Prompt Title: Broadcasting in Spark.

Broadcasting in Spark involves distributing data to all nodes in the cluster, so that each task running on the nodes can access the data without having to transfer it over the network repeatedly. When you broadcast a 300 MB file in Spark, it gets replicated across all the nodes in the cluster, which can increase the total size of the data to be stored in memory.

However, it is unlikely that the broadcasted file would grow from 300 MB to 3 GB, unless there are other factors at play such as compression, serialization or other data transformations that can expand the data size. Additionally, if you are using a compression codec like Snappy or Gzip, the size of the broadcasted data can increase due to the overhead of compression.

Another possibility could be that the broadcasted data is being cached in memory on the nodes, which can cause the memory usage to grow significantly. This can be mitigated by tuning the amount of memory allocated to Spark and the size of the broadcasted data, and by considering alternative solutions such as using a shared file system or distributed caching frameworks like Redis or Memcached.

Overall, it's important to carefully consider the size and format of the data being broadcasted in Spark, as well as the resources available on the cluster, to avoid memory and performance issues.