In standalone mode, Spark follows the master-slave architecture, very much like Hadoop, MapReduce, and YARN. The compute master daemon is called Spark master and runs on one master node. Spark master can be made highly available using ZooKeeper. You can also add more standby masters on the fly if needed.

The compute slave daemon is called a worker, and it exists on each slave node. The worker daemon does the following:

• Reports the availability of the compute resources on a slave node, such as the number of cores, memory, and others, to the Spark master
• Spawns the executor when asked to do so by the Spark master
• Restarts the executor if it dies

There is, at most, one executor per application, per slave machine.

Both Spark master and the worker are very lightweight. Typically, memory allocation between 500 MB to 1 GB is sufficient. This value can be set in conf/spark-env.sh by setting the SPARK_DAEMON_MEMORY parameter. For example, the following configuration will set the memory to 1 gigabits for both the master and worker daemon. Make sure you have sudo as the super user before running it:

    $ echo "export SPARK_DAEMON_MEMORY=1g" >> /opt/infoobjects/spark/conf/spark-env.sh

By default, each slave node has one worker instance running on it. Sometimes, you may have a few machines that are more powerful than others. In that case, you can spawn more than one worker on that machine with the following configuration (only on those machines):

    $ echo "export SPARK_WORKER_INSTANCES=2" >> /opt/infoobjects/spark/conf/spark-env.sh

The Spark worker, by default, uses all the cores on the slave machine for its executors. If you would like to limit the number of cores the worker could use, you can set it to the number of your choice (for example, 12), using the following configuration:

    $ echo "export SPARK_WORKER_CORES=12" >> /opt/infoobjects/spark/conf/spark-env.sh

The Spark worker, by default, uses all of the available RAM (1 GB for executors). Note that you cannot allocate how much memory each specific executor will use (you can control this from the driver configuration). To assign another value to the total memory (for example, 24 GB) to be used by all the executors combined, execute the following setting:

    $ echo "export SPARK_WORKER_MEMORY=24g" >> /opt/infoobjects/spark/conf/spark-env.sh

There are some settings you can do at the driver level:

• To specify the maximum number of CPU cores to be used by a given application across the cluster, you can set the spark.cores.max configuration in Spark submit  or  Spark shellas follows:

        $ spark-submit --conf spark.cores.max=12

• To specify the amount of memory that each executor should be allocated (the minimum recommendation is 8 GB), you can set the spark.executor.memory configuration in Spark submit or Spark shell as follows:

        $ spark-submit --conf spark.executor.memory=8g

The following diagram depicts the high-level architecture of a Spark cluster: