diff --git a/fnal_column_analysis_tools/processor/executor.py b/fnal_column_analysis_tools/processor/executor.py
index a6006bb5d..0f00c8f59 100644
--- a/fnal_column_analysis_tools/processor/executor.py
+++ b/fnal_column_analysis_tools/processor/executor.py
@@ -74,14 +74,21 @@ def _work_function(item):
 @lru_cache(maxsize=128)
 def _get_chunking(filelist, treename, chunksize):
     items = []
-    for fn in filelist:
-        nentries = uproot.numentries(fn, treename)
+    executor = None if len(filelist) < 5 else concurrent.futures.ThreadPoolExecutor(10)
+    for fn, nentries in uproot.numentries(filelist, treename, total=False, executor=executor).items():
         for index in range(nentries // chunksize + 1):
             items.append((fn, chunksize, index))
     return items
 
 
-def run_uproot_job(fileset, treename, processor_instance, executor, executor_args={}, chunksize=500000):
+def _get_chunking_lazy(filelist, treename, chunksize):
+    for fn in filelist:
+        nentries = uproot.numentries(fn, treename)
+        for index in range(nentries // chunksize + 1):
+            yield (fn, chunksize, index)
+
+
+def run_uproot_job(fileset, treename, processor_instance, executor, executor_args={}, chunksize=500000, maxchunks=None):
     '''
     A convenience wrapper to submit jobs for a file set, which is a
     dictionary of dataset: [file list] entries.  Supports only uproot
@@ -97,6 +104,7 @@ def run_uproot_job(fileset, treename, processor_instance, executor, executor_arg
                 for item in items: accumulator += function(item)
     executor_args: extra arguments to pass to executor
     chunksize: number of entries to process at a time in the data frame
+    maxchunks: maximum number of chunks to process per dataset
     '''
     if not isinstance(fileset, Mapping):
         raise ValueError("Expected fileset to be a mapping dataset: list(files)")
@@ -105,7 +113,13 @@ def run_uproot_job(fileset, treename, processor_instance, executor, executor_arg
 
     items = []
     for dataset, filelist in tqdm(fileset.items(), desc='Preprocessing'):
-        for chunk in _get_chunking(tuple(filelist), treename, chunksize):
+        if maxchunks is not None:
+            chunks = _get_chunking_lazy(tuple(filelist), treename, chunksize)
+        else:
+            chunks = _get_chunking(tuple(filelist), treename, chunksize)
+        for ichunk, chunk in enumerate(chunks):
+            if (maxchunks is not None) and (ichunk > maxchunks):
+                break
             items.append((dataset, chunk[0], treename, chunk[1], chunk[2], processor_instance))
 
     output = processor_instance.accumulator.identity()