Which version of Spark are you using? I just tried the example below on 1.5.1
and it seems to work as expected:
scala> val res = df.groupBy("key").count.agg(min("count"), avg("count"))
res: org.apache.spark.sql.DataFrame = [min(count): bigint, avg(count): double]
scala> res.show
+----------+----------+
|min(count)|avg(count)|
+----------+----------+
| 1| 1.0|
+----------+----------+
scala> res.printSchema
root
|-- min(count): long (nullable = true)
|-- avg(count): double (nullable = true)
On Oct 21, 2015, at 11:12 AM, Carol McDonald <[email protected]> wrote:
> This used to work :
>
> // What's the min number of bids per item? what's the average? what's the
> max?
> auction.groupBy("item", "auctionid").count.agg(min("count"),
> avg("count"),max("count")).show
>
> // MIN(count) AVG(count) MAX(count)
> // 1 16.992025518341308 75
>
> but this now gives an error
>
> val res = auction.groupBy("item", "auctionid").count.agg(min("count"),
> avg("count"),max("count"))
>
> <console>:42: error: Float does not take parameters
>
> val res = auction.groupBy("item", "auctionid").count.agg(min("count"),
> avg("count"),max("count"))
>
> min and max still work .
>
> Do I need to cast the count to a float ?
>
> auction.groupBy("item", "auctionid").count.agg(min("count"),
> max("count")).show
>
> MIN(count) MAX(count)
> 1 75
