How Does SparkSQL Subquery Support Differ Between Versions 2.0 and Earlier?

Subquery Support in SparkSQL

Introduction

Subqueries are often used in SQL to retrieve nested information or perform comparisons. While SparkSQL supports certain subqueries, its support is not comprehensive across all versions. This article aims to provide an overview of SparkSQL's subquery capabilities and discuss the limitations in earlier versions.

Spark 2.0

From Spark 2.0 onwards, both correlated and uncorrelated subqueries are fully supported. This allows for more complex SQL queries involving nested data.

Examples:

select * from l where exists (select * from r where l.a = r.c)
select * from l where a in (select c from r)

Spark < 2.0

In Spark versions prior to 2.0, subqueries are only supported in the FROM clause, similar to Hive versions prior to 0.12. Subqueries in the WHERE clause are not supported.

For example, the following query will fail in Spark < 2.0:

sqlContext.sql(
 "select sal from samplecsv where sal < (select MAX(sal) from samplecsv)"
).collect().foreach(println)

Planned Features

In addition to the current support, Spark has planned features to enhance its subquery capabilities:

• SPARK-23945: Support for using a single-column DataFrame as input to Column.isin().
• SPARK-18455: General support for correlated subquery processing.

Conclusion

SparkSQL's support for subqueries has evolved significantly over the years. While earlier versions support only a limited subset, Spark 2.0 and above offer comprehensive support for both correlated and uncorrelated subqueries. Planned features aim to further improve this support in future releases.

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