Add dataproc tpcds example notebook #607
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| Original file line number | Diff line number | Diff line change | ||||
|---|---|---|---|---|---|---|
| @@ -0,0 +1,331 @@ | ||||||
| { | ||||||
| "cells": [ | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "d2ceb94f-8fe1-4b5e-aca0-95603ed385c6", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "# Install packages" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "221bdbaf-997e-4e6d-b6f7-2e870ee94ae3", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "sparkmeasure_version='0.27'" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "67246397-0aab-4c0f-bd7d-36063dd6386b", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "%pip install --quiet \\\n", | ||||||
| " tpcds_pyspark \\\n", | ||||||
| " pandas \\\n", | ||||||
| " sparkmeasure=={sparkmeasure_version}.0 \\\n", | ||||||
| " matplotlib" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "77190a13-c5aa-454a-925c-b3aa2c6fa99d", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "# Import modules" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "53ec0b9b-a94b-4176-9123-48329941cd69", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "from importlib.resources import files\n", | ||||||
| "from pyspark.sql import SparkSession\n", | ||||||
| "from tpcds_pyspark import TPCDS\n", | ||||||
| "import glob\n", | ||||||
| "import os\n", | ||||||
| "import pandas as pd\n", | ||||||
| "import re\n", | ||||||
| "import time" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "7a0c86bb-f557-4f20-8899-d7d27023ad50", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "# Init a SparkSession with RAPIDS Spark" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "0d54c758-44df-4aa1-afaa-f9c23c569313", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "## Detect Scala Version used in PySpark package" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "5745a225-d3b6-4613-8de5-d56d838e2548", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
|
Contributor
There was a problem hiding this comment. syntax: Regex needs escaping for the dot -
Suggested change
Collaborator
Author
There was a problem hiding this comment. @gerashegalov Same here. This dataproc version is matching your original version for this regexp. I want to ignore this suggestion.
Collaborator
There was a problem hiding this comment. This is a legitimate improvement although it does not change anything in practice. We can fix it in the original notebook |
||||||
| "spark_sql_jar_path, *_ = glob.glob(f\"/usr/lib/spark/jars/spark-sql_*jar\")\n", | ||||||
| "spark_sql_jar = os.path.basename(spark_sql_jar_path)\n", | ||||||
| "scala_version = re.search(r'^spark-sql_(\\d+.\\d+)-.*\\.jar$', spark_sql_jar).group(1)\n", | ||||||
| "scala_version" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "e5c44f7f-5079-407b-9ecc-e9e862847590", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "## Create Spark Session" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "4f87e87a-a78a-4462-9a41-176412850cf1", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "spark = (\n", | ||||||
| " SparkSession.builder\n", | ||||||
| " .appName(\"NDS Example\") \\\n", | ||||||
| " .config(\"spark.rapids.sql.enabled\", \"true\") \\\n", | ||||||
| " .getOrCreate()\n", | ||||||
| ")\n", | ||||||
| "spark" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "d66b75e8-8074-4ce3-9456-a75749ccf3a2", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "# Verify SQL Acceleration on GPU can be enabled by checking the query plan" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "3342862a-30dd-4610-af7f-d3ef69af7038", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "spark.conf.set('spark.rapids.sql.enabled', True)\n", | ||||||
| "sum_df = spark.range(1000).selectExpr('SUM(*)')\n", | ||||||
| "sum_df.collect()\n", | ||||||
| "sum_df.explain()" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "9fd0b86e-4ed6-40a4-9b5f-926a982ccd95", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "# TPCDS App" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "0ec05e43-464c-4d98-b8d7-f775dd2196eb", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "# https://github.com/LucaCanali/Miscellaneous/tree/master/Performance_Testing/TPCDS_PySpark/tpcds_pyspark/Queries\n", | ||||||
| "# queries = None to run all (takes much longer)\n", | ||||||
| "queries = None\n", | ||||||
| "queries = [\n", | ||||||
| " 'q14a',\n", | ||||||
| " 'q14b',\n", | ||||||
| " 'q23a',\n", | ||||||
| " 'q23b',\n", | ||||||
| " # 'q24a',\n", | ||||||
| " # 'q24b',\n", | ||||||
| " # 'q88',\n", | ||||||
| "]\n", | ||||||
| "\n", | ||||||
| "demo_start = time.time()\n", | ||||||
| "tpcds = TPCDS(data_path='gs://GCS_PATH_TO_TPCDS_DATA/', num_runs=1, queries_repeat_times=1, queries=queries)" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "4ea56daf-3cd4-4f8a-ac4d-d19518b936ac", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "## Register TPC-DS tables before running queries" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "56becf51-525d-412f-b89b-f3d593441428", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "tpcds.map_tables() " | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "b56a0c59-582f-40c0-939f-d11c639776d6", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "## Measure Apache Spark GPU" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "e378bc6f-a26c-4f88-8765-f00ae6fa682d", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "tpcds.spark.conf.set('spark.rapids.sql.enabled', True)\n", | ||||||
| "%time tpcds.run_TPCDS()\n", | ||||||
| "gpu_grouped_results = tpcds.grouped_results_pdf.copy()\n", | ||||||
| "gpu_grouped_results" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "e33eb475-d7e1-4c79-9d4e-8cbbb072647f", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "## Measure Apache Spark CPU" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "972f3ab6-25f9-45db-b0d8-a2c743a45d9b", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "tpcds.spark.conf.set('spark.rapids.sql.enabled', False)\n", | ||||||
| "%time tpcds.run_TPCDS()\n", | ||||||
| "cpu_grouped_results = tpcds.grouped_results_pdf.copy()\n", | ||||||
| "cpu_grouped_results" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "markdown", | ||||||
| "id": "46b069a0-935d-4f1d-958b-1b4b1c156b85", | ||||||
| "metadata": {}, | ||||||
| "source": [ | ||||||
| "## Show Speedup Factors achieved by GPU" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "db48d7ef-00b9-4744-8cc1-c80225170c03", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "res = pd.merge(cpu_grouped_results, gpu_grouped_results, on='query', how='inner', suffixes=['_cpu', '_gpu'])\n", | ||||||
| "res['speedup'] = res['elapsedTime_cpu'] / res['elapsedTime_gpu']\n", | ||||||
| "res = res.sort_values(by='elapsedTime_cpu', ascending=False)\n", | ||||||
| "res" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "be7ee04a-b3fe-46ab-9c7e-cfd02592b65d", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "demo_dur = time.time() - demo_start\n", | ||||||
| "print(f\"CPU and GPU run took: {demo_dur=} seconds\")" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "c267bf53-0147-4a50-9656-ad369873e8f6", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "res.plot(title='TPC-DS query elapsedTime on CPU vs GPU (lower is better)', \n", | ||||||
| " kind='bar', x='query', y=['elapsedTime_cpu', 'elapsedTime_gpu'],\n", | ||||||
| " color=['blue', '#76B900'])" | ||||||
| ] | ||||||
| }, | ||||||
| { | ||||||
| "cell_type": "code", | ||||||
| "execution_count": null, | ||||||
| "id": "bd1ec37e-7570-48e3-887c-85e112141988", | ||||||
| "metadata": { | ||||||
| "tags": [] | ||||||
| }, | ||||||
| "outputs": [], | ||||||
| "source": [ | ||||||
| "res.plot(title='Speedup factors of TPC-DS queries on GPU', kind='bar', \n", | ||||||
| " x='query', y='speedup', color='#76B900')" | ||||||
| ] | ||||||
| } | ||||||
| ], | ||||||
| "metadata": { | ||||||
| "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": "3.10.2" | ||||||
| } | ||||||
| }, | ||||||
| "nbformat": 4, | ||||||
| "nbformat_minor": 5 | ||||||
| } | ||||||
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syntax: String formatting error -
sparkmeasure_versionvariable won't be interpolated inside single quotes. Use f-stringThere was a problem hiding this comment.
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@gerashegalov I think my GCP dataproc version notebook is matching your original notebook. So I think I should ignore this suggestion. How do you think?
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Yes the IPython magic already does variable substitution