Hmm, I think I got what Jingnan means. The lambda function is x != i and i
is not evaluated when the lambda function was defined. So the pipelined rdd
is rdd.filter(lambda x: x != i).filter(lambda x: x != i), rather than
having the values of i substituted. Does that make sense to you, Sean?
On
Dear Spark users,
I ran the Python code below on a simple RDD, but it gave strange results.
The filtered RDD contains non-existent elements which were filtered away
earlier. Any idea why this happened?
```
rdd = spark.sparkContext.parallelize([0,1,2])
for i in range(3):
print("RDD is ",
