In Spark, how do you configure components?
💡 Model Answer
Spark configuration can be set in several ways. Programmatically, you can create a SparkConf object and pass it to SparkContext, or use SparkSession.builder.config("key", "value"). For example: SparkSession.builder.appName("MyApp").config("spark.executor.memory", "4g").getOrCreate(). You can also set properties via spark-submit using the --conf flag, e.g., spark-submit --conf spark.sql.shuffle.partitions=200 myapp.py. Environment variables such as SPARK_WORKER_MEMORY or SPARK_DRIVER_MEMORY can also influence configuration. Cluster managers like YARN or Kubernetes allow you to set defaults at the cluster level. Common properties include spark.executor.instances, spark.executor.cores, spark.sql.shuffle.partitions, and spark.serializer. Proper configuration ensures optimal resource usage, performance, and fault tolerance.
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