Spark 2 Student Book
Spark 2 Student Book
subclassed sparkcontext follows the separation of concerns among the sparkcontext and sparksession in that subclasses do not manage the storage or extraction of the stored data. the default storage store is org.apache.spark.sql.execution.datasources.csv.csvoutputformat. the csvoutputformat stores its data in org.csvdatareader instances. the csvdatareader is a subtype of the datareader abstraction. spark stores the data in objects of type dataframe when you pass in cached = false or readschema = false to the csv() or json() methods.
in the df.load() method, you can also pass a path to the input database to spark. in the case of hive, load looks in the hive metastore for the table specified. the hive metastore is where the spark sql engine caches hive queries.
you can also use the dataframe.createtempview() method with a hive metastore which will result in a temporary view getting added to the metastore. this is useful for data sets that you want to run spark jobs on without having to go back and set up the metastore each time.
this will transfer the data from hdfs to your local machine and create a table in memory that you can query using spark sql. following is the syntax to load a schema using column data type as the schema. hence, no type information is required to load a schema for columns with string and int data types.
both dataframe and dataset classes have the same functionality but dataframe is a more powerful and sophisticated class that is used by all kind of data mining or machine learning algorithms. to convert a dataframe to a dataset use thetodataset() method as shown in the following line of code:
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