diff --git a/src/datafilters/dataset_builder.py b/src/datafilters/dataset_builder.py
index d221764..c60bf7f 100644
--- a/src/datafilters/dataset_builder.py
+++ b/src/datafilters/dataset_builder.py
@@ -4,14 +4,12 @@
 from collections.abc import Collection, Iterable
 from datetime import datetime
 from pathlib import Path
-
+from typing import TypeVar
 import polars as pl
-from cfg_param_wrapper import CfgDict
 from polars import DataFrame, Expr
 from tqdm import tqdm
 
 from .base_filters import Column, Comparable, DataFilter, FastComparable
-from .custom_toml import TomlCustomCommentDecoder, TomlCustomCommentEncoder
 
 
 def current_time() -> datetime:
@@ -22,6 +20,9 @@ def _time(_=None) -> datetime:
     return current_time()
 
 
+T = TypeVar("T")
+
+
 class DatasetBuilder:
     def __init__(self, origin: str, db_path: Path) -> None:
         super().__init__()
@@ -31,8 +32,8 @@ def __init__(self, origin: str, db_path: Path) -> None:
 
         self.filepath: Path = db_path
 
-        self.basic_schema = {"path": str, "checkedtime": pl.Datetime}
-        self.filter_type_schema = self.basic_schema.copy()
+        self.basic_schema: dict[str, pl.DataType | type] = {"path": str, "checkedtime": pl.Datetime}
+        self.filter_type_schema: dict[str, pl.DataType | type] = self.basic_schema.copy()
 
         self.build_schema: dict[str, Expr] = {"checkedtime": pl.col("path").apply(_time)}
         if os.path.exists(self.filepath):
@@ -97,8 +98,8 @@ def populate_df(
             cond: Expr = now - pl.col("checkedtime") < datetime.fromtimestamp(trim_age_limit)
             if trim_check_exists:
                 cond &= pl.col("path").apply(os.path.exists)
-
             self.df = self.df.filter(cond)
+
         from_full_to_relative: dict[str, Path] = self.absolute_dict(lst)
         # add new paths to the dataframe with missing data
         existing_paths = set(self.df.select(pl.col("path")).to_series())
@@ -119,32 +120,37 @@ def populate_df(
             pl.any(pl.col(col).is_null() for col in updated_df.columns if col in search_cols)
         )
         if len(unfinished):
-            with tqdm(desc="Gathering file info...", total=len(unfinished)) as t:
+            with (
+                tqdm(desc="Gathering file info...", unit="file", total=len(unfinished)) as total_t,
+                tqdm(desc="Processing chunks...", unit="chunk", total=len(unfinished) // chunksize) as sub_t,
+            ):
                 save_timer = datetime.now()
                 collected_data: DataFrame = DataFrame(schema=self.filter_type_schema)
-                for nulls, group in (
-                    unfinished.with_columns(self.filter_type_schema)
-                    .with_row_count("idx")
-                    .with_columns(pl.col("idx") // chunksize)
-                    .groupby("idx", *(pl.col(col).is_not_null() for col in self.build_schema))
+                for nulls, group in unfinished.with_columns(self.filter_type_schema).groupby(
+                    *(pl.col(col).is_not_null() for col in self.build_schema)
                 ):
-                    t.set_postfix_str(str(nulls))
-                    new_data = group.drop("idx").with_columns(
-                        **{
-                            col: expr
-                            for truth, (col, expr) in zip(nulls[1:], self.build_schema.items())  # type: ignore
-                            if not truth
-                        }
-                    )
-                    collected_data = pl.concat([collected_data, new_data], how="diagonal")
-                    t.update(len(group))
-                    if ((new_time := datetime.now()) - save_timer).total_seconds() > save_interval:
-                        self.df = self.df.update(collected_data, on="path")
-                        self.save_df()
-                        t.set_postfix_str(f"Autosaved at {current_time()}")
-                        collected_data = collected_data.clear()
-                        save_timer = new_time
-            self.df = self.df.update(collected_data, on="path").rechunk()
+                    group_expr = {
+                        col: expr
+                        for truth, (col, expr) in zip(nulls, self.build_schema.items())  # type: ignore
+                        if not truth
+                    }
+                    subgroups = list(self._split_into_chunks(group, chunksize=chunksize))
+                    total_t.set_postfix_str(str(tuple(group_expr.keys())))
+                    sub_t.total = len(subgroups)
+                    sub_t.n = 0
+
+                    for idx, subgroup in subgroups:
+                        new_data: DataFrame = subgroup.with_columns(**group_expr)
+                        collected_data = pl.concat((collected_data, new_data), how="diagonal")
+                        if ((new_time := datetime.now()) - save_timer).total_seconds() > save_interval:
+                            self.df = self.df.update(collected_data, on="path")
+                            self.save_df()
+                            collected_data = collected_data.clear()
+                            save_timer = new_time
+                        sub_t.set_postfix_str(str(idx))
+                        sub_t.update(1)
+                        total_t.update(len(subgroup))
+                self.df = self.df.update(collected_data, on="path").rechunk()
             self.save_df()
         return
 
@@ -152,6 +158,8 @@ def filter(self, lst, sort_col="path") -> list[Path]:
         assert (
             sort_col in self.df.columns
         ), f"the column '{sort_col}' is not in the database. Available columns: {self.df.columns}"
+        if len(self.unready_filters):
+            warnings.warn(f"{len(self.unready_filters)} filters are not initialized and will not be populated")
 
         from_full_to_relative: dict[str, Path] = self.absolute_dict(lst)
         paths: set[str] = set(from_full_to_relative.keys())
@@ -192,6 +200,15 @@ def _make_schema_compliant(data_frame: DataFrame, schema) -> DataFrame:
         """adds columns from the schema to the dataframe. (not in-place)"""
         return pl.concat([data_frame, DataFrame(schema=schema)], how="diagonal")
 
+    @staticmethod
+    def _split_into_chunks(df: DataFrame, chunksize: int, column="_idx"):
+        return (
+            ((idx, len(part)), part.drop(column))
+            for idx, part in df.with_row_count(column)
+            .with_columns(pl.col(column) // chunksize)
+            .groupby(column, maintain_order=True)
+        )
+
     def __enter__(self, *args, **kwargs):
         self.__init__(*args, **kwargs)
         return self