Phoenix spark and dynamic columns

[Paul Jones]

Is it possible to save a dataframe into a table where the columns are dynamic?

For instance, I have a loaded a CSV file with header (key, cat1, cat2) into a 
dataframe. All values are strings. I created a table like this: create table 
mytable ("KEY" varchar not null primary key); The code is as follows:

    val df = sqlContext.read
        .format("com.databricks.spark.csv")
        .option("header", "true")
        .option("inferSchema", "true")
        .option("delimiter", "\t")
        .load("saint.tsv")
    
    df.write
        .format("org.apache.phoenix.spark")
        .mode("overwrite")
        .option("table", "mytable")
        .option("zkUrl", "servier:2181/hbase")
        .save()

The CSV files I process always have a key column but I don’t know what the 
other columns will be until I start processing. The code above fails my example 
unless I create static columns named cat1 and cat2. Can I change the save 
somehow to run an upsert specifying the names/column types thus saving into 
dynamic columns?

Thanks in advance,
Paul