diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..c0e89cd --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,29 @@ +name: Deploy docs + +on: + push: + branches: + - main + workflow_dispatch: + +permissions: + contents: write + +jobs: + deploy: + name: Deploy MkDocs to GitHub Pages + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Deploy docs + env: + GIT_COMMITTER_NAME: CI + GIT_COMMITTER_EMAIL: ci-bot@example.com + run: uv run --group docs mkdocs gh-deploy --force diff --git a/.gitignore b/.gitignore index 8fc3eb5..cabd295 100644 --- a/.gitignore +++ b/.gitignore @@ -156,6 +156,9 @@ venv.bak/ # mkdocs documentation /site +# Superpowers AI planning docs +docs/superpowers/ + # mypy .mypy_cache/ .dmypy.json diff --git a/README.md b/README.md index 0d95fb8..a232eac 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,17 @@ This is a prototype for querying STAC or CMR style _metadata_ about Zarr arrays This concept was conceived by the team at [Earthmover](https://www.earthmover.io/) and is outlined in their whitepaper Level 2 Data Collections in Zarr / Icechunk. +## Why + +The Earthmover whitepaper outlines several rationales for storing +_metadata_ in a Zarr store. The most compelling cases are + +- **Heterogeneous Arrays** - With the advent of Virtualizarr we are often representing chunks from source files that we don't control. For Level 2 and Level 3 datasets like Sentinel 2 this means that virtual Zarr arrays have varying `dtypes`, `codecs` and `crs` values. +If the source arrays are heterogeneous, they cannot be concatenated along a dimension to form a single datacube. Because of this we need an alternative to select or discover these arrays other than the normal coordinate or dimensional slicing we use with datacubes. + +- **Synchornization** - Our current metadata management solutions (STAC, CMR, ODC) all use disconnected metadata stores which reference raw data assets in object storage. +This can present problems as systems require complex, fragile orchestration to maintain consistency between metadata indexes and source data. Using Icechunk as store can alleviate this as array data and metadata updates can be completed in a single atomic transaction. + ## Schema To store this _metadata_, zarr-datafusion-search uses a convention where the Zarr store represents each metadata "field" with a 1-dimensional array in a root group named `"meta"`. @@ -28,6 +39,12 @@ In addition, DataFusion-Python supports [_custom table providers_](https://dataf > you must use the same version of DataFusion-Python as the version of > DataFusion used to compile the custom table provider. +## Installation +```bash +uv add zarr-datafusion-search +``` + +## Usage ```py from zarr_datafusion_search import ZarrTable from datafusion import SessionContext diff --git a/docs/DEVELOP.md b/docs/DEVELOP.md new file mode 100644 index 0000000..ef88c01 --- /dev/null +++ b/docs/DEVELOP.md @@ -0,0 +1,36 @@ +# Contributing + +## Rust + +From the project root, run: + +```bash +cargo test +``` + +A suite of benchmarks are available (though the remote S3 benchmarks use data in a +protected bucket and require credentials). Benchmarks are in separate binaries and +can be run via: + +```bash +cargo bench --bench datetime_local +cargo bench --bench bbox_colunms_local +cargo bench --bench bbox_local +``` + +## Python bindings + +From the `python/` directory, run: + +```bash +uv run --no-project maturin develop --uv +``` + +The `--no-project` flag is necessary to avoid building the Rust code (in release +mode) an extra time before we even reach the `maturin develop` command. + +Prefix all `uv run` commands with `--no-project`. For example: + +```bash +uv run --no-project pytest +``` diff --git a/docs/api/zarr-table.md b/docs/api/zarr-table.md new file mode 100644 index 0000000..a33ad93 --- /dev/null +++ b/docs/api/zarr-table.md @@ -0,0 +1,3 @@ +# ZarrTable + +::: zarr_datafusion_search.ZarrTable diff --git a/docs/architecture/query-pipeline.md b/docs/architecture/query-pipeline.md new file mode 100644 index 0000000..5370af9 --- /dev/null +++ b/docs/architecture/query-pipeline.md @@ -0,0 +1,48 @@ +# Query Pipeline + +The library uses [Apache Datafusion](https://datafusion.apache.org/) as the +foundation of its expression parser and execution planner. To interact with +the custom Zarr schema described in [zarr.md](./zarr.md) we use a custom +Datafusion +[TableProvider](https://datafusion.apache.org/library-user-guide/custom-table-providers.html) + + +The `ZarrTableProvider` uses several techniques to try and improve filter +execution performance. There are 2 main drivers of execution cost. + +1. Retrieving and deserializing Zarr chunks. +2. Evaluating expressions against chunks. + +#### Non-indexed columns +In the most basic case, the TableProvider looks at the "columns" used in the +expression predicate and retrieves each chunk for the metadata arrays of these +"columns" to build a `RecordBatch` with a schema of just the predicate columns. + +This `RecordBatch` is evaluated against the filter to find matching rows. If it +contains matching rows and there are additional projected "columns" in +expression, the chunk for that column's array is loaded and the indexes for the +matching rows are selected. + +These selected rows are combined and added to the `RecordBatchStream`. + +![](../diagrams/chunk_scanning.png) + +#### Indexed columns +While the approach described above can be highly performant for many types of +columns and expressions, it can be slow for `dtypes` that are slow to +deserialize (variable length types) or when the predicate evaluation is +expensive (geodatafusion functions). + +In these cases we want to use additional metadata or materialized indexes to +1. Reduce the number of chunks we retrieve and deserialize. +2. Reduce the number of values we check against the filter predicate. + +Currently we support using [geo-index](https://github.com/georust/geo-index) +R-tree indexes. In this case the "basic" pipeline is short circuited. The +`TableProvder` checks if the "column" in the predicate has an index. If an +index exists, it uses it find the "rows" that satisfy the predicate. The +`TableProvider` fetches only the chunks for those rows and then only decodes +those portions of the chunks. The filter predicate is never used against the +raw values. + +![](../diagrams/index_scanning.png) diff --git a/docs/architecture/zarr.md b/docs/architecture/zarr.md new file mode 100644 index 0000000..8b011d0 --- /dev/null +++ b/docs/architecture/zarr.md @@ -0,0 +1,55 @@ +# Zarr Structure + +`zarr-datafusion-search` uses a set of 1D arrays to store metadata, +treating them as if they were columns in a columnar storage format like +Parquet. + +#### Schema +The library requires a special group named `meta`. It discovers each array stored in the `meta` group and +maps its Zarr v3 `dtype` to an Arrow type to build an +[Arrow schema](https://arrow.apache.org/cookbook/py/schema.html). These are the +current supported type mappings. + +## Supported dtype mappings + +| Zarr v3 dtype | Arrow type | Notes | +|---|---|---| +| `bool` | `Boolean` | | +| `int8` | `Int8` | | +| `int16` | `Int16` | | +| `int32` | `Int32` | | +| `int64` | `Int64` | | +| `uint8` | `UInt8` | | +| `uint16` | `UInt16` | | +| `uint32` | `UInt32` | | +| `uint64` | `UInt64` | | +| `float16` | `Float16` | | +| `float32` | `Float32` | | +| `float64` | `Float64` | | +| `bytes` | `BinaryView` | When the field name is `bbox`, mapped to WKB with EPSG:4326 CRS via the GeoArrow extension type instead | +| `r` (raw bits) | `BinaryView` | | +| `string` | `Utf8View` | | +| `numpy.datetime64[s]` | `Timestamp(Second, None)` | | +| `numpy.datetime64[ms]` | `Timestamp(Millisecond, None)` | | +| `numpy.datetime64[us]` | `Timestamp(Microsecond, None)` | | +| `numpy.datetime64[ns]` | `Timestamp(Nanosecond, None)` | | +| `complex64` | ā€” | Not supported | +| `complex128` | ā€” | Not supported | + +### Chunking +Because the `meta` arrays are combined by Datafusion into a single schema they +need to maintain chunk alignment. This means that during array creation time, +the chunk size must be the same for each array. And as data is appended, it needs to be appended to each array simultaneously. +Because the chunks are aligned across arrays, the library can treat the combined chunks similar to a Parquet row group. + +This diagram demonstrates the chunk alignment +![](../diagrams/chunk_scanning.png) + +### Indexes +`zarr-datafusion-search` supports the optional use of materialized indexes to improve +scanning performance. We use the following convention, if a `meta` +array is used in the filter predicate `zarr-datafusion-search` will look for a +group called `indexes` and search for an array of the same name as the +predicate array. Currently the only index type supported are R-tree indexes +generated by the [geo-index](https://github.com/georust/geo-index) +library but we will continue to expand index support. diff --git a/docs/assets/ds_favicon.png b/docs/assets/ds_favicon.png new file mode 100644 index 0000000..db95a32 Binary files /dev/null and b/docs/assets/ds_favicon.png differ diff --git a/docs/assets/logo_no_text.png b/docs/assets/logo_no_text.png new file mode 100644 index 0000000..f9f3773 Binary files /dev/null and b/docs/assets/logo_no_text.png differ diff --git a/docs/diagrams/Index_scanning.excalidraw b/docs/diagrams/Index_scanning.excalidraw new file mode 100644 index 0000000..5cec015 --- /dev/null +++ b/docs/diagrams/Index_scanning.excalidraw @@ -0,0 +1,3240 @@ +{ + "type": "excalidraw", + "version": 2, + "source": "https://excalidraw.com", + "elements": [ + { + "id": "title", + "type": "text", + "x": 374.16552734375, + "y": 8, + "width": 151.6689453125, + "height": 27.5, + "angle": 0, + 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mode 100644 index 0000000..7ac0a7f --- /dev/null +++ b/docs/index.md @@ -0,0 +1,24 @@ +# zarr-datafusion-search + +A prototype for querying STAC or CMR style _metadata_ about Zarr arrays and groups +using [DataFusion](https://datafusion.apache.org/), an extensible query engine written +in Rust. + +This concept was conceived by the team at [Earthmover](https://www.earthmover.io/) +and is outlined in their whitepaper *Level 2 Data Collections in Zarr / Icechunk*. + +## Why + +The Earthmover whitepaper outlines several rationales for storing +_metadata_ in a Zarr store. The most compelling cases are + +- **Heterogeneous Arrays** - With the advent of Virtualizarr we are often representing chunks from source files that we don't control. For Level 2 and Level 3 datasets like Sentinel 2 this means that virtual Zarr arrays have varying `dtypes`, `codecs` and `crs` values. +If the source arrays are heterogeneous, they cannot be concatenated along a dimension to form a single datacube. Because of this we need an alternative to select or discover these arrays other than the normal coordinate or dimensional slicing we use with datacubes. + +- **Synchronization** - Our current metadata management solutions (STAC, CMR, ODC) all use disconnected metadata stores which reference raw data assets in object storage. +This can present problems as systems require complex, fragile orchestration to maintain consistency between metadata indexes and source data. Using Icechunk as store can alleviate this as array data and metadata updates can be completed in a single atomic transaction. + + +- **[User Guide](user-guide/installation.md)** ā€” Installation, quickstart +- **[API Reference](api/zarr-table.md)** ā€” `ZarrTable` class documentation +- **[Developer Docs](DEVELOP.md)** ā€” Contributing and running benchmarks diff --git a/docs/overrides/main.html b/docs/overrides/main.html new file mode 100644 index 0000000..0af326a --- /dev/null +++ b/docs/overrides/main.html @@ -0,0 +1,8 @@ +{% extends "base.html" %} + +{% block outdated %} + You're not viewing the latest version. + + Click here to go to latest. + +{% endblock %} diff --git a/docs/overrides/stylesheets/extra.css b/docs/overrides/stylesheets/extra.css new file mode 100644 index 0000000..a6a7b1c --- /dev/null +++ b/docs/overrides/stylesheets/extra.css @@ -0,0 +1,41 @@ +:root, +[data-md-color-scheme="default"] { + --md-primary-fg-color: #cf3f02; + --md-default-fg-color: #443f3f; + --boxShadowD: 0px 12px 24px 0px rgba(68, 63, 63, 0.08), + 0px 0px 4px 0px rgba(68, 63, 63, 0.08); +} +body { + margin: 0; + padding: 0; +} +h1, +h2, +h3, +h4, +h5, +h6 { + font-family: var(--md-heading-font); + font-weight: bold; +} +.md-typeset h1, +.md-typeset h2 { + font-weight: normal; + color: var(--md-default-fg-color); +} +.md-typeset h3, +.md-typeset h4 { + font-weight: bold; + color: var(--md-default-fg-color); +} +.md-button, +.md-typeset .md-button { + font-family: var(--md-heading-font); +} +.md-content .supheading { + font-family: var(--md-heading-font); + text-transform: uppercase; + color: var(--md-primary-fg-color); + font-size: 0.75rem; + font-weight: bold; +} diff --git a/docs/user-guide/installation.md b/docs/user-guide/installation.md new file mode 100644 index 0000000..860d4f6 --- /dev/null +++ b/docs/user-guide/installation.md @@ -0,0 +1,29 @@ +# Installation + +## Requirements + +- Python 3.10 or later +- `datafusion >= 52.0` + +## Install from PyPI + +```bash +pip install zarr-datafusion-search +``` + +## Install with uv + +```bash +uv add zarr-datafusion-search +``` + +## DataFusion version compatibility + +`zarr-datafusion-search` is compiled against a specific version of the +DataFusion engine. The `datafusion` Python package you have installed must +match. Check the `datafusion>=` constraint in +[`python/pyproject.toml`](https://github.com/developmentseed/zarr-datafusion-search/blob/main/python/pyproject.toml) +for the required version. + +If you see an error like `abi3 wheel is not compatible` or a symbol not found +at import time, this is almost always a DataFusion version mismatch. diff --git a/docs/user-guide/quickstart.md b/docs/user-guide/quickstart.md new file mode 100644 index 0000000..ab11582 --- /dev/null +++ b/docs/user-guide/quickstart.md @@ -0,0 +1,126 @@ +# Quickstart + +This guide walks through querying a Zarr store with SQL in three steps: +create a store, register it as a DataFusion table, and run queries. + +## 1. Create a Zarr store + +A `zarr-datafusion-search` store is a Zarr v3 group containing a `meta/` +subgroup. Each 1D array inside `meta/` becomes a SQL column. The simplest +way to build a test store is with `zarr-python` and `shapely`: + +```python +import numpy as np +import shapely +import zarr +from zarr.dtype import VariableLengthBytes, VariableLengthUTF8 + +root = zarr.open_group("my_store.zarr", mode="w", zarr_format=3) +meta = root.create_group("meta") + +# Timestamps +meta.create_array( + "date", + data=np.array(["2023-01-01", "2023-01-02", "2023-01-03"], dtype="datetime64[ms]"), +) + +# String metadata +collection = meta.create_array( + "collection", + shape=(3,), + dtype=VariableLengthUTF8(), +) +collection[:] = ["sentinel-2", "sentinel-2", "landsat-8"] + +# Bounding boxes stored as WKB +bbox = meta.create_array( + "bbox", + shape=(3,), + dtype=VariableLengthBytes(), +) +bbox[:] = shapely.to_wkb([ + shapely.box(-10.0, -10.0, 10.0, 10.0), + shapely.box(-20.0, -20.0, 20.0, 20.0), + shapely.box( 30.0, 30.0, 50.0, 50.0), +]) +``` + +## 2. Register the store + +Use `ZarrTable.from_obstore` to open the store through +[obstore](https://github.com/developmentseed/obstore), then register it +with a DataFusion `SessionContext`: + +```python +from datafusion import SessionContext +from obstore.store import LocalStore +from zarr_datafusion_search import ZarrTable + +store = LocalStore("my_store.zarr") +zarr_table = await ZarrTable.from_obstore(store, "/meta") + +ctx = SessionContext() +ctx.register_table("my_data", zarr_table) +``` + +## 3. Query with SQL + +Once registered, any DataFusion SQL query works against the table: + +```python +# Inspect the schema +df = ctx.sql("SELECT * FROM my_data LIMIT 5") +print(df.schema()) +df.show() + +# Filter by date +df = ctx.sql(""" + SELECT date, collection + FROM my_data + WHERE date >= '2023-01-02' +""") +df.show() +``` + +## 4. Spatial queries + +Spatial SQL functions are provided by `geodatafusion`, which is included as a +dependency and requires no separate install. Call `register_all` to make the +functions available in your session: + +```python +from geodatafusion import register_all + +register_all(ctx) + +df = ctx.sql(""" + SELECT date, collection + FROM my_data + WHERE ST_Intersects( + bbox, + ST_GeomFromText('POLYGON((-15 -15, -15 15, 15 15, 15 -15, -15 -15))') + ) +""") +df.show() +``` + +## Using an Icechunk store + +If your data lives in an [Icechunk](https://icechunk.io) repository, open a +read-only session and pass it directly to `ZarrTable.from_icechunk`: + +```python +import icechunk +from zarr_datafusion_search import ZarrTable + +storage = icechunk.local_filesystem_storage("my_repo") +repo = icechunk.Repository.open(storage) +session = repo.readonly_session("main") + +zarr_table = await ZarrTable.from_icechunk(session=session, group_path="/meta") + +ctx = SessionContext() +ctx.register_table("my_data", zarr_table) +``` + +From here the SQL interface is identical to the local store example above. diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..a0467f2 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,115 @@ +site_name: zarr-datafusion-search +repo_name: developmentseed/zarr-datafusion-search +repo_url: https://github.com/developmentseed/zarr-datafusion-search +site_description: Query Zarr metadata with DataFusion SQL +site_author: Development Seed +site_url: https://developmentseed.github.io/zarr-datafusion-search/ +docs_dir: docs + +extra: + social: + - icon: "fontawesome/brands/github" + link: "https://github.com/developmentseed" + - icon: "material/web" + link: "https://developmentseed.org/" + +exclude_docs: | + superpowers/ + +nav: + - index.md + - User Guide: + - user-guide/installation.md + - user-guide/quickstart.md + - API Reference: + - api/zarr-table.md + - Architecture: + - architecture/zarr.md + - architecture/query-pipeline.md + - Developer Docs: DEVELOP.md + +theme: + language: en + name: material + custom_dir: docs/overrides + logo: assets/logo_no_text.png + favicon: assets/ds_favicon.png + palette: + - media: "(prefers-color-scheme)" + toggle: + icon: material/brightness-auto + name: Switch to light mode + - media: "(prefers-color-scheme: light)" + primary: default + accent: deep orange + toggle: + icon: material/brightness-7 + name: Switch to dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: default + accent: deep orange + toggle: + icon: material/brightness-4 + name: Switch to system preference + + font: + text: Roboto + code: Roboto Mono + + features: + - content.code.annotate + - content.code.copy + - navigation.indexes + - navigation.instant + - navigation.tracking + - search.suggest + - search.share + +extra_css: + - overrides/stylesheets/extra.css + +watch: + - python/python + +plugins: + - search + - mkdocstrings: + handlers: + python: + paths: [python/python] + options: + allow_inspection: false + docstring_style: google + docstring_section_style: list + line_length: 80 + separate_signature: true + show_root_heading: true + show_signature_annotations: true + show_source: false + show_symbol_type_toc: true + signature_crossrefs: true + +markdown_extensions: + - admonition + - attr_list + - def_list + - footnotes + - md_in_html + - pymdownx.details + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + - pymdownx.highlight: + anchor_linenums: true + - pymdownx.inlinehilite + - pymdownx.magiclink: + hide_protocol: true + repo_url_shortener: true + - pymdownx.smartsymbols + - pymdownx.snippets + - pymdownx.superfences + - pymdownx.tasklist: + custom_checkbox: true + - toc: + permalink: true diff --git a/pyproject.toml b/pyproject.toml index b89f414..9bba442 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,3 +13,7 @@ dependencies = [ [dependency-groups] dev = ["ipykernel>=6.30.1"] +docs = [ + "mkdocs-material>=9.5", + "mkdocstrings[python]>=0.29", +] diff --git a/python/python/zarr_datafusion_search/_rust.pyi b/python/python/zarr_datafusion_search/_rust.pyi new file mode 100644 index 0000000..050d463 --- /dev/null +++ b/python/python/zarr_datafusion_search/_rust.pyi @@ -0,0 +1,94 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from collections.abc import Awaitable + +def ___version() -> str: ... + +class ZarrTable: + """A DataFusion table provider that exposes a Zarr metadata store as a SQL-queryable table. + + `ZarrTable` implements the DataFusion `TableProvider` protocol via the FFI boundary, + so it can be registered directly with a `SessionContext` using + `register_table_provider`. + + Use one of the async class methods to construct an instance: + + - [`from_icechunk`][zarr_datafusion_search.ZarrTable.from_icechunk] ā€” from an Icechunk session + - [`from_obstore`][zarr_datafusion_search.ZarrTable.from_obstore] ā€” from an obstore object store + + Example: + ```python + import asyncio + from datafusion import SessionContext + from zarr_datafusion_search import ZarrTable + + async def main(): + table = await ZarrTable.from_obstore(store, "/meta") + ctx = SessionContext() + ctx.register_table_provider("items", table) + df = ctx.sql("SELECT date, collection FROM items LIMIT 10") + df.show() + + asyncio.run(main()) + ``` + """ + + @classmethod + def from_icechunk( + cls, + session: Any, + group_path: str, + ) -> Awaitable[ZarrTable]: + """Create a ZarrTable from an Icechunk session. + + Args: + session: An open `icechunk.Session` pointing to the store. + group_path: Absolute path to the Zarr group containing the metadata + arrays (e.g. `"/meta"`). + + Returns: + An awaitable that resolves to a `ZarrTable` instance. + + Example: + ```python + table = await ZarrTable.from_icechunk(session, "/meta") + ``` + """ + ... + + @classmethod + def from_obstore( + cls, + store: Any, + group_path: str, + ) -> Awaitable[ZarrTable]: + """Create a ZarrTable from an obstore object store. + + Args: + store: Any obstore-compatible object store (e.g. `obstore.store.S3Store`, + `obstore.store.LocalStore`). + group_path: Absolute path to the Zarr group containing the metadata + arrays (e.g. `"/meta"`). + + Returns: + An awaitable that resolves to a `ZarrTable` instance. + + Example: + ```python + import obstore.store + store = obstore.store.LocalStore("/path/to/data.zarr") + table = await ZarrTable.from_obstore(store, "/meta") + ``` + """ + ... + + def __datafusion_table_provider__(self, session: Any) -> Any: + """Return the FFI `TableProvider` capsule for DataFusion registration. + + This is called automatically by `SessionContext.register_table_provider`. + You do not need to call it directly. + """ + ... diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..2cc5ff5 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1250 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" + +[[package]] +name = "appnope" +version = "0.1.4" +source = { registry = 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