A Spark newbie here. I recently started playing around with Spark on my local machine on two cores by using the command:
pyspark --master local[2]
I have a 393Mb text file which has almost a million rows. I wanted to perform some data manipulation operation. I am using the built-in dataframe functions of PySpark to perform simple operations like groupBy
, sum
, max
, stddev
.
However, when I do the exact same operations in pandas on the exact same dataset, pandas seems to defeat pyspark by a huge margin in terms of latency.
I was wondering what could be a possible reason for this. I have a couple of thoughts.
Thanks for looking. Much appreciated.
Because:
You can go on like this for a long time...
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