增加 Spark 工人核心

Question

I have installed Spark on master and 2 workers. The original core number per worker is 8. When I start the master, the workers are work properly without any problem, but the problem is in Spark GUI each worker has only 2 cores assigned.

Kindly, how can I increase the number of the cores in which each worker works with 8 cores?

Answer 1

The setting which controls cores per executor is spark.executor.cores . See doc . It can be set either via spark-submit cmd argument or in spark-defaults.conf . The file is usually located in /etc/spark/conf (ymmv). YOu can search for the conf file with find / -type f -name spark-defaults.conf

spark.executor.cores 8

However the setting does not guarantee that each executor will always get all the available cores. This depends on your workload.

If you schedule tasks on a dataframe or rdd, spark will run a parallel task for each partition of the dataframe. A task will be scheduled to an executor (separate jvm) and the executor can run multiple tasks in parallel in jvm threads on each core.

Also an exeucutor will not necessarily run on a separate worker. If there is enough memory, 2 executors can share a worker node.

In order to use all the cores the setup in your case could look as follows:

given you have 10 gig of memory on each node

spark.default.parallelism 14
spark.executor.instances 2
spark.executor.cores 7
spark.executor.memory 9g

Setting memory to 9g will make sure, each executor is assigned to a separate node. Each executor will have 7 cores available. And each dataframe operation will be scheduled to 14 concurrent tasks, which will be distributed x 7 to each executor. You can also repartition a dataframe, instead of setting default.parallelism . One core and 1gig of memory is left for the operating system.