The spark tool is a convenience wrapper for running spark clusters
on top of Slurm. Note that this script is still experimental. In addition it
will require modification to work on different clusters:
- change the settings in the 'Configuration' section
- change the sbatch template script at the end of 'spark' to reflect the nodes/partition/memory/cpu configuration of the target slurm cluster.
The follwing was run in an sinteractive session so that the spark shell (or pyspark in my case) didn't itself run on the login node:
$ module load spark/2.1.1
$ spark
NAME
spark - administer spark clusters on compute nodes
SYNOPSIS
spark cmd [options]
COMMANDS
help - show this help or help for specific cmd
start - start a new spark cluster
list - list clusters
show - show cluster details
stop - shut down a cluster
clean - clean up the directory where cluster
info is stored (/data/wresch/.spark-on-biowulf)
DESCRIPTION
This tool is used to start, stop, monitor, and
use spark clusters running on compute nodes.
Start a cluster with 3 nodes and a max runtime of 120 minutes:
$ spark start -t 120 3
INFO: Submitted job for cluster RjHxpj
List your currently active spark clusters
$ spark list
Cluster id Slurm jobid state
---------- ------------ --------------------
RjHxpj 43451296 RUNNING
With more detail
$ spark list -d
Cluster id Slurm jobid state
---------- ------------ --------------------
RjHxpj 43451296 RUNNING
nodes: 2
max_time: 120
spark: 2.1.1
job_id: 43451296
START: 2017-06-19 13:16:03
nodelist: cn[0443-0444]
master: spark://cn0443:7077
master_webui: http://cn0443:8080
webui tunnel: ssh -L 55555:cn0443:8080 -N x.x.x
As you can see there is a ssh command that will set up a tunnel between your local machine, and the spark cluster if you want to see the spark master's web ui.
Here is how you would connect to the cluster with pyspark using one executor per node:
$ pyspark --master spark://cn0443:7077 --executor-memory=40g
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.1.1
/_/
Using Python version 2.7.13 (default, Dec 20 2016 23:09:15)
SparkSession available as 'spark'.
>>> txt = spark.sparkContext.textFile("sqlite3.c")
>>> txt.count()
202918
>>> defines = txt.filter(lambda l: l.startswith('#define'))
>>> defines.count()
2492
>>> defines.first()
u'#define SQLITE_CORE 1'
>>> txt.map(lambda l: len(l)).reduce(lambda a, b: a if (a>b) else b)
260
>>> Ctrl-D
Or if you prefere a jupyter notebook you can create a wrapper script like the following:
#! /bin/bash
export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS="notebook --port=3939 --no-browser"
exec pyspark --executor-memory=40g "$@"
spark-submit the pi example with multiple executors per node
$ spark-submit \
--driver-memory=3g \
--master "spark://cn0443:7077" \
--deploy-mode client \
--executor-cores=2 \
--executor-memory=3g \
./pi.py
[...snip...]
/pi.py:36, took 562.603120 s
PI=3.141584232000
Shut down the cluster
$ spark stop Rj
INFO: Sending cluster RjHxpj/43451296 shutdown signal
INFO: May take a couple of minutes to shut down cleanly
$ spark list
Cluster id Slurm jobid state
---------- ------------ --------------------
Include inactive clusters in the output
$ spark list -di
Cluster id Slurm jobid state
---------- ------------ --------------------
RjHxpj 43451296 COMPLETED
nodes: 2
max_time: 30
spark: 2.1.1
job_id: 43451296
START: 2017-06-19 13:16:03
nodelist: cn[0443-0444]
master: spark://cn0443:7077
master_webui: http://cn0443:8080
webui tunnel: ssh -L 55555:cn0443:8080 -N biowulf.nih.gov
DONE: 2017-06-19 13:26:31
Clean up the spark directory
$ spark clean
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent