I have a Spark job that reads from a postgresql (v9.5) table, and write result
to parquet. The code flow is not complicated, basically
case class MyCaseClass(field1: String, field2: String)
val df = spark.read.format("jdbc")...load()
df.createOrReplaceTempView(...)
val newdf = spark.sql("seslect field1, field2 from
mytable").as[MyCaseClass].map { row =>
val fieldX = ... // extract something from field2
(field1, fileldX)
}.filter { ... /* filter out field 3 that's not valid */ }
newdf.write.mode(...).parquet(destPath)
This job worked correct without a problem. But it's doesn't look working ok
(the job looks like hanged) when adding more fields. The refactored job looks
as below
...
val newdf = spark.sql("seslect field1, field2, ... fieldN from
mytable").as[MyCaseClassWithMoreFields].map { row =>
...
NewCaseClassWithMoreFields(...) // all fields plus fieldX
}.filter { ... }
newdf.write.mode(...).parquet(destPath)
Basically what the job does is extracting some info from one of a field in db
table, appends that newly extracted field to the original row, and then dumps
the whole new table to parquet.
new filed + (original field1 + ... + original fieldN)
...
...
Records loaded by spark sql to spark job (before refactored) are around 8MM,
this remains the same, but when the refactored spark runs, it looks hanging
there without progress. The only output on the console is (there is no crash,
no exceptions thrown)
WARN HeartbeatReceiver:66 - Removing executor driver with no recent
heartbeats: 137128 ms exceeds timeout 120000 ms
Memory in top command looks like
VIRT RES SHR %CPU %MEM
15.866g 8.001g 41.4m 740.3 25.6
The command used to submit spark job is
spark-submit --class ... --master local[*] --driver-memory 10g
--executor-memory 10g ... --files ... --driver-class-path ... <jar file> ...
How can I debug or check which part of my code might cause the problem (so I
can improve it)?
Thanks
