Description

Spark is Hadoop MapReduce done in memory


Software Category: devel

For detailed information, visit the Spark website.

Available Versions

To find the available versions and learn how to load them, run:

module spider spark

The output of the command shows the available Spark module versions.

For detailed information about a particular Spark module, including how to load the module, run the module spider command with the module’s full version label. For example:

module spider spark/3.4.1

Using Spark interactively

There are three ways to use Spark interactively:

1. Shell prompt
2. Open OnDemand PySpark
3. Open OnDemand Desktop

If you need the Web UI you must use the third method (OOD Desktop).

Shell prompt

First submit an ijob.

Scala/PySpark

To start up a Scala or PySpark shell prompt, run spark-shell or pyspark. For example:

$ spark-shell
...
Spark context Web UI available at http://udc-xxxx-xx:4040
Spark context available as 'sc' (master = local[*], app id = local-1633023285536).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 3.1.2
      /_/

Using Scala version 2.12.10 (OpenJDK 64-Bit Server VM, Java 11.0.2)
Type in expressions to have them evaluated.
Type :help for more information.

scala>

R

To start an R prompt, you must load R first. Then run sparkR. If the R version is different from 4.1.0, you will see a warning message:

package ‘SparkR’ was built under R version 4.1.0

We recommend loading the closest available version.

Open OnDemand PySpark

Python users can run Spark in a JupyterLab interface via the PySpark Interactive App on Open OnDemand.

Open OnDemand Desktop

Spark provides a user interface (UI) for you to monitor your Spark job. If you intend to use the Web UI, you must request a Desktop session through Open OnDemand.

The URL is displayed upon launching Spark and is of the form http://udc-xxxx-xx:4040 where udc-xxxx-xx is the hostname of the compute node. You can either right-click on the link and select “Open Link,” or enter localhost:4040 in the browser.

Jupyter notebook/lab

You can redirect pyspark to open a Jupyter notebook/lab as follows. The jupyter command is provided by the jupyterlab module.

Set two environment variables:

export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS=lab

If you’d prefer a notebook session, replace lab with notebook.

Navigate to your working directory and run:

pyspark

This will start up Jupyter inside a browser automatically. Use the “Python 3” kernel.

The example below estimates the value of pi in a PySpark session running on 16 cores, with the JupyterLab window on the left and the Spark Web UI event timeline on the right. Note that the SparkContext object sc is initialized automatically.

Slurm Script Templates for Batch Jobs

Local mode on a single node

#!/bin/bash
#SBATCH -p standard     # partition
#SBATCH -A myaccount    # your allocation
#SBATCH -N 1            # number of nodes
#SBATCH -c 10           # number of cores per node
#SBATCH -t 10:00:00     # time

module purge
module load spark

spark-submit script.py

You must initialize SparkContext explicitly in your script, e.g.:

from pyspark import SparkContext
sc = SparkContext("local[*]")

The Spark log is written to slurm-<JOB_ID>.out. After the job is finished, use the seff <JOB_ID> command to verify that the cores are used effectively:

$ seff 1232109
...
Cores per node: 10
CPU Utilized: 01:17:16
CPU Efficiency: 82.20% of 01:34:00 core-walltime
...

If the CPU efficiency is much lower, please consider using fewer cores for your future jobs.

Standalone cluster mode using multiple nodes

We gratefully acknowledge Advanced Research Computing at the University of Michigan for the spark-start script. The following is implemented for module versions 4.0.0 and above.

Before requesting multiple nodes, please make sure that your job can use a full standard node effectively. When you request N nodes in the standalone cluster mode, one node is set aside as the master node and the remaining N-1 nodes are worker nodes. Thus, running on 2 nodes will have the same effect as running on 1 node.

#!/bin/bash
#SBATCH -A myallocation  # replace with your actual allocation
#SBATCH -p parallel      # partition
#SBATCH -N 3             # number of nodes
#SBATCH -c 96            # cores per node
#SBATCH --mem=100G       # memory per node
#SBATCH -t 10:00:00      # time

SPARK_VERSION=4.0.0
#---------------------------
# do not modify this section
TEC=$(((SLURM_NNODES-1)*SLURM_CPUS_PER_TASK))
EM=$((SLURM_MEM_PER_NODE/1024))
module purge
module load spark/$SPARK_VERSION
spark-start
source ${HOME}/.spark-local/${SLURM_JOB_ID}/spark/conf/spark-env.sh
#---------------------------

spark-submit --master $SPARK_MASTER_URL --executor-memory ${EM}G --total-executor-cores $TEC pi.py

It is recommended that all cores (96 per parallel node) be requested. Advanced users may make a copy of $SPARK_HOME/bin/spark-start and customize it before submission; specify the path to your local copy in the Slurm script.

Temporary files are created in ~/.spark-local/<JOB_ID> and may be safely removed after the job completes.