Jason Xu created SPARK-38388:
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Summary: Repartition + Stage retries could lead to incorrect data
Key: SPARK-38388
URL: https://issues.apache.org/jira/browse/SPARK-38388
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.1.1, 2.4.0
Environment: Spark 2.4 and 3.1
Reporter: Jason Xu
Spark repartition uses RoundRobinPartitioning, the generated results is
non-deterministic when data has some randomness and stage/task retries happen.
The bug can be triggered when upstream data has some randomness, a repartition
is called on them, then followed by result stage (could be more stages).
As the pattern shows below:
upstream stage (data with randomness) -> (repartition shuffle) -> result stage
When one executor goes down at result stage, some tasks of that stage might
have finished, others would fail, shuffle files on that executor also get lost,
some tasks from previous stage (upstream data generation, repartition) will
need to rerun to generate dependent shuffle data files.
Because data has some randomness, regenerated data in upstream retried tasks is
slightly different, repartition then generates inconsistent ordering, then
tasks at result stage will be retried generating different data.
This is similar but different to
https://issues.apache.org/jira/browse/SPARK-23207, fix for it uses extra local
sort to make the row ordering deterministic, the sorting algorithm it uses
simply compares row/record binaries. But in this case, upstream data has some
randomness, the sorting algorithm doesn't help keep the order, thus
RoundRobinPartitioning introduced non-deterministic result.
The following code returns 998818, instead of 1000000:
{code:java}
import scala.sys.process._
import org.apache.spark.TaskContext
case class TestObject(id: Long, value: Double)
val ds = spark.range(0, 1000 * 1000, 1).repartition(100,
$"id").withColumn("val", rand()).repartition(100).map { row =>
if (TaskContext.get.stageAttemptNumber == 0 &&
TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId > 98)
{
throw new Exception("pkill -f java".!!)
}
TestObject(row.getLong(0), row.getDouble(1))
}
ds.toDF("id", "value").write.saveAsTable("tmp.test_table")
spark.sql("select count(distinct id) from tmp.test_table").show{code}
Command:
{code:java}
spark-shell --num-executors 10 (--conf spark.dynamicAllocation.enabled=false
--conf spark.shuffle.service.enabled=false){code}
To simulate the issue, disable external shuffle service is needed (if it's also
enabled by default in your environment), this is to trigger shuffle file loss
and previous stage retries.
In our production, we have external shuffle service enabled, this data
correctness issue happened when there were node losses.
Although there's some non-deterministic factor in upstream data, user wouldn't
expect to see incorrect result.
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