Unable to read data when using nested field as identifier
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- Java
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Description
### Apache Iceberg version
1.10.1 (latest release)
### Query engine
Spark
### Please describe the bug 🐞
We have a table using some nested fields as identifier, when we read individual columns (`select *` works fine) from such table an error is thrown:
```
java.lang.IllegalArgumentException: Cannot find required field for ID 20
at org.apache.iceberg.relocated.com.google.common.base.Preconditions.checkArgument(Preconditions.java:190)
at org.apache.iceberg.data.DeleteFilter.fileProjection(DeleteFilter.java:304)
at org.apache.iceberg.data.DeleteFilter.(DeleteFilter.java:100)
at org.apache.iceberg.spark.source.BaseReader$SparkDeleteFilter.(BaseReader.java:205)
at org.apache.iceberg.spark.source.BatchDataReader.open(BatchDataReader.java:116)
at org.apache.iceberg.spark.source.BatchDataReader.open(BatchDataReader.java:43)
at org.apache.iceberg.spark.source.BaseReader.next(BaseReader.java:141)
at org.apache.spark.sql.execution.datasources.v2.PartitionIterator.hasNext(DataSourceRDD.scala:120)
at org.apache.spark.sql.execution.datasources.v2.MetricsIterator.hasNext(DataSourceRDD.scala:158)
at org.apache.spark.sql.execution.datasources.v2.DataSourceRDD$$anon$1.$anonfun$hasNext$1(DataSourceRDD.scala:63)
at org.apache.spark.sql.execution.datasources.v2.DataSourceRDD$$anon$1.$anonfun$hasNext$1$adapted(DataSourceRDD.scala:63)
at scala.Option.exists(Option.scala:376)
at org.apache.spark.sql.execution.datasources.v2.DataSourceRDD$$anon$1.hasNext(DataSourceRDD.scala:63)
at org.apache.spark.sql.execution.datasources.v2.DataSourceRDD$$anon$1.advanceToNextIter(DataSourceRDD.scala:97)
at org.apache.spark.sql.execution.datasources.v2.DataSourceRDD$$anon$1.hasNext(DataSourceRDD.scala:63)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.columnartorow_nextBatch_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.hashAgg_doAggregateWithoutKey_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvaluator$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:43)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:140)
at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:104)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:54)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166)
at org.apache.spark.scheduler.Task.run(Task.scala:141)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:621)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:624)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:829)
```
### Willingness to contribute
- [x] I can contribute a fix for this bug independently
- [x] I would be willing to contribute a fix for this bug with guidance from the Iceberg community
- [ ] I cannot contribute a fix for this bug at this time
Contributor guide
Research direction
Start at org.apache.iceberg.data.DeleteFilter.fileProjection, then follow the Spark reader path through BaseReader.java and BatchDataReader.java shown in the stack trace. Reproduce the failure with a table using a nested identifier and an individual-column read; done means the read succeeds without the missing-field exception while select * continues to work.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- java, spark
- Domain
- data, databases
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100