External Files

Beacon reads files directly in a FROM clause. Every supported format has a read_* table function. The function takes a path or a glob. You register nothing first:

-- one file
SELECT * FROM read_parquet('profiles/2024.parquet') LIMIT 10;

-- a glob across many files
SELECT * FROM read_netcdf('argo/**/*.nc') WHERE temperature > 20;

-- a list of paths or globs
SELECT * FROM read_csv(['a.csv', 'b.csv']);

Beacon resolves a path against its storage root. That root is either a local directory or one S3-compatible bucket, chosen at startup. See Object Storage.

Formats

Format	Function	STORED AS	Recognized files
Parquet	read_parquet	PARQUET	.parquet
GeoParquet	read_geoparquet	GEOPARQUET	.geoparquet
CSV / TSV	read_csv	CSV	.csv, .tsv
Arrow IPC	read_arrow	ARROW	.arrow, .feather
NetCDF	read_netcdf	NC	.nc
HDF5	read_hdf5	HDF5, H5	.h5, .hdf5
Zarr	read_zarr	ZARR	zarr.json marker
Atlas	read_atlas	ATLAS	atlas.json marker
GeoTIFF / COG	read_tiff	TIFF	.tif, .tiff
BBF	read_bbf	BBF	.bbf
Delta Lake	read_delta	DELTA	_delta_log/ directory
Apache Iceberg	read_iceberg	ICEBERG	metadata/ directory
Icechunk	read_icechunk	ICECHUNK	repository directory
ODV ASCII	read_odv_ascii	not supported	.txt

Beacon finds every format in the dataset store automatically. Delta Lake, Apache Iceberg, Icechunk and ODV ASCII are the exception. Point a function at them. For Delta, Iceberg and Icechunk, you can also use an external table.

Capability matrix

The table above says how to read each format. This one says what you get.

Format	On an S3 datasets store	Pushdown	Query output	Schema function	Array format
Parquet	Full	Predicate + projection, row-group pruning	Yes	read_parquet_schema	No
GeoParquet	Full	Projection; no st_* pushdown yet	Yes	read_geoparquet_schema	No
CSV / TSV	Full	Projection only	Yes	read_csv_schema	No
Arrow IPC	Full	Projection only	Yes	read_arrow_schema	No
NetCDF	Anonymous only, or full with the Rust reader	Projection + dimension selection	Yes	read_netcdf_schema	Yes
HDF5	Anonymous only, or full with the Rust reader	Projection + dimension selection	Yes	read_hdf5_schema	Yes
Zarr	Full	Projection + dimension selection, chunk pruning	No	read_zarr_schema	Yes
Atlas	Full	Predicate + projection, file-level pruning	Yes	read_atlas_schema	Yes
GeoTIFF / COG	Full	Projection, range requests	No	read_tiff_schema	Yes
BBF	Full	Predicate + projection	No	read_bbf_schema	No
Delta Lake	Full	Predicate + projection, file skipping	No, but see below	read_delta_schema	No
Apache Iceberg	Full	Predicate + projection, file skipping	No	read_iceberg_schema	No
Icechunk	Full, see Icechunk	Projection + dimension selection, chunk pruning	No	read_icechunk_schema	Yes
ODV ASCII	Full	Projection only	Yes	read_odv_ascii_schema	No

Reading the columns:

• On an S3 datasets store — how the reader behaves when the server's datasets store is a bucket rather than a local directory. NetCDF and HDF5 read through a pure-Rust reader by default. That reader goes through the object store and authenticates normally. A server that sets BEACON_NETCDF_USE_RUST_READER=false or BEACON_HDF5_USE_RUST_READER=false reads through netCDF-c instead. It then needs the bucket to allow anonymous reads: netCDF-c opens a file by URL and never sees the credential chain. Every other reader authenticates normally already. This is a property of the server's store, not of the query — paths in SQL are relative either way.
• Pushdown — how much of a query reaches storage instead of running after the read. Predicate means a WHERE clause prunes data. Projection means a narrow SELECT reads fewer columns. Atlas is the strongest: its collection statistics drop whole files before any array is opened.
• Query output — whether a query result can be written back in that format, with COPY TO or an output.format on the API. Writing rows into an existing table is a different capability: Delta Lake external tables accept INSERT INTO, and managed tables accept the full INSERT / UPDATE / DELETE set, but neither is a query output format.
• Schema function — the _schema counterpart that returns columns and types without a scan. See Inspect a schema.
• Array format — whether the file holds N-dimensional arrays, which Beacon flattens into rows. See Arrays to tables.

See inside a file

Check the columns and types of an unfamiliar dataset first. Every reader has a _schema counterpart that does this without a read of any data:

SELECT * FROM read_netcdf_schema('argo/**/*.nc');

SUMMARIZE also gives value ranges, distinct counts and null shares. It profiles every column in one pass:

SUMMARIZE (SELECT * FROM read_netcdf('argo/**/*.nc'));

Inspect a schema compares all four ways and says what each one costs.

Read many files at once

A glob (*, **) expands across directories. One call can therefore cover thousands of files. Beacon merges their schemas. It also prunes the files that cannot match your filters.

SELECT platform, avg(temperature) AS t
FROM read_netcdf('argo/**/*.nc')
WHERE depth < 100
GROUP BY platform;

Array formats such as Zarr and Atlas use a marker file. Point at zarr.json or atlas.json, not at the chunk files.

Some files share a schema but have different columns. Combine those files with UNION BY NAME.

Give files a table name

A read_* call fits an ad-hoc query. When many queries share one source, register it once as an external table. Then query it by name:

CREATE EXTERNAL TABLE ocean_profiles
STORED AS PARQUET
LOCATION 'profiles/';

SELECT * FROM ocean_profiles LIMIT 10;

See also

• Table Functions: every reader signature in one place.
• Data Sources: the full read model.
• Object Storage: running against a bucket.