# vectra 0.11.3

## Bug fixes

* `collect_chunked()` and `chunk_feeder()` now consume their input node, matching
  every other terminal (`collect()`, `write_vtr()`). The streaming batch cursor
  previously drained the plan without invalidating the handle, so collecting the
  same node again re-drove an already-drained spill plan and returned wrong or
  empty data instead of raising the documented "already consumed" error.

* The holistic aggregates (`median()`, `n_distinct()`) and `kmer()` now bound the
  fan-in of their external record merge. The shared record sort-merge opened
  every spilled run at once, so a genuinely larger-than-RAM aggregate could grow
  its resident read buffers with the run count and exhaust the process file-handle
  table. It now reduces the runs to a bounded fan-in over multiple passes first,
  as the row sort behind `arrange()`/grouped `summarise()` already did, keeping
  peak memory and open handles bounded regardless of input size.

* `propagate()` no longer stops at a fixed 20 levels of hierarchy. A parent-child
  chain deeper than 20 within a batch left the deepest rows `NA`; propagation now
  runs to convergence, so an arbitrarily deep hierarchy resolves fully.

* `resolve()` and `propagate()` coerce their foreign-key and primary-key columns
  to a common type before matching. A key pair stored in different numeric types
  (for example a `double` foreign key against an integer primary key) could
  silently fail to match; they are now compared like with like.

* `lookup(.report = TRUE)`, the default, no longer materializes the whole fact
  table. It collected the entire fact table into memory purely to count its rows
  for a diagnostic message; the count and the unmatched-key preview now stream in
  bounded memory.

# vectra 0.11.2

## Bug fixes

* The gzip (`.gz`) reader now streams. It previously read the whole compressed
  file into memory and inflated it whole into a second buffer, so the readable
  size was capped at available RAM, and its size query used a 32-bit `ftell`, so
  a `.gz` past 2 GB compressed failed to open at all on Windows. It now feeds the
  raw deflate stream through miniz's `tinfl` coroutine into a 32 KB wrapping
  window (which doubles as the LZ dictionary) and serves bytes from that window,
  with 64-bit file offsets throughout; peak memory is the window plus one input
  block, independent of file size. A `.gz` whose inflated size exceeds RAM (and a
  compressed size past 2 GB) now reads fine. Enables `tbl_csv()` on multi-GB
  compressed streams.

* The gzip reader now follows concatenated gzip members, so a multi-member `.gz`
  (as produced by `bgzip` and `cat a.gz b.gz`) reads whole instead of stopping
  at the first member. The header is parsed field by field with no fixed size
  cap. This affects `tbl_csv()`, `tbl_fasta()`, `tbl_fastq()`, and `tbl_bed()`
  on any `.gz` input.

* A truncated or corrupt `.gz` now fails loudly. The scanners distinguish a
  hard decode error from a clean end of stream, so a partial compressed file
  raises an error instead of silently returning a short read.

# vectra 0.11.1

## Bug fixes

* A query is now consumed by exactly one terminal operation. `collect()` and
  `append_vtr()` join `write_*()` in invalidating the node once its pull cursor
  is drained, so a second terminal op on the same node (for example
  `collect()`-ing a pipeline to inspect it and then `write_vtr()`-ing the same
  object) raises a clear "already consumed" error instead of re-driving an
  exhausted plan. Previously the second pass returned empty or, on a
  multi-spill plan, silently reinterpreted a string column's bytes as doubles
  (#5).

* `offload()` shards are re-collectable: a shard rebuilds a fresh scan on each
  access, so a partition stays an iterable list of shards under the
  consume-once rule.

* `vec_builder_*` now errors on a type-mismatched or dictionary-deferred array
  instead of reinterpreting raw bytes, matching the guard the writer already
  applied at its own boundary.

# vectra 0.11.0

## Delimited-file reader gains a `delim` argument

* `tbl_csv()` takes a `delim` argument (default `","`), so tab- and
  semicolon-separated files read natively without a transcode step. `delim =
  "\t"` streams a GBIF occurrence export (SIMPLE_CSV) straight through; `delim =
  ";"` reads the semicolon exports common in European data. Quoting stays RFC
  4180 for any delimiter, so a quoted field may still contain the delimiter,
  newlines, and doubled quotes.

## Feature-space nearest-neighbour tools

* New `feature_knn()`: nearest-neighbour search in *predictor* space rather than
  on coordinates. For each streamed query row it returns the mean distance to
  the nearest `k` (or nearest `percentage`%) of a resident reference cloud, with
  a Euclidean or Mahalanobis metric. The query side streams one batch at a time
  so the projection side can exceed memory; the reference cloud is materialized
  once, whitened for the chosen metric, and scanned in parallel (a bounded
  max-heap keeps peak memory at O(k) per thread). This is the
  environmental-novelty counterpart to the coordinate-based `spatial_knn()`.
* New `rast_feature_distance()`: the same distance computed out-of-core over a
  projection raster. The reference raster is read once and indexed; the
  projection raster streams one tile-row strip at a time and the distance
  surface is written aligned to its grid. This is the streaming distance surface
  behind an environmental-novelty / transferability diagnostic such as MOP
  (Owens et al. 2013); the strict non-analogous-conditions layers compose from a
  per-band range reduce plus `rast_calc()`.
* The Species Distribution Models vignette gains a transferability / novelty
  section covering both.

## Bounded memory across the remaining streaming paths

* The streaming operations that still grew resident state with the input size
  or with key skew are now bounded. Interval joins run as a serial sweep-merge
  over externally sorted sides; k-mer counting streams through an external
  sort-merge (`rec_spill`) instead of a hash table that grew with the input;
  grouped top-1 (`slice_min`/`slice_max`, `n = 1`) keeps one champion per open
  group via a `(key, row-id)` sort; fuzzy joins stream the probe side and spill
  the build side when it overflows the budget; and ungrouped windows with mixed
  orderings decompose into a chain of single-spec streaming nodes rather than
  materializing the table. A shared `rec_spill` external merge and a shared
  `key_snap` group-boundary detector back these paths, so there is one
  implementation of each rather than several.

# vectra 0.10.8

## Bounded-memory joins under key skew

* Hash joins now keep a bounded memory peak regardless of how skewed the join
  keys are. When the materialized build side exceeds the memory budget
  (`vectra_mem()`), both sides grace-hash spill into 64 run-file partitions and
  join one partition at a time. A partition that is itself still over budget is
  re-partitioned by its sub-join with a depth-salted hash (a murmur3 finalizer,
  not a bare XOR, so colliding keys actually redistribute across levels rather
  than landing in the same bucket again). A partition that a single dominant key
  value makes un-splittable -- hashing cannot separate identical keys at any
  depth -- drops at the third level to a block-nested-loop: the build side is
  read in budget-sized blocks and the probe side is re-scanned once per block.
  Peak memory is one build block plus one probe batch plus a one-bit-per-row
  matched bitset, so no join retains an unbounded resident partition. Applies to
  all five kinds (inner, left, right, full, semi, anti). Partition files are
  opened lazily on first row, so a hot key no longer creates 63 empty spill
  files per level.

## Fixes

* An empty build partition no longer corrupts the heap on a `full_join()`. The
  hash-table constructor floors its slot allocation to one row; the true build
  count is now recorded separately, so the finalize pass over a build-empty
  partition reads zero rows instead of a nonexistent row 0. Surfaced by the
  one-sided partitions the recursive spill routinely produces.
* The sorted-input merge-join path is now consistent with the hash path in three
  cases it previously disagreed on: a match group beginning at build row 0 no
  longer spins forever (the group cursor used a positive-only sentinel that
  conflated position 0 with "inactive"); an unmatched build row under
  `full_join()` is emitted exactly once rather than doubled by the finalize
  pass; and an `NA` key never matches (both-`NA` at an equal-compare point is
  treated as unmatched, as in the hash path).

# vectra 0.10.7

## Fixes

* The overlay engine again builds without OpenMP (e.g. the default macOS CRAN
  toolchain). The serial fallback in `C_overlay_run` called `process_tile()`
  with one argument short of its signature, so the no-OpenMP branch failed to
  compile; it now passes the point-in-polygon flag like the parallel branch.
* Encoding a string column of duplicate empty strings no longer triggers
  undefined behavior. The DICT_1D dictionary encoder compared a hash-table
  cache candidate with `memcmp(str, s, len)`; when every string is empty the
  heap data pointer is `NULL` and `len` is 0, so `memcmp(NULL, NULL, 0)` tripped
  the UBSan nonnull check on CRAN's ASAN/UBSAN runner. The comparison is now
  short-circuited on `len == 0`, where the already-checked length equality
  makes the strings equal. Output is unchanged. Fixed in vendored tdc
  (`gcol33/tdc`).
* An audit swept the rest of this undefined-behavior class (a `NULL` pointer
  with length 0 passed to `memcmp`/`memcpy`) across the engine and vendored
  tdc. Two more sites are fixed: a blocked `fuzzy_join()` whose probe block
  column is all empty strings compared block keys with `memcmp(build, NULL, 0)`,
  and tdc's string min/max stats copied a zero-length prefix from a `NULL`
  all-empty-string heap. Both are now length-guarded; results are unchanged.

# vectra 0.10.6

## Bounded-memory top-N and fuzzy join

* `slice_min()`/`slice_max(..., with_ties = FALSE)` no longer materialize their
  input. The streaming top-N node keeps at most `k` rows in a size-`k` max-heap
  (fixed-width values overwritten in place, strings held as per-slot owned copies
  freed on eviction); a non-winning row costs one comparison and no copy, so peak
  memory is `min(k, n)` rows rather than the whole child. NA in the order column
  now always sorts last, independent of direction, matching dplyr and the
  `with_ties = TRUE` path -- an earlier version treated NA as the maximum under
  `slice_max()`.
* `fuzzy_join()` now streams the probe side instead of materializing both inputs
  and the whole cross-product of matches. The build side is materialized once and,
  with a blocking column, indexed by exact block key; the probe side streams one
  batch at a time, and each batch's matches are computed, ordered, and emitted in
  chunks before the next batch is pulled. Peak memory is the build side plus one
  probe batch plus that batch's matches, and the `(probe, distance)` output order
  is preserved without a global sort. These were the last two operators that
  buffered their whole input, so every verb is now bounded-memory.

# vectra 0.10.5

## Spill-safe window functions

* Ordered ungrouped windows that need the whole table sorted (`rank()`,
  `dense_rank()`, `percent_rank()`, `cume_dist()`, `row_number(col)`,
  `roll_*()`, `lag()`, `lead()`, `ntile()`) now stream, closing the last window
  case that materialized the whole table. A single spill-safe global sort is
  inserted below the window, then one forward pass computes each spec from
  bounded running state, so peak memory is one batch plus the sort's own spill
  buffer rather than the whole table. Two ordering tricks keep the awkward cases
  single-pass: `cume_dist()` sorts descending so `count(<= v)` is known when each
  value group opens, and `lead()` is computed as `lag()` on the row-id-reversed
  stream. When one `mutate()` mixes specs that need conflicting sort orders (for
  example `rank(x)` and `rank(desc(x))` together), the node falls back to the
  in-memory path; splitting them into separate `mutate()` calls keeps each
  streaming.

# vectra 0.10.4

## Spill-safe window functions

* Grouped window functions (`group_by()` followed by `mutate()` with
  `row_number()`, `rank()`, `lag()`, `cumsum()`, `roll_sum()`, and the rest) no
  longer materialize the whole table in memory. The window node now sorts on the
  group keys (external, spill-safe), processes one group at a time, and restores
  the original row order, so peak memory is a single group rather than the full
  input -- the same bound `group_by() |> summarise()` already had. Results are
  unchanged: rows come back in original order, and cumulative windows still run
  in arrival order within each group.

* Ungrouped cumulative windows (`mutate(cs = cumsum(x))` and the rest of the
  `cumsum`/`cummean`/`cummin`/`cummax`/`row_number()` family, with no
  `group_by()`) now stream one batch at a time with O(1) running state, so a
  running aggregate over a larger-than-RAM table holds only one batch. Ordered
  ungrouped windows that need the whole table sorted (`rank()`, `dense_rank()`,
  `percent_rank()`, `cume_dist()`, `row_number(col)`, `roll_*()`, `lag()`,
  `lead()`, `ntile()`) keep the in-memory path.

# vectra 0.10.3

## Bug fixes

* The parallel `.vtr` reader no longer risks an intermittent crash when a read
  fails mid-collect. Row groups are decoded on OpenMP worker threads; on an
  I/O or decode failure (a truncated or removed file, a short read, a corrupt
  block) the reader used to raise the error directly from a worker thread,
  where the R error mechanism's longjmp corrupts the master thread's stack.
  The reader now allocates every batch on the master thread and only fills them
  from disk in parallel, capturing the first failure and re-raising it once the
  parallel region joins, so a failed read is a clean, catchable R error.

## BED streaming scan backend

* `tbl_bed()` streams a BED (Browser Extensible Data) file of genomic features
  as a lazy table, one feature per row, with the standard BED columns in order
  (`chrom`, `start`, `end`, `name`, `score`, `strand`, `thickStart`,
  `thickEnd`, `itemRgb`, `blockCount`, `blockSizes`, `blockStarts`; extra
  fields past the twelfth as `V13`, `V14`, ...). The column count is fixed by
  the first feature line and every later line must match. Fields are
  whitespace-delimited (tab or space); blank, `#`, `track`, and `browser` lines
  are skipped; gzip (`.bed.gz`) input is read transparently, and the scan
  reports its feature count on completion (`quiet = TRUE` suppresses it).
* Coordinates are read faithfully: `start` is 0-based and `end` half-open, both
  returned exactly as stored. Paired with the existing `interval_join()`, this
  makes vectra a streaming genome-interval overlap engine. For base-overlap
  semantics matching bedtools and `GenomicRanges::findOverlaps()`, use
  `interval_join(..., closed = FALSE)`, which requires a strictly positive
  overlap and so does not pair abutting features. Recovery-tested against
  `findOverlaps` and on explicit half-open boundary (off-by-one) cases.
* A malformed feature line is a loud error, not a silent drop: an inconsistent
  field count, a non-integer `start`/`end`, or fewer than three fields stops
  the scan. Optional integer fields (`score`, `thickStart`, `thickEnd`,
  `blockCount`) accept `.` or `NA` as missing.

# vectra 0.10.2

## `kmer()` k-mer spectrum node

* `kmer(x, seq, k, by = , canonical = )` counts every k-mer of a nucleotide
  column, grouped by zero or more key columns, returning one row per distinct
  (group, k-mer) with a `kmer` string and an integer `count`. It is the
  set-wise companion to the per-row `seq_*` family: a blocking step like
  `summarise()`, but only the k-mer table is held, not the input, so a spectrum
  over a larger-than-RAM read set stays bounded. Each k-mer is packed into 2
  bits per base and counted in a native open-addressing hash (k in `1:32`); a
  window containing any non-`ACGT` base is skipped, matching dedicated k-mer
  counters. `canonical = TRUE` collapses a k-mer with its reverse complement.
  Recovery-tested against a hand-rolled tabulation (ungrouped, by-group,
  canonical, non-ACGT skipping, streaming invariance).

* Internal: the group-key store (`KeyArena`) shared by `summarise()` and
  `kmer()`, and the 2-bit base encoding shared by `seq_*` and `kmer()`, are now
  single-sourced (`key_arena`, `seq_util`).

## Fixes

* Reading a `.vtr` no longer holds an OS file handle open for the scan node's
  whole lifetime. The reader loads the row-group index into memory at open and
  reopens the file per read (as the parallel reader already did), so an idle
  scan node -- one created or already collected but not yet garbage-collected --
  keeps no descriptor. A tight `tbl(f) |> collect()` loop previously leaked one
  handle per iteration until the OS refused further opens and `vtr1_open_tdc`
  failed (crashing when the failure landed mid-decode); it now runs unbounded.
* A data.frame lifted into a lazy node (`tbl_xlsx()`, and the data.frame inputs
  to `write_csv()` / `write_sqlite()` / `write_tiff()`) now owns its temporary
  `.vtr` for the node's lifetime and unlinks it when the node is freed, instead
  of deleting it when the creating call returned.

# vectra 0.10.1

## FASTA / FASTQ streaming scan backends

* `tbl_fasta()` and `tbl_fastq()` stream a biological-sequence file as a lazy
  table, one record per row: `id`, `desc`, `seq` for FASTA and an additional
  `qual` for FASTQ. `id` is the first whitespace-delimited token of the header
  and `desc` is the remainder (an empty string when absent). Records stream one
  batch at a time, so a read set larger than RAM never fully materializes, and
  the `seq_*` expression family works directly on the `seq` column. Gzip input
  (`.fasta.gz`, `.fq.gz`, ...) is read transparently through the same vendored
  miniz path CSV uses. A record cut short --- a header where a `>`/`@` is
  expected, a FASTQ record missing a line, or a quality string whose length
  does not match its sequence --- is a loud error rather than a silent drop,
  and the scan reports how many records it read on completion (`quiet = TRUE`
  suppresses it). Recovery-tested against `Biostrings` and `ShortRead`.

* The byte reader that backs the streaming text scans (plain and gzip) is now a
  shared `byte_reader` used by both the CSV and FASTA/FASTQ backends.

# vectra 0.10.0

## `seq_*` biological-sequence expressions

* A family of `seq_*` functions now works directly inside `mutate()`,
  `filter()`, and `summarise()` over a sequence held in an ordinary string
  column, computed per row in C and parallelized across rows: `seq_length`,
  `seq_gc`, `seq_revcomp`, `seq_complement`, `seq_reverse`, `seq_transcribe`
  (DNA<->RNA), `seq_translate` (standard genetic code), `seq_subseq`, and
  `seq_dist` (Levenshtein / Damerau-Levenshtein / Hamming edit distance to a
  reference column or constant). A missing or unparseable cell yields `NA`, the
  same contract as the `st_*` geometry and embedding-distance families.
  Complement handles the IUPAC ambiguity codes. See `?seq_expressions`.

# vectra 0.9.12

## `compress = "small"` parallelizes the candidate sweep

* The try-all-pick-smallest encoder now trial-encodes a column's candidate
  specs across threads: each thread encodes a disjoint slice of candidates into
  its own scratch buffer and the smallest record is reduced. Large row groups
  (the ones where the sweep dominates encode time) compress markedly faster on
  multi-core machines. The chosen spec is independent of thread count — ties
  break to the lowest candidate index — so `"small"` files are byte-identical
  regardless of how many cores encode them, and never larger than `"fast"`. The
  sweep stays serial for small blocks and inside an existing parallel region.

## All-null row groups are pruned during scan

* A filter comparison against a column whose values are all `NA` in a row group
  (`x > 5`, `x == "a"`, `x != 5`, ...) is `NA` for every row, which the filter
  drops. The scan now recognizes this from the row group's null count and skips
  the whole group without reading it, even for numeric columns that carry no
  min/max (an all-`NA` column has none). Results are unchanged; the pruning only
  avoids reading groups that could not have produced a row.

# vectra 0.9.11

## `compress = "small"` now does adaptive per-column encoding

* `write_vtr(..., compress = "small")` previously behaved identically to
  `"fast"`. It now performs try-all-pick-smallest: each column is trial-encoded
  under a set of candidate tdc specs — alternative models (delta, second-order
  and FCM/DFCM float predictors, numeric and string dictionaries, sparse-zero)
  crossed with stronger entropy coders (optimal-parse LZ, split-stream LZ, FSE,
  4-stream Huffman, per-lane) — and the smallest block record is kept. The
  `"fast"` encoding is always a candidate, so `"small"` files are never larger
  than `"fast"` (about 16-40% smaller on mixed data). Encode is slower in
  proportion to the number of candidates; decode and the on-disk format are
  unchanged.
* `compress = "none"` now works on string columns (previously errored).

## Faster `collect()` on dictionary-encoded string columns

* `collect()` on a string column now interns each unique value once and fills
  the result by index, instead of hashing every row. The direct-read path
  decodes the on-disk dictionary block into (unique values + per-row indices)
  via the new `tdc_decode_block_dict` primitive and carries it through a
  `VecArray` dictionary side-channel to the fill. On 5M rows of wide, heavily-
  duplicated strings the collect drops from ~0.34s to ~0.03s. Results are
  unchanged; NA, empty, and UTF-8 values round-trip identically.

## Fixes

* Fixed two correctness bugs in the tdc codec that surfaced through the new
  `"small"` encoder: sparse-zero blocks and single-element all-zero columns
  could fail to decode. Both are covered by new regression tests.

# vectra 0.9.10

## One memory knob for the whole engine

* A single ceiling, `options(vectra.memory = "8GB")`, now governs every part of
  the engine that buffers before spilling. It is resolved by the new exported
  `vectra_mem()` (accepts a byte count or a `"512MB"` / `"8GB"` string). The
  auto-detected default is half of system RAM, floored at 1 GB; an explicit
  value is honored as given. Row-group size (`batch_size`) is a separate
  cache-locality control and is unaffected.
* The external sort's spill threshold, the self-overlay working-set cap, and the
  streaming spatial flush / partition-routing buffers all derive their budget
  from `vectra_mem()` instead of separate constants. The per-subsystem options
  `vectra.spatial_flush`, `vectra.partition_budget`, `vectra.overlay_mem_limit`,
  and `vectra.overlay_parse_chunk` are removed; per-call `flush_rows` (an
  explicit row cap) remains the override on the streaming spatial verbs and
  `offload()`.

## Joins spill to disk instead of running out of memory

* When a join's build (right) side outgrows `vectra_mem()`, the engine switches
  to a grace-hash join: both sides are hash-partitioned by key into run-files
  and joined one partition at a time, so peak memory stays bounded. The result
  is identical to the in-memory join for every kind (inner, left, right, full,
  semi, anti), including composite keys and many-to-many matches.

# vectra 0.9.9

## Faster `spatial_overlay()`

* Each distinct input geometry is decoded from its stored WKB once per overlay
  batch and shared, read-only, across every tile it falls in. A feature that
  spans many tiles was previously decoded again in each of them; on a dense
  world protected-area union a single large feature can recur in thousands of
  tiles, which made WKB decoding the largest single cost of the overlay. The
  per-tile clipping, noding, and attribution are unchanged, so the result is
  identical.
* A piece is a face of the arrangement of all input boundaries, so it lies
  wholly inside or outside every input up to snap-rounding slivers along the
  boundary. `spatial_overlay()` now credits each whole face to the inputs whose
  interior contains the face's representative point, and the piece geometry is
  that face. This replaces intersecting every face with each partially covering
  input, the largest remaining cost once decoding is shared; per-input covered
  area stays within about the noding precision times the face perimeter of the
  exact value (well inside the 1e-4 coverage tolerance), and thin boundary
  slivers no longer appear as separate pieces. Pass `exact = TRUE` to restore
  the previous behaviour, where each face is intersected with every covering
  input and credited that exact area.
* Together these take the end-to-end ~470k-feature world protected-area union
  from about 15 minutes to about 5, with the coverage invariant still holding
  exactly (0 offenders), on a 32-thread desktop.

# vectra 0.9.8

## New features

* Embedding columns. `as_embedding()` packs numeric vectors into a hex float32
  blob held in an ordinary character column. The distance functions `cosine()`
  (cosine distance), `l2()` (Euclidean distance), and `dot()` (inner product)
  decode the blob inside the engine and run inside `mutate()` / `filter()`,
  parallelized over rows. Pair them with `slice_min()` / `slice_max()` for
  nearest-neighbour search.
* Time-series resampling. `resample()` buckets a `Date` / `POSIXct` column to a
  calendar grid and aggregates within each bucket, the time-series form of
  `group_by()` + `summarise()`. `floor_time()` exposes the bucket key on its
  own for use inside `mutate()` / `filter()`.
* Time-based rolling aggregates inside `mutate()`: `roll_sum()`, `roll_mean()`,
  `roll_min()`, `roll_max()`, and `roll_n()` over a trailing datetime window
  `(time - every, time]`, respecting an upstream `group_by()`.
* `interval_join()` joins two tables on range overlap: a row of `x` matches a
  row of `y` when their `[start, end]` intervals overlap. Supports an optional
  equality key, inner or left output, and open or closed interval ends.

## Bug fixes

* `write_vtr()` no longer triggers an UndefinedBehaviorSanitizer report when the
  written data frame has a zero-row double column (CRAN M1 sanitizer check). The
  bulk-copy fast path in `df_to_batch()` called `memcpy()` over the column
  unconditionally; for an empty column `REAL()` yields a degenerate pointer that
  clang's alignment sanitizer flagged. The copy is now skipped when there are no
  rows.

# vectra 0.9.7

## Geometry functions in mutate(), filter(), and summarise()

* A family of `st_*` geometry functions now runs inside the expression verbs,
  on the GEOS C library straight off the hex-WKB geometry column, with no
  per-batch round-trip through `sf`. `tbl(f) |> filter(st_area(geometry) > 1e6)`
  prunes the stream in C, and `mutate(geometry = st_centroid(geometry))` adds a
  new hex-WKB geometry column. See `?geom_expressions`.
* Measures (return a number): `st_area()`, `st_length()` / `st_perimeter()`,
  `st_x()`, `st_y()`, `st_npoints()`, `st_ngeometries()`, and the binary
  `st_distance()`.
* Predicates (return TRUE/FALSE): unary `st_is_valid()`, `st_is_empty()`,
  `st_is_simple()`, and the binary topological relations `st_intersects()`,
  `st_within()`, `st_contains()`, `st_overlaps()`, `st_touches()`,
  `st_crosses()`, `st_equals()`, `st_disjoint()`, `st_covers()`,
  `st_covered_by()`. The second geometry is another geometry column, a constant
  `sf`/`sfc` object (parsed once and reused across the stream), or a hex-WKB
  string.
* Transforms (return a geometry as hex-WKB): `st_centroid()`,
  `st_point_on_surface()`, `st_boundary()`, `st_envelope()`,
  `st_convex_hull()`, `st_make_valid()`, the parameterized `st_buffer(g, dist)`
  and `st_simplify(g, tol)`, and the type name `st_geometry_type()`.
* The per-row decode is parallelized with OpenMP. A missing or unparseable
  geometry yields `NA` for that row rather than an error.

## Documentation

* New vignettes covering the spatial surface added since 0.9.1: "Geometry
  functions in expressions" (the `st_*` functions inside `mutate()`/`filter()`/
  `summarise()`), "Coverage and topology" (the set-wise verbs:
  `spatial_polygonize()`, `spatial_line_merge()`, `spatial_simplify()`,
  `spatial_eliminate()`, `spatial_explode()`, `spatial_topology()`,
  `spatial_centerline()`, `spatial_construct()`, `spatial_snap_grid()`,
  `spatial_locate()`), and "Network analysis" (`spatial_network()`,
  `spatial_route()`, `spatial_service_area()`).

## Bug fixes

* Fixed installation failure on R-devel with clang 22 (CRAN's
  `r-devel-linux-x86_64-fedora-clang`). Six source files included `<omp.h>`
  directly after R's headers; clang 22's `omp.h` begins with a `declare
  variant match(...)` clause that R's `match` -> `Rf_match` macro rewrote into
  invalid syntax. All OpenMP usage now routes through `vec_omp.h`, which
  forward-declares the runtime functions instead of including the wrapper.

# vectra 0.9.6

## Network analysis

* `spatial_network()` builds a routable graph from a line layer: nodes at line
  endpoints (snapped within `tolerance`), edges weighted by geometry length or a
  `weight` column, optionally directed with one-way codes (`direction`,
  `weight_to`). The graph and the shortest-path solver are native C (a
  binary-heap Dijkstra over a CSR adjacency, no `igraph` dependency); the graph
  is held resident in a `vectra_network` object, the network counterpart of a
  resident `sf` `y`, while the query verbs stream.
* `spatial_route()` streams a layer of origins past a resident network and
  returns the shortest path from each origin to one or more destinations `to`,
  one row per (origin, destination) with the cost and the route geometry. With
  `geometry = FALSE` it returns only the cost, so a destination set per origin is
  the origin-destination cost matrix in long form. Unreachable pairs return an
  infinite cost rather than dropping the row.
* `spatial_service_area()` streams origins and, per origin, returns the part of
  the network reachable within a cost budget -- the convex-hull service area
  (`output = "polygon"`), the reachable edges (`"lines"`), or the reachable
  nodes (`"nodes"`). A vector `cost` returns nested travel-cost isochrone bands,
  one row per (origin, band).
* The solver parallelises over a batch of origins with OpenMP; the graph is the
  resident budget while the query side scales by streaming.

# vectra 0.9.5

## Coverage cleanup

* `spatial_eliminate()` cleans a polygon coverage by absorbing every feature
  smaller than `max_area` into a neighbour (the QGIS "Eliminate"): each sliver
  joins the neighbour it shares the longest border with, or the largest-area
  neighbour with `into = "largest_area"`. An area-rooted union-find collapses
  chains of slivers so a connected run flows to its single largest member, whose
  attributes survive, and a sliver with no neighbour is kept unchanged. Rides the
  partition tier alongside `spatial_dissolve()`.

# vectra 0.9.4

## Centerline and planar topology

* `spatial_centerline()` traces the centerline (medial axis) of each streamed
  polygon from the Voronoi diagram of its densified boundary: the Voronoi edges
  that fall inside the polygon are its skeleton, merged into maximal lines.
  `density` sets the boundary sampling and `prune` drops the short spurs the
  skeleton grows toward convex corners. The usual approximation for river or road
  centerlines from a filled shape; non-polygon geometry passes through unchanged.
* `spatial_topology()` decomposes a polygon coverage into the arcs of its planar
  topology: the unioned boundaries are noded so a shared border becomes one arc,
  tagged with the identifiers of the (up to two) polygons on either side -- two
  for an internal shared edge, one for an outer edge. Rides the partition tier
  and is the inverse of `spatial_polygonize()`.

# vectra 0.9.3

## Set-wise topology verbs and linear referencing

* `spatial_polygonize()` builds the polygonal faces enclosed by a line network
  (the QGIS "Polygonize", the inverse of taking polygon boundaries): a group's
  lines are unioned and noded, then the faces of that arrangement are returned,
  one per row. Like `spatial_dissolve()` and `spatial_construct()` it rides the
  partition tier, with an optional `by` to polygonize within groups.
* `spatial_line_merge()` sews line segments that meet end to end into maximal
  linestrings (the line counterpart of a dissolve), one row per chain; segments
  meeting at a junction of degree greater than two stay separate.
* `spatial_simplify()` simplifies a polygon coverage without tearing shared
  edges: boundaries are unioned so a shared border is one line, noded into arcs,
  each arc simplified once with its junction endpoints pinned, and
  re-polygonized, so adjacent polygons stay edge-matched with no slivers. This is
  the topology-preserving simplification a per-feature
  `spatial_map(~ sf::st_simplify())` cannot give, because that simplifies each
  polygon's copy of a shared border independently. Each simplified face keeps its
  source polygon's attributes.
* `spatial_locate()` locates streamed points along a resident line layer (linear
  referencing): each point gets its nearest line's identifier, the measure
  (distance along that line), and the perpendicular offset, with an optional
  `snap` onto the line. The inverse (a measure back to a point) is
  `sf::st_line_interpolate()` through `spatial_map()`.
* The partition tier shared by `spatial_dissolve()`, `spatial_construct()`, and
  the three new set-wise verbs is now a single internal `.partition_each` router
  rather than re-inlined in each verb.

# vectra 0.9.2

## Two-layer `spatial_overlay()`

* `spatial_overlay()` gains a second layer `y`: instead of self-unioning one
  layer it nodes two layers into one planar partition and carries the attributes
  of the covering `x`-record and `y`-record onto each piece. A `how` argument
  selects which pieces to keep -- `"intersection"` (covered by both),
  `"union"` (every piece of either, the absent side filled with `NA`),
  `"identity"` (all of `x` split by `y`), or `"symdiff"` (pieces in exactly one
  layer). `vars_y` selects the carried `y` columns, and a name shared with an `x`
  column is disambiguated with a `.x` / `.y` suffix. `y` accepts an `sf` object
  or a file path (`layer_y` / `query_y`) read in batches, and must share the CRS
  of `x`. With `y = NULL` (the default) the behaviour is unchanged. This reuses
  the existing noding, deduplication, component-tiling, and streaming machinery,
  so a two-layer overlay scales the same way the self-union does.

## `spatial_explode()`

* New `spatial_explode()` streams a query and splits every multipart geometry
  into its single-part components -- a `MULTIPOLYGON` into one row per polygon, a
  `MULTILINESTRING` into linestrings, a `MULTIPOINT` into points, and a
  `GEOMETRYCOLLECTION` into its members (recursively) -- copying the source
  attributes onto each part. Single-part and empty geometries pass through as one
  row. An optional `part` argument names a 1-based part-index column. It is the
  streaming counterpart of the QGIS "multipart to singleparts" tool, processing
  one batch at a time, and the inverse of `spatial_dissolve()`.

## `spatial_construct()`

* New `spatial_construct()` builds a set-wise geometry construction from a whole
  feature set -- the constructions a per-feature `spatial_map()` cannot express.
  A `kind` argument selects it: `"convex_hull"`, `"concave_hull"`, `"envelope"`,
  `"oriented_box"`, `"enclosing_circle"`, `"inscribed_circle"`, `"pole"` (the
  pole of inaccessibility, the centre of the maximum inscribed circle),
  `"voronoi"`, and `"delaunay"`. Like `spatial_dissolve()` it rides the partition
  tier: a `by` argument routes the layer into one shard per group and builds one
  construction per group, with `NULL` constructing from the whole layer. The
  enclosing kinds emit one feature per group; the tessellations emit one polygon
  per cell, each carrying the group's `by` values.

## `spatial_snap_grid()` and `spatial_snap()`

* New `spatial_snap_grid()` rounds a streamed layer's coordinates to a regular
  grid of a given spacing and repairs the result, one batch at a time. It is the
  fixed-precision snap-rounding `spatial_overlay()` applies internally, exposed
  as a standalone verb, so a layer can be cleaned of slivers or pre-noded to a
  common precision without running a full overlay. The snap runs in C straight
  off the hex-WKB column, one cleaned geometry per input feature.
* New `spatial_snap()` snaps a streamed layer's vertices and edges toward a
  resident reference layer when they lie within a tolerance (the QGIS "snap
  geometries to layer"), closing the small gaps and overshoots between two layers
  that should share a boundary. The reference layer stays resident while the left
  stream flows past one batch at a time.

## `spatial_knn()`

* New `spatial_knn()` finds, for each feature of a streamed layer, the `k`
  nearest features of a small resident layer, returning one row per (left,
  neighbour) pair with the neighbour's rank, identifier, and distance. Where
  `spatial_join()` with `st_nearest_feature` attaches only the single nearest
  match, this returns the top `k` and the distances -- the nearest-`k` query and
  the building block of a distance matrix. Distances follow `sf::st_distance()`
  (planar in CRS units, or great-circle metres when spherical geometry is on).

## `spatial_smooth()`

* New `spatial_smooth()` rounds the corners of streamed lines and polygons by
  Chaikin corner-cutting, one batch at a time. Each iteration replaces every
  vertex with two points a quarter and three-quarters along its adjacent edges;
  open lines keep their endpoints, polygon rings are cut cyclically. The
  smoothing is computed directly on the coordinates (no GEOS call), so it is
  dependency-light. Densifying and sampling points along a line stay
  `spatial_map()` recipes (`~ sf::st_segmentize(.x, dfMaxLength)`,
  `~ sf::st_line_sample(.x, n)`).

## `spatial_split()`

* New `spatial_split()` cuts a streamed layer against a small resident `blade`
  layer (the QGIS "split with lines"), one batch at a time: a polygon is divided
  into the faces the blade carves out, a line into the arcs between crossings, and
  each piece is emitted as its own row with the source attributes copied. A
  feature the blade misses passes through as a single piece. With
  `extract = "points"` it instead returns the points where each feature meets the
  blade (the "line intersections" tool), dropping features that do not cross. The
  split is built from \pkg{sf}/GEOS noding and polygonization and expects planar
  coordinates.

# vectra 0.9.1

## `spatial_overlay()` noding and deduplication

* `spatial_overlay()` now nodes each tile with fixed-precision snap-rounding
  (`GEOSUnaryUnionPrec`) at a grid derived from the layer extent, instead of
  floating-point noding. Floating noding throws on dense overlapping linework
  and falls back to a full snap-rounding retry of the whole component, which on
  large protected-area layers dominated the run. Fixed-precision noding is
  deterministic and single-pass, so the per-tile cost is flat and the overlap
  coverage invariant holds (`maxerr < 1e-4`) without the previous coverage
  warning. A new `precision` argument overrides the derived grid size.
* Byte-identical input geometries are now deduplicated before the overlay
  (`dedup = TRUE`, the default): each distinct geometry is overlaid once and its
  attributes fanned back to every duplicate source, so a layer with repeated
  sites does the topology work once. On a ~470k-feature world protected-area
  union this cut the distinct geometry count by about three quarters and the
  end-to-end run from roughly 50 to 17 minutes. Set `dedup = FALSE` to disable.

## Streaming GeoPackage output

* An [sf::st_write()] method for a `vectra_node` (also reached via
  `sf::write_sf()`) writes a result to a vector file one batch at a time,
  appending each, so a multi-million-feature output is never held in memory as
  one `sf` object the way `collect_sf() |> st_write()` would. Resolving a dense
  overlay and writing the ~3M-piece GeoPackage this way keeps peak memory near
  the overlay's own (a few GB) instead of spiking on the write.
* Grouped `slice_min()` / `slice_max()` (`n = 1`) now emits its winners in
  bounded row batches rather than one block, so a downstream streaming writer
  sees the result incrementally.

## Streaming grouped `slice_min()` / `slice_max()`

* Grouped `slice_min()` / `slice_max()` with `n = 1, with_ties = FALSE` now
  streams: it holds only the running winner per group, so peak memory scales
  with the number of groups (the result size), not the input length. The
  previous path ranked every input row through the window operator, which
  materialized all columns -- including a large geometry string column -- and
  could exhaust memory (`builder realloc failed (str data)`) when resolving a
  dense overlay whose geometry dwarfs RAM. The whole winning row, geometry and
  all attributes included, is still kept. Other grouped cases (`n > 1` or
  `with_ties = TRUE`) are unchanged.

## Lower-memory `spatial_overlay()`

* `spatial_overlay()` now encodes and parses the input geometry a feature batch
  at a time rather than materializing the whole layer's WKB at once. Connected
  components are derived from the bounding boxes after parsing, so the result is
  byte-identical; only the transient input copy is bounded. The batch size scales
  with available RAM (`read_chunk`, or `getOption("vectra.overlay_parse_chunk")`),
  and the default working-set budget is capped at half of total RAM when it can be
  detected, so a many-core machine cannot scale the overlay past what it can hold.
* `spatial_overlay()` can read its input directly from a vector file (`x` a path,
  with `layer =` or `query =`) instead of a pre-loaded `sf` object, reading the
  layer in feature batches. The full layer is never held in memory, so peak usage
  tracks the cleaned geometry rather than the source size: a world protected-area
  layer that needs ~11 GB to load with `sf::st_read()` overlays in ~5 GB this way,
  bringing a larger-than-RAM layer within reach of a 16 GB machine.

## Raster and vector toolbox

* `polygonize(raster)` vectorises a raster into polygon features, the inverse of
  `rasterize()`: cells are read one tile-row strip at a time and (by default)
  dissolved by value into one polygon per value through `spatial_dissolve()`.
* `contours(raster, levels)` traces iso-lines with marching squares over a
  haloed strip pass, then joins each level's segments into continuous lines.
* `mask(raster, polygon)` clips a raster to an `sf` polygon layer one strip at a
  time, keeping the pixels whose centre falls inside (or, with `inverse = TRUE`,
  outside) it. It is the raster counterpart of `spatial_clip()`.
* `rast_calc(rasters, expr)` evaluates a cellwise expression across aligned
  rasters (map algebra): band indices like `(nir - red) / (nir + red)`,
  reclassification, and arithmetic across layers, streamed strip by strip.
* `mosaic(rasters, fun)` merges rasters sharing a resolution and cell grid onto
  their union, resolving overlap with `first` / `last` / `mean` / `sum` / `min`
  / `max`, one output strip at a time.
* `proximity(raster, target)` computes the exact Euclidean distance from every
  cell to the nearest feature (non-NA, or matching `target`) in CRS units, via
  the separable Felzenszwalb-Huttenlocher distance transform: a row pass, an
  out-of-core transpose, a column pass, and a transpose back, each over tile-row
  strips so the whole grid is never resident. Squared distances scale by the x
  and y resolution, so the result is exact on anisotropic cells.

## Native libgeos compute paths

* `spatial_filter()`, `spatial_join()`, `spatial_clip()`, and
  `spatial_dissolve()` now run their geometry operation natively on the GEOS C
  API (via `libgeos`) straight off the hex-WKB geometry column, with no per-batch
  round-trip through `sf`. The resident side -- the locator layer, the join
  target, the clip mask -- is parsed once into a GEOS spatial index and each
  streamed batch is tested, matched, or cut in C, parallel across rows.
  `spatial_filter()` and `spatial_join()` cover the topological predicates
  (intersects, within, contains, overlaps, covers, covered by, touches,
  crosses); `spatial_join()` returns the per-row match lists from C and attaches
  the resident attributes in R without decoding the left side.
* The native predicate set extends beyond the topological ones: `equals`,
  within-distance (`sf::st_is_within_distance`, radius passed as `dist =`, found
  by querying the index with each feature's envelope grown by the radius), and,
  for `spatial_join()`, nearest feature (`sf::st_nearest_feature`, one resident
  match per row via the index's nearest-neighbour traversal). `spatial_filter()`
  also runs `disjoint` natively (a row matches when it is disjoint from at least
  one resident feature). A disjoint *join* keeps the `sf` path, since its matches
  are the bounding-box complement a spatial index cannot prune.
* Coordinate-assembled (`coords`) point input runs natively too: each point is
  built in C from its x/y columns and matched against the index, instead of being
  assembled into an `sf` layer per batch. This covers `spatial_filter()` (every
  predicate but disjoint, which stays on `sf` as it does for the join) and
  `spatial_join()` (topological, within-distance, and nearest, with the emitted
  point geometry also built in C).
* `zonal()` with polygon zones now assigns each pixel centre to its polygon
  natively: the polygons are parsed once into the index and every tile-row
  strip's centres are located in C, so `sf` is touched only to read the polygons
  in. Geographic polygons with spherical geometry on (`sf::sf_use_s2()`) keep the
  `sf` point-in-polygon path.
* The native paths run on projected or unprojected planar data, where they equal
  the previous `sf` result exactly. Geographic coordinates with spherical
  geometry on (`sf::sf_use_s2()`), a disjoint join, and extra `sf::st_union()` /
  `sf::st_join()` arguments keep the `sf` path, so its semantics are unchanged.

## Documentation

* New `vignette("spatial")` walks the out-of-core GIS toolbox as one workflow,
  with inline canvas animations for the raster-to-points bridge, select by
  location, rasterization, and the cost-model tiers.
* The quickstart vignette leads with animated views of the streaming memory
  envelope, what has to fit in RAM, and the lazy pull-based plan, and its
  on-disk-format description now matches the tdc codec.

## Two-sided streamed spatial join

* `spatial_join(x, y, partition = grid(cellsize))` joins two larger-than-RAM
  layers by binning both to a uniform spatial grid and joining one shard at a
  time, for the case where neither side fits in memory as a resident `sf`
  object. `y` becomes a streamed `vectra_node`; each left feature is assigned to
  the single grid cell of its reference point while each right feature is
  replicated to every cell its bounding box overlaps, so a left row is emitted
  exactly once and the result equals the resident join. This is exact for point
  left geometries (the dominant case -- tagging a huge point set with the
  polygon it falls in). `grid(cellsize, origin)` defines the partition grid. The
  partition path serves the topological predicates (intersects, within,
  contains, ...) and `sf::st_nearest_feature`, for which each left feature
  searches its own cell and the eight around it (the nearest is found when it
  lies within one cell of the left reference cell).

## Streamed warp (resample / reproject)

* `warp(raster, template, method)` resamples or reprojects a `.vec` raster onto
  a target grid, walking the *output* one tile-row strip at a time. Each strip's
  target pixel centres are projected into the source CRS (via PROJ through `sf`
  only when the two CRSs differ), mapped through the source geotransform, and
  sampled from the bounded source window they fall in -- so the whole output grid
  is never resident and the source is read in windows rather than held whole.
  `method` is `"near"`, `"bilinear"`, or `"cubic"` (Catmull-Rom), following the
  GDAL / `terra::project()` convention; kernels that reach off the source extent
  or touch nodata return `NA`. `template` borrows a grid from another raster or
  is given as `list(crs =, extent =, res =, dims =)`. The C sampler keeps the
  interpolation native; projection stays in PROJ.

## Streamed focal and terrain

* `focal(raster, w, fun)` applies a moving window to a `.vec` raster, reading
  the input one tile-row strip at a time -- each strip expanded by the kernel
  radius (a halo read) so window neighbours are available without ever holding
  the whole grid resident. When `path` is given the output is streamed straight
  back to a new `.vec` one tile-row at a time, so neither the input nor the
  output band is ever fully in memory: the raster op that runs out of core where
  an in-memory engine needs the whole raster at once. The window is a weight
  matrix (or a single odd integer); `fun` is one of `"sum"`, `"mean"`, `"min"`,
  `"max"`, `"sd"`, `"median"`, computed in C, with `na.rm` matching the resident
  behaviour at edges.
* `terrain(raster, v)` derives DEM products with Horn's 3x3 method on the same
  haloed strip pass: `"slope"`, `"aspect"`, `"hillshade"`, `"TPI"`,
  `"roughness"`, `"TRI"`. The return follows the input -- one matrix for a single
  `v`, a named list (or a multi-band `.vec`) for several -- and matches
  `terra::terrain()` / `terra::shade()`.

## Streamed dissolve

* `spatial_dissolve(x, by, .fun)` unions the geometries within each `by` group
  into a single feature (the GIS "Dissolve" tool), optionally summarising
  attributes through a named list of functions. Dissolve needs every geometry of
  a group together, so it rides the partition tier: `x` is spilled once and
  routed into one shard per group in a single bounded pass, then each shard is
  unioned with `sf`. With no `by` the whole layer dissolves into one feature.

## Streamed zonal statistics

* `zonal(raster, zones, fun)` summarises a raster within zones one tile-row
  strip at a time, so the whole grid never has to be resident. Zones come from a
  second raster aligned to the value grid (the `terra::zonal()` pattern) or from
  an `sf` polygon layer (each pixel assigned the polygon its centre falls in).
  The per-zone moments are folded in memory as strips arrive -- peak memory is
  one strip plus the small per-zone table -- and `fun` may name several of
  `"mean"`, `"sum"`, `"count"`, `"min"`, `"max"`, `"sd"` at once. Raster zones
  are `sf`-free; `sd` is derived from the streamed moments with no second pass.

## Streamed vector-to-raster

* `rasterize(x, template, field, fun)` folds a larger-than-RAM point stream into
  a fixed raster grid one batch at a time. The grid is held resident while the
  points flow past, so peak memory is the grid plus one batch -- the streaming
  counterpart to `terra::rasterize()` on a point set that has to fit in RAM. The
  per-cell reduction (`"count"`, `"sum"`, `"mean"`, `"min"`, `"max"`) is
  accumulated in C. Points arrive either as two coordinate columns (the default,
  `sf`-free path) or from a hex-WKB point-geometry column. The result is an
  in-memory georeferenced matrix, or a `.vec` raster when `path` is given.

## Streamed select-by-location and clip/erase

* `spatial_filter(x, y, predicate)` keeps the rows of a streamed layer `x` whose
  geometry satisfies an `sf` binary predicate against a small resident layer `y`
  (select by location), filtering the billion-row stream one batch at a time
  while `y` stays in memory. Rows are filtered, never duplicated, and the output
  carries `x`'s schema unchanged; `negate = TRUE` keeps the non-matching rows
  (select by location, inverted).

* `spatial_clip(x, mask, erase)` cuts a streamed layer's geometry against a
  small resident `mask`: the intersection by default (the GIS "Clip" tool), or
  the difference with `erase = TRUE` (the "Erase" tool). The mask is dissolved
  once and held resident while the stream flows past one batch at a time.

* The run-file spill machinery shared by the streamed spatial verbs
  (`spatial_map`/`join`/`filter`/`clip`/`overlay`) is now a single internal
  accumulator, so all of them flush, finalize, and clean up identically.

# vectra 0.8.2

## Bug fixes

* `ifelse()` (and `if_else()`) now returns the correct type when its two
  branches differ. Previously `ifelse(int64_col, x, y)` with a `double` or `NA`
  other branch labelled the result column int64 while the evaluator produced
  doubles, so the kept int64 values came back as ~4.6e18 garbage (and triggered
  a spurious "int64 value exceeds 2^53" warning). The result column now adopts
  the common type of the two branches, matching the evaluator. In particular
  `ifelse(year > 0, year, NA)` is a clean way to blank out sentinel years.

# vectra 0.8.1

## Polygon self-overlay

* `spatial_overlay(x)` splits a polygon `sf` layer along all its own overlaps
  into disjoint pieces (the "Union (single layer)" overlay), returning a lazy
  node with one row per piece per covering polygon. Resolve the duplicates with
  a grouped `slice_min()`/`slice_max()` -- e.g. earliest designation year wins,
  `group_by(piece_id) |> slice_min(year)`. The overlay runs in C on the GEOS C
  API (via `libgeos`). Each feature is parsed once, in parallel -- repaired and
  snapped to a fixed-precision grid -- then features are grouped into connected
  components from their bounding boxes. Each component is one overlay job whose
  boundary linework is noded once and polygonised into faces (a single noding
  pass, so cost tracks the number of pieces, not how deeply polygons overlap);
  the few components too large for the memory budget are tiled over their own
  extent and clipped, so no single noding pass is ever large. Jobs run one per
  OpenMP thread (`threads`) and stream to a `.vtr` in batches sized to a
  `mem_limit` budget, so peak memory stays bounded regardless of layer size. The
  snapping grid is derived from the data's coordinate magnitude and checked
  against a coverage invariant (the piece areas covering an input sum to its
  area), so pieces come out disjoint and their areas reconstruct the union.
  Scales to layers a single `sf::st_intersection()` cannot hold at once (a 470k
  marine-protection layer overlays in bounded memory where the in-memory call
  exhausts RAM).

# vectra 0.8.0

## Group-aware slicing

* `slice_min()` and `slice_max()` now respect `group_by()`: they keep the n
  smallest/largest rows *within each group* and return the whole winning row
  (every column, including geometry carried as a string), rather than a global
  top-n. `with_ties = FALSE` returns exactly n per group via a deterministic
  ordered `row_number()`; `with_ties = TRUE` keeps rows tied at the nth value.
  Previously a grouped `slice_min()`/`slice_max()` silently ignored the grouping
  and returned a single global result.
* `row_number()` accepts an order column: `row_number(col)` and
  `row_number(desc(col))` assign a deterministic 1..n within each partition,
  ordered by the column (the unordered `row_number()` is unchanged).
  `rank(desc(col))` is also supported.

## Streamed spatial operations

* `spatial_map(x, fn)` streams a lazy query through an `sf` transform (buffer,
  centroid, CRS transform, simplify, ...) one batch at a time and returns a new
  lazy node, so a per-feature geometry operation runs on a table larger than
  RAM at one-batch peak memory.
* `spatial_join(x, y, join)` joins a streamed left side `x` against a small
  resident `sf` object `y` with an `sf` binary predicate (`st_intersects` by
  default): the spatial analogue of a hash join with the small side resident.
  The dominant use is tagging a huge point set with the polygon it falls in.
  Both-sides-huge joins compose with `offload(by = ...)`: partition on a spatial
  grid key, join each shard, recombine.
* `collect_sf(x)` materializes a spatial query as an `sf` object.
* Geometry rides through the engine as hex-encoded WKB in an ordinary string
  column (no new column type), losslessly round-tripped; the CRS is carried on
  the node. Topology stays with `sf`/GEOS — `sf` is an optional dependency
  (Suggests).

# vectra 0.7.1

* Cap the OpenMP team to two threads under `R CMD check`. When CRAN's
  `_R_CHECK_LIMIT_CORES_` is set, the package now lowers its default team size
  to two so the parallel string, fuzzy-join, sort, and window kernels stay
  within the check farm's two-core limit. The fuzzy-join match phase also
  clamps its requested thread count to the available maximum, matching the
  blocked fuzzy-lookup path. Outside a check the package still uses every
  available core.

# vectra 0.7.0

## Streaming consumption

* `collect_chunked(x, f, .init)` folds a function over a query one batch at a
  time. The engine pulls a single batch into R, applies `f(acc, chunk)`, frees
  the batch, and moves on, so a result larger than RAM can be reduced to a
  small summary (a running count, per-group sufficient statistics, the
  cross-products behind a linear fit) in one bounded-memory pass.
* `chunk_feeder(.source)` turns a query into a resettable generator following
  the `data(reset)` protocol that `biglm::bigglm()` expects, so a generalized
  linear model can be fitted out-of-core: each iteratively reweighted pass
  streams through the engine without ever holding the full design matrix.
  `.source` is a factory returning a fresh node, replayed on every reset.
* New C pull interface (`C_node_optimize`, `C_node_next_batch`) backs both
  verbs; per-batch conversion reuses the existing column converter, so the
  chunked and materializing paths share one code path.

## Offloading and out-of-core fits

* `offload()` is one verb with two return shapes. `offload(x)` materializes a
  query once to a `.vtr` and returns a node that streams from that file: it
  holds the same rows as `x` (an identity on values) and changes only the cost
  profile, since replaying it is a disk scan instead of a re-run of the upstream
  pipeline. `chunk_feeder()` accepts an offloaded node directly, so an iterative
  consumer such as `biglm::bigglm()` reads the prepared columns from disk on
  every reweighted pass rather than rebuilding them each time.
* `offload(x, by = ...)` splits a query into disjoint shards in a single
  streaming pass, one per key value (`method = "level"`), per value range
  (`"range"`), or per hash bucket (`"hash"`); `"auto"` picks level for a
  discrete key and range for a numeric one. The result is list-like: `length()`,
  `names()` (the keys), `p[["key"]]`, and `lapply(p, ...)` all work, turning a
  model that couples within a group into independent per-shard fits. The union
  of the shards reproduces the input; row totals are checked.
* `group_map()` and `group_modify()` run a function on each shard of a
  partition. `group_map()` reads each shard into a data.frame, hands it to the
  function with its key, and returns the results keyed by shard (one fit per
  group). `group_modify()` binds per-shard data.frames into one table and
  restores the key as a column. A purrr-style `~` formula works for either.
* `collect_chunked()` is now a generic and gains a `combine` argument: supplying
  it declares the reduction a monoid (with `.init` as identity), which lets the
  fold run over the shards of a partition and merge the partial results. A
  `commutative` flag declares the merge order-free.
* Offloaded streams carry a cost grade (passes over the data, peak memory, I/O
  class), shown by `print()` and `explain()` -- the label a plan reads to choose
  between a one-pass fold, an external sort (`arrange()`), and a partition.

# vectra 0.6.3

## Fixes

* `summarise()` / `summarize()` now accept namespace-qualified
  aggregation calls (`vectra::n()`, `vectra::sum(x)`,
  `vectra:::mean(x)`). Previously `parse_agg_expr` ran
  `as.character()` on the call head and dispatched on its result; for
  a `pkg::fn` call that yielded the length-3 vector
  `c("::", "pkg", "fn")`, and the subsequent `if (!fn %in% valid_aggs)`
  triggered "the condition has length > 1" under R >= 4.2. The parser
  now unwraps `::` / `:::` and uses the bare function name.

# vectra 0.6.2

## CRAN archive-issue fixes

Resolves the three findings the auto-check email surfaced for the
2026-05-06 archived 0.5.1 release.

* DESCRIPTION: replaced "gridded" (flagged as a possibly-misspelled word
  in the CRAN incoming pretest) with "raster".
* gcc-ASAN heap-buffer-overflow in the LZ decode path
  (`tdc/src/api/decode_impl.c`, surfaced through `read_rg_tdc_with_fp` in
  `vtr1_tdc.c`): the consolidated decode pipeline now always allocates
  scratch buffers with a +16-byte wildcopy slack, so `tdc_match_copy`'s
  SIMD overshoot stays within the allocation. The `decode_ex.c` variant
  that was missing this slack on 0.5.1 is gone (folded into the shared
  `driver_decode_block_impl`). The ASAN-under-vignettes regression check
  is now part of the GitHub Actions sanitizer workflow so a future
  drift would be caught locally instead of at CRAN's BDR memcheck.
* rchk PROTECT findings in `src/r_bridge.c`, `src/r_bridge_io.c`,
  `src/vtr1_tdc.c`, and `src/collect.c`: every `Rf_getAttrib` /
  `Rf_mkString` result that crossed an allocating call (`R_alloc`,
  `Rf_warning`, `Rf_setAttrib`, `Rf_asReal`, `Rf_asInteger`,
  `parse_*`) is now `PROTECT`ed and balanced with a matching
  `UNPROTECT`. Touches `apply_annotation`, `C_write_vtr`,
  `C_write_vtr_tdc`, `parse_quantize`, and `parse_spatial`.

# vectra 0.6.1

## Fixes

* `src/vec_omp.h` and call sites: stop including `<omp.h>` and forward-declare
  the three OpenMP runtime functions vectra calls (`omp_get_max_threads`,
  `omp_get_thread_num`, `omp_in_parallel`). clang 21's bundled omp.h wrapper
  contains an unbalanced `#pragma omp end declare variant` that breaks
  compilation of `block.c` (and any other vectra TU that includes the
  wrapper) under r-devel-linux-x86_64-debian-clang. The bug is in the wrapper
  itself, so an `#ifdef _OPENMP` guard around `#include <omp.h>` is not
  enough — when `-fopenmp` is on the compile line, `_OPENMP` is defined and
  the broken wrapper is pulled in. Skipping the wrapper avoids the bug; the
  `#pragma omp ...` directives elsewhere in `src/` are still recognised and
  the runtime symbols resolve at link time via `libomp`. Fixes the
  compilation error that caused vectra 0.5.1 to be archived from CRAN.

# vectra 0.6.0

## Raster format (`.vec`)

A new tiled raster format and accompanying API for larger-than-RAM gridded
data. Each tile is encoded as a self-describing tdc block (PRED_2D +
BYTE_SHUFFLE + LZ); decoding is parallel across tiles.

* `vec_write_raster(x, path, ...)`: write a numeric matrix or 3D
  `(rows, cols, bands)` array to `.vec`. Storage dtypes: `f64`, `f32`,
  `i8`/`u8`, `i16`/`u16`, `i32`/`u32`, `i64`/`u64`. `compression` controls
  per-tile codec probing — `"fast"`, `"balanced"`, or `"max"` (six-spec probe
  per tile). Decode cost is unchanged across levels because each tile records
  its own codec spec.
* `vec_open_raster(path)` / `vec_close_raster(r)`: lazy open returning a
  metadata + handle list (`vectra_raster`). The handle is auto-finalized on
  garbage collection.
* `vec_read_window(r, band, level, cols, rows)`: decode a window of a chosen
  band, with overview-level support. Pixels outside the raster come back as
  `NA`. Tile decode is parallelized across worker threads (Phase 5a).
* `vec_extract_points(r, x, y)`: sample band values at `(x, y)` points.
* `vec_build_overviews(path, levels, resampling)`: append `n_levels - 1`
  reduced-resolution copies in place. Resampling kernels: `"nearest"`,
  `"average"`, `"bilinear"`, `"mode"`, `"gauss"`. The file's `n_levels` is
  updated atomically.
* `vec_to_tiff(path, output, compression)`: export `.vec` level-0 pixels to
  GeoTIFF. Compression is `"none"`, `"deflate"`, or `"lzw"`; LZW also applies
  horizontal differencing (Predictor 2) for integer pixel types, matching the
  layout libtiff/GDAL produce by default. Inherits dtype, geotransform,
  EPSG, and nodata from the source.

## Time cubes

* `vec_write_time_cube(x, times, path, layout, ...)`: write a 4D
  `(rows, cols, bands, time)` array. Two layouts:
  - `"image"` (default): one tile per `(band, time, ty, tx)` — optimal for
    "give me one full image at time T" reads.
  - `"pixel"`: one tile per `(band, ty, tx)` holding the full time stack as
    `[tw*th, n_time]` — optimal for "give me the time series at pixel
    `(x, y)`" reads.
* `vec_read_pixel_series(r, x, y, band)`: full time series at a single
  pixel as a numeric vector. On pixel-major files this is one tile decode;
  on image-major files the reader scans the index for distinct time stamps
  and decodes one tile per stamp.
* `vec_read_time_slice(r, time, band, level, cols, rows)`: read a single
  time slice as a matrix.
* `vec_raster_times(r, band, level)`: distinct time stamps, in ascending
  order.
* `vec_raster_layout(r)`: query whether an open raster is `"image"` or
  `"pixel"` layout.
* `print.vectra_raster()`: prints dimensions, dtype, geotransform, EPSG,
  nodata, and band names.

## GeoTIFF reader and writer

* Reader: tiled and Cloud-Optimized GeoTIFF (COG) inputs go through the same
  block abstraction as strip TIFFs (strips collapse to `n_blocks_x = 1`).
  Edge-block padding is handled in `block_stored_rows()`.
* `tiff_band_names()`: parse `<Item role="description">` entries from
  `GDAL_METADATA` (tag 42112). Pure-R scanner, no `xml2` dependency.
* `tiff_crs(path)`: read the EPSG code, geographic-vs-projected flag, and
  citation string from the GeoKey directory (tags 34735/34737).
* `write_tiff()` gains `tiled`, `tile_size`, `bigtiff`, and `crs` arguments.
  - `tiled = TRUE` emits TIFF tags 322/323/324/325 in place of strip tags.
    `tile_size` accepts a single integer (square) or a length-2 `c(w, h)`;
    both dimensions must be positive multiples of 16. Default 256. Tiled
    output is the layout required for Cloud-Optimized GeoTIFF.
  - `bigtiff = "auto"` (default) auto-promotes to BigTIFF (magic `0x002B`,
    64-bit offsets) when the expected raw payload exceeds the classic-TIFF
    4 GB ceiling; `TRUE` forces BigTIFF; `FALSE` forces classic TIFF. Tiled
    BigTIFF is not yet supported.
  - `crs` accepts an integer EPSG code, an `"EPSG:xxxx"` string, or a list
    with `$epsg`, `$geographic`, and optional `$citation`. Outputs round-trip
    through `terra::rast()` for 4326, 3857, and 31287.

## Fixes

* `collect()` / `block_array_gather`: empty-string slots now shortcut to
  `R_BlankString`. Previously the gather paths called `Rf_mkCharLenCE(NULL,
  0, ...)` and the dedup cache called `memcmp(NULL, ...)` when a batch
  happened to contain only empty/`NA` strings, tripping UBSAN's nonnull
  check even though the length was zero.

## Internal

* C-side `*_push` helpers (`vec_buf_push`, `vec_array_push`, ...)
  consolidated into a single `vec_grow_to` growth primitive.

# vectra 0.5.1

## CRAN resubmission fixes (0.5.0 incoming pretest feedback)

* `configure` / `configure.win`: rewritten as POSIX `/bin/sh` (previously
  `#!/usr/bin/env bash` with `set -o pipefail` and `[[ ... ]]`). Bash is
  not guaranteed on all CRAN build hosts.
* `src/window.c`: the OpenMP task-parallel merge sort helper was defined
  unconditionally but called only from `#ifdef _OPENMP` branches, producing
  a clang `-Wunneeded-internal-declaration` warning under Debian's
  no-OpenMP build. The definition now shares the guard.
* Vendored `tdc`: all `fprintf(stderr, ...)` debug/timing prints are routed
  through a `TDC_LOG(...)` macro that is a no-op unless
  `TDC_ENABLE_STDERR_LOG` is defined at build time, so the released `.so`
  contains no `stderr` / `fprintf` symbols. Addresses the WRE §1.6.4 policy
  forbidding compiled code from writing to stdout/stderr.

## Fixes

* `collect()`: fix use-after-free in the cross-batch CHARSXP dedup cache. Each
  slot stored a raw pointer into the decoder's heap buffer, which is freed
  when the batch is consumed; the next batch's hash-collision `memcmp` then
  dereferenced freed memory. Manifested as segfaults on the second consecutive
  `collect()` of a large multi-rowgroup string-heavy `.vtr` (register,
  backbones), more likely under the parallel reader where batches accumulate
  before the serial consumer loop. Now verifies cache hits against
  `CHAR(sexp)`, which points into the still-alive interned CHARSXP body.

# vectra 0.5.0

## Compression backend rewire

* Replaced the bespoke v4 codec with `tdc`, a standalone typed-dimensional
  compression library vendored into `src/tdc/`. Encode and decode go through
  a self-describing block record (model + transform chain + entropy) rather
  than per-column tag constants. Deleted `vtr_codec.c`, `vtr_encodings.c`,
  `vtr_compress.c`, `vtr1.c`, and `vtr_codec_internal.h`.
* The `.vtr` on-disk format is a deliberate breaking change: pre-0.5 files
  are not readable. `write_vtr()` and `append_vtr()` write the new container;
  `tbl()` reads only the new container.
* Per-row-group column statistics (min/max) are carried in the container
  index so the scan layer can still prune unreachable row groups.
* Parallel row-group reads are preserved.
* Custom vendoring via `tools/vendor_tdc.sh` and `configure` / `configure.win`
  pull the latest upstream `tdc` on every install when the source checkout
  is present; the pre-vendored copy is used otherwise.

## Known regression

* The v4 dict-defer CHARSXP fast path is gone — duplicate strings now hit R's
  CHARSXP hash per row. Will be re-implemented on top of `tdc`'s
  dictionary-encoded varlen output when it becomes a hot spot.

## Fixes

* `man/write_vtr.Rd`: replaced a literal percent sign in the `compress`
  argument description that produced malformed Rd output on build.
* Windows: `write_vtr()`, `append_vtr()` and `delete_vtr()` now use
  `MoveFileEx` with a short retry loop for the final temp-to-target swap.
  Previously, a preceding `tbl()` read could leave the target file mmap'd
  pending GC, and the swap would fail with a sharing violation.

# vectra 0.4.1

## Star schema and lookup

* New `vtr_schema()`, `link()`, and `lookup()` functions for star-schema
  workflows. Register a fact table with named dimension links once, then
  pull columns from any dimension without writing explicit joins. Only
  referenced dimensions are scanned.
* `lookup()` reports unmatched keys per dimension by default, catching
  referential integrity issues before they propagate NAs silently.
* Supports both `"left"` (default) and `"inner"` join modes, named keys
  for differing column names, and reusable schema objects across multiple
  queries.

# vectra 0.3.2

* Fix misaligned `int64_t` memory access in `vtr_codec.c` (UBSAN).
  Dictionary encoding wrote and read 8-byte offsets through an unaligned
  pointer; delta decoding had the same issue. All fixed with `memcpy`.

# vectra 0.3.1

* CRAN submission fixes: title case, quoted technical terms in DESCRIPTION,
  corrected documentation URLs.

# vectra 0.3.0

## File operations

* `append_vtr(df, path)`: append a data.frame as a new row group to an
  existing `.vtr` file. Existing row groups are never rewritten.
* `delete_vtr(path, row_ids)`: logically delete rows by 0-based physical
  index. Writes a tombstone side file (`<path>.del`); the `.vtr` file is
  never modified. Deletions are cumulative and excluded automatically on the
  next `tbl()` call.
* `diff_vtr(old_path, new_path, key_col)`: key-based logical diff between
  two `.vtr` files. Returns a list with `added` (a lazy `vectra_node`) and
  `deleted` (a vector of key values). Implemented as a single-pass C streaming
  engine with O(n_unique_keys) memory.

## Expressions

* `tolower()`, `toupper()`, `trimws()`: case conversion and whitespace
  trimming for string columns in `filter()` and `mutate()`.
* `levenshtein(x, y)` / `levenshtein_norm(x, y)`: Levenshtein edit distance
  and normalised variant (0–1). Supports column-vs-column and column-vs-literal
  comparisons. Optional `max_dist` argument for early termination.
* `dl_dist(x, y)` / `dl_dist_norm(x, y)`: Damerau-Levenshtein distance
  (counts transpositions as cost 1) and normalised variant.
* `jaro_winkler(x, y)`: Jaro-Winkler similarity (0–1, higher = more similar).
  All string-similarity functions propagate `NA` and work in `filter()` and
  `mutate()`.
* `resolve(fk, pk, value)`: scalar self-join — looks up `value` where
  `pk == fk` within the same batch. Useful for denormalising parent-child
  tables without a join.
* `propagate(parent_id, id, seed)`: tree-traversal aggregation — propagates
  non-NA `seed` values down a parent-child hierarchy until all reachable nodes
  are filled. Converges in O(depth) passes.

## Format

* `.vtr` format version 4 with a two-layer codec (no external dependencies):
  - Encoding: `PLAIN` (default), `DICTIONARY` (string columns with < 50%
    unique values), `DELTA` (monotonically increasing `int64` columns).
  - Compression: custom LZ77 byte compressor (`LZ_VTR`, ~120 lines of C).
    Applied after encoding; skipped for buffers < 64 bytes or when
    compression does not reduce size.
  Files written with v4 are typically 30–60% smaller than v3. `tbl()` reads
  v1–v4 files; `write_vtr()` always writes v4.

# vectra 0.2.2

## Query optimizer

* Column pruning: scan nodes only read columns needed by the query plan.
* Predicate pushdown: filter predicates are attached to scan nodes and use
  `.vtr` v3 per-rowgroup min/max statistics to skip entire row groups.

## Engine

* `.vtr` format version 3 with per-column per-rowgroup statistics (min/max).
* O(n log n) `rank()` and `dense_rank()` (replaces O(n²) comparison-based).
* Nested expressions in `summarise()`: `summarise(m = mean(x + y))` auto-inserts
  a hidden mutate.

## Expressions

* `year()`, `month()`, `day()`, `hour()`, `minute()`, `second()`: date/time
  component extraction for Date and POSIXct columns.
* `as.Date()` and `as.POSIXct()` literals in filter expressions (e.g.
  `filter(date > as.Date("2020-01-01"))`).
* `as.Date(string_col)`: convert ISO-format date strings to Date values.
* `nchar()`: returns string length as integer.
* `substr(x, start, stop)`: substring extraction (1-based, like R).
* `grepl(pattern, x)`: fixed string matching (no regex).
* `paste0(a, b)`: two-argument string concatenation.
* `gsub(pattern, replacement, x)` / `sub()`: fixed-string replacement.
* `startsWith()` / `endsWith()`: string prefix/suffix matching.
* `pmin()` / `pmax()`: element-wise minimum/maximum.
* `log2()`, `log10()`, `sign()`, `trunc()`: additional math functions.

## Aggregation

* `sd()` and `var()`: sample standard deviation and variance via Welford's
  online algorithm. Returns NA for groups with fewer than 2 values (R semantics).
* `first()` and `last()`: first and last non-NA value per group. Both support
  `na.rm = TRUE`.

## Verbs

* `slice_min()` and `slice_max()` gain a working `with_ties` parameter
  (default `TRUE`). Ties at the boundary are now included by default; use
  `with_ties = FALSE` for exactly `n` rows.
* `count()` and `tally()` gain a working `sort` parameter. `sort = TRUE`
  returns results in descending order of the count column.
* `transmute()` and `reframe()` now support `across()`.
* `distinct(.keep_all = TRUE)` with a column subset now emits a message when
  falling back to R.

## Utilities

* `glimpse()`: preview column names, types, and first few values without
  collecting the full result.
* `collect()` now works on data.frames (no-op), so `slice_min(...) |> collect()`
  works regardless of the `with_ties` path.

## Documentation

* New quickstart vignette: `vignette("quickstart")`.
* `@details` sections added to `filter()`, `mutate()`, `summarise()`,
  `arrange()`, `distinct()`, `count()`, and join functions.

# vectra 0.2.1

## Engine

* External merge sort with 1 GB memory budget and automatic spill-to-disk.
* Sort-based `group_by() |> summarise()` path for spill-safe aggregation.
* Chunked FULL join finalize (65,536 rows per batch).
* Automatic type coercion (`int64 <-> double`) in join keys and `bind_rows()`.
* `rank()` and `dense_rank()` window functions.

## Type system

* `.vtr` format version 2 with per-column annotations.
* Date, POSIXct, and factor columns roundtrip through `write_vtr()` / `collect()`.
* `where()` predicates work in `select()`, `rename()`, `relocate()`, and `across()`.

## Infrastructure

* Engine reference vignette (`vignette("engine")`).
* 17-scenario benchmark suite with baseline snapshots and regression thresholds.
* ASAN/UBSAN CI job on Linux.
* Benchmark smoke job on PRs.

# vectra 0.1.0

* Initial release.
* Custom columnar on-disk format (`.vtr`) with multi-row-group support.
* dplyr-compatible verbs: `filter()`, `select()`, `mutate()`, `transmute()`,
  `rename()`, `relocate()`, `group_by()`, `summarise()`, `count()`, `tally()`,
  `distinct()`, `reframe()`, `arrange()`, `slice_head()`, `slice_tail()`,
  `slice_min()`, `slice_max()`, `pull()`.
* Hash joins: `left_join()`, `inner_join()`, `right_join()`, `full_join()`,
  `semi_join()`, `anti_join()`.
* `bind_rows()` and `bind_cols()` for combining queries.
* Window functions: `row_number()`, `lag()`, `lead()`, `cumsum()`, `cummean()`,
  `cummin()`, `cummax()`.
* `across()` support in `mutate()` and `summarise()`.
* `explain()` for inspecting the execution plan.
* `tidyselect` integration for column selection helpers.
* Data sources: `.vtr`, CSV, SQLite, GeoTIFF.
* Data sinks: `write_csv()`, `write_sqlite()`, `write_tiff()`.
