fix(read): decode on the calling thread when rayon's pool has one thread
Full reads of chunked datasets handed their chunks to rayon. With a one-thread pool (concurrent_read --decode-threads 1, RAYON_NUM_THREADS=1) every thread reading through a File queued behind that single worker, so 16 readers decoded on one core: per-thread CPU time showed one thread doing all the decoding and the readers almost none, and full reads stopped at about 2x one thread. The cached full-read path and the uncached reader behind verify_provenance now decode inline when the pool cannot parallelise (parallel_read::pool_can_parallelise). The File's chunk cache was the suspect but not the cause: datasets over its budget were already read without inserting, and skipping its lookups gained only a few percent at 16 threads. The regression test keeps a one-thread global pool's worker busy and requires a full read and verify_provenance to finish anyway; before the fix both waited for the worker (timed out). Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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@@ -40,6 +40,7 @@ fn decompress_all_chunks(
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{
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if let Some(pl) = pipeline
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&& parallel_read::should_use_parallel(chunks.len())
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&& parallel_read::pool_can_parallelise()
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{
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// Seed from the first chunk's address and count for determinism.
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let seed = chunks.first().map(|c| c.address).unwrap_or(0) ^ (chunks.len() as u64);
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@@ -1056,7 +1057,9 @@ pub fn read_chunked_data_cached(
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// Decompress what the cache didn't have, a bounded batch at a time — in
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// parallel with the `parallel` feature (this path, the one the facade
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// uses, was sequential; only the uncached reader was parallel). Chunks are
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// uses, was sequential; only the uncached reader was parallel), unless the
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// pool has one thread: then every reading thread would queue behind that
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// one worker, so each decodes its own chunks instead. Chunks are
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// cached only when the whole dataset fits: pushing a larger dataset
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// through the cache just evicts each chunk moments after inserting it.
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let cache_them = total_bytes <= cache.max_bytes();
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@@ -1073,12 +1076,13 @@ pub fn read_chunked_data_cached(
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};
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for batch in misses.chunks(DECODE_BATCH) {
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#[cfg(feature = "parallel")]
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let decoded: Vec<Result<Vec<u8>, FormatError>> = if batch.len() >= 4 {
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use rayon::prelude::*;
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batch.par_iter().map(decode).collect()
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} else {
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batch.iter().map(decode).collect()
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};
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let decoded: Vec<Result<Vec<u8>, FormatError>> =
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if batch.len() >= 4 && parallel_read::pool_can_parallelise() {
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use rayon::prelude::*;
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batch.par_iter().map(decode).collect()
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} else {
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batch.iter().map(decode).collect()
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};
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#[cfg(not(feature = "parallel"))]
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let decoded: Vec<Result<Vec<u8>, FormatError>> = batch.iter().map(decode).collect();
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@@ -27,6 +27,20 @@ pub fn should_use_parallel(chunk_count: usize) -> bool {
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chunk_count > PARALLEL_THRESHOLD
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}
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/// Whether handing a read's chunks to rayon can decode them faster than the
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/// calling thread would alone.
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///
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/// `false` when the pool the work would go to (the current pool inside a
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/// rayon worker, else the global one) has a single thread. Handing work to
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/// that pool is then worse than useless: the caller blocks while the one
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/// worker decodes, and every other thread reading at the same time queues
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/// behind the same worker, so N reader threads decode on one core. (That is
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/// how full reads with `--decode-threads 1` stopped scaling at about 2x in
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/// the `concurrent_read` benchmark.)
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pub fn pool_can_parallelise() -> bool {
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rayon::current_num_threads() > 1
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}
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/// Decompress chunks in parallel using lane-partitioned assignment.
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///
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/// Instead of naive `par_iter`, chunks are deterministically assigned to lanes
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