This is the snippet:
from pyspark import SparkContext
from pyspark.sql.session import SparkSession
sc = SparkContext(
On spark-shell console, enter the variable name and see the data type. As an alternative, you can tab twice after variable named. and it will show necessary function which could be applied. Example of a DataFrame object.
res23: org.apache.spark.sql.DataFrame = [order_id: string, book_name: string ... 1 more field]
This is an indicator of a Spark version mismatch. Before Spark 2.3 show
method took only two arguments:
def show(self, n=20, truncate=True):
since 2.3 it takes three arguments:
def show(self, n=20, truncate=True, vertical=False):
In your case Python client seems to invoke the latter one, while the JVM backend uses the older version.
Since SparkContext
initialization undergone significant changes in 2.4, which would cause failure on SparkContext.__init__
, you're likely using:
You can confirm that by checking versions directly from your session, Python:
sc.version
vs. JVM:
sc._jsc.version()
Problems like this, are usually a result of misconfigured PYTHONPATH
(either directly, or by using pip
installed PySpark
on top per-existing Spark binaries) or SPARK_HOME
.