angelosnm opened a new issue, #1723:
URL: https://github.com/apache/sedona/issues/1723
I have set up a standalone Spark cluster where PySpark jobs are being sent.
These jobs are having the below config where S3/MinIO is being used as HDFS
(using the S3A package) to read raster files:
```python
config = (
SedonaContext.builder()
.master(spark_endpoint) \
.appName("RasterProcessingWithSedona") \
.config("spark.driver.host", socket.gethostbyname(socket.gethostname()))
\
.config("spark.driver.port", "2222") \
.config("spark.blockManager.port", "36859") \
.config("spark.executor.memory", "16g") \
.config("spark.executor.cores", "4") \
.config("spark.driver.memory", "10g") \
.config("spark.hadoop.fs.s3a.endpoint", s3_endpoint) \
.config("spark.hadoop.fs.s3a.access.key", s3_access_key_id) \
.config("spark.hadoop.fs.s3a.secret.key", s3_secret_access_key) \
.config("spark.hadoop.fs.s3a.connection.ssl.enabled", "false") \
.config("spark.hadoop.fs.s3a.path.style.access", "true") \
.config(
'spark.jars.packages',
'org.apache.sedona:sedona-spark-shaded-3.5_2.12:1.6.1,'
'org.datasyslab:geotools-wrapper:1.6.1-28.2'
)
.getOrCreate()
)
```
Then, the raster/tif files are being accessed as per below:
```python
raster_path = "s3a://data/BFA"
rawDf = sedona.read.format("binaryFile").option("recursiveFileLookup",
"true").option("pathGlobFilter", "*.tif*").load(raster_path)
rawDf.createOrReplaceTempView("rawdf")
rawDf.show()
```
And this code. returns the error mentioned in the "Actual behavior" entry.
If this code runs under local mode it runs normally
## Expected behavior
## Actual behavior
```bash
Py4JJavaError: An error occurred while calling o66.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0
in stage 3.0 failed 4 times, most recent failure: Lost task 0.3 in stage 3.0
(TID 5) (192.168.18.112 executor 1): TaskResultLost (result lost from block
manager)
Driver stacktrace:
at
org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2856)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2792)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2791)
at
scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at
scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2791)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1247)
at
org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1247)
at scala.Option.foreach(Option.scala:407)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1247)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:3060)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2994)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2983)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:989)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2393)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2414)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2433)
at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:530)
at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:483)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:61)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:4334)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:3316)
at
org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:4324)
at
org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:546)
at
org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:4322)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:125)
at
org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:201)
at
org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:108)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:66)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:4322)
at org.apache.spark.sql.Dataset.head(Dataset.scala:3316)
at org.apache.spark.sql.Dataset.take(Dataset.scala:3539)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:280)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:315)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at
py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Thread.java:750)
```
## Steps to reproduce the problem
## Settings
Sedona version = 1.6.1
Apache Spark version = 3.5.2
Apache Flink version = N/A
API type = Python
Scala version = 2.12
JRE version = 1.8.0_432
Python version = 3.11.10
Environment = Standalone
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