How to configure environment variables in Linux for Spark and Java?
Proper configuration of environment variables is essential to ensure that Spark and Java work properly in a Linux environment. Start by editing the RC base configuration file, which contains the necessary settings for your user session. It is important to add the specific paths appropriately:
-
Java path: add a comment to identify the lines containing the Java settings.
#export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64export PATH=$PATH:$JAVA_HOME/bin
JAVA_HOME
: Specifies the folder where Java is installed.
-
Settings for Spark: Set the SPARK_HOME
variable in a similar way, pointing to the folder where you unzipped Spark.
#export SPARK_HOME=/home/spark/spark/sparkexport PATH=$PATH:$SPARK_HOME/bin
-
Variables for Python and PySpark: Essential for using Spark with Python.
export PYTHONPATH=$SPARK_HOME/python:$PYTHONPATHexport PYSPARK_PYTHON=python3
Don't forget to save the changes and reload the configuration file with the source
command. This ensures that the new configurations are active without the need to reboot the system.
How to run a Spark process using command line?
Running Spark processes via command line, although effective, can be somewhat complicated due to the amount of information it generates. Here's how to do it in a basic way:
-
Setting up the environment: make sure you have everything configured correctly and navigate to the folder where Spark resides.
-
Using PySpark: To run live code similar to logging into the Python interpreter, use PySpark
.
-
Using spark-submit
for .py
scripts: If you want to run a Python file with Spark, the spark-submit
command is essential.
During execution, you will receive messages in the terminal indicating the progress and success of the operations performed by Spark. However, these logs may hide the results you are looking for, so it is important to review the output carefully.
What challenges does using the command line present and how can they be mitigated?
The command line is powerful, but it can lead to a sea of logs and messages, making it difficult to distinguish the relevant results. This method is ideal when:
- The code is fully tested.
- You need to run it in production environments.
- You want to perform demonstrations with a subset of data.
Although functional, for educational purposes and in situations where you need to understand the state of processes, it is advisable to look for alternatives. One option is to integrate Anaconda, which facilitates access to results and interaction with the Spark environment in a more user-friendly and didactic way.
For those interested in running and learning about Spark more comfortably, we recommend the next session that will explore how to configure Anaconda and make the process more accessible. And remember, there is always a community ready to help: leave your questions or comments for support, keep learning and exploring the world of Spark!
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