SparkSQL: Hive sub-query leads to full table scan

[#1]

I have a huge Hive Table, that I want to process in Spark. To avoid a full table scan in my test purposes, I first run this code:

%spark
val df1 = sqlContext.sql("select * from mydb.mytable limit 10")
df1.show()

That works very fast (1 Sec). Now I added another query to "transform" the result a little bit - Now Hive seems to make a full table scan, as this execution takes more than 10 minutes!

%spark
val df2 = sqlContext.sql("SELECT concat_ws(\":\", lpad(cast((hashvalue % 200) as String), 3, \"0\"), concat_ws(\"_\", name, number, test, step, cast(starttime as STRING))) as key, hashvalue, valuelist from (select * from mydb.mytable limit 10) a")
df2.show()

1. Is this normal behavior?

2. Is there a way to avoid full table scans, when using sub-queries?

Thank you!

[#2]

I am not completly sure, but I think i came across an information that the limit statement cause a repartioning, so you have significant performance impact by using it. Instead you should use TABLESAMPLE or rewrite the query if it is important which row you get (and not only the limitation)

[#3]

Thank you! Can you give me some details about this or do you have some helpful links?