Para executar um aplicativo usando um contexto de ignição, primeiro é necessário executar um job de Slurm que inicie um master e alguns workers. Há algumas coisas que você terá que tomar cuidado ao usar o Slurm:
- não inicie o Spark como um daemon
- faz com que os funcionários do Spark usem apenas os núcleos e a memória solicitados para o trabalho do Slurm
- para executar o mestre e o trabalhador no mesmo trabalho que você precisará ramificar em algum lugar do seu script
Estou trabalhando com os binários do Linux instalados em $HOME/spark-1.5.2-bin-hadoop2.6/ . Lembre-se de substituir <username> e <shared folder> por alguns valores válidos no script.
#!/bin/bash
#start_spark_slurm.sh
#SBATCH --nodes=3
# ntasks per node MUST be one, because multiple slaves per work doesn't
# work well with slurm + spark in this script (they would need increasing
# ports among other things)
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=4
#SBATCH --mem-per-cpu=500
# Beware! $HOME will not be expanded and invalid paths will result Slurm jobs
# hanging indefinitely with status CG (completing) when calling scancel!
#SBATCH --output="/home/<username>/spark/logs/%j.out"
#SBATCH --error="/home/<username>/spark/logs/%j.err"
#SBATCH --time=01:00:00
# This section will be run when started by sbatch
if [ "$1" != 'srunning' ]; then
this=$0
# I experienced problems with some nodes not finding the script:
# slurmstepd: execve(): /var/spool/slurm/job123/slurm_script:
# No such file or directory
# that's why this script is being copied to a shared location to which
# all nodes have access to:
script=/<shared folder>/${SLURM_JOBID}_$(basename "$0")
cp "$this" "$script"
# This might not be necessary on all clusters
module load scala/2.10.4 java/jdk1.7.0_25 cuda/7.0.28
export sparkLogs=$HOME/spark/logs
export sparkTmp=$HOME/spark/tmp
mkdir -p "$sparkLogs" "$sparkTmp"
export SPARK_ROOT=$HOME/spark-1.5.2-bin-hadoop2.6/
export SPARK_WORKER_DIR=$sparkLogs
export SPARK_LOCAL_DIRS=$sparkLogs
export SPARK_MASTER_PORT=7077
export SPARK_MASTER_WEBUI_PORT=8080
export SPARK_WORKER_CORES=$SLURM_CPUS_PER_TASK
export SPARK_DAEMON_MEMORY=$(( $SLURM_MEM_PER_CPU * $SLURM_CPUS_PER_TASK / 2 ))m
export SPARK_MEM=$SPARK_DAEMON_MEMORY
srun "$script" 'srunning'
# If run by srun, then decide by $SLURM_PROCID whether we are master or worker
else
source "$SPARK_ROOT/sbin/spark-config.sh"
source "$SPARK_PREFIX/bin/load-spark-env.sh"
if [ $SLURM_PROCID -eq 0 ]; then
export SPARK_MASTER_IP=$(hostname)
MASTER_NODE=$(scontrol show hostname $SLURM_NODELIST | head -n 1)
# The saved IP address + port is necessary alter for submitting jobs
echo "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" > "$sparkLogs/${SLURM_JOBID}_spark_master"
"$SPARK_ROOT/bin/spark-class" org.apache.spark.deploy.master.Master \
--ip $SPARK_MASTER_IP \
--port $SPARK_MASTER_PORT \
--webui-port $SPARK_MASTER_WEBUI_PORT
else
# $(scontrol show hostname) is used to convert e.g. host20[39-40]
# to host2039 this step assumes that SLURM_PROCID=0 corresponds to
# the first node in SLURM_NODELIST !
MASTER_NODE=spark://$(scontrol show hostname $SLURM_NODELIST | head -n 1):7077
"$SPARK_ROOT/bin/spark-class" org.apache.spark.deploy.worker.Worker $MASTER_NODE
fi
fi
Agora para iniciar o trabalho sbatch e depois disso example.jar :
mkdir -p $HOME/spark/logs
jobid=$(sbatch ./start_spark_slurm.sh)
jobid=${jobid##Submitted batch job }
MASTER_WEB_UI=''
while [ -z "$MASTER_WEB_UI" ]; do
sleep 1s
if [ -f $HOME/spark/logs/$jobid.err ]; then
MASTER_WEB_UI=$(sed -nE 's|.*Started MasterWebUI at (http://[0-9.:]*)||p' $HOME/spark/logs/$jobid.err)
fi
done
MASTER_ADDRESS=$(cat $HOME/spark/logs/${jobid}_spark_master)
$HOME/spark-1.5.2-bin-hadoop2.6/bin/spark-submit --master $MASTER_ADDRESS example.jar
firefox $MASTER_WEB_UI