From 660521e457f63396b05643f9f832ed90f54dfb84 Mon Sep 17 00:00:00 2001 From: Filipe Fernandes Date: Wed, 7 Oct 2026 12:24:11 -0300 Subject: [PATCH 1/6] add new packages --- .binder/environment-python_and_r.yml | 2 ++ .binder/environment.yml | 2 ++ 2 files changed, 4 insertions(+) diff --git a/.binder/environment-python_and_r.yml b/.binder/environment-python_and_r.yml index cac69d32..f855291a 100644 --- a/.binder/environment-python_and_r.yml +++ b/.binder/environment-python_and_r.yml @@ -27,6 +27,7 @@ dependencies: - jupyter - lxml - matplotlib-base + - mlcroissant - nbclassic - netcdf4 - numpy @@ -36,6 +37,7 @@ dependencies: - palettable - pandas - pocean-core>=3.3.0 + - prometheus_client - pyobis - pysgrid - pyugrid diff --git a/.binder/environment.yml b/.binder/environment.yml index 868e266f..4cb41346 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -27,6 +27,7 @@ dependencies: - jupyter - lxml - matplotlib-base + - mlcroissant - nbclassic - netcdf4 - numpy @@ -36,6 +37,7 @@ dependencies: - palettable - pandas - pocean-core>=3.3.0 + - prometheus_client - pyobis - pysgrid - pyugrid From 6712eb6dfe754403feab5d740658c0ce1d86b001 Mon Sep 17 00:00:00 2001 From: Filipe Fernandes Date: Wed, 7 Oct 2026 12:24:17 -0300 Subject: [PATCH 2/6] relock --- .binder/conda-lock.yml | 19518 +++++++++++++++------------------------ 1 file changed, 7199 insertions(+), 12319 deletions(-) diff --git a/.binder/conda-lock.yml b/.binder/conda-lock.yml index ed4e555f..2e3a5b40 100644 --- a/.binder/conda-lock.yml +++ b/.binder/conda-lock.yml @@ -9,14 +9,14 @@ # To update a single package to the latest version compatible with the version constraints in the source: # conda-lock lock --lockfile conda-lock.yml --update PACKAGE # To re-solve the entire environment, e.g. after changing a version constraint in the source file: -# conda-lock -f environment-python_and_r.yml --lockfile conda-lock.yml +# conda-lock -f environment-python_and_r.yml -f environment.yml --lockfile conda-lock.yml version: 1 metadata: content_hash: - 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name: _python_abi3_support @@ -85,10 +73,10 @@ package: dependencies: cpython: '' python-gil: '' - url: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda + url: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_3.conda hash: - md5: aaa2a381ccc56eac91d63b6c1240312f - sha256: a3967b937b9abf0f2a99f3173fa4630293979bd1644709d89580e7c62a544661 + md5: 3845f3d75991bae0fb90884662f4327c + sha256: 2a7204314663eeda5dec482a956f0e2eaf289bd5b9953eaaaad0e81aa64638f2 category: main optional: false - name: _python_abi3_support @@ -98,10 +86,10 @@ package: dependencies: cpython: '' python-gil: '' - url: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda + url: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_3.conda hash: - md5: aaa2a381ccc56eac91d63b6c1240312f - sha256: a3967b937b9abf0f2a99f3173fa4630293979bd1644709d89580e7c62a544661 + md5: 3845f3d75991bae0fb90884662f4327c + sha256: 2a7204314663eeda5dec482a956f0e2eaf289bd5b9953eaaaad0e81aa64638f2 category: main optional: false - 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url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hf451053_7.conda hash: - md5: ab136e4c34e97f34fb621d2592a393d8 - sha256: 9485ba49e8f47d2b597dd399e88f4802e100851b27c21d7525625b0b4025a5d9 + md5: 4ec2684c73812cc2c3d78379384a39cc + sha256: da867f5092eb0cb746d353694f0098031fd9817a4ce7d5743121209ae0f406ca category: main optional: false - name: zstd @@ -31456,13 +26336,13 @@ package: manager: conda platform: win-64 dependencies: - libzlib: '>=1.3.1,<2.0a0' + libzlib: '>=1.3.2,<2.0a0' ucrt: '>=10.0.20348.0' vc: '>=14.3,<15' vc14_runtime: '>=14.44.35208' - url: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda + url: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_7.conda hash: - md5: 053b84beec00b71ea8ff7a4f84b55207 - sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2 + md5: e4ac308c39d6d0e131154976da67cf3b + sha256: ca7daae4f218a11fab82cc2857f0ea518ec3f46acec60490485347a4c22c6b3e category: main optional: false From 03470dfbddb3291ea2d6614637f198657fa20a19 Mon Sep 17 00:00:00 2001 From: Filipe Fernandes Date: Wed, 7 Oct 2026 12:24:35 -0300 Subject: [PATCH 3/6] Showcase latest ERDDAP features --- .../2026-10-10-ERDDAP_latest_features.ipynb | 677 ++++++++++++++++++ 1 file changed, 677 insertions(+) create mode 100644 jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb diff --git a/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb b/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb new file mode 100644 index 00000000..d8388134 --- /dev/null +++ b/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb @@ -0,0 +1,677 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import subprocess\n", + "import sys\n", + "\n", + "COLAB = \"google.colab\" in sys.modules\n", + "\n", + "\n", + "def _install(package):\n", + " if COLAB:\n", + " ans = input(f\"Install { package }? [y/n]:\")\n", + " if ans.lower() in [\"y\", \"yes\"]:\n", + " subprocess.check_call(\n", + " [sys.executable, \"-m\", \"pip\", \"install\", \"--quiet\", package]\n", + " )\n", + " print(f\"{ package } installed!\")\n", + "\n", + "\n", + "def _colab_install_missing_deps(deps):\n", + " import importlib\n", + "\n", + " for dep in deps:\n", + " if importlib.util.find_spec(dep) is None:\n", + " if dep == \"iris\":\n", + " dep = \"scitools-iris\"\n", + " _install(dep)\n", + "\n", + "\n", + "deps = [\"erddapy\"]\n", + "_colab_install_missing_deps(deps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploring ERDDAP latest features\n", + "\n", + "\n", + "Created: 2026-10-10\n", + "\n", + "\n", + "ERDDAP 2.30.0 introduced some interesting new features:\n", + "\n", + "- A metrics response to help ERDDAP maintainers and users get information on the server itself;\n", + "- A croissant metadata spec response to facilitate dataset interoperability with Machine Learning (ML);\n", + "- Support for the table optimized `parquet` format;\n", + "- Message Queuing Telemetry Transport Technical (MQTT) for interaction with IoT;\n", + "\n", + "\n", + "https://docs.mlcommons.org/croissant/docs/croissant-spec-1.0.html\n", + "\n", + "https://mqtt.org/faq/\n", + "\n", + "\n", + "Let's start by asking the server version to be sure we have one that has the new features" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('docker', '2.30.0')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import requests\n", + "from prometheus_client.parser import text_string_to_metric_families\n", + "\n", + "\n", + "\n", + "def metrics_erddap_build_info(url):\n", + " metrics = f\"{url}/metrics\"\n", + " r = requests.get(metrics, allow_redirects=True, timeout=10)\n", + " r.raise_for_status()\n", + "\n", + " for family in text_string_to_metric_families(r.text):\n", + " if family.name == \"ERDDAP_build_info\":\n", + " data = family.samples[0].labels\n", + " deployment_info = data[\"deployment_info\"]\n", + " version = data[\"version_full\"]\n", + " return deployment_info, version\n", + "\n", + "\n", + "\n", + "server = \"https://gliders.ioos.us/erddap\"\n", + "\n", + "metrics_erddap_build_info(server)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! We got a docker deployment of the minimun required version. Supposed you are training a ML model for QA/QC of glider data and your model requires the data to be `croissant` spec compliant." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from erddapy import ERDDAP\n", + "from erddapy.core.url import urlopen\n", + "\n", + "\n", + "e = ERDDAP(server=server, protocol=\"tabledap\")\n", + "\n", + "e.dataset_id = \"amelia-20180501T0000\"\n", + "e.response = \"croissant\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "\n", + "jsonld = urlopen(e.get_download_url())\n", + "metadata = json.loads(jsonld.read().decode())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metadata is rich and hard to read as a human, let's check if it passes croissant validation to ensure it can be fed in ML workflows." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "\n", + "with open(\"metadata.json\", \"w\") as f:\n", + " content = json.dumps(metadata, indent=2)\n", + " f.write(content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When loading with the `mlcroissant` we can check it for compliance explorer this format a bit further." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:absl:WARNING: The JSON-LD `@context` is not standard. Refer to the official @context (e.g., from the example datasets in https://github.com/mlcommons/croissant/tree/main/datasets/1.0). The different keys are: {'samplingRate', 'equivalentProperty'}\n", + "WARNING:absl:Found the following 2 warning(s) during the validation:\n", + " - [Metadata(amelia-20180501T0000)] Property \"http://mlcommons.org/croissant/citeAs\" is recommended, but does not exist.\n", + " - [Metadata(amelia-20180501T0000)] Property \"https://schema.org/version\" is recommended, but does not exist.\n" + ] + } + ], + "source": [ + "import mlcroissant\n", + "\n", + "\n", + "dataset = mlcroissant.Dataset(jsonld=\"metadata.json\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are some warnings, but no errors and the dataset is loaded. Let's check what can we do with it. We can access them again via the `metada.issues` object." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found the following 2 warning(s) during the validation:\n", + " - [Metadata(amelia-20180501T0000)] Property \"http://mlcommons.org/croissant/citeAs\" is recommended, but does not exist.\n", + " - [Metadata(amelia-20180501T0000)] Property \"https://schema.org/version\" is recommended, but does not exist.\n" + ] + } + ], + "source": [ + "print(dataset.metadata.issues.report())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ERDDAP's `croissant` is metadata only. We need to attach the data whe passing it to a ML workflow. Implementing a ML workflow from here should be easier than writing your own data loaders/converters, etc.\n", + "\n", + "\n", + "Now, imagine you do have a ML worklfow that downloads many files. While setting a ML workflow is beyond the scope of this notebook, we can show you how to quickly obtain `parquet` files that are lightweight and easy to load in many ML workflows." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trajectorywmo_idprofile_idtimelatitudelongitudedepthconductivityconductivity_qcdensity...salinity_qctemperaturetemperature_qctime_qctime_uvtime_uv_qcuu_qcvv_qc
0amelia-20180501T0000480193412018-05-01 14:58:48.317000+00:0037.539598-74.85649225.3500003.44320NaN1025.799805...NaN8.0939NaNNaNNaNNaNNaNNaNNaNNaN
1amelia-20180501T0000480193412018-05-01 14:58:48.317000+00:0037.539598-74.85649223.9500013.44254NaN1025.776001...NaN8.1080NaNNaNNaNNaNNaNNaNNaNNaN
2amelia-20180501T0000480193412018-05-01 14:58:48.317000+00:0037.539598-74.85649222.2199993.46240NaN1025.745361...NaN8.3210NaNNaNNaNNaNNaNNaNNaNNaN
3amelia-20180501T0000480193412018-05-01 14:58:48.317000+00:0037.539598-74.85649220.8600013.48436NaN1025.682251...NaN8.5911NaNNaNNaNNaNNaNNaNNaNNaN
4amelia-20180501T0000480193412018-05-01 14:58:48.317000+00:0037.539598-74.85649219.4900003.55269NaN1025.579468...NaN9.3318NaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
35876amelia-20180501T0000480193413972018-05-14 12:29:12.947000+00:0037.475571-75.07957911.0100003.69139NaN1024.308472...NaN12.0024NaNNaNNaNNaNNaNNaNNaNNaN
35877amelia-20180501T0000480193413972018-05-14 12:29:12.947000+00:0037.475571-75.0795798.2400003.88118NaN1023.760315...NaN14.2628NaNNaNNaNNaNNaNNaNNaNNaN
35878amelia-20180501T0000480193413972018-05-14 12:29:12.947000+00:0037.475571-75.0795796.2900003.88696NaN1023.779907...NaN14.2783NaNNaNNaNNaNNaNNaNNaNNaN
35879amelia-20180501T0000480193413972018-05-14 12:29:12.947000+00:0037.475571-75.0795793.7100003.89055NaN1023.776733...NaN14.2993NaNNaNNaNNaNNaNNaNNaNNaN
35880amelia-20180501T0000480193413972018-05-14 12:29:12.947000+00:0037.475571-75.0795791.8000003.89293NaN1023.763733...NaN14.3251NaNNaNNaNNaNNaNNaNNaNNaN
\n", + "

35881 rows × 73 columns

\n", + "
" + ], + "text/plain": [ + " trajectory wmo_id profile_id \\\n", + "0 amelia-20180501T0000 4801934 1 \n", + "1 amelia-20180501T0000 4801934 1 \n", + "2 amelia-20180501T0000 4801934 1 \n", + "3 amelia-20180501T0000 4801934 1 \n", + "4 amelia-20180501T0000 4801934 1 \n", + "... ... ... ... \n", + "35876 amelia-20180501T0000 4801934 1397 \n", + "35877 amelia-20180501T0000 4801934 1397 \n", + "35878 amelia-20180501T0000 4801934 1397 \n", + "35879 amelia-20180501T0000 4801934 1397 \n", + "35880 amelia-20180501T0000 4801934 1397 \n", + "\n", + " time latitude longitude depth \\\n", + "0 2018-05-01 14:58:48.317000+00:00 37.539598 -74.856492 25.350000 \n", + "1 2018-05-01 14:58:48.317000+00:00 37.539598 -74.856492 23.950001 \n", + "2 2018-05-01 14:58:48.317000+00:00 37.539598 -74.856492 22.219999 \n", + "3 2018-05-01 14:58:48.317000+00:00 37.539598 -74.856492 20.860001 \n", + "4 2018-05-01 14:58:48.317000+00:00 37.539598 -74.856492 19.490000 \n", + "... ... ... ... ... \n", + "35876 2018-05-14 12:29:12.947000+00:00 37.475571 -75.079579 11.010000 \n", + "35877 2018-05-14 12:29:12.947000+00:00 37.475571 -75.079579 8.240000 \n", + "35878 2018-05-14 12:29:12.947000+00:00 37.475571 -75.079579 6.290000 \n", + "35879 2018-05-14 12:29:12.947000+00:00 37.475571 -75.079579 3.710000 \n", + "35880 2018-05-14 12:29:12.947000+00:00 37.475571 -75.079579 1.800000 \n", + "\n", + " conductivity conductivity_qc density ... salinity_qc \\\n", + "0 3.44320 NaN 1025.799805 ... NaN \n", + "1 3.44254 NaN 1025.776001 ... NaN \n", + "2 3.46240 NaN 1025.745361 ... NaN \n", + "3 3.48436 NaN 1025.682251 ... NaN \n", + "4 3.55269 NaN 1025.579468 ... NaN \n", + "... ... ... ... ... ... \n", + "35876 3.69139 NaN 1024.308472 ... NaN \n", + "35877 3.88118 NaN 1023.760315 ... NaN \n", + "35878 3.88696 NaN 1023.779907 ... NaN \n", + "35879 3.89055 NaN 1023.776733 ... NaN \n", + "35880 3.89293 NaN 1023.763733 ... NaN \n", + "\n", + " temperature temperature_qc time_qc time_uv time_uv_qc u u_qc \\\n", + "0 8.0939 NaN NaN NaN NaN NaN NaN \n", + "1 8.1080 NaN NaN NaN NaN NaN NaN \n", + "2 8.3210 NaN NaN NaN NaN NaN NaN \n", + "3 8.5911 NaN NaN NaN NaN NaN NaN \n", + "4 9.3318 NaN NaN NaN NaN NaN NaN \n", + "... ... ... ... ... ... .. ... \n", + "35876 12.0024 NaN NaN NaN NaN NaN NaN \n", + "35877 14.2628 NaN NaN NaN NaN NaN NaN \n", + "35878 14.2783 NaN NaN NaN NaN NaN NaN \n", + "35879 14.2993 NaN NaN NaN NaN NaN NaN \n", + "35880 14.3251 NaN NaN NaN NaN NaN NaN \n", + "\n", + " v v_qc \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "... .. ... \n", + "35876 NaN NaN \n", + "35877 NaN NaN \n", + "35878 NaN NaN \n", + "35879 NaN NaN \n", + "35880 NaN NaN \n", + "\n", + "[35881 rows x 73 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "\n", + "fname = e.download_file(file_type=\"parquet\")\n", + "df = pd.read_parquet(fname)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the `croissant` metadata and the `parquet` files one can set up tensoflow/pytorch ML workflows. The last missing piece would be a workflow that is trained on near real time data. That is where MQTT could play a role. With MQTT datasets can be updated remotely, directly from the instrument acquiring the data, and a ML workflow could re-run when the ERDDAP reports that a new data just arrived." + ] + } + ], + "metadata": { + "celltoolbar": "Edit Metadata", + "colab": { + "authorship_tag": "ABX9TyP4APRhllnmg4K4cwSXUwCh", + "include_colab_link": true, + "provenance": [] + }, + "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": "3.14.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From e99bdfdb930deac352eebbc718da615c6c517dd7 Mon Sep 17 00:00:00 2001 From: Filipe Fernandes Date: Wed, 7 Oct 2026 12:38:33 -0300 Subject: [PATCH 4/6] update --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 05ee7ef5..36f4f94f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: - id: check-yaml - repo: https://github.com/psf/black-pre-commit-mirror - rev: 26.5.1 + rev: "26.10.0" hooks: - id: black language_version: python3 From e507ad2f0e6d9065593d627b1defb99d03e7f4ef Mon Sep 17 00:00:00 2001 From: Filipe Fernandes Date: Wed, 7 Oct 2026 12:38:41 -0300 Subject: [PATCH 5/6] lints --- .../2026-10-10-ERDDAP_latest_features.ipynb | 7 ------- 1 file changed, 7 deletions(-) diff --git a/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb b/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb index d8388134..c7c79a0a 100644 --- a/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb +++ b/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb @@ -87,7 +87,6 @@ "from prometheus_client.parser import text_string_to_metric_families\n", "\n", "\n", - "\n", "def metrics_erddap_build_info(url):\n", " metrics = f\"{url}/metrics\"\n", " r = requests.get(metrics, allow_redirects=True, timeout=10)\n", @@ -101,7 +100,6 @@ " return deployment_info, version\n", "\n", "\n", - "\n", "server = \"https://gliders.ioos.us/erddap\"\n", "\n", "metrics_erddap_build_info(server)" @@ -123,7 +121,6 @@ "from erddapy import ERDDAP\n", "from erddapy.core.url import urlopen\n", "\n", - "\n", "e = ERDDAP(server=server, protocol=\"tabledap\")\n", "\n", "e.dataset_id = \"amelia-20180501T0000\"\n", @@ -138,7 +135,6 @@ "source": [ "import json\n", "\n", - "\n", "jsonld = urlopen(e.get_download_url())\n", "metadata = json.loads(jsonld.read().decode())" ] @@ -158,7 +154,6 @@ "source": [ "import json\n", "\n", - "\n", "with open(\"metadata.json\", \"w\") as f:\n", " content = json.dumps(metadata, indent=2)\n", " f.write(content)" @@ -190,7 +185,6 @@ "source": [ "import mlcroissant\n", "\n", - "\n", "dataset = mlcroissant.Dataset(jsonld=\"metadata.json\")" ] }, @@ -626,7 +620,6 @@ "source": [ "import pandas as pd\n", "\n", - "\n", "fname = e.download_file(file_type=\"parquet\")\n", "df = pd.read_parquet(fname)\n", "df" From df5edff9390d446498ddda5026066f53d63e2214 Mon Sep 17 00:00:00 2001 From: Filipe Fernandes Date: Thu, 8 Oct 2026 11:04:27 -0300 Subject: [PATCH 6/6] parquet vs csv --- .../2026-10-10-ERDDAP_latest_features.ipynb | 138 +++++++++++++++++- 1 file changed, 130 insertions(+), 8 deletions(-) diff --git a/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb b/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb index c7c79a0a..42c23f04 100644 --- a/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb +++ b/jupyterbook/content/code_gallery/data_access_notebooks/2026-10-10-ERDDAP_latest_features.ipynb @@ -84,6 +84,7 @@ ], "source": [ "import requests\n", + "import pandas as pd\n", "from prometheus_client.parser import text_string_to_metric_families\n", "\n", "\n", @@ -175,7 +176,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "WARNING:absl:WARNING: The JSON-LD `@context` is not standard. Refer to the official @context (e.g., from the example datasets in https://github.com/mlcommons/croissant/tree/main/datasets/1.0). The different keys are: {'samplingRate', 'equivalentProperty'}\n", + "WARNING:absl:WARNING: The JSON-LD `@context` is not standard. Refer to the official @context (e.g., from the example datasets in https://github.com/mlcommons/croissant/tree/main/datasets/1.0). The different keys are: {'equivalentProperty', 'samplingRate'}\n", "WARNING:absl:Found the following 2 warning(s) during the validation:\n", " - [Metadata(amelia-20180501T0000)] Property \"http://mlcommons.org/croissant/citeAs\" is recommended, but does not exist.\n", " - [Metadata(amelia-20180501T0000)] Property \"https://schema.org/version\" is recommended, but does not exist.\n" @@ -221,13 +222,136 @@ "ERDDAP's `croissant` is metadata only. We need to attach the data whe passing it to a ML workflow. Implementing a ML workflow from here should be easier than writing your own data loaders/converters, etc.\n", "\n", "\n", - "Now, imagine you do have a ML worklfow that downloads many files. While setting a ML workflow is beyond the scope of this notebook, we can show you how to quickly obtain `parquet` files that are lightweight and easy to load in many ML workflows." + "Now, imagine you do have a ML worklfow that downloads many files. While setting a ML workflow is beyond the scope of this notebook, we can show you how to quickly obtain `parquet` files that are lightweight and easy to load in many ML workflows.\n", + "\n", + "\n", + "Below let's compare the two most common formats for tables: `parquet` and `csv` for file size, reading efficience, etc." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 124 ms, sys: 53.5 ms, total: 178 ms\n", + "Wall time: 5.65 s\n" + ] + } + ], + "source": [ + "%%time\n", + "csv = e.download_file(file_type=\"csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 10.8 ms, sys: 4.13 ms, total: 14.9 ms\n", + "Wall time: 7 s\n" + ] + } + ], + "source": [ + "%%time\n", + "parquet = e.download_file(file_type=\"parquet\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The download times are similar, with the `parquet` format just a bit slower. However, it is hard to separate network time from the ERDDAP's internal time to pre-process the files, maybe creating the parquet file on the server-side here is the culprit for the extra time. Let's check the file size to be sure." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "parquet: 1.9 MB\n", + "csv: 13.7 MB\n" + ] + } + ], + "source": [ + "from humanize import naturalsize\n", + "\n", + "print(f\"parquet: {naturalsize(parquet.stat().st_size)}\")\n", + "print(f\"csv: {naturalsize(csv.stat().st_size)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The parquet file is an order of magnitude lower! The difference can be huge when dealing with large datasets or slow internet connection. Let's check the reading times now." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 248 ms, sys: 30 ms, total: 278 ms\n", + "Wall time: 278 ms\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: DtypeWarning: Columns (4: latitude, 5: longitude, 6: depth, 7: conductivity, 9: density, 14: lat_uv, 17: lon_uv, 20: precise_lat, 21: precise_lon, 23: pressure, 64: temperature, 67: time_uv, 69: u, 71: v) have mixed types. Specify dtype option on import or set low_memory=False.\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "df = pd.read_csv(csv)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 69.7 ms, sys: 241 ms, total: 311 ms\n", + "Wall time: 79.8 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "df = pd.read_parquet(parquet)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { @@ -612,16 +736,12 @@ "[35881 rows x 73 columns]" ] }, - "execution_count": 8, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pandas as pd\n", - "\n", - "fname = e.download_file(file_type=\"parquet\")\n", - "df = pd.read_parquet(fname)\n", "df" ] }, @@ -629,6 +749,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "It is no surprise that the optimized table binary format (`parquet`) will load much faster.\n", + "\n", "With the `croissant` metadata and the `parquet` files one can set up tensoflow/pytorch ML workflows. The last missing piece would be a workflow that is trained on near real time data. That is where MQTT could play a role. With MQTT datasets can be updated remotely, directly from the instrument acquiring the data, and a ML workflow could re-run when the ERDDAP reports that a new data just arrived." ] } @@ -655,7 +777,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.5" + "version": "3.14.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": {