Difference between revisions of "Spark on farm"
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Line 14: | Line 14: | ||
cd ${TMPDIR} | cd ${TMPDIR} | ||
− | + | ||
+ | echo "spark.executor.extraJavaOptions -Djava.io.tmpdir=${TMPDIR}" > spark-defaults.conf | ||
+ | |||
PYSPARK_PYTHON=<PATH_TO_VIRTUALENV_BIN_PYTHON> \ | PYSPARK_PYTHON=<PATH_TO_VIRTUALENV_BIN_PYTHON> \ | ||
− | <SPARK_HOME>/bin/spark-class org.apache.spark.deploy.worker.Worker ${SPARK_MASTER} -c ${NUM_CORES} -m ${MEMORY} -d ${TMPDIR} | + | <SPARK_HOME>/bin/spark-class \ |
+ | org.apache.spark.deploy.worker.Worker ${SPARK_MASTER} \ | ||
+ | -c ${NUM_CORES} -m ${MEMORY} -d ${TMPDIR} --properties-file spark-defaults.conf | ||
4) Submit the previous script to the farm | 4) Submit the previous script to the farm |
Revision as of 15:48, 14 February 2019
In order to launch a Spark cluster, follow these steps:
1) Deploy a Spark installation on your ~
2) On a UI, start a spark master using this command and write down the master url provided
${SPARK_HOME}/sbin/start-master.sh -h $(hostname)
3) Create a submit script like this one, substituting <VARIABLES>
SPARK_MASTER=<MASTER_URL> NUM_CORES=1 MEMORY=4g cd ${TMPDIR} echo "spark.executor.extraJavaOptions -Djava.io.tmpdir=${TMPDIR}" > spark-defaults.conf PYSPARK_PYTHON=<PATH_TO_VIRTUALENV_BIN_PYTHON> \ <SPARK_HOME>/bin/spark-class \ org.apache.spark.deploy.worker.Worker ${SPARK_MASTER} \ -c ${NUM_CORES} -m ${MEMORY} -d ${TMPDIR} --properties-file spark-defaults.conf
4) Submit the previous script to the farm
qsub -q <QUEUE> -t 1-<NUM_WORKERS> <SUBMIT_SCRIPT>
5) After a few minutes, workers should start registering to the master.
6) Create your SparkContext connecting it to the <MASTER_URL>
spark = SparkSession.builder.master(<MASTER_URL>).getOrCreate() sc = spark.sparkContext
7) Enjoy!