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Antalya 25.8: disable *Local table engine and table function #1522

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0959073
disable iceberglocal
zvonand Mar 13, 2026
2e1df01
remove all iceberglocal mentions from tests
zvonand Mar 13, 2026
8160a5d
remove other appearances
zvonand Mar 13, 2026
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29 changes: 0 additions & 29 deletions src/Storages/ObjectStorage/registerStorageObjectStorage.cpp
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -278,21 +278,6 @@ void registerStorageIceberg(StorageFactory & factory)
.has_builtin_setting_fn = DataLakeStorageSettings::hasBuiltin,
});
# endif
factory.registerStorage(
IcebergLocalDefinition::storage_engine_name,
[&](const StorageFactory::Arguments & args)
{
const auto storage_settings = getDataLakeStorageSettings(*args.storage_def);
auto configuration = std::make_shared<StorageLocalIcebergConfiguration>(storage_settings);
return createStorageObjectStorage(args, configuration);
},
{
.supports_settings = true,
.supports_sort_order = true,
.supports_schema_inference = true,
.source_access_type = AccessTypeObjects::Source::FILE,
.has_builtin_setting_fn = DataLakeStorageSettings::hasBuiltin,
});
}

#endif
Expand Down Expand Up @@ -347,20 +332,6 @@ void registerStorageDeltaLake(StorageFactory & factory)
.has_builtin_setting_fn = DataLakeStorageSettings::hasBuiltin,
});
# endif
factory.registerStorage(
DeltaLakeLocalDefinition::storage_engine_name,
[&](const StorageFactory::Arguments & args)
{
const auto storage_settings = getDataLakeStorageSettings(*args.storage_def);
auto configuration = std::make_shared<StorageLocalDeltaLakeConfiguration>(storage_settings);
return createStorageObjectStorage(args, configuration);
},
{
.supports_settings = true,
.supports_schema_inference = true,
.source_access_type = AccessTypeObjects::Source::FILE,
.has_builtin_setting_fn = StorageObjectStorageSettings::hasBuiltin,
});
}
#endif

Expand Down
7 changes: 1 addition & 6 deletions src/TableFunctions/TableFunctionObjectStorage.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -330,12 +330,7 @@ template class TableFunctionObjectStorage<HudiClusterDefinition, StorageS3HudiCo
#if USE_AVRO
void registerTableFunctionIceberg(TableFunctionFactory & factory)
{
factory.registerFunction<TableFunctionIcebergLocal>(
{.documentation
= {.description = R"(The table function can be used to read the Iceberg table stored locally.)",
.examples{{IcebergLocalDefinition::name, "SELECT * FROM icebergLocal(filename)", ""}},
.category = FunctionDocumentation::Category::TableFunction},
.allow_readonly = false});
UNUSED(factory);
}
#endif
}
7 changes: 0 additions & 7 deletions src/TableFunctions/TableFunctionObjectStorageCluster.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -157,13 +157,6 @@ void registerTableFunctionIcebergCluster(TableFunctionFactory & factory)
.category = FunctionDocumentation::Category::TableFunction},
.allow_readonly = false});

factory.registerFunction<TableFunctionIcebergLocalCluster>(
{.documentation
= {.description = R"(The table function can be used to read the Iceberg table stored on shared storage in parallel for many nodes in a specified cluster.)",
.examples{{IcebergLocalClusterDefinition::name, "SELECT * FROM icebergLocalCluster(cluster, filename, format, [,compression])", ""}},
.category = FunctionDocumentation::Category::TableFunction},
.allow_readonly = false});

# if USE_AWS_S3
factory.registerFunction<TableFunctionIcebergS3Cluster>(
{.documentation
Expand Down
20 changes: 0 additions & 20 deletions src/TableFunctions/TableFunctionObjectStorageClusterFallback.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -295,26 +295,6 @@ void registerTableFunctionObjectStorageClusterFallback(TableFunctionFactory & fa
}
);

factory.registerFunction<TableFunctionIcebergLocalClusterFallback>(
{
.documentation = {
.description=R"(The table function can be used to read the Iceberg table stored on shared disk in parallel for many nodes in a specified cluster or from single node.)",
.examples{
{
"icebergLocal",
"SELECT * FROM icebergLocal(filename)", ""
},
{
"icebergLocal",
"SELECT * FROM icebergLocal(filename) "
"SETTINGS object_storage_cluster='cluster'", ""
},
},
.category = FunctionDocumentation::Category::TableFunction
},
.allow_readonly = false
}
);
#endif

#if USE_AVRO && USE_AWS_S3
Expand Down
198 changes: 6 additions & 192 deletions tests/integration/test_storage_delta/test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -316,19 +316,6 @@ def create_delta_table(
"""
+ allow_dynamic_metadata_for_datalakes_suffix
)
elif storage_type == "local":
# For local storage, we need to use the absolute path
user_files_path = os.path.join(
SCRIPT_DIR, f"{cluster.instances_dir_name}/node1/database/user_files"
)
table_path = os.path.join(user_files_path, table_name)
instance.query(
f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=DeltaLakeLocal('{table_path}', {format})
"""
)
else:
raise Exception(f"Unknown delta lake storage type: {storage_type}")

Expand All @@ -345,10 +332,6 @@ def default_upload_directory(
return started_cluster.default_azure_uploader.upload_directory(
local_path, remote_path, **kwargs
)
elif storage_type == "local":
return started_cluster.local_uploader.upload_directory(
local_path, remote_path, **kwargs
)
else:
raise Exception(f"Unknown delta storage type: {storage_type}")

Expand All @@ -374,7 +357,7 @@ def create_initial_data_file(

@pytest.mark.parametrize(
"use_delta_kernel, storage_type",
[("1", "s3"), ("0", "s3"), ("0", "azure"), ("1", "local")],
[("1", "s3"), ("0", "s3"), ("0", "azure")],
)
def test_single_log_file(started_cluster, use_delta_kernel, storage_type):
instance = get_node(started_cluster, use_delta_kernel)
Expand All @@ -393,7 +376,7 @@ def test_single_log_file(started_cluster, use_delta_kernel, storage_type):
table_path = os.path.join(user_files_path, TABLE_NAME)

# We need to exclude the leading slash for local storage protocol file://
delta_path = table_path if storage_type == "local" else f"/{TABLE_NAME}"
delta_path = f"/{TABLE_NAME}"
write_delta_from_file(spark, parquet_data_path, delta_path)

files = default_upload_directory(
Expand All @@ -420,7 +403,7 @@ def test_single_log_file(started_cluster, use_delta_kernel, storage_type):

@pytest.mark.parametrize(
"use_delta_kernel, storage_type",
[("1", "s3"), ("0", "s3"), ("0", "azure"), ("1", "local")],
[("1", "s3"), ("0", "s3"), ("0", "azure")],
)
def test_partition_by(started_cluster, use_delta_kernel, storage_type):
instance = get_node(started_cluster, use_delta_kernel)
Expand All @@ -435,7 +418,7 @@ def test_partition_by(started_cluster, use_delta_kernel, storage_type):
table_path = os.path.join(user_files_path, TABLE_NAME)

# We need to exclude the leading slash for local storage protocol file://
delta_path = table_path if storage_type == "local" else f"/{TABLE_NAME}"
delta_path = f"/{TABLE_NAME}"

write_delta_from_df(
spark,
Expand Down Expand Up @@ -465,7 +448,7 @@ def test_partition_by(started_cluster, use_delta_kernel, storage_type):

@pytest.mark.parametrize(
"use_delta_kernel, storage_type",
[("1", "s3"), ("0", "s3"), ("0", "azure"), ("1", "local")],
[("1", "s3"), ("0", "s3"), ("0", "azure")],
)
def test_checkpoint(started_cluster, use_delta_kernel, storage_type):
instance = get_node(started_cluster, use_delta_kernel)
Expand All @@ -480,7 +463,7 @@ def test_checkpoint(started_cluster, use_delta_kernel, storage_type):
)
table_path = os.path.join(user_files_path, TABLE_NAME)
# We need to exclude the leading slash for local storage protocol file://
delta_path = table_path if storage_type == "local" else f"/{TABLE_NAME}"
delta_path = f"/{TABLE_NAME}"

write_delta_from_df(
spark,
Expand Down Expand Up @@ -3272,141 +3255,6 @@ def insert(i):
assert len(file_names) == sum(success)


def test_writes_spark_compatibility(started_cluster):
instance = started_cluster.instances["node1"]
instance_disabled_kernel = cluster.instances["node_with_disabled_delta_kernel"]
minio_client = started_cluster.minio_client
bucket = started_cluster.minio_bucket
table_name = randomize_table_name("test_writes")
result_file = f"{table_name}_data"

schema = pa.schema([("id", pa.int32()), ("name", pa.string())])
empty_arrays = [pa.array([], type=pa.int32()), pa.array([], type=pa.string())]
write_deltalake(
f"file:///{result_file}",
pa.Table.from_arrays(empty_arrays, schema=schema),
mode="overwrite",
)

LocalUploader(instance).upload_directory(f"/{result_file}/", f"/{result_file}/")
files = (
instance.exec_in_container(["bash", "-c", f"ls /{result_file}"])
.strip()
.split("\n")
)
assert len(files) == 1
assert "_delta_log" == files[0]
assert "" in instance.exec_in_container(
["bash", "-c", f"ls /{result_file}/_delta_log"]
)

instance.query(
f"CREATE TABLE {table_name} (id Int32, name String) ENGINE = DeltaLakeLocal('/{result_file}') SETTINGS output_format_parquet_compression_method = 'none'"
)
instance.query(
f"INSERT INTO {table_name} SELECT number, toString(number) FROM numbers(10)"
)

LocalDownloader(instance).download_directory(f"/{result_file}/", f"/{result_file}/")

files = (
instance.exec_in_container(["bash", "-c", f"ls /{result_file}"])
.strip()
.split("\n")
)
assert len(files) == 2
pfile = files[0] if files[0].endswith(".parquet") else files[1]

table = pq.read_table(f"/{result_file}/{pfile}")
df = table.to_pandas()
assert (
"0 0 0\n1 1 1\n2 2 2\n3 3 3\n4 4 4\n5 5 5\n6 6 6\n7 7 7\n8 8 8\n9 9 9"
in str(df)
)

spark = started_cluster.spark_session
df = spark.read.format("delta").load(f"/{result_file}").collect()
assert (
"[Row(id=0, name='0'), Row(id=1, name='1'), Row(id=2, name='2'), Row(id=3, name='3'), Row(id=4, name='4'), Row(id=5, name='5'), Row(id=6, name='6'), Row(id=7, name='7'), Row(id=8, name='8'), Row(id=9, name='9')]"
== str(df)
)

instance.query(
f"INSERT INTO {table_name} SELECT number, toString(number) FROM numbers(10, 10)"
)
LocalDownloader(instance).download_directory(f"/{result_file}/", f"/{result_file}/")
files = (
instance.exec_in_container(["bash", "-c", f"ls /{result_file}"])
.strip()
.split("\n")
)
assert len(files) == 3

df = spark.read.format("delta").load(f"/{result_file}").collect()
assert (
"[Row(id=10, name='10'), Row(id=11, name='11'), Row(id=12, name='12'), Row(id=13, name='13'), Row(id=14, name='14'), Row(id=15, name='15'), Row(id=16, name='16'), Row(id=17, name='17'), Row(id=18, name='18'), Row(id=19, name='19'), Row(id=0, name='0'), Row(id=1, name='1'), Row(id=2, name='2'), Row(id=3, name='3'), Row(id=4, name='4'), Row(id=5, name='5'), Row(id=6, name='6'), Row(id=7, name='7'), Row(id=8, name='8'), Row(id=9, name='9')]"
== str(df)
)


@pytest.mark.parametrize("partitioned", [False, True])
@pytest.mark.parametrize("limit_enabled", [False, True])
def test_write_limits(started_cluster, partitioned, limit_enabled):
instance = started_cluster.instances["node1"]
minio_client = started_cluster.minio_client
bucket = started_cluster.minio_bucket
table_name = randomize_table_name("test_write_limits")
result_file = f"{table_name}_data"

schema = pa.schema([("id", pa.int32()), ("name", pa.string())])
empty_arrays = [pa.array([], type=pa.int32()), pa.array([], type=pa.string())]
write_deltalake(
f"file:///{result_file}",
pa.Table.from_arrays(empty_arrays, schema=schema),
mode="overwrite",
partition_by=["id"] if partitioned else [],
)
LocalUploader(instance).upload_directory(f"/{result_file}/", f"/{result_file}/")
files = (
instance.exec_in_container(["bash", "-c", f"ls /{result_file}"])
.strip()
.split("\n")
)
assert len(files) == 1

instance.query(
f"CREATE TABLE {table_name} (id Int32, name String) ENGINE = DeltaLakeLocal('/{result_file}') SETTINGS output_format_parquet_compression_method = 'none'"
)

num_rows = 1000000
partitions_num = 5
limit_rows = 10 if limit_enabled else (num_rows + 1)
instance.query(
f"INSERT INTO {table_name} SELECT number % {partitions_num}, randomString(10) FROM numbers({num_rows}) SETTINGS delta_lake_insert_max_rows_in_data_file = {limit_rows}, max_insert_block_size = 1000, min_chunk_bytes_for_parallel_parsing = 1000"
)

files = LocalDownloader(instance).download_directory(f"/{result_file}/", f"/{result_file}/")
data_files = [file for file in files if file.endswith(".parquet")]
assert len(data_files) > 0, f"No data files: {files}"

if partitioned:
if limit_enabled:
assert len(data_files) > partitions_num, f"Data files: {data_files}"
else:
assert len(data_files) == partitions_num, f"Data files: {data_files}"
else:
if limit_enabled:
assert len(data_files) > 1, f"Data files: {data_files}"
else:
assert len(data_files) == 1, f"Data files: {data_files}"

assert num_rows == int(instance.query(f"SELECT count() FROM {table_name}"))

spark = started_cluster.spark_session
df = spark.read.format("delta").load(f"/{result_file}")
assert df.count() == num_rows


def test_column_mapping_id(started_cluster):
node = started_cluster.instances["node1"]
table_name = randomize_table_name("test_column_mapping_id")
Expand Down Expand Up @@ -3540,37 +3388,3 @@ def s3_function(path):
)


def test_write_column_order(started_cluster):
instance = started_cluster.instances["node1"]
minio_client = started_cluster.minio_client
bucket = started_cluster.minio_bucket
table_name = randomize_table_name("test_write_column_order")
result_file = f"{table_name}_data"
schema = pa.schema([("c1", pa.int32()), ("c0", pa.string())])
empty_arrays = [pa.array([], type=pa.int32()), pa.array([], type=pa.string())]
write_deltalake(
f"file:///{result_file}",
pa.Table.from_arrays(empty_arrays, schema=schema),
mode="overwrite",
)
LocalUploader(instance).upload_directory(f"/{result_file}/", f"/{result_file}/")

instance.query(
f"CREATE TABLE {table_name} (c0 String, c1 Int32) ENGINE = DeltaLakeLocal('/{result_file}') SETTINGS output_format_parquet_compression_method = 'none'"
)
num_rows = 10
instance.query(
f"INSERT INTO {table_name} (c1, c0) SELECT number as c1, toString(number % 2) as c0 FROM numbers(10)"
)

assert num_rows == int(instance.query(f"SELECT count() FROM {table_name}"))
assert (
"0\t0\n1\t1\n0\t2\n1\t3\n0\t4\n1\t5\n0\t6\n1\t7\n0\t8\n1\t9"
== instance.query(f"SELECT c0, c1 FROM {table_name}").strip()
)

instance.query(
f"INSERT INTO {table_name} (c1, c0) SELECT c1, c0 FROM generateRandom('c1 Int32, c0 String', 16920040705558589162, 7706, 3) LIMIT {num_rows}"
)

assert num_rows * 2 == int(instance.query(f"SELECT count() FROM {table_name}"))
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