RDD (Resilient Distributed Dataset) is the fundamental data structure for distributed data processing in Apache Spark. An RDD is an immutable distributed collection of data that can be processed in parallel. RDDs are fault-tolerant and can be created from data stored in external storage systems, such as HDFS (Hadoop Distributed File System), or by transforming existing RDDs using operations called transformations.
There are several ways to create an empty RDD in Spark. Here are a few examples:
- Using the
parallelize
method: You can create an empty RDD by calling theparallelize
method on an empty list. For example:
sc = SparkContext()
empty_rdd = sc.parallelize([])
2. Using the emptyRDD
method: You can also create an empty RDD using the emptyRDD
method of the SparkContext. For example:
sc = SparkContext()
empty_rdd = sc.emptyRDD()
- Using the
range
method: You can create an empty RDD by calling therange
method with an end value of 0. For example:
sc = SparkContext() empty_rdd = sc.range(0)