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spark:running-spark-on-single-machine-or-on-cluster [2014/11/07 16:09]
straka created |
spark:running-spark-on-single-machine-or-on-cluster [2023/11/07 12:48] (current)
straka [Starting Spark Cluster] |
| ===== Running Spark on Single Machine ===== | ===== Running Spark on Single Machine ===== |
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| Spark computations can be started both on desktop machines and on cluster machines, either by specifying ''MASTER'' to one of ''local'' modes, or by not speficying MASTER at all (''local[*]'' is used then). | Spark computations can be started both on desktop machines and on cluster machines, either by specifying ''MASTER'' to one of ''local'' modes, or by not specifying MASTER at all (''local[*]'' is used then). |
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| Note that when you use ''qrsh'' or ''qsub'', your job can usually use one core, so you should specify ''MASTER=local''. If you do not, Spark will use all cores on the machine, even though SGE gave you onle one. | Note that when you use ''sbatch'' or ''srun'' to run a cluster locally, your job is by default expected to use just a single core, so you should specify ''MASTER=local''. If you do not, Spark will use all cores on the machine, even though Slurm gave you only one. |
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| ===== Starting Spark Cluster ===== | ===== Starting Spark Cluster ===== |
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| | Spark cluster can be started using Slurm. The cluster is user-specific, but it can be used for several consecutive Spark computations. |
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| | The Spark cluster can be started using one of the following two commands: |
| | * ''spark-sbatch'': start a Spark cluster via an ''sbatch'' <file>spark-sbatch [sbatch args] workers memory_per_workerG[:python_memoryG] command [arguments...]</file> |
| | * ''spark-srun'': start a Spark cluster via an ''srun'' <file>spark-srun [salloc args] workers memory_per_workerG[:python_memoryG] [command arguments...]</file> |
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| | Both ''spark-sbatch'' and ''spark-srun'' commands start a Spark cluster with the specified number of workers, each with the given amount of memory. Then they set ''MASTER'' and ''SPARK_ADDRESS'' to the address of the Spark master and ''SPARK_WEBUI'' to the URL of the master web interface. Both these values are also written on standard output, and the ''SPARK_WEBUI'' is added to the Slurm job Comment. Finally, the specified command is started; when ''spark-srun'' is used, the command may be empty, in which case ''bash'' is opened. |
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| | ==== Memory Specification ==== |
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| | TL;DR: Good default is ''2G''. |
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| | The memory for each worker is specified using the following format: <file>spark_memory_per_workerG[:memory_per_Python_processG]</file> |
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| | The Spark memory limits the Java heap, and half of it is reserved for memory storage of cached RDDs. The second value sets a memory limit of every Python process and is by default set to ''2G''. |
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| | ==== Examples ==== |
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| | Start Spark cluster with 10 workers 2GB RAM each and then run interactive shell. The cluster stops after the shell is exited. |
| | <file>spark-srun 10 2G</file> |
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| | Start Spark cluster with 20 workers 4GB RAM each in the ''cpu-ms'' partition, and run ''screen'' in it, so that several computations can be performed using this cluster. The cluster has to be stopped manually (either by quitting the scree or calling ''scancel''). |
| | <file>spark-sbatch -p cpu-ms 20 4G screen -D -m</file> |
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