├── .gitignore
├── LICENSE
├── README.md
└── public
├── ingest.ipynb
├── noaa.ipynb
└── overture.ipynb
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | share/python-wheels/
24 | *.egg-info/
25 | .installed.cfg
26 | *.egg
27 | MANIFEST
28 |
29 | # PyInstaller
30 | # Usually these files are written by a python script from a template
31 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
32 | *.manifest
33 | *.spec
34 |
35 | # Installer logs
36 | pip-log.txt
37 | pip-delete-this-directory.txt
38 |
39 | # Unit test / coverage reports
40 | htmlcov/
41 | .tox/
42 | .nox/
43 | .coverage
44 | .coverage.*
45 | .cache
46 | nosetests.xml
47 | coverage.xml
48 | *.cover
49 | *.py,cover
50 | .hypothesis/
51 | .pytest_cache/
52 | cover/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | .pybuilder/
76 | target/
77 |
78 | # Jupyter Notebook
79 | .ipynb_checkpoints
80 |
81 | # IPython
82 | profile_default/
83 | ipython_config.py
84 |
85 | # pyenv
86 | # For a library or package, you might want to ignore these files since the code is
87 | # intended to run in multiple environments; otherwise, check them in:
88 | # .python-version
89 |
90 | # pipenv
91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
94 | # install all needed dependencies.
95 | #Pipfile.lock
96 |
97 | # poetry
98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
99 | # This is especially recommended for binary packages to ensure reproducibility, and is more
100 | # commonly ignored for libraries.
101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102 | #poetry.lock
103 |
104 | # pdm
105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
106 | #pdm.lock
107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108 | # in version control.
109 | # https://pdm.fming.dev/#use-with-ide
110 | .pdm.toml
111 |
112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
113 | __pypackages__/
114 |
115 | # Celery stuff
116 | celerybeat-schedule
117 | celerybeat.pid
118 |
119 | # SageMath parsed files
120 | *.sage.py
121 |
122 | # Environments
123 | .env
124 | .venv
125 | env/
126 | venv/
127 | ENV/
128 | env.bak/
129 | venv.bak/
130 |
131 | # Spyder project settings
132 | .spyderproject
133 | .spyproject
134 |
135 | # Rope project settings
136 | .ropeproject
137 |
138 | # mkdocs documentation
139 | /site
140 |
141 | # mypy
142 | .mypy_cache/
143 | .dmypy.json
144 | dmypy.json
145 |
146 | # Pyre type checker
147 | .pyre/
148 |
149 | # pytype static type analyzer
150 | .pytype/
151 |
152 | # Cython debug symbols
153 | cython_debug/
154 |
155 | # PyCharm
156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158 | # and can be added to the global gitignore or merged into this file. For a more nuclear
159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder.
160 | #.idea/
161 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2024 Fused
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 |
2 | Fused Example Jupyter Notebooks
3 |
4 |
5 | 🌎 Code to Map. Instantly.
6 |
7 |
8 |
9 | 
10 |
11 | This repo is a public collection of Jupyter Notebooks that showcase Fused User Defined Functions (UDFs).
12 |
13 | Fused is the glue layer that interfaces data platforms and data tools via a managed serverless API. With Fused, you can write, share, or discover UDFs which are the building blocks of serverless geospatial operations. UDFs are Python functions that turn into live HTTP endpoints that load their output into any tools that can call an API.
14 |
15 | ## Quickstart
16 |
17 | ### 1. Install Fused Python SDK
18 |
19 | [](https://pypi.python.org/pypi/fused)
20 |
21 | The Fused Python SDK is available at [PyPI](https://pypi.org/project/fused/). Use the standard Python [installation tools](https://packaging.python.org/en/latest/tutorials/installing-packages/). UDFs this repo expect the most recent version.
22 |
23 | ```bash
24 | python3 -m venv .venv
25 | source .venv/bin/activate
26 | pip install fused
27 | ```
28 | ### 2. TODO
29 |
30 | ## Walkthrough TODO
31 |
32 |
33 | ## Ecosystem
34 |
35 | Build any scale workflows with the [Fused Python SDK](https://docs.fused.io/python-sdk/overview) and [Workbench webapp](https://docs.fused.io/workbench/overview), and integrate them into your stack with the [Fused Hosted API](https://docs.fused.io/hosted-api/overview).
36 |
37 | 
38 |
39 | ## Documentation
40 |
41 | Fused documentation is in [docs.fused.io](https://docs.fused.io/).
42 |
43 | ## Contribution guidelines
44 |
45 | All UDF contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
46 |
47 | ## License
48 |
49 | [MIT License](./LICENSE)
50 |
51 |
--------------------------------------------------------------------------------
/public/ingest.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "attachments": {},
5 | "cell_type": "markdown",
6 | "metadata": {
7 | "id": "UhnqNrO6UhHj"
8 | },
9 | "source": [
10 | "# How-to: Ingestion\n",
11 | "\n",
12 | "This example notebook shows how to use Fused to ingest data into an S3 bucket."
13 | ]
14 | },
15 | {
16 | "attachments": {},
17 | "cell_type": "markdown",
18 | "metadata": {
19 | "id": "cN6FpPH4WZms"
20 | },
21 | "source": [
22 | "## Ingest data\n",
23 | "\n",
24 | "Fused delivers speed advantages thanks to spatial partitioning. Geospatial operations between two or more datasets are usually for spatially overlapping or neighboring areas - and usually for localized areas of interest. Breaking down datasets across geographic chunks loads only the relevant data for each operation.\n",
25 | "\n",
26 | "The [`fused.ingest()`](/python-sdk/api/top-level-functions/#fused.ingest) method loads data into an S3 bucket and automatically geo partitions it.\n",
27 | "\n",
28 | "Datasets ingested with Fused are spatially partitioned collections of Parquet files. Each file has one or more chunks, which are a further level of spatial partitioning.\n",
29 | "\n",
30 | "Columns in a dataset are grouped into tables. An ingested dataset contains a `main` table with the original input columns and a `fused` table containing spatial metadata.\n",
31 | "\n",
32 | "The `ingest()` method has many configuration options, which the API documentation explains. The following sections cover a few different ingestion use cases.\n",
33 | "\n",
34 | "Pro tip: While Fused is generally used to ingest files, it's also possible to pass the `GeoDataFrame` directly to `fused.ingest()`."
35 | ]
36 | },
37 | {
38 | "attachments": {},
39 | "cell_type": "markdown",
40 | "metadata": {},
41 | "source": [
42 | "### Default ingestion\n",
43 | "By default ingestion tries to create a certain number of files (`target_num_files=20`). The number of rows per file and chunk are chosen to meet this target. Note that 20 files is only a target and the actual number of files generated can vary."
44 | ]
45 | },
46 | {
47 | "cell_type": "code",
48 | "execution_count": 6,
49 | "metadata": {},
50 | "outputs": [],
51 | "source": [
52 | "import fused\n",
53 | "\n",
54 | "\n",
55 | "job = fused.ingest(\n",
56 | " input=\"https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/TRACT/tl_rd22_11_tract.zip\",\n",
57 | " output=f\"fd://census/dc_tract\",\n",
58 | ")\n",
59 | "job_id = job.run_remote(overwrite=True)"
60 | ]
61 | },
62 | {
63 | "cell_type": "markdown",
64 | "metadata": {},
65 | "source": [
66 | "While the job is running, follow its logs."
67 | ]
68 | },
69 | {
70 | "cell_type": "code",
71 | "execution_count": null,
72 | "metadata": {},
73 | "outputs": [],
74 | "source": [
75 | "job_id.tail_logs()"
76 | ]
77 | },
78 | {
79 | "cell_type": "markdown",
80 | "metadata": {},
81 | "source": [
82 | "### Row-based ingestion\n",
83 | "\n",
84 | "Our basic ingestion is row-based, where the user set the maximum number of rows per each chunk and file."
85 | ]
86 | },
87 | {
88 | "cell_type": "code",
89 | "execution_count": 8,
90 | "metadata": {},
91 | "outputs": [],
92 | "source": [
93 | "job = fused.ingest(\n",
94 | " input=\"https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/TRACT/tl_rd22_11_tract.zip\",\n",
95 | " explode_geometries=True,\n",
96 | " partitioning_method=\"rows\",\n",
97 | " partitioning_maximum_per_file=100,\n",
98 | " partitioning_maximum_per_chunk=10,\n",
99 | ")\n",
100 | "job_id = job.run_remote(overwrite=True)"
101 | ]
102 | },
103 | {
104 | "attachments": {},
105 | "cell_type": "markdown",
106 | "metadata": {},
107 | "source": [
108 | "### Area-based ingestion\n",
109 | "\n",
110 | "Fused also supports area-based ingestion, where the number of rows in each partition is determined by the sum of their area.\n"
111 | ]
112 | },
113 | {
114 | "cell_type": "code",
115 | "execution_count": 10,
116 | "metadata": {},
117 | "outputs": [],
118 | "source": [
119 | "job = fused.ingest(\n",
120 | " input=\"https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/TRACT/tl_rd22_11_tract.zip\",\n",
121 | " output=f\"fd://census/dc_tract_area\",\n",
122 | " explode_geometries=True,\n",
123 | " partitioning_method=\"area\",\n",
124 | " partitioning_maximum_per_file=None,\n",
125 | " partitioning_maximum_per_chunk=None,\n",
126 | ")\n",
127 | "job_id = job.run_remote(overwrite=True)"
128 | ]
129 | },
130 | {
131 | "attachments": {},
132 | "cell_type": "markdown",
133 | "metadata": {},
134 | "source": [
135 | "### Geometry subdivision\n",
136 | "\n",
137 | "It's also possible to subdivide geometries in the ingestion process."
138 | ]
139 | },
140 | {
141 | "cell_type": "code",
142 | "execution_count": 11,
143 | "metadata": {},
144 | "outputs": [],
145 | "source": [
146 | "job = fused.ingest(\n",
147 | " input=\"https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/TRACT/tl_rd22_11_tract.zip\",\n",
148 | " output=f\"fd://census/dc_tract_geometry\",\n",
149 | " explode_geometries=True,\n",
150 | " partitioning_method=\"area\",\n",
151 | " partitioning_maximum_per_file=None,\n",
152 | " partitioning_maximum_per_chunk=None,\n",
153 | " subdivide_start=0.001,\n",
154 | " subdivide_stop=0.0001,\n",
155 | " subdivide_method=\"area\",\n",
156 | ")\n",
157 | "job_id = job.run_remote(overwrite=True)"
158 | ]
159 | },
160 | {
161 | "attachments": {},
162 | "cell_type": "markdown",
163 | "metadata": {},
164 | "source": [
165 | "Once ingestion completes, [`fused.open_table`](/python-sdk/api/experimental/#fused._experimental.open_table) returns the corresponding [`Table`](/python-sdk/api/experimental/#fused.models.Table) object.\n",
166 | "\n",
167 | "The notebook _repr_ provides insight into the Table structure.\n",
168 | "\n",
169 | "- Each table has one or more _files_, which are spatially partitioned.\n",
170 | "- Each file has one or more _chunks_, which are again spatially partitioned within the file.\n",
171 | "\n",
172 | "Optionally, tables can be part of a `Dataset`, which consists of one or more _tables_.\n"
173 | ]
174 | },
175 | {
176 | "cell_type": "code",
177 | "execution_count": 12,
178 | "metadata": {},
179 | "outputs": [
180 | {
181 | "data": {
182 | "text/html": [
183 | "