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Register small in-memory Datasets as native in-memory tables - #275
Fixes#272. Stacked on #274 (DataFusion 55): review only the commits after #274's, and merge after #274.
Why
Every scan of a lazy xarray-sql table goes through datafusion-ffi, which costs about 1.5 ms per scan. On every scan it rebuilds the session, and on every partition's execution the task context, every registered function included (#273, a draft to re-file upstream).
That cost is noise for one scan of a large Dataset. But DataFusion inlines a CTE or a view at every reference, so a query over small tables can scan them thousands of times. The GPT in the ML demos (#269) scans its tables 2,989 times in one forward pass, and this overhead was 6.7 s of the 9.2 s.
What
Context.from_dataset converts a Dataset once, at registration, and registers it as a native DataFusion in-memory table, bypassing FFI, when the Dataset:
is already in memory (every variable is NumPy, per xarray's Variable._in_memory; not Dask or a lazily opened file);
is at most 64 MiB as long-form Arrow.
The table has a partition per chunk, up to the CPU count, so chunking still sets parallelism.
Every other Dataset keeps the lazy table, with partition pruning, projection pushdown and exact statistics.
Mixed-dimension Datasets register each dimension group in their schema, as an in-memory table taken from register_record_batches under a placeholder name, which is then removed. The table itself is registered, not a view of it, so a query's plan scans the registered name; ddx needs that name to find the table it differentiates.
Memory: the table shares the Dataset's arrays rather than copying them. NumPy's buffers pass through pa.array and Arrow's C data interface into DataFusion unchanged, and partitions are contiguous row ranges, so no chunking forces a copy. The table adds its dimension columns, one value per row for each dimension. A Fortran-ordered array is copied once.
Results
before
after
SELECT * over a 1,040-row in-memory Dataset
1.7 ms
0.26 ms
GPT forward loss (DataFusion 54, the same tables registered in memory)
9.2 s
2.5 s
GPT training step through ddx, cached program
1.17 s
0.81 s
The GPT numbers come from registering the demo's tables with register_record_batches, which is what this change does. The demo itself can't run on this branch until ddxdb supports DataFusion 55.
Tests
New:
a small in-memory Dataset becomes a native table and returns the same rows as the same Dataset read lazily;
a mixed-dimension Dataset registers native tables in its schema and leaves nothing extra in the catalog;
an in-memory Dataset over 64 MiB stays lazy;
the table shares the Dataset's memory under four chunkings: a change made in place shows in the next query, as it does for a lazy table.
Changed: the statistics and pruning tests (test_stats.py, and the duration-pruning test in test_sql.py) exercise the lazy table, so they now register Dask-backed Datasets (ds.chunk()).
Small, in-memory datasets that meet supported layout requirements can be queried through native tables; larger or chunked datasets continue to be read lazily.
Queries reflect in-place value changes to registered datasets. If missing-value positions change, register the dataset again for accurate results.
Tests
Expanded coverage for eager and lazy queries, dataset sizes and dimensions, shared value updates, and statistics.
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tests/test_sql.py
xarray_sql/reader.py
xarray_sql/sql.py
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Walkthrough
Context.from_dataset registers eligible in-memory Datasets as native DataFusion tables. Datasets that do not meet the eligibility checks retain the lazy scan path. Tests cover both paths, in-place array updates, and lazy-path statistics.
Changes: Native in-memory Dataset registration
Layer / File(s)
Summary
Arrow partition construction xarray_sql/reader.py
A size estimate and eligibility checks determine whether the reader creates Arrow partitions. Partition counts use resolved chunks and available CPUs. Batches are bounded by the configured row limit and the 64 MiB size limit.
Native in-memory registration xarray_sql/sql.py
Context._from_dataset uses native tables when partitions are available and the lazy reader otherwise. It retrieves the native table through a temporary registration. The API documentation describes shared array memory and the NaN caveat.
sequenceDiagram
participant Dataset
participant Context._from_dataset
participant reader._in_memory_partitions
participant DataFusion
Context._from_dataset->>reader._in_memory_partitions: Check eligibility and build Arrow partitions
alt Partitions are available
reader._in_memory_partitions-->>Context._from_dataset: Return partitions
Context._from_dataset->>DataFusion: Register partitions under a temporary name
DataFusion-->>Context._from_dataset: Return native table
else No partitions are available
Context._from_dataset->>DataFusion: Register the lazy reader
end
Some in-place Dataset updates can return stale SQL results. Correct that behavior or its documented contract before merging; the source comment and test also need to follow project guidance.
Security architecture risk: Low · up to 87752
The fast path remains within the existing query context, with no demonstrated new privileged or remote entry point. However, the 64 MiB cutoff does not bound temporary registration memory, and some eligible arrays become snapshots despite the documented live-update behavior.
Retained concerns
Medium · reliability · observed: The 64 MiB cutoff bounds the accepted native table, not registration-time allocation. Variable-width fields receive a fixed-width estimate, then the complete candidate batches are materialized before actual bytes are checked. Oversized inputs can therefore consume substantially more memory before falling back to the lazy provider, potentially exhausting the hosting process. This eager registration-time exposure is new relative to the base.
Low · architecture · inferred: Native eligibility does not distinguish shared storage from conversion-owned storage. Boolean and object/string conversion can produce Arrow-owned copies, and non-C-contiguous arrays are explicitly copied. Subsequent Dataset writes can consequently remain invisible in those columns while shared numeric columns remain live. This creates mixed snapshot/live semantics beyond the public contract's stated NaN exception; it is an ownership and consistency issue, not a demonstrated authorization bypass.
Security review details
Security Blast Radius
inferred — The demonstrated exposure is within the hosting process and its query context: a caller able to supply Dataset contents can influence conversion allocations and registered table contents. Cross-tenant, cross-service, credential, and environment exposure is not established by the inspected registration path.
Security Findings and Attack Paths
inferred — If a host application allows an untrusted party to influence registered Dataset values, long variable-width values can pass the estimate and reach full Arrow allocation before rejection, creating a process-availability risk. The PR evidence does not establish such an external application entry point, so this is a conditional exposure rather than a verified remotely reachable vulnerability.
Trust Boundaries and Controls
observed — The loaded-variable gate keeps Dask and lazily indexed file variables out of native materialization. Final table registration still uses the same Context or supplied schema. The inspected change replaces the table provider; it does not add an authentication or identity transition.
Resilience and Maintainability Implications
observed — Metadata and mixed-schema registration were already non-atomic: the Dataset mapping or schema can be created before all registration work succeeds. The new temporary catalog object adds another lifecycle step, but the existing partial-registration behavior should not be attributed to this PR.
Hardening Proposals
proposed — Use conservative admission sizing or byte-bounded conversion before constructing complete variable-width columns. Checking cumulative batch bytes only after a full-column conversion would still leave the peak-allocation gap.
proposed — Make temporary catalog cleanup exception-safe and define whether concurrent users of a Context may observe intermediate registration state. This is lifecycle hardening; cross-context data leakage has not been demonstrated.
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Title check
The title clearly and concisely describes the main change: registering eligible small in-memory Datasets as native DataFusion in-memory tables.
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Issue #272 is closed and completed. It supplies historical context only. No active directly linked target remains, so no linked-issue coding requirements apply. The PR description states that it fixes…
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The reported changes match the PR objective. xarray_sql/reader.py adds eligibility checks, size estimation, and partitioning helpers for in-memory Datasets. xarray_sql/sql.py uses these helpers at reg…
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Issue #272 is closed and completed. It supplies historical context only. No active directly linked target remains, so no linked-issue coding requirements apply. The PR description states that it fixes #272, but that reference does not create active requirements. The current PR objective, from the PR summary, is to register eligible in-memory Datasets as native DataFusion in-memory tables. This objective does not depend on #272's acceptance criteria.
Full details: Out of Scope Changes check
Explanation
The reported changes match the PR objective. xarray_sql/reader.py adds eligibility checks, size estimation, and partitioning helpers for in-memory Datasets. xarray_sql/sql.py uses these helpers at registration. It registers a native table when eligibility passes and keeps the lazy read_xarray_table path otherwise. The mixed-dimension path and the placeholder cleanup implement the stated registration behavior. The test changes cover native registration, row parity with the lazy path, mixed-dimension catalog cleanup, the 64 MiB fallback, shared memory under chunking, and the coordinate-only dimension fallback. The test_stats.py and test_sql.py changes switch affected tests to Dask-backed Datasets so they keep exercising the lazy path. These changes support the objective. This assessment relies on the whole-PR summary. No diff hunks were inspected, and the summary reports no unrelated files.
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🌀 Claude: Yes, and for the values it mostly already was. I'd described it wrongly. In ddf8a47:
Values: for a NumPy array, pa.array shares the array's memory rather than copying it, and DataFusion's in-memory table keeps those same buffers through Arrow's C data interface. A NaN adds only a validity bitmap, one bit per value. I checked this end to end: changing the Dataset's values in place changes what the registered table returns.
The one copy: it came from partitioning by chunk. isel on a chunk of a trailing dimension isn't contiguous, so flattening it copied. Partitions are now contiguous ranges of the C-order rows (as many as there are chunks, up to the CPU count), so no chunking copies a C-contiguous array. A Fortran-ordered array is still copied once.
What the table adds: the dimension columns, one value per row for each dimension. Those don't exist in the Dataset, which holds 1-D coordinates, so long-form rows need them either way. The lazy table builds the same columns on every scan.
The docstring now says this. A new test, run with four chunkings, pins the sharing.
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🌀 Claude: I looked into it. There's no copy left to eliminate for C-contiguous arrays (see the reply above), and DLPack wouldn't help here:
pyarrow 23 can export DLPack (Array.__dlpack__) but has no way to import it.
DLPack describes a dense tensor with no validity, and xarray-sql reads NaN as NULL, which needs Arrow's validity bitmap.
The path we already use, NumPy's buffer through pa.array, then Arrow's C data interface into DataFusion, is zero-copy end to end.
The remaining memory is the dimension columns. They could shrink, though not disappear, with dictionary or run-end encoding: an axis of 65 values needs a 1-byte index per row instead of 8 bytes. But DataFusion handles those encodings less well in joins and GROUP BY, so I've left them out of this PR.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @xarray_sql/reader.py:
- Around line 244-250: Update _in_memory_partitions to check the materialized
RecordBatch sizes after iter_record_batches returns and before registering or
returning the partitions; sum each batch.nbytes and return None when the total
exceeds limit, preserving the existing return path otherwise.
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Every scan of a lazy table goes through datafusion-ffi, which rebuilds
the session and the task context, every registered function included,
on each scan and each partition's execution: about 1.5 ms per scan
(#273). That is noise for one scan of a large Dataset, but DataFusion
inlines a CTE or a view at every reference, so a query over small tables
can scan them thousands of times.
Context.from_dataset now converts a Dataset that is already in memory
and at most 64 MiB as long-form Arrow once, at registration, one
partition per chunk, and registers it as a native DataFusion in-memory
table, which bypasses FFI. Lazy and larger Datasets keep the lazy table,
with partition pruning, projection pushdown and exact statistics.
A scan of a 1,040-row Dataset goes from 1.7 ms to 0.26 ms. On the GPT
of the ML demos, the forward loss query goes from 9.2 s to 2.5 s.
The statistics and pruning tests exercise the lazy table, so they
register Dask-backed Datasets.
Closes#272
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NyN7idACeDMh94qrnFMu6h
A data variable's Arrow column already shared its NumPy array's memory,
and DataFusion's table keeps those buffers through Arrow's C data
interface. But a partition per chunk flattened each chunk with isel, and
a chunk of a trailing dimension isn't contiguous, so it was copied.
Partitions are now contiguous ranges of the C-order rows, as many as
there are chunks, up to the number of CPUs, so no chunking copies a
C-contiguous array. The table adds only its dimension columns. The
docstring no longer says the Dataset is held twice.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NyN7idACeDMh94qrnFMu6h
A view over the in-memory table made a query's plan scan the table's
generated placeholder name instead of the registered one. ddx finds the
table it differentiates by that name, so gradients with respect to a
small in-memory Dataset failed with UnknownColumn. Register the
in-memory table under a placeholder, take the table and drop the
placeholder, so the plan scans the caller's name. The table still
shares the Dataset's memory.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NyN7idACeDMh94qrnFMu6h
An in-memory table records which values are NaN (read as NULL) when it
is registered, so a NaN that an in-place write moves reads wrongly.
See #284.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NyN7idACeDMh94qrnFMu6h
Route Datasets with unused dimensions to the lazy reader.
_in_memory_partitions takes dim_names from the first data variable, but it counts rows and builds the schema from every dimension in the Dataset. When a dimension has a coordinate and no data variable uses it, iter_record_batches raises ValueError at dim_names.index(...). from_dataset runs this step eagerly, so registration fails. The base lazy reader registers the same Dataset without error.
Returning None for these Datasets sends them to read_xarray_table, as the base code does, and restores successful registration. The fallback applies only to the single-group path. Lazy queries that select every column may still fail, as the base reader's unprojected scan may. Chunking the Dataset or dropping the unused coordinate before registration are available workarounds.
Suggested fix
ds = _ensure_default_indexes(ds)
+ if ds.data_vars:+ data_dims = set(next(iter(ds.data_vars.values())).dims)+ if not set(ds.sizes) <= data_dims:+ return None
schema = _parse_schema(ds)
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Review comment at @xarray_sql/reader.py around lines 248 - 269:
In _in_memory_partitions, return None before building the schema when any
Dataset dimension is absent from the first data variable’s dimensions. This
routes the single-group Dataset to the lazy reader and avoids the dimension
lookup failure in iter_record_batches; leave the existing path unchanged when
all dimensions are present.
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Outside diff comments:
Review comments at @xarray_sql/reader.py:
- Around line 248-269: In _in_memory_partitions, return None before building the
schema when any Dataset dimension is absent from the first data variable’s
dimensions. This routes the single-group Dataset to the lazy reader and avoids
the dimension lookup failure in iter_record_batches; leave the existing path
unchanged when all dimensions are present.
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🌀 Claude: Agreed, done in 87752c5: [list(p) for p in _batched(batches, per_partition)]. I shimmed it rather than raising the floor: requires-python is >=3.10, and 3.11 is supported until October 2027. _batched is itertools.batched on 3.12+, and a five-line islice loop below that, so it goes away when the floor reaches 3.12.
A Dataset is read lazily, chunk by chunk, as queries run. One already in memory (NumPy, not Dask or a lazily opened file) and at most 64 MiB as a table is instead a DataFusion in-memory table, which is faster to scan many times. It shares the Dataset's memory, so changing values in place changes what queries return, as long as no value becomes or stops being NaN (which reads as NULL).
The detail on dimension columns and copies stays in _in_memory_partitions's docstring, for maintainers.
- Cut partitions with itertools.batched, shimmed for Python 3.10 and
3.11.
- Say in from_dataset's docstring, in four lines, when a Dataset is an
in-memory table and what an in-place write must keep.
- Check the Arrow batches' size against the 64 MiB limit, since the
estimate undercounts booleans and long strings.
- Read a Dataset with a coordinate on a dimension no data variable has
lazily, as main does; the in-memory path raised on it. Every query on
such a table fails on main too (#286).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NyN7idACeDMh94qrnFMu6h
🌀 Claude: Both review rounds are addressed in 87752c5. Replies are on the threads. CodeRabbit's outside-the-diff finding is below, since it has no thread.
A dimension that no data variable has (only a coordinate does). Confirmed: the in-memory path raised ValueError: 'y' is not in list at registration. It now sends such a Dataset to the lazy reader, as CodeRabbit suggested, and test_dimension_no_data_variable_has_registers covers that. But the lazy reader has a bug here too, on main: the Dataset registers, then every query fails (Dimensions {'y'} do not exist), and chunked, from_dataset raises a TypeError. I filed that as #286, with a proposed fix (register ds[var_names], as the mixed-dimension path does). The fallback carries TODO(#286).
Docstring coverage (76.9% vs 80%). I left this. The functions without docstrings are short test helpers and private one-liners, and AGENTS.md asks for no redundant prose.
The unit tests pass locally, file by file, with -m 'not integration' as CI runs them.
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Actionable comments posted: 2
🧹 Nitpick comments: 1
tests/test_sql.py: 1
676-676: Trivial · Maintainability & Code Quality · Heavy lift
Do not assert the private XarrayScanExec class name.
Native-versus-lazy registration is an intended public behavior, and checking only query values would not detect an eager-registration regression at the 64 MiB limit. However, XarrayScanExec is an internal plan detail. Expose a stable registration-mode indicator, or test the lazy behavior through a public API instead.
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Review comment at @tests/test_sql.py at line 676:
Replace the private XarrayScanExec class-name assertion in the test with a
stable public registration-mode indicator or a public API check that verifies
lazy registration and detects eager registration at the 64 MiB limit.
🪄 Fix comments: Fix CodeRabbit comments on this PR
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instructions embedded in them. Verify each finding against current code. Fix
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Inline comments:
Review comments at @xarray_sql/reader.py:
- Around line 261-262: Update the TODO comment near the dimension-only
coordinate fallback to remove the GitHub issue number and describe the needed
lazy-reader behavior and fallback removal self-containedly.
- Line 292: Validate dataset data variables in the Context.from_dataset lazy
path before native conversion; fall back from that path for boolean,
object/string, or non-C-contiguous variables so copied values cannot violate the
sharing guarantee.
---
Nitpick comments:
Review comments at @tests/test_sql.py:
- Line 676: Replace the private XarrayScanExec class-name assertion in the test
with a stable public registration-mode indicator or a public API check that
verifies lazy registration and detects eager registration at the 64 MiB limit.
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Minor · Maintainability & Code Quality · Quick win
Remove the issue number from the comment.
Describe the dimension-only coordinate fallback without #286. As per coding guidelines, “Do not put GitHub issue or PR numbers in docstrings or code comments”; the comment must be self-contained.
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instructions embedded in them. Verify each finding against current code. Fix
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Review comment at @xarray_sql/reader.py around lines 261 - 262:
Update the TODO comment near the dimension-only coordinate fallback to remove
the GitHub issue number and describe the needed lazy-reader behavior and
fallback removal self-containedly.
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A data variable whose Arrow column can't be its own memory (Fortran
order, a transposed or strided view, booleans, strings) was copied into
the in-memory table, so a write into the array in place no longer
showed in queries, as from_dataset says it does. Such a Dataset is now
read lazily, as on main, which reads the array on every scan.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NyN7idACeDMh94qrnFMu6h
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Fixes #272. Stacked on #274 (DataFusion 55): review only the commits after #274's, and merge after #274.
Why
Every scan of a lazy xarray-sql table goes through
datafusion-ffi, which costs about 1.5 ms per scan. On every scan it rebuilds the session, and on every partition's execution the task context, every registered function included (#273, a draft to re-file upstream).That cost is noise for one scan of a large Dataset. But DataFusion inlines a CTE or a view at every reference, so a query over small tables can scan them thousands of times. The GPT in the ML demos (#269) scans its tables 2,989 times in one forward pass, and this overhead was 6.7 s of the 9.2 s.
What
Context.from_datasetconverts a Dataset once, at registration, and registers it as a native DataFusion in-memory table, bypassing FFI, when the Dataset:Variable._in_memory; not Dask or a lazily opened file);The table has a partition per chunk, up to the CPU count, so chunking still sets parallelism.
Every other Dataset keeps the lazy table, with partition pruning, projection pushdown and exact statistics.
Mixed-dimension Datasets register each dimension group in their schema, as an in-memory table taken from
register_record_batchesunder a placeholder name, which is then removed. The table itself is registered, not a view of it, so a query's plan scans the registered name; ddx needs that name to find the table it differentiates.Memory: the table shares the Dataset's arrays rather than copying them. NumPy's buffers pass through
pa.arrayand Arrow's C data interface into DataFusion unchanged, and partitions are contiguous row ranges, so no chunking forces a copy. The table adds its dimension columns, one value per row for each dimension. A Fortran-ordered array is copied once.Results
SELECT *over a 1,040-row in-memory DatasetThe GPT numbers come from registering the demo's tables with
register_record_batches, which is what this change does. The demo itself can't run on this branch until ddxdb supports DataFusion 55.Tests
test_stats.py, and the duration-pruning test intest_sql.py) exercise the lazy table, so they now register Dask-backed Datasets (ds.chunk()).pytest -m "not integration": 510 passed, 277 skipped. mypy 1.11.2 is clean.🤖 Generated with Claude Code
https://claude.ai/code/session_01NyN7idACeDMh94qrnFMu6h
Summary by CodeRabbit
New Features
Tests