Hi Spark Developers,
I just ran some very simple operations on a dataset. I was surprise by the
execution plan of take(1), head() or first().
For your reference, this is what I did in pyspark 1.5:
df=sqlContext.read.parquet("someparquetfiles")
df.head()
The above lines take over 15 minutes. I was frustrated because I can do
better without using spark :) Since I like spark, so I tried to figure out
why. It seems the dataframe requires 3 stages to give me the first row. It
reads all data (which is about 1 billion rows) and run Limit twice.
Instead of head(), show(1) runs much faster. Not to mention that if I do:
df.rdd.take(1) //runs much faster.
Is this expected? Why head/first/take is so slow for dataframe? Is it a bug
in the optimizer? or I did something wrong?
Best Regards,
Jerry
