May 2017
Intermediate to advanced
294 pages
7h 33m
English
Content preview from Apache Spark 2.x Cookbook
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How it works...
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 ...
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