[data](deps): Bump moto[s3] from 2.3.1 to 3.0.5 in /python/requirements/data_processing#27
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…ect#22158) * better filter of column length * address comments * more
Continuing docs overhaul, tune now has: - [x] better landing page - [x] a getting started guide - [x] user guide was cut down, partially merged with FAQ, and partially integrated with tutorials - [x] the new user guide contains guides to tune features and practical integrations - [x] we rewrote some of the feature guides for clarity - [x] we got rid of sphinx-gallery for this sub-project (only data and core left), as it looks bad and is unnecessarily complicated anyway (plus, makes the build slower) - [x] sphinx-gallery examples are now moved to markdown notebook, as started in ray-project#22030. - [x] Examples are tested in the new framework, of course. There's still a lot one can do, but this is already getting too large. Will follow up with more fine-tuning next week. Co-authored-by: Antoni Baum <antoni.baum@protonmail.com> Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>
Cpp worker tests should be part of ray core.
Allow auto-close of stale PRs, with a shorter time limit.
ray-project#22129) Followup from ray-project#21788. Previously we had a lot of `serialized_runtime_env == "{}" || serialized_runtime_env == ""` scattered around the C++ code; this PR puts this in a helper function.
…roject#22146) Previously code incorrectly assumed that an exiting worker would disconnect from the raylet promptly to release resources. This isn't the case if the worker is owning references. This PR plumbs through the right release resources call even in this scenario. Closes ray-project#10960 Co-authored-by: SangBin Cho <rkooo567@gmail.com>
…ay-project#22188) Added lines to autoscaler configuration yaml to create a service account that is used to give the autoscaler permissions to list and read pods and patch the cluster CRD for up/downscaling.
…ct#22209) Bring back the old way of running commands for non-k8s tests. This also fixes the regression from many_drivers.py
…ct#22204) Unflake pg test (pg test 3 times out occasionally)+ add pg tests that weren't running
ray-project#21488 (comment) : > We discussed this issue in a bit more detail and came to the conclusion that we should set the CPU resource requirement for each actor in the actor pool to 0, to make the Ray Pool compatible/same behavior as the Python multiprocessing pool. Would that work for you @yogeveran ? (very similar to solution 4 mentioned above, but with 0.0 instead of 0.1, so it works in all cases).
Add unit test to check addresses of various formats are correctly passed to `get_job_submission_client_cluster_info`.
…ad of `execution_plan`) and re-instate Pong learning test. (ray-project#22126)
…ct from going out of scope (ray-project#22120) When a Ray program first creates an ObjectRef (via ray.put or task call), we add it with a ref count of 0 in the C++ backend because the language frontend will increment the initial local ref once we return the allocated ObjectID, then delete the local ref once the ObjectRef goes out of scope. Thus, there is a brief window where the object ref will appear to be out of scope. This can cause problems with async protocols that check whether the object is in scope or not, such as the previous bug fixed in ray-project#19910. Now that we plan to enable lineage reconstruction to automatically recover lost objects, this race condition can also be problematic because we use the ref count to decide whether an object needs to be recovered or not. This PR avoids these race conditions by incrementing the local ref count in the C++ backend when executing ray.put() and task calls. The frontend is then responsible for skipping the initial local ref increment when creating the ObjectRef. This is the same fix used in ray-project#19910, but generalized to all initial ObjectRefs. This is a re-merge for ray-project#21719 with a fix for removing the owned object ref if creation fails.
…into helper class (ray-project#22215) Separate Scheduler Resource Reporting logic into a separate class for better readability and maintainability.
…2189) As title, after infra got updated, we need to merge the PR so that test can run ray without redis.
…2244)" (ray-project#22626) Breaks train_torch_linear_test.py.
…fer (ray-project#22639) Currently object transfers assume that the object size is fixed. This is a bad assumption during failures, especially with lineage reconstruction enabled and tasks with nondeterministic outputs. This PR cleans up the handling and hopefully guards against two cases where the object size may change during a transfer: 1. The object manager's size information does not match the object in the local plasma store (due to async notifications). --> the object manager overwrites its own information if it finds that the physical object has a different size. 2. The receiver's created buffer size does not match the sender's object size. --> the receiver destroys the previous buffer and creates a new buffer with the correct size. This might cause some transient errors but eventually object transfer should succeed. Unfortunately I couldn't trigger this from Python because it depends on some pretty specific timing conditions. However, I did add some unit tests for case 2 (this is the majority of the PR).
…ay-project#22643) `test_job_submission` has become [flakey](https://flakey-tests.ray.io/) due to timeout. This change increases the timeout in `test_failed_job_status` from 10 to 25 seconds.
Whenever we spill, we try to spill all spillable objects. We also try to fuse small objects together to reduce total IOPS. If there aren't enough objects in the object store to meet the fusion threshold, we spill the objects anyway to avoid liveness issues. However, the current logic always spills once we reach the end of the spillable objects or once we've reached the fusion threshold. This can produce lots of unfused objects if they are created concurrently with the spill. This PR changes the spill logic: once we reach the end of the spillable objects, if the last batch of spilled objects is under the fusion threshold, we'll only spill it if we don't have other spills pending too. This gives the pending spills time to finish, and then we can re-evaluate whether it's necessary to spill the remaining objects. Liveness is also preserved.
…e init args, support nested (ray-project#22646) - Moved all `Deployment` instance creation to `DeploymentNode` level with only relevant info passed into it from `generate.py`. This abstraction makes more sense and less leaky. - In `DeploymentNode`, we leverage ray core DAG's `_PyObjScanner` to find and replace only Deployment nodes init args & kwargs to deployment handle, which is only specific to `Deployment` instance, but not `DeploymentNode` itself. However this is the simplest and most robust way to handle nested args at `DAGNode` level. - This implementation lives in ray core DAGNode level so we don't need to expose `_PyObjScanner` directly. - Added serve pipeline tests to BUILD CI.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
…on/requirements/tune/accelerate-0.5.1
Bumps [moto[s3]](https://github.com/spulec/moto) from 2.3.1 to 3.0.5. - [Release notes](https://github.com/spulec/moto/releases) - [Changelog](https://github.com/spulec/moto/blob/master/CHANGELOG.md) - [Commits](getmoto/moto@2.3.1...3.0.5) --- updated-dependencies: - dependency-name: moto[s3] dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com>
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…BRT: "corrupted size vs. prev_size") (ray-project#58660) ## Summary This PR fixes a heap corruption bug that causes the driver to crash with SIGABRT. The issue is caused by a use-after-free when the `RayletClient` object is destroyed while an asynchronous RPC callback is still pending. ## Problem Description ### Scenario A Ray Data job (Ray 2.50.0) with pipeline `read_parquet -> filter -> map_batches -> write` running for 4+ hours, where workers use elastic resources with low job priority causing frequent worker deaths due to pod preemption, crashes the driver with SIGABRT: ``` corrupted size vs. prev_size *** SIGABRT received at time=1761916578 on cpu 30 *** PC: @ 0x7f073569d9fc (unknown) pthread_kill Aborted (core dumped) ``` ### Trigger Conditions After reproducing with an ASan image, Asan reveals the actual use-after-free at: ``` #0 0x7ff282967361 in std::__atomic_base<long>::fetch_sub(long, std::memory_order) /usr/include/c++/11/bits/atomic_base.h:628 #1 0x7ff282967361 in std::__atomic_base<long>::operator--(int) /usr/include/c++/11/bits/atomic_base.h:377 #2 0x7ff282967361 in operator() src/ray/raylet_rpc_client/raylet_client.cc:338 #3 0x7ff282967361 in __invoke_impl<void, ray::rpc::RayletClient::PinObjectIDs(const ray::rpc::Address&, const std::vector<ray::ObjectID>&, const ray::ObjectID&, ray::rpc::ClientCallback<ray::rpc::PinObjectIDsReply>&)::<lambda(ray::Status, ray::rpc::PinObjectIDsReply&&)>&, const ray::Status&, ray::rpc::PinObjectIDsReply> /usr/include/c++/11/bits/invoke.h:61 #4 0x7ff282967361 in __invoke_r<void, ray::rpc::RayletClient::PinObjectIDs(const ray::rpc::Address&, const std::vector<ray::ObjectID>&, const ray::ObjectID&, ray::rpc::ClientCallback<ray::rpc::PinObjectIDsReply>&)::<lambda(ray::Status, ray::rpc::PinObjectIDsReply&&)>&, const ray::Status&, ray::rpc::PinObjectIDsReply> /usr/include/c++/11/bits/invoke.h:111 #5 0x7ff282967361 in _M_invoke /usr/include/c++/11/bits/std_function.h:290 #6 0x7ff2829fbadf in std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>::operator()(ray::Status const&, ray::rpc::PinObjectIDsReply&&) const /usr/include/c++/11/bits/std_function.h:590 #7 0x7ff2829fbadf in ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Create<ray::rpc::NodeManagerService, ray::rpc::PinObjectIDsRequest, ray::rpc::PinObjectIDsReply>(std::weak_ptr<ray::rpc::RetryableGrpcClient>, std::unique_ptr<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply>, std::default_delete<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply> > > (ray::rpc::NodeManagerService::Stub::*)(grpc::ClientContext*, ray::rpc::PinObjectIDsRequest const&, grpc::CompletionQueue*), std::shared_ptr<ray::rpc::GrpcClient<ray::rpc::NodeManagerService> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, ray::rpc::PinObjectIDsRequest, std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>, long)::{lambda(ray::Status const&)#2}::operator()(ray::Status const&) const bazel-out/k8-dbg/bin/src/ray/rpc/_virtual_includes/retryable_grpc_client/ray/rpc/retryable_grpc_client.h:293 #8 0x7ff2829fbadf in void std::__invoke_impl<void, ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Create<ray::rpc::NodeManagerService, ray::rpc::PinObjectIDsRequest, ray::rpc::PinObjectIDsReply>(std::weak_ptr<ray::rpc::RetryableGrpcClient>, std::unique_ptr<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply>, std::default_delete<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply> > > (ray::rpc::NodeManagerService::Stub::*)(grpc::ClientContext*, ray::rpc::PinObjectIDsRequest const&, grpc::CompletionQueue*), std::shared_ptr<ray::rpc::GrpcClient<ray::rpc::NodeManagerService> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, ray::rpc::PinObjectIDsRequest, std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>, long)::{lambda(ray::Status const&)#2}&, ray::Status>(std::__invoke_other, ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Create<ray::rpc::NodeManagerService, ray::rpc::PinObjectIDsRequest, ray::rpc::PinObjectIDsReply>(std::weak_ptr<ray::rpc::RetryableGrpcClient>, std::unique_ptr<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply>, std::default_delete<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply> > > (ray::rpc::NodeManagerService::Stub::*)(grpc::ClientContext*, ray::rpc::PinObjectIDsRequest const&, grpc::CompletionQueue*), std::shared_ptr<ray::rpc::GrpcClient<ray::rpc::NodeManagerService> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, ray::rpc::PinObjectIDsRequest, std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>, long)::{lambda(ray::Status const&)#2}&, ray::Status&&) /usr/include/c++/11/bits/invoke.h:61 #9 0x7ff2829fbadf in std::enable_if<is_invocable_r_v<void, ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Create<ray::rpc::NodeManagerService, ray::rpc::PinObjectIDsRequest, ray::rpc::PinObjectIDsReply>(std::weak_ptr<ray::rpc::RetryableGrpcClient>, std::unique_ptr<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply>, std::default_delete<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply> > > (ray::rpc::NodeManagerService::Stub::*)(grpc::ClientContext*, ray::rpc::PinObjectIDsRequest const&, grpc::CompletionQueue*), std::shared_ptr<ray::rpc::GrpcClient<ray::rpc::NodeManagerService> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, ray::rpc::PinObjectIDsRequest, std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>, long)::{lambda(ray::Status const&)#2}&, ray::Status>, void>::type std::__invoke_r<void, ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Create<ray::rpc::NodeManagerService, ray::rpc::PinObjectIDsRequest, ray::rpc::PinObjectIDsReply>(std::weak_ptr<ray::rpc::RetryableGrpcClient>, std::unique_ptr<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply>, std::default_delete<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply> > > (ray::rpc::NodeManagerService::Stub::*)(grpc::ClientContext*, ray::rpc::PinObjectIDsRequest const&, grpc::CompletionQueue*), std::shared_ptr<ray::rpc::GrpcClient<ray::rpc::NodeManagerService> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, ray::rpc::PinObjectIDsRequest, std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>, long)::{lambda(ray::Status const&)#2}&, ray::Status>(ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Create<ray::rpc::NodeManagerService, ray::rpc::PinObjectIDsRequest, ray::rpc::PinObjectIDsReply>(std::weak_ptr<ray::rpc::RetryableGrpcClient>, std::unique_ptr<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply>, std::default_delete<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply> > > (ray::rpc::NodeManagerService::Stub::*)(grpc::ClientContext*, ray::rpc::PinObjectIDsRequest const&, grpc::CompletionQueue*), std::shared_ptr<ray::rpc::GrpcClient<ray::rpc::NodeManagerService> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, ray::rpc::PinObjectIDsRequest, std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>, long)::{lambda(ray::Status const&)#2}&, ray::Status&&) /usr/include/c++/11/bits/invoke.h:111 #10 0x7ff2829fbadf in std::_Function_handler<void (ray::Status), ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Create<ray::rpc::NodeManagerService, ray::rpc::PinObjectIDsRequest, ray::rpc::PinObjectIDsReply>(std::weak_ptr<ray::rpc::RetryableGrpcClient>, std::unique_ptr<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply>, std::default_delete<grpc::ClientAsyncResponseReader<ray::rpc::PinObjectIDsReply> > > (ray::rpc::NodeManagerService::Stub::*)(grpc::ClientContext*, ray::rpc::PinObjectIDsRequest const&, grpc::CompletionQueue*), std::shared_ptr<ray::rpc::GrpcClient<ray::rpc::NodeManagerService> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, ray::rpc::PinObjectIDsRequest, std::function<void (ray::Status const&, ray::rpc::PinObjectIDsReply&&)>, long)::{lambda(ray::Status const&)#2}>::_M_invoke(std::_Any_data const&, ray::Status&&) /usr/include/c++/11/bits/std_function.h:290 #11 0x7ff2834657e9 in std::function<void (ray::Status)>::operator()(ray::Status) const /usr/include/c++/11/bits/std_function.h:590 #12 0x7ff2834657e9 in ray::rpc::RetryableGrpcClient::RetryableGrpcRequest::Fail(ray::Status const&) bazel-out/k8-dbg/bin/src/ray/rpc/_virtual_includes/retryable_grpc_client/ray/rpc/retryable_grpc_client.h:109 #13 0x7ff2834657e9 in operator() src/ray/rpc/retryable_grpc_client.cc:30 #14 0x7ff2834657e9 in __invoke_impl<void, ray::rpc::RetryableGrpcClient::~RetryableGrpcClient()::<lambda()>&> /usr/include/c++/11/bits/invoke.h:61 #15 0x7ff2834657e9 in __invoke_r<void, ray::rpc::RetryableGrpcClient::~RetryableGrpcClient()::<lambda()>&> /usr/include/c++/11/bits/invoke.h:111 #16 0x7ff2834657e9 in _M_invoke /usr/include/c++/11/bits/std_function.h:290 2025-11-14 16:15:05,608 INFO streaming_executor_state.py:511 -- Running activate tasks is {'MapBatches(QwenInfer)': ['MapBatches(QwenInfer)-79153', 'MapBatches(QwenInfer)-80170', 'MapBatches(QwenInfer)-80225', 'MapBatches(QwenInfer)-80299', 'MapBatches(QwenInfer)-82624'], 'MapBatches(drop_columns)->Write': ['MapBatches(drop_columns)->Write-25244', 'MapBatches(drop_columns)->Write-34438', 'MapBatches(drop_columns)->Write-34439', 'MapBatches(drop_columns)->Write-34440', 'MapBatches(drop_columns)->Write-34441']} #17 0x7ff2834e2407 in std::function<void ()>::operator()() const /usr/include/c++/11/bits/std_function.h:590 #18 0x7ff2834e2407 in EventTracker::RecordExecution(std::function<void ()> const&, std::shared_ptr<StatsHandle>) src/ray/common/event_stats.cc:112 #19 0x7ff2834bea54 in operator() src/ray/common/asio/instrumented_io_context.cc:110 #20 0x7ff2834bea54 in __invoke_impl<void, instrumented_io_context::post(std::function<void()>, std::string, int64_t)::<lambda()>&> /usr/include/c++/11/bits/invoke.h:61 #21 0x7ff2834bea54 in __invoke_r<void, instrumented_io_context::post(std::function<void()>, std::string, int64_t)::<lambda()>&> /usr/include/c++/11/bits/invoke.h:111 #22 0x7ff2834bea54 in _M_invoke /usr/include/c++/11/bits/std_function.h:290 #23 0x7ff28242fb5b in std::function<void ()>::operator()() const /usr/include/c++/11/bits/std_function.h:590 #24 0x7ff28242fb5b in boost::asio::detail::binder0<std::function<void ()> >::operator()() external/boost/boost/asio/detail/bind_handler.hpp:60 #25 0x7ff28242fb5b in void boost::asio::asio_handler_invoke<boost::asio::detail::binder0<std::function<void ()> > >(boost::asio::detail::binder0<std::function<void ()> >&, ...) external/boost/boost/asio/handler_invoke_hook.hpp:88 #26 0x7ff28242fb5b in void boost_asio_handler_invoke_helpers::invoke<boost::asio::detail::binder0<std::function<void ()> >, std::function<void ()> >(boost::asio::detail::binder0<std::function<void ()> >&, std::function<void ()>&) external/boost/boost/asio/detail/handler_invoke_helpers.hpp:54 #27 0x7ff28242fb5b in void boost::asio::detail::asio_handler_invoke<boost::asio::detail::binder0<std::function<void ()> >, std::function<void ()> >(boost::asio::detail::binder0<std::function<void ()> >&, boost::asio::detail::binder0<std::function<void ()> >*) external/boost/boost/asio/detail/bind_handler.hpp:111 #28 0x7ff28242fb5b in void boost_asio_handler_invoke_helpers::invoke<boost::asio::detail::binder0<std::function<void ()> >, boost::asio::detail::binder0<std::function<void ()> > >(boost::asio::detail::binder0<std::function<void ()> >&, boost::asio::detail::binder0<std::function<void ()> >&) external/boost/boost/asio/detail/handler_invoke_helpers.hpp:54 #29 0x7ff28242fb5b in boost::asio::detail::executor_op<boost::asio::detail::binder0<std::function<void ()> >, std::allocator<void>, boost::asio::detail::scheduler_operation>::do_complete(void*, boost::asio::detail::scheduler_operation*, boost::system::error_code const&, unsigned long) external/boost/boost/asio/detail/executor_op.hpp:70 #30 0x7ff2838607d6 in boost::asio::detail::scheduler_operation::complete(void*, boost::system::error_code const&, unsigned long) external/boost/boost/asio/detail/scheduler_operation.hpp:40 #31 0x7ff2838607d6 in boost::asio::detail::scheduler::do_run_one(boost::asio::detail::conditionally_enabled_mutex::scoped_lock&, boost::asio::detail::scheduler_thread_info&, boost::system::error_code const&) external/boost/boost/asio/detail/impl/scheduler.ipp:492 #32 0x7ff283892d35 in boost::asio::detail::scheduler::run(boost::system::error_code&) external/boost/boost/asio/detail/impl/scheduler.ipp:210 #33 0x7ff2838981e0 in boost::asio::io_context::run() external/boost/boost/asio/impl/io_context.ipp:63 2025-11-14 16:15:05,742 INFO streaming_executor_state.py:511 -- Running activate tasks is {'MapBatches(QwenInfer)': ['MapBatches(QwenInfer)-79153', 'MapBatches(QwenInfer)-80170', 'MapBatches(QwenInfer)-80225', 'MapBatches(QwenInfer)-80299', 'MapBatches(QwenInfer)-82624'], 'MapBatches(drop_columns)->Write': ['MapBatches(drop_columns)->Write-25244', 'MapBatches(drop_columns)->Write-34438', 'MapBatches(drop_columns)->Write-34439', 'MapBatches(drop_columns)->Write-34440', 'MapBatches(drop_columns)->Write-34441']} #34 0x7ff281e9d0aa in operator() src/ray/core_worker/core_worker_process.cc:193 #35 0x7ff281e9d247 in run external/boost/boost/thread/detail/thread.hpp:120 #36 0x7ff282503c47 in thread_proxy external/boost/libs/thread/src/pthread/thread.cpp:179 #37 0x7ff28b013ac2 in start_thread nptl/pthread_create.c:442 #38 0x7ff28b0a58bf (/lib/x86_64-linux-gnu/libc.so.6+0x1268bf) 0x50c003fd3d30 is located 112 bytes inside of 120-byte region [0x50c003fd3cc0,0x50c003fd3d38) freed by thread T68 here: 2025-11-14 16:15:05,876 INFO streaming_executor_state.py:511 -- Running activate tasks is {'MapBatches(QwenInfer)': ['MapBatches(QwenInfer)-79153', 'MapBatches(QwenInfer)-80170', 'MapBatches(QwenInfer)-80225', 'MapBatches(QwenInfer)-80299', 'MapBatches(QwenInfer)-82624'], 'MapBatches(drop_columns)->Write': ['MapBatches(drop_columns)->Write-25244', 'MapBatches(drop_columns)->Write-34438', 'MapBatches(drop_columns)->Write-34439', 'MapBatches(drop_columns)->Write-34440', 'MapBatches(drop_columns)->Write-34441']} #0 0x7ff28b39924f in operator delete(void*, unsigned long) ../../../../src/libsanitizer/asan/asan_new_delete.cpp:172 #1 0x7ff281eceb5f in __gnu_cxx::new_allocator<std::_Sp_counted_ptr_inplace<ray::rpc::RayletClient, std::allocator<ray::rpc::RayletClient>, (__gnu_cxx::_Lock_policy)2> >::deallocate(std::_Sp_counted_ptr_inplace<ray::rpc::RayletClient, std::allocator<ray::rpc::RayletClient>, (__gnu_cxx::_Lock_policy)2>*, unsigned long) /usr/include/c++/11/ext/new_allocator.h:145 #2 0x7ff281eceb5f in std::allocator_traits<std::allocator<std::_Sp_counted_ptr_inplace<ray::rpc::RayletClient, std::allocator<ray::rpc::RayletClient>, (__gnu_cxx::_Lock_policy)2> > >::deallocate(std::allocator<std::_Sp_counted_ptr_inplace<ray::rpc::RayletClient, std::allocator<ray::rpc::RayletClient>, (__gnu_cxx::_Lock_policy)2> >&, std::_Sp_counted_ptr_inplace<ray::rpc::RayletClient, std::allocator<ray::rpc::RayletClient>, (__gnu_cxx::_Lock_policy)2>*, unsigned long) /usr/include/c++/11/bits/alloc_traits.h:496 #3 0x7ff281eceb5f in std::__allocated_ptr<std::allocator<std::_Sp_counted_ptr_inplace<ray::rpc::RayletClient, std::allocator<ray::rpc::RayletClient>, (__gnu_cxx::_Lock_policy)2> > >::~__allocated_ptr() /usr/include/c++/11/bits/allocated_ptr.h:74 #4 0x7ff281eceb5f in std::_Sp_counted_ptr_inplace<ray::rpc::RayletClient, std::allocator<ray::rpc::RayletClient>, (__gnu_cxx::_Lock_policy)2>::_M_destroy() /usr/include/c++/11/bits/shared_ptr_base.h:538 #5 0x7ff282a73f0a in std::_Sp_counted_base<(__gnu_cxx::_Lock_policy)2>::_M_release() /usr/include/c++/11/bits/shared_ptr_base.h:184 #6 0x7ff282a73f0a in std::__shared_count<(__gnu_cxx::_Lock_policy)2>::~__shared_count() /usr/include/c++/11/bits/shared_ptr_base.h:705 #7 0x7ff282a73f0a in std::__shared_ptr<ray::RayletClientInterface, (__gnu_cxx::_Lock_policy)2>::~__shared_ptr() /usr/include/c++/11/bits/shared_ptr_base.h:1154 #8 0x7ff282a73f0a in std::shared_ptr<ray::RayletClientInterface>::~shared_ptr() /usr/include/c++/11/bits/shared_ptr.h:122 #9 0x7ff282a73f0a in std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> >::~pair() /usr/include/c++/11/bits/stl_pair.h:211 #10 0x7ff282a73f0a in void __gnu_cxx::new_allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > >::destroy<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > >(std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> >*) /usr/include/c++/11/ext/new_allocator.h:168 #11 0x7ff282a73f0a in void std::allocator_traits<std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > > >::destroy<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > >(std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > >&, std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> >*) /usr/include/c++/11/bits/alloc_traits.h:535 #12 0x7ff282a73f0a in void absl::lts_20230802::container_internal::map_slot_policy<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >::destroy<std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > > >(std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > >*, absl::lts_20230802::container_internal::map_slot_type<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >*) external/com_google_absl/absl/container/internal/container_memory.h:421 #13 0x7ff282a73f0a in void absl::lts_20230802::container_internal::FlatHashMapPolicy<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >::destroy<std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > > >(std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > >*, absl::lts_20230802::container_internal::map_slot_type<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >*) external/com_google_absl/absl/container/flat_hash_map.h:578 #14 0x7ff282a73f0a in void absl::lts_20230802::container_internal::common_policy_traits<absl::lts_20230802::container_internal::FlatHashMapPolicy<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >, void>::destroy<std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > > >(std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > >*, absl::lts_20230802::container_internal::map_slot_type<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >*) external/com_google_absl/absl/container/internal/common_policy_traits.h:50 #15 0x7ff282a73f0a in absl::lts_20230802::container_internal::raw_hash_set<absl::lts_20230802::container_internal::FlatHashMapPolicy<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >, absl::lts_20230802::hash_internal::Hash<ray::NodeID>, std::equal_to<ray::NodeID>, std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > > >::erase(absl::lts_20230802::container_internal::raw_hash_set<absl::lts_20230802::container_internal::FlatHashMapPolicy<ray::NodeID, std::shared_ptr<ray::RayletClientInterface> >, absl::lts_20230802::hash_internal::Hash<ray::NodeID>, std::equal_to<ray::NodeID>, std::allocator<std::pair<ray::NodeID const, std::shared_ptr<ray::RayletClientInterface> > > >::iterator) external/com_google_absl/absl/container/internal/raw_hash_set.h:2183 #16 0x7ff282a73f0a in ray::rpc::RayletClientPool::Disconnect(ray::NodeID) src/ray/raylet_rpc_client/raylet_client_pool.cc:114 #17 0x7ff282a7aa61 in operator() src/ray/raylet_rpc_client/raylet_client_pool.cc:69 #18 0x7ff282a7ac66 in __invoke_impl<void, ray::rpc::RayletClientPool::GetDefaultUnavailableTimeoutCallback(ray::gcs::GcsClient*, ray::rpc::RayletClientPool*, const ray::rpc::Address&)::<lambda()>&> /usr/include/c++/11/bits/invoke.h:61 #19 0x7ff282a7ac66 in __invoke_r<void, ray::rpc::RayletClientPool::GetDefaultUnavailableTimeoutCallback(ray::gcs::GcsClient*, ray::rpc::RayletClientPool*, const ray::rpc::Address&)::<lambda()>&> /usr/include/c++/11/bits/invoke.h:111 #20 0x7ff282a7ac66 in _M_invoke /usr/include/c++/11/bits/std_function.h:290 #21 0x7ff28346a1ac in std::function<void ()>::operator()() const /usr/include/c++/11/bits/std_function.h:590 #22 0x7ff28346a1ac in ray::rpc::RetryableGrpcClient::CheckChannelStatus(bool) src/ray/rpc/retryable_grpc_client.cc:85 #23 0x7ff28346c06a in operator() src/ray/rpc/retryable_grpc_client.cc:45 ``` This is a **non-deterministic race condition** that occurs under the following sequence: 1. Worker A's pod is preempted → Worker A dies 2. Objects on Worker A are lost 3. Objects are found on Worker B → `PinObjectIDs` RPC is initiated 4. Worker B dies or becomes unavailable → `CheckChannelStatus` detects this → `Disconnect` is called 5. The `RayletClient` corresponding to Worker B on the driver is destroyed 6. RPC callback executes and accesses the already-freed `RayletClient` → use-after-free triggers crash Whether the use-after-free occurs depends on the relative timing of steps 5 and 6. In scenarios with frequent pod preemptions, object recovery frequently triggers `PinObjectIDs`, making this race condition more likely to occur. ### Root Cause In `RayletClient::PinObjectIDs`, the RPC callback lambda directly captured the raw `this` pointer: ```cpp auto rpc_callback = [this, callback = std::move(callback)](...) { pins_in_flight_--; // Accessing member via 'this' pointer ... }; ``` If the `RayletClient` object is destroyed before the async RPC callback executes, the callback will access freed memory through the dangling `this` pointer, leading to heap corruption and SIGABRT with the error message "corrupted size vs. prev_size". ## Solution The fix ensures that the `RayletClient` object remains alive during the asynchronous callback execution by: 1. **Inheriting from `std::enable_shared_from_this<RayletClient>`**: The class already inherits from this base class (line 43 in `raylet_client.h`), which enables safe shared pointer management. 2. **Capturing `shared_from_this()` in the lambda**: Instead of capturing the raw `this` pointer, the callback now captures a `shared_ptr` to the object. The `shared_from_this()` is called before incrementing `pins_in_flight_` to ensure proper lifetime management: ```cpp // Capture shared_from_this() before incrementing to ensure object lifetime // is extended for the async callback, preventing use-after-free. auto self = shared_from_this(); pins_in_flight_++; auto rpc_callback = [self, callback = std::move(callback)]( Status status, rpc::PinObjectIDsReply &&reply) { self->pins_in_flight_--; callback(status, std::move(reply)); }; ``` This ensures that the `RayletClient` object's lifetime is extended until the callback completes, preventing the use-after-free bug. By capturing the shared pointer before incrementing the counter, we also ensure that if `shared_from_this()` were to fail (though it shouldn't in normal usage), we don't leave the counter in an inconsistent state. ## Code Changes - **File**: `src/ray/raylet_rpc_client/raylet_client.cc` - **Method**: `RayletClient::PinObjectIDs` - **Change**: Replace `this` capture with `shared_from_this()` capture in the RPC callback lambda Signed-off-by: dragongu <andrewgu@vip.qq.com> Co-authored-by: gulonglong <gulonglong@stepfun.com>
simonsays1980
pushed a commit
that referenced
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Feb 28, 2026
ray-project#61034) Currently, there is a chance that a worker can crash on the `getenv` syscall from the otel lazy initialization. We found the race is between `setenv` on the user thread (`setenv(RBLN_DEVICES)`) and `getenv` on the worker internal thread. However, we can't forbid `setenv` on a user's thread; the only thing we can do is not call `getenv` once the user's thread starts. Here is the backtrace of the crash we found by intercepting the `getenv`: ``` [getenv_preload] setenv name=RBLN_DEVICES value= overwrite=1 [getenv_preload] setenv backtrace: #0 /home/ray/getenv_trace_preload.so(setenv+0x73) [0x748a77ea870b] #1 ray::IDLE(+0x224d5b) [0x59f10aeead5b] #2 ray::IDLE(+0x13dfc3) [0x59f10ae03fc3] #3 ray::IDLE(_PyEval_EvalFrameDefault+0x313) [0x59f10adf3703] #4 ray::IDLE(+0x184bfd) [0x59f10ae4abfd] #5 ray::IDLE(+0x19da04) [0x59f10ae63a04] #6 ray::IDLE(_PyEval_EvalFrameDefault+0x115a) [0x59f10adf454a] #7 ray::IDLE(_PyFunction_Vectorcall+0x6c) [0x59f10ae03dfc] #8 ray::IDLE(_PyEval_EvalFrameDefault+0x49ae) [0x59f10adf7d9e] #9 ray::IDLE(_PyFunction_Vectorcall+0x6c) [0x59f10ae03dfc] #10 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x9a9333) [0x748a76270333] #11 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(_ZNSt17_Function_handlerIFN3ray6StatusERKNS0_3rpc7AddressENS2_8TaskTypeESsRKNS0_4core11RayFunctionERKSt13unordered_mapISsdSt4hashISsESt8equal_toISsESaISt4pairIKSsdEEERKSt6vectorISt10shared_ptrINS0_9RayObjectEESaISQ_EERKSN_INS2_15ObjectReferenceESaISV_EERSH_S10_PSN_ISG_INS0_8ObjectIDESQ_ESaIS12_EES15_PSN_ISG_IS11_bESaIS16_EERSO_INS0_17LocalMemoryBufferEEPbPSsS1E_RKSN_INS0_16ConcurrencyGroupESaIS1F_EESsbbblRKSt8optionalISsEEPFS1_S5_S6_SsSA_SM_SU_SZ_SsSsS15_S15_S19_S1C_S1D_S1E_S1E_S1J_SsbbblS1L_EE9_M_invokeERKSt9_Any_dataS5_OS6_OSsSA_SM_SU_SZ_S10_S10_OS15_S1X_OS19_S1C_OS1D_OS1E_S20_S1J_S1W_ObS21_S21_OlS1N_+0x1ab) [0x748a761786ab] #12 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(_ZN3ray4core10CoreWorker11ExecuteTaskERKNS_17TaskSpecificationESt8optionalISt13unordered_mapISsSt6vectorISt4pairIldESaIS9_EESt4hashISsESt8equal_toISsESaIS8_IKSsSB_EEEEPS7_IS8_INS_8ObjectIDESt10shared_ptrINS_9RayObjectEEESaISP_EESS_PS7_IS8_ISL_bESaIST_EEPN6google8protobuf16RepeatedPtrFieldINS_3rpc20ObjectReferenceCountEEEPbPSsS15_+0x1166) [0x748a76320a96] #13 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(_ZNSt17_Function_handlerIFN3ray6StatusERKNS0_17TaskSpecificationESt8optionalISt13unordered_mapISsSt6vectorISt4pairIldESaIS9_EESt4hashISsESt8equal_toISsESaIS8_IKSsSB_EEEEPS7_IS8_INS0_8ObjectIDESt10shared_ptrINS0_9RayObjectEEESaISP_EESS_PS7_IS8_ISL_bESaIST_EEPN6google8protobuf16RepeatedPtrFieldINS0_3rpc20ObjectReferenceCountEEEPbPSsS15_ESt5_BindIFMNS0_4core10CoreWorkerEFS1_S4_SK_SS_SS_SW_S13_S14_S15_S15_EPS19_St12_PlaceholderILi1EES1D_ILi2EES1D_ILi3EES1D_ILi4EES1D_ILi5EES1D_ILi6EES1D_ILi7EES1D_ILi8EES1D_ILi9EEEEE9_M_invokeERKSt9_Any_dataS4_OSK_OSS_S1U_OSW_OS13_OS14_OS15_S1Y_+0x87) [0x748a762e8647] #14 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xb5186d) [0x748a7641886d] #15 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xb557c5) [0x748a7641c7c5] #16 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x103e3eb) [0x748a769053eb] #17 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x1034f0b) [0x748a768fbf0b] #18 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xb6f21b) [0x748a7643621b] #19 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x15893cb) [0x748a76e503cb] #20 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x158ad69) [0x748a76e51d69] #21 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x158b472) [0x748a76e52472] #22 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(_ZN3ray4core10CoreWorker20RunTaskExecutionLoopEv+0x132) [0x748a762e4252] #23 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(_ZN3ray4core21CoreWorkerProcessImpl26RunWorkerTaskExecutionLoopEv+0x41) [0x748a76336bd1] #24 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x8a45c1) [0x748a7616b5c1] #25 ray::IDLE(_PyEval_EvalFrameDefault+0x6fb) [0x59f10adf3aeb] #26 ray::IDLE(_PyFunction_Vectorcall+0x6c) [0x59f10ae03dfc] #27 ray::IDLE(_PyEval_EvalFrameDefault+0x6fb) [0x59f10adf3aeb] #28 ray::IDLE(+0x1d5cac) [0x59f10ae9bcac] #29 ray::IDLE(PyEval_EvalCode+0x85) [0x59f10ae9bbf5] #30 ray::IDLE(+0x20732a) [0x59f10aecd32a] #31 ray::IDLE(+0x201d13) [0x59f10aec7d13] #32 ray::IDLE(+0x976be) [0x59f10ad5d6be] #33 ray::IDLE(_PyRun_SimpleFileObject+0x1bb) [0x59f10aec23db] #34 ray::IDLE(_PyRun_AnyFileObject+0x44) [0x59f10aec1f74] #35 ray::IDLE(Py_RunMain+0x371) [0x59f10aebf3e1] #36 ray::IDLE(Py_BytesMain+0x37) [0x59f10ae8f447] #37 /lib/x86_64-linux-gnu/libc.so.6(+0x29d90) [0x748a77baad90] #38 /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0x80) [0x748a77baae40] #39 ray::IDLE(+0x1c930e) [0x59f10ae8f30e] [getenv_preload] getenv name=OTEL_CPP_EXPORTER_OTLP_METRICS_RETRY_BACKOFF_MULTIPLIER [getenv_preload] backtrace: #0 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x10a9d17) [0x7321ce3c9d17] #1 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x10abe2b) [0x7321ce3cbe2b] #2 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x1050ffc) [0x7321ce370ffc] #3 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x104f4d7) [0x7321ce36f4d7] #4 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x1045833) [0x7321ce365833] #5 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xa6c760) [0x7321cdd8c760] #6 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xe69d9a) [0x7321ce189d9a] #7 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(_ZN3ray3rpc14ClientCallImplINS0_16HealthCheckReplyEE15OnReplyReceivedEv+0x165) [0x7321ce18c005] #8 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(_ZNSt17_Function_handlerIFvvEZN3ray3rpc17ClientCallManager29PollEventsFromCompletionQueueEiEUlvE_E9_M_invokeERKSt9_Any_data+0x15) [0x7321cdd8e475] #9 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x103e3eb) [0x7321ce35e3eb] #10 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x1034f0b) [0x7321ce354f0b] #11 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xb6f21b) [0x7321cde8f21b] #12 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x15893cb) [0x7321ce8a93cb] #13 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x158ad69) [0x7321ce8aad69] #14 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0x158b472) [0x7321ce8ab472] #15 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xa6bb54) [0x7321cdd8bb54] #16 /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so(+0xba2250) [0x7321cdec2250] #17 /lib/x86_64-linux-gnu/libc.so.6(+0x94ac3) [0x7321cf66eac3] #18 /lib/x86_64-linux-gnu/libc.so.6(+0x1268d0) [0x7321cf7008d0] *** SIGSEGV received at time=1770862205 on cpu 1 *** PC: @ 0x748a77bc5c1d (unknown) getenv @ 0x748a77bc3520 (unknown) (unknown) {"asctime":"2026-02-11 18:10:05,910","levelname":"E","message":"*** SIGSEGV received at time=1770862205 on cpu 1 ***","filename":"logging.cc","lineno":474} {"asctime":"2026-02-11 18:10:05,910","levelname":"E","message":"PC: @ 0x748a77bc5c1d (unknown) getenv","filename":"logging.cc","lineno":474} {"asctime":"2026-02-11 18:10:05,910","levelname":"E","message":" @ 0x748a77bc3520 (unknown) (unknown)","filename":"logging.cc","lineno":474} Fatal Python error: Segmentation fault ``` According to the backtrace, we can identify that it is the `OtlpGrpcMetricExporterOptions`, [which called `getenv(OTEL_CPP_EXPORTER_OTLP_METRICS_RETRY_BACKOFF_MULTIPLIER)`](https://github.com/open-telemetry/opentelemetry-cpp/blob/13ad05a6f431efb76995cffb1225d26b45374749/exporters/otlp/src/otlp_grpc_metric_exporter_options.cc#L47), getting initialized by calling `InitOpenTelemetryExporter` in the `metrics_agent_client_->WaitForServerReady()` callback, that causes the issue. This PR moves `OtlpGrpcMetricExporterOptions` into `OpenTelemetryMetricRecorder` (so that we keep otel details encapsulated) and moves its initialization early to `stats::Init()`, to force the `OtlpGrpcMetricExporterOptions` to be initialized early, so that we don't call `getenv` afterward. --------- Signed-off-by: Rueian Huang <rueiancsie@gmail.com>
simonsays1980
pushed a commit
that referenced
this pull request
Feb 28, 2026
## Description
grpc 1.57.1 will call `GetEnv("GRPC_EXPERIMENTAL_PICKFIRST_LB_CONFIG")`
on every grpc channel establishment for parsing load-balancing policy.
This causes race conditions between user tasks as they are allowed to do
setenv at anytime. This PR upgrades the grpc lib to 1.58.0 to get rid of
the `GetEnv("GRPC_EXPERIMENTAL_PICKFIRST_LB_CONFIG")`.
```
(gdb) bt
#0 __pthread_kill_implementation (no_tid=0, signo=11, threadid=129183804413504) at ./nptl/pthread_kill.c:44
#1 __pthread_kill_internal (signo=11, threadid=129183804413504) at ./nptl/pthread_kill.c:78
#2 __GI___pthread_kill (threadid=129183804413504, signo=signo@entry=11) at ./nptl/pthread_kill.c:89
#3 0x00007580a7545476 in __GI_raise (sig=11) at ../sysdeps/posix/raise.c:26
#4 <signal handler called>
#5 __pthread_kill_implementation (no_tid=0, signo=11, threadid=129183804413504) at ./nptl/pthread_kill.c:44
#6 __pthread_kill_internal (signo=11, threadid=129183804413504) at ./nptl/pthread_kill.c:78
#7 __GI___pthread_kill (threadid=129183804413504, signo=signo@entry=11) at ./nptl/pthread_kill.c:89
#8 0x00007580a7545476 in __GI_raise (sig=11) at ../sysdeps/posix/raise.c:26
#9 <signal handler called>
#10 __GI_getenv (name=0x7580a6a078c2 "PC_EXPERIMENTAL_PICKFIRST_LB_CONFIG") at ./stdlib/getenv.c:84
#11 0x00007580a67e8b8a in grpc_core::GetEnv(char const*) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#12 0x00007580a649601f in grpc_core::ShufflePickFirstEnabled() () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#13 0x00007580a64960ed in grpc_core::json_detail::FinishedJsonObjectLoader<grpc_core::(anonymous namespace)::PickFirstConfig, 1ul, void>::LoadInto(grpc_core::experimental::Json const&, grpc_core::JsonArgs const&, void*, grpc_core::ValidationErrors*) const () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#14 0x00007580a6787384 in grpc_core::json_detail::LoadWrapped::LoadInto(grpc_core::experimental::Json const&, grpc_core::JsonArgs const&, void*, grpc_core::ValidationErrors*) const ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#15 0x00007580a6497b07 in grpc_core::(anonymous namespace)::PickFirstFactory::ParseLoadBalancingConfig(grpc_core::experimental::Json const&) const ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#16 0x00007580a67c18a7 in grpc_core::LoadBalancingPolicyRegistry::ParseLoadBalancingConfig(grpc_core::experimental::Json const&) const ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#17 0x00007580a66ad9b8 in grpc_core::ClientChannel::OnResolverResultChangedLocked(grpc_core::Resolver::Result) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#18 0x00007580a66ae452 in grpc_core::ClientChannel::ResolverResultHandler::ReportResult(grpc_core::Resolver::Result) ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#19 0x00007580a63bc603 in grpc_core::PollingResolver::OnRequestCompleteLocked(grpc_core::Resolver::Result) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#20 0x00007580a63bcb2d in std::_Function_handler<void (), grpc_core::PollingResolver::OnRequestComplete(grpc_core::Resolver::Result)::{lambda()#1}>::_M_invoke(std::_Any_data const&)
() from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#21 0x00007580a67cbf46 in grpc_core::WorkSerializer::WorkSerializerImpl::Run(std::function<void ()>, grpc_core::DebugLocation const&) ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#22 0x00007580a67cc0ea in grpc_core::WorkSerializer::Run(std::function<void ()>, grpc_core::DebugLocation const&) ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#23 0x00007580a63bd117 in grpc_core::PollingResolver::OnRequestComplete(grpc_core::Resolver::Result) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#24 0x00007580a63b3f86 in grpc_core::(anonymous namespace)::AresClientChannelDNSResolver::AresRequestWrapper::OnHostnameResolved(void*, absl::lts_20230802::Status) ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#25 0x00007580a67c44c4 in grpc_core::ExecCtx::Flush() () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#26 0x00007580a63408a2 in grpc_core::ExecCtx::~ExecCtx() () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#27 0x00007580a6740343 in grpc_call_start_batch () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#28 0x00007580a5e281e9 in grpc::internal::CallOpSet<grpc::internal::CallOpSendInitialMetadata, grpc::internal::CallOpSendMessage, grpc::internal::CallOpRecvInitialMetadata, grpc::internal::CallOpRecvMessage<google::protobuf::MessageLite>, grpc::internal::CallOpClientSendClose, grpc::internal::CallOpClientRecvStatus>::ContinueFillOpsAfterInterception() ()
from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#29 0x00007580a5e2d809 in grpc::internal::BlockingUnaryCallImpl<google::protobuf::MessageLite, google::protobuf::MessageLite>::BlockingUnaryCallImpl(grpc::ChannelInterface*, grpc::inte--Type <RET> for more, q to quit, c to continue without paging--c
rnal::RpcMethod const&, grpc::ClientContext*, google::protobuf::MessageLite const&, google::protobuf::MessageLite*) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#30 0x00007580a62d76ea in opentelemetry::proto::collector::metrics::v1::MetricsService::Stub::Export(grpc::ClientContext*, opentelemetry::proto::collector::metrics::v1::ExportMetricsServiceRequest const&, opentelemetry::proto::collector::metrics::v1::ExportMetricsServiceResponse*) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#31 0x00007580a62ca40c in opentelemetry::v1::exporter::otlp::OtlpGrpcClient::DelegateExport(opentelemetry::proto::collector::metrics::v1::MetricsService::StubInterface*, std::unique_ptr<grpc::ClientContext, std::default_delete<grpc::ClientContext> >&&, std::unique_ptr<google::protobuf::Arena, std::default_delete<google::protobuf::Arena> >&&, opentelemetry::proto::collector::metrics::v1::ExportMetricsServiceRequest&&, opentelemetry::proto::collector::metrics::v1::ExportMetricsServiceResponse*) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#32 0x00007580a62c23ed in opentelemetry::v1::exporter::otlp::OtlpGrpcMetricExporter::Export(opentelemetry::v1::sdk::metrics::ResourceMetrics const&) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#33 0x00007580a62c0334 in (anonymous namespace)::OpenTelemetryMetricExporter::Export(opentelemetry::v1::sdk::metrics::ResourceMetrics const&) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#34 0x00007580a62e5fdf in opentelemetry::v1::sdk::metrics::PeriodicExportingMetricReader::CollectAndExportOnce()::{lambda()#1}::operator()() const::{lambda(opentelemetry::v1::sdk::metrics::ResourceMetrics&)#1}::operator()(opentelemetry::v1::sdk::metrics::ResourceMetrics&) const () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#35 0x00007580a62ee7a6 in opentelemetry::v1::sdk::metrics::MetricReader::Collect(opentelemetry::v1::nostd::function_ref<bool (opentelemetry::v1::sdk::metrics::ResourceMetrics&)>) () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#36 0x00007580a62e5085 in std::thread::_State_impl<std::thread::_Invoker<std::tuple<opentelemetry::v1::sdk::metrics::PeriodicExportingMetricReader::CollectAndExportOnce()::{lambda()#1}> > >::_M_run() () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#37 0x00007580a6997be0 in execute_native_thread_routine () from /home/ray/anaconda3/lib/python3.10/site-packages/ray/_raylet.so
#38 0x00007580a7597ac3 in start_thread (arg=<optimized out>) at ./nptl/pthread_create.c:442
#39 0x00007580a76298d0 in clone3 () at ../sysdeps/unix/sysv/linux/x86_64/clone3.S:81
```
Signed-off-by: Rueian Huang <rueiancsie@gmail.com>
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Bumps moto[s3] from 2.3.1 to 3.0.5.
Changelog
Sourced from moto[s3]'s changelog.
... (truncated)
Commits
19c5108Prepare release 3.0.5 (#4892)58ff549add Entity Id when listing (#4891)a3d3618S3 improvements: pull request #4890 from bblommers/s3-improvements182e45aAdded support for custom AWS Lambda images. (#4889)790b5d5Add implementation for cognitoidp admin_set_user_mfa_preference (#4884)142249fS3:CompleteMultipartUpload - error on empty part list8ba4bb6S3 - ListObjectVersions: Limit DeleteMarkers by Prefixf15e451S3 - Refactor tests6733947S3Control - Access Points8b81481Addition of s3control support in server mode (#4885)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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