Github user jackylk commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/2659#discussion_r212785152
--- Diff:
integration/spark-datasource/src/test/scala/org/apache/spark/sql/carbondata/datasource/SparkCarbonDataSourceTest.scala
---
@@ -285,6 +285,44 @@ class SparkCarbonDataSourceTest extends FunSuite with
BeforeAndAfterAll {
spark.sql("drop table if exists date_parquet_table")
}
+ test("test write with array type with filter") {
+ spark.sql("drop table if exists carbon_table")
+ spark.sql("drop table if exists parquet_table")
+ import spark.implicits._
+ val df = spark.sparkContext.parallelize(1 to 10)
+ .map(x => ("a" + x % 10, Array("b", "c"), x))
+ .toDF("c1", "c2", "number")
+
+ df.write
+ .format("parquet").saveAsTable("parquet_table")
+ spark.sql("create table carbon_table(c1 string, c2 array<string>,
number int) using carbon")
+ spark.sql("insert into carbon_table select * from parquet_table")
+ assert(spark.sql("select * from carbon_table").count() == 10)
+ TestUtil.checkAnswer(spark.sql("select * from carbon_table where
c1='a1' and c2[0]='b'"), spark.sql("select * from parquet_table where c1='a1'
and c2[0]='b'"))
+ TestUtil.checkAnswer(spark.sql("select * from carbon_table"),
spark.sql("select * from parquet_table"))
+ spark.sql("drop table if exists carbon_table")
+ spark.sql("drop table if exists parquet_table")
+ }
+
+ test("test write with struct type with filter") {
+ spark.sql("drop table if exists carbon_table")
+ spark.sql("drop table if exists parquet_table")
+ import spark.implicits._
+ val df = spark.sparkContext.parallelize(1 to 10)
+ .map(x => ("a" + x % 10, ("b", "c"), x))
+ .toDF("c1", "c2", "number")
+
+ df.write
+ .format("parquet").saveAsTable("parquet_table")
+ spark.sql("create table carbon_table(c1 string, c2 struct<a1:string,
a2:string>, number int) using carbon")
--- End diff --
can you create more complex schema with array and struct
---
