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https://issues.apache.org/jira/browse/SPARK-48950?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17883967#comment-17883967
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Thomas Newton commented on SPARK-48950:
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I've been doing a bit more experimentation and I have some more info to share.
Firstly I've attached dumps of some of the outputs I've got from running my
reproduce script. [^corrupt_data_examples.zip]
Checking these we can see that the majority of the time the bug occurs in just
a simple double type column but there is also one occurrence in the array of
double column.
I also did some testing just using local spark session on a single node and I
was also able to reproduce. NOTE: to reproduce its important to enable retries
e.g. `SparkSession.builder.master("local[12, 10]")`. Its also generally a bit
slower than reproducing on a multi-node cluster, I assume because the code is
just running slower.
I also tried reproducing with the data stored on the local filesystem. In this
case I've not been able to reproduce, so I suspect the problem is originating
from getting corrupt data from hadoop-azure when reading Azure blob storage.
Maybe the problem is just a case of we need to be using some checksums (from
what I could tell spark does not make use of the parquet checksums) but I would
really like to understand why reverting
[https://github.com/apache/spark/pull/39950] solves or at least reduces the
frequency of the problem.
> Corrupt data from parquet scans
> -------------------------------
>
> Key: SPARK-48950
> URL: https://issues.apache.org/jira/browse/SPARK-48950
> Project: Spark
> Issue Type: Bug
> Components: Input/Output
> Affects Versions: 3.5.0, 4.0.0, 3.5.1, 3.5.2
> Environment: Spark 3.5.0
> Running on kubernetes
> Using Azure Blob storage with hierarchical namespace enabled
> Reporter: Thomas Newton
> Priority: Major
> Labels: correctness
> Attachments: corrupt_data_examples.zip, example_task_errors.txt,
> generate_data_to_reproduce_spark-48950.ipynb, job_dag.png,
> reproduce_spark-48950.py, sql_query_plan.png
>
>
> Its very rare and non-deterministic but since Spark 3.5.0 we have started
> seeing a correctness bug in parquet scans when using the vectorized reader.
> We've noticed this on double type columns where occasionally small groups
> (typically 10s to 100s) of rows are replaced with crazy values like
> `-1.29996470e+029, 3.56717569e-184, 7.23323243e+307, -1.05929677e+045,
> -7.60562076e+240, -3.18088886e-064, 2.89435993e-116`. I think this is the
> result of interpreting uniform random bits as a double type. Most of my
> testing has been on an array of double type column but we have also seen it
> on un-nested plain double type columns.
> I've been testing this by adding a filter that should return zero results but
> will return non-zero if the parquet scan has problems. I've attached
> screenshots of this from the Spark UI.
> I did a `git bisect` and found that the problem starts with
> [https://github.com/apache/spark/pull/39950], but I haven't yet understood
> why. Its possible that this change is fine but it reveals a problem
> elsewhere? I did also notice [https://github.com/apache/spark/pull/44853]
> which appears to be a different implementation of the same thing so maybe
> that could help.
> Its not a major problem by itself but another symptom appears to be that
> Parquet scan tasks fail at a rate of approximately 0.03% with errors like
> those in the attached `example_task_errors.txt`. If I revert
> [https://github.com/apache/spark/pull/39950] I get exactly 0 task failures on
> the same test.
>
> The problem seems to be a bit dependant on how the parquet files happen to be
> organised on blob storage so I don't yet have a reproduce that I can share
> that doesn't depend on private data.
> I tested on a pre-release 4.0.0 and the problem was still present.
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