Ngone51 opened a new pull request #32385:
URL: https://github.com/apache/spark/pull/32385
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### What changes were proposed in this pull request?
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This PR proposes to add checksum support for shuffle blocks. The basic idea
is:
On the mapper side, we'll wrap a `CheckedOutputStream` upon the
`FileOutputStream` to calculate the checksum (use the same checksum calculator
`Adler32` with broadcast) for each shuffle block (a.k.a partition) at the same
time when we writing map output files. And similar to the index file, we'll
have a checksum file to save these checksums.
On the reducer side, we'll also wrap a `CheckedInputStream` upon the
`FileInputStream` to read the block. When block corruption is detected, we'll
try to diagnose corruption for the cause:
First, we'll use the `CheckedInputStream` to consume the remaining data of
the corrupted block to calculate the checksum (`c1`);
Second, the reducer send an RPC request called `DiagnoseCorruption` (which
contains `c1`) to the server (where the reducer executed)
Third, the server will read (using a very small memory) the corresponding
block back from the disk and calculate the checksum (`c2`) again for it. And
also read back the checksum(`c3`) of the block saved in the checksum file.
Then, if `c2 != c3`, we'll suspect the corruption is caused by the disk issue.
Otherwise, if `c1 != c3`, we'll suspect the corruption is caused by the network
issue. Otherwise, the cause remains unknown. The server then will reply to the
reducer with `CorruptionCause` containing the cause.
Fourth, the reducer needs to take action after it receives the cause. If
it's a disk issue or unknown, it will throw fetch failure directly. If it's a
network issue, it will re-fetch the block later. Also note that, if the
corruption happens inside `BufferReleasingInputStream`, the reducer will throw
the fetch failure immediately no matter what the cause is since the data has
been partially consumed by downstream RDDs. If corruption happens again after
retry, the reducer will throw the fetch failure directly this time without the
diagnosis.
Overall, I think we don't introduce severe overhead with this proposal. In a
normal case, the checksum is calculated in the streaming way as well as other
streams, e.g., encryption, compression. And the major overhead here is that we
need an extra data file traverse in the error case in order to calculate the
checksum (`c2`).
And the proposal in this PR is much simpler compared to the previous one
https://github.com/apache/spark/pull/15894/ (abandoned due to complexity ),
which introduce more overhead as it need to traverse the data file twice for
every block. In that proposal, the checksum is appended to each block data, so
it's also invasive to the existing code.
### Why are the changes needed?
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Shuffle data corruption is a long-standing issue in Spark. For example, in
SPARK-18105, people continually reports corruption issue. However, data
corruption is difficult to reproduce in most cases and even harder to tell the
root cause. We don't know if it's a Spark issue or not. With the checksum
support for the shuffle, Spark itself can at least distinguish the cause
between disk and network, which is very important for users.
### Does this PR introduce _any_ user-facing change?
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Yes.
1) Added a conf `spark.shuffle.checksum` to let user enables/disables the
checksum (enabled by default)
2) With checksum enabled, users can know the possible cause of corruption
rather than "Stream is corrupted" only.
### How was this patch tested?
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Added an end-to-end unit test in `ShuffleSuite`.
I'll add more tests if the community accepts the proposal.
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