sqlContext.read.parquet
can take multiple paths as input. If you want just day=5
and day=6
, you can simply add two paths like:
val dataframe = sqlContext
.read.parquet("file:///your/path/data=jDD/year=2015/month=10/day=5/",
"file:///your/path/data=jDD/year=2015/month=10/day=6/")
If you have folders under day=X
, like say country=XX
, country
will automatically be added as a column in the dataframe
.
EDIT: As of Spark 1.6 one needs to provide a “basepath”-option in order for Spark to generate columns automatically. In Spark 1.6.x the above would have to be re-written like this to create a dataframe with the columns “data”, “year”, “month” and “day”:
val dataframe = sqlContext
.read
.option("basePath", "file:///your/path/")
.parquet("file:///your/path/data=jDD/year=2015/month=10/day=5/",
"file:///your/path/data=jDD/year=2015/month=10/day=6/")