Daniel-Davies commented on code in PR #38867:
URL: https://github.com/apache/spark/pull/38867#discussion_r1060550263
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala:
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@@ -4601,6 +4601,231 @@ case class ArrayExcept(left: Expression, right:
Expression) extends ArrayBinaryL
newLeft: Expression, newRight: Expression): ArrayExcept = copy(left =
newLeft, right = newRight)
}
+@ExpressionDescription(
+ usage = "_FUNC_(x, pos, val) - Places val into index pos of array x (array
indices start at 1)",
+ examples = """
+ Examples:
+ > SELECT _FUNC_(array(1, 2, 3, 4), 5, 5);
+ [1,2,3,4,5]
Review Comment:
Thanks for the quick response @beliefer - two questions:
1. I can't find any function descriptions that tell users that arrays are 0
based; could you link some? For example, here is the live documentation for
[elementAt](https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.element_at.html),
which uses 1 based indexing.
2. The SQL99 standard specifies that SQL Arrays use 1 based indexing. For
example, PostGres uses 1 based index Arrays also. Even if we ignored all other
legacy spark functions, is there a reason you prefer 0 based indexing?
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