CurrentCatalog¶

Overview¶

The CurrentCatalog expression returns the name of the current catalog in the Spark session. This is a leaf expression that produces a string value representing the active catalog context without requiring any input parameters.

Syntax¶

SELECT current_catalog();

// DataFrame API usage
import org.apache.spark.sql.functions._
df.select(expr("current_catalog()"))

Arguments¶

Argument	Type	Description
None	N/A	This expression takes no arguments

Return Type¶

StringType - Returns a non-nullable string representing the current catalog name.

Supported Data Types¶

This expression does not accept input data types as it is a parameterless leaf expression that generates its own output.

Algorithm¶

Accesses the current Spark session's catalog context
Retrieves the active catalog name from the session state
Returns the catalog name as a string value
Operates as a constant expression during query execution
Does not perform any computation on input data

Partitioning Behavior¶

This expression has neutral partitioning behavior:

Preserves existing partitioning schemes as it doesn't affect data distribution
Does not require shuffle operations since it produces the same constant value across all partitions
Can be computed independently on each partition without coordination

Edge Cases¶

Never returns null values (nullable = false)
Always returns a valid catalog name string
Behavior is consistent across all nodes in a distributed environment
Result remains constant throughout the execution of a single query
No special handling required for empty datasets

Code Generation¶

This expression is marked as Unevaluable, meaning it does not support Tungsten code generation and cannot be directly evaluated. The actual catalog name resolution is handled during query planning and analysis phases, with the result being substituted as a literal value in the optimized plan.

Examples¶

-- Get the current catalog name
SELECT current_catalog();
-- Output: spark_catalog

-- Use in a WHERE clause
SELECT table_name FROM information_schema.tables 
WHERE table_catalog = current_catalog();

-- Use in a complex query
SELECT current_catalog() as catalog_name, count(*) as table_count
FROM information_schema.tables
GROUP BY current_catalog();

// DataFrame API usage
import org.apache.spark.sql.functions._

// Select current catalog
val catalogDF = spark.sql("SELECT current_catalog()")
catalogDF.show()

// Use with other expressions
val df = spark.range(1)
val result = df.select(expr("current_catalog()").alias("catalog"))
result.show()

// Combine with metadata queries
val tablesDF = spark.sql("""
  SELECT current_catalog() as catalog, 
         count(*) as table_count
  FROM information_schema.tables
""")