Spark Must-Know Differences:
➤ RDD vs DataFrame:
- RDD: Low-level API, unstructured data, more control.
- DataFrame: High-level API, optimized, structured data.
➤ DataFrame vs Dataset:
- DataFrame: Untyped API, ease of use, suitable for Python.
- Dataset: Typed API, compile-time safety, best with Scala/Java.
➤ map() vs flatMap():
- map(): Transforms each element, returns a new RDD with the same number of elements.
- flatMap(): Transforms each element and flattens the result, can return a different number of elements.
➤ filter() vs where():
- filter(): Filters rows based on a condition, commonly used in RDDs.
- where(): SQL-like filtering, more intuitive in DataFrames.
➤ collect() vs take():
- collect(): Retrieves the entire dataset to the driver.
- take(): Retrieves a specified number of rows, safer for large datasets.
➤ cache() vs persist():
- cache(): Stores data in memory only.
- persist(): Stores data with a specified storage level (memory, disk, etc.).
➤ select() vs selectExpr():
- select(): Selects columns with standard column expressions.
- selectExpr(): Selects columns using SQL expressions.
➤ join() vs union():
- join(): Combines rows from different DataFrames based on keys.
- union(): Combines rows from DataFrames with the same schema.
➤ withColumn() vs withColumnRenamed():
- withColumn(): Creates or replaces a column.
- withColumnRenamed(): Renames an existing column.
➤ groupBy() vs agg():
- groupBy(): Groups rows by a column or columns.
- agg(): Performs aggregate functions on grouped data.
➤repartition() vs coalesce():
- repartition(): Increases or decreases the number of partitions, performs a full shuffle.
- coalesce(): Reduces the number of partitions without a full shuffle, more efficient for reducing partitions.
➤ orderBy() vs sort():
- orderBy(): Returns a new DataFrame sorted by specified columns, supports both ascending and descending.
- sort(): Alias for orderBy(), identical in functionality.
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