LuciferYang opened a new pull request, #50127:
URL: https://github.com/apache/spark/pull/50127

   ### What changes were proposed in this pull request?
   This pr reverts commit 971b9a40c42a1d8c050941ce8088fdfc6ac442bc.
   
   
   ### Why are the changes needed?
   A crash related to RocksDB has occurred in the macOS daily test after 
upgrade to 9.10.0:
   - https://github.com/apache/spark/actions/runs/13608353684/job/38042505327
   - https://github.com/apache/spark/actions/runs/13464386260/job/37626883152
   
   ```
   #
   # A fatal error has been detected by the Java Runtime Environment:
   #
   #  SIGSEGV (0xb) at pc=0x0000000137105814, pid=32170, tid=707595
   #
   # JRE version: OpenJDK Runtime Environment Zulu21.40+17-CRaC-CA (21.0.6+7) 
(build 21.0.6+7-LTS)
   # Java VM: OpenJDK 64-Bit Server VM Zulu21.40+17-CRaC-CA (21.0.6+7-LTS, 
mixed mode, sharing, tiered, compressed oops, compressed class ptrs, g1 gc, 
bsd-aarch64)
   # Problematic frame:
   # C  [librocksdbjni16177160674801731666.jnilib+0x1d814]  
Java_org_rocksdb_FlushOptions_disposeInternalJni+0x360
   #
   # No core dump will be written. Core dumps have been disabled. To enable 
core dumping, try "ulimit -c unlimited" before starting Java again
   #
   # An error report file with more information is saved as:
   # /Users/runner/work/spark/spark/sql/core/hs_err_pid32170.log
   21:02:35.349 ERROR org.apache.spark.util.Utils: Aborting task
   org.apache.spark.SparkException: [CANNOT_WRITE_STATE_STORE.CANNOT_COMMIT] 
Error writing state store files for provider 
RocksDBStateStore[id=(op=0,part=1),dir=file:/Users/runner/work/spark/spark/sql/core/target/tmp/streaming.metadata-aa0706db-1b92-485b-b2e6-f356f91ffda6/state/0/1].
 Cannot perform commit during state checkpoint. SQLSTATE: 58030
        at 
org.apache.spark.sql.errors.QueryExecutionErrors$.failedToCommitStateFileError(QueryExecutionErrors.scala:2203)
        at 
org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider$RocksDBStateStore.commit(RocksDBStateStoreProvider.scala:243)
        at 
org.apache.spark.sql.execution.streaming.TransformWithStateExec.$anonfun$processDataWithPartition$5(TransformWithStateExec.scala:431)
        at 
scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at org.apache.spark.util.Utils$.timeTakenMs(Utils.scala:481)
        at 
org.apache.spark.sql.execution.streaming.StateStoreWriter.timeTakenMs(statefulOperators.scala:384)
        at 
org.apache.spark.sql.execution.streaming.StateStoreWriter.timeTakenMs$(statefulOperators.scala:384)
   21:02:35.351 ERROR 
org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask: Aborting 
commit for partition 1 (task 6, attempt 0, stage 3.0)
   
   21:02:35.351 ERROR 
org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask: Aborted 
commit for partition 1 (task 6, attempt 0, stage 3.0)
   
   - transformWithState - streaming with rocksdb and invalid processor should 
fail (encoding = Avro) (without changelog checkpointing)
   21:02:35.352 WARN org.apache.spark.scheduler.TaskSetManager: Lost task 1.0 
in stage 3.0 (TID 6) (localhost executor driver): TaskKilled (Stage cancelled: 
[STATEFUL_PROCESSOR_CANNOT_PERFORM_OPERATION_WITH_INVALID_HANDLE_STATE] Failed 
to perform stateful processor operation=get_value_state with invalid handle 
state=INITIALIZED. SQLSTATE: 42802)
   
   21:02:35.372 WARN 
org.apache.spark.sql.execution.streaming.ResolveWriteToStream: 
spark.sql.adaptive.enabled is not supported in streaming DataFrames/Datasets 
and will be disabled.
   
   Warning: [1237.706s][warning][os] Loading hsdis library failed
   #
   # If you would like to submit a bug report, please visit:
   #   http://www.azul.com/support/
   # The crash happened outside the Java Virtual Machine in native code.
   # See problematic frame for where to report the bug.
   #
   ```
   
   
   ### Does this PR introduce _any_ user-facing change?
   No
   
   
   ### How was this patch tested?
   Pass GitHub Actions
   
   
   ### Was this patch authored or co-authored using generative AI tooling?
   No


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