Hi all,
I'm running Spark on Kubernetes on AWS using only spot instances for executors
with dynamic allocation enabled. This particular job is being
triggered by Airflow and it hit this bug [1] 6 times in a row. However, I had
recently switched to using PersistentVolumeClaims in Spark with
spark.shuffle.sort.io.plugin.class=org.apache.spark.shuffle.KubernetesLocalDiskShuffleDataIO
but kept
spark.dynamicAllocation.shuffleTracking.enabled=true. Upon review, I see under
the notes for spark.dynamicAllocation.enabled [2] that these
configurations are "or" not "and". However, when setting
spark.dynamicAllocation.shuffleTracking.enabled=false, my job crashes with the
message
org.apache.spark.SparkException: Dynamic allocation of executors requires one
of the following conditions: 1) enabling external shuffle
service through spark.shuffle.service.enabled. 2) enabling shuffle tracking
through spark.dynamicAllocation.shuffleTracking.enabled. 3)
enabling shuffle blocks decommission through spark.decommission.enabled and
spark.storage.decommission.shuffleBlocks.enabled. 4)
(Experimental) configuring spark.shuffle.sort.io.plugin.class to use a custom
ShuffleDataIO who's ShuffleDriverComponents supports reliable
storage.
Am I hitting this bug unavoidably? Or is there a configuration I'm missing to
enable
spark.shuffle.sort.io.plugin.class=org.apache.spark.shuffle.KubernetesLocalDiskShuffleDataIO
to replace
spark.dynamicAllocation.shuffleTracking.enabled=true?
Using Spark 3.5.1 - here's my full spark-defaults.conf just in case
spark.checkpoint.compress
true
spark.driver.cores
1
spark.driver.maxResultSize
2g
spark.driver.memory
5140m
spark.dynamicAllocation.enabled
true
spark.dynamicAllocation.executorAllocationRatio
0.33
spark.dynamicAllocation.maxExecutors
20
spark.dynamicAllocation.sustainedSchedulerBacklogTimeout
30
spark.eventLog.enabled
true
spark.executor.cores
3
spark.executor.logs.rolling.enableCompression
true
spark.executor.logs.rolling.maxRetainedFiles
48
spark.executor.logs.rolling.strategy
time
spark.executor.logs.rolling.time.interval
hourly
spark.hadoop.fs.s3a.impl
org.apache.hadoop.fs.s3a.S3AFileSystem
spark.hadoop.fs.s3a.connection.ssl.enabled
false
spark.hadoop.fs.s3a.fast.upload
true
spark.kryo.registrationRequired
false
spark.kryo.unsafe
false
spark.kryoserializer.buffer
1m
spark.kryoserializer.buffer.max
1g
spark.kubernetes.driver.limit.cores
750m
spark.kubernetes.driver.ownPersistentVolumeClaim
true
spark.kubernetes.driver.request.cores
750m
spark.kubernetes.driver.reusePersistentVolumeClaim
true
spark.kubernetes.driver.waitToReusePersistentVolumeClaim
true
spark.kubernetes.executor.limit.cores
3700m
spark.kubernetes.executor.request.cores
3700m
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.claimName
OnDemand
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.path
/data/spark-x/executor-x
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.readOnly
false
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.sizeLimit
20Gi
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.storageClass
ebs-sc
spark.kubernetes.namespace
spark
spark.serializer
org.apache.spark.serializer.KryoSerializer
spark.shuffle.sort.io.plugin.class
org.apache.spark.shuffle.KubernetesLocalDiskShuffleDataIO
spark.sql.orc.compression.codec
zlib
spark.sql.pyspark.jvmStacktrace.enabled
true
spark.sql.sources.partitionOverwriteMode
dynamic
spark.sql.streaming.kafka.useDeprecatedOffsetFetching
false
spark.submit.deployMode
cluster
Thanks,
Aaron
[1] https://issues.apache.org/jira/browse/SPARK-45858
[2] https://spark.apache.org/docs/latest/configuration.html#dynamic-allocation
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