{ "cells": [ { "cell_type": "markdown", "id": "87378ca2", "metadata": {}, "source": [ "# Using Spark EntitySets (BETA)" ] }, { "cell_type": "raw", "id": "ac77ea82", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. note::\n", " Support for Spark EntitySets is still in Beta. While the key functionality has been implemented, development is ongoing to add the remaining functionality.\n", " \n", " All planned improvements to the Featuretools/Spark integration are `documented on Github `_. If you see an open issue that is important for your application, please let us know by upvoting or commenting on the issue. If you encounter any errors using Spark dataframes in EntitySets, or find missing functionality that does not yet have an open issue, please create a `new issue on Github `_." ] }, { "cell_type": "markdown", "id": "01778198", "metadata": {}, "source": [ "Creating a feature matrix from a very large dataset can be problematic if the underlying pandas dataframes that make up the EntitySet cannot easily fit in memory. To help get around this issue, Featuretools supports creating ``EntitySet`` objects from Spark dataframes. A Spark ``EntitySet`` can then be passed to ``featuretools.dfs`` or ``featuretools.calculate_feature_matrix`` to create a feature matrix, which will be returned as a Spark dataframe. In addition to working on larger than memory datasets, this approach also allows users to take advantage of the parallel and distributed processing capabilities offered by Spark and Spark.\n", "\n", "This guide will provide an overview of how to create a Spark ``EntitySet`` and then generate a feature matrix from it. If you are already familiar with creating a feature matrix starting from pandas dataframes, this process will seem quite familiar, as there are no differences in the process. There are, however, some limitations when using Spark dataframes, and those limitations are reviewed in more detail below.\n", "\n", "## Creating EntitySets\n", "\n", "Spark ``EntitySets`` require PySpark. Both can be installed directly with ``pip install featuretools[spark]``. Java is also required for PySpark and may need to be installed, see [the Spark documentation](https://spark.apache.org/docs/latest/index.html) for more details. We will create a very small Spark dataframe for this example. Spark dataframes can also be created from pandas dataframes, Spark dataframes, or read in directly from a file." ] }, { "cell_type": "code", "execution_count": null, "id": "20acbac9", "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "import pyspark.sql as sql\n", "\n", "spark = (\n", " sql.SparkSession.builder.master(\"local[2]\")\n", " .config(\n", " \"spark.driver.extraJavaOptions\", \"-Dio.netty.tryReflectionSetAccessible=True\"\n", " )\n", " .config(\"spark.sql.shuffle.partitions\", \"2\")\n", " .config(\"spark.driver.bindAddress\", \"127.0.0.1\")\n", " .getOrCreate()\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "af6545db", "metadata": {}, "outputs": [], "source": [ "import featuretools as ft\n", "import pyspark.pandas as ps\n", "\n", "ps.set_option(\"compute.default_index_type\", \"distributed\")\n", "\n", "id = [0, 1, 2, 3, 4]\n", "values = [12, -35, 14, 103, -51]\n", "spark_df = ps.DataFrame({\"id\": id, \"values\": values})\n", "spark_df" ] }, { "cell_type": "markdown", "id": "3c27b229", "metadata": {}, "source": [ "Now that we have our Spark dataframe, we can start to create the ``EntitySet``. Inferring Woodwork logical types for the columns in a Spark dataframe can be computationally expensive. To avoid this expense, logical type inference can be skipped by supplying a dictionary of logical types using the `logical_types` parameter when calling `es.add_dataframe()`. Logical types can be specified as Woodwork LogicalType classes, or their equivalent string representation. For more information on using Woodwork types refer to the [Woodwork Typing in Featuretools](../getting_started/woodwork_types.ipynb) guide.\n", "\n", "Aside from supplying the logical types, the rest of the process of creating an `EntitySet` is the same as if we were using pandas DataFrames." ] }, { "cell_type": "code", "execution_count": null, "id": "b5ca0be3", "metadata": {}, "outputs": [], "source": [ "from woodwork.logical_types import Double, Integer\n", "\n", "es = ft.EntitySet(id=\"spark_es\")\n", "es = es.add_dataframe(\n", " dataframe_name=\"spark_input_df\",\n", " dataframe=spark_df,\n", " index=\"id\",\n", " logical_types={\"id\": Integer, \"values\": Double},\n", ")\n", "\n", "es" ] }, { "cell_type": "markdown", "id": "9d1b8525", "metadata": {}, "source": [ "## Running DFS\n", "\n", "We can pass the ``EntitySet`` we created above to ``featuretools.dfs`` in order to create a feature matrix. If the ``EntitySet`` we pass to ``dfs`` is made of Spark dataframes, the feature matrix we get back will be a Spark dataframe." ] }, { "cell_type": "code", "execution_count": null, "id": "bc48d985", "metadata": {}, "outputs": [], "source": [ "feature_matrix, features = ft.dfs(\n", " entityset=es,\n", " target_dataframe_name=\"spark_input_df\",\n", " trans_primitives=[\"negate\"],\n", " max_depth=1,\n", ")\n", "feature_matrix" ] }, { "cell_type": "markdown", "id": "a4f8ccfd", "metadata": {}, "source": [ "This feature matrix can be saved to disk or converted to a pandas dataframe and brought into memory, using the appropriate Spark dataframe methods.\n", "\n", "While this is a simple example to illustrate the process of using Spark dataframes with Featuretools, this process will also work with an ``EntitySet`` containing multiple dataframes, as well as with aggregation primitives.\n", "\n", "## Limitations\n", "\n", "The key functionality of Featuretools is available for use with a Spark ``EntitySet``, and work is ongoing to add the remaining functionality that is available when using a pandas ``EntitySet``. There are, however, some limitations to be aware of when creating a Spark ``Entityset`` and then using it to generate a feature matrix. The most significant limitations are reviewed in more detail in this section." ] }, { "cell_type": "raw", "id": "71efc4dc", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ".. note::\n", " If the limitations of using a Spark ``EntitySet`` are problematic for your problem, you may still be able to compute a larger-than-memory feature matrix by partitioning your data as described in :doc:`performance`." ] }, { "cell_type": "markdown", "id": "854c0156", "metadata": {}, "source": [ "### Supported Primitives\n", "\n", "When creating a feature matrix from a Spark ``EntitySet``, only certain primitives can be used. Primitives that rely on the order of the entire dataframe or require an entire column for computation are currently not supported when using a Spark ``EntitySet``. Multivariable and time-dependent aggregation primitives also are not currently supported.\n", "\n", "To obtain a list of the primitives that can be used with a Spark ``EntitySet``, you can call ``featuretools.list_primitives()``. This will return a table of all primitives. Any primitive that can be used with a Spark ``EntitySet`` will have a value of ``True`` in the ``spark_compatible`` column." ] }, { "cell_type": "code", "execution_count": null, "id": "bed6a8c8", "metadata": {}, "outputs": [], "source": [ "primitives_df = ft.list_primitives()\n", "spark_compatible_df = primitives_df[primitives_df[\"spark_compatible\"] == True]\n", "spark_compatible_df.head()" ] }, { "cell_type": "code", "execution_count": null, "id": "1e5baee7", "metadata": {}, "outputs": [], "source": [ "spark_compatible_df.tail()" ] }, { "cell_type": "markdown", "id": "8abc5442", "metadata": {}, "source": [ "### DataFrame Limitations\n", "\n", "Featuretools stores the DataFrames that make up an EntitySet as Woodwork DataFrames, which include additional typing information about the columns that are in the DataFrame. When adding a DataFrame to an `EntitySet`, Woodwork will attempt to infer the logical types for any columns that do not have a logical type defined. This inference process can be quite expensive for Spark DataFrames. In order to skip type inference and speed up the process of adding a Spark DataFrame to an `EntitySet`, users can specify the logical type to use for each column in the DataFrame. A list of available logical types can be obtained by running ``featuretools.list_logical_types()``. To learn more about the limitations of a Spark dataframe with Woodwork typing, see the [Woodwork guide on Spark dataframes](https://woodwork.alteryx.com/en/stable/guides/using_woodwork_with_dask_and_spark.html#Spark-DataFrame-Example).\n", "\n", "By default, Woodwork checks that pandas dataframes have unique index values. Because performing this same check with Spark could be computationally expensive, this check is not performed when adding a Spark dataframe to an `EntitySet`. When using Spark dataframes, users must ensure that the supplied index values are unique.\n", "\n", "When using a pandas DataFrames, the ordering of the underlying DataFrame rows is maintained by Featuretools. For a Spark DataFrame, the ordering of the DataFrame rows is not guaranteed, and Featuretools does not attempt to maintain row order in a Spark DataFrame. If ordering is important, close attention must be paid to any output to avoid issues.\n", "\n", "### EntitySet Limitations\n", "\n", "When creating a Featuretools ``EntitySet`` that will be made of Spark dataframes, all of the dataframes used to create the ``EntitySet`` must be of the same type, either all Spark dataframe, all Dask dataframes, or all pandas dataframes. Featuretools does not support creating an ``EntitySet`` containing a mix of Spark, Dask, and pandas dataframes.\n", "\n", "Additionally, ``EntitySet.add_interesting_values()`` cannot be used in Spark EntitySets to find interesting values; however, it can be used set a column's interesting values with the `values` parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "6e13e5ef", "metadata": {}, "outputs": [], "source": [ "values_dict = {\"values\": [12, 103]}\n", "es.add_interesting_values(dataframe_name=\"spark_input_df\", values=values_dict)\n", "\n", "es[\"spark_input_df\"].ww.columns[\"values\"].metadata" ] }, { "cell_type": "markdown", "id": "80f6b033", "metadata": {}, "source": [ "\n", "### DFS Limitations\n", "\n", "There are a few key limitations when generating a feature matrix from a Spark ``EntitySet``.\n", "\n", "If a ``cutoff_time`` parameter is passed to ``featuretools.dfs()`` it should be a single cutoff time value, or a pandas dataframe. The current implementation will still work if a Spark dataframe is supplied for cutoff times, but a ``.to_pandas()`` call will be made on the dataframe to convert it into a pandas dataframe. This conversion will result in a warning, and the process could take a considerable amount of time to complete depending on the size of the supplied dataframe.\n", "\n", "Additionally, Featuretools does not currently support the use of the ``approximate`` or ``training_window`` parameters when working with Spark EntitySets, but should in future releases.\n", "\n", "Finally, if the output feature matrix contains a boolean column with ``NaN`` values included, the column type may have a different datatype than the same feature matrix generated from a pandas ``EntitySet``. If feature matrix column data types are critical, the feature matrix should be inspected to make sure the types are of the proper types, and recast as necessary.\n", "\n", "### Other Limitations\n", "\n", "Currently ``featuretools.encode_features()`` does not work with a Spark dataframe as input. This will hopefully be resolved in a future release of Featuretools.\n", "\n", "The utility function ``featuretools.make_temporal_cutoffs()`` will not work properly with Spark inputs for ``instance_ids`` or ``cutoffs``. However, as noted above, if a ``cutoff_time`` dataframe is supplied to ``dfs``, the supplied dataframe should be a pandas dataframe, and this can be generated by supplying pandas inputs to ``make_temporal_cutoffs()``.\n", "\n", "The use of ``featuretools.remove_low_information_features()`` cannot currently be used with a Spark feature matrix.\n", "\n", "When manually defining a ``Feature``, the ``use_previous`` parameter cannot be used if this feature will be applied to calculate a feature matrix from a Spark ``EntitySet``." ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 5 }