How to add a new Struct column to a DataFrame - scala

Question

I'm currently trying to extract a database from MongoDB and use Spark to ingest into ElasticSearch with geo_points.
The Mongo database has latitude and longitude values, but ElasticSearch requires them to be casted into the geo_point type.
Is there a way in Spark to copy the lat and lon columns to a new column that is an array or struct?
Any help is appreciated!

Answer 1

I assume you start with some kind of flat schema like this:
root
|-- lat: double (nullable = false)
|-- long: double (nullable = false)
|-- key: string (nullable = false)
First lets create example data:
import org.apache.spark.sql.Row
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.types._
val rdd = sc.parallelize(
Row(52.23, 21.01, "Warsaw") :: Row(42.30, 9.15, "Corte") :: Nil)
val schema = StructType(
StructField("lat", DoubleType, false) ::
StructField("long", DoubleType, false) ::
StructField("key", StringType, false) ::Nil)
val df = sqlContext.createDataFrame(rdd, schema)
An easy way is to use an udf and case class:
case class Location(lat: Double, long: Double)
val makeLocation = udf((lat: Double, long: Double) => Location(lat, long))
val dfRes = df.
withColumn("location", makeLocation(col("lat"), col("long"))).
drop("lat").
drop("long")
dfRes.printSchema
and we get
root
|-- key: string (nullable = false)
|-- location: struct (nullable = true)
| |-- lat: double (nullable = false)
| |-- long: double (nullable = false)
A hard way is to transform your data and apply schema afterwards:
val rddRes = df.
map{case Row(lat, long, key) => Row(key, Row(lat, long))}
val schemaRes = StructType(
StructField("key", StringType, false) ::
StructField("location", StructType(
StructField("lat", DoubleType, false) ::
StructField("long", DoubleType, false) :: Nil
), true) :: Nil
)
sqlContext.createDataFrame(rddRes, schemaRes).show
and we get an expected output
+------+-------------+
| key| location|
+------+-------------+
|Warsaw|[52.23,21.01]|
| Corte| [42.3,9.15]|
+------+-------------+
Creating nested schema from scratch can be tedious so if you can I would recommend the first approach. It can be easily extended if you need more sophisticated structure:
case class Pin(location: Location)
val makePin = udf((lat: Double, long: Double) => Pin(Location(lat, long))
df.
withColumn("pin", makePin(col("lat"), col("long"))).
drop("lat").
drop("long").
printSchema
and we get expected output:
root
|-- key: string (nullable = false)
|-- pin: struct (nullable = true)
| |-- location: struct (nullable = true)
| | |-- lat: double (nullable = false)
| | |-- long: double (nullable = false)
Unfortunately you have no control over nullable field so if is important for your project you'll have to specify schema.
Finally you can use struct function introduced in 1.4:
import org.apache.spark.sql.functions.struct
df.select($"key", struct($"lat", $"long").alias("location"))

Answer 2

Try this:
import org.apache.spark.sql.functions._
df.registerTempTable("dt")
dfres = sql("select struct(lat,lon) as colName from dt")