From 57b255ab5aa9f4b3937ca2d1ae3c1fa820e66862 Mon Sep 17 00:00:00 2001 From: betolink Date: Wed, 2 Oct 2024 22:21:28 -0500 Subject: [PATCH] another incremental update, maybe we can finish this next week --- .gitignore | 3 +- README.md | 2 + _quarto.yml | 4 +- notebooks/environment.yml => environment.yml | 4 +- figures/figure-4.png | Bin 0 -> 89146 bytes figures/figure-5.png | Bin 104078 -> 86835 bytes ...arison.ipynb => all-access-patterns.ipynb} | 158 ++--- notebooks/benchmarks.csv | 44 ++ notebooks/byte_ranges.pkl.gz | Bin 0 -> 548505 bytes notebooks/h5logger.py | 140 +++++ notebooks/h5py-atl03-benchmarks.csv | 21 + notebooks/h5py-atl03.ipynb | 387 ++++++++++++ notebooks/h5py-atl06.ipynb | 312 ++++++++++ notebooks/h5py-benchmarks.csv | 61 ++ notebooks/h5py-default.png | Bin 0 -> 88090 bytes notebooks/h5py-original.csv | 11 + notebooks/h5py.ipynb | 308 ---------- notebooks/logs/.placeholder | 0 notebooks/{optimize.py => optimize-atl03.py} | 0 notebooks/optimize-atl06.py | 573 ++++++++++++++++++ notebooks/ros3.png | Bin 2396 -> 0 bytes notebooks/stats-default.png | Bin 0 -> 88810 bytes notebooks/stats.png | Bin 64678 -> 0 bytes paper.qmd | 30 +- 24 files changed, 1641 insertions(+), 417 deletions(-) rename notebooks/environment.yml => environment.yml (85%) create mode 100644 figures/figure-4.png rename notebooks/{portable-full-comparison.ipynb => all-access-patterns.ipynb} (66%) create mode 100644 notebooks/benchmarks.csv create mode 100644 notebooks/byte_ranges.pkl.gz create mode 100644 notebooks/h5logger.py create mode 100644 notebooks/h5py-atl03-benchmarks.csv create mode 100644 notebooks/h5py-atl03.ipynb create mode 100644 notebooks/h5py-atl06.ipynb create mode 100644 notebooks/h5py-benchmarks.csv create mode 100644 notebooks/h5py-default.png create mode 100644 notebooks/h5py-original.csv delete mode 100644 notebooks/h5py.ipynb delete mode 100644 notebooks/logs/.placeholder rename notebooks/{optimize.py => optimize-atl03.py} (100%) create mode 100755 notebooks/optimize-atl06.py delete mode 100644 notebooks/ros3.png create mode 100644 notebooks/stats-default.png delete mode 100644 notebooks/stats.png diff --git a/.gitignore b/.gitignore index c8c6efb..9b5e924 100644 --- a/.gitignore +++ b/.gitignore @@ -7,4 +7,5 @@ docs /notebooks/*files/ *.pyc __pycache__/ - +/site_libs/manuscript-notebook/ +.ipynb_checkpoints/ diff --git a/README.md b/README.md index 4ebd16d..3d86a4a 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,8 @@ This repository contains use case gathering, benchmarking, and prototyping work related to cloud-optimization of ICESat-2 data, with the overall goal of better enabling cloud access patterns for the ICESat-2 community. The audience of this repository includes ICESat-2 data providers and tool and service developers with experience and interest in developing solutions to improve the performance of ICESat-2 data in the cloud. +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/nsidc/cloud-optimized-icesat2/main?labpath=notebooks%2F) + ## Level of Support diff --git a/_quarto.yml b/_quarto.yml index d4673a6..3c8a5a7 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -2,9 +2,7 @@ project: type: manuscript render: - paper.qmd - - notebooks/portable-full-comparison.ipynb - - notebooks/h5py.ipynb - - optimize.py + - notebooks/h5py-atl03.ipynb output-dir: docs manuscript: article: paper.qmd diff --git a/notebooks/environment.yml b/environment.yml similarity index 85% rename from notebooks/environment.yml rename to environment.yml index 85dc00a..55a4fb2 100644 --- a/notebooks/environment.yml +++ b/environment.yml @@ -3,14 +3,16 @@ channels: - conda-forge dependencies: - python=3.11 - - 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s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\n", - "Processing format: rechunked-4mb, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-4mb.h5\n", - "Processing format: rechunked-8mb, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.h5\n", - "Processing format: rechunked-8mb-kerchunk, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.json\n", - "Processing format: original, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.h5\n", - "Processing format: original-kerchunk, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.json\n", - "Processing format: page-only-4mb, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-4mb.h5\n", - "Processing format: page-only-8mb, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-8mb.h5\n", - "Processing format: rechunked-4mb, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-4mb.h5\n", - "Processing format: rechunked-8mb, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.h5\n", - "Processing format: rechunked-8mb-kerchunk, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.json\n" + "Processing format: original, link: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01.h5\n" ] } ], @@ -308,24 +282,21 @@ " )\n", " return ds[variable]\n", "\n", + "\n", "for key, dataset in test_dict.items():\n", " for k, link in dataset[\"links\"].items():\n", + " # log_filename = f\"logs/fsspec-xarray-{key}-{k}-default.log\" \n", + " # # Create a new FileHandler for each iteration\n", + " # file_handler = logging.FileHandler(log_filename)\n", + " # file_handler.setLevel(logging.DEBUG)\n", "\n", - " log_filename = f\"logs/fsspec-xarray-{key}-{k}-default.log\"\n", - "\n", - " \n", - " # Create a new FileHandler for each iteration\n", - " file_handler = logging.FileHandler(log_filename)\n", - " file_handler.setLevel(logging.DEBUG)\n", - "\n", - " # Add the handler to the root logger\n", - " logging.getLogger().addHandler(file_handler)\n", + " # # Add the handler to the root logger\n", + " # logging.getLogger().addHandler(file_handler)\n", " print (f\"Processing format: {k}, link: {link}\")\n", " start = time.time()\n", " if \"kerchunk\" in k or link.endswith(\".json\"):\n", " ds = kerchunk_result(link, dataset[\"group\"], dataset[\"variable\"])\n", " data_mean = ds.mean()\n", - " \n", " elapsed = time.time() - start\n", " kerchunk_benchmarks.append(\n", " {\"tool\": \"kerchunk\",\n", @@ -354,8 +325,8 @@ " \"bytes_requested\": fo.cache.total_requested_bytes,\n", " \"shape\": ds[dataset[\"variable\"]].values.shape,\n", " \"mean\": data_mean})\n", - " logging.getLogger().removeHandler(file_handler)\n", - " file_handler.close()" + " # logging.getLogger().removeHandler(file_handler)\n", + " # file_handler.close()" ] }, { @@ -370,15 +341,24 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 59, "id": "a7aacf16-8276-4a50-b5af-3103056d73f4", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -386,24 +366,34 @@ } ], "source": [ - "df = pd.DataFrame.from_dict(kerchunk_benchmarks + regular_xarray_benchmarks)\n", + "df = pd.DataFrame.from_dict(kerchunk_benchmarks + regular_xarray_benchmarks + regular_h5py_benchmarks)\n", "\n", - "pivot_df = df.pivot_table(index=['tool','dataset'], columns=['format'], values='time', aggfunc='mean')\n", + "plt.figure(figsize=(20, 5)) \n", + "plt.style.use('ggplot')\n", + "# plt.style.use('grayscale')\n", "\n", - "# Plotting\n", - "pivot_df.plot(kind='bar', figsize=(15, 5))\n", + "pivot_df = df.pivot_table(index=['tool',], columns=['format', ], values='time', aggfunc='mean')\n", + "baseline_original = pivot_df['original'].max()\n", + "kerchunk_original = pivot_df['original-kerchunk'].max()\n", "\n", - "plt.title(\"Out of the box I/O parameters\", fontsize=10)\n", - "plt.suptitle('Cloud-optimized HDF5 access performance (less is better)', fontsize=14)\n", + "# Plotting\n", + "pivot_df.plot(kind='barh', figsize=(20, 8), fontsize=14, width=0.8)\n", "\n", - "plt.xlabel('Tool')\n", - "plt.ylabel('Time in seconds')\n", - "# plt.xticks(rotation=90)\n", - "plt.legend(title='Format')\n", - "# plt.grid(True)\n", + "plt.suptitle('Cloud-optimized HDF5 performance (less is better)', fontsize=18)\n", + "plt.title(\"Out of the box I/O parameters\", fontsize=14)\n", + "plt.xlabel('Mean Time (S)')\n", + "plt.ylabel('Library', fontsize=16)\n", "plt.xticks(rotation=0)\n", - "# plt.grid(True)\n", - "# plt.grid(axis='y', which='major', linestyle='-')\n", + "plt.legend(title='Format', fontsize=14)\n", + "plt.grid(False)\n", + "\n", + "\n", + "plt.axvline(x=baseline_original, color='red', linestyle='--', linewidth=2, label=f\"Baseline: {baseline_original:.2f}\")\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"stats.png\", transparent=True, dpi=150)\n", + "\n", "plt.show()" ] }, @@ -420,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 57, "id": "98c29558-de50-44af-87e9-074092fcd0ac", "metadata": { "tags": [] @@ -430,24 +420,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01.json\n", - "Unable to synchronously open file (file signature not found)\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.json\n", - "Unable to synchronously open file (file signature not found)\n", "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.json\n", - "Unable to synchronously open file (file signature not found)\n", "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-4mb.h5\n", "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-8mb.h5\n", "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.json\n", - "Unable to synchronously open file (file signature not found)\n" + "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.h5\n" ] } ], @@ -460,17 +437,8 @@ " if \"kerchunk\" in k or link.endswith(\".json\"):\n", " continue \n", " print (f\"Processing: {link}\")\n", - " log_filename = f\"logs/fsspec-h5py-{key}-{k}_default.log\"\n", - " \n", - " # Create a new FileHandler for each iteration\n", - " file_handler = logging.FileHandler(log_filename)\n", - " file_handler.setLevel(logging.DEBUG)\n", - "\n", - " # Add the handler to the root logger\n", - " logging.getLogger().addHandler(file_handler)\n", - " # this is mostly IO so no perf_counter is needed\n", " start = time.time()\n", - " fo = fs.open(link, mode=\"rb\")\n", + " fo = fs.open(link, cache_type=None, mode=\"rb\")\n", " with h5py.File(fo) as f:\n", " path = f\"{dataset['group']}/{dataset['variable']}\"\n", " data = f[path][:]\n", @@ -486,10 +454,6 @@ " \"shape\": data.shape,\n", " \"bytes_requested\": fo.cache.total_requested_bytes,\n", " \"mean\": data_mean})\n", - "\n", - " logging.getLogger().removeHandler(file_handler) \n", - " file_handler.close()\n", - " \n", " except Exception as e:\n", " print(e)" ] @@ -507,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 56, "id": "d8fa6dca-f408-4298-beca-f2839d4c3b67", "metadata": { "tags": [] @@ -515,7 +479,7 @@ "outputs": [ { "data": { - "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6)) \n", + "plt.style.use('ggplot')\n", + "\n", + "x_max = max(df[\"time\"])\n", + "pivot_df = df.pivot_table(index=['driver', 'optimized-read'], columns=['format', ], values='time', aggfunc='mean')\n", + "baseline_original = pivot_df['original'].max()\n", + "\n", + "# Plotting\n", + "pivot_df.plot(kind='barh', figsize=(20, 8), fontsize=14, width=0.5)\n", + "\n", + "plt.xlim(0, x_max)\n", + "\n", + "plt.suptitle('Cloud-optimized HDF5 performance (less is better)', fontsize=18)\n", + "# plt.title(\"Default I/O parameters (ATL03_20181120182818_08110112_006_02.h5: 7GB)\", fontsize=14)\n", + "plt.xlabel('Mean Time (S)')\n", + "plt.ylabel('Access Pattern', fontsize=16)\n", + "plt.xticks(rotation=0)\n", + "plt.legend(title='Format', fontsize=14, loc='upper right', bbox_to_anchor=(1.15, 1.015))\n", + "plt.grid(False)\n", + "\n", + "plt.axvline(x=baseline_original, color='red', linestyle='--', linewidth=2, label=f\"Baseline: {baseline_original:.2f}\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"h5py-default.png\", transparent=True, dpi=150)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c39fb04d-f35d-43e6-a506-26b5d7edc0c5", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(\"h5py-atl03-benchmarks.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "4b3fbc1c-25de-49a8-b58d-5f845cb7f53b", + "metadata": {}, + "source": [ + "The following cell is experimental, it plots the access pattern signature and the reads on a remote HDF5 file, optimized or not, we can only record the info using fsspec for now as ROS3 logging requires to compile h5py from scratch using custom flags. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2fb9f951-6a83-4e09-b3e0-0c00f67eea73", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.lines import Line2D\n", + "import matplotlib.patches as patches\n", + "import numpy as np\n", + "\n", + "fig, axs = plt.subplots(ncols=1, nrows=len(ranges), figsize=(18, 18), sharex=True)\n", + "\n", + "for index, range_stats in enumerate(ranges):\n", + " rdf = range_stats[\"ranges\"]\n", + " file_size = range_stats[\"file_size\"]\n", + "\n", + " bins = [0, 1 * 1024, 10 * 1024, 100 * 1024, np.inf]\n", + " colors = ['red', 'orange', 'purple', 'blue']\n", + " labels = ['< 1KB', '1KB - 10KB', '10KB - 100KB', '> 100KB']\n", + " rdf['color'] = pd.cut(rdf['size'], bins=bins, labels=colors)\n", + " rdf['label'] = pd.cut(rdf['size'], bins=bins, labels=labels)\n", + "\n", + " for i, row in rdf.iterrows():\n", + " rect = patches.Rectangle((row['start'], 0), row['end']-row['start'], 1, \n", + " linewidth=1, edgecolor=row['color'], facecolor=row['color'], alpha=0.3)\n", + " axs[index].add_patch(rect)\n", + "\n", + " axs[index].set_xlim(0, 1.1e9)\n", + " axs[index].set_ylim(0, 1)\n", + " axs[index].set_yticklabels(\"\")\n", + " axs[index].set_yticks([])\n", + " xticks = [\n", + " 1024*1024,\n", + " 10*1024*1024,\n", + " 100*1024*1024,\n", + " 1024*1024*1024,\n", + " 10*1024*1024*1024,\n", + " ]\n", + " xtick_labels = [\n", + " '1 MB',\n", + " '10 MB',\n", + " '100 MB',\n", + " '1 GB',\n", + " '10GB'\n", + " ]\n", + " axs[index].set_xticks(xticks)\n", + " axs[index].set_xticklabels(xtick_labels)\n", + "\n", + "# The last axis will retain the x-ticks\n", + "axs[-1].tick_params(axis='x', which='both', bottom=True, labelbottom=True)\n", + "\n", + "# Create custom legend handles\n", + "legend_elements = [Line2D([0], [0], color=color, lw=2, label=label) for color, label in zip(colors, labels)]\n", + "# plt.legend(handles=legend_elements, title=\"Request Size\", loc='upper right')\n", + "\n", + "handles, labels = axs[0].get_legend_handles_labels()\n", + "fig.legend(handles=legend_elements, loc='upper right')\n", + "\n", + "# plt.suptitle(f'ATL06 Read Pattern. File Size: {round(file_size/1e6,2)} MB, Total Requests:{len(rdf)}, Requests <10kb: {len(rdf[rdf[\"size\"]<10000])}', fontsize=18)\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1441c37f-60f6-42da-8c4f-a3399331d5e8", + "metadata": {}, + "outputs": [], + "source": [ + "# import holoviews as hv\n", + "# from holoviews.operation.datashader import rasterize\n", + "# import datashader as ds\n", + "# hv.extension(\"bokeh\")\n", + "\n", + "# xticks = [\n", + "# (1024*1024, '1MB'),\n", + "# (10*1024*1024, '10MB'),\n", + "# (100*1024*1024, '100MB'),\n", + "# (1024*1024*1024, '1GB'),\n", + "# (1024*1024*1024*10, '10GB')\n", + "# ]\n", + "\n", + "# # Function to create Rectangles\n", + "# def get_rectangles(ranges):\n", + "# rectangles = hv.Rectangles([]) # Start with an empty set of rectangles\n", + "# for i, row in ranges[0][\"ranges\"].iterrows():\n", + "# rect = (row['start'], 0, row['end'], 1) # Define rectangle bounds\n", + "# rectangles = rectangles * hv.Rectangles([rect]).opts(\n", + "# color=row['color'], \n", + "# line_color=row['color'], \n", + "# line_width=1\n", + "# )\n", + "# return rectangles\n", + "\n", + "# # Create an overlay of all rectangles\n", + "# rectangles = get_rectangles(ranges)\n", + "\n", + "# # Rasterize the plot using Datashader\n", + "# rasterized_rectangles = rasterize(rectangles, width=1200, height=300)\n", + "\n", + "# # Customize the plot with xticks and limits\n", + "# rasterized_rectangles.opts(\n", + "# xlabel='File Offset', ylabel='', xticks=xticks, \n", + "# xlim=(0, file_size), ylim=(0, 1), \n", + "# show_legend=True, legend_position='top_right'\n", + "# )\n" + ] + } + ], + "metadata": { + "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": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/h5py-atl06.ipynb b/notebooks/h5py-atl06.ipynb new file mode 100644 index 0000000..d17fdc8 --- /dev/null +++ b/notebooks/h5py-atl06.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "12725ef5-255d-4b78-b4db-c27717db0d25", + "metadata": {}, + "source": [ + "# Testing ROS3 and fsspec with h5py on cloud optimized HDF5 files \n", + "\n", + "This notebook tests both I/O drivers on cloud optimized HDF5 files from the ICESat-2 mission. \n", + "\n", + "> Note: The ROS3 driver is only available in the Conda distribution of h5py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ac69e2f-87bc-4253-acab-54e2b0fa0348", + "metadata": {}, + "outputs": [], + "source": [ + "import fsspec\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import h5py\n", + "\n", + "from dask.distributed import Client, LocalCluster\n", + "import dask.bag as db\n", + "from dask.diagnostics import ProgressBar\n", + "\n", + "from h5logger import parse_fsspec_log, read_file\n", + "\n", + "\n", + "for library in (h5py, fsspec):\n", + " print(f'{library.__name__} v{library.__version__}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87720bcc-7764-4c01-87cb-81d3eb1aa1b2", + "metadata": {}, + "outputs": [], + "source": [ + "test_dict = {\n", + " \"ATL06\": {\n", + " \"files\": {\n", + " \"original\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl06/ATL06_20200811143458_07210811_006_01.h5\",\n", + " \"page-only-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl06/ATL06_20200811143458_07210811_006_01_page_8mb.h5\",\n", + " \"rechunked-2mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl06/ATL06_20200811143458_07210811_006_01_rechunked-2mb-repacked.h5\",\n", + " \"rechunked-4mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl06/ATL06_20200811143458_07210811_006_01_rechunked-4mb-repacked.h5\",\n", + " \"rechunked-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl06/ATL06_20200811143458_07210811_006_01_rechunked-4mb-repacked.h5\",\n", + " },\n", + " \"variables\": [\"/gt1l/land_ice_segments/h_li\", \"/gt1l/land_ice_segments/latitude\", \"/gt1l/land_ice_segments/longitude\"]\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "410ddda8-8182-4cf5-a825-09f8ba7021c6", + "metadata": {}, + "outputs": [], + "source": [ + "if \"dask_client\" not in locals():\n", + " cluster = LocalCluster(threads_per_worker=1)\n", + " dask_client = Client(cluster)\n", + " dask_client" + ] + }, + { + "cell_type": "markdown", + "id": "823228b5-6700-4abb-8c28-4f8e69d76431", + "metadata": {}, + "source": [ + "The importance of caching and over-reads with remote files\n", + "\n", + "* **simple**: Caches entire files on disk.\n", + "* **blockcache**: Caches file data in chunks (blocks) on disk.\n", + "* **bytes**: Caches entire files in memory.\n", + "* **none**: Does not use caching on any request" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c5225bb-361b-4146-ad01-eff90b762d9f", + "metadata": {}, + "outputs": [], + "source": [ + "num_runs = 1\n", + "benchmarks = []\n", + "ranges = []\n", + "\n", + "#the real default is readahead with 5MB of block sizes, we disabled to test real times without caching anything\n", + "default_io_params = {\n", + " \"fsspec_params\": {\n", + " \"skip_instance_cache\": True,\n", + " \"cache_type\": \"none\"\n", + " # \"cache_type\": \"first\", # could be first, or cachiing the entier file with simple, \n", + " # \"block_size\": 4*1024*1024\n", + " },\n", + " \"h5py_params\": {}\n", + "}\n", + "\n", + "# we can fine-tune these\n", + "optimized_io_params ={\n", + " \"fsspec_params\": {\n", + " \"cache_type\": \"blockcache\", # could be first, or cachiing the entier file with simple, \n", + " \"block_size\": 8*1024*1024\n", + " },\n", + " \"h5py_params\" : {\n", + " \"page_buf_size\": 16*1024*1024,\n", + " \"rdcc_nbytes\": 4*1024*1024\n", + " }\n", + "}\n", + "\n", + "for optimized_read in [False, True]:\n", + " for driver in [\"ros3\", \"fsspec\"]:\n", + " for run in range(num_runs): # Running N times\n", + " for dataset_name, dataset_item in test_dict.items():\n", + " # Inner loop (parallelized)\n", + " urls = dataset_item[\"files\"].items() \n", + " benchmark_list = [(run, dataset_name, dataset_item[\"variables\"], flavor, url, optimized_read, driver, default_io_params, optimized_io_params) for flavor, url in urls]\n", + " bag = db.from_sequence(benchmark_list, npartitions=len(benchmark_list))\n", + " result = bag.map(read_file)\n", + " with ProgressBar():\n", + " results = result.compute()\n", + " for result in results:\n", + " if len(result[\"benchmark\"]):\n", + " benchmarks.append(result[\"benchmark\"])\n", + " # For now we can only log I/O with fsspec\n", + " if result[\"benchmark\"][\"driver\"] == \"fsspec\":\n", + " ranges.append(result[\"ranges\"])\n", + " \n", + "df = pd.DataFrame.from_dict(benchmarks)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c672ed19-0733-4ebd-8863-61b110b855fb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96f782cf-c2da-4523-ac19-6cef6b865579", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 6)) \n", + "plt.style.use('ggplot')\n", + "\n", + "x_max = max(df[\"time\"])\n", + "pivot_df = df.pivot_table(index=['driver', 'optimized-read'], columns=['format', ], values='time', aggfunc='mean')\n", + "baseline_original = pivot_df['original'].max()\n", + "\n", + "# Plotting\n", + "pivot_df.plot(kind='barh', figsize=(20, 8), fontsize=14, width=0.5)\n", + "\n", + "plt.xlim(0, x_max)\n", + "\n", + "plt.suptitle('Cloud-optimized HDF5 performance (less is better)', fontsize=18)\n", + "# plt.title(\"Default I/O parameters (ATL03_20181120182818_08110112_006_02.h5: 7GB)\", fontsize=14)\n", + "plt.xlabel('Mean Time (S)')\n", + "plt.ylabel('Access Pattern', fontsize=16)\n", + "plt.xticks(rotation=0)\n", + "plt.legend(title='Format', fontsize=14, loc='upper right', bbox_to_anchor=(1.15, 1.015))\n", + "plt.grid(False)\n", + "\n", + "plt.axvline(x=baseline_original, color='red', linestyle='--', linewidth=2, label=f\"Baseline: {baseline_original:.2f}\")\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"stats-default.png\", transparent=True, dpi=150)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c39fb04d-f35d-43e6-a506-26b5d7edc0c5", + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv(\"h5py-benchmarks.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "345d2b51-10d0-4db9-9810-38a6b13b19d8", + "metadata": {}, + "outputs": [], + "source": [ + "ranges[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fb9f951-6a83-4e09-b3e0-0c00f67eea73", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.lines import Line2D\n", + "import matplotlib.patches as patches\n", + "import numpy as np\n", + "\n", + "fig, axs = plt.subplots(ncols=1, nrows=len(ranges), figsize=(18, 18), sharex=True)\n", + "\n", + "for index, range_stats in enumerate(ranges):\n", + " rdf = range_stats[\"ranges\"]\n", + " file_size = range_stats[\"file_size\"]\n", + "\n", + " bins = [0, 1 * 1024, 10 * 1024, 100 * 1024, np.inf]\n", + " colors = ['red', 'orange', 'purple', 'blue']\n", + " labels = ['< 1KB', '1KB - 10KB', '10KB - 100KB', '> 100KB']\n", + " rdf['color'] = pd.cut(rdf['size'], bins=bins, labels=colors)\n", + " rdf['label'] = pd.cut(rdf['size'], bins=bins, labels=labels)\n", + "\n", + " for i, row in rdf.iterrows():\n", + " rect = patches.Rectangle((row['start'], 0), row['end']-row['start'], 1, \n", + " linewidth=1, edgecolor=row['color'], facecolor=row['color'], alpha=0.3)\n", + " axs[index].add_patch(rect)\n", + "\n", + " axs[index].set_xlim(0, 1.1e8)\n", + " axs[index].set_ylim(0, 1) \n", + " axs[index].set_yticklabels(\"\")\n", + " axs[index].set_yticks([])\n", + "\n", + "\n", + "# The last axis will retain the x-ticks\n", + "axs[-1].tick_params(axis='x', which='both', bottom=True, labelbottom=True)\n", + "\n", + "# Create custom legend handles\n", + "legend_elements = [Line2D([0], [0], color=color, lw=2, label=label) for color, label in zip(colors, labels)]\n", + "# plt.legend(handles=legend_elements, title=\"Request Size\", loc='upper right')\n", + "\n", + "handles, labels = axs[0].get_legend_handles_labels()\n", + "fig.legend(handles=legend_elements, loc='upper right')\n", + "\n", + "plt.suptitle(f'ATL06 Read Pattern. File Size: {round(file_size/1e6,2)} MB, Total Requests:{len(rdf)}, Requests <10kb: {len(rdf[rdf[\"size\"]<10000])}', fontsize=18)\n", + "plt.tight_layout()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1441c37f-60f6-42da-8c4f-a3399331d5e8", + "metadata": {}, + "outputs": [], + "source": [ + "# import holoviews as hv\n", + "# hv.extension(\"bokeh\")\n", + "\n", + "# xticks = [\n", + "# (1024, '1KB'),\n", + "# (1024*1024, '1MB'),\n", + "# (10*1024*1024, '10MB'),\n", + "# (100*1024*1024, '100MB'),\n", + "# (1024*1024*1024, '1GB')\n", + "# ]\n", + "\n", + "# rectangles = hv.Overlay()\n", + "\n", + "# for index, row in rdf.iterrows():\n", + "# # Create a rectangle for each row\n", + "# rect = hv.Rectangles((row['start'], 0, row['end'], 1), label=row['label']).opts(\n", + "# color=row['color'],\n", + "# line_color=row['color'],\n", + "# line_width=1,\n", + "# alpha=0.7 # Optional: Set transparency for better visibility\n", + "# )\n", + "# rectangles *= rect # Overlay the rectangle on top of the previous ones\n", + "\n", + "# # Customize and display the plot\n", + "# rectangles.opts(\n", + "# width=1200, height=300, xlim=(0, file_size), ylim=(0, 1),\n", + "# xlabel='File Offset', ylabel='', xticks=xticks, show_legend=True, legend_position='top_right'\n", + "# )" + ] + } + ], + "metadata": { + "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": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/h5py-benchmarks.csv b/notebooks/h5py-benchmarks.csv new file mode 100644 index 0000000..a534636 --- /dev/null +++ b/notebooks/h5py-benchmarks.csv @@ -0,0 +1,61 @@ 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"cell_type": "markdown", - "id": "12725ef5-255d-4b78-b4db-c27717db0d25", - "metadata": {}, - "source": [ - "# Testing ROS3 and fsspec with h5py on cloud optimized HDF5 files \n", - "\n", - "This notebook tests both I/O drivers on cloud optimized HDF5 files from the ICESat-2 mission. \n", - "\n", - "> Note: The ROS3 driver is only available in the Conda distribution of h5py" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3ac69e2f-87bc-4253-acab-54e2b0fa0348", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "xarray v2024.6.0\n", - "h5py v3.11.0\n", - "fsspec v2024.6.0\n", - "h5coro v0.0.6\n", - "zarr v2.18.2\n" - ] - } - ], - "source": [ - "import xarray as xr\n", - "import h5py\n", - "import fsspec\n", - "import s3fs\n", - "import logging\n", - "import re\n", - "import time\n", - "import numpy as np\n", - "import zarr\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "from h5coro import h5coro, s3driver, filedriver\n", - "driver = s3driver.S3Driver\n", - "\n", - "logger = logging.getLogger('fsspec')\n", - "logger.setLevel(logging.DEBUG)\n", - "\n", - "for library in (xr, h5py, fsspec, h5coro, zarr):\n", - " print(f'{library.__name__} v{library.__version__}')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "87720bcc-7764-4c01-87cb-81d3eb1aa1b2", - "metadata": {}, - "outputs": [], - "source": [ - "test_dict = {\n", - " \"1GB\": {\n", - " \"links\": {\n", - " \"original\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01.h5\",\n", - " \"original-kerchunk\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01.json\",\n", - " \"page-only-4mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\",\n", - " \"page-only-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\",\n", - " \"rechunked-4mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-4mb.h5\",\n", - " \"rechunked-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.h5\",\n", - " \"rechunked-8mb-kerchunk\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.json\",\n", - " \n", - " },\n", - " \"group\": \"/gt1l/heights\",\n", - " \"variable\": \"h_ph\"\n", - " },\n", - " \"7GB\": {\n", - " \"links\": {\n", - " \"original\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.h5\",\n", - " \"original-kerchunk\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.json\",\n", - " \"page-only-4mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-4mb.h5\",\n", - " \"page-only-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-8mb.h5\",\n", - " \"rechunked-4mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-4mb.h5\",\n", - " \"rechunked-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.h5\",\n", - " \"rechunked-8mb-kerchunk\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.json\",\n", - " },\n", - " \"group\": \"/gt1l/heights\",\n", - " \"variable\": \"h_ph\"\n", - " }\n", - "}\n", - "\n", - "fs = s3fs.S3FileSystem(anon=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "54990389-9e89-476f-928a-af4844caa595", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-8mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.h5\n" - ] - } - ], - "source": [ - "h5py_fsspec_benchmarks = []\n", - "\n", - "\n", - "for key, dataset in test_dict.items():\n", - " for k, link in dataset[\"links\"].items():\n", - " try:\n", - " if \"kerchunk\" in k or link.endswith(\".json\"):\n", - " continue \n", - " print (f\"Processing: {link}\")\n", - " log_filename = f\"logs/fsspec-h5py-{key}-{k}_default.log\"\n", - " \n", - " # Create a new FileHandler for each iteration\n", - " file_handler = logging.FileHandler(log_filename)\n", - " file_handler.setLevel(logging.DEBUG)\n", - "\n", - " # Add the handler to the root logger\n", - " logging.getLogger().addHandler(file_handler)\n", - " # this is mostly IO so no perf_counter is needed\n", - " io_params = {\n", - " \"fsspec_params\": {},\n", - " \"h5py_params\": {}\n", - " }\n", - " \n", - " if \"rechunked\" in link or \"page\" in link:\n", - " io_params ={\n", - " \"fsspec_params\": {\n", - " \"cache_type\": \"blockcache\",\n", - " \"block_size\": 8*1024*1024\n", - " },\n", - " \"h5py_params\" : {\n", - " \"page_buf_size\": 32*1024*1024,\n", - " \"rdcc_nbytes\": 4*1024*1024\n", - " }\n", - " }\n", - " \n", - " start = time.time()\n", - " fo = fs.open(link, mode=\"rb\", **io_params[\"fsspec_params\"])\n", - " with h5py.File(fo, **io_params[\"h5py_params\"]) as f:\n", - " path = f\"{dataset['group']}/{dataset['variable']}\"\n", - " data = f[path][:]\n", - " data_mean = data.mean()\n", - " elapsed = time.time() - start\n", - " h5py_fsspec_benchmarks.append(\n", - " {\"tool\": \"h5py-fsspec\",\n", - " \"dataset\": key,\n", - " \"cloud-aware\": \"yes\",\n", - " \"format\": k,\n", - " \"file\": link,\n", - " \"time\": elapsed,\n", - " \"shape\": data.shape,\n", - " \"bytes_requested\": fo.cache.total_requested_bytes,\n", - " \"mean\": data_mean})\n", - "\n", - " logging.getLogger().removeHandler(file_handler) \n", - " file_handler.close()\n", - " \n", - " except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4efd9495-5a7f-4502-a9d4-20c7494f16db", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20230618223036_13681901_006_01_rechunked-100k-page-8mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-8mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-4mb.h5\n", - "Processing: s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.h5\n" - ] - } - ], - "source": [ - "h5py_ros3_benchmarks = []\n", - "\n", - "for key, dataset in test_dict.items():\n", - " for k, link in dataset[\"links\"].items():\n", - " try:\n", - " if \"kerchunk\" in k or link.endswith(\".json\"):\n", - " continue\n", - " print (f\"Processing: {link}\")\n", - "\n", - " h5py_params = {\n", - " \"mode\": \"r\",\n", - " \"driver\": \"ros3\",\n", - " \"aws_region\": \"us-west-2\".encode(\"utf-8\"),\n", - " }\n", - "\n", - " \n", - " if \"rechunked\" in link or \"page\" in link:\n", - " h5py_params[\"page_buf_size\"] = 32*1024*1024\n", - " h5py_params[\"rdcc_nbytes\"] = 4*1024*1024\n", - " \n", - " start = time.time()\n", - " with h5py.File(link, **h5py_params) as f:\n", - " path = f\"{dataset['group']}/{dataset['variable']}\"\n", - " data = f[path][:]\n", - " data_mean = data.mean()\n", - " elapsed = time.time() - start\n", - " h5py_ros3_benchmarks.append(\n", - " {\"tool\": \"h5py-ros3\",\n", - " \"dataset\": key,\n", - " \"cloud-aware\": \"yes\",\n", - " \"format\": k,\n", - " \"file\": link,\n", - " \"time\": elapsed,\n", - " \"shape\": data.shape,\n", - " \"bytes_requested\": None, # metrics not easily available in ROS3\n", - " \"mean\": data_mean})\n", - "\n", - " \n", - " except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "96f782cf-c2da-4523-ac19-6cef6b865579", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pd.DataFrame.from_dict(h5py_ros3_benchmarks + h5py_fsspec_benchmarks)\n", - "\n", - "pivot_df = df.pivot_table(index=['tool','dataset'], columns=['format'], values='time', aggfunc='mean')\n", - "\n", - "# Plotting\n", - "pivot_df.plot(kind='bar', figsize=(10, 6))\n", - "\n", - "plt.suptitle('Cloud-optimized HDF5 I/O performance (less is better)', fontsize=14)\n", - "plt.title(\"Informed I/O parameters\", fontsize=10)\n", - "plt.xlabel('Tool')\n", - "plt.ylabel('Time (seconds)')\n", - "plt.xticks(rotation=0)\n", - "plt.legend(title='Format')\n", - "plt.tight_layout()\n", - "ax = plt.gca()\n", - "plt.grid(False)\n", - "# ax.yaxis.grid(True)\n", - "\n", - "plt.show()" - ] - } - ], - "metadata": { - "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": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/logs/.placeholder b/notebooks/logs/.placeholder deleted file mode 100644 index e69de29..0000000 diff --git a/notebooks/optimize.py b/notebooks/optimize-atl03.py similarity index 100% rename from notebooks/optimize.py rename to notebooks/optimize-atl03.py diff --git a/notebooks/optimize-atl06.py b/notebooks/optimize-atl06.py new file mode 100755 index 0000000..2add637 --- /dev/null +++ b/notebooks/optimize-atl06.py @@ -0,0 +1,573 @@ +#!/usr/bin/env pythonw +"""Creates a cloud-optimized version of an ICESat-2 HDF5 file.""" + +# Copyright (c) 2024, University of Washington +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, +# this list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the University of Washington nor the names of its +# contributors may be used to endorse or promote products derived from this +# software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE UNIVERSITY OF WASHINGTON AND CONTRIBUTORS +# “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED +# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE UNIVERSITY OF WASHINGTON OR +# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; +# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, +# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF +# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import os +import sys +import argparse +import traceback +import h5py +from h5py import h5p, h5f +import numpy as np +import time + +__version_ = "1.0.1" + + +# ----------------------------------------------------------------------------- +def h5_s2b(s: str) -> bytes: + """Converts a Python3 string to ASCII (bytes) for low-level calls. + + Args: + s: Python string + + Returns: + `s` as bytes + """ + try: + b = s.encode("ascii", "ignore") + except (UnicodeEncodeError, AttributeError): + b = s + return b + + +# ------------------------------------------------------------------------------ +def init(): + """Parses arguments""" + + parser = argparse.ArgumentParser( + description="""Re-writes ICESat-2 HDF5 files using cloud optimization techniques.""" + ) + + parser.add_argument("infile", help="Input HDF5 file.") + + parser.add_argument("outfile", help="Output HDF5 file.") + + parser.add_argument( + "-min_chunk", + "--min_chunk", + required=False, + type=int, + default=500, + help="""Do not resize any dataset with chunksize < this size (num elements).""", + ) + + parser.add_argument( + "-seg_chunk", + "--seg_chunk", + required=False, + type=int, + default=100000, + help="""New chunksize for segment (non_photon) datasets (num elements).""", + ) + + parser.add_argument( + "-ph_chunk", + "--ph_chunk", + required=False, + type=int, + default=1000000, + help="""New chunksize for any photon datasets (num elements).""", + ) + + parser.add_argument( + "-gzip", + "--gzip", + required=False, + type=int, + default=-1, + help="""New gzip level (if dataset is compressed; 0=disable, -1=do not change)""", + ) + + parser.add_argument( + "-fs_space_page", + "--fs_space_page", + dest="fs_space_page", + action="store_true", + required=False, + default=False, + help="Use H5F_FSPACE_STRATEGY_PAGE.", + ) + + parser.add_argument( + "-fs_page_size", + "--fs_page_size", + required=False, + type=int, + default=0.0, + help="""Filespace page size (bytes), 0=do not change (requires -fs_space_page).""", + ) + + parser.add_argument( + "-cache_slots", + "--cache_slots", + required=False, + type=int, + default=0, + help="""Number of cache slots, 0=do not change. (requires -fs_space_page)""", + ) + + parser.add_argument( + "-chunk_cache", + "--chunk_cache", + required=False, + type=int, + default=0.0, + help="""Size of the chunk cache (bytes), 0=do not change. (requires -fs_space_page)""", + ) + + parser.add_argument( + "-user_blocksize", + "--user_blocksize", + required=False, + type=int, + default=0.0, + help="""User block size (bytes), 0=do not change.""", + ) + + parser.add_argument( + "-meta_blocksize", + "--meta_blocksize", + required=False, + type=int, + default=0.0, + help="""Metadata block size (bytes), 0=do not change.""", + ) + + return parser.parse_args() + + +# ------------------------------------------------------------------------------ +def full_path(obj): + """Returns the full pathname of an object""" + + if obj.parent is not None and obj.name != "/": + return os.path.join(full_path(obj.parent), obj.name) + else: + return "" + + +# ------------------------------------------------------------------------------ +def print_stats_hdr(): + """Prints the stats header""" + + print( + " {0:40s} {1:7s} {2:10s} {3:10s} {4:7s} {5:10s} {6:10s}".format( + "name", + "src_zip", + "src_size", + "src_chunks", + "dst_zip", + "dst_nbytes", + "dst_chunks", + ) + ) + print( + " {0:40s} {1:7s} {2:10s} {3:10s} {4:7s} {5:10s} {6:10s}".format( + "----", + "-------", + "----------", + "----------", + "-------", + "----------", + "----------", + ) + ) + + +# ------------------------------------------------------------------------------ +def print_stats(stats: dict): + """Prints stats""" + + print( + " {0:40s} {1:7d} {2:10d} {3:10d} {4:7d} {5:10d} {6:10d}".format( + stats["name"], + stats["src_gzip"], + stats["src_size"], + stats["src_chunks"], + stats["dst_gzip"], + stats["dst_nbytes"], + stats["dst_chunks"], + ) + ) + + +# ------------------------------------------------------------------------------ +def copy_attributes(src, dest): + """Copies attributes from an input object to an output object""" + + if isinstance(src, h5py.Dataset): + for name, value in src.attrs.items(): + # Avoid attributes that belong to dimension scales and the datasets + # they are attached to + if name in ("CLASS", "NAME", "DIMENSION_LIST", "REFERENCE_LIST"): + continue + dest.attrs.create(name, data=value, dtype=value.dtype) + else: + for key in src.attrs.keys(): + dest.attrs.create(key, data=src.attrs[key], dtype=src.attrs[key].dtype) + + +# ------------------------------------------------------------------------------ +def copy_dataset(args, src, dst, name, photons=False): + """Copies a dataset from an input group to an output group""" + + stats = { + "name": name, + "src_gzip": 0, + "src_size": 0, + "src_nbytes": 0, + "src_chunks": 0, + "dst_gzip": 0, + "dst_nbytes": 0, + "dst_chunks": 0, + } + + src_dset = src[name] + chunks = src_dset.chunks + maxshape = src_dset.maxshape + compress = src_dset.compression + compress_opts = src_dset.compression_opts + + if src_dset.compression_opts is None: + stats["src_gzip"] = -1 + else: + stats["src_gzip"] = src_dset.compression_opts + + stats["src_size"] = src_dset.size + stats["src_nbytes"] = src_dset.size + + if src_dset.chunks is None: + stats["src_chunks"] = -1 + else: + stats["src_chunks"] = src_dset.chunks[0] + + # Optionally modify compression (for compressed datasets) + if compress == "gzip": + if args.gzip == 0: + compress = None + compress_opts = None + elif args.gzip > 0: + compress_opts = args.gzip + + # Optionally modify chunking (for select chunked datasets) + # + # This simplistic approach is probably not optimal for 2D or 3D + # datasets, but it's a start. + # + # The 'variable' dimensions for ICESat-2 data are the left-most. + # Do not change any chunksize less than args and do not change + # any chunksize that is equal to maxshape. + if maxshape[0] is None and chunks[0] is not None: + if chunks[0] > args.min_chunk: + chunks = list(chunks) + if photons: + chunks[0] = args.ph_chunk + else: + chunks[0] = args.seg_chunk + chunks = tuple(chunks) + + dst_dset = dst.create_dataset( + name, + shape=src_dset.shape, + dtype=src_dset.dtype, + maxshape=src_dset.maxshape, + chunks=chunks, + compression=compress, + compression_opts=compress_opts, + shuffle=src_dset.shuffle, + fillvalue=src_dset.fillvalue, + track_order=True, + ) + dst_dset.write_direct(np.array(src_dset)) + if src_dset.is_scale: + dst_dset.make_scale(h5py.h5ds.get_scale_name(src_dset.id)) + + copy_attributes(src_dset, dst_dset) + + if dst_dset.compression_opts is None: + stats["dst_gzip"] = -1 + else: + stats["dst_gzip"] = dst_dset.compression_opts + + stats["dst_size"] = dst_dset.size + stats["dst_nbytes"] = dst_dset.size + + if dst_dset.chunks is None: + stats["dst_chunks"] = -1 + else: + stats["dst_chunks"] = dst_dset.chunks[0] + + print_stats(stats) + + return stats + + +# ------------------------------------------------------------------------------ +def copy_group(args, src, dst, name): + """Copies a group from an input file to an output file + + Could do something with stats here. ie: + min/max within each group, etc. Not sure its worth it now. + """ + + src_grp = src[name] + if name != "/": + dst_grp = dst.create_group(name, track_order=True) + else: + dst_grp = dst + + # Copy group attributes + copy_attributes(src_grp, dst_grp) + + path = full_path(src_grp) + print(f"\nGroup: {path}\n") + print_stats_hdr() + + # This hack will only work for ATL03 + if name == "land_ice_segments": + photons = True + else: + photons = False + + # Copy child datasets + for item in src_grp.keys(): + if isinstance(src_grp[item], h5py.Dataset): + if item == "control": + continue + copy_dataset(args, src_grp, dst_grp, item, photons=photons) + + # Copy child groups + for item in src_grp.keys(): + if isinstance(src_grp[item], h5py.Group): + copy_group(args, src_grp, dst_grp, item) + + +# ------------------------------------------------------------------------------ +def optimize_file(args): + """Copies an ICESat-2 HDF5 file to a new cloud-optimized file. + + Uses the low-level h5py APID to open/query the input file and to create + the output file. I started using existing code that was written with an + old h5py and then realized (too late) new h5py supports all these things. + + So, then switches to the high-level APID to actually copy the data. + """ + + strategies = [ + "H5F_FSPACE_STRATEGY_FSM_AGGR", + "H5F_FSPACE_STRATEGY_PAGE", + "H5F_FSPACE_STRATEGY_AGGR", + "H5F_FSPACE_STRATEGY_NONE", + ] + + if not os.path.isfile(args.infile): + print(f"{args.infile} : file does not exist.") + sys.exit(3) + + # Open the input file + try: + in_fid = h5f.open(h5_s2b(args.infile), h5f.ACC_RDONLY) + except Exception as e: + print(f"Error opening input file - {e}") + sys.exit(3) + + # Get info from the file creation plist + try: + fcpl = in_fid.get_create_plist() + fs_strategy = fcpl.get_file_space_strategy() + fspace_page_size = fcpl.get_file_space_page_size() + user_blocksize = fcpl.get_userblock() + except Exception as e: + print(f"Error reading the file creation plist - {e}") + in_fid.close() + sys.exit(3) + + # Get info from the file access plist + try: + fapl = in_fid.get_access_plist() + cache_info = fapl.get_cache() + meta_blocksize = fapl.get_meta_block_size() + except Exception as e: + print(f"Error reading the file access plist - {e}") + in_fid.close() + sys.exit(3) + + cache_slots = cache_info[1] + chunk_cache = cache_info[2] + + strategy_str = strategies[fs_strategy[0]] + print("--") + print(f"Original fs_strategy = {strategy_str}") + print(f"Original fspace_page_size = {fspace_page_size}") + print(f"Original cache_slots = {cache_slots}") + print(f"Original chunk_cache = {chunk_cache}") + print(f"Original userblock_size = {user_blocksize}") + print(f"Original meta_blocksize = {meta_blocksize}") + print("--") + + # Apply any optional argument values + if args.fs_space_page: + fs_strategy = list(fs_strategy) + cache_info = list(cache_info) + fs_strategy[0] = 1 + if args.fs_page_size > 0.0: + fspace_page_size = args.fs_page_size + if args.chunk_cache > 0.0: + cache_info[2] = args.chunk_cache + if args.cache_slots > 0.0: + cache_info[1] = args.cache_slots + fs_strategy = tuple(fs_strategy) + cache_info = tuple(cache_info) + + if args.user_blocksize > 0.0: + user_blocksize = args.user_blocksize + if args.meta_blocksize > 0.0: + meta_blocksize = args.meta_blocksize + + # Create a new file create property list + try: + fcpl = h5p.create(h5p.FILE_CREATE) + fcpl.set_link_creation_order(h5p.CRT_ORDER_TRACKED | h5p.CRT_ORDER_INDEXED) + fcpl.set_attr_creation_order(h5p.CRT_ORDER_TRACKED | h5p.CRT_ORDER_INDEXED) + fcpl.set_file_space_strategy(fs_strategy[0], fs_strategy[1], fs_strategy[2]) + fcpl.set_file_space_page_size(fspace_page_size) + fcpl.set_userblock(user_blocksize) + except Exception as e: + print(f"Error creating the file creation plist - {e}") + in_fid.close() + sys.exit(3) + + # Create a new file access property list + try: + fapl = h5p.create(h5p.FILE_ACCESS) + fapl.set_cache(cache_info[0], cache_info[1], cache_info[2], cache_info[3]) + fapl.set_meta_block_size(meta_blocksize) + except Exception as e: + print(f"Error creating the file access plist - {e}") + in_fid.close() + sys.exit(3) + + # Create the output file + try: + out_fid = h5f.create(h5_s2b(args.outfile), h5f.ACC_TRUNC, fcpl=fcpl, fapl=fapl) + except Exception as e: + print(f"Error creating the output file - {e}") + sys.exit(3) + + # Get info from the file creation plist + try: + fcpl = out_fid.get_create_plist() + fs_strategy = fcpl.get_file_space_strategy() + fspace_page_size = fcpl.get_file_space_page_size() + user_blocksize = fcpl.get_userblock() + except Exception as e: + print(f"Error reading the file creation plist - {e}") + in_fid.close() + out_fid.close() + sys.exit(3) + + # Get info from the file access plist + try: + fapl = out_fid.get_access_plist() + cache_info = fapl.get_cache() + meta_blocksize = fapl.get_meta_block_size() + except Exception as e: + print(f"Error reading the file access plist - {e}") + in_fid.close() + out_fid.close() + sys.exit(3) + + cache_slots = cache_info[1] + chunk_cache = cache_info[2] + + strategy_str = strategies[fs_strategy[0]] + print("--") + print(f"Revised fs_strategy = {strategy_str}") + print(f"Revised fspace_page_size = {fspace_page_size}") + print(f"Revised cache_slots = {cache_slots}") + print(f"Revised chunk_cache = {chunk_cache}") + print(f"Revised userblock_size = {user_blocksize}") + print(f"Revised meta_blocksize = {meta_blocksize}") + print("--") + + # Close the files so we can reopen them in the high-level APID + in_fid.close() + out_fid.close() + + # Read from input file and write to output file + try: + beg_time = time.time() + + with h5py.File(args.infile, "r") as src, h5py.File(args.outfile, "r+") as dst: + copy_group(args, src, dst, "/") + + # Attach dimension scales in the output file exactly how it is in + # the input file + def attach(name, obj): + if isinstance(obj, h5py.Dataset): + for idx, dim in enumerate(obj.dims, start=0): + for scale in dim.values(): + print(f"Dimscale {scale.name} attached to {obj.name} at dim #{idx}") + dst[obj.name].dims[idx].attach_scale(dst[scale.name]) + + src.visititems(attach) + + end_time = time.time() + + except Exception as e: + print(f"Error reading/writing data - {e}") + print(traceback.format_exc()) + in_fid.close() + out_fid.close() + sys.exit(3) + + # Print file sizes/change + src_stats = os.stat(args.infile) + dst_stats = os.stat(args.outfile) + src_mb = src_stats.st_size / (1024 * 1024) + dst_mb = dst_stats.st_size / (1024 * 1024) + schange = (dst_mb / src_mb) * 100.0 + print("--") + print("Total read/write time=", end_time - beg_time) + print(f"File Sizes (MiB): input={src_mb}, output={dst_mb}, change={schange}%") + print("--") + + +# ------------------------------------------------------------------------------ +def run(): + args = init() + optimize_file(args) + + +# ------------------------------------------------------------------------------- +if __name__ == "__main__": + run() diff --git 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z!#YmnvqGc9UAFK=_gC^PyLR-kC#JmhE6t=Eq;$5J+C9&a#kdewWr=)u{;Ax;*;})D z5~-ea!zn^Lux~o40zcLY;=;;J%56&t-?XB)ty`?hV-uV2p>|V+AJ*6^(BNi3`__u$ zEwJsAW3lT4eb{USI(cVn)pq()7m)*c0}2!04}#$3=pN$00f*|^(1mh#Z~(htHc(6p z7WZ8J!zg4lej=bC-2W)Y<_x!uf~beq^U=tLqQdD?Q0K%;d9mYGD;yiw`nZ^qza@RvhtAkWE3-R+&r!9bupQZf^pG QW~w=?Ih{zWJ!HJ=PpZ-&G5`Po literal 0 HcmV?d00001 diff --git a/notebooks/h5logger.py b/notebooks/h5logger.py new file mode 100644 index 0000000..a3e5757 --- /dev/null +++ b/notebooks/h5logger.py @@ -0,0 +1,140 @@ +import re +import numpy as np +import pandas as pd +import logging +import s3fs +import fsspec +import time +import h5py +from datetime import datetime +from uuid import uuid4 + + +def parse_fsspec_log(log_path): + """ + This method only parses fsspec logs that have a FileSize: attached to them. + """ + head_line = re.compile('\s*(read: 0 - \d+)') + fsize_line = re.compile('FileSize: (\d+)') + # range_line = re.compile('\s* read: (?P[0-9]+) - (?P[0-9]+)') + range_line = re.compile('\s* read: (?P[0-9]+) - (?P[0-9]+)(?:\s*,\s*.*?:\s*(?P[0-9]+)\s*hits,\s*(?P[0-9]+)\s*misses)?') + + + + ranges = list() + with open(log_path) as logtxt: + for line in logtxt: + if head_line.match(line): + break + else: + raise RuntimeError('HEAD line not found in the log file') + + for line in logtxt: + match = fsize_line.match(line) + if match: + fsize = int(match.group(1)) + break + else: + raise RuntimeError('FILESIZE line not found in the log file') + + logtxt.seek(0) + for line in logtxt: + match = range_line.match(line) + if match: + start=int(match.group(1)) + end=int(match.group(2)) + hits=match.group(3) + missed=match.group(4) + rsize=end-start+1 + + ranges.append({"start": start, "end": end, "size": rsize, "hits": hits, "missed": missed}) + + df = pd.DataFrame(ranges, columns=['start', 'end', 'size', 'hits', 'missed']) + return df + +def read_file(info): + h5py_fsspec_benchmarks = {} + ranges = None + file_size = None + block_size = None + iteration, dataset, variables, flavor, url, optimized_read, driver, default_io_params, optimized_io_params = info + if url.endswith(".json"): + return {} + io_params = default_io_params + if optimized_read: + if "rechunked" in url or "page" in url: + optimized = "yes" + print(f"Reading: {url} with optimized I/O parameters") + io_params = optimized_io_params + block_size = io_params["fsspec_params"]["block_size"] + else: + # we cannot read the original file with optimized parameters + optimized = "no" + print(f"Reading: {url} with default parameters") + else: + optimized = "no" + print(f"Reading: {url} with default parameters") + cache_type = io_params["fsspec_params"]["cache_type"] + + # this is mostly IO so no perf_counter is needed + start = time.time() + if driver == "fsspec": + fs = s3fs.S3FileSystem(anon=True) + logger = logging.getLogger('fsspec') + logger.setLevel(logging.DEBUG) + file_info = fs.info(url) + file_size = file_info['size'] + file_name = url.split("/")[-1] + current_time = datetime.now() + formatted_time = current_time.strftime(f"%Y-%m-%d_%H-%M-%S-{uuid4()}") + log_filename = f"logs/fsspec-{file_name}-{driver}-{optimized}-{formatted_time}.log" + # Create a new FileHandler for each iteration + file_handler = logging.FileHandler(log_filename) + file_handler.setLevel(logging.DEBUG) + # Add the handler to the root logger + logging.getLogger().addHandler(file_handler) + with fs.open(url, mode="rb", **io_params["fsspec_params"]) as fo: + with h5py.File(fo, **io_params["h5py_params"]) as f: + for variable in variables: + data = f[variable][:] + data_mean = data.mean() + req_bytes = fo.cache.total_requested_bytes + logger.debug(f"FileSize: {file_size}") + logging.getLogger().removeHandler(file_handler) + file_handler.close() + ranges = parse_fsspec_log(log_filename) + else: + cloud_params = { + "mode": "r", + "driver": "ros3", + "aws_region": "us-west-2".encode("utf-8") + } + with h5py.File(url, **io_params["h5py_params"], **cloud_params) as f: + for variable in variables: + data = f[variable][:] + data_mean = data.mean() + req_bytes = None # not available + elapsed = time.time() - start + return { + "benchmark": { + "iteration": iteration, + "library": "h5py", + "driver": driver, + "dataset": dataset, + "optimized-read": optimized, + "format": flavor, + "file": url, + "time": elapsed, + "shape": data.shape, + "bytes_requested": req_bytes, + "mean": data_mean}, + "ranges": { + "file": url, + "optimized-read": optimized, + "cache_type": cache_type, + "block_size": block_size, + "time": time, + "bytes_requested": req_bytes, + "file_size": file_size, + "ranges": ranges} + } diff --git a/notebooks/h5py-atl03-benchmarks.csv b/notebooks/h5py-atl03-benchmarks.csv new file mode 100644 index 0000000..5b8b471 --- /dev/null +++ b/notebooks/h5py-atl03-benchmarks.csv @@ -0,0 +1,21 @@ +,iteration,library,driver,dataset,optimized-read,format,file,time,shape,bytes_requested,mean 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file mode 100644 index 0000000..ffce00d --- /dev/null +++ b/notebooks/h5py-atl03.ipynb @@ -0,0 +1,387 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "12725ef5-255d-4b78-b4db-c27717db0d25", + "metadata": {}, + "source": [ + "# Testing access times to cloud optimized HDF5 files with the fsspec and ROS3 drivers.\n", + "\n", + "This notebook tests both I/O drivers on cloud optimized HDF5 files from the ICESat-2 mission. \n", + "\n", + "> Note: The ROS3 driver is only available in the Conda distribution of h5py" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3ac69e2f-87bc-4253-acab-54e2b0fa0348", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h5py v3.11.0\n", + "fsspec v2024.9.0\n" + ] + } + ], + "source": [ + "import fsspec\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import h5py\n", + "\n", + "from dask.distributed import Client, LocalCluster\n", + "import dask.bag as db\n", + "from dask.diagnostics import ProgressBar\n", + "\n", + "from h5logger import parse_fsspec_log, read_file\n", + "\n", + "\n", + "for library in (h5py, fsspec):\n", + " print(f'{library.__name__} v{library.__version__}')" + ] + }, + { + "cell_type": "markdown", + "id": "94d6203f-2af2-4c64-9b4f-2354c480bd4c", + "metadata": {}, + "source": [ + "The folowing dictionary is generic enough that we can use it for different datasets, we only require file URLS and the variables we want to read from them using h5py. \n", + "The tests take for granted that the original file has no cloud optimizations and can not be read using cloud optimized patterns, the next check is to verify if the keywords \"paged\" or \"rechunked\" are present in the file name, it's presumed to be cloud optimized. \n", + "\n", + "This notebook uses dask to speed up the testing, we issue requests to each file at the same time, first looping using default parameters, this is to learn what happens when we access the different flavors without knowing that some are cloud optimized. Then we use optimized I/O parameters, we do the same for both fsspec and the HDF5 native driver ROS3. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "87720bcc-7764-4c01-87cb-81d3eb1aa1b2", + "metadata": {}, + "outputs": [], + "source": [ + "test_dict = {\n", + " \"ATL03-7GB\": {\n", + " \"files\": {\n", + " \"original\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02.h5\",\n", + " \"page-only-4mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-4mb.h5\",\n", + " \"page-only-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02-page-only-8mb.h5\",\n", + " \"rechunked-4mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-4mb.h5\",\n", + " \"rechunked-8mb\": \"s3://its-live-data/test-space/cloud-experiments/h5cloud/atl03/ATL03_20181120182818_08110112_006_02_rechunked-100k-page-8mb.h5\",\n", + " },\n", + " \"variables\": [\"/gt1l/heights/h_ph\", \"/gt1l/heights/lat_ph\", \"/gt1l/heights/lon_ph\"]\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "410ddda8-8182-4cf5-a825-09f8ba7021c6", + "metadata": {}, + "outputs": [], + "source": [ + "# If there is a dask_client cluster let's not create new ones.\n", + "if \"dask_client\" not in locals():\n", + " cluster = LocalCluster(threads_per_worker=1)\n", + " dask_client = Client(cluster)\n", + " dask_client" + ] + }, + { + "cell_type": "markdown", + "id": "823228b5-6700-4abb-8c28-4f8e69d76431", + "metadata": {}, + "source": [ + "The importance of caching and over-reads with remote files\n", + "\n", + "* **simple**: Caches entire files on disk.\n", + "* **blockcache**: Caches file data in chunks (blocks) on memory.\n", + "* **bytes**: Caches entire files in memory.\n", + "* **none**: Does not use caching on any request" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3c5225bb-361b-4146-ad01-eff90b762d9f", + "metadata": {}, + "outputs": [], + "source": [ + "num_runs = 1\n", + "benchmarks = []\n", + "ranges = []\n", + "\n", + "#the real default is readahead with 5MB of block sizes, we disabled to test real times without caching anything\n", + "default_io_params = {\n", + " \"fsspec_params\": {\n", + " \"skip_instance_cache\": True,\n", + " \"cache_type\": \"none\"\n", + " # \"cache_type\": \"first\", # could be first, or cachiing the entier file with simple, \n", + " # \"block_size\": 4*1024*1024\n", + " },\n", + " \"h5py_params\": {}\n", + "}\n", + "\n", + "# we can fine-tune these\n", + "optimized_io_params ={\n", + " \"fsspec_params\": {\n", + " \"cache_type\": \"blockcache\", # could be first, or cachiing the entier file with simple, \n", + " \"block_size\": 8*1024*1024\n", + " },\n", + " \"h5py_params\" : {\n", + " \"page_buf_size\": 16*1024*1024,\n", + " \"rdcc_nbytes\": 4*1024*1024\n", + " }\n", + "}\n", + "\n", + "for optimized_read in [False, True]:\n", + " for driver in [\"ros3\", \"fsspec\"]:\n", + " for run in range(num_runs): # Running N times\n", + " for dataset_name, dataset_item in test_dict.items():\n", + " # Inner loop (parallelized)\n", + " urls = dataset_item[\"files\"].items() \n", + " benchmark_list = [(run, dataset_name, dataset_item[\"variables\"], flavor, url, optimized_read, driver, default_io_params, optimized_io_params) for flavor, url in urls]\n", + " bag = db.from_sequence(benchmark_list, npartitions=len(benchmark_list))\n", + " result = bag.map(read_file)\n", + " with ProgressBar():\n", + " results = result.compute()\n", + " for result in results:\n", + " if len(result[\"benchmark\"]):\n", + " benchmarks.append(result[\"benchmark\"])\n", + " # For now we can only log I/O with fsspec\n", + " if result[\"benchmark\"][\"driver\"] == \"fsspec\":\n", + " ranges.append(result[\"ranges\"])\n", + " \n", + "df = pd.DataFrame.from_dict(benchmarks)" + ] + }, + { + "cell_type": "markdown", + "id": "c9c48844-8ba6-456f-b6a7-05ad782ea07c", + "metadata": {}, + "source": [ + "Now that we have collected the information we need we are going to plot how the drivers and the parameters performed.\n", + "The \"baseline\" is what HDF5 and fsspec do when they don't use cloud optimized parameters on a non-optimized file. Here is when we see the worst performance due te many small serial request to cloud storage. Presumably, the best case would be when we use optimized I/O that aligns to the scheme used for a cloud optimized file. E.G. if a file was optimized using paged aggregation and page sizes of 4MB, the best performance should be when we tell the I/O driver that we should read 4MB at the time. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "96f782cf-c2da-4523-ac19-6cef6b865579", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "