Hi Spark-users:
I have a dataframe "df_t" which was generated from other dataframes by
several transformations. And then I did something very simple, just
counting the rows, that is the following code:
(A)
df_t_1 = df_t.groupby(["Id","key"]).count().withColumnRenamed("count",
"cnt1")
df_t_2 = df_t.groupby("Id").count().withColumnRenamed("count", "cnt2")
df_t_3 = df_t_1.join(df_t_2, ["Id"])
df_t.join(df_t_3, ["Id","key"])
When I run this query, I got the error that "key" is missing during
joining. However, the column "key" is clearly in the dataframe dt. What is
strange is that: if I first do this:
data = df_t.collect(); df_t = spark.createDataFrame(data); (B)
then (A) can run without error. However, the code (B) should not change
the dataframe dt_t at all. Why the snippet (A) can run with (B) but
failed without (B)? Also, A different joining sequence can also complete
without error:
(C)
df_t_1 = df_t.groupby(["Id","key"]).count().withColumnRenamed("count",
"cnt1")
df_t_2 = df_t.groupby("Id").count().withColumnRenamed("count", "cnt2")
df_t.join(df_t_1, ["Id","key"]).join(df_t_2, ["Id"])
But (A) and (C) are conceptually the same and should produce the same
result. What could possibly go wrong here? Any hints to track down
the problem is appreciated. I am using spark 2.1.
