vanzin commented on a change in pull request #26470: [SPARK-27042][SS]
Invalidate cached Kafka producer in case of task retry
URL: https://github.com/apache/spark/pull/26470#discussion_r346542556
##########
File path:
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/CachedKafkaProducer.scala
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@@ -93,6 +93,10 @@ private[kafka010] object CachedKafkaProducer extends
Logging {
.setAuthenticationConfigIfNeeded()
.build()
val key = toCacheKey(updatedKafkaParams)
+ if (TaskContext.get != null && TaskContext.get.attemptNumber >= 1) {
Review comment:
> 2 jobs must write the same TopicPartition at the same time
Why at the same time? All you need to hit my case is two tasks on the first
job using different producers, and can't that happen e.g. if you have executors
with more than one core?
It seems like Spark would eventually self-heal at the cost of some failed
tasks, but can't we do better? As Jungtaek said we already have to return the
producer to the pool. It's just a matter of, instead of returning it,
invalidating it if an error is detected.
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