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[WIP] Support Current domain in ingestion code #565

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[WIP] Support Current domain in ingestion code #565

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748f266
Consider Current Domain
kounelisagis Feb 11, 2025
efd6d46
Modify test
kounelisagis Feb 11, 2025
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12 changes: 10 additions & 2 deletions apis/python/src/tiledb/vector_search/ingestion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -412,12 +412,20 @@ def read_source_metadata(
if source_type == "TILEDB_ARRAY":
schema = tiledb.ArraySchema.load(source_uri)
size = np.int64(schema.domain.dim(1).domain[1]) + 1
dimensions = np.int64(schema.domain.dim(0).domain[1]) + 1
# In the case that current domain is non-empty, we need to consider it
if hasattr(schema, "current_domain") and not schema.current_domain.is_empty:
dimensions = np.int64(schema.current_domain.dim(0).domain[1]) + 1
else:
dimensions = np.int64(schema.domain.dim(0).domain[1]) + 1
return size, dimensions, schema.attr(0).dtype
if source_type == "TILEDB_SPARSE_ARRAY":
schema = tiledb.ArraySchema.load(source_uri)
size = np.int64(schema.domain.dim(0).domain[1]) + 1
dimensions = np.int64(schema.domain.dim(1).domain[1]) + 1
# In the case that current domain is non-empty, we need to consider it
if hasattr(schema, "current_domain") and not schema.current_domain.is_empty:
dimensions = np.int64(schema.current_domain.dim(1).domain[1]) + 1
else:
dimensions = np.int64(schema.domain.dim(1).domain[1]) + 1
return size, dimensions, schema.attr(0).dtype
if source_type == "TILEDB_PARTITIONED_ARRAY":
with tiledb.open(source_uri, "r", config=config) as source_array:
Expand Down
124 changes: 108 additions & 16 deletions apis/python/test/test_ingestion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1536,23 +1536,24 @@ def test_ivf_flat_copy_centroids_uri(tmp_path):
centroids = np.array([[1, 1, 1, 1], [2, 2, 2, 2]], dtype=np.float32)
centroids_in_size = centroids.shape[0]
dimensions = centroids.shape[1]
domain = tiledb.Domain(
*[
tiledb.Dim(
name="rows",
domain=(0, dimensions - 1),
tile=dimensions,
dtype=np.dtype(np.int32),
),
tiledb.Dim(
name="cols",
domain=(0, np.iinfo(np.dtype("int32")).max),
tile=100000,
dtype=np.dtype(np.int32),
),
]
)
schema = tiledb.ArraySchema(
domain=tiledb.Domain(
*[
tiledb.Dim(
name="rows",
domain=(0, dimensions - 1),
tile=dimensions,
dtype=np.dtype(np.int32),
),
tiledb.Dim(
name="cols",
domain=(0, np.iinfo(np.dtype("int32")).max),
tile=100000,
dtype=np.dtype(np.int32),
),
]
),
domain=domain,
sparse=False,
attrs=[
tiledb.Attr(
Expand All @@ -1570,6 +1571,17 @@ def test_ivf_flat_copy_centroids_uri(tmp_path):
with tiledb.open(centroids_uri, mode="w", timestamp=index_timestamp) as A:
A[0:dimensions, 0:centroids_in_size] = centroids.transpose()

ctx = tiledb.Ctx()
ndrect = tiledb.NDRectangle(ctx, domain)
range_one = (0, 1)
range_two = (0, 2)
ndrect.set_range(0, range_one[0], range_one[1])
ndrect.set_range(1, range_two[0], range_two[1])

current_domain = tiledb.CurrentDomain(ctx)
current_domain.set_ndrectangle(ndrect)
A.schema.set_current_domain(current_domain)

# Create the index.
index_uri = os.path.join(tmp_path, "array")
index = ingest(
Expand Down Expand Up @@ -2010,3 +2022,83 @@ def test_ivf_flat_taskgraph_query(tmp_path):
queries, k=k, nprobe=nprobe, nthreads=8, mode=Mode.LOCAL, num_partitions=10
)
assert accuracy(result, gt_i) > MINIMUM_ACCURACY


# def test_ingestion_current_domain(tmp_path):
# # ################################################################################################
# # # First set up the data.
# # ################################################################################################
# # data = np.array(
# # [
# # [1.0, 1.1, 1.2, 1.3],
# # [2.0, 2.1, 2.2, 2.3],
# # [3.0, 3.1, 3.2, 3.3],
# # [4.0, 4.1, 4.2, 4.3],
# # [5.0, 5.1, 5.2, 5.3],
# # ],
# # dtype=np.float32,
# # )
# # training_data = data[1:3]

# # ################################################################################################
# # # Test we can ingest, query, update, and consolidate.
# # ################################################################################################
# # index_uri = os.path.join(tmp_path, "array")
# # index = ingest(
# # index_type="IVF_FLAT",
# # index_uri=index_uri,
# # input_vectors=data,
# # training_input_vectors=training_data,
# # )

# # ======

# dimensions = 128
# schema = tiledb.ArraySchema(
# domain=tiledb.Domain(
# *[
# tiledb.Dim(
# name="rows",
# domain=(0, dimensions - 1),
# tile=dimensions,
# dtype=np.dtype(np.int32),
# ),
# tiledb.Dim(
# name="cols",
# domain=(0, np.iinfo(np.dtype("int32")).max),
# tile=100000,
# dtype=np.dtype(np.int32),
# ),
# ]
# ),
# sparse=False,
# attrs=[
# tiledb.Attr(
# name="attr",
# dtype="float32",
# filters=tiledb.FilterList([tiledb.ZstdFilter()]),
# )
# ],
# cell_order="col-major",
# tile_order="col-major",
# )
# uri = os.path.join(tmp_path, "array")
# tiledb.Array.create(uri, schema)

# index_timestamp = int(time.time() * 1000)
# with tiledb.open(uri, mode="w", timestamp=index_timestamp) as A:
# A[0:dimensions, 0:dimensions] = np.random.rand(dimensions, dimensions).astype(
# np.float32
# )

# data = np.random.rand(1000, dimensions).astype(np.float32)

# # Create the index.
# index_uri = os.path.join(tmp_path, "array")
# index = ingest(
# index_type="IVF_FLAT",
# index_uri=index_uri,
# input_vectors=data,
# copy_centroids_uri=uri,
# partitions=centroids_in_size,
# )
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