From 7b78c1da931dc5b1ff9cf5676c6ca2c8c8b48522 Mon Sep 17 00:00:00 2001
From: Uchenna Ugoh <61969079+ugohuche@users.noreply.github.com>
Date: Fri, 26 Jul 2024 21:14:55 +0100
Subject: [PATCH] Added TPC-H Q13 and Q22

---
 tpch/notebooks/q13/execute.ipynb        | 396 +++++++++++++++++++++++
 tpch/notebooks/q13/kernel-metadata.json |  15 +
 tpch/notebooks/q22/execute.ipynb        | 409 ++++++++++++++++++++++++
 tpch/notebooks/q22/kernel-metadata.json |  15 +
 4 files changed, 835 insertions(+)
 create mode 100644 tpch/notebooks/q13/execute.ipynb
 create mode 100644 tpch/notebooks/q13/kernel-metadata.json
 create mode 100644 tpch/notebooks/q22/execute.ipynb
 create mode 100644 tpch/notebooks/q22/kernel-metadata.json

diff --git a/tpch/notebooks/q13/execute.ipynb b/tpch/notebooks/q13/execute.ipynb
new file mode 100644
index 000000000..1b2526132
--- /dev/null
+++ b/tpch/notebooks/q13/execute.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {
+    "papermill": {
+     "duration": 46.481932,
+     "end_time": "2024-07-25T23:10:36.288698",
+     "exception": false,
+     "start_time": "2024-07-25T23:09:49.806766",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip uninstall apache-beam -y && pip install -U pandas polars pyarrow narwhals "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {
+    "papermill": {
+     "duration": 0.522877,
+     "end_time": "2024-07-25T23:10:36.819167",
+     "exception": false,
+     "start_time": "2024-07-25T23:10:36.296290",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import polars as pl\n",
+    "\n",
+    "pd.options.mode.copy_on_write = True\n",
+    "pd.options.future.infer_string = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.02756,
+     "end_time": "2024-07-25T23:10:36.854110",
+     "exception": false,
+     "start_time": "2024-07-25T23:10:36.826550",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import Any\n",
+    "import narwhals as nw\n",
+    "\n",
+    "def q13(\n",
+    "    customer_ds_raw: Any,\n",
+    "    orders_ds_raw: Any\n",
+    ") -> Any:\n",
+    "\n",
+    "    customer_ds = nw.from_native(customer_ds_raw)\n",
+    "    orders_ds = nw.from_native(orders_ds_raw)\n",
+    "    \n",
+    "    var1 = \"special\"\n",
+    "    var2 = \"requests\"\n",
+    "\n",
+    "    orders_ds = orders_ds.filter(~nw.col(\"o_comment\").str.contains(f\"{var1}.*{var2}\"))\n",
+    "    \n",
+    "    result = (\n",
+    "        customer_ds.join(orders_ds, left_on=\"c_custkey\", right_on=\"o_custkey\", how=\"left\")\n",
+    "        .group_by(\"c_custkey\")\n",
+    "        .agg(nw.col(\"o_orderkey\").len().alias(\"c_count\"))\n",
+    "        .group_by(\"c_count\")\n",
+    "        .agg(nw.col(\"c_count\").len().alias(\"len\"))\n",
+    "        .select(nw.col(\"c_count\"), nw.col(\"len\").alias(\"custdist\"))\n",
+    "        .sort(by=[\"custdist\", \"c_count\"], descending=[True, True])\n",
+    "    )\n",
+    "\n",
+    "    return nw.to_native(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {
+    "papermill": {
+     "duration": 0.016452,
+     "end_time": "2024-07-25T23:10:36.878001",
+     "exception": false,
+     "start_time": "2024-07-25T23:10:36.861549",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dir_ = \"/kaggle/input/tpc-h-data-parquet-s-2/\"\n",
+    "customer = dir_ + 'customer.parquet'\n",
+    "orders = dir_ + 'orders.parquet'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {
+    "papermill": {
+     "duration": 0.016664,
+     "end_time": "2024-07-25T23:10:36.902043",
+     "exception": false,
+     "start_time": "2024-07-25T23:10:36.885379",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "IO_FUNCS = {\n",
+    "    'pandas': lambda x: pd.read_parquet(x, engine='pyarrow'),\n",
+    "    'pandas[pyarrow]': lambda x: pd.read_parquet(x, engine='pyarrow', dtype_backend='pyarrow'),\n",
+    "    'polars[eager]': lambda x: pl.read_parquet(x),\n",
+    "    'polars[lazy]': lambda x: pl.scan_parquet(x),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.015614,
+     "end_time": "2024-07-25T23:10:36.924894",
+     "exception": false,
+     "start_time": "2024-07-25T23:10:36.909280",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "results = {}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006996,
+     "end_time": "2024-07-25T23:10:36.939302",
+     "exception": false,
+     "start_time": "2024-07-25T23:10:36.932306",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## pandas via Narwhals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {
+    "papermill": {
+     "duration": 44.926739,
+     "end_time": "2024-07-25T23:11:21.873211",
+     "exception": false,
+     "start_time": "2024-07-25T23:10:36.946472",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'pandas'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q13(fn(customer), fn(orders))\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006853,
+     "end_time": "2024-07-25T23:11:21.887504",
+     "exception": false,
+     "start_time": "2024-07-25T23:11:21.880651",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## pandas, pyarrow dtypes, via Narwhals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {
+    "papermill": {
+     "duration": 44.733391,
+     "end_time": "2024-07-25T23:12:06.628137",
+     "exception": false,
+     "start_time": "2024-07-25T23:11:21.894746",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'pandas[pyarrow]'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q13(fn(customer), fn(orders))\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006896,
+     "end_time": "2024-07-25T23:12:06.642200",
+     "exception": false,
+     "start_time": "2024-07-25T23:12:06.635304",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Polars read_parquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {
+    "papermill": {
+     "duration": 13.901571,
+     "end_time": "2024-07-25T23:12:20.550910",
+     "exception": false,
+     "start_time": "2024-07-25T23:12:06.649339",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'polars[eager]'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q13(fn(customer), fn(orders))\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {
+    "papermill": {
+     "duration": 0.0075,
+     "end_time": "2024-07-25T23:12:20.566105",
+     "exception": false,
+     "start_time": "2024-07-25T23:12:20.558605",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Polars scan_parquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {
+    "papermill": {
+     "duration": 10.393542,
+     "end_time": "2024-07-25T23:12:30.967063",
+     "exception": false,
+     "start_time": "2024-07-25T23:12:20.573521",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'polars[lazy]'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q13(fn(customer), fn(orders)).collect()\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007808,
+     "end_time": "2024-07-25T23:12:30.982613",
+     "exception": false,
+     "start_time": "2024-07-25T23:12:30.974805",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017013,
+     "end_time": "2024-07-25T23:12:31.007464",
+     "exception": false,
+     "start_time": "2024-07-25T23:12:30.990451",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "with open('results.json', 'w') as fd:\n",
+    "    json.dump(results, fd)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "none",
+   "dataSources": [
+    {
+     "sourceId": 167796934,
+     "sourceType": "kernelVersion"
+    }
+   ],
+   "dockerImageVersionId": 30673,
+   "isGpuEnabled": false,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 164.526043,
+   "end_time": "2024-07-25T23:12:31.536428",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-07-25T23:09:47.010385",
+   "version": "2.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tpch/notebooks/q13/kernel-metadata.json b/tpch/notebooks/q13/kernel-metadata.json
new file mode 100644
index 000000000..6511ee943
--- /dev/null
+++ b/tpch/notebooks/q13/kernel-metadata.json
@@ -0,0 +1,15 @@
+{
+  "id": "marcogorelli/narwhals-tpch-q22-s2",
+  "title": "Narwhals TPCH Q22 S2",
+  "code_file": "execute.ipynb",
+  "language": "python",
+  "kernel_type": "notebook",
+  "is_private": "false",
+  "enable_gpu": "false",
+  "enable_tpu": "false",
+  "enable_internet": "true",
+  "dataset_sources": [],
+  "competition_sources": [],
+  "kernel_sources": ["marcogorelli/tpc-h-data-parquet-s-2"],
+  "model_sources": []
+}
\ No newline at end of file
diff --git a/tpch/notebooks/q22/execute.ipynb b/tpch/notebooks/q22/execute.ipynb
new file mode 100644
index 000000000..337c1bf7d
--- /dev/null
+++ b/tpch/notebooks/q22/execute.ipynb
@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {
+    "papermill": {
+     "duration": 49.860489,
+     "end_time": "2024-07-26T12:50:30.931456",
+     "exception": false,
+     "start_time": "2024-07-26T12:49:41.070967",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip uninstall apache-beam -y && pip install -U pandas polars pyarrow narwhals "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {
+    "papermill": {
+     "duration": 0.563589,
+     "end_time": "2024-07-26T12:50:31.502451",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:30.938862",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import polars as pl\n",
+    "\n",
+    "pd.options.mode.copy_on_write = True\n",
+    "pd.options.future.infer_string = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {
+    "papermill": {
+     "duration": 0.031352,
+     "end_time": "2024-07-26T12:50:31.541220",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:31.509868",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from typing import Any\n",
+    "import narwhals as nw\n",
+    "\n",
+    "def q22(\n",
+    "    customer_ds_raw: Any,\n",
+    "    orders_ds_raw: Any,\n",
+    ") -> Any:\n",
+    "\n",
+    "    customer_ds = nw.from_native(customer_ds_raw)\n",
+    "    orders_ds = nw.from_native(orders_ds_raw)\n",
+    "    \n",
+    "    \n",
+    "    query1 = (\n",
+    "        customer_ds.with_columns(nw.col(\"c_phone\").str.slice(0, 2).alias(\"cntrycode\"))\n",
+    "        .filter(nw.col(\"cntrycode\").str.contains(\"13|31|23|29|30|18|17\"))\n",
+    "        .select(\"c_acctbal\", \"c_custkey\", \"cntrycode\")\n",
+    "    )\n",
+    "\n",
+    "    query2 = query1.filter(nw.col(\"c_acctbal\") > 0.0).select(\n",
+    "        nw.col(\"c_acctbal\").mean().alias(\"avg_acctbal\")\n",
+    "    )\n",
+    "\n",
+    "    query3 = orders_ds.select(nw.col(\"o_custkey\").unique()).with_columns(\n",
+    "        nw.col(\"o_custkey\").alias(\"c_custkey\")\n",
+    "    )\n",
+    "\n",
+    "    final_query = (\n",
+    "        query1.join(query3, left_on=\"c_custkey\", right_on=\"c_custkey\", how=\"left\")\n",
+    "        .filter(nw.col(\"o_custkey\").is_null())\n",
+    "        .join(query2, how=\"cross\")\n",
+    "        .filter(nw.col(\"c_acctbal\") > nw.col(\"avg_acctbal\"))\n",
+    "        .group_by(\"cntrycode\")\n",
+    "        .agg(\n",
+    "            nw.col(\"c_acctbal\").count().alias(\"numcust\"),\n",
+    "            nw.col(\"c_acctbal\").sum().round(2).alias(\"totacctbal\"),\n",
+    "        )\n",
+    "        .sort(\"cntrycode\")\n",
+    "    )\n",
+    "\n",
+    "    return nw.to_native(final_query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {
+    "papermill": {
+     "duration": 0.016247,
+     "end_time": "2024-07-26T12:50:31.564892",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:31.548645",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dir_ = \"/kaggle/input/tpc-h-data-parquet-s-2/\"\n",
+    "customer = dir_ + 'customer.parquet'\n",
+    "orders = dir_ + 'orders.parquet'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017416,
+     "end_time": "2024-07-26T12:50:31.589507",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:31.572091",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "IO_FUNCS = {\n",
+    "    'pandas': lambda x: pd.read_parquet(x, engine='pyarrow'),\n",
+    "    'pandas[pyarrow]': lambda x: pd.read_parquet(x, engine='pyarrow', dtype_backend='pyarrow'),\n",
+    "    'polars[eager]': lambda x: pl.read_parquet(x),\n",
+    "    'polars[lazy]': lambda x: pl.scan_parquet(x),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {
+    "papermill": {
+     "duration": 0.015402,
+     "end_time": "2024-07-26T12:50:31.612243",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:31.596841",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "results = {}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006963,
+     "end_time": "2024-07-26T12:50:31.626579",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:31.619616",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## pandas via Narwhals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {
+    "papermill": {
+     "duration": 10.559142,
+     "end_time": "2024-07-26T12:50:42.192870",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:31.633728",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'pandas'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q22(fn(customer), fn(orders))\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008478,
+     "end_time": "2024-07-26T12:50:42.209200",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:42.200722",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## pandas, pyarrow dtypes, via Narwhals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {
+    "papermill": {
+     "duration": 7.07373,
+     "end_time": "2024-07-26T12:50:49.292349",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:42.218619",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'pandas[pyarrow]'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q22(fn(customer), fn(orders))\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {
+    "papermill": {
+     "duration": 0.00828,
+     "end_time": "2024-07-26T12:50:49.309227",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:49.300947",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Polars read_parquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {
+    "papermill": {
+     "duration": 6.202911,
+     "end_time": "2024-07-26T12:50:55.520546",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:49.317635",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'polars[eager]'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q22(fn(customer), fn(orders))\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008436,
+     "end_time": "2024-07-26T12:50:55.537904",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:55.529468",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Polars scan_parquet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
+   "metadata": {
+    "papermill": {
+     "duration": 12.591074,
+     "end_time": "2024-07-26T12:51:08.137474",
+     "exception": false,
+     "start_time": "2024-07-26T12:50:55.546400",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tool = 'polars[lazy]'\n",
+    "fn = IO_FUNCS[tool]\n",
+    "timings = %timeit -o q22(fn(customer), fn(orders)).collect()\n",
+    "results[tool] = timings.all_runs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008232,
+     "end_time": "2024-07-26T12:51:08.154223",
+     "exception": false,
+     "start_time": "2024-07-26T12:51:08.145991",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Save"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {
+    "papermill": {
+     "duration": 0.017523,
+     "end_time": "2024-07-26T12:51:08.180173",
+     "exception": false,
+     "start_time": "2024-07-26T12:51:08.162650",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "with open('results.json', 'w') as fd:\n",
+    "    json.dump(results, fd)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "none",
+   "dataSources": [
+    {
+     "sourceId": 167796934,
+     "sourceType": "kernelVersion"
+    }
+   ],
+   "dockerImageVersionId": 30673,
+   "isGpuEnabled": false,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 90.628471,
+   "end_time": "2024-07-26T12:51:08.710737",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-07-26T12:49:38.082266",
+   "version": "2.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tpch/notebooks/q22/kernel-metadata.json b/tpch/notebooks/q22/kernel-metadata.json
new file mode 100644
index 000000000..6511ee943
--- /dev/null
+++ b/tpch/notebooks/q22/kernel-metadata.json
@@ -0,0 +1,15 @@
+{
+  "id": "marcogorelli/narwhals-tpch-q22-s2",
+  "title": "Narwhals TPCH Q22 S2",
+  "code_file": "execute.ipynb",
+  "language": "python",
+  "kernel_type": "notebook",
+  "is_private": "false",
+  "enable_gpu": "false",
+  "enable_tpu": "false",
+  "enable_internet": "true",
+  "dataset_sources": [],
+  "competition_sources": [],
+  "kernel_sources": ["marcogorelli/tpc-h-data-parquet-s-2"],
+  "model_sources": []
+}
\ No newline at end of file