Add shards to array strategy (zarr-developers#2822)

* Add shards to array strategy

* Prioritize v3 over v2 in property tests

---------

Co-authored-by: Davis Bennett <[email protected]>

Author: dcherian

changes/2822.feature.rst

@@ -0,0 +1 @@
+Add arbitrary `shards` to Hypothesis strategy for generating arrays.

src/zarr/testing/strategies.py

@@ -96,7 +96,7 @@ def clear_store(x: Store) -> Store:
 # So we map a clear to reset the store.
 stores = st.builds(MemoryStore, st.just({})).map(clear_store)
 compressors = st.sampled_from([None, "default"])
-zarr_formats: st.SearchStrategy[ZarrFormat] = st.sampled_from([2, 3])
+zarr_formats: st.SearchStrategy[ZarrFormat] = st.sampled_from([3, 2])
 array_shapes = npst.array_shapes(max_dims=4, min_side=0)
 
 
@@ -166,6 +166,32 @@ def numpy_arrays(
     return draw(npst.arrays(dtype=dtype, shape=shapes))
 
 
+@st.composite  # type: ignore[misc]
+def chunk_shapes(draw: st.DrawFn, *, shape: tuple[int, ...]) -> tuple[int, ...]:
+    # We want this strategy to shrink towards arrays with smaller number of chunks
+    # 1. st.integers() shrinks towards smaller values. So we use that to generate number of chunks
+    numchunks = draw(
+        st.tuples(*[st.integers(min_value=0 if size == 0 else 1, max_value=size) for size in shape])
+    )
+    # 2. and now generate the chunks tuple
+    return tuple(
+        size // nchunks if nchunks > 0 else 0
+        for size, nchunks in zip(shape, numchunks, strict=True)
+    )
+
+
+@st.composite  # type: ignore[misc]
+def shard_shapes(
+    draw: st.DrawFn, *, shape: tuple[int, ...], chunk_shape: tuple[int, ...]
+) -> tuple[int, ...]:
+    # We want this strategy to shrink towards arrays with smaller number of shards
+    # shards must be an integral number of chunks
+    assert all(c != 0 for c in chunk_shape)
+    numchunks = tuple(s // c for s, c in zip(shape, chunk_shape, strict=True))
+    multiples = tuple(draw(st.integers(min_value=1, max_value=nc)) for nc in numchunks)
+    return tuple(m * c for m, c in zip(multiples, chunk_shape, strict=True))
+
+
 @st.composite  # type: ignore[misc]
 def np_array_and_chunks(
     draw: st.DrawFn, *, arrays: st.SearchStrategy[np.ndarray] = numpy_arrays
@@ -175,19 +201,7 @@ def np_array_and_chunks(
     Returns: a tuple of the array and a suitable random chunking for it.
     """
     array = draw(arrays)
-    # We want this strategy to shrink towards arrays with smaller number of chunks
-    # 1. st.integers() shrinks towards smaller values. So we use that to generate number of chunks
-    numchunks = draw(
-        st.tuples(
-            *[st.integers(min_value=0 if size == 0 else 1, max_value=size) for size in array.shape]
-        )
-    )
-    # 2. and now generate the chunks tuple
-    chunks = tuple(
-        size // nchunks if nchunks > 0 else 0
-        for size, nchunks in zip(array.shape, numchunks, strict=True)
-    )
-    return (array, chunks)
+    return (array, draw(chunk_shapes(shape=array.shape)))
 
 
 @st.composite  # type: ignore[misc]
@@ -210,7 +224,12 @@ def arrays(
     zarr_format = draw(zarr_formats)
     if arrays is None:
         arrays = numpy_arrays(shapes=shapes, zarr_formats=st.just(zarr_format))
-    nparray, chunks = draw(np_array_and_chunks(arrays=arrays))
+    nparray = draw(arrays)
+    chunk_shape = draw(chunk_shapes(shape=nparray.shape))
+    if zarr_format == 3 and all(c > 0 for c in chunk_shape):
+        shard_shape = draw(st.none() | shard_shapes(shape=nparray.shape, chunk_shape=chunk_shape))
+    else:
+        shard_shape = None
     # test that None works too.
     fill_value = draw(st.one_of([st.none(), npst.from_dtype(nparray.dtype)]))
     # compressor = draw(compressors)
@@ -223,7 +242,8 @@ def arrays(
     a = root.create_array(
         array_path,
         shape=nparray.shape,
-        chunks=chunks,
+        chunks=chunk_shape,
+        shards=shard_shape,
         dtype=nparray.dtype,
         attributes=attributes,
         # compressor=compressor,  # FIXME
@@ -236,7 +256,8 @@ def arrays(
     assert a.name is not None
     assert isinstance(root[array_path], Array)
     assert nparray.shape == a.shape
-    assert chunks == a.chunks
+    assert chunk_shape == a.chunks
+    assert shard_shape == a.shards
     assert array_path == a.path, (path, name, array_path, a.name, a.path)
     assert a.basename == name, (a.basename, name)
     assert dict(a.attrs) == expected_attrs