Thanks. Setting the driver memory property worked for K=1000 . But when I
increased K to1500 I get the following error:
15/06/19 09:38:44 INFO ContextCleaner: Cleaned accumulator 7
15/06/19 09:38:44 INFO BlockManagerInfo: Removed broadcast_34_piece0 on
172.31.3.51:45157 in memory (size: 1568.0 B, free: 10.4 GB)
15/06/19 09:38:44 INFO BlockManagerInfo: Removed broadcast_34_piece0 on
172.31.9.50:59356 in memory (size: 1568.0 B, free: 73.6 GB)
15/06/19 09:38:44 INFO BlockManagerInfo: Removed broadcast_34_piece0 on
172.31.9.50:60934 in memory (size: 1568.0 B, free: 73.6 GB)
15/06/19 09:38:44 INFO BlockManagerInfo: Removed broadcast_34_piece0 on
172.31.15.51:37825 in memory (size: 1568.0 B, free: 73.6 GB)
15/06/19 09:38:44 INFO BlockManagerInfo: Removed broadcast_34_piece0 on
172.31.15.51:60610 in memory (size: 1568.0 B, free: 73.6 GB)
15/06/19 09:38:44 INFO ContextCleaner: Cleaned shuffle 5
Exception in thread "Thread-2" java.lang.OutOfMemoryError: Requested array
size exceeds VM limit
at java.util.Arrays.copyOf(Arrays.java:2367)
at
java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:130)
at
java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:114)
at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:587)
at java.lang.StringBuilder.append(StringBuilder.java:214)
at py4j.Protocol.getOutputCommand(Protocol.java:305)
at py4j.commands.CallCommand.execute(CallCommand.java:82)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
Exception in thread "Thread-300" java.lang.OutOfMemoryError: Requested
array size exceeds VM limit
at java.util.Arrays.copyOf(Arrays.java:2367)
at
java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:130)
at
java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:114)
at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:587)
at java.lang.StringBuilder.append(StringBuilder.java:214)
at py4j.Protocol.getOutputCommand(Protocol.java:305)
at py4j.commands.CallCommand.execute(CallCommand.java:82)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
Is there any method/guideline through which I can understand the memory
requirement before hand and make appropriate configurations?
Regards,
Rogers Jeffrey L
On Thu, Jun 18, 2015 at 8:14 PM, Rogers Jeffrey <rogers.john2...@gmail.com>
wrote:
> I am submitting the application from a python notebook. I am launching
> pyspark as follows:
>
> SPARK_PUBLIC_DNS=ec2-54-165-202-17.compute-1.amazonaws.com
> SPARK_WORKER_CORES=8 SPARK_WORKER_MEMORY=15g SPARK_MEM=30g OUR_JAVA_MEM=30g
> SPARK_DAEMON_JAVA_OPTS="-XX:MaxPermSize=30g -Xms30g -Xmx30g" IPYTHON=1
> PYSPARK_PYTHON=/usr/bin/python SPARK_PRINT_LAUNCH_COMMAND=1
> ./spark/bin/pyspark --master spark://
> 54.165.202.17.compute-1.amazonaws.com:7077 --deploy-mode client
>
> I guess I should be adding another extra argument --conf
> "spark.driver.memory=15g" . Is that correct?
>
> Regards,
> Rogers Jeffrey L
>
> On Thu, Jun 18, 2015 at 7:50 PM, Xiangrui Meng <men...@gmail.com> wrote:
>
>> With 80,000 features and 1000 clusters, you need 80,000,000 doubles to
>> store the cluster centers. That is ~600MB. If there are 10 partitions,
>> you might need 6GB on the driver to collect updates from workers. I
>> guess the driver died. Did you specify driver memory with
>> spark-submit? -Xiangrui
>>
>> On Thu, Jun 18, 2015 at 12:22 PM, Rogers Jeffrey
>> <rogers.john2...@gmail.com> wrote:
>> > Hi All,
>> >
>> > I am trying to run KMeans clustering on a large data set with 12,000
>> points
>> > and 80,000 dimensions. I have a spark cluster in Ec2 stand alone mode
>> with
>> > 8 workers running on 2 slaves with 160 GB Ram and 40 VCPU.
>> >
>> > My Code is as Follows:
>> >
>> > def convert_into_sparse_vector(A):
>> > non_nan_indices=np.nonzero(~np.isnan(A) )
>> > non_nan_values=A[non_nan_indices]
>> > dictionary=dict(zip(non_nan_indices[0],non_nan_values))
>> > return Vectors.sparse (len(A),dictionary)
>> >
>> > X=[convert_into_sparse_vector(A) for A in complete_dataframe.values ]
>> > sc=SparkContext(appName="parallel_kmeans")
>> > data=sc.parallelize(X,10)
>> > model = KMeans.train(data, 1000, initializationMode="k-means||")
>> >
>> > where complete_dataframe is a pandas data frame that has my data.
>> >
>> > I get the error: Py4JNetworkError: An error occurred while trying to
>> connect
>> > to the Java server.
>> >
>> > The error trace is as follows:
>> >
>> >> ---------------------------------------- Exception happened during
>> >> processing of request from ('127.0.0.1', 41360) Traceback (most recent
>> >> call last): File "/usr/lib64/python2.6/SocketServer.py", line 283,
>> >> in _handle_request_noblock
>> >> self.process_request(request, client_address) File
>> >> "/usr/lib64/python2.6/SocketServer.py", line 309, in process_request
>> >> self.finish_request(request, client_address) File
>> >> "/usr/lib64/python2.6/SocketServer.py", line 322, in finish_request
>> >> self.RequestHandlerClass(request, client_address, self) File
>> >> "/usr/lib64/python2.6/SocketServer.py", line 617, in __init__
>> >> self.handle() File "/root/spark/python/pyspark/accumulators.py",
>> >> line 235, in handle
>> >> num_updates = read_int(self.rfile) File
>> >> "/root/spark/python/pyspark/serializers.py", line 544, in read_int
>> >> raise EOFError EOFError
>> >> ----------------------------------------
>> >>
>> >>
>> ---------------------------------------------------------------------------
>> >> Py4JNetworkError Traceback (most recent call
>> >> last) <ipython-input-13-3dd00c2c5e93> in <module>()
>> >> ----> 1 model = KMeans.train(data, 1000,
>> initializationMode="k-means||")
>> >>
>> >> /root/spark/python/pyspark/mllib/clustering.pyc in train(cls, rdd, k,
>> >> maxIterations, runs, initializationMode, seed, initializationSteps,
>> >> epsilon)
>> >> 134 """Train a k-means clustering model."""
>> >> 135 model = callMLlibFunc("trainKMeansModel",
>> >> rdd.map(_convert_to_vector), k, maxIterations,
>> >> --> 136 runs, initializationMode, seed,
>> >> initializationSteps, epsilon)
>> >> 137 centers = callJavaFunc(rdd.context,
>> model.clusterCenters)
>> >> 138 return KMeansModel([c.toArray() for c in centers])
>> >>
>> >> /root/spark/python/pyspark/mllib/common.pyc in callMLlibFunc(name,
>> >> *args)
>> >> 126 sc = SparkContext._active_spark_context
>> >> 127 api = getattr(sc._jvm.PythonMLLibAPI(), name)
>> >> --> 128 return callJavaFunc(sc, api, *args)
>> >> 129
>> >> 130
>> >>
>> >> /root/spark/python/pyspark/mllib/common.pyc in callJavaFunc(sc, func,
>> >> *args)
>> >> 119 """ Call Java Function """
>> >> 120 args = [_py2java(sc, a) for a in args]
>> >> --> 121 return _java2py(sc, func(*args))
>> >> 122
>> >> 123
>> >>
>> >> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
>> >> __call__(self, *args)
>> >> 534 END_COMMAND_PART
>> >> 535
>> >> --> 536 answer = self.gateway_client.send_command(command)
>> >> 537 return_value = get_return_value(answer,
>> >> self.gateway_client,
>> >> 538 self.target_id, self.name)
>> >>
>> >> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
>> >> send_command(self, command, retry)
>> >> 367 if retry:
>> >> 368 #print_exc()
>> >> --> 369 response = self.send_command(command)
>> >> 370 else:
>> >> 371 response = ERROR
>> >>
>> >> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
>> >> send_command(self, command, retry)
>> >> 360 the Py4J protocol.
>> >> 361 """
>> >> --> 362 connection = self._get_connection()
>> >> 363 try:
>> >> 364 response = connection.send_command(command)
>> >>
>> >> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
>> >> _get_connection(self)
>> >> 316 connection = self.deque.pop()
>> >> 317 except Exception:
>> >> --> 318 connection = self._create_connection()
>> >> 319 return connection
>> >> 320
>> >>
>> >> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
>> >> _create_connection(self)
>> >> 323 connection = GatewayConnection(self.address, self.port,
>> >> 324 self.auto_close, self.gateway_property)
>> >> --> 325 connection.start()
>> >> 326 return connection
>> >> 327
>> >>
>> >> /root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py in
>> >> start(self)
>> >> 430 'server'
>> >> 431 logger.exception(msg)
>> >> --> 432 raise Py4JNetworkError(msg)
>> >> 433
>> >> 434 def close(self):
>> >>
>> >> Py4JNetworkError: An error occurred while trying to connect to the
>> >> Java server
>> >
>> >
>> > Is there any specific setting that I am missing , that causes this
>> error?
>> >
>> > Thanks and Regards,
>> > Rogers Jeffrey L
>>
>
>
