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feat(storage): multi-stream support in AsyncMultiRangeDownloader - #18537
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This pull request introduces multi-stream scaling and unfinalized object support in AsyncMultiRangeDownloader (MRD) by implementing an internal stream pool (_StreamPool) and stream manager (_ManagedStream). Feedback on these changes focuses on optimizing concurrency and resource management. Key recommendations include releasing the lock before awaiting the stream factory in acquire_stream to prevent blocking concurrent tasks, tracking background tasks to avoid garbage collection, initializing and updating self._routing_token to prevent AttributeError and preserve routing state, and properly handling pool closure to avoid resource leaks.
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/gcbrun(b183f7f) |
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some comments on descriptions
Just mention single-stream (because each stream maps to 1 grpc channel , the underlying TCP connections per channel depends on the gRPC's xDS for direct path)
change this to single-stream
Aren't we going with the hybrid approach ? |
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@raj-prince Please address my comment - b/575739236#comment2 |
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Great catch, I was not aware - opening stream with read-handle comes with default metadata. Fixed the override scenario. Enabled all existing MRD systems test in test_rapid.py with multi-stream-configs. |
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Description
This PR adds optional multi-stream support to
AsyncMultiRangeDownloader(MRD), enabling downstream consumers likegcsfsto scale throughput beyond single-stream limits on high-bandwidth storage classes like RAPID.By default, existing single-stream behavior is fully preserved with zero pooling or load-tracking overhead (no lock acquisition or pending range/byte accounting). Multi-stream is strictly opt-in via the
stream_configparameter.Summary of changes:
MRDStreamConfig: Added a configuration dataclass to bundle multi-stream settings (min_streams,max_streams,target_io_depth,target_bytes).stream_config=Noneby default): Whenstream_configis omitted, the pool is completely bypassed (_pool = None). Requests execute directly on the primary stream with no byte/range counting, locking, or load calculation._StreamPool): When enabled, range requests are distributed across streams using a hybrid selection strategy (least-loaded across basemin_streams, first-fit across dynamically scaled streams). If load exceeds target capacity, additional streams are opened in the background in proportion to overall load without blocking active reads on already-open streams.read_handleandrouting_token(with redirect-aware retry handling) to connect directly to the same backend without repeating lookup overhead.Benchmark Results (20 GiB download on RAPID)
min_streams = max_streams)test_reads.py execution
All 6 benchmark cases in
tests/perf/microbenchmarks/time_based/reads/test_reads.pyexecuted and PASSED (total elapsed time:3m 41.69s):seq_regional_96p_1c_10240MiB_64KiB_1rangesseq_rapid_96p_1c_10240MiB_64KiB_1rangesrand_regional_1p_1c_10240MiB_64KiB_1rangesrand_regional_1p_16c_10240MiB_64KiB_1rangesrand_rapid_1p_1c_10240MiB_64KiB_1rangesrand_rapid_1p_16c_10240MiB_64KiB_1ranges