rucio-register
Command and API to add Butler specific information to Rucio metadata.
This is a guide to using the rucio-register command for registering Butler files with Rucio.
Butler files are expected to be located in a Rucio directory structure, below a directory named for a Rucio scope. For example, if the root of the Rucio directory is "/rucio/disks/xrd1/rucio" and the Rucio scope is "test", the files should be located below "/rucio/disks/xrd1/rucio/test".
Example
The command "rucio-register" registers files with Rucio. This command requires a YAML configuration file which specifies the Rucio rse and scope, as well as the root of the directory where files are deposited, and the external reference to the Rucio RSE. This configuration file can be specified on the command line, or in the environment variable RUCIO_REGISTER_CONFIG.
The command can register data-products or raws:
for data products:
rucio-register data-products --log-level INFO --repo /rucio/disks/xrd1/rucio/test --collections HSC/runs/RC2/w_2023_32/DM-40356/20230814T170253Z --dataset-type visitSummary --rucio-dataset rubin_dataset --rucio-register-config register_config.yaml
for raws:
rucio-register raws --log-level INFO --repo /rucio/disks/xrd1/rucio/test --rucio-dataset rubin_dataset --collections LATISS/raw/all --rucio-register-config register_config.yaml \*
Note that for raws, this is similar to how one uses the butler command
This command looks for files registered in the butler repo "/repo/main" using the "dataset-type" and "collections" arguments to query the butler. Note that the repo name's suffix is the Rucio "scope". In this example, that scope is "main".
The resulting datasets' files are registered with Rucio, as specified in the "config.yaml" file. Additionally, those files are registered with the Rucio dataset specified by the "rucio-dataset" argument.
for zip files:
rucio-register zips --rucio-dataset rubin_dataset --log-level INFO --rucio-register-config /home/lsst/rucio_register/examples/register_config.yaml --zip-file file:///rucio/disks/xrd1/rucio/test/something/2c8f9e54-9757-54c0-9119-4c3ac812a2da.zip
Note for zip files, register a single zip file at a time.
for dimension record YAML files:
rucio-register dimensions --rucio-dataset rubin_dataset --log-level INFO --rucio-register-config /home/lsst/rucio_register/examples/register_config.yaml --dimension-file file:///rucio/disks/xrd1/rucio/test/something/dimensions.yaml
Note for zip files, register a single zip file at a time.
config.yaml
The config.yaml file includes information which specifies the Rucio RSE to use, the Rucio scope, the local root of the RSE, and the URL prefix of the location where Rucio stores the files.
rucio_rse: "XRD1"
scope: "main"
rse_root: "/rucio/disks/xrd1/rucio"
dtn_url: "root://xrd1:1094//rucio"
export-datasets
Command and to dump Butler dataset, dimension, and calibration validity range data to a YAML file.
This command works alongside "rucio-register". It can be used to record all the files registered into Rucio so that their transfer and ingestion at the destination can be confirmed. In addition, it preserves dimension data and calibration validity range data that is not otherwise transferred via Rucio. This additional data can be useful for repeated ingests of raw and calibration data into Butler repositories.
Examples
To record the dimension values (notably not including the visit dimension, which would have to be regenerated) for a set of raw images:
export-datasets \
--root /sdf/group/rubin/lsstdata/offline/instrument/ \
--filename Dataset-LSSTCam-NoTract-20250101-0000.yaml \
--collections LSSTCam/raw/all \
--where "instrument='LSSTCam' and day_obs=20250101 and exposure.seq_num IN (1..99)" \
--limit 30000 \
/repo/main raw
--root is needed here since the original files are ingested as full URLs with direct.
To record the datasets created by a multi-site processing workflow:
export-datasets \
--filename Dataset-LSSTCam-Tract2024-Step3-Group5-metadata.yaml \
--collections step3/group5 \
--where "tract=2024" \
$LOCAL_REPO '*_metadata'
Note the use of a glob pattern to select dataset types of interest.
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