smohr003 created SPARK-24233:
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Summary: union operation on read of dataframe does nor produce
correct result
Key: SPARK-24233
URL: https://issues.apache.org/jira/browse/SPARK-24233
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.1.0
Reporter: smohr003
I know that I can use wild card * to read all subfolders. But, I am trying to
use .par and .schema to speed up the read process.
val absolutePath = "adl://datalakename.azuredatalakestore.net/testU/"
Seq((1, "one"), (2, "two")).toDF("k",
"v").write.mode("overwrite").parquet(absolutePath + "1")
Seq((11, "one"), (22, "two")).toDF("k",
"v").write.mode("overwrite").parquet(absolutePath + "2")
Seq((111, "one"), (222, "two")).toDF("k",
"v").write.mode("overwrite").parquet(absolutePath + "3")
Seq((1111, "one"), (2222, "two")).toDF("k",
"v").write.mode("overwrite").parquet(absolutePath + "4")
Seq((2, "one"), (2, "two")).toDF("k",
"v").write.mode("overwrite").parquet(absolutePath + "5")
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.\{FileSystem, Path}
import java.net.URI
def readDir(path: String): DataFrame = {
val fs = FileSystem.get(new URI(path), new Configuration())
val subDir = fs.listStatus(new Path(path)).map(i => i.getPath.toString)
var df = spark.read.parquet(subDir.head)
val dfSchema = df.schema
subDir.tail.par.foreach(p => df =
df.union(spark.read.schema(dfSchema).parquet(p)).select(df.columns.head,
df.columns.tail:_*))
df
}
val dfAll = readDir(absolutePath)
dfAll.count
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