Reading DataFrame from partitioned parquet file

Question

How to read partitioned parquet with condition as dataframe,

this works fine,

val dataframe = sqlContext.read.parquet("file:///home/msoproj/dev_data/dev_output/aln/partitions/data=jDD/year=2015/month=10/day=25/*") 

Partition is there for day=1 to day=30 is it possible to read something like(day = 5 to 6) or day=5,day=6,

val dataframe = sqlContext.read.parquet("file:///home/msoproj/dev_data/dev_output/aln/partitions/data=jDD/year=2015/month=10/day=??/*") 

If I put * it gives me all 30 days data and it too big.

Answer 1

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/")