Add memory-size partitioning to from_dataframe; change partition size params to partition_rows and partition_bytes

benchmarks/benchmarks.py

@@ -123,7 +123,7 @@ def time_save_big_catalog():
             "catalog_type": "object",
             "lowest_order": 6,
             "highest_order": 10,
-            "threshold": 500,
+            "partition_rows": 500,
         }
 
         catalog = lsdb.from_dataframe(mock_partition_df, margin_threshold=None, **kwargs)

docs/tutorials/import_catalogs.ipynb

@@ -115,7 +115,7 @@
     "    catalog_type=\"object\",\n",
     "    lowest_order=2,\n",
     "    highest_order=5,\n",
-    "    threshold=100,\n",
+    "    partition_rows=100,\n",
     ")\n",
     "\n",
     "# Save it to disk in HATS format\n",

src/lsdb/loaders/dataframe/dataframe_catalog_loader.py

@@ -1,18 +1,20 @@
 from __future__ import annotations
 
-import math
 import re
 import warnings
 
 import astropy.units as u
 import hats as hc
+import hats.pixel_math.healpix_shim as hp
 import nested_pandas as npd
 import numpy as np
 import pandas as pd
 import pyarrow as pa
 from hats.catalog import CatalogType, TableProperties
+from hats.io.size_estimates import get_mem_size_per_row
 from hats.pixel_math import HealpixPixel, generate_histogram
 from hats.pixel_math.healpix_pixel_function import get_pixel_argsort
+from hats.pixel_math.sparse_histogram import supplemental_count_histogram
 from hats.pixel_math.spatial_index import (
     SPATIAL_INDEX_COLUMN,
     SPATIAL_INDEX_ORDER,
@@ -44,8 +46,8 @@ def __init__(
         lowest_order: int = 0,
         highest_order: int = 7,
         drop_empty_siblings: bool = False,
-        partition_size: int | None = None,
-        threshold: int | None = None,
+        partition_rows: int | None = None,
+        partition_bytes: int | None = None,
         should_generate_moc: bool = True,
         moc_max_order: int = 10,
         use_pyarrow_types: bool = True,
@@ -71,10 +73,12 @@ def __init__(
         drop_empty_siblings : bool, default False
             When determining final partitionining, if 3 of 4 pixels are empty,
             keep only the non-empty pixel
-        partition_size : int or None, default None
-            The desired partition size, in number of rows.
-        threshold : int or None, default None
-            The maximum number of data points per pixel.
+        partition_rows : int or None, default None
+            The maximum partition size, in number of rows. If specified,
+            'partition_bytes' must be None.
+        partition_bytes : int or None, default None
+            The maximum partition size, in bytes. If specified,
+            'partition_rows' must be None.
         should_generate_moc : bool, default True
             Should we generate a MOC (multi-order coverage map) of the data.
             It can improve performance when joining/crossmatching to other hats-sharded datasets.
@@ -92,7 +96,7 @@ def __init__(
         self.lowest_order = lowest_order
         self.highest_order = highest_order
         self.drop_empty_siblings = drop_empty_siblings
-        self.threshold = self._calculate_threshold(partition_size, threshold)
+        self.partition_rows, self.partition_bytes = self._calculate_threshold(partition_rows, partition_bytes)
 
         if ra_column is None:
             ra_column = self._find_column("ra")
@@ -103,11 +107,31 @@ def __init__(
         if dataframe[ra_column].isna().any() or dataframe[dec_column].isna().any():
             raise ValueError(f"NaN values found in {ra_column}/{dec_column} columns")
 
+        # Don't allow deprecated arguments.
+        for deprecated_arg in ["threshold", "partition_size"]:
+            if deprecated_arg in kwargs:
+                raise ValueError(
+                    f"'{deprecated_arg}' is deprecated; use 'partition_rows' or 'partition_bytes' instead."
+                )
+
+        # Don't allow users to specify hats_max_rows or hats_max_bytes directly.
+        for invalid_arg in ["hats_max_rows", "hats_max_bytes"]:
+            if invalid_arg in kwargs:
+                raise ValueError(
+                    f"{invalid_arg} should not be provided in kwargs; "
+                    "use 'partition_rows' or 'partition_bytes' instead"
+                )
+
+        # Set partitioning kwarg to pass to catalog info creation.
+        if self.partition_rows is not None:
+            kwargs = dict(kwargs, hats_max_rows=self.partition_rows)
+        elif self.partition_bytes is not None:
+            kwargs = dict(kwargs, hats_max_bytes=self.partition_bytes)
+
         self.catalog_info = self._create_catalog_info(
             ra_column=ra_column,
             dec_column=dec_column,
             total_rows=len(self.dataframe),
-            hats_max_rows=self.threshold,
             **kwargs,
         )
         self.should_generate_moc = should_generate_moc
@@ -130,28 +154,50 @@ def _find_column(self, search_term: str) -> str:
             raise ValueError(f"Found {n_matches} possible columns for {search_term}")
         return matches[0]
 
-    def _calculate_threshold(self, partition_size: int | None = None, threshold: int | None = None) -> int:
-        """Calculates the number of points per HEALPix pixel (threshold) for the desired partition size."""
+    def _calculate_threshold(
+        self,
+        partition_rows: int | None = None,
+        partition_bytes: int | None = None,
+    ) -> tuple[int | None, int | None]:
+        """Verifies partition thresholds and sets default if necessary.
+
+        If partition_rows is provided, no partition exceeds the maximum number of rows;
+        if partition_bytes is provided, no partition exceeds the maximum memory size.
+
+        If neither partition_rows nor partition_bytes is provided, a default
+        partition size of approximately 1 GiB is used.
+
+        Parameters
+        ----------
+        partition_rows : int or None, default None
+            The desired partition size maximum, in number of rows.
+        partition_bytes : int or None, default None
+            The desired partition size maximum, in bytes.
+
+        Returns
+        -------
+        tuple[int or None, int or None]
+            The validated partition_rows and partition_bytes.
+
+        Raises
+        ------
+        ValueError
+            If both partition_rows and partition_bytes are specified.
+        """
         self.df_total_memory = self.dataframe.memory_usage(deep=True).sum()
         if self.df_total_memory > (1 << 30) or len(self.dataframe) > 1_000_000:
             warnings.warn(
                 "from_dataframe is not intended for large datasets. "
                 "Consider using hats-import: https://hats-import.readthedocs.io/",
                 RuntimeWarning,
             )
-        if threshold is not None and partition_size is not None:
-            raise ValueError("Specify only one: threshold or partition_size")
-        if threshold is None:
-            if partition_size is not None:
-                # Round the number of partitions to the next integer, otherwise the
-                # number of pixels per partition may exceed the threshold
-                num_partitions = math.ceil(len(self.dataframe) / partition_size)
-                threshold = len(self.dataframe) // num_partitions
-            else:
-                # Each partition in memory will be of roughly 1Gib
-                partition_memory = self.df_total_memory / len(self.dataframe)
-                threshold = math.ceil((1 << 30) / partition_memory)
-        return threshold
+
+        if partition_rows is not None and partition_bytes is not None:
+            raise ValueError("Specify only one: partition_rows or partition_bytes")
+        if partition_rows is None and partition_bytes is None:
+            # Default to 1 GiB partitions
+            partition_bytes = 1 << 30
+        return partition_rows, partition_bytes
 
     def _create_catalog_info(
         self,
@@ -238,18 +284,38 @@ def _compute_pixel_list(self) -> list[HealpixPixel]:
         list[HealpixPixel]
             HEALPix pixels for the final partitioning.
         """
-        raw_histogram = generate_histogram(
-            self.dataframe,
-            highest_order=self.highest_order,
-            ra_column=self.catalog_info.ra_column,
-            dec_column=self.catalog_info.dec_column,
-        )
+        # Generate histograms.
+        if self.partition_rows is not None:
+            row_count_histogram = generate_histogram(
+                self.dataframe,
+                highest_order=self.highest_order,
+                ra_column=self.catalog_info.ra_column,
+                dec_column=self.catalog_info.dec_column,
+            )
+            mem_size_histogram = None
+        else:
+            row_mem_sizes = get_mem_size_per_row(self.dataframe)
+            mapped_pixels = hp.radec2pix(
+                self.highest_order,
+                self.dataframe[self.catalog_info.ra_column].values,
+                self.dataframe[self.catalog_info.dec_column].values,
+            )
+            (row_count_sparse_histo, mem_size_sparse_histo) = supplemental_count_histogram(
+                mapped_pixels,
+                row_mem_sizes,
+                highest_order=self.highest_order,
+            )
+            row_count_histogram = row_count_sparse_histo.to_array()
+            mem_size_histogram = mem_size_sparse_histo.to_array()
+
+        # Generate alignment.
         alignment = hc.pixel_math.generate_alignment(
-            raw_histogram,
+            row_count_histogram,
             highest_order=self.highest_order,
             lowest_order=self.lowest_order,
-            threshold=self.threshold,
+            threshold=(self.partition_rows if self.partition_rows is not None else self.partition_bytes),
             drop_empty_siblings=self.drop_empty_siblings,
+            mem_size_histogram=mem_size_histogram,
         )
         pixel_list = list({HealpixPixel(tup[0], tup[1]) for tup in alignment if not tup is None})
         return list(np.array(pixel_list)[get_pixel_argsort(pixel_list)])

src/lsdb/loaders/dataframe/from_dataframe.py

@@ -17,8 +17,8 @@ def from_dataframe(
     lowest_order: int = 0,
     highest_order: int = 7,
     drop_empty_siblings: bool = True,
-    partition_size: int | None = None,
-    threshold: int | None = None,
+    partition_rows: int | None = None,
+    partition_bytes: int | None = None,
     margin_order: int = -1,
     margin_threshold: float | None = 5.0,
     should_generate_moc: bool = True,
@@ -50,10 +50,12 @@ def from_dataframe(
     drop_empty_siblings : bool, default True
         When determining final partitionining, if 3 of 4 pixels are empty,
         keep only the non-empty pixel
-    partition_size : int or None, default None
-        The desired partition size, in number of rows.
-    threshold : int or None, default None
-        The maximum number of data points per pixel.
+    partition_rows : int or None, default None
+        The desired partition size, in number of rows. Only one of
+        `partition_rows` or `partition_bytes` should be specified.
+    partition_bytes : int or None, default None
+        The desired partition size, in bytes. Only one of
+        `partition_rows` or `partition_bytes` should be specified.
     margin_order : int, default -1
         The order at which to generate the margin cache.
     margin_threshold : float or None, default 5
@@ -91,8 +93,8 @@ def from_dataframe(
         lowest_order=lowest_order,
         highest_order=highest_order,
         drop_empty_siblings=drop_empty_siblings,
-        partition_size=partition_size,
-        threshold=threshold,
+        partition_rows=partition_rows,
+        partition_bytes=partition_bytes,
         should_generate_moc=should_generate_moc,
         moc_max_order=moc_max_order,
         use_pyarrow_types=use_pyarrow_types,

tests/lsdb/catalog/test_catalog.py

@@ -921,7 +921,7 @@ def divme(df, _=None):
         return 1 / df["a"]
 
     # Force every row into a separate partition
-    nfc = lsdb.from_dataframe(nf, ra_column="a", dec_column="b", partition_size=1)
+    nfc = lsdb.from_dataframe(nf, ra_column="a", dec_column="b", partition_rows=1)
 
     with pytest.raises(RuntimeError, match=r"function divme to partition 3: Not so fast"):
         nfc.map_partitions(divme, include_pixel=False).compute()

tests/lsdb/io/test_to_hats.py

@@ -163,7 +163,7 @@ def test_save_catalog_point_map(small_sky_order1_df, tmp_path):
         catalog_name="small_sky_order1",
         lowest_order=6,
         highest_order=8,
-        threshold=500,
+        partition_rows=500,
     )
 
     small_sky_order1_catalog.write_catalog(