .
> NAML
> 
>




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Re: Spark Java Heap Error

2016-09-13 Thread Baktaawar 
I put driver memory as 6gb instead of 8(half of 16). But does 2 gb make
this difference?

On Tuesday, September 13, 2016, neil90 [via Apache Spark User List] <
ml-node+s1001560n27704...@n3.nabble.com> wrote:

> Double check your Driver Memory in your Spark Web UI make sure the driver
> Memory is close to half of 16gb available.
>
> --
> If you reply to this email, your message will be added to the discussion
> below:
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> Java-Heap-Error-tp27669p27704.html
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> 
> .
> NAML
> 
>




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Re: Spark Java Heap Error

2016-09-12 Thread Baktaawar 
Hi 

I even tried the dataframe.cache() action to carry out the cross tab
transformation. However still I get the 
same OOM error. 


recommender_ct.cache()
---
Py4JJavaError Traceback (most recent call last)
 in ()
> 1 recommender_ct.cache()

/Users/i854319/spark/python/pyspark/sql/dataframe.pyc in cache(self)
375 """
376 self.is_cached = True
--> 377 self._jdf.cache()
378 return self
379 

/Users/i854319/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in
__call__(self, *args)
811 answer = self.gateway_client.send_command(command)
812 return_value = get_return_value(
--> 813 answer, self.gateway_client, self.target_id, self.name)
814 
815 for temp_arg in temp_args:

/Users/i854319/spark/python/pyspark/sql/utils.pyc in deco(*a, **kw)
 43 def deco(*a, **kw):
 44 try:
---> 45 return f(*a, **kw)
 46 except py4j.protocol.Py4JJavaError as e:
 47 s = e.java_exception.toString()

/Users/i854319/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py in
get_return_value(answer, gateway_client, target_id, name)
306 raise Py4JJavaError(
307 "An error occurred while calling {0}{1}{2}.\n".
--> 308 format(target_id, ".", name), value)
309 else:
310 raise Py4JError(

Py4JJavaError: An error occurred while calling o40.cache.
: java.lang.OutOfMemoryError
at
java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123)
at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117)
at
java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
at
java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877)
at
java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
at
org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
at
org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:101)
at
org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:301)
at
org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2055)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:707)
at 
org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:706)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:706)
at
org.apache.spark.sql.execution.ConvertToUnsafe.doExecute(rowFormatConverters.scala:38)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at
org.apache.spark.sql.execution.columnar.InMemoryRelation.buildBuffers(InMemoryColumnarTableScan.scala:129)
at
org.apache.spark.sql.execution.columnar.InMemoryRelation.(InMemoryColumnarTableScan.scala:118)
at
org.apache.spark.sql.execution.columnar.InMemoryRelation$.apply(InMemoryColumnarTableScan.scala:41)
at
org.apache.spark.sql.execution.CacheManager$$anonfun$cacheQuery$1.apply(CacheManager.scala:93)
at
org.apache.spark.sql.execution.CacheManager.writeLock(CacheManager.scala:60)
at
org.apache.spark.sql.execution.CacheManager.cacheQuery(CacheManager.scala:84)
at org.apache.spark.sql.DataFrame.persist(DataFrame.scala:1581)
at org.apache.spark.sql.DataFrame.cache(DataFrame.scala:1590)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.Refl

Re: Spark Java Heap Error

2016-09-09 Thread Baktaawar 
Hi Thanks

I tried that. But got this error. Again OOM. I am not sure what to do now.
For spark.driver.maxResultSize i kept 2g. Rest I did as mentioned above.
16Gb for driver and 2g for executor. I have 16Gb mac. Please help. I am
very delayed on my work because of this and not able to move ahead. My
dataset is 56K rows and 8k columns mostly sparse. The column names are
though long strings.

-

Py4JJavaError Traceback (most recent call last)

 in ()

> 1 recommender_ct.show()


/Users/i854319/spark/python/pyspark/sql/dataframe.pyc in show(self, n,
truncate)

255 +---+-+

256 """

--> 257 print(self._jdf.showString(n, truncate))

258

259 def __repr__(self):


/Users/i854319/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py in
__call__(self, *args)

811 answer = self.gateway_client.send_command(command)

812 return_value = get_return_value(

--> 813 answer, self.gateway_client, self.target_id, self.name)

814

815 for temp_arg in temp_args:


/Users/i854319/spark/python/pyspark/sql/utils.pyc in deco(*a, **kw)

 43 def deco(*a, **kw):

 44 try:

---> 45 return f(*a, **kw)

 46 except py4j.protocol.Py4JJavaError as e:

 47 s = e.java_exception.toString()


/Users/i854319/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py in
get_return_value(answer, gateway_client, target_id, name)

306 raise Py4JJavaError(

307 "An error occurred while calling {0}{1}{2}.\n".

--> 308 format(target_id, ".", name), value)

309 else:

310 raise Py4JError(


Py4JJavaError: An error occurred while calling o40.showString.

: java.lang.OutOfMemoryError: Java heap space

at java.util.Arrays.copyOf(Arrays.java:3332)

at
java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:137)

at
java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:121)

at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:421)

at java.lang.StringBuilder.append(StringBuilder.java:136)

at scala.StringContext.standardInterpolator(StringContext.scala:123)

at scala.StringContext.s(StringContext.scala:90)

at
org.apache.spark.sql.execution.QueryExecution.toString(QueryExecution.scala:70)

at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:52)

at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086)

at org.apache.spark.sql.DataFrame.org
$apache$spark$sql$DataFrame$$execute$1(DataFrame.scala:1498)

at org.apache.spark.sql.DataFrame.org
$apache$spark$sql$DataFrame$$collect(DataFrame.scala:1505)

at
org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1375)

at
org.apache.spark.sql.DataFrame$$anonfun$head$1.apply(DataFrame.scala:1374)

at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2099)

at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1374)

at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1456)

at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:170)

at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)

at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

at java.lang.reflect.Method.invoke(Method.java:498)

at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)

at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)

at py4j.Gateway.invoke(Gateway.java:259)

at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)

at py4j.commands.CallCommand.execute(CallCommand.java:79)

at py4j.GatewayConnection.run(GatewayConnection.java:209)

at java.lang.Thread.run(Thread.java:745)
ᐧ

On Wed, Sep 7, 2016 at 10:52 AM, neil90 [via Apache Spark User List] <
ml-node+s1001560n27673...@n3.nabble.com> wrote:

> If your in local mode just allocate all your memory you want to use to
> your Driver(that acts as the executor in local mode) don't even bother
> changing the executor memory. So your new settings should look like this...
>
> spark.driver.memory  16g
> spark.driver.maxResultSize   2g
> spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value
>
> You might need to change your spark.driver.maxResultSize settings if you
> plan on doing a collect on the entire rdd/dataframe.
>
> --
> If you reply to this email, your message will be added to the discussion
> below:
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> Java-Heap-Error-tp27669p27673.html
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>