Converting from HiPSCat#

This page discusses topics around setting up a pipeline to generate a new valid HATS catalog from an existing HiPSCat catalog. There are several breaking incompatibilities between these two versions of the format, and users should migrate catalogs to the new format before using HATS or LSDB later than v0.4.

At a minimum, you need arguments that include where to find the original files, and where to put the output files. A minimal arguments block will look something like:

import hats_import.pipeline as runner
from hats_import.hipscat_conversion.arguments import ConversionArguments

args = ConversionArguments(
    input_catalog_path="./hipscat_catalogs/my_catalog",
    output_path="./hats_catalogs/",
    output_artifact_name="my_catalog",
)
runner.pipeline(args)

More details on each of these parameters is provided in sections below.

For the curious, see the API documentation for hats_import.hipscat_conversion.arguments.ConversionArguments.

Dask setup#

We will either use a user-provided dask Client, or create a new one with arguments:

dask_tmp - str - directory for dask worker space. this should be local to the execution of the pipeline, for speed of reads and writes. For much more information, see Temporary files and disk usage

dask_n_workers - int - number of workers for the dask client. Defaults to 1.

dask_threads_per_worker - int - number of threads per dask worker. Defaults to 1.

If you find that you need additional parameters for your dask client (e.g are creating a SLURM worker pool), you can instead create your own dask client and pass along to the pipeline, ignoring the above arguments. This would look like:

from dask.distributed import Client
from hats_import.pipeline import pipeline_with_client

args = ConversionArguments(...)
with Client('scheduler:port') as client:
    pipeline_with_client(args, client)

If you’re running within a .py file, we recommend you use a main guard to potentially avoid some python threading issues with dask:

from hats_import.pipeline import pipeline

def conversion_pipeline():
    args = ConversionArguments(...)
    pipeline(args)

if __name__ == '__main__':
    conversion_pipeline()

Input Catalog#

For this pipeline, you will need to have already transformed your catalog into HiPSCat parquet format. Provide the path to the catalog data with the argument input_catalog_path.

Progress Reporting#

By default, we will display some progress bars during pipeline execution. To disable these (e.g. when you expect no output to standard out), you can set progress_bar=False.

There are several stages to the pipeline execution, and you can expect progress reporting to look like the following:

Mapping  : 100%|██████████| 2352/2352 [9:25:00<00:00, 14.41s/it]
Reducing : 100%|██████████| 2385/2385 [00:43<00:00, 54.47it/s]
Finishing: 100%|██████████| 4/4 [00:03<00:00,  1.15it/s]

For very long-running pipelines (e.g. multi-TB inputs), you can get an email notification when the pipeline completes using the completion_email_address argument. This will send a brief email, for either pipeline success or failure.

Output#

Where?#

You must specify a name for the new HATS table, using output_artifact_name. It’s totally fine to simply use the name of the original input HiPSCat catalog, so long as they will be written to different directories.

You must specify where you want your HATS table to be written, using output_path. This path should be the base directory for your catalogs, as the full path for the HATS table will take the form of output_path/output_artifact_name.

If you’re writing to cloud storage, or otherwise have some filesystem credential dict, initialize output_path using universal_pathlib’s utilities.

In addition, you can specify directories to use for various intermediate files:

dask worker space (dask_tmp)
sharded parquet files (tmp_dir)

Most users are going to be ok with simply setting the tmp_dir for all intermediate file use. For more information on these parameters, when you would use each, and demonstrations of temporary file use see Temporary files and disk usage

What next?#

You can validate that your new HATS catalog meets both the HATS/LSDB expectations, as well as your own expectations of the data contents. You can follow along with the Manual catalog verification.

Converting from HiPSCat

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