RDD is big collection of data items.As we are dealing with big data, those collections are big enough that they can not fit in one node.So they needs to be partitioned across nodes.So spark automatically partitions RDDs and distribute partitions across nodes.
Default partitioning behavior
Default partitioning behavior depends upon how the RDD has been created like whether it has been created from external sources like Cassandra table, HDFS file etc or by transforming one RDD into another new RDD.
Spark uses partitioner property to determine the algorithm to determine on which worker that particular record of RDD should be stored on.
When Spark reads a file from HDFS, it creates a single partition for a single input split. Input split is set by the Hadoop InputFormat used to read this file. For instance, if you use textFile() it would be TextInputFormat in Hadoop, which would return you a single partition for a single block of HDFS (but the split between partitions would be done on line split, not the exact block split), unless you have a compressed text file. In case of compressed file you would get a single partition for a single file (as compressed text files are not splittable).
When you call rdd.repartition(x) it would perform a shuffle of the data from N partititons you have in rdd to x partitions you want to have, partitioning would be done on round robin basis.
If you have a 30GB uncompressed text file stored on HDFS, then with the default HDFS block size setting (128MB) it would be stored in 235 blocks, which means that the RDD you read from this file would have 235 partitions. When you call repartition(1000) your RDD would be marked as to be repartitioned, but in fact it would be shuffled to 1000 partitions only when you will execute an action on top of this RDD (lazy execution concept)