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         


        

Answer 1

you need to provide mergeSchema = true option. like mentioned below (this is from 1.6.0):

val dataframe = sqlContext.read.option("mergeSchema", "true").parquet("file:///your/path/data=jDD")

This will read all the parquet files into dataframe and also creates columns year, month and day in the dataframe data.

Ref: https://spark.apache.org/docs/1.6.0/sql-programming-guide.html#schema-merging

Answer 2

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/")

Answer 3

If you want to read for multiple days, for example day = 5 and day = 6 and want to mention the range in the path itself, wildcards can be used:

val dataframe = sqlContext
  .read
  .parquet("file:///your/path/data=jDD/year=2015/month=10/day={5,6}/*")

Wildcards can also be used to specify a range of days:

val dataframe = sqlContext
  .read
  .parquet("file:///your/path/data=jDD/year=2015/month=10/day=[5-10]/*")

This matches all days from 5 to 10.