Retain keys with null values while writing JSON in spark
I am trying to write a JSON file using spark. There are some keys that have null
as value. These show up just fine in the DataSet
, but when I write the file, the keys get dropped. How do I ensure they are retained?
code to write the file:
ddp.coalesce(20).write().mode("overwrite").json("hdfs://localhost:9000/user/dedupe_employee");
part of JSON data from source:
"event_header": {
"accept_language": null,
"app_id": "App_ID",
"app_name": null,
"client_ip_address": "IP",
"event_id": "ID",
"event_timestamp": null,
"offering_id": "Offering",
"server_ip_address": "IP",
"server_timestamp": 1492565987565,
"topic_name": "Topic",
"version": "1.0"
}
Output:
"event_header": {
"app_id": "App_ID",
"client_ip_address": "IP",
"event_id": "ID",
"offering_id": "Offering",
"server_ip_address": "IP",
"server_timestamp": 1492565987565,
"topic_name": "Topic",
"version": "1.0"
}
In the above example keys accept_language
, app_name
and event_timestamp
have been dropped.
Solution 1:
Apparently, spark does not provide any option to handle nulls. So following custom solution should work.
import com.fasterxml.jackson.module.scala.DefaultScalaModule
import com.fasterxml.jackson.module.scala.experimental.ScalaObjectMapper
import com.fasterxml.jackson.databind.ObjectMapper
case class EventHeader(accept_language:String,app_id:String,app_name:String,client_ip_address:String,event_id: String,event_timestamp:String,offering_id:String,server_ip_address:String,server_timestamp:Long,topic_name:String,version:String)
val ds = Seq(EventHeader(null,"App_ID",null,"IP","ID",null,"Offering","IP",1492565987565L,"Topic","1.0")).toDS()
val ds1 = ds.mapPartitions(records => {
val mapper = new ObjectMapper with ScalaObjectMapper
mapper.registerModule(DefaultScalaModule)
records.map(mapper.writeValueAsString(_))
})
ds1.coalesce(1).write.text("hdfs://localhost:9000/user/dedupe_employee")
This will produce output as :
{"accept_language":null,"app_id":"App_ID","app_name":null,"client_ip_address":"IP","event_id":"ID","event_timestamp":null,"offering_id":"Offering","server_ip_address":"IP","server_timestamp":1492565987565,"topic_name":"Topic","version":"1.0"}
Solution 2:
If you are on Spark 3, you can add
spark.sql.jsonGenerator.ignoreNullFields false