Creating hive table using parquet file metadata

Here's a solution I've come up with to get the metadata from parquet files in order to create a Hive table.

First start a spark-shell (Or compile it all into a Jar and run it with spark-submit, but the shell is SOO much easier)

import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.DataFrame


val df=sqlContext.parquetFile("/path/to/_common_metadata")

def creatingTableDDL(tableName:String, df:DataFrame): String={
  val cols = df.dtypes
  var ddl1 = "CREATE EXTERNAL TABLE "+tableName + " ("
  //looks at the datatypes and columns names and puts them into a string
  val colCreate = (for (c <-cols) yield(c._1+" "+c._2.replace("Type",""))).mkString(", ")
  ddl1 += colCreate + ") STORED AS PARQUET LOCATION '/wherever/you/store/the/data/'"
  ddl1
}

val test_tableDDL=creatingTableDDL("test_table",df,"test_db")

It will provide you with the datatypes that Hive will use for each column as they are stored in Parquet. E.G: CREATE EXTERNAL TABLE test_table (COL1 Decimal(38,10), COL2 String, COL3 Timestamp) STORED AS PARQUET LOCATION '/path/to/parquet/files'

I'd just like to expand on James Tobin's answer. There's a StructField class which provides Hive's data types without doing string replacements.

// Tested on Spark 1.6.0.

import org.apache.spark.sql.DataFrame

def dataFrameToDDL(dataFrame: DataFrame, tableName: String): String = {
    val columns = dataFrame.schema.map { field =>
        "  " + field.name + " " + field.dataType.simpleString.toUpperCase
    }

    s"CREATE TABLE $tableName (\n${columns.mkString(",\n")}\n)"
}

This solves the IntegerType problem.

scala> val dataFrame = sc.parallelize(Seq((1, "a"), (2, "b"))).toDF("x", "y")
dataFrame: org.apache.spark.sql.DataFrame = [x: int, y: string]

scala> print(dataFrameToDDL(dataFrame, "t"))
CREATE TABLE t (
  x INT,
  y STRING
)

This should work with any DataFrame, not just with Parquet. (e.g., I'm using this with a JDBC DataFrame.)

As an added bonus, if your target DDL supports nullable columns, you can extend the function by checking StructField.nullable.