SQL on Spark: How do I get all values of DISTINCT? - sql

Question

So, assume I have the following table:
Name | Color
------------------------------
John | Blue
Greg | Red
John | Yellow
Greg | Red
Greg | Blue
I would like to get a table of the distinct colors for each name - how many and their values. Meaning, something like this:
Name | Distinct | Values
--------------------------------------
John | 2 | Blue, Yellow
Greg | 2 | Red, Blue
Any ideas how to do so?

Answer 1

collect_list will give you a list without removing duplicates.
collect_set will automatically remove duplicates
so just
select
Name,
count(distinct color) as Distinct, # not a very good name
collect_set(Color) as Values
from TblName
group by Name
this feature is implemented since spark 1.6.0 check it out:
https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
/**
* Aggregate function: returns a set of objects with duplicate elements eliminated.
*
* For now this is an alias for the collect_set Hive UDAF.
*
* #group agg_funcs
* #since 1.6.0
*/
def collect_set(columnName: String): Column = collect_set(Column(columnName))

Answer 2

For PySPark; I come from an R/Pandas background, so I'm actually finding Spark Dataframes a little easier to work with.
To do this:
Setup a Spark SQL context
Read your file into a dataframe
Register your dataframe as a temp table
Query it directly using SQL syntax
Save results as objects, output to files..do your thing
Here's a class I created to do this:
class SQLspark():
def __init__(self, local_dir='./', hdfs_dir='/users/', master='local', appname='spark_app', spark_mem=2):
self.local_dir = local_dir
self.hdfs_dir = hdfs_dir
self.master = master
self.appname = appname
self.spark_mem = int(spark_mem)
self.conf = (SparkConf()
.setMaster(self.master)
.setAppName(self.appname)
.set("spark.executor.memory", self.spark_mem))
self.sc = SparkContext(conf=self.conf)
self.sqlContext = SQLContext(self.sc)
def file_to_df(self, input_file):
# import file as dataframe, all cols will be imported as strings
df = self.sqlContext.read.format("com.databricks.spark.csv").option("header", "true").option("delimiter", "\t").option("inferSchema", "true").load(input_file)
# # cache df object to avoid rebuilding each time
df.cache()
# register as temp table for querying, use 'spark_df' as table name
df.registerTempTable("spark_df")
return df
# you also cast a spark dataframe as a pandas df
def sparkDf_to_pandasDf(self, input_df):
pandas_df = input_df.toPandas()
return pandas_df
def find_distinct(self, col_name):
my_query = self.sqlContext.sql("""SELECT distinct {} FROM spark_df""".format(col_name))
# now do your thing with the results etc
my_query.show()
my_query.count()
my_query.collect()
###############
if __name__ == '__main__':
# instantiate class
# see function for variables to input
spark = TestETL(os.getcwd(), 'hdfs_loc', "local", "etl_test", 10)
# specify input file to process
tsv_infile = 'path/to/file'