How to prepare datasets#

This guide shows how to assemble input datasets for coffea processors. The goal is to build a fileset mapping that the coffea.processor.Runner can chunk and distribute efficiently.

Fileset formats#

Coffea supports three fileset formats. The most flexible format (recommended) maps each file to its tree name:

Format 1: File-to-tree mapping (recommended)#

from coffea import processor
from coffea.nanoevents import NanoAODSchema

fileset = {
    "DYJets": {
        "files": {
            "/store/mc/.../nano_dy.root": "Events",
            "/store/mc/.../nano_dy_2.root": "Events",
        },
        "metadata": {"year": 2018, "is_mc": True},
    },
    "DataMu": {
        "files": {
            "/store/data/.../nano_data.root": "Events",
        },
        "metadata": {"year": 2018, "is_mc": False},
    },
}

runner = processor.Runner(
    executor=processor.IterativeExecutor(),
    schema=NanoAODSchema,
)

result = runner(fileset, processor_instance=my_processor)

The top-level keys label datasets; they propagate into events.metadata['dataset']
files is a dictionary mapping file paths to tree names (e.g., "Events")
metadata is merged into each chunk’s metadata dictionary and is available inside your processor via events.metadata
This format allows different tree names per file

Format 2: Single tree name per dataset#

If all files in a dataset use the same tree name, you can specify it once:

fileset = {
    "DYJets": {
        "treename": "Events",
        "files": [
            "/store/mc/.../nano_dy.root",
            "/store/mc/.../nano_dy_2.root",
        ],
        "metadata": {"year": 2018, "is_mc": True},
    },
}

result = runner(fileset, processor_instance=my_processor)

Format 3: Global tree name#

If all files across all datasets use the same tree name, pass it to the runner:

fileset = {
    "DYJets": {
        "files": ["/store/mc/.../nano_dy.root"],
        "metadata": {"year": 2018},
    },
}

result = runner(fileset, processor_instance=my_processor, treename="Events")

Note: Format 1 (file-to-tree mapping) is most flexible and is recommended for new analyses.

Mix local and remote files#

Uproot accepts local paths, XRootD URLs, and glob patterns. Coffea simply forwards them to uproot.

fileset = {
    "DYJets": {
        "files": {
            "root://cmsxrootd.fnal.gov//store/mc/Run3Summer23/.../nano_1.root": "Events",
            "root://cmsxrootd.fnal.gov//store/mc/Run3Summer23/.../nano_2.root": "Events",
        },
        "metadata": {"is_mc": True},
    },
    "LocalTest": {
        "files": {
            "nano_test.root": "Events",
        },
        "metadata": {"is_mc": False},
    },
}

Chunks can span files in different storage systems; coffea relies on uproot to stream the data.

Discover files programmatically#

coffea.dataset_tools.rucio_utils helps when the list of files lives in Rucio or in JSON manifests.

from coffea.dataset_tools.rucio_utils import get_dataset_files_replicas

outfiles, outsites, sites_counts = get_dataset_files_replicas(
    dataset="/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v1/NANOAODSIM",
)

# chose site from which to extract files
site = max(sites_counts, key=sites_counts.get)
files_by_site = []
for i, (files, sites) in enumerate(zip(outfiles, outsites)):
    iS = sites.index(site)
    files_by_site.append(files[iS])

# Convert list to dict mapping files to tree name (Format 1)
fileset = {
    "DYJets": {
        "files": {f: "Events" for f in files_by_site},
        "metadata": {"is_mc": True},
    }
}

# Or use Format 2 if all files have same tree
fileset_alt = {
    "DYJets": {
        "treename": "Events",
        "files": files_by_site,
        "metadata": {"is_mc": True},
    }
}

Output example:

print("site:\n", site)
print("\nfileset:\n", fileset)
print("\nfileset_alt:\n", fileset_alt)

site:
 T2_DE_DESY

fileset:
 {'DYJets':
    {'files':
      {'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/0CF0CDED-7582-7A49-84CD-\
        0E5F73DE27B0.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/0E17EB7B-5779-2741-ABBE-\
        3465CC7C6174.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/1332F4CB-CEBC-0C4C-BA1E-\
        2D82BD7F7294.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/1778E98F-966D-204F-9F3F-\
        61DB3DB07616.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/2060156F-8F7B-5B4C-B2BE-\
        7C7823494E17.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/3415A617-3335-B341-94D8-\
        809C1B8012A7.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/42217818-CFFD-8B42-B357-\
        B7D1CE8881D1.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/53CB5FBD-F338-1F48-A516-\
        3862AC0F87B1.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/7140ABB2-6BB4-BF4D-B469-\
        9C3C9E01C566.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/78AB9B61-1B4D-7644-89A7-\
        42945E27D39F.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/90D780AD-4691-5749-98FF-\
        089926B03C3C.root': 'Events',
       'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/93820618-447B-844C-96FE-4A2488ED5170.root': 'Events', 'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/A05BE01E-9983-0C42-B981-D31FEC2A02D1.root': 'Events', 'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/B0CC5BCA-2D15-CA40-B246-0BFBBC5BD092.root': 'Events', 'root://dcache-cms-xrootd.desy.de:1094//store/mc/RunIISummer20UL18NanoAODv9/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8/NANOAODSIM/106X_upgrade2018_realistic_v16_L1v1-v1/120000/BF8F5312-B200-B747-9DAF-5ABE838BEA43.root': 'Events', 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             ],
      'metadata': {'is_mc': True}
    }
}

You can cache results in JSON and feed them directly to the runner later.

Store filesets in JSON#

import json

with open("fileset.json", "w") as fout:
    json.dump(fileset, fout, indent=2)

# Later, reload the fileset
with open("fileset.json", "r") as fin:
    loaded_fileset = json.load(fin)

result = runner(loaded_fileset, processor_instance=my_processor)

Storing filesets in JSON is a convenient way to share dataset definitions with collaborators.

Add custom metadata#

Metadata travels with each chunk and is available as events.metadata.

fileset = {
    "TTJets": {
        "files": {
            "ttbar.root": "Events",
        },
        "metadata": {
            "cross_section": 831.76,
            "year": 2022,
        },
    },
}

Inside your processor:

def process(self, events):
    dataset = events.metadata["dataset"]  # "TTJets"
    xsec = events.metadata.get("cross_section", 1.0)
    year = events.metadata.get("year")
    # Use metadata in your analysis
    ...

Use metadata for cross sections, era tags, or analysis-specific flags.

Keep the fileset small during development#

Create reduced filesets for unit tests by limiting the number of files.

# Take only first file from each dataset
mini_fileset = {}
for dataset, info in fileset.items():
    first_file = list(info["files"].items())[0]
    mini_fileset[dataset] = {
        "files": {first_file[0]: first_file[1]},
        "metadata": info.get("metadata", {}),
    }

Run your processor with mini_fileset locally, then switch back to the full fileset for production. You can also use NanoEventsFactory.from_root(..., entry_stop=N) to limit the number of events processed.

Tips & tricks#

Use NanoEventsFactory.from_root(..., entry_stop=N) alongside a reduced fileset to validate schemas before launching large jobs.
Preserve bookkeeping like n_events and the sum of generator weights in metadata so weight calculations stay consistent across processors.
Keep JSON filesets under version control; they document exactly which inputs were analyzed.
When combining many small files, merge them upstream if possible—large numbers of tiny files increase scheduler overhead during preprocessing.