perf:Add additional sharding benchmarks

changes/3712.misc.md

@@ -0,0 +1 @@
+Added benchmarks for Morton order computation in sharded arrays.

tests/benchmarks/test_indexing.py

@@ -50,3 +50,218 @@ def test_slice_indexing(
 
     data[:] = 1
     benchmark(getitem, data, indexer)
+
+
+# Benchmark for Morton order optimization with power-of-2 shards
+# Morton order is used internally by sharding codec for chunk iteration
+morton_shards = (
+    (16,) * 3,  # With 2x2x2 chunks: 8x8x8 = 512 chunks per shard
+    (32,) * 3,  # With 2x2x2 chunks: 16x16x16 = 4096 chunks per shard
+)
+
+
+@pytest.mark.parametrize("store", ["memory"], indirect=["store"])
+@pytest.mark.parametrize("shards", morton_shards, ids=str)
+def test_sharded_morton_indexing(
+    store: Store,
+    shards: tuple[int, ...],
+    benchmark: BenchmarkFixture,
+) -> None:
+    """Benchmark sharded array indexing with power-of-2 chunks per shard.
+
+    This benchmark exercises the Morton order iteration path in the sharding
+    codec, which benefits from the hypercube and vectorization optimizations.
+    The Morton order cache is cleared before each iteration to measure the
+    full computation cost.
+    """
+    from zarr.core.indexing import _morton_order
+
+    # Create array where each shard contains many small chunks
+    # e.g., shards=(32,32,32) with chunks=(2,2,2) means 16x16x16 = 4096 chunks per shard
+    shape = tuple(s * 2 for s in shards)  # 2 shards per dimension
+    chunks = (2,) * 3  # Small chunks to maximize chunks per shard
+
+    data = create_array(
+        store=store,
+        shape=shape,
+        dtype="uint8",
+        chunks=chunks,
+        shards=shards,
+        compressors=None,
+        filters=None,
+        fill_value=0,
+    )
+
+    data[:] = 1
+    # Read a sub-shard region to exercise Morton order iteration
+    indexer = (slice(shards[0]),) * 3
+
+    def read_with_cache_clear() -> None:
+        _morton_order.cache_clear()
+        getitem(data, indexer)
+
+    benchmark(read_with_cache_clear)
+
+
+# Benchmark with larger chunks_per_shard to make Morton order impact more visible
+large_morton_shards = (
+    (32,) * 3,  # With 1x1x1 chunks: 32x32x32 = 32768 chunks per shard
+)
+
+
+@pytest.mark.parametrize("store", ["memory"], indirect=["store"])
+@pytest.mark.parametrize("shards", large_morton_shards, ids=str)
+def test_sharded_morton_indexing_large(
+    store: Store,
+    shards: tuple[int, ...],
+    benchmark: BenchmarkFixture,
+) -> None:
+    """Benchmark sharded array indexing with large chunks_per_shard.
+
+    Uses 1x1x1 chunks to maximize chunks_per_shard (32^3 = 32768), making
+    the Morton order computation a more significant portion of total time.
+    The Morton order cache is cleared before each iteration.
+    """
+    from zarr.core.indexing import _morton_order
+
+    # 1x1x1 chunks means chunks_per_shard equals shard shape
+    shape = tuple(s * 2 for s in shards)  # 2 shards per dimension
+    chunks = (1,) * 3  # 1x1x1 chunks: chunks_per_shard = shards
+
+    data = create_array(
+        store=store,
+        shape=shape,
+        dtype="uint8",
+        chunks=chunks,
+        shards=shards,
+        compressors=None,
+        filters=None,
+        fill_value=0,
+    )
+
+    data[:] = 1
+    # Read one full shard
+    indexer = (slice(shards[0]),) * 3
+
+    def read_with_cache_clear() -> None:
+        _morton_order.cache_clear()
+        getitem(data, indexer)
+
+    benchmark(read_with_cache_clear)
+
+
+@pytest.mark.parametrize("store", ["memory"], indirect=["store"])
+@pytest.mark.parametrize("shards", large_morton_shards, ids=str)
+def test_sharded_morton_single_chunk(
+    store: Store,
+    shards: tuple[int, ...],
+    benchmark: BenchmarkFixture,
+) -> None:
+    """Benchmark reading a single chunk from a large shard.
+
+    This isolates the Morton order computation overhead by minimizing I/O.
+    Reading one chunk from a shard with 32^3 = 32768 chunks still requires
+    computing the full Morton order, making the optimization impact clear.
+    The Morton order cache is cleared before each iteration.
+    """
+    from zarr.core.indexing import _morton_order
+
+    # 1x1x1 chunks means chunks_per_shard equals shard shape
+    shape = tuple(s * 2 for s in shards)  # 2 shards per dimension
+    chunks = (1,) * 3  # 1x1x1 chunks: chunks_per_shard = shards
+
+    data = create_array(
+        store=store,
+        shape=shape,
+        dtype="uint8",
+        chunks=chunks,
+        shards=shards,
+        compressors=None,
+        filters=None,
+        fill_value=0,
+    )
+
+    data[:] = 1
+    # Read only a single chunk (1x1x1) from the shard
+    indexer = (slice(1),) * 3
+
+    def read_with_cache_clear() -> None:
+        _morton_order.cache_clear()
+        getitem(data, indexer)
+
+    benchmark(read_with_cache_clear)
+
+
+# Benchmark for morton_order_iter directly (no I/O)
+morton_iter_shapes = (
+    (8, 8, 8),  # 512 elements
+    (16, 16, 16),  # 4096 elements
+    (32, 32, 32),  # 32768 elements
+)
+
+
+@pytest.mark.parametrize("shape", morton_iter_shapes, ids=str)
+def test_morton_order_iter(
+    shape: tuple[int, ...],
+    benchmark: BenchmarkFixture,
+) -> None:
+    """Benchmark morton_order_iter directly without I/O.
+
+    This isolates the Morton order computation to measure the
+    optimization impact without array read/write overhead.
+    The cache is cleared before each iteration.
+    """
+    from zarr.core.indexing import _morton_order, morton_order_iter
+
+    def compute_morton_order() -> None:
+        _morton_order.cache_clear()
+        # Consume the iterator to force computation
+        list(morton_order_iter(shape))
+
+    benchmark(compute_morton_order)
+
+
+@pytest.mark.parametrize("store", ["memory"], indirect=["store"])
+@pytest.mark.parametrize("shards", large_morton_shards, ids=str)
+def test_sharded_morton_write_single_chunk(
+    store: Store,
+    shards: tuple[int, ...],
+    benchmark: BenchmarkFixture,
+) -> None:
+    """Benchmark writing a single chunk to a large shard.
+
+    This is the clearest end-to-end demonstration of Morton order optimization.
+    Writing a single chunk to a shard with 32^3 = 32768 chunks requires
+    computing the full Morton order, but minimizes I/O overhead.
+
+    Expected improvement: ~160ms (matching Morton computation speedup of ~178ms).
+    The Morton order cache is cleared before each iteration.
+    """
+    import numpy as np
+
+    from zarr.core.indexing import _morton_order
+
+    # 1x1x1 chunks means chunks_per_shard equals shard shape
+    shape = tuple(s * 2 for s in shards)  # 2 shards per dimension
+    chunks = (1,) * 3  # 1x1x1 chunks: chunks_per_shard = shards
+
+    data = create_array(
+        store=store,
+        shape=shape,
+        dtype="uint8",
+        chunks=chunks,
+        shards=shards,
+        compressors=None,
+        filters=None,
+        fill_value=0,
+    )
+
+    # Write data for a single chunk
+    write_data = np.ones((1, 1, 1), dtype="uint8")
+    indexer = (slice(1), slice(1), slice(1))
+
+    def write_with_cache_clear() -> None:
+        _morton_order.cache_clear()
+        data[indexer] = write_data
+
+    benchmark(write_with_cache_clear)