java.lang.RuntimeException: Unsupported literal type class java.util.ArrayList [1]
This happens in PySpark
when a Python list is provide where a scalar is required.
Assuming id0 is an integer column in the DataFrame df,
the following code throws the above error.
v = [1, 2, 3 …The easist way to define a UDF in PySpark is to use the @udf tag,
and similarly the easist way to define a Pandas UDF in PySpark is to use the @pandas_udf tag.
Pandas UDFs are preferred to UDFs for server reasons.
First, pandas UDFs are typically much faster than UDFs.
Second, pandas UDFs are more flexible than UDFs on parameter passing.
Both UDFs and pandas UDFs can take multiple columns as parameters.
In addition,
pandas UDFs can take a DataFrame as parameter
(when passed to the apply
It is suggested that you use Python script instead of Shell script as much as possible.
If you do have to stick with Shell script,
you can use =~ for regular expression matching in Bash.
This make Bash syntax extremely flexible and powerful.
For example,
you can match multiple strings using …
I countered the issue when using PySpark locally
(the issue can happen to a cluster as well).
It turned out to be caused by a misconfiguration of the environment variable JAVA_HOME in Docker.
PySpark: Exception: Java gateway process exited before sending the driver its port number
rb/wb when read/write pickle files.