SPARK SQL - case when then

I'm new to SPARK-SQL. Is there an equivalent to "CASE WHEN 'CONDITION' THEN 0 ELSE 1 END" in SPARK SQL ?

select case when 1=1 then 1 else 0 end from table

Thanks Sridhar

6 Answers

Before Spark 1.2.0

The supported syntax (which I just tried out on Spark 1.0.2) seems to be

SELECT IF(1=1, 1, 0) FROM table 

This recent thread links to the SQL parser source, which may or may not help depending on your comfort with Scala. At the very least the list of keywords starting (at time of writing) on line 70 should help.

Here's the direct link to the source for convenience: .

Update for Spark 1.2.0 and beyond

As of Spark 1.2.0, the more traditional syntax is supported, in response to SPARK-3813: search for "CASE WHEN" in the test source. For example:

SELECT CASE WHEN key = 1 THEN 1 ELSE 2 END FROM testData 

Update for most recent place to figure out syntax from the SQL Parser

The parser source can now be found here.

Update for more complex examples

In response to a question below, the modern syntax supports complex Boolean conditions.

SELECT CASE WHEN id = 1 OR id = 2 THEN "OneOrTwo" ELSE "NotOneOrTwo" END AS IdRedux FROM customer 

You can involve multiple columns in the condition.

SELECT CASE WHEN id = 1 OR state = 'MA' THEN "OneOrMA" ELSE "NotOneOrMA" END AS IdRedux FROM customer 

You can also nest CASE WHEN THEN expression.

SELECT CASE WHEN id = 1 THEN "OneOrMA" ELSE CASE WHEN state = 'MA' THEN "OneOrMA" ELSE "NotOneOrMA" END END AS IdRedux FROM customer 

For Spark 2.+ Spark when function

From documentation:

Evaluates a list of conditions and returns one of multiple possible result expressions. If otherwise is not defined at the end, null is returned for unmatched conditions.

 // Example: encoding gender string column into integer. // Scala: people.select(when(col("gender") === "male", 0) .when(col("gender") === "female", 1) .otherwise(2)) // Java: people.select(when(col("gender").equalTo("male"), 0) .when(col("gender").equalTo("female"), 1) .otherwise(2)) 

This syntax worked for me in Databricks:

 select org, patient_id, case when (age is null) then 'Not Available' when (age < 15) then 'Less than 15' when (age >= 15 and age < 25) then '15 to 25' when (age >= 25 and age < 35) then '25 to 35' when (age >= 35 and age < 45) then '35 to 45' when (age >= 45) then '45 and Older' end as age_range from demo 

The decode() function analog of Oracle SQL for SQL Spark can be implemented as follows:

​ case ​ ​ ​ when exp1 in ('a','b','c') ​ ​ ​ ​ then element_at(map('a','A','b','B','c','C'), exp1) ​ ​ ​ else exp1 ​ ​ end 

Based on my current production code, this works val identifierDF = tempIdentifierDF.select(tempIdentifierDF("t_item_account_id"), when(tempIdentifierDF("h_description").contains(tempIdentifierDF("t_cusip")),100) .when(tempIdentifierDF("h_description").contains(tempIdentifierDF("t_ticker")),100) .when(tempIdentifierDF("h_description").contains(tempIdentifierDF("t_isin")),100) .when(tempIdentifierDF("h_description").contains(tempIdentifierDF("t_sedol")),100) .when(tempIdentifierDF("h_description").contains(tempIdentifierDF("t_valoren")),100) .otherwise(0) .alias("identifier_in_description_score") ) 

Spark DataFrame API (Python version) also enable to do next query:

df.selectExpr('time', \ 'CASE WHEN (time > 1) THAN time * 1.1 ELSE time END AS updated_time')