Oh, sorry. So neither SQL nor Spark SQL is preferred. Then you may write
you own aggregation with |aggregateByKey|:
|users.aggregateByKey((0,Set.empty[String]))({case ((count, seen), user) =>
(count +1, seen + user)
}, {case ((count0, seen0), (count1, seen1)) =>
(count0 + count1, seen0 ++ seen1)
}).mapValues {case (count, seen) =>
(count, seen.size)
}
|
On 12/5/14 3:47 AM, Arun Luthra wrote:
Is that Spark SQL? I'm wondering if it's possible without spark SQL.
On Wed, Dec 3, 2014 at 8:08 PM, Cheng Lian <lian.cs....@gmail.com
<mailto:lian.cs....@gmail.com>> wrote:
You may do this:
|table("users").groupBy('zip)('zip, count('user), countDistinct('user))
|
On 12/4/14 8:47 AM, Arun Luthra wrote:
I'm wondering how to do this kind of SQL query with PairRDDFunctions.
SELECT zip, COUNT(user), COUNT(DISTINCT user)
FROM users
GROUP BY zip
In the Spark scala API, I can make an RDD (called "users") of
key-value pairs where the keys are zip (as in ZIP code) and the
values are user id's. Then I can compute the count and distinct
count like this:
val count = users.mapValues(_ => 1).reduceByKey(_ + _)
val countDistinct = users.distinct().mapValues(_ =>
1).reduceByKey(_ + _)
Then, if I want count and countDistinct in the same table, I have
to join them on the key.
Is there a way to do this without doing a join (and without using
SQL or spark SQL)?
Arun
