The spark json read is unforgiving of things like missing elements from some
json records, or mixed types.
If you want to pass invalid json files through spark you're best doing an
initial parse through the Jackson APIs using a defined schema first, then you
can set types like Option[String] where a column is optional, then convert the
validated back into a new string variable, then read the string as a dataframe.
Thanks,
Ewan
On 3 Jun 2016 22:03, Jerry Wong <jerry.king2.w...@gmail.com> wrote:
Hi,
I met a problem of empty field in the nested JSON file with Spark SQL. For
instance,
There are two lines of JSON file as follows,
{
"firstname": "Jack",
"lastname": "Nelson",
"address": {
"state": "New York",
"city": "New York"
}
}{
"firstname": "Landy",
"middlename": "Ken",
"lastname": "Yong",
"address": {
"state": "California",
"city": "Los Angles"
}
}
I use Spark SQL to get the files like,
val row = sqlContext.sql("SELECT firstname, middlename, lastname,
address.state, address.city FROM jsontable")
The compile will tell me the error of line1: no "middlename".
How do I handle this case in the SQL sql?
Many thanks in advance!
Jerry
