How to set Apache Spark Executor memory

How can I increase the memory available for Apache spark executor nodes?

How can I increase the memory available for Apache spark executor nodes?

I have a 2 GB file that is suitable to loading in to Apache Spark. I am running apache spark for the moment on 1 machine, so the driver and executor are on the same machine. The machine has 8 GB of memory.

When I try count the lines of the file after setting the file to be cached in memory I get these errors:

2014-10-25 22:25:12 WARN  CacheManager:71 - Not enough space to cache partition rdd_1_1 in memory! Free memory is 278099801 bytes.

I looked at the documentation here and set spark.executor.memory to 4g in $SPARK_HOME/conf/spark-defaults.conf

The UI shows this variable is set in the Spark Environment. You can find screenshot here

However when I go to the Executor tab the memory limit for my single Executor is still set to 265.4 MB. I also still get the same error.

I tried various things mentioned here but I still get the error and don't have a clear idea where I should change the setting.

I am running my code interactively from the spark-shell

Why learn Apache Spark in 2020?

Why learn Apache Spark in 2020?

This video on "Apache Spark in 2020" will provide you with the detailed and comprehensive knowledge about the current IT Job trends based on Apache Spark and why learn Apache Spark in 2020? What is new in Apache Spark? What is Apache Spark? Top 5 Reasons to learn Spark. Salary trends of Spark Developer. Components of Spark. Skills required by Spark Developer. Companies using Apache Spark

This Edureka video on "Apache Spark in 2020" will provide you with the detailed and comprehensive knowledge about the current IT Job trends based on Apache Spark and why is it important to learn it in the year 2020. This video will cover the following topics:

  • Why do we hear the word Spark in 2020?
  • What is Apache Spark
  • Top 5 Reasons to learn Spark
  • Salary trends of Spark Developer
  • Components of Spark
  • Skills required by Spark Developer
  • Companies using Apache Spark

Apache Spark Tutorial - Apache Spark Full Course - Learn Apache Spark

Apache Spark Tutorial - Apache Spark Full Course - Learn Apache Spark

This video will help you understand and learn Apache Spark in detail. This Spark tutorial is ideal for both beginners as well as professionals who want to master Apache Spark concepts.

This video will help you understand and learn Apache Spark in detail. This Spark tutorial is ideal for both beginners as well as professionals who want to master Apache Spark concepts. Below are the topics covered in this Spark tutorial for beginners:

2:44 Introduction to Apache Spark

3:49 What is Spark?

5:34 Spark Eco-System

7:44 Why RDD?

16:44 RDD Operations

18:59 Yahoo Use-Case

21:09 Apache Spark Architecture

24:24 RDD

26:59 Spark Architecture

31:09 Demo

39:54 Spark RDD

41:09 Spark Applications

41:59 Need For RDDs

43:34 What are RDDs?

44:24 Sources of RDDs

45:04 Features of RDDs

46:39 Creation of RDDs

50:19 Operations Performed On RDDs

50:49 Narrow Transformations

51:04 Wide Transformations

51:29 Actions

51:44 RDDs Using Spark Pokemon Use-Case

1:05:19 Spark DataFrame

1:06:54 What is a DataFrame?

1:08:24 Why Do We Need Dataframes?

1:09:54 Features of DataFrames

1:11:09 Sources Of DataFrames

1:11:34 Creation Of DataFrame

1:24:44 Spark SQL

1:25:14 Why Spark SQL?

1:27:09 Spark SQL Advantages Over Hive

1:31:54 Spark SQL Success Story

1:33:24 Spark SQL Features

1:37:15 Spark SQL Architecture

1:39:40 Spark SQL Libraries

1:42:15 Querying Using Spark SQL

1:45:50 Adding Schema To RDDs

1:55:05 Hive Tables

1:57:50 Use Case: Stock Market Analysis with Spark SQL

2:16:50 Spark Streaming

2:18:10 What is Streaming?

2:25:46 Spark Streaming Overview

2:27:56 Spark Streaming workflow

2:31:21 Streaming Fundamentals

2:33:36 DStream

2:38:56 Input DStreams

2:40:11 Transformations on DStreams

2:43:06 DStreams Window

2:47:11 Caching/Persistence

2:48:11 Accumulators

2:49:06 Broadcast Variables

2:49:56 Checkpoints

2:51:11 Use-Case Twitter Sentiment Analysis

3:00:26 Spark MLlib

3:00:31 MLlib Techniques

3:01:46 Demo

3:11:51 Use Case: Earthquake Detection Using Spark

3:24:01 Visualizing Result

3:25:11 Spark GraphX

3:26:01 Basics of Graph

3:27:56 Types of Graph

3:38:56 GraphX

3:40:42 Property Graph

3:48:37 Creating & Transforming Property Graph

3:56:17 Graph Builder

4:02:22 Vertex RDD

4:07:07 Edge RDD

4:11:37 Graph Operators

4:24:37 GraphX Demo

4:34:24 Graph Algorithms

4:34:40 PageRank

4:38:29 Connected Components

4:40:39 Triangle Counting

4:44:09 Spark GraphX Demo

4;57:54 MapReduce vs Spark

5:13:03 Kafka with Spark Streaming

5:23:38 Messaging System

5:21:15 Kafka Components

2:23:45 Kafka Cluster

5:24:15 Demo

5:48:56 Kafka Spark Streaming Demo

6:17:16 PySpark Tutorial

6:21:26 PySpark Installation

6:47:06 Spark Interview Questions

Hadoop vs Spark | Hadoop MapReduce vs Spark

Hadoop vs Spark | Hadoop MapReduce vs Spark

🔥Intellipaat Big Data Hadoop Course: https://intellipaat.com/big-data-hadoop-training/ In this video on Hadoop vs Spark you will understand about the top Big...

In this video on Hadoop vs Spark you will understand about the top Big Data solutions used in the IT industry, and which one should you use for better performance. So in this Hadoop MapReduce vs Spark comparison some important parameters have been taken into consideration to tell you the difference between Hadoop and Spark also which one is preferred over the other in certain aspects in detail.

Why Hadoop is important

Big data hadoop is one of the best technological advances that is finding increased applications for big data and in a lot of industry domains. Data is being generated hugely in each and every industry domain and to process and distribute effectively hadoop is being deployed everywhere and in every industry.