Add batched tests (#73)

stac_geoparquet/arrow/_api.py

@@ -146,6 +146,10 @@ def stac_table_to_ndjson(
 ) -> None:
     """Write STAC Arrow to a newline-delimited JSON file.
 
+    !!! note
+        This function _appends_ to the JSON file at `dest`; it does not overwrite any
+        existing data.
+
     Args:
         table: STAC in Arrow form. This can be a pyarrow Table, a pyarrow
             RecordBatchReader, or any other Arrow stream object exposed through the

tests/test_arrow.py

@@ -1,3 +1,4 @@
+import itertools
 import json
 from io import BytesIO
 from pathlib import Path
@@ -7,6 +8,7 @@
 import pytest
 
 from stac_geoparquet.arrow import (
+    DEFAULT_JSON_CHUNK_SIZE,
     parse_stac_items_to_arrow,
     parse_stac_ndjson_to_arrow,
     stac_table_to_items,
@@ -33,38 +35,54 @@
     "us-census",
 ]
 
+CHUNK_SIZES = [2, DEFAULT_JSON_CHUNK_SIZE]
 
-@pytest.mark.parametrize("collection_id", TEST_COLLECTIONS)
-def test_round_trip_read_write(collection_id: str):
+
+@pytest.mark.parametrize(
+    "collection_id,chunk_size", itertools.product(TEST_COLLECTIONS, CHUNK_SIZES)
+)
+def test_round_trip_read_write(collection_id: str, chunk_size: int):
     with open(HERE / "data" / f"{collection_id}-pc.json") as f:
         items = json.load(f)
 
-    table = pa.Table.from_batches(parse_stac_items_to_arrow(items))
+    table = parse_stac_items_to_arrow(items, chunk_size=chunk_size).read_all()
     items_result = list(stac_table_to_items(table))
 
     for result, expected in zip(items_result, items):
         assert_json_value_equal(result, expected, precision=0)
 
 
-@pytest.mark.parametrize("collection_id", TEST_COLLECTIONS)
-def test_round_trip_write_read_ndjson(collection_id: str, tmp_path: Path):
+@pytest.mark.parametrize(
+    "collection_id,chunk_size", itertools.product(TEST_COLLECTIONS, CHUNK_SIZES)
+)
+def test_round_trip_write_read_ndjson(
+    collection_id: str, chunk_size: int, tmp_path: Path
+):
     # First load into a STAC-GeoParquet table
     path = HERE / "data" / f"{collection_id}-pc.json"
-    table = pa.Table.from_batches(parse_stac_ndjson_to_arrow(path))
+    table = parse_stac_ndjson_to_arrow(path, chunk_size=chunk_size).read_all()
 
     # Then write to disk
     stac_table_to_ndjson(table, tmp_path / "tmp.ndjson")
 
-    # Then read back and assert tables match
-    table = pa.Table.from_batches(parse_stac_ndjson_to_arrow(tmp_path / "tmp.ndjson"))
+    with open(path) as f:
+        orig_json = json.load(f)
+
+    rt_json = []
+    with open(tmp_path / "tmp.ndjson") as f:
+        for line in f:
+            rt_json.append(json.loads(line))
+
+    # Then read back and assert JSON data matches
+    assert_json_value_equal(orig_json, rt_json, precision=0)
 
 
 def test_table_contains_geoarrow_metadata():
     collection_id = "naip"
     with open(HERE / "data" / f"{collection_id}-pc.json") as f:
         items = json.load(f)
 
-    table = pa.Table.from_batches(parse_stac_items_to_arrow(items))
+    table = parse_stac_items_to_arrow(items).read_all()
     field_meta = table.schema.field("geometry").metadata
     assert field_meta[b"ARROW:extension:name"] == b"geoarrow.wkb"
     assert json.loads(field_meta[b"ARROW:extension:metadata"])["crs"]["id"] == {
@@ -107,7 +125,7 @@ def test_to_parquet_two_geometry_columns():
     with open(HERE / "data" / "3dep-lidar-copc-pc.json") as f:
         items = json.load(f)
 
-    table = pa.Table.from_batches(parse_stac_items_to_arrow(items))
+    table = parse_stac_items_to_arrow(items).read_all()
     with BytesIO() as bio:
         to_parquet(table, bio)
         bio.seek(0)