Arun sqc interop

.github/workflows/ci.yml

@@ -246,12 +246,16 @@ jobs:
       unidist: ${{ steps.filter.outputs.unidist }}
       engines: ${{ steps.engines.outputs.engines }}
       experimental: ${{ steps.experimental.outputs.experimental }}
+      test-native-dataframe-mode: ${{ steps.filter.outputs.test-native-dataframe-mode }}
     steps:
     - uses: actions/checkout@v4
     - uses: dorny/paths-filter@v3
       id: filter
       with:
         filters: |
+          test-native-dataframe-mode:
+            - 'modin/core/storage_formats/pandas/native_query_compiler.py'
+            - 'modin/core/storage_formats/base/query_compiler.py'
           shared: &shared
             - 'modin/core/execution/dispatching/**'
           ray:
@@ -293,7 +297,7 @@ jobs:
     name: test-ubuntu (engine unidist ${{matrix.unidist-backend}}, python ${{matrix.python-version}})
     services:
       moto:
-        image: motoserver/moto
+        image: motoserver/moto:5.0.13
         ports:
           - 5000:5000
         env:
@@ -382,7 +386,7 @@ jobs:
       # Using workaround https://github.com/actions/runner/issues/822#issuecomment-1524826092
       moto:
         # we only need moto service on Ubuntu and for group_4 task or python engine
-        image: ${{ (matrix.os == 'ubuntu' && (matrix.engine == 'python' || matrix.test_task == 'group_4')) && 'motoserver/moto' || '' }}
+        image: ${{ (matrix.os == 'ubuntu' && (matrix.engine == 'python' || matrix.test_task == 'group_4')) && 'motoserver/moto:5.0.13' || '' }}
         ports:
           - 5000:5000
         env:
@@ -462,6 +466,7 @@ jobs:
         if: matrix.engine == 'python' || matrix.test_task == 'group_4'
       - run: python -m pytest modin/tests/interchange/dataframe_protocol/pandas/test_protocol.py
         if: matrix.engine == 'python' || matrix.test_task == 'group_4'
+      - run: python -m pytest modin/tests/polars/test_dataframe.py
       - run: |
           python -m pip install lazy_import
           python -m pytest modin/tests/pandas/integrations/
@@ -507,7 +512,7 @@ jobs:
     name: test-${{ matrix.os }}-sanity (engine ${{ matrix.execution.name }}, python ${{matrix.python-version}})
     services:
       moto:
-        image: ${{ matrix.os != 'windows' && 'motoserver/moto' || '' }}
+        image: ${{ matrix.os != 'windows' && 'motoserver/moto:5.0.13' || '' }}
         ports:
           - 5000:5000
         env:
@@ -622,7 +627,7 @@ jobs:
     name: test experimental
     services:
       moto:
-        image: motoserver/moto
+        image: motoserver/moto:5.0.13
         ports:
           - 5000:5000
         env:
@@ -664,6 +669,45 @@ jobs:
           python-version: ${{matrix.python-version}}
       - run: python -m pytest modin/tests/experimental/spreadsheet/test_general.py
 
+  test-native-dataframe-mode:
+    needs: [ lint-flake8, execution-filter]
+    if: ${{ needs.execution-filter.outputs.test-native-dataframe-mode == 'true' }}
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+    strategy:
+      matrix:
+        python-version: ["3.9"]
+    env:
+      MODIN_NATIVE_DATAFRAME_MODE: "Pandas"
+    name: test-native-dataframe-mode python ${{matrix.python-version}})
+    steps:
+      - uses: actions/checkout@v4
+      - uses: ./.github/actions/mamba-env
+        with:
+          environment-file: environment-dev.yml
+          python-version: ${{matrix.python-version}}
+      - run: python -m pytest modin/tests/pandas/dataframe/test_binary.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_default.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_indexing.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_iter.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_join_sort.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_map_metadata.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_pickle.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_reduce.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_udf.py
+      - run: python -m pytest modin/tests/pandas/dataframe/test_window.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_binary.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_default.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_indexing.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_iter.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_join_sort.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_map_metadata.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_pickle.py
+      - run: python -m pytest modin/tests/pandas/native_df_mode/test_window.py
+      - uses: ./.github/actions/upload-coverage
+
   merge-coverage-artifacts:
     needs: [test-internals, test-api-and-no-engine, test-defaults, test-all-unidist, test-all, test-experimental, test-sanity]
     if: always()  # we need to run it regardless of some job being skipped, like in PR

.github/workflows/push-to-main.yml

@@ -19,7 +19,7 @@ jobs:
         shell: bash -l {0}
     services:
       moto:
-        image: motoserver/moto
+        image: motoserver/moto:5.0.13
         ports:
           - 5000:5000
         env:

docs/_static/custom.js

@@ -3,9 +3,8 @@ document.addEventListener("DOMContentLoaded", function () {
   script.type = "module";
   script.id = "runllm-widget-script"
 
-  script.src = "https://cdn.jsdelivr.net/npm/@runllm/search-widget@stable/dist/run-llm-search-widget.es.js";
+  script.src = "https://widget.runllm.com";
 
-  script.setAttribute("version", "stable");
   script.setAttribute("runllm-keyboard-shortcut", "Mod+j"); // cmd-j or ctrl-j to open the widget.
   script.setAttribute("runllm-name", "Modin");
   script.setAttribute("runllm-position", "BOTTOM_RIGHT");

docs/usage_guide/optimization_notes/index.rst

@@ -37,6 +37,38 @@ Range-partitioning is not a silver bullet, meaning that enabling it is not alway
 a link to the list of operations that have support for range-partitioning and practical advices on when one should
 enable it: :doc:`operations that support range-partitioning </usage_guide/optimization_notes/range_partitioning_ops>`.
 
+Dynamic-partitioning in Modin
+"""""""""""""""""""""""""""""
+
+Ray engine experiences slowdowns when running a large number of small remote tasks at the same time. Ray Core recommends to `avoid tiny task`_.
+When modin DataFrame has a large number of partitions, some functions produce a large number of remote tasks, which can cause slowdowns. 
+To solve this problem, Modin suggests using dynamic partitioning. This approach reduces the number of remote tasks 
+by combining multiple partitions into a single virtual partition and perform a common remote task on them.
+
+Dynamic partitioning is typically used for operations that are fully or partially executed on all partitions separately.
+
+.. code-block:: python
+
+    import modin.pandas as pd
+    from modin.config import context
+
+    df = pd.DataFrame(...)
+
+    with context(DynamicPartitioning=True):
+        df.abs()
+
+Dynamic partitioning is also not always useful, and this approach is usually used for medium-sized DataFrames with a large number of columns.
+If the number of columns is small, the number of partitions will be close to the number of CPUs, and Ray will not have this problem.
+If the DataFrame has too many rows, this is also not a good case for using Dynamic-partitioning, since each task is no longer tiny and performing 
+the combined tasks carries more overhead than assigning them separately.
+
+Unfortunately, the use of Dynamic-partitioning depends on various factors such as data size, number of CPUs, operations performed, 
+and it is up to the user to determine whether Dynamic-partitioning will give a boost in his case or not.
+
+..
+  TODO: Define heuristics to automatically enable dynamic partitioning without performance penalty.
+  `Issue #7370 <https://github.com/modin-project/modin/issues/7370>`_
+
 Understanding Modin's partitioning mechanism
 """"""""""""""""""""""""""""""""""""""""""""
 
@@ -311,3 +343,4 @@ an inner join you may want to swap left and right DataFrames.
 Note that result columns order may differ for first and second ``merge``.
 
 .. _range-partitioning: https://www.techopedia.com/definition/31994/range-partitioning
+.. _`avoid tiny task`: https://docs.ray.io/en/latest/ray-core/tips-for-first-time.html#tip-2-avoid-tiny-tasks

environment-dev.yml

@@ -70,3 +70,4 @@ dependencies:
       - git+https://github.com/modin-project/modin-spreadsheet.git@49ffd89f683f54c311867d602c55443fb11bf2a5
       # The `numpydoc` version should match the version installed in the `lint-pydocstyle` job of the CI.
       - numpydoc==1.6.0
+      - polars

modin/config/__init__.py

@@ -23,6 +23,7 @@
     CpuCount,
     DaskThreadsPerWorker,
     DocModule,
+    DynamicPartitioning,
     Engine,
     EnvironmentVariable,
     GithubCI,
@@ -39,6 +40,7 @@
     MinPartitionSize,
     MinRowPartitionSize,
     ModinNumpy,
+    NativeDataframeMode,
     NPartitions,
     PersistentPickle,
     ProgressBar,
@@ -68,6 +70,7 @@
     "CpuCount",
     "GpuCount",
     "Memory",
+    "NativeDataframeMode",
     # Ray specific
     "IsRayCluster",
     "RayRedisAddress",
@@ -95,6 +98,7 @@
     "AsyncReadMode",
     "ReadSqlEngine",
     "IsExperimental",
+    "DynamicPartitioning",
     # For tests
     "TrackFileLeaks",
     "TestReadFromSqlServer",

modin/config/envvars.py

@@ -332,6 +332,24 @@ class CpuCount(EnvironmentVariable, type=int):
 
     varname = "MODIN_CPUS"
 
+    @classmethod
+    def _put(cls, value: int) -> None:
+        """
+        Put specific value if CpuCount wasn't set by a user yet.
+
+        Parameters
+        ----------
+        value : int
+            Config value to set.
+
+        Notes
+        -----
+        This method is used to set CpuCount from cluster resources internally
+        and should not be called by a user.
+        """
+        if cls.get_value_source() == ValueSource.DEFAULT:
+            cls.put(value)
+
     @classmethod
     def _get_default(cls) -> int:
         """
@@ -874,6 +892,18 @@ class DaskThreadsPerWorker(EnvironmentVariable, type=int):
     default = 1
 
 
+class DynamicPartitioning(EnvironmentVariable, type=bool):
+    """
+    Set to true to use Modin's dynamic-partitioning implementation where possible.
+
+    Please refer to documentation for cases where enabling this options would be beneficial:
+    https://modin.readthedocs.io/en/stable/usage_guide/optimization_notes/index.html#dynamic-partitioning-in-modin
+    """
+
+    varname = "MODIN_DYNAMIC_PARTITIONING"
+    default = False
+
+
 def _check_vars() -> None:
     """
     Check validity of environment variables.
@@ -913,4 +943,26 @@ def _check_vars() -> None:
         )
 
 
+class NativeDataframeMode(EnvironmentVariable, type=str):
+    """
+    Configures the query compiler to process Modin data.
+
+    When this config is set to ``Default``, ``PandasQueryCompiler`` is used,
+    which leads to Modin executing dataframes in distributed fashion.
+    When set to a string (e.g., ``pandas``), ``NativeQueryCompiler`` is used,
+    which handles the dataframes without distributing,
+    falling back to native library functions (e.g., ``pandas``).
+
+    This could be beneficial for handling relatively small dataframes
+    without involving additional overhead of communication between processes.
+    """
+
+    varname = "MODIN_NATIVE_DATAFRAME_MODE"
+    choices = (
+        "Default",
+        "Pandas",
+    )
+    default = "Default"
+
+
 _check_vars()

modin/core/dataframe/algebra/groupby.py

@@ -655,9 +655,11 @@ def aggregate_on_dict(grp_obj, *args, **kwargs):
                     )
 
             native_res_part = [] if native_agg_res is None else [native_agg_res]
-            result = pandas.concat(
-                [*native_res_part, *custom_results], axis=1, copy=False
-            )
+            parts = [*native_res_part, *custom_results]
+            if parts:
+                result = pandas.concat(parts, axis=1, copy=False)
+            else:
+                result = pandas.DataFrame(columns=result_columns)
 
             # The order is naturally preserved if there's no custom aggregations
             if preserve_aggregation_order and len(custom_aggs):