scala - Is there a way to add extra metadata for Spark dataframes?

Question

Is it possible to add extra meta data to DataFrames?

Reason

I have Spark DataFrames for which I need to keep extra information. Example: A DataFrame, for which I want to "remember" the highest used index in an Integer id column.

Current solution

I use a separate DataFrame to store this information. Of course, keeping this information separately is tedious and error-prone.

Is there a better solution to store such extra information on DataFrames?

Answer 1

To expand and Scala-fy nealmcb's answer (the question was tagged scala, not python, so I don't think this answer will be off-topic or redundant), suppose you have a DataFrame:

import org.apache.spark.sql
val df = sc.parallelize(Seq.fill(100) { scala.util.Random.nextInt() }).toDF("randInt")

And some way to get the max or whatever you want to memoize on the DataFrame:

val randIntMax = df.rdd.map { case sql.Row(randInt: Int) => randInt }.reduce(math.max)

sql.types.Metadata can only hold strings, booleans, some types of numbers, and other metadata structures. So we have to use a Long:

val metadata = new sql.types.MetadataBuilder().putLong("columnMax", randIntMax).build()

DataFrame.withColumn() actually has an overload that permits supplying a metadata argument at the end, but it's inexplicably marked [private], so we just do what it does — use Column.as(alias, metadata):

val newColumn = df.col("randInt").as("randInt_withMax", metadata)
val dfWithMax = df.withColumn("randInt_withMax", newColumn)

dfWithMax now has (a column with) the metadata you want!

dfWithMax.schema.foreach(field => println(s"${field.name}: metadata=${field.metadata}"))
> randInt: metadata={}
> randInt_withMax: metadata={"columnMax":2094414111}

Or programmatically and type-safely (sort of; Metadata.getLong() and others do not return Option and may throw a "key not found" exception):

dfWithMax.schema("randInt_withMax").metadata.getLong("columnMax")
> res29: Long = 209341992

Attaching the max to a column makes sense in your case, but in the general case of attaching metadata to a DataFrame and not a column in particular, it appears you'd have to take the wrapper route described by the other answers.