Optimizing queries using partition pruning

When predicate push-down optimization is not applicable—for example, if all stripes contain records that match the predicate condition—a query with a WHERE clause might need to read the entire data set. This becomes a bottleneck over a large table. Partition pruning is another optimization method; it exploits query semantics to avoid reading large amounts of data unnecessarily.

Partition pruning is possible when data within a table is split across multiple logical partitions. Each partition corresponds to a particular value of a partition column and is stored as a subdirectory within the table root directory on HDFS. Where applicable, only the required partitions (subdirectories) of a table are queried, thereby avoiding unnecessary I/O.

Spark supports saving data in a partitioned layout seamlessly, through the partitionBy method available during data source write operations. To partition the "people" table by the “age” column, you can use the following command:


As a result, records are automatically partitioned by the age field and then saved into different directories: for example, peoplePartitioned/age=1/, peoplePartitioned/age=2/, and so on.

After partitioning the data, subsequent queries can omit large amounts of I/O when the partition column is referenced in predicates. For example, the following query automatically locates and loads the file under peoplePartitioned/age=20/and omits all others:

val peoplePartitioned = spark.read.format("orc").load("peoplePartitioned")
spark.sql("SELECT * FROM peoplePartitioned WHERE age = 20")