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[Data] Improve stability of Parquet metadata prefetch task #42044

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merged 3 commits into from Dec 21, 2023
Merged

[Data] Improve stability of Parquet metadata prefetch task #42044

merged 3 commits into from Dec 21, 2023

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c21
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@c21 c21 commented Dec 20, 2023

Why are these changes needed?

This PR is a fix for Parquet metadata prefetching task, when reading a large amount of Parquet files on S3 (>50k). Before this PR, the Parquet prefetch metadata task is running on head node (w/ DEFAULT scheduling strategy), and not retry on S3 transient exception. So it can fail very quickly because it launches too many request from same node, and throttled by S3.

This PR does 3 things:

  • Fix scheduling strategy to use SPREAD same as read task, to spread out metadata prefetch task across cluster. This avoids hit S3 w/ too many requests from same node.
  • Auto-retry on OSError, where S3 throws transient error such as Access Denied, Read Timeout.
  • Extract num_cpus default value out as a variable. So we can tune the value to control the concurrency of prefetch metadata task for particular workload. Sometime num_cpus=0.5 does not work well.

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  • I've signed off every commit(by using the -s flag, i.e., git commit -s) in this PR.
  • I've run scripts/format.sh to lint the changes in this PR.
  • I've included any doc changes needed for https://docs.ray.io/en/master/.
    • I've added any new APIs to the API Reference. For example, if I added a
      method in Tune, I've added it in doc/source/tune/api/ under the
      corresponding .rst file.
  • I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
  • Testing Strategy
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    • This PR is not tested :(

@c21
Improve stability of Parquet metadata prefetch task
132bbf5 
Signed-off-by: Cheng Su <scnju13@gmail.com>
@c21
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c21 commented Dec 20, 2023

It's hard to understand a unit test given it depends on failure from prefetching metadata.

I can run the Parquet metadata prefetching release test, to make sure it still works - https://github.com/ray-project/ray/blob/master/release/release_tests.yaml#L5485 .

@c21
Fix unit test
e3b9013 
Signed-off-by: Cheng Su <scnju13@gmail.com>
scottjlee
scottjlee approved these changes Dec 20, 2023
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python/ray/data/datasource/parquet_datasource.py
# The application-level exceptions to retry for metadata prefetching task.
# Default to retry on `OSError` because AWS S3 would throw this transient
# error when load is too high.
RETRY_EXCEPTIONS_FOR_META_FETCH_TASK = [OSError]
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should we add this in general for all FileBasedDatasource reads?

ray/python/ray/data/datasource/file_based_datasource.py

Line 68 in 9740642

OPEN_FILE_RETRY_ON_ERRORS = ["AWS Error SLOW_DOWN"]

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Only Parquet has this metadata prefetching. So we can add it first here. For all file-based datasources, looks like the expoential backoff retry is working.

python/ray/data/datasource/parquet_datasource.py
@@ -47,6 +47,15 @@
FRAGMENTS_PER_META_FETCH = 6
PARALLELIZE_META_FETCH_THRESHOLD = 24

# The `num_cpus` for each metadata prefetching task.
# Default to 0.5 instead of 1 because it is cheaper than normal read task.
NUM_CPUS_FOR_META_FETCH_TASK = 0.5
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so currently, there is no configurable way (e.g. through DataContext) to modify this right?

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yes, I am hesitating to add to DataContext (which exposed to users). If we see this is a value needed by users, we can expose it later. For now, let's start w/ a varilable, so at least we (internal developer) can change it.

raulchen
raulchen approved these changes Dec 20, 2023
View reviewed changes
python/ray/data/tests/test_parquet.py Outdated
@@ -206,7 +206,10 @@ def test_parquet_read_meta_provider(ray_start_regular_shared, fs, data_path):
pq.write_table(table, path2, filesystem=fs)

class TestMetadataProvider(DefaultParquetMetadataProvider):
def prefetch_file_metadata(self, fragments):
def prefetch_file_metadata(self, fragments, **ray_remote_args):
assert ray_remote_args["num_cpus"] == 0.5
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Suggested change
assert ray_remote_args["num_cpus"] == 0.5
assert ray_remote_args["num_cpus"] == NUM_CPUS_FOR_META_FETCH_TASK

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updated.

@c21
Address comment
0502ba9 
Signed-off-by: Cheng Su <scnju13@gmail.com>
@c21
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c21 commented Dec 21, 2023

Reran the Parquet metadata release test, no regression - https://buildkite.com/ray-project/release/builds/4714#018c89f0-5bde-4011-835d-90d244d2c3f0 .

@c21 c21 merged commit 6fdc9e3 into ray-project:master Dec 21, 2023
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@c21 c21 deleted the fix-prefetch branch December 21, 2023 01:41
vickytsang pushed a commit to ROCm/ray that referenced this pull request Jan 12, 2024
@c21 @vickytsang
[Data] Improve stability of Parquet metadata prefetch task (ray-proje…
170aabb 
…ct#42044)

This PR is a fix for Parquet metadata prefetching task, when reading a large amount of Parquet files on S3 (>50k). Before this PR, the Parquet prefetch metadata task is running on head node (w/ `DEFAULT` scheduling strategy), and not retry on S3 transient exception. So it can fail very quickly because it launches too many request from same node, and throttled by S3.

This PR does 3 things:
* Fix scheduling strategy to use `SPREAD` same as read task, to spread out metadata prefetch task across cluster. This avoids hit S3 w/ too many requests from same node.
* Auto-retry on `OSError`, where S3 throws transient error such as `Access Denied`, `Read Timeout`.
* Extract `num_cpus` default value out as a variable. So we can tune the value to control the concurrency of prefetch metadata task for particular workload. Sometime `num_cpus=0.5` does not work well.

Signed-off-by: Cheng Su <scnju13@gmail.com>
@bveeramani bveeramani mentioned this pull request Feb 6, 2024
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@raulchen raulchen raulchen approved these changes

@scottjlee scottjlee scottjlee approved these changes

@ericl ericl Awaiting requested review from ericl ericl is a code owner

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