Considering import pyspark.sql.functions as psf There are two types of broadcasting: sc.broadcast() to copy python objects to every node for a more efficient use of psf.isin psf.broadcast inside a join to copy your pyspark dataframe to every node when the dataframe is small: df1.join(psf.broadcast(df2)). It is usually used for cartesian products (CROSS JOIN in pig). … Read more
Long story short, Spark (including PySpark) is not designed to handle multiple contexts in a single application. If you’re interested in JVM side of the story I would recommend reading SPARK-2243 (resolved as won’t fix). There is a number of design decisions made in PySpark which reflects that including, but not limited to a singleton … Read more
float() is not a Spark function, you need the function cast(): from pyspark.sql.functions import col df_temp.select(*(col(c).cast(“float”).alias(c) for c in df_temp.columns))
These are few ways to write If-Else / When-Then-Else / When-Otherwise expression in pyspark. Sample dataframe df = spark.createDataFrame([(1,1),(2,2),(3,3)],[‘id’,’value’]) df.show() #+—+—–+ #| id|value| #+—+—–+ #| 1| 1| #| 2| 2| #| 3| 3| #+—+—–+ #Desired Output: #+—+—–+———-+ #| id|value|value_desc| #+—+—–+———-+ #| 1| 1| one| #| 2| 2| two| #| 3| 3| other| #+—+—–+———-+ Option#1: withColumn() … Read more
You need a flattening UDF; starting from your own df: spark.version # u’2.2.0′ from pyspark.sql import functions as F import pyspark.sql.types as T def fudf(val): return reduce (lambda x, y:x+y, val) flattenUdf = F.udf(fudf, T.ArrayType(T.IntegerType())) df2 = df.groupBy(“store”).agg(F.collect_list(“values”)) df2.show(truncate=False) # +—–+———————————————-+ # |store| collect_list(values) | # +—–+———————————————-+ # |1 |[WrappedArray(1, 2, 3), WrappedArray(4, 5, 6)]| … Read more
You are not using the correct sum function but the built-in function sum (by default). So the reason why the build-in function won’t work is that’s it takes an iterable as an argument where as here the name of the column passed is a string and the built-in function can’t be applied on a string. … Read more
Create sample data: from pyspark.sql import Row x = [Row(col1=”xx”, col2=”yy”, col3=”zz”, col4=[123,234])] rdd = sc.parallelize([Row(col1=”xx”, col2=”yy”, col3=”zz”, col4=[123,234])]) df = spark.createDataFrame(rdd) df.show() #+—-+—-+—-+———-+ #|col1|col2|col3| col4| #+—-+—-+—-+———-+ #| xx| yy| zz|[123, 234]| #+—-+—-+—-+———-+ Use getItem to extract element from the array column as this, in your actual case replace col4 with collect_set(TIMESTAMP): df = df.withColumn(“col5”, … Read more
In the past, you were able to simply pass a dictionary to spark.createDataFrame(), but this is now deprecated: mylist = [ {“type_activity_id”:1,”type_activity_name”:”xxx”}, {“type_activity_id”:2,”type_activity_name”:”yyy”}, {“type_activity_id”:3,”type_activity_name”:”zzz”} ] df = spark.createDataFrame(mylist) #UserWarning: inferring schema from dict is deprecated,please use pyspark.sql.Row instead # warnings.warn(“inferring schema from dict is deprecated,” As this warning message says, you should use pyspark.sql.Row instead. … Read more