Could someone provide some insight/examples on the usage of this API?
https://spark.apache.org/docs/latest/api/scala/org/apache/spark/sql/jdbc/JdbcConnectionProvider.html
Why is it needed since this is an abstract class and there isn't any
concrete implementation of it? Thanks a lot in
Thanks Sean
Andy
From: Sean Owen
Date: Wednesday, January 5, 2022 at 3:38 PM
To: Andrew Davidson , Nicholas Gustafson
Cc: "user @spark"
Subject: Re: Newbie pyspark memory mgmt question
There is no memory leak, no. You can .cache() or .persist() DataFrames, and
that can use memory until
There is no memory leak, no. You can .cache() or .persist() DataFrames, and
that can use memory until you .unpersist(), but you're not doing that and
they are garbage collected anyway.
Hard to say what's running out of memory without knowing more about your
data size, partitions, cluster size, etc
Hi
I am running into OOM problems. My cluster should be much bigger than I need. I
wonder if it has to do with the way I am writing my code. Below are three style
cases. I wonder if they cause memory to be leaked?
Case 1 :
df1 = spark.read.load( cvs file)
df1 = df1.someTransform()
df1 =
Just thought I'd do a quick bump and add the dev mailing list - in case
there is some insight there
Feels like this should be categorized as a bug for spark 3.2.0
On Wed, Dec 29, 2021 at 5:25 PM Abdeali Kothari
wrote:
> Hi,
> I am using pyspark for some projects. And one of the things we are
hm,
If I understand correctly
from pyspark.sql import SparkSession
from pyspark import SparkContext
from pyspark.sql import SQLContext, HiveContext
import sys
def spark_session(appName):
return SparkSession.builder \
.appName(appName) \
.enableHiveSupport() \
Did you install and configure the proper Spark kernel (SparkMagic) on
your Jupyter Lab or Hub? See
https://github.com/jupyter/jupyter/wiki/Jupyter-kernels for more info...
On 1/5/22 4:01 AM, 流年以东” wrote:
In the process of using pyspark,there is no spark context when opening
jupyter and
