One query is going to get executed on the data frame. It will do the same optimization so that the processing can be done in a faster way. In the custom memory management scheme, lot of spaces can be saved because as the data is stored in the off memory of heap memory. That means it is not always dealing with the data. That is the in-memory data. Here the data will be stored in off heap memory and that is no garbage collection over it in case of data frames. In data frames the query optimizer is the optimized execution plan. And when the optimization is done, the final execution takes place on the RDD. As the query optimizer is working on the queries. The execution plan will take the lesser time to do the queries execution and also the processing.
Apache Spark Dataframe and Datasets:
We shall be discussing Spark datasets. The datasets are the data structure in Spark SQL. It is an extension to data frame and another data structure in spark. It is nothing but one extension from the data frames to have some more advantageous. Data set provides that object-oriented programming interface. It is encoding features. The encoding feature is a primary concept of civilization. Encoding techniques can be applied, can be adopted on these datasets. Encoders are used to convert JVM objects and sparks internal binary format. These are the different advantages of our datasets.
Why we should use the data sets? The spark SQL query for data sets are very optimized. The queries execution plans are there. After doing the queries execution optimized. Only the queries will get executed for the faster and quick retrieval of data. Different tools like Catalyst query optimizer and these framework returns the data flow graph from the queries. This data flow graph will indicate that how the data we will be flowing from sub queries to the other sub queries. Using datasets, we can analyze that data during the runtime. And this feature is not possible for RDD data frame. During the runtime, we can perform the respective analysis on our data, which is absent in our data frames. These are serializable and quarriable. So, we can save that to the persistent storage. We can keep them in their respective persistent strategies.