Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext

Question

Anyon know Why I keeo getting this error in Jupyter Notebooks??? I've been trying to load my Tensorflow model into Apache Spark vis SparlFlowbut I can't seem to figure out how to get past this error. Any help would be much appreciated.

First Jupyter cell:

from sparkflow.graph_utils import build_graph
from sparkflow.tensorflow_async import SparkAsyncDL
import tensorflow as tf
from pyspark.ml.feature import VectorAssembler, OneHotEncoder
from pyspark.ml.pipeline import Pipeline


 from pyspark.sql import SparkSession
    from tensorflow.keras import layers
    from tensorflow.keras import losses

Second Jupyter cell:

def lstm_model(X_train, y_train):
    # Reshapes to input neuron
    inputs= keras.Input(shape = (X_train.shape[1], 1))\
    #Training Layers
    x_1 = layers.LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))(inputs)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.LSTM(units = 50, return_sequences = True)(x_1)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.LSTM(units = 50, return_sequences = True)(x_1)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.LSTM(units = 50, return_sequences = True)(x_1)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.Flatten()(x_1)

    # 1 output neuron for each column prediction
    output = Dense(units=1)(x_1)
    return losses.MeanSquaredError(y_train,output)

Third Jupyter Cell:

def dataframe_input(pandas_dataframe):

    train_data = pandas_dataframe[self.column_name].values

    # Reshaping to a 2D array
    train_data = train_data.reshape(-1,1)
    print(train_data.dtype)
    print(type(train_data))
    print(train_data.shape) 

    # Feature Scaling
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled_train_data =scaler.fit_transform(train_data)

    # Initialzing each x_train and y_train datasets for each column
    X_train = []
    y_train = []

    # Appending scaled training data to each dataset
    for i in range(self.timesteps, len(train_data)):
        X_train.append(scaled_train_data[i - self.timesteps:i, 0])
        y_train.append(scaled_train_data[i, 0])

    # Numpy array creation, Keras requires numpy arrays for Inputs
    X_train, y_train = np.array(X_train, dtype=int), np.array(y_train)
    print(X_train.shape)
    print(X_train.dtype)

    # Reshaping to a 3D matrix (970, 30, 1)
    #X_train = np.reshape(X_train, (X_train[0], X_train[1], 1))
    print(X_train.shape)
    return X_train, y_train

Fourth Jupyter Cell( Where Im getting the error):

# Spark Session
# In order to use APIs of SQL, HIVE, and Streaming, no need to create separate contexts as sparkSession includes all the APIs.
spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .getOrCreate()

# Reading CSVto a Spark DataFrame
df = spark.read.option("inferSchema", "true").csv('"../csv_test_files/stats.csv"')

# Convert the Spark dataframe into a Pandas Dataframe
pandas_dataframe = df.select("*").toPandas()

# Get the input and ouput data for passing to the model
X_train, y_train = dataframe_input(pandas_dataframe)

Error Output:

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-25-5143cc437b69> in <module>
      3 spark = SparkSession \
      4     .builder \
----> 5     .appName("Python Spark SQL basic example") \
      6     .getOrCreate()
      7 

~/anaconda3/lib/python3.7/site-packages/pyspark/sql/session.py in getOrCreate(self)
    171                     for key, value in self._options.items():
    172                         sparkConf.set(key, value)
--> 173                     sc = SparkContext.getOrCreate(sparkConf)
    174                     # This SparkContext may be an existing one.
    175                     for key, value in self._options.items():

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in getOrCreate(cls, conf)
    365         with SparkContext._lock:
    366             if SparkContext._active_spark_context is None:
--> 367                 SparkContext(conf=conf or SparkConf())
    368             return SparkContext._active_spark_context
    369 

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in __init__(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer, conf, gateway, jsc, profiler_cls)
    134         try:
    135             self._do_init(master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
--> 136                           conf, jsc, profiler_cls)
    137         except:
    138             # If an error occurs, clean up in order to allow future SparkContext creation:

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer, conf, jsc, profiler_cls)
    196 
    197         # Create the Java SparkContext through Py4J
--> 198         self._jsc = jsc or self._initialize_context(self._conf._jconf)
    199         # Reset the SparkConf to the one actually used by the SparkContext in JVM.
    200         self._conf = SparkConf(_jconf=self._jsc.sc().conf())

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in _initialize_context(self, jconf)
    304         Initialize SparkContext in function to allow subclass specific initialization
    305         """
--> 306         return self._jvm.JavaSparkContext(jconf)
    307 
    308     @classmethod

~/anaconda3/lib/python3.7/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1523         answer = self._gateway_client.send_command(command)
   1524         return_value = get_return_value(
-> 1525             answer, self._gateway_client, None, self._fqn)
   1526 
   1527         for temp_arg in temp_args:

~/anaconda3/lib/python3.7/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: java.net.BindException: Can't assign requested address: Service 'sparkDriver' failed after 16 retries (on a random free port)! Consider explicitly setting the appropriate binding address for the service 'sparkDriver' (for example spark.driver.bindAddress for SparkDriver) to the correct binding address.
    at java.base/sun.nio.ch.Net.bind0(Native Method)
    at java.base/sun.nio.ch.Net.bind(Net.java:461)
    at java.base/sun.nio.ch.Net.bind(Net.java:453)
    at java.base/sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:227)
    at io.netty.channel.socket.nio.NioServerSocketChannel.doBind(NioServerSocketChannel.java:128)
    at io.netty.channel.AbstractChannel$AbstractUnsafe.bind(AbstractChannel.java:558)
    at io.netty.channel.DefaultChannelPipeline$HeadContext.bind(DefaultChannelPipeline.java:1283)
    at io.netty.channel.AbstractChannelHandlerContext.invokeBind(AbstractChannelHandlerContext.java:501)
    at io.netty.channel.AbstractChannelHandlerContext.bind(AbstractChannelHandlerContext.java:486)
    at io.netty.channel.DefaultChannelPipeline.bind(DefaultChannelPipeline.java:989)
    at io.netty.channel.AbstractChannel.bind(AbstractChannel.java:254)
    at io.netty.bootstrap.AbstractBootstrap$2.run(AbstractBootstrap.java:364)
    at io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:163)
    at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:403)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:463)
    at io.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)
    at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)
    at java.base/java.lang.Thread.run(Thread.java:834)

Answer 1

Seems like you have too many running SparkSessions. In the default configuration you can only have 16, because there are 16 retries to get a port for Spark's job overview page.

This could be because you work on a busy cluster with many users running jobs, or, e.g., because you have a lot of Jupyter notebooks with SparkSessions running.

Depending on which resource manager you use there are different ways to check how many SparkSessions are currently open.

To circumvent the problem you can also increase the number of retries to find an unused port Spark makes when creating the SparkSession. For this you have to set the config parameter spark.port.maxRetries to a larger value (see also here: https://spark.apache.org/docs/latest/configuration.html):

spark = SparkSession.builder.config('spark.port.maxRetries', 100).getOrCreate()