Kontinuation opened a new issue, #1766: URL: https://github.com/apache/datafusion-comet/issues/1766
### What is the problem the feature request solves? The `native_datafusion` parquet scanner throws the following error when reading parquet files on S3: ``` org.apache.comet.CometNativeException: External: Generic S3 error: Error performing PUT http://169.254.169.254/latest/api/token in 1.79000475s, after 10 retries, max_retries: 10, retry_timeout: 180s - HTTP error: error sending request at org.apache.comet.Native.executePlan(Native Method) at org.apache.comet.CometExecIterator.$anonfun$getNextBatch$2(CometExecIterator.scala:150) at org.apache.comet.CometExecIterator.$anonfun$getNextBatch$2$adapted(CometExecIterator.scala:149) at org.apache.comet.vector.NativeUtil.getNextBatch(NativeUtil.scala:157) at org.apache.comet.CometExecIterator.$anonfun$getNextBatch$1(CometExecIterator.scala:149) at org.apache.comet.Tracing$.withTrace(Tracing.scala:31) at org.apache.comet.CometExecIterator.getNextBatch(CometExecIterator.scala:147) at org.apache.comet.CometExecIterator.hasNext(CometExecIterator.scala:170) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:491) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460) at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:893) at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:893) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:367) at org.apache.spark.rdd.RDD.iterator(RDD.scala:331) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166) at org.apache.spark.scheduler.Task.run(Task.scala:141) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620) at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64) at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623) at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136) at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635) at java.base/java.lang.Thread.run(Thread.java:840) ``` The error message suggests that the S3 client is [retrieving token on EC2 instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html), even though I am not running comet on EC2. It is also quite common to configure credentials for accessing AWS S3 by specifying Hadoop configurations, for instance: ```python spark = (SparkSession.builder .config("spark.hadoop.fs.s3a.aws.credentials.provider", "org.apache.hadoop.fs.s3a.AnonymousAWSCredentialsProvider") .getOrCreate()) ``` Currently there's no mechanism for using credentials configured as Hadoop S3 configurations when reading S3 using the native datafusion parquet reader. We'd better build some compatibility layer to recognize Hadoop S3 configurations in native parquet reader for compatibility with Vanilla Spark. ### Describe the potential solution _No response_ ### Additional context _No response_ -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
