The number of executors in a Spark-based application can be set by passing the
--num-executors command line argument to the
For example, to set the number of executors to 4, you would use the following command:
spark-submit --num-executors 4 <other arguments> <your application>
Alternatively, you can set the number of executors programmatically using the
SparkConf object by calling the
set("spark.executor.instances", <num>) method and passing the desired number of executors as the argument.
from pyspark import SparkConf, SparkContext conf = SparkConf().setAppName("MyApp").setMaster("local") conf.set("spark.executor.instances", "4") sc = SparkContext(conf=conf)
In this case, the
setMaster("local") sets the master to run the application locally, but if you want to run it on a cluster you should replace it with the cluster url.
It’s worth noting that the number of executors should be chosen based on the resources available on the cluster and the requirements of the specific application. In general, it’s a good practice to set the number of executors to a number close to the number of cores available on the cluster. Also, you should set the executor memory size using the
--executor-memory flag or
spark.executor.memory configuration property.
The number of executors in a Spark application is typically determined by the resources available on the cluster and the requirements of the specific application. Here are a few things to consider when determining the number of executors:
- Number of cores: A good rule of thumb is to set the number of executors to a number close to the number of cores available on the cluster. This allows for efficient resource utilization and can help ensure that your application completes in a reasonable amount of time.
- Memory requirements: Each executor requires a certain amount of memory to operate. You should set the amount of executor memory using the
spark.executor.memoryconfiguration property and make sure that you have enough memory available to accommodate all of the executors.
- Data size: The size of the input data also plays an important role when determining the number of executors. Applications that process large datasets may require more executors to ensure that the data can be processed in a reasonable amount of time.
- Task parallelism: The number of tasks that can be run in parallel also affects the number of executors. Applications that have a high degree of task parallelism will require more executors to ensure that all tasks can be run simultaneously.
It’s worth noting that the optimal number of executors will vary depending on the specific application and cluster, so it may be necessary to experiment with different configurations to find the best setting for a particular application.
Spark important urls to refer