pyacid 0.1.0a1

Astronomical Catalog Inference Driver: XMATCH SQL over HATS-partitioned Parquet via DuckDB

acid — Astronomical Catalog Inference Driver

SQL-driven crossmatching and analysis of HEALPix-partitioned astronomical catalogs, executed by DuckDB. acid extends SQL with an XMATCH(...) predicate for spherical-distance joins, runs each anchor partition independently against a boundary-safe margin cache, and aggregates the results.

Reads and writes the HATS format used by LINCC Frameworks (LSDB, hats-import) and by published catalogs such as Gaia DR3 and Rubin DP1.

---

Quick start

One-shot query

import acid

r = acid.sql(
    """
    SELECT a.id, b.id AS b_id, XMATCH_DISTANCE(b) AS d_arcsec
    FROM dia_source AS a
    JOIN object AS b ON XMATCH(radius_arcsec => 1.0)
    JOIN lightcurve AS lc ON a.id = lc.object_id
    """,
    catalogs="catalogs.yaml",
    workers=8,
)

r.df()              # -> pandas.DataFrame
r.arrow()           # -> pyarrow.Table
r.to_polars()       # -> polars.DataFrame
for batch in r.batches(): ...      # -> RecordBatch iterator
r.write_parquet("results.parquet", layout="single")

Exploratory session (recommended for repeated queries)

with acid.connect("catalogs.yaml", workers=8) as s:
    r1 = s.sql("SELECT a.id FROM a JOIN b ON XMATCH(r => 1.0)")
    r2 = s.sql("SELECT a.id FROM a JOIN b ON XMATCH(r => 0.5)")

    # Promote an intermediate to a real catalog usable in later queries.
    s.materialize(
        "nearby",
        "SELECT a.id, a.ra, a.dec FROM a JOIN b ON XMATCH(r => 1.0)",
    )
    r3 = s.sql("SELECT * FROM nearby JOIN c ON XMATCH(r => 5.0, mode => 'all')")

A Session keeps the worker pool, the DuckDB connection in each worker, and the parsed registry alive across queries. On a 64-core box it's typically 5–10× faster than calling acid.sql() in a loop.

CLI

# Query execution (--db accepts a directory of HATS catalogs or a YAML file)
acid query "SELECT COUNT(*) FROM object" --db datasets/ --out /tmp/result
acid query -f query.sql --db catalogs.yaml --out results/ --workers 32
echo "SELECT ..." | acid query --db datasets/ --out results/
acid validate "SELECT ..." --db datasets/

# Download catalogs (HTTP, SSH, or local; full or spatial subset)
acid download https://data.lsdb.io/hats/two_mass/two_mass /data/two_mass
acid download https://data.lsdb.io/hats/two_mass/two_mass /data/two_mass --cone 50,-50,10
acid download user@server:/hats/gaia /data/gaia --columns ra,dec,mag --cone 180,0,5

# Inspect catalogs (local or remote)
acid inspect /data/two_mass                          # summary
acid inspect schema /data/two_mass                   # column schema
acid inspect https://data.lsdb.io/hats/two_mass/two_mass  # remote

# Build margin caches locally
acid hats build-margin /data/two_mass --margin-arcsec 5.0 --workers 16

results/ is itself a valid HATS catalog (lsdb.open_catalog(...) and hats.read_hats(...) will read it). Downloaded subsets are also valid HATS catalogs with rebuilt _metadata.

Catalog registry

The simplest way: point --db at a directory of HATS catalogs. Each subdirectory with a properties file becomes a table named after the directory. Margin caches (dataproduct_type=margin) are auto-skipped.

For more control, use a YAML file:

catalogs:
  dia_source:
    path: /data/dia_source      # HATS root, or CatalogCollection root
    # Auto-detected from <path>/properties when present:
    #   ra_col            (from hats_col_ra)
    #   dec_col           (from hats_col_dec)
    #   hpix_order        (from <path>/partition_info.csv)
    #   neighbor_path     (from collection.properties or sibling '_margin' dir)
    #   neighbor_margin_arcsec  (from hats_margin_threshold)
    #   npix_suffix       (from hats_npix_suffix; default '.parquet')
    # Any auto-detected value can be overridden here.

  object:
    path: /data/object_collection    # a CatalogCollection root works too

  lightcurve:
    path: /data/lightcurve
    hpix_order: 5                    # explicit when partition_info.csv is absent

# Named MOC footprints for IN_MOC() filtering.
# Each entry is a path to a FITS file (HEALPix image or MOC FITS).
mocs:
  des_dr2: /data/mocs/des_dr2.fits
  known_artifacts: /data/mocs/artifacts.fits
  # If a catalog has a point_map.fits at its root, IN_MOC(<alias>, '<catalog_name>')
  # auto-loads it — no explicit entry needed.

---

What XMATCH does

JOIN  b ON XMATCH(radius_arcsec => 1.0)                   -- nearest, inner
JOIN  b ON XMATCH(r => 1.0)                               -- 'r' is an alias
JOIN  b ON XMATCH(r => 1.0, mode => 'all')                -- every match within r
LEFT JOIN b ON XMATCH(r => 1.0)                           -- keep unmatched anchors

-- Distance is exposed as a SELECT-level function over the right alias.
SELECT a.id, XMATCH_DISTANCE(b) AS d FROM a JOIN b ON XMATCH(r => 1.0)
WHERE  XMATCH_DISTANCE(b) < 0.5

-- Ordinary joins, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT/OFFSET,
-- DISTINCT all work; cross-partition reduction is handled internally.
SELECT a.id, COUNT(*) AS n, AVG(XMATCH_DISTANCE(b)) AS avg_d
FROM a
JOIN  b ON XMATCH(r => 1.0)
JOIN  lightcurve AS lc ON a.id = lc.object_id
GROUP BY a.id
ORDER BY n DESC LIMIT 100

-- Footprint filtering via MOC (Multi-Order Coverage maps):
-- Restrict rows to a survey footprint or sky region.
SELECT a.id, a.ra, a.dec
FROM a JOIN b ON XMATCH(r => 1.0)
WHERE IN_MOC(a, 'des_dr2')              -- anchor inside DES footprint
  AND NOT IN_MOC(b, 'known_artifacts')  -- exclude artifact regions

-- IN_MOC also works in SELECT projections (per-row boolean):
SELECT a.id, IN_MOC(a, 'des_dr2') AS in_des FROM a

Semantics, in short:

• All XMATCHes in a query use the anchor (first FROM) table's coordinates, even after a mode => 'all' expansion.
• A right-table radius must be ≤ that catalog's neighbor_margin_arcsec. Otherwise we'd silently miss boundary pairs; the analyzer rejects the query.
• ORDER BY ... LIMIT K pushes the top-K to each partition first; the reducer re-sorts the union and applies the global LIMIT/OFFSET.
• Aggregates / GROUP BY / DISTINCT / HAVING run in a phase-2 reducer over the per-partition Parquet output.

---

Python API surface

# Sessions (preferred for EDA)
s = acid.connect(catalogs, *, workers=1, duckdb_threads=None, cache_dir=None)
s.sql(query, *, output=None, inmem_row_limit=50_000_000) -> acid.Result
s.run(query, *, output=None)                              -> ExecutionResult
s.validate(query)                                          -> QueryPlan
s.explain(query)                                           -> str (rewritten SQL)
s.list_catalogs()                                          -> list[str]
s.add_catalog(name, **kwargs)                              -> TableSpec
s.materialize(name, query, *, ra_col=None, dec_col=None, overwrite=False) -> TableSpec
s.register_moc(name, source)                           # FITS path, mocpy.MOC, or (N,2) ranges
s.close()    # or use as a context manager

# One-shot convenience (transient Session under the hood)
acid.sql(query, *, catalogs, output=None, workers=1, ...)  -> acid.Result
acid.run(query, *, catalogs, output, workers=1)            -> ExecutionResult

# Result
r.num_rows, r.column_names, r.schema
r.column(name)         -> pa.ChunkedArray
r.arrow()              -> pa.Table
r.df() / r.to_pandas() -> pandas.DataFrame
r.to_polars()          -> polars.DataFrame
r.to_pylist()          -> list[dict]
r.batches(batch_size=None) -> Iterator[pa.RecordBatch]
r.head(n=10)           -> Result
r.write_parquet(path, layout="hats"|"single") -> Path
len(r), for batch in r: ...

# Errors (all inherit from acid.AcidError)
acid.RegistryError       # catalog config (missing path, mixed Norder, ...)
acid.ParseError          # SQL parse failures
acid.ValidationError     # unsupported XMATCH constructs
acid.ExecutionError      # per-partition execution failures
acid.SessionClosedError  # method called on a closed Session

---

Layout assumptions

• Catalogs follow the HATS layout: <root>/dataset/Norder=N/Dir=D/Npix=P.parquet (or Npix=P/*.parquet when hats_npix_suffix='/').
• Margin caches live as sibling catalogs (HATS canonical), at <root>/margin_cache/..., or any sibling dir matching <name>_margin*. collection.properties is consumed if present.
• Adaptive (per-pixel) Norder is supported: a catalog's partition_info.csv may list pixels at any orders, and XMATCH/ordinary joins across mixed-Norder catalogs are run via a refinement-tree enumeration that emits one work unit per coarsest cursor pixel where every joined catalog has ≤ 1 partition. Output is itself a valid HATS catalog whose partition_info.csv reflects the refinement.

---

What's the speed story?

• DuckDB does the heavy SQL work and Parquet I/O. acid is roughly a Python orchestrator + a vectorized SciPy cKDTree crossmatcher.
• Each partition is independent → embarrassingly parallel across HEALPix pixels.
• The per-worker DuckDB connection is opened once and reused for every partition the worker processes; temp namespace is dropped between partitions. With a Session, the connection survives across queries.
• Top-K queries push the LIMIT to each partition. Aggregates write partial data to disk and reduce centrally.
• Column pruning: anchor and right tables are TEMP views over read_parquet(), so the final SELECT only pulls referenced columns from disk. Wide catalogs (150+ columns) don't slow down narrow SELECTs.
• Auto-spill: when output_dir is unset and the running result exceeds inmem_row_limit (default 50M rows), acid spills to a tempdir rather than OOM-ing the parent.

See bench/match_all.py and bench/session_vs_oneshot.py for microbenchmarks.

---

Install

With uv (recommended for development)

uv sync --dev          # creates .venv, installs all deps + test + hats
uv run pytest          # run tests

With pip

pip install -e .
# extras:  pip install -e .[hats,dev]

Requires Python 3.10+, DuckDB 1.x, SQLGlot ≥ 27, PyArrow ≥ 14, NumPy ≥ 1.24, SciPy ≥ 1.10, PyYAML ≥ 6.

---

Status

• v0 (correctness): XMATCH inner/left, mode 'nearest'/'all', chains, ordinary joins, distance via XMATCH_DISTANCE(alias).

• v1 (scale): views + narrow side-tables, vectorized matcher, worker initializer, auto-spill, top-K pushdown, manifest.

• v1.1 (HATS spec): writes valid HATS catalogs, reads canonical property keys, supports hats_npix_suffix='/', auto-discovers margin siblings via collection.properties.

• v2 (EDA): persistent Session, per-worker DuckDB connection, Result wrapper, materialize().

• v3 (adaptive Norder): per-catalog PartitionIndex, refinement-tree tuple enumeration, vectorized pure-numpy ang2pix_nested exposed as a DuckDB UDF for per-pixel row filtering, LEFT-XMATCH/JOIN over partitions without coverage.

• v4 (MOC footprint filtering): IN_MOC(<alias>, '<name>') in WHERE restricts rows to a named sky region (Multi-Order Coverage map). Supports NOT IN_MOC, multiple predicates (AND-combined via mocpy set ops), catalog auto-resolution from point_map.fits, and use in SELECT/ORDER BY/ CASE expressions. Three-level optimization: catalog-footprint scoping, cursor-pixel intersection, and partition-level pruning — all via the existing _healpix_29 row-group pushdown fast path.

• v5 (catalog ops): acid hats build-margin builds HATS margin caches locally (validated against hats-import). acid download generates point_map.fits, auto-includes HATS RA/Dec/healpix columns. acid query accepts --db <directory> for zero-config usage, fails fast on errors, shows tqdm progress, shuffles work for load balancing. Bare column resolution via schema introspection. LocalFetcher for local I/O.

Tests: 176 passing (~30s parallel via pytest-xdist). Fixtures cached across runs.

Known limitations

• XMATCH must be the entire ON predicate. Compound predicates like XMATCH(...) AND b.mag < 20 are rejected.
• No CTEs / subqueries in the anchor position.
• RIGHT / FULL / CROSS JOIN XMATCH not supported.
• IN_MOC must be in conjunctive WHERE position (top-level AND-chain, optionally negated). Disjunctive use (IN_MOC(...) OR ...) and IN_MOC in JOIN ON are rejected.