Description Within PySpark, even though a DataFrame can be saved to S3/S3A (not AWS, but a S3-compliant storage), its data are saved unencrypted despite that setting fs.s3a.encryption.algorithm (SSE-C) and fs.s3a.encryption.key are used. Reproducibility Generate the key as followed: encKey=$(openssl rand…
As far as I know, Spark executors handle many tasks at the same time to guarantee processing data parallelly.Here comes the question. When connecting to external data storage,say mysql,how many tasks are there to finishi this job?In other words,are multiple…
When running my job, I am getting the following exception: Exception in User Class: org.apache.spark.SparkException : Job aborted due to stage failure: Task 32 in stage 2.0 failed 4 times, most recent failure: Lost task 32.3 in stage 2.0 (TID…
I have a pyspark job that is distributed in multiple code files in this structure: flexible_clendar - Cache - redis_main.py - Helpers - helpers.py - Spark - spark_main.py - main.py In the 'main.py' I'm using the functions from 'helpers.py', 'redis_main.py',…
When I am trying to create a sparksession I get this error: spark = SparkSession.builder.appName("Practice").getOrCreate() py4j.protocol.Py4JError: org.apache.spark.api.python.PythonUtils.getPythonAuthSocketTimeout does not exist in the JVM This is my code: import pyspark from pyspark.sql import SparkSession spark = SparkSession.builder.appName("Practice").getOrCreate() What am I doing…
Could someone please help me understand why we need to use PySpark or SprakSQL etc if the source and target of my data is the same DB? For example, lets say I need to load data to table X in…
I am using a Ubuntu 20.04.4 server with 2xNVidia A100 GPUs. Spark (3.3.0) works fine normally, but when I try to use GPUs through RAPIDS, it just keeps waiting without loading data. I tried loading data as CSV and parquet…
I'm running a Spark application in AWS EMR. The code is like this: with SparkSession.builder.appName(f"Spark App").getOrCreate() as spark: dataframe = spark.read.format('jdbc').options( ... ).load() print("Log A") max_date_result = dataframe.agg(max_(date_format('date', 'yyyy-MM-dd')).alias('max_date')).collect()[0] print("Log B") This application always gets stuck for a long time…
I have an azure storage account (Storage gen2) and need to copy files like config.yaml, text files, gz files to reference them inside my code. I have tried the steps listed in https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/synapse-file-mount-api, but what this does is to mount…
EDIT 1: Tried with network_mode: host on the worker nodes, same result I am setting up a multi-node multi-docker cluster of spark, in standalone configuration: 1 node with 1 spark master and X workers docker-compose for master+worker node: version: '2'…