diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..ad15714 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,41 @@ +name: Test + +on: + workflow_dispatch: + push: + branches: + - main + - develop + tags: + pull_request: + +jobs: + tests: + strategy: + matrix: + python-version: [3.11, 3.12, 3.13, 3.14] + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v5 + + - name: Install uv + uses: astral-sh/setup-uv@v7 + with: + version: "0.10.*" + python-version: ${{ matrix.python-version }} + enable-cache: true + + - name: Run pre-commit + run: uv run pre-commit run --all-files + + - name: Run tests + run: uv run pytest --cov-branch --cov-report=xml + + - name: Upload coverage to Codecov + if: matrix.python-version == '3.14' + uses: codecov/codecov-action@v5 + with: + token: ${{ secrets.CODECOV_TOKEN }} + files: coverage.xml diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8ed4cdf --- /dev/null +++ b/.gitignore @@ -0,0 +1,20 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info +.coverage +coverage.xml + +# Virtual environments +.venv + +minio-data/ + +# dev docs +dev-docs/ + +data/ +.worktrees/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..d92ff2c --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,17 @@ +default_stages: [pre-commit, pre-push] +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.2 + hooks: + - id: ruff + args: ["--fix", "--show-fixes"] + - id: ruff-format + + - repo: local + hooks: + - id: ty + name: ty (type check) + entry: uv run ty check + language: system + types: [python] + pass_filenames: false diff --git a/.pyiceberg.yaml b/.pyiceberg.yaml new file mode 100644 index 0000000..c9a8c61 --- /dev/null +++ b/.pyiceberg.yaml @@ -0,0 +1,9 @@ +catalog: + default: + type: rest + uri: http://localhost:8181 + warehouse: s3://warehouse/ + s3.endpoint: http://localhost:9000 + s3.access-key-id: admin + s3.secret-access-key: password + s3.path-style-access: "true" diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/README.md b/README.md index d49b10b..6d5baaa 100644 --- a/README.md +++ b/README.md @@ -1 +1,191 @@ # icestac + +icestac maps STAC concepts onto Apache Iceberg. + +The current implementation covers STAC Items with schema utilities for the flattened item layout defined by [STAC GeoParquet](https://radiantearth.github.io/stac-geoparquet-spec/), a small write API, and runnable PyIceberg examples. + +Each collection gets one Iceberg table named with its STAC collection ID. Clients can locate an item table from a collection ID without a metadata lookup. Each row represents one item, and an item's identity is scoped to its collection. Requesting an item by collection and ID should return one current record, not multiple versions. + +The `collections` table name is reserved for planned collection metadata. Collection metadata is not implemented yet. + +## Storage conventions + +Use PyIceberg for catalog and table administration, including namespace and table creation, partitioning, sorting, and table properties. icestac does not choose a default table layout. + +The schema utilities and `put_items` handle the STAC-facing parts. They work with an Arrow table (`pyarrow.Table` or `arro3.core.Table`) using the [STAC GeoParquet](https://radiantearth.github.io/stac-geoparquet-spec/) column mapping: item properties are top-level columns, while geometry is WKB and links and assets remain nested structures. This describes the in-memory Arrow input, not a compliant STAC GeoParquet file; file compliance also requires GeoParquet and STAC metadata. `get_schema_from_items` checks the required fields and their basic Arrow types, then creates an Iceberg schema. It does not perform full STAC validation. + +The inferred schema marks `id` as an Iceberg identifier field. Iceberg identifier fields are schema metadata, not uniqueness constraints. Starting with a table that has unique `(collection, id)` pairs and writing through `put_items` preserves the one-current-row convention. Native writes that bypass `put_items`, such as appends, can create duplicate IDs and violate it. + +## Put items + +Create a table for one collection with native PyIceberg, then pass its items to `put_items`: + +```python +from pyiceberg.catalog import load_catalog + +from icestac.constants import DEFAULT_NAMESPACE +from icestac.schema import get_schema_from_items +from icestac.write import put_items + +# items is an Arrow table containing items from one collection. +collection_id = "my-collection" +catalog = load_catalog() +catalog.create_namespace_if_not_exists(DEFAULT_NAMESPACE) +schema = get_schema_from_items(items) +table = catalog.create_table( + identifier=(DEFAULT_NAMESPACE, collection_id), + schema=schema, +) +put_items(table, items) +``` + +`put_items` accepts items from one collection and checks that their `collection` values match the collection named by the table. It rejects duplicate IDs within an input batch. Matching items are complete replacements, so omitted optional fields are cleared, including nested link fields. New IDs are inserted. + +Collection IDs can contain periods. Pass catalog identifiers as tuples such as `(namespace, collection_id)` instead of interpolated dotted strings; a catalog backend may still reject a particular ID. Do not use `collections` as an item table name. + +For append semantics, use native PyIceberg: + +```python +table.append(df=items) +``` + +### Schema evolution + +New fields raise an error unless you pass `evolve_schema=True`. Schema evolution and the replacement write happen in one Iceberg transaction: + +```python +put_items(table, items, evolve_schema=True) +``` + +To update a schema without writing items, use native PyIceberg: + +```python +from pyiceberg.catalog import load_catalog + +from icestac.constants import DEFAULT_NAMESPACE +from icestac.schema import get_schema_from_items + +catalog = load_catalog() +table = catalog.load_table((DEFAULT_NAMESPACE, collection_id)) +with table.update_schema() as update: + update.union_by_name(get_schema_from_items(items)) +``` + +### Concurrent writes + +Each batch, including optional schema evolution, commits as one Iceberg transaction. A concurrent commit conflict raises `CommitFailedException`; refresh the table and retry from current state rather than replaying a stale replacement. A `CommitStateUnknownException` means the commit outcome is unknown, so refresh and reconcile before retrying. + +## Table layout + +Choose the layout when creating the table. For example, partition by month of `datetime`, sort by `datetime`, and set a Parquet row group limit: + +```python +from pyiceberg.partitioning import PartitionField, PartitionSpec +from pyiceberg.table import TableProperties +from pyiceberg.table.sorting import SortField, SortOrder +from pyiceberg.transforms import MonthTransform + +schema = get_schema_from_items(items) +datetime_id = schema.find_field("datetime").field_id +table = catalog.create_table( + identifier=(DEFAULT_NAMESPACE, collection_id), + schema=schema, + partition_spec=PartitionSpec( + PartitionField( + source_id=datetime_id, + field_id=1000, + transform=MonthTransform(), + name="datetime_month", + ) + ), + sort_order=SortOrder(SortField(source_id=datetime_id)), + properties={TableProperties.PARQUET_ROW_GROUP_LIMIT: "50000"}, +) +``` + +Omit `partition_spec` for an unpartitioned table. Interval items with a null `datetime` go into a null partition. To partition them by their start time, use the `start_datetime` field with `MonthTransform()`. + +Geometry uses WKB in an Iceberg binary column. The output files lack the metadata required for GeoParquet and STAC GeoParquet compliance. + +## Local demo + +Run these commands from the repository root. You need [uv](https://docs.astral.sh/uv/), Docker Compose, and DuckDB for the query examples. + +### Start the services + +```bash +docker compose up -d +``` + +- Iceberg REST catalog: `http://localhost:8181` +- MinIO API: `http://localhost:9000`; console: `http://localhost:9001` +- The `mc` service creates the `warehouse` bucket. + +The checked-in `.pyiceberg.yaml` points to these services. The credentials (`admin` / `password`) are for local development only. See [PyIceberg configuration](https://py.iceberg.apache.org/configuration/) for other environments. + +### Load HLS items + +```bash +uv run main.py +``` + +The demo downloads January–August 2026 HLS STAC GeoParquet from public S3 into `data/`, reuses cached files, and writes each month's items to the `HLSS30_2.0` collection table. Each batch must fit in memory. + +It keeps the original `HLSS30_2.0` collection ID, partitions by month of `datetime`, sorts by DuckDB's Hilbert index of each bbox's lower-left coordinates, and caps Parquet row groups at 50,000 rows. Compatible schema changes use `evolve_schema=True`; rerunning the demo reuses the existing table and replaces the cached batches again. + +### Query with DuckDB + +The query selects items overlapping the contiguous United States bounding box (longitude -125 to -66, latitude 24 to 50) from April 1, 2026 inclusive through July 1, 2026 exclusive; the bbox predicates test bounding-box overlap, not exact geometry intersection. + +```sql +INSTALL iceberg; LOAD iceberg; +INSTALL httpfs; LOAD httpfs; + +CREATE OR REPLACE SECRET minio ( + TYPE S3, + KEY_ID 'admin', + SECRET 'password', + ENDPOINT 'localhost:9000', + USE_SSL false, + URL_STYLE 'path' +); + +ATTACH 'icestac' AS catalog ( + TYPE ICEBERG, + ENDPOINT 'http://localhost:8181', + AUTHORIZATION_TYPE 'none' +); + +SELECT count(*) FROM catalog.icestac."HLSS30_2.0"; + +EXPLAIN ANALYZE SELECT count(*) +FROM catalog.icestac."HLSS30_2.0" +WHERE bbox.xmin <= -90.0 + AND bbox.xmax >= -100.0 + AND bbox.ymin <= 50.0 + AND bbox.ymax >= 40.0 + AND datetime >= TIMESTAMPTZ '2026-04-01 00:00:00+00' + AND datetime < TIMESTAMPTZ '2026-07-01 00:00:00+00' +; + +``` + +### Delete the demo table + +**This deletes the table and its data.** + +```python +from pyiceberg.catalog import load_catalog + +from icestac.constants import DEFAULT_NAMESPACE + +load_catalog().purge_table((DEFAULT_NAMESPACE, "HLSS30_2.0")) +``` + +To remove only the catalog entry and keep the files, use `drop_table(...)` instead. + +## Tests + +```bash +uv run pytest +``` diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..7cebaf0 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,52 @@ +services: + rest: + image: apache/iceberg-rest-fixture + container_name: iceberg-rest + networks: + iceberg_net: + ports: + - 8181:8181 + environment: + - AWS_ACCESS_KEY_ID=admin + - AWS_SECRET_ACCESS_KEY=password + - AWS_REGION=us-east-1 + - CATALOG_WAREHOUSE=s3://warehouse/ + - CATALOG_IO__IMPL=org.apache.iceberg.aws.s3.S3FileIO + - CATALOG_S3_ENDPOINT=http://minio:9000 + minio: + image: minio/minio + container_name: minio + environment: + - MINIO_ROOT_USER=admin + - MINIO_ROOT_PASSWORD=password + - MINIO_DOMAIN=minio + networks: + iceberg_net: + aliases: + - warehouse.minio + ports: + - 9001:9001 + - 9000:9000 + volumes: + - ./minio-data:/data + command: ["server", "/data", "--console-address", ":9001"] + mc: + depends_on: + - minio + image: minio/mc + container_name: mc + networks: + iceberg_net: + environment: + - AWS_ACCESS_KEY_ID=admin + - AWS_SECRET_ACCESS_KEY=password + - AWS_REGION=us-east-1 + entrypoint: | + /bin/sh -c " + until (/usr/bin/mc alias set minio http://minio:9000 admin password) do echo '...waiting...' && sleep 1; done; + /usr/bin/mc mb -p minio/warehouse; + /usr/bin/mc policy set public minio/warehouse; + tail -f /dev/null + " +networks: + iceberg_net: diff --git a/main.py b/main.py new file mode 100644 index 0000000..f760ea9 --- /dev/null +++ b/main.py @@ -0,0 +1,124 @@ +import asyncio +import logging +from pathlib import Path + +import pyarrow as pa +from obstore.store import LocalStore, S3Store +from pyiceberg.catalog import load_catalog +from pyiceberg.exceptions import TableAlreadyExistsError +from pyiceberg.partitioning import PartitionField, PartitionSpec +from pyiceberg.table import TableProperties +from pyiceberg.table.sorting import SortField, SortOrder +from pyiceberg.transforms import MonthTransform +from rustac import DuckdbClient + +from icestac.constants import DEFAULT_NAMESPACE +from icestac.write import put_items +from icestac.schema import get_schema_from_items + +logger = logging.getLogger("icestac-demo") + +HLS_STAC_GEOPARQUET_BUCKET = "nasa-maap-data-store" +HLS_STAC_GEOPARQUET_PREFIX = "file-staging/nasa-map/hls-stac-geoparquet-archive/v2" +MAX_ROW_GROUP_SIZE = 50_000 + + +def read_hls_items(client: DuckdbClient, path: Path) -> pa.Table: + """Read an HLS batch and sort it by bbox Hilbert index.""" + # Keep GeoParquet geometry as WKB for Iceberg's binary column. + client.execute("SET enable_geoparquet_conversion = false") + client.execute("SET TimeZone = 'UTC'") + items = pa.table( + client.query_to_table( + """ + SELECT *, + CASE WHEN isfinite(bbox.xmin) AND isfinite(bbox.ymin) + THEN ST_Hilbert( + bbox.xmin, bbox.ymin, + {min_x: -180.0, min_y: -90.0, + max_x: 180.0, max_y: 90.0}::BOX_2D + )::BIGINT + END AS hilbert_idx + FROM read_parquet(?, hive_partitioning = false) + ORDER BY hilbert_idx + """, + [str(path)], + ) + ) + if not len(items): + raise ValueError(f"No items found in {path}") + if items["hilbert_idx"].null_count: + raise ValueError(f"Missing or non-finite bbox coordinates in {path}") + return items + + +async def run() -> None: + """Load January–August 2026 HLS items into the local Iceberg catalog.""" + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s:%(name)s:%(message)s", + datefmt="%Y-%m-%dT%H:%M:%S%z", + ) + catalog = load_catalog() + client = DuckdbClient() + data_dir = Path("data") + data_dir.mkdir(exist_ok=True) + local_store = LocalStore(data_dir) + source_store = S3Store( + bucket=HLS_STAC_GEOPARQUET_BUCKET, + prefix=HLS_STAC_GEOPARQUET_PREFIX, + region="us-west-2", + skip_signature=True, + ) + collection_id = "HLSS30_2.0" + table = None + + for month in range(1, 9): + path = ( + f"{collection_id}/year=2026/month={month}/" + f"{collection_id}-2026-{month}.parquet" + ) + try: + local_store.head(path) + except FileNotFoundError: + logger.info("Downloading %s", path) + response = await source_store.get_async(path) + await local_store.put_async(path, response) + + logger.info("Reading and sorting %s", path) + items = read_hls_items(client, data_dir / path) + + if month == 1: + schema = get_schema_from_items(items) + datetime_id = schema.find_field("datetime").field_id + catalog.create_namespace_if_not_exists(DEFAULT_NAMESPACE) + try: + table = catalog.create_table( + identifier=(DEFAULT_NAMESPACE, collection_id), + schema=schema, + partition_spec=PartitionSpec( + PartitionField( + source_id=datetime_id, + field_id=1000, + transform=MonthTransform(), + name="datetime_month", + ) + ), + sort_order=SortOrder( + SortField(source_id=schema.find_field("hilbert_idx").field_id) + ), + properties={ + TableProperties.PARQUET_ROW_GROUP_LIMIT: str(MAX_ROW_GROUP_SIZE) + }, + ) + except TableAlreadyExistsError: + table = catalog.load_table((DEFAULT_NAMESPACE, collection_id)) + logger.info("Using existing table %s", collection_id) + + logger.info("Loading %s items for 2026-%02d", len(items), month) + assert table is not None + put_items(table, items, evolve_schema=True) + + +if __name__ == "__main__": + asyncio.run(run()) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c828005 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,58 @@ +[project] +name = "icestac" +version = "0.0.0" +description = "Manage STAC metadata in Apache Iceberg" +readme = "README.md" +authors = [ + { name = "hrodmn", email = "henry@developmentseed.org" } +] +classifiers = [ + "Intended Audience :: Developers", + "Intended Audience :: Information Technology", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", +] +requires-python = ">=3.11" +dependencies = [ + "pyarrow>=23.0.0", + "pyiceberg[pyiceberg-core]>=0.12.0", + "rustac[arrow]>=0.9.3", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[dependency-groups] +deploy = [ + "aws-cdk-lib>=2.236.0", +] +dev = [ + "obstore>=0.9.4", + "pre-commit>=4.5.1", + "pytest>=9.0.2", + "pytest-cov>=7.0.0", + "ruff>=0.15.2", + "sqlalchemy>=2.0.46", + "ty>=0.0.18", +] + +[tool.ty.rules] +all = "error" + +[tool.pytest.ini_options] +addopts = [ + "-v", + "--cov-config=pyproject.toml", + "--cov=src", + "--cov-report=term-missing" +] +filterwarnings = [ + "ignore::DeprecationWarning:pyiceberg.*", + "ignore::pydantic.PydanticDeprecatedSince20", + "ignore::DeprecationWarning:pydantic.*", +] diff --git a/src/icestac/__init__.py b/src/icestac/__init__.py new file mode 100644 index 0000000..646031a --- /dev/null +++ b/src/icestac/__init__.py @@ -0,0 +1 @@ +"""icestac""" diff --git a/src/icestac/constants.py b/src/icestac/constants.py new file mode 100644 index 0000000..4df4b90 --- /dev/null +++ b/src/icestac/constants.py @@ -0,0 +1,2 @@ +DEFAULT_NAMESPACE = "icestac" +COLLECTIONS_TABLE_NAME = "collections" diff --git a/src/icestac/schema.py b/src/icestac/schema.py new file mode 100644 index 0000000..131ed12 --- /dev/null +++ b/src/icestac/schema.py @@ -0,0 +1,145 @@ +import pyarrow as pa +from arro3.core import Schema as ArrowSchema +from arro3.core import Table as ArrowTable +from pyiceberg.io.pyarrow import _pyarrow_to_schema_without_ids, schema_to_pyarrow +from pyiceberg.schema import Schema as IcebergSchema +from pyiceberg.schema import assign_fresh_schema_ids +from pyiceberg.types import ( + BinaryType, + ListType, + StringType, + StructType, + TimestampType, + TimestamptzType, +) + +ItemsInput = pa.Table | ArrowTable +REQUIRED_FIELDS = frozenset( + {"geometry", "type", "id", "datetime", "links", "collection", "assets"} +) +NON_NULLABLE_FIELDS = frozenset({"type", "id", "links", "collection", "assets"}) +LINK_TYPE = pa.list_( + pa.struct( + [ + pa.field("href", pa.string(), nullable=False), + pa.field("rel", pa.string(), nullable=False), + pa.field("type", pa.string()), + pa.field("title", pa.string()), + ] + ) +) +EMPTY_ITEMS_ERROR = "Cannot infer or load a schema from an empty Arrow table" + + +def enforce_required_fields(schema: ArrowSchema) -> ArrowSchema: + """Make required fields non-null and give empty links a concrete type.""" + arrow_schema = pa.schema(schema) + fields = [] + for field in arrow_schema: + if field.name in NON_NULLABLE_FIELDS: + field = field.with_nullable(False) + if ( + field.name == "links" + and pa.types.is_list(field.type) + and pa.types.is_null(field.type.value_type) + ): + field = field.with_type(LINK_TYPE) + fields.append(field) + return ArrowSchema.from_arrow(pa.schema(fields, metadata=arrow_schema.metadata)) + + +def validate_schema(schema: pa.Schema | ArrowSchema | IcebergSchema) -> None: + """Validate the structural fields and types of a flattened STAC schema.""" + if isinstance(schema, IcebergSchema): + schema_fields = set(schema.column_names) + else: + schema_fields = set(schema.names) + missing_fields = REQUIRED_FIELDS - schema_fields + + if missing_fields: + raise ValueError( + f"Schema is missing required STAC fields: {sorted(missing_fields)}" + ) + + if isinstance(schema, IcebergSchema): + fields = {field.name: field.field_type for field in schema.fields} + expected = { + "type": StringType, + "id": StringType, + "collection": StringType, + "geometry": BinaryType, + "datetime": (TimestampType, TimestamptzType), + "links": ListType, + "assets": StructType, + } + invalid = [ + name + for name, field_type in expected.items() + if not isinstance(fields[name], field_type) + ] + else: + arrow_schema = pa.schema(schema) + invalid = [] + for name, predicate in ( + ("type", pa.types.is_string), + ("id", pa.types.is_string), + ("collection", pa.types.is_string), + ("geometry", pa.types.is_binary), + ("datetime", pa.types.is_timestamp), + ("links", pa.types.is_list), + ("assets", pa.types.is_struct), + ): + field_type = arrow_schema.field(name).type + if name in ("type", "id", "collection") and pa.types.is_dictionary( + field_type + ): + field_type = field_type.value_type + if not predicate(field_type): + invalid.append(name) + + if invalid: + raise ValueError( + "Unsupported types for STAC fields: " + ", ".join(sorted(invalid)) + ) + + +def _align_empty_links(items: pa.Table, schema: IcebergSchema) -> pa.Table: + """Align empty links with the table's nested schema before writing.""" + if not all(not links for links in items["links"].to_pylist()): + return items + + links_type = schema_to_pyarrow(schema, include_field_ids=False).field("links").type + links = items["links"].cast(links_type) + links_index = items.schema.get_field_index("links") + links_field = ( + items.schema.field(links_index).with_type(links_type).with_nullable(False) + ) + return items.set_column(links_index, links_field, links) + + +def prepare_arrow_table(items: ItemsInput) -> pa.Table: + """Check an Arrow table's structural schema and normalize required fields.""" + if isinstance(items, ArrowTable): + table = pa.table(items) + elif isinstance(items, pa.Table): + table = items + else: + raise TypeError("items must be a pyarrow.Table or arro3.core.Table") + if len(table) == 0: + raise ValueError(EMPTY_ITEMS_ERROR) + + validate_schema(table.schema) + schema = enforce_required_fields(ArrowSchema.from_arrow(table.schema)) + return table.cast(pa.schema(schema)) + + +def get_schema_from_items(items: ItemsInput) -> IcebergSchema: + """Derive an Iceberg schema from a structurally valid Arrow table.""" + arrow_table = prepare_arrow_table(items) + schema_without_ids = _pyarrow_to_schema_without_ids(arrow_table.schema) + schema = assign_fresh_schema_ids(schema_without_ids) + return IcebergSchema( + *schema.fields, + schema_id=schema.schema_id, + identifier_field_ids=[schema.find_field("id").field_id], + ) diff --git a/src/icestac/write.py b/src/icestac/write.py new file mode 100644 index 0000000..6b6d4fe --- /dev/null +++ b/src/icestac/write.py @@ -0,0 +1,79 @@ +import warnings +from pyiceberg.table import Table, TableProperties +from pyiceberg.table.upsert_util import create_match_filter, has_duplicate_rows + +from icestac.constants import COLLECTIONS_TABLE_NAME +from icestac.schema import ItemsInput, _align_empty_links, prepare_arrow_table + + +def put_items( + table: Table, + items: ItemsInput, + *, + evolve_schema: bool = False, +) -> None: + """Replace or insert a batch of STAC items in an Iceberg table. + + Matching IDs are complete replacements: omitted optional fields are + cleared. Commit retries are disabled because PyIceberg cannot safely replay + the match against refreshed table state. Commit failures are propagated; + an unknown commit outcome requires caller reconciliation before retrying. + """ + + arrow_table = prepare_arrow_table(items) + expected_collection_id = table.name()[-1] + if expected_collection_id == COLLECTIONS_TABLE_NAME: + raise ValueError( + f"Cannot load items into reserved table {COLLECTIONS_TABLE_NAME!r}" + ) + + collection_ids = set(arrow_table.column("collection").unique().to_pylist()) + if collection_ids != {expected_collection_id}: + raise ValueError( + f"Items for {expected_collection_id!r} contain collection ids " + f"{sorted(map(str, collection_ids))}" + ) + + # PyIceberg retries a staged merge against refreshed metadata without + # rerunning the original lookup, which can duplicate an ID. + retry_metadata = table.metadata + previous_retries = retry_metadata.properties.get(TableProperties.COMMIT_NUM_RETRIES) + retry_metadata.properties[TableProperties.COMMIT_NUM_RETRIES] = "0" + + try: + with table.transaction() as transaction: + if evolve_schema: + with transaction.update_schema() as update: + update.union_by_name(arrow_table.schema) + + arrow_table = _align_empty_links( + arrow_table, transaction.table_metadata.schema() + ) + # Reject duplicate incoming IDs before staging any write. + if has_duplicate_rows(arrow_table, ["id"]): + raise ValueError( + "Duplicate rows found in source dataset based on the key " + "columns. No write executed" + ) + with warnings.catch_warnings(): + # New IDs legitimately have no existing records to delete. + warnings.filterwarnings( + "ignore", + message="^Delete operation did not match any records$", + category=UserWarning, + module=r"^pyiceberg\.table$", + ) + transaction.overwrite( + df=arrow_table, + overwrite_filter=create_match_filter(arrow_table, ["id"]), + ) + finally: + # A refresh during commit means another metadata object owns the + # policy; never overwrite it with this call's temporary value. + if table.metadata is retry_metadata: + properties = retry_metadata.properties + if properties.get(TableProperties.COMMIT_NUM_RETRIES) == "0": + if previous_retries is None: + properties.pop(TableProperties.COMMIT_NUM_RETRIES, None) + else: + properties[TableProperties.COMMIT_NUM_RETRIES] = previous_retries diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..dc8e569 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,99 @@ +import gc +import tempfile +from pathlib import Path +from typing import Any, Generator + +import pyarrow as pa +import pytest +import rustac +from pyiceberg.catalog import Catalog, load_catalog + +from icestac.constants import DEFAULT_NAMESPACE + + +@pytest.fixture +def temp_warehouse(): + """Create a temporary warehouse directory for testing.""" + with tempfile.TemporaryDirectory() as tmpdir: + yield Path(tmpdir) + + +@pytest.fixture +def test_catalog(temp_warehouse: Path) -> Generator[Catalog, None, None]: + """Create a temporary SQL catalog for testing.""" + catalog = load_catalog( + "test_catalog", + **{ + "type": "sql", + "uri": f"sqlite:///{temp_warehouse}/catalog.db", + "warehouse": str(temp_warehouse), + }, + ) + catalog.create_namespace_if_not_exists(DEFAULT_NAMESPACE) + yield catalog + + gc.collect() + catalog.close() + + +@pytest.fixture +def test_collection_id(): + """Provide a test collection ID.""" + return "test-collection" + + +@pytest.fixture +def sample_stac_item(): + """Provide a sample STAC item for testing.""" + return { + "type": "Feature", + "stac_version": "1.0.0", + "id": "test-item-001", + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [-180.0, -90.0], + [180.0, -90.0], + [180.0, 90.0], + [-180.0, 90.0], + [-180.0, -90.0], + ] + ], + }, + "bbox": [-180.0, -90.0, 180.0, 90.0], + "properties": { + "datetime": "2024-01-01T00:00:00Z", + "title": "Test Item", + }, + "collection": "test-collection", + "links": [], + "assets": { + "data": {"href": "https://example.com/data.tif", "type": "image/tiff"} + }, + } + + +@pytest.fixture +def sample_stac_items(sample_stac_item) -> list[dict[str, Any]]: + """Provide a list of sample STAC items for testing.""" + items = [] + for i in range(3): + item = sample_stac_item.copy() + item["id"] = f"test-item-{i:03d}" + item["properties"] = sample_stac_item["properties"].copy() + item["properties"]["datetime"] = f"2024-01-{i + 1:02d}T00:00:00Z" + items.append(item) + + return items + + +@pytest.fixture +def sample_stac_item_arrow_table(sample_stac_items) -> pa.Table: + return pa.table(rustac.to_arrow(sample_stac_items)) + + +@pytest.fixture +def items(sample_stac_item_arrow_table: pa.Table) -> pa.Table: + """Provide a batch in the public Arrow ingestion format.""" + return sample_stac_item_arrow_table diff --git a/tests/helpers.py b/tests/helpers.py new file mode 100644 index 0000000..35d7873 --- /dev/null +++ b/tests/helpers.py @@ -0,0 +1,14 @@ +from typing import Any + +import pyarrow as pa +import rustac + + +def items_to_arrow(items: list[dict[str, Any]]) -> pa.Table: + """Convert test STAC fixtures to the public Arrow input format.""" + return pa.table(rustac.to_arrow(items)) + + +def items_to_list(items: pa.Table) -> list[dict[str, Any]]: + """Reconstruct Arrow rows for test assertions.""" + return rustac.from_arrow(items)["features"] diff --git a/tests/test_main.py b/tests/test_main.py new file mode 100644 index 0000000..c36d1f4 --- /dev/null +++ b/tests/test_main.py @@ -0,0 +1,97 @@ +import asyncio +from copy import deepcopy +from pathlib import Path + +import pyarrow as pa +import pyarrow.parquet as pq +import pytest +from pyiceberg.table import TableProperties +from rustac import DuckdbClient, RustacError + +import main +from tests.helpers import items_to_arrow + + +@pytest.fixture +def duckdb_client(): + """Use the installed spatial extension without downloading during tests.""" + try: + return DuckdbClient(install_extensions=False) + except RustacError as exc: + pytest.skip(f"DuckDB extensions are not installed: {exc}") + + +def test_demo_loads_cached_batches_and_can_rerun( + tmp_path, monkeypatch, test_catalog, sample_stac_item, duckdb_client +) -> None: + """Load real Parquet through DuckDB and Iceberg, including schema evolution.""" + monkeypatch.chdir(tmp_path) + monkeypatch.setattr(main, "load_catalog", lambda: test_catalog) + monkeypatch.setattr(main, "DuckdbClient", lambda: duckdb_client) + for month in range(1, 9): + batch = [] + for index, xmin in enumerate((120.0, -120.0, 0.0)): + item = deepcopy(sample_stac_item) + item["id"] = f"{month}-{index}" + item["collection"] = "HLSS30_2.0" + item["bbox"] = [xmin, 0.0, xmin + 1, 1.0] + item["properties"]["datetime"] = f"2026-{month:02d}-01T00:00:00Z" + if month >= 6: + item["properties"]["new_field"] = True + batch.append(item) + path = Path( + f"data/HLSS30_2.0/year=2026/month={month}/HLSS30_2.0-2026-{month}.parquet" + ) + path.parent.mkdir(parents=True) + pq.write_table(items_to_arrow(batch), path) + prepared = main.read_hls_items(duckdb_client, path) + keys = prepared["hilbert_idx"].to_pylist() + assert keys == sorted(keys) + assert len(set(keys)) == 3 + assert pa.types.is_binary(prepared.schema.field("geometry").type) + assert "year" not in prepared.column_names + assert "month" not in prepared.column_names + + asyncio.run(main.run()) + asyncio.run(main.run()) + + table = test_catalog.load_table(("icestac", "HLSS30_2.0")) + result = table.scan().to_arrow() + assert len(result) == 24 + assert len(result["id"].unique()) == 24 + assert result["collection"].unique().to_pylist() == ["HLSS30_2.0"] + assert table.schema().identifier_field_ids == [ + table.schema().find_field("id").field_id + ] + assert result["new_field"].null_count == 15 + assert table.properties[TableProperties.PARQUET_ROW_GROUP_LIMIT] == "50000" + assert ( + table.sort_order().fields[0].source_id + == table.schema().find_field("hilbert_idx").field_id + ) + + +@pytest.mark.parametrize("case", ["empty", "null_bbox", "nonfinite_bbox"]) +def test_read_hls_items_rejects_invalid_batches( + tmp_path, items, duckdb_client, case +) -> None: + """Reject unusable batches before creating or writing an Iceberg table.""" + if case == "empty": + items = items.slice(0, 0) + else: + bbox_type = items.schema.field("bbox").type + value = ( + None + if case == "null_bbox" + else {"xmin": float("nan"), "ymin": 0.0, "xmax": 1.0, "ymax": 1.0} + ) + items = items.set_column( + items.schema.get_field_index("bbox"), + "bbox", + pa.array([value] * len(items), type=bbox_type), + ) + path = tmp_path / "items.parquet" + pq.write_table(items, path) + + with pytest.raises(ValueError, match="No items found|bbox coordinates"): + main.read_hls_items(duckdb_client, path) diff --git a/tests/test_schema.py b/tests/test_schema.py new file mode 100644 index 0000000..3df4438 --- /dev/null +++ b/tests/test_schema.py @@ -0,0 +1,141 @@ +import pyarrow as pa +import pytest +from pyiceberg.schema import Schema +from pyiceberg.types import ListType +import rustac + +from icestac.schema import get_schema_from_items, validate_schema + + +def test_get_schema_from_items(items: pa.Table) -> None: + """Derive an Iceberg schema from a flattened Arrow table.""" + schema = get_schema_from_items(items) + + assert isinstance(schema, Schema) + assert schema.find_field("title").field_id > 0 + assert schema.find_field("id").required + assert schema.identifier_field_ids == [schema.find_field("id").field_id] + assert str(schema.find_field("geometry").field_type) == "binary" + + nested_paths = ( + "bbox.xmin", + "bbox.ymax", + "links.element.href", + "assets.data.href", + ) + nested_ids = [schema.find_field(path).field_id for path in nested_paths] + assert all(field_id > 0 for field_id in nested_ids) + assert len(nested_ids) == len(set(nested_ids)) + links_type = schema.find_field("links").field_type + assert isinstance(links_type, ListType) + assert links_type.element_id > 0 + + +def test_get_schema_from_items_rejects_empty_table(items: pa.Table) -> None: + with pytest.raises( + ValueError, match="Cannot infer or load a schema from an empty Arrow table" + ): + get_schema_from_items(items.slice(0, 0)) + + +def test_public_ingestion_rejects_dictionary_inputs(sample_stac_item) -> None: + for items in (sample_stac_item, [sample_stac_item]): + with pytest.raises(TypeError, match="pyarrow.Table or arro3.core.Table"): + get_schema_from_items(items) + + +def test_public_ingestion_accepts_arro3_table(sample_stac_item) -> None: + items = rustac.to_arrow([sample_stac_item]) + + assert get_schema_from_items(items).find_field("id") + + +def test_get_schema_from_items_accepts_semantically_unvalidated_arrow( + sample_stac_item, +) -> None: + invalid_temporal_item = { + **sample_stac_item, + "properties": {"datetime": None}, + } + items = pa.table(rustac.to_arrow([invalid_temporal_item])) + + assert get_schema_from_items(items).find_field("datetime") + + +def test_get_schema_from_items_rejects_missing_required_field(items: pa.Table) -> None: + with pytest.raises(ValueError, match="missing required STAC fields.*'id'"): + get_schema_from_items(items.drop(["id"])) + + +def test_get_schema_from_items_rejects_invalid_field_type(items: pa.Table) -> None: + index = items.schema.get_field_index("id") + invalid = items.set_column(index, "id", pa.array([1, 2, 3], type=pa.int64())) + + with pytest.raises(ValueError, match="Unsupported types for STAC fields: id"): + get_schema_from_items(invalid) + + +def test_get_schema_from_items_rejects_non_string_type(items: pa.Table) -> None: + index = items.schema.get_field_index("type") + invalid = items.set_column(index, "type", pa.array([1, 2, 3], type=pa.int64())) + + with pytest.raises(ValueError, match="Unsupported types for STAC fields: type"): + get_schema_from_items(invalid) + + +def test_get_schema_from_items_rejects_null_required_field(items: pa.Table) -> None: + index = items.schema.get_field_index("id") + invalid = items.set_column( + index, "id", pa.array([None, None, None], type=pa.string()) + ) + + with pytest.raises(ValueError, match="Casting field 'id' with null values"): + get_schema_from_items(invalid) + + +def test_get_schema_from_items_rejects_null_links(items: pa.Table) -> None: + """Empty-link normalization must not replace null links with empty lists.""" + invalid = items.set_column( + items.schema.get_field_index("links"), + "links", + pa.array([[], None, []], type=pa.list_(pa.null())), + ) + + with pytest.raises(ValueError, match="Casting field 'links' with null values"): + get_schema_from_items(invalid) + + +def test_validate_schema_valid(items: pa.Table) -> None: + """A valid inferred schema passes structural validation.""" + validate_schema(get_schema_from_items(items)) + + +def test_validate_schema_missing_required_field() -> None: + schema = pa.schema( + [ + ("type", pa.string()), + ("geometry", pa.binary()), + ("collection", pa.string()), + ("datetime", pa.timestamp("ms")), + ] + ) + + with pytest.raises(ValueError, match="missing required STAC fields.*'id'"): + validate_schema(schema) + + +def test_validate_schema_rejects_invalid_field_type() -> None: + schema = pa.schema( + [ + ("type", pa.string()), + ("id", pa.int64()), + ("geometry", pa.binary()), + ("collection", pa.string()), + ("datetime", pa.timestamp("ms")), + ("links", pa.list_(pa.string())), + ("assets", pa.struct([])), + ] + ) + + with pytest.raises(ValueError, match="Unsupported types for STAC fields: id"): + validate_schema(schema) diff --git a/tests/test_write.py b/tests/test_write.py new file mode 100644 index 0000000..128c96f --- /dev/null +++ b/tests/test_write.py @@ -0,0 +1,673 @@ +from concurrent.futures import ThreadPoolExecutor +from copy import deepcopy +from threading import Barrier +from typing import Any + +import pyarrow as pa +import pytest +import rustac +from pyiceberg.catalog import Catalog +from pyiceberg.exceptions import CommitFailedException +from pyiceberg.table import Table, TableProperties + +from icestac.constants import COLLECTIONS_TABLE_NAME, DEFAULT_NAMESPACE +from icestac.schema import get_schema_from_items +from icestac.write import put_items +from tests.helpers import items_to_arrow, items_to_list + + +@pytest.mark.filterwarnings( + "error:Delete operation did not match any records:UserWarning" +) +def test_put_items_inserts_items( + test_catalog: Catalog, + test_collection_id: str, + items: pa.Table, +) -> None: + """Insert a batch into a native Iceberg table.""" + expected_items = items_to_list(items) + + # Create the table + iceberg_schema = get_schema_from_items(items) + assert iceberg_schema.identifier_field_ids == [ + iceberg_schema.find_field("id").field_id + ] + table = test_catalog.create_table( + schema=iceberg_schema, + identifier=(DEFAULT_NAMESPACE, test_collection_id), + ) + + # Put items through the standalone writer. + put_items(table, items) + + # Verify records were inserted + result = table.scan().to_arrow() + assert len(result) == len(expected_items) + assert sorted(result.column("id").to_pylist()) == sorted( + item["id"] for item in expected_items + ) + + +def test_put_items_accepts_dotted_collection_ids( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + item = deepcopy(sample_stac_item) + item["collection"] = "foo.bar" + items = items_to_arrow([item]) + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, item["collection"]), + ) + + put_items(table, items) + + assert table.name() == (DEFAULT_NAMESPACE, "foo.bar") + assert table.scan().to_arrow().column("collection").to_pylist() == ["foo.bar"] + + +def test_put_items_rejects_different_collection( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + items = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + different = deepcopy(sample_stac_item) + different["collection"] = "other-collection" + + with pytest.raises(ValueError, match="other-collection"): + put_items(table, items_to_arrow([different])) + + assert len(table.scan().to_arrow()) == 0 + + +def test_put_items_rejects_reserved_collections_table( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, COLLECTIONS_TABLE_NAME), + ) + evolved = deepcopy(sample_stac_item) + evolved["properties"]["new_field"] = True + + with pytest.raises(ValueError, match="reserved table 'collections'"): + put_items(table, items_to_arrow([evolved]), evolve_schema=True) + + table.refresh() + assert len(table.scan().to_arrow()) == 0 + with pytest.raises(ValueError, match="Could not find field"): + table.schema().find_field("new_field") + + +def test_put_items_inserts_items_explicitly( + test_catalog: Catalog, + items: pa.Table, +) -> None: + """Insert a batch through the standalone writer.""" + expected_items = items_to_list(items) + + # Create the table + iceberg_schema = get_schema_from_items(items) + table = test_catalog.create_table( + schema=iceberg_schema, + identifier=(DEFAULT_NAMESPACE, expected_items[0]["collection"]), + ) + + # Put items through the standalone writer. + put_items(table, items) + + # Verify records were inserted + result = table.scan().to_arrow() + assert len(result) == len(expected_items) + + +def test_put_items_replaces_existing_items( + test_catalog: Catalog, + items: pa.Table, +) -> None: + """Replace existing records with the same ID.""" + expected_items = items_to_list(items) + + # Create the table + iceberg_schema = get_schema_from_items(items) + table = test_catalog.create_table( + schema=iceberg_schema, + identifier=(DEFAULT_NAMESPACE, expected_items[0]["collection"]), + ) + + # Put initial items + put_items(table, items) + + # Modify items (same IDs but different data) + modified_items = [] + for item in expected_items: + modified_item = item.copy() + modified_item["properties"] = item["properties"].copy() + modified_item["properties"]["title"] = f"Updated {item['properties']['title']}" + modified_items.append(modified_item) + + # Replace the existing items. + put_items(table, items_to_arrow(modified_items)) + + # Verify only the original record count exists and they have updated titles + result = table.scan().to_arrow() + assert len(result) == len(expected_items) + + # Check that titles were updated + titles = result.column("title").to_pylist() + assert all(title.startswith("Updated") for title in titles) + + +def test_put_items_reconstructs_evolved_existing_item( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + """An existing item can gain a top-level property during replacement.""" + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + put_items(table, initial) + + replacement = deepcopy(sample_stac_item) + replacement["properties"]["processing:software"] = {"version": "1.0"} + put_items(table, items_to_arrow([replacement]), evolve_schema=True) + + reconstructed = rustac.from_arrow(table.scan().to_arrow())["features"] + assert len(reconstructed) == 1 + assert reconstructed[0]["id"] == replacement["id"] + assert reconstructed[0]["properties"] == replacement["properties"] + + +def test_put_items_reconstructs_nested_asset_and_property_evolution( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + """Nested asset fields and flattened properties evolve in one replacement.""" + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + put_items(table, initial) + + replacement = deepcopy(sample_stac_item) + replacement["properties"]["processing:software"] = {"version": "1.0"} + replacement["assets"]["data"]["roles"] = ["data"] + replacement["assets"]["thumbnail"] = { + "href": "https://example.com/thumbnail.jpg", + "type": "image/jpeg", + } + put_items(table, items_to_arrow([replacement]), evolve_schema=True) + + reconstructed = rustac.from_arrow(table.scan().to_arrow())["features"][0] + assert reconstructed["properties"]["processing:software"] == {"version": "1.0"} + assert reconstructed["assets"] == replacement["assets"] + + +def test_put_items_clears_omitted_optional_fields( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + """Omitted optional values are cleared instead of being retained.""" + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + put_items(table, initial) + + replacement = deepcopy(sample_stac_item) + replacement["properties"] = {"datetime": sample_stac_item["properties"]["datetime"]} + replacement["assets"]["data"] = {"href": "https://example.com/replacement.tif"} + put_items(table, items_to_arrow([replacement])) + + reconstructed = rustac.from_arrow(table.scan().to_arrow())["features"][0] + assert "title" not in reconstructed["properties"] + assert reconstructed["assets"] == { + "data": {"href": replacement["assets"]["data"]["href"]} + } + + +@pytest.mark.parametrize("populate_first", [False, True]) +def test_competing_replacement_writes_do_not_duplicate_ids( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], + monkeypatch: pytest.MonkeyPatch, + populate_first: bool, +) -> None: + """Concurrent replacement writes fail rather than replay stale matches.""" + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + if populate_first: + put_items(table, initial) + first = test_catalog.load_table(table.name()) + second = test_catalog.load_table(table.name()) + assert TableProperties.COMMIT_NUM_RETRIES not in first.metadata.properties + + first_item = deepcopy(sample_stac_item) + first_item["properties"]["title"] = "First" + second_item = deepcopy(sample_stac_item) + second_item["properties"]["title"] = "Second" + barrier = Barrier(2) + original_commit = Table._do_commit + + def synchronized_commit(self, updates, requirements): + barrier.wait(timeout=10) + return original_commit(self, updates, requirements) + + monkeypatch.setattr(Table, "_do_commit", synchronized_commit) + + def write(item: dict[str, Any], target: Table) -> Exception | None: + try: + put_items(target, items_to_arrow([item])) + except Exception as error: # noqa: BLE001 + return error + return None + + with ThreadPoolExecutor(max_workers=2) as executor: + errors = list( + executor.map( + lambda args: write(*args), + ((first_item, first), (second_item, second)), + ) + ) + + assert sum(error is not None for error in errors) == 1 + assert any(isinstance(error, CommitFailedException) for error in errors) + table.refresh() + result = table.scan().to_arrow() + assert len(result) == 1 + assert result.column("id").to_pylist() == [sample_stac_item["id"]] + + +def test_failed_put_restores_retry_policy( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], + monkeypatch: pytest.MonkeyPatch, +) -> None: + items = items_to_arrow([sample_stac_item]) + retry_property = TableProperties.COMMIT_NUM_RETRIES + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + properties={retry_property: "7"}, + ) + + def fail_commit(self, updates, requirements): + raise CommitFailedException("controlled commit failure") + + monkeypatch.setattr(Table, "_do_commit", fail_commit) + with pytest.raises(CommitFailedException, match="controlled commit failure"): + put_items(table, items) + + assert table.metadata.properties[retry_property] == "7" + + +def test_successful_put_restores_retry_policy( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + items = items_to_arrow([sample_stac_item]) + retry_property = TableProperties.COMMIT_NUM_RETRIES + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + properties={retry_property: "7"}, + ) + + put_items(table, items) + + assert table.metadata.properties[retry_property] == "7" + + +def test_failed_put_does_not_overwrite_refreshed_retry_policy( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], + monkeypatch: pytest.MonkeyPatch, +) -> None: + items = items_to_arrow([sample_stac_item]) + retry_property = TableProperties.COMMIT_NUM_RETRIES + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + properties={retry_property: "7"}, + ) + + def refresh_then_fail(self, updates, requirements): + self.refresh() + raise CommitFailedException("controlled commit failure") + + monkeypatch.setattr(Table, "_do_commit", refresh_then_fail) + with pytest.raises(CommitFailedException, match="controlled commit failure"): + put_items(table, items) + + assert table.metadata.properties[retry_property] == "7" + + +def test_put_items_multiple_batches( + test_catalog: Catalog, + items: pa.Table, +) -> None: + """Put multiple batches into one table.""" + expected_items = items_to_list(items) + + # Create the table + iceberg_schema = get_schema_from_items(items) + table = test_catalog.create_table( + schema=iceberg_schema, + identifier=(DEFAULT_NAMESPACE, expected_items[0]["collection"]), + ) + + # Put the first batch + put_items(table, items_to_arrow(expected_items[:2])) + + result = table.scan().to_arrow() + assert len(result) == min(2, len(expected_items)) + + # Put the second batch + if len(expected_items) > 2: + put_items(table, items_to_arrow(expected_items[2:])) + + result = table.scan().to_arrow() + assert len(result) == len(expected_items) + + +def test_put_items_supports_interval_datetime( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + interval_item = deepcopy(sample_stac_item) + interval_item["properties"] = { + "datetime": None, + "start_datetime": "2024-01-01T00:00:00Z", + "end_datetime": "2024-01-02T00:00:00Z", + } + interval_items = items_to_arrow([interval_item]) + iceberg_schema = get_schema_from_items(interval_items) + table = test_catalog.create_table( + schema=iceberg_schema, + identifier=(DEFAULT_NAMESPACE, interval_item["collection"]), + ) + + put_items(table, interval_items) + + assert table.scan().to_arrow().column("id").to_pylist() == [interval_item["id"]] + + +def test_put_items_different_schema( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + initial = items_to_arrow([sample_stac_item]) + iceberg_schema = get_schema_from_items(initial) + table = test_catalog.create_table( + schema=iceberg_schema, + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + put_items(table, initial) + + item_new_schema = deepcopy(sample_stac_item) + item_new_schema["properties"]["new_field"] = True + with pytest.raises(ValueError, match="Update the schema first"): + put_items(table, items_to_arrow([item_new_schema])) + + +def test_put_items_evolves_schema( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + put_items(table, initial) + + evolved_item = deepcopy(sample_stac_item) + evolved_item["id"] = "evolved-item" + evolved_item["properties"]["processing:software"] = { + "Atmospheric Correction": "6.0" + } + put_items(table, items_to_arrow([evolved_item]), evolve_schema=True) + + table.refresh() + assert table.schema().find_field("processing:software.Atmospheric Correction") + assert len(table.scan().to_arrow()) == 2 + + +def test_populated_link_fields_survive_reconstruction( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + item = deepcopy(sample_stac_item) + item["links"] = [ + { + "href": "https://example.com/query", + "rel": "data", + "type": "application/json", + "title": "Query", + "method": "POST", + "headers": {"content-type": "application/json"}, + "body": {"limit": 1}, + "merge": True, + } + ] + input_items = items_to_arrow([item]) + table = test_catalog.create_table( + schema=get_schema_from_items(input_items), + identifier=(DEFAULT_NAMESPACE, item["collection"]), + ) + + put_items(table, input_items) + + reconstructed = rustac.from_arrow(table.scan().to_arrow())["features"][0] + assert reconstructed["links"] == item["links"] + + +def test_put_items_clears_extended_links_without_schema_evolution( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + populated = deepcopy(sample_stac_item) + populated["links"] = [ + { + "href": "https://example.com/query", + "rel": "data", + "method": "POST", + "headers": {"content-type": "application/json"}, + "body": {"limit": 1}, + "merge": True, + } + ] + populated_table = items_to_arrow([populated]) + table = test_catalog.create_table( + schema=get_schema_from_items(populated_table), + identifier=(DEFAULT_NAMESPACE, populated["collection"]), + ) + put_items(table, populated_table) + + replacement = deepcopy(populated) + replacement["links"] = [] + put_items(table, items_to_arrow([replacement])) + + reconstructed = rustac.from_arrow(table.scan().to_arrow())["features"][0] + assert reconstructed["links"] == [] + + +def test_populated_links_require_evolution_after_empty_schema( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + empty_links = deepcopy(sample_stac_item) + empty_links["links"] = [] + empty_links_table = items_to_arrow([empty_links]) + table = test_catalog.create_table( + schema=get_schema_from_items(empty_links_table), + identifier=(DEFAULT_NAMESPACE, empty_links["collection"]), + ) + put_items(table, empty_links_table) + + populated = deepcopy(sample_stac_item) + populated["id"] = "populated-links" + populated["links"] = [ + { + "href": "https://example.com/query", + "rel": "data", + "method": "POST", + "headers": {"content-type": "application/json"}, + "body": {"limit": 1}, + "merge": True, + } + ] + populated_table = items_to_arrow([populated]) + with pytest.raises(ValueError, match="Update the schema first"): + put_items(table, populated_table) + + put_items(table, populated_table, evolve_schema=True) + reconstructed = rustac.from_arrow(table.scan().to_arrow())["features"] + assert ( + next(item for item in reconstructed if item["id"] == populated["id"])["links"] + == populated["links"] + ) + + +def test_arrow_temporal_semantics_are_callers_responsibility( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + invalid = deepcopy(sample_stac_item) + invalid["properties"] = { + "datetime": None, + "start_datetime": "2024-01-02T00:00:00Z", + "end_datetime": "2024-01-01T00:00:00Z", + } + arrow_items = items_to_arrow([invalid]) + table = test_catalog.create_table( + schema=get_schema_from_items(arrow_items), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + + put_items(table, arrow_items) + + assert table.scan().to_arrow().column("id").to_pylist() == [sample_stac_item["id"]] + + +def test_arrow_structural_edge_cases_follow_arrow_schema( + sample_stac_item: dict[str, Any], +) -> None: + arrow = items_to_arrow([sample_stac_item]) + geometry_index = arrow.schema.get_field_index("geometry") + null_geometry = arrow.set_column( + geometry_index, + "geometry", + pa.array([None], type=pa.binary()), + ) + + assert get_schema_from_items(null_geometry).find_field("geometry") + + +def test_put_items_accepts_arro3_table( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + items = rustac.to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + + put_items(table, items) + + assert table.scan().to_arrow().column("id").to_pylist() == [sample_stac_item["id"]] + + +def test_put_items_accepts_dictionary_columns( + test_catalog: Catalog, + items: pa.Table, +) -> None: + """Dictionary-encoded columns can be written without manual re-encoding.""" + for name in ("id", "collection", "title"): + items = items.set_column( + items.schema.get_field_index(name), name, items[name].dictionary_encode() + ) + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, items["collection"][0].as_py()), + ) + + put_items(table, items) + + result = table.scan().to_arrow() + assert sorted(zip(result["id"].to_pylist(), result["title"].to_pylist())) == sorted( + zip(items["id"].to_pylist(), items["title"].to_pylist()) + ) + + +def test_put_items_rejects_non_arrow_inputs( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + + with pytest.raises(TypeError, match="pyarrow.Table"): + put_items(table, sample_stac_item) + + +def test_put_items_rejects_invalid_arrow_schema( + test_catalog: Catalog, + items: pa.Table, +) -> None: + table = test_catalog.create_table( + schema=get_schema_from_items(items), + identifier=(DEFAULT_NAMESPACE, "test-collection"), + ) + id_index = items.schema.get_field_index("id") + invalid = items.set_column( + id_index, + "id", + pa.array([1, 2, 3], type=pa.int64()), + ) + + with pytest.raises(ValueError, match="Unsupported types for STAC fields: id"): + put_items(table, invalid) + assert len(table.scan().to_arrow()) == 0 + + +def test_put_items_does_not_evolve_schema_when_write_fails( + test_catalog: Catalog, + sample_stac_item: dict[str, Any], +) -> None: + initial = items_to_arrow([sample_stac_item]) + table = test_catalog.create_table( + schema=get_schema_from_items(initial), + identifier=(DEFAULT_NAMESPACE, sample_stac_item["collection"]), + ) + put_items(table, initial) + before = rustac.from_arrow(table.scan().to_arrow())["features"] + + evolved_item = deepcopy(sample_stac_item) + evolved_item["properties"]["new_field"] = True + + evolved = items_to_arrow([evolved_item, evolved_item]) + with pytest.raises(ValueError, match="Duplicate rows"): + put_items(table, evolved, evolve_schema=True) + + table.refresh() + assert rustac.from_arrow(table.scan().to_arrow())["features"] == before + with pytest.raises(ValueError, match="Could not find field"): + table.schema().find_field("new_field") diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..b7af234 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1552 @@ 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