docs: fewer round trips for remote files in the browser, counted
CHANGELOG (Unreleased): the v1 B-tree lookup, `Storage::hint`, the walks that go on past a missing node, and the counts before and after on an h5py file like the reviewer's (3000 datasets, 198 MB, earliest and latest libver, 1 MiB and 64 KiB blocks), the corpus read lazily at 512 B and 64 KiB blocks, and the Node/Chromium suite. known-issues (browser limits, round trips): the new counts, why the passes cannot go lower (the chain of addresses), why merging nearby requests does not help such a file, and that a second listing refetches at 1 MiB blocks when the file's metadata blocks exceed `cacheSize`. range-reads.md M4 status and the viewer README follow. Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
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## Unreleased
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### Remote files in the browser: fewer round trips to list a group or open a dataset (2026-09-27)
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- **Opening one dataset of a v1 (symbol table) group no longer reads the
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whole group.** A name is looked up down the group's B-tree, as
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libhdf5's `H5G__stab_lookup` does (binary search on the node keys, names
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in the local heap compared bytewise, then one symbol table node); only
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when that finds no hard link of that name (a soft link, or a B-tree out
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of name order, where libhdf5 would report it missing) is every entry
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read, as before. Local files benefit too (a lookup read O(entries)).
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In a group holding two entries of one name, the B-tree's is now the one
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found, as in libhdf5.
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- **`Storage::hint(offset, len)`** (clawhdf5-format): a parser says what
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it reads next — a B-tree node's or symbol table node's body, an object
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header's first chunk and continuation chunks, the symbol table nodes a
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B-tree leaf names, a dense group's name index header and heap blocks,
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and in a listing every child's object header. Every backend ignores it
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but the browser's restartable reader (`clawhdf5_wasm::lazy`), which
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fetches the hinted blocks it lacks together with the blocks a pass
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missed, within the call's `maxFetch` budget; a pass that misses
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nothing ignores them, so a hint never adds a round trip, and results
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never depend on hints.
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- The v1 and v2 B-tree walks of a listing descend into every child after
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one fails (before, the siblings were only read, so their subtrees came
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a pass later), then return the first error: same results and errors.
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- Counted on tank, 2026-09-27, with `CLAWHDF5_WASM_LIST_FILE=<file>
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CLAWHDF5_WASM_READ=/d1500 cargo test --release -p clawhdf5-wasm --test
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lazy listing_cost_of_a_given_file -- --nocapture` on an h5py file like
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the reviewer's (3000 datasets of 16384 `f32`, 198 MB, h5py 3.16 /
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HDF5 2.0), passes / requests / bytes, before -> after:
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| file, block size | `list('/')` | open + read one dataset |
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|---|---|---|
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| earliest, 1 MiB | 6 / 73 / 192.5 MB -> 4 / 68 / 192.5 MB | 7 / 74 / 193.6 MB -> 6 / 5 / 5.2 MB |
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| earliest, 64 KiB | 8 / 531 / 35.2 MB -> 5 / 530 / 35.3 MB | 9 / 515 / 34.1 MB -> 8 / 7 / 0.52 MB |
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| latest, 1 MiB | 9 / 98 / 196.5 MB -> 5 / 86 / 196.5 MB | 8 / 7 / 6.7 MB -> 7 / 7 / 6.7 MB |
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| latest, 64 KiB | 11 / 452 / 29.6 MB -> 6 / 454 / 30.5 MB | 9 / 8 / 0.58 MB -> 8 / 8 / 0.58 MB |
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The listing's passes now follow the depth of the group's index (the
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chain index levels -> symbol table nodes or heap objects -> child
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headers); its bytes are the child headers, which h5py spreads through
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the file (at 1 MiB blocks most of it). The whole corpus read lazily
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(`CLAWHDF5_WASM_CORPUS`, 656 files, every object listed, described and
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read): 27 513 -> 27 496 passes, 3 001 -> 2 962 requests and 194.8 ->
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195.3 MB at 64 KiB blocks; 41 342 -> 37 517 passes, 32 578 -> 29 511
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requests, 65.4 -> 65.7 MB at 512 B. The Node and Chromium suite
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(`examples/wasm-viewer/test/run.sh`) passes unchanged (the 200 MB file
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still takes 5 requests, 6 MiB); its corpus comparison fetched 33.54 ->
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33.61 MB.
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- Tests: the listing budgets (`listing_a_large_group_takes_a_few_passes`,
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512-byte blocks) are tightened to the new counts (FileBuilder, 600
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children: 5 -> 4 passes; h5py, 2000 children: 8 -> 5 and 11 -> 6); new
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`reading_one_dataset_of_a_large_group_fetches_a_few_blocks` (h5py
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earliest: 529 requests, 333 kB -> at most 6 requests, 27 kB), v1
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lookups against the listing (and with a name moved out of B-tree
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order), hints riding only on misses and within the fetch budget.
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### Deterministic errors on damaged chunked datasets (2026-09-27)
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- A read through the file's chunk cache listed a damaged dataset's chunks in
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hash-map order, seeded per `File`, so two opens of the same file could
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@@ -607,6 +607,16 @@ fast path within benchmark noise.
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missing blocks: listing 3000 datasets went from 185 passes to 6.
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The 32-bit risk below is covered by a Node test that reads data at
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3 GiB from a mock server and is refused a 4 GiB file.
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- Fewer round trips (2026-09-27, later): the walks descend into every
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child after a failure (not only read the siblings), and parsers
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call `Storage::hint` for what they read next (node bodies, object
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header chunks, a dense group's heap blocks, a listing's child
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headers); `LazyStorage` fetches hinted blocks only along with a
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pass's real misses and within `maxFetch`, so hints never add a
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round trip nor change a result. A v1 group's name is looked up down
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its B-tree (`H5G__stab_lookup`), not by listing it. 3000 datasets
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list in 4 passes (earliest) and 5 (latest) at 1 MiB blocks, and
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opening one of them costs 5 requests, not 74 (CHANGELOG).
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**M5 — SWMR and growth (later, separate design).** `Storage::len()` may grow;
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add `File::refresh()` that re-reads the superblock/EOF and invalidates cached
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+23
-4
@@ -1051,10 +1051,29 @@ cache, but:
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header, and every node of a level of the group's index, in one pass
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(since 2026-09-27; it was one round trip per header block): 3000
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datasets of an h5py file took 6 passes at 1 MiB blocks, 9 for a
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`libver="latest"` file (dense links). Each pass re-parses what the
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call reads (CPU, not network). With headers spread through the file
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(h5py writes each next to its data) a listing still fetches most of
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the file at 1 MiB blocks; a smaller `blockSize` fetches less.
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`libver="latest"` file (dense links). **Since 2026-09-27 (later):**
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4 and 5 passes (5 and 6 at 64 KiB, from 8 and 11): the index walks go
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on past a missing node, and parsers hint what they read next
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(`Storage::hint`: node bodies, the heap's blocks, each child's
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header), which the lazy reader fetches with a pass's misses. That is
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the depth of the chain (index levels, then symbol table nodes or
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heap objects, then headers) plus the pass that finishes; it cannot
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go lower without reading structures before their addresses are
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known. Opening one dataset of a v1 group looks its name up down the
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group's B-tree (it read every entry: 74 requests, 193 MB at 1 MiB
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blocks for one 64 KiB dataset of the 3000; now 5 requests, 5 MB).
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Each pass re-parses what the call reads (CPU, not network). With
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headers spread through the file (h5py writes each next to its data)
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a listing still fetches most of the file at 1 MiB blocks (192 of
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198 MB; 35 MB in 530 requests at 64 KiB); a smaller `blockSize`
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fetches less. Merging nearby requests does not help such a file: the
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blocks a listing needs are five or six apart at 64 KiB, so fewer requests would
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mean fetching most of the file. Listing it a second time is free at
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64 KiB blocks, but at 1 MiB its metadata blocks (192 MB) exceed the
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64 MiB `cacheSize`, so they are fetched again (the earliest file: 4
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passes, 50 requests); a larger `cacheSize` keeps them. A file's paged
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aggregation (metadata in pages) is not used to fetch its metadata in
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one request.
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- **Memory:** a call keeps every block it reads until it finishes (the
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cache budget applies between calls). It may fetch at most `maxFetch`
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bytes (512 MiB by default, at most 1 GiB), and a single read longer
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@@ -70,8 +70,10 @@ are fetched (in parallel, adjacent blocks in one request), and the pass is
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run again, until one completes (`docs/design/range-reads.md`, M4). Opening
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costs one request (the first block, which also gives the file's size);
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listing a group whose metadata is in blocks already fetched costs none,
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and otherwise a round trip per level of the group's index plus one for
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its children's headers, all fetched together;
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and otherwise about a round trip per level of the group's index plus one
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for its children's headers, all fetched together (the reader fetches
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what it knows it reads next along with what a pass missed); opening one
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object looks its name up in the group's index, not the whole group;
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reading a chunked dataset costs a round trip for its chunk index (a few
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for a deep one) and one batch of requests for its chunks. Every answer is
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checked — a `206` with exactly the bytes asked for, from the same file
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