ivasilyev1 opened a new pull request, #58929:
URL: https://github.com/apache/spark/pull/58929
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### What changes were proposed in this pull request?
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This PR changes 2 things:
- In `OffsetSeq` emit the rebinded SQLConf for both `OffsetSeqMetadata` and
`OffsetSeqMetadataV2`
- In `OfflineStateRepartitionUtils.isRepartitionBatch` add matched case to
ensure `shufflePartitions` and `previousShufflePartitions` exist
### Why are the changes needed?
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1. If you propose a new API, clarify the use case for a new API.
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These changes are necessary because in the current implementation
`OfflineStateRepartitionUtils.isRepartitionBatch` assumes that
`spark.sql.shuffle.partitions` will always be present in the offset metadata
and accesses the keys directly with `.get`.
With the changes in offset log format v2, it is possible for the offset log
to not contain this key if the current batch did not successfully complete.
When this happens, the checks in
`OfflineStateRepartitionUtils.isRepartitionBatch` will cause spark to crash:
```java
26/09/19 14:05:17 ERROR MicroBatchExecution: Query [id =
41da1d72-c034-4a36-9ae3-fdc0e1297405, runId =
4eb3a723-257b-45c7-a2e0-72dff79ebf5b] terminated with error
java.util.NoSuchElementException: None.get
at scala.None$.get(Option.scala:627)
at scala.None$.get(Option.scala:626)
at
org.apache.spark.sql.execution.streaming.state.OfflineStateRepartitionUtils$.isRepartitionBatch(OfflineStateRepartitionUtils.scala:54)
at
org.apache.spark.sql.execution.streaming.runtime.MicroBatchExecution.checkUnfinishedRepartitionBatch(MicroBatchExecution.scala:564)
at
org.apache.spark.sql.execution.streaming.runtime.MicroBatchExecution.initializeExecution(MicroBatchExecution.scala:494)
at
org.apache.spark.sql.execution.streaming.runtime.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:604)
at
org.apache.spark.sql.execution.streaming.runtime.StreamExecution.$anonfun$runStream$1(StreamExecution.scala:353)
at
scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
at
org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:810)
at
org.apache.spark.sql.execution.streaming.runtime.StreamExecution.org$apache$spark$sql$execution$streaming$runtime$StreamExecution$$runStream(StreamExecution.scala:313)
at
org.apache.spark.sql.execution.streaming.runtime.StreamExecution$$anon$1.run(StreamExecution.scala:236)
Traceback (most recent call last):
```
This behavior can be recreated with the following script to reliably trigger
the crash:
```python
import time
from pathlib import Path
from pyspark.sql import SparkSession
def get_spark():
spark = (
SparkSession.builder
.master("local[2]")
.appName("offset-log-bug")
.config("spark.sql.streaming.offsetLog.formatVersion", "2")
.config("spark.sql.streaming.checkUnfinishedRepartitionOnRestart",
"true")
.getOrCreate()
)
return spark
def fail_on_second_batch(batch_df, batch_id):
if batch_id == 1:
raise RuntimeError("Failing on second batch")
batch_df.count()
def main():
input_dir = Path("input")
input_dir.mkdir(exist_ok=True)
( input_dir/"file1.txt" ).write_text("1\n")
( input_dir/"file2.txt" ).write_text("2\n")
spark = get_spark()
df = spark.readStream.format("text").option("maxFilesPerTrigger",
"1").load("input")
query = (
df.writeStream
.foreachBatch(fail_on_second_batch)
.option("checkpointLocation", "checkpoint")
.trigger(availableNow=True)
.start()
)
try:
query.awaitTermination()
except:
print("#####################")
print("Expected first run failure")
print("Shutting down spark and recreating")
print("Continue in 5 seconds")
print("#####################")
time.sleep(5)
finally:
query.stop()
spark.stop()
spark = get_spark()
df = (spark.readStream.format("text").option("maxFilesPerTrigger",
"1").load("input"))
query = (
df.writeStream
.foreachBatch(lambda batch_df, batch_id: batch_df.count())
.option("checkpointLocation", "checkpoint")
.trigger(availableNow=True)
.start()
)
query.awaitTermination()
if __name__ == "__main__":
main()
```
### Does this PR introduce _any_ user-facing change?
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I am unsure if this counts as a user-facing change, but the noticeable
change would be that `spark.sql.shuffle.partitions` key is always present in
the offset metadata
### How was this patch tested?
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I tested the crash condition with the above python script. After introducing
these change and compiling a spark distribution, the query no longer crashes
and instead finishes successfully
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