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ClickHouse/tests/integration/test_storage_iceberg/test.py
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import glob
import json
import logging
import os
import time
import uuid
from datetime import datetime
import pyspark
import pytest
from azure.storage.blob import BlobServiceClient
from minio.deleteobjects import DeleteObject
from pyspark.sql.functions import (
current_timestamp,
monotonically_increasing_id,
row_number,
)
from pyspark.sql.readwriter import DataFrameWriter, DataFrameWriterV2
from pyspark.sql.types import (
ArrayType,
BooleanType,
DateType,
IntegerType,
StringType,
StructField,
StructType,
TimestampType,
)
from pyspark.sql.window import Window
import helpers.client
from helpers.cluster import ClickHouseCluster, ClickHouseInstance, is_arm
from helpers.s3_tools import (
AzureUploader,
HDFSUploader,
LocalUploader,
S3Uploader,
get_file_contents,
list_s3_objects,
prepare_s3_bucket,
)
from helpers.test_tools import TSV
SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
def get_spark():
builder = (
pyspark.sql.SparkSession.builder.appName("spark_test")
.config(
"spark.sql.catalog.spark_catalog",
"org.apache.iceberg.spark.SparkSessionCatalog",
)
.config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")
.config("spark.sql.catalog.spark_catalog.type", "hadoop")
.config("spark.sql.catalog.spark_catalog.warehouse", "/iceberg_data")
.config(
"spark.sql.extensions",
"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions",
)
.master("local")
)
return builder.master("local").getOrCreate()
@pytest.fixture(scope="module")
def started_cluster():
try:
cluster = ClickHouseCluster(__file__, with_spark=True)
with_hdfs = True
if is_arm():
with_hdfs = False
cluster.add_instance(
"node1",
main_configs=[
"configs/config.d/query_log.xml",
"configs/config.d/cluster.xml",
"configs/config.d/named_collections.xml",
"configs/config.d/filesystem_caches.xml",
],
user_configs=["configs/users.d/users.xml"],
with_minio=True,
with_azurite=True,
with_hdfs=with_hdfs,
stay_alive=True,
)
cluster.add_instance(
"node2",
main_configs=[
"configs/config.d/query_log.xml",
"configs/config.d/cluster.xml",
"configs/config.d/named_collections.xml",
"configs/config.d/filesystem_caches.xml",
],
user_configs=["configs/users.d/users.xml"],
stay_alive=True,
)
cluster.add_instance(
"node3",
main_configs=[
"configs/config.d/query_log.xml",
"configs/config.d/cluster.xml",
"configs/config.d/named_collections.xml",
"configs/config.d/filesystem_caches.xml",
],
user_configs=["configs/users.d/users.xml"],
stay_alive=True,
)
logging.info("Starting cluster...")
cluster.start()
prepare_s3_bucket(cluster)
logging.info("S3 bucket created")
cluster.spark_session = get_spark()
cluster.default_s3_uploader = S3Uploader(
cluster.minio_client, cluster.minio_bucket
)
cluster.azure_container_name = "mycontainer"
cluster.blob_service_client = cluster.blob_service_client
container_client = cluster.blob_service_client.create_container(
cluster.azure_container_name
)
cluster.container_client = container_client
cluster.default_azure_uploader = AzureUploader(
cluster.blob_service_client, cluster.azure_container_name
)
cluster.default_hdfs_uploader = HDFSUploader(cluster)
cluster.default_local_uploader = LocalUploader(cluster.instances["node1"])
yield cluster
finally:
cluster.shutdown()
def run_query(instance, query, stdin=None, settings=None):
# type: (ClickHouseInstance, str, object, dict) -> str
logging.info("Running query '{}'...".format(query))
result = instance.query(query, stdin=stdin, settings=settings)
logging.info("Query finished")
return result
def write_iceberg_from_file(
spark, path, table_name, mode="overwrite", format_version="1", partition_by=None
):
if mode == "overwrite":
if partition_by is None:
spark.read.load(f"file://{path}").writeTo(table_name).tableProperty(
"format-version", format_version
).using("iceberg").create()
else:
spark.read.load(f"file://{path}").writeTo(table_name).partitionedBy(
partition_by
).tableProperty("format-version", format_version).using("iceberg").create()
else:
spark.read.load(f"file://{path}").writeTo(table_name).append()
def write_iceberg_from_df(
spark, df, table_name, mode="overwrite", format_version="1", partition_by=None
):
if mode == "overwrite":
if partition_by is None:
df.writeTo(table_name).tableProperty(
"format-version", format_version
).using("iceberg").create()
else:
df.writeTo(table_name).tableProperty(
"format-version", format_version
).partitionedBy(partition_by).using("iceberg").create()
else:
df.writeTo(table_name).append()
def generate_data(spark, start, end):
a = spark.range(start, end, 1).toDF("a")
b = spark.range(start + 1, end + 1, 1).toDF("b")
b = b.withColumn("b", b["b"].cast(StringType()))
a = a.withColumn(
"row_index", row_number().over(Window.orderBy(monotonically_increasing_id()))
)
b = b.withColumn(
"row_index", row_number().over(Window.orderBy(monotonically_increasing_id()))
)
df = a.join(b, on=["row_index"]).drop("row_index")
return df
def get_creation_expression(
storage_type,
table_name,
cluster,
format="Parquet",
table_function=False,
allow_dynamic_metadata_for_data_lakes=False,
run_on_cluster=False,
**kwargs,
):
allow_dynamic_metadata_for_datalakes_suffix = (
" SETTINGS allow_dynamic_metadata_for_data_lakes = 1"
if allow_dynamic_metadata_for_data_lakes
else ""
)
if storage_type == "s3":
if "bucket" in kwargs:
bucket = kwargs["bucket"]
else:
bucket = cluster.minio_bucket
if run_on_cluster:
assert table_function
return f"icebergS3Cluster('cluster_simple', s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"
else:
if table_function:
return f"icebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"
else:
return (
f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"""
+ allow_dynamic_metadata_for_datalakes_suffix
)
elif storage_type == "azure":
if run_on_cluster:
assert table_function
return f"""
icebergAzureCluster('cluster_simple', azure, container = '{cluster.azure_container_name}', storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})
"""
else:
if table_function:
return f"""
icebergAzure(azure, container = '{cluster.azure_container_name}', storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})
"""
else:
return (
f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergAzure(azure, container = {cluster.azure_container_name}, storage_account_url = '{cluster.env_variables["AZURITE_STORAGE_ACCOUNT_URL"]}', blob_path = '/iceberg_data/default/{table_name}/', format={format})"""
+ allow_dynamic_metadata_for_datalakes_suffix
)
elif storage_type == "hdfs":
if run_on_cluster:
assert table_function
return f"""
icebergHDFSCluster('cluster_simple', hdfs, filename= 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')
"""
else:
if table_function:
return f"""
icebergHDFS(hdfs, filename= 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')
"""
else:
return (
f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergHDFS(hdfs, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')"""
+ allow_dynamic_metadata_for_datalakes_suffix
)
elif storage_type == "local":
assert not run_on_cluster
if table_function:
return f"""
icebergLocal(local, path = '/iceberg_data/default/{table_name}/', format={format})
"""
else:
return (
f"""
DROP TABLE IF EXISTS {table_name};
CREATE TABLE {table_name}
ENGINE=IcebergLocal(local, path = '/iceberg_data/default/{table_name}/', format={format})"""
+ allow_dynamic_metadata_for_datalakes_suffix
)
else:
raise Exception(f"Unknown iceberg storage type: {storage_type}")
def check_schema_and_data(instance, table_expression, expected_schema, expected_data):
schema = instance.query(f"DESC {table_expression}")
data = instance.query(f"SELECT * FROM {table_expression} ORDER BY ALL")
schema = list(
map(
lambda x: x.split("\t")[:2],
filter(lambda x: len(x) > 0, schema.strip().split("\n")),
)
)
data = list(
map(
lambda x: x.split("\t"),
filter(lambda x: len(x) > 0, data.strip().split("\n")),
)
)
assert expected_schema == schema
assert expected_data == data
def get_uuid_str():
return str(uuid.uuid4()).replace("-", "_")
def create_iceberg_table(
storage_type,
node,
table_name,
cluster,
format="Parquet",
**kwargs,
):
node.query(
get_creation_expression(storage_type, table_name, cluster, format, **kwargs)
)
def create_initial_data_file(
cluster, node, query, table_name, compression_method="none"
):
node.query(
f"""
INSERT INTO TABLE FUNCTION
file('{table_name}.parquet')
SETTINGS
output_format_parquet_compression_method='{compression_method}',
s3_truncate_on_insert=1 {query}
FORMAT Parquet"""
)
user_files_path = os.path.join(
SCRIPT_DIR, f"{cluster.instances_dir_name}/node1/database/user_files"
)
result_path = f"{user_files_path}/{table_name}.parquet"
return result_path
def default_upload_directory(
started_cluster, storage_type, local_path, remote_path, **kwargs
):
if storage_type == "local":
return started_cluster.default_local_uploader.upload_directory(
local_path, remote_path, **kwargs
)
elif storage_type == "hdfs":
return started_cluster.default_hdfs_uploader.upload_directory(
local_path, remote_path, **kwargs
)
elif storage_type == "s3":
print(kwargs)
return started_cluster.default_s3_uploader.upload_directory(
local_path, remote_path, **kwargs
)
elif storage_type == "azure":
return started_cluster.default_azure_uploader.upload_directory(
local_path, remote_path, **kwargs
)
else:
raise Exception(f"Unknown iceberg storage type: {storage_type}")
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_single_iceberg_file(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_single_iceberg_file_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
write_iceberg_from_df(spark, generate_data(spark, 0, 100), TABLE_NAME)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert instance.query(f"SELECT * FROM {TABLE_NAME}") == instance.query(
"SELECT number, toString(number + 1) FROM numbers(100)"
)
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_partition_by(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_partition_by_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
write_iceberg_from_df(
spark,
generate_data(spark, 0, 10),
TABLE_NAME,
mode="overwrite",
format_version=format_version,
partition_by="a",
)
files = default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
assert len(files) == 14 # 10 partitiions + 4 metadata files
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 10
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_multiple_iceberg_files(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_multiple_iceberg_files_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
write_iceberg_from_df(
spark,
generate_data(spark, 0, 100),
TABLE_NAME,
mode="overwrite",
format_version=format_version,
)
files = default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
# ['/iceberg_data/default/test_multiple_iceberg_files/data/00000-1-35302d56-f1ed-494e-a85b-fbf85c05ab39-00001.parquet',
# '/iceberg_data/default/test_multiple_iceberg_files/metadata/version-hint.text',
# '/iceberg_data/default/test_multiple_iceberg_files/metadata/3127466b-299d-48ca-a367-6b9b1df1e78c-m0.avro',
# '/iceberg_data/default/test_multiple_iceberg_files/metadata/snap-5220855582621066285-1-3127466b-299d-48ca-a367-6b9b1df1e78c.avro',
# '/iceberg_data/default/test_multiple_iceberg_files/metadata/v1.metadata.json']
assert len(files) == 5
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
write_iceberg_from_df(
spark,
generate_data(spark, 100, 200),
TABLE_NAME,
mode="append",
format_version=format_version,
)
files = default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
"",
)
assert len(files) == 9
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 200
assert instance.query(f"SELECT * FROM {TABLE_NAME} ORDER BY 1") == instance.query(
"SELECT number, toString(number + 1) FROM numbers(200)"
)
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_types(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_types_" + format_version + "_" + storage_type + "_" + get_uuid_str()
)
data = [
(
123,
"string",
datetime.strptime("2000-01-01", "%Y-%m-%d"),
["str1", "str2"],
True,
)
]
schema = StructType(
[
StructField("a", IntegerType()),
StructField("b", StringType()),
StructField("c", DateType()),
StructField("d", ArrayType(StringType())),
StructField("e", BooleanType()),
]
)
df = spark.createDataFrame(data=data, schema=schema)
df.printSchema()
write_iceberg_from_df(
spark, df, TABLE_NAME, mode="overwrite", format_version=format_version
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 1
assert (
instance.query(f"SELECT a, b, c, d, e FROM {TABLE_NAME}").strip()
== "123\tstring\t2000-01-01\t['str1','str2']\ttrue"
)
table_function_expr = get_creation_expression(
storage_type, TABLE_NAME, started_cluster, table_function=True
)
assert (
instance.query(f"SELECT a, b, c, d, e FROM {table_function_expr}").strip()
== "123\tstring\t2000-01-01\t['str1','str2']\ttrue"
)
assert instance.query(f"DESCRIBE {table_function_expr} FORMAT TSV") == TSV(
[
["a", "Nullable(Int32)"],
["b", "Nullable(String)"],
["c", "Nullable(Date)"],
["d", "Array(Nullable(String))"],
["e", "Nullable(Bool)"],
]
)
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs"])
def test_cluster_table_function(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_iceberg_cluster_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
def add_df(mode):
write_iceberg_from_df(
spark,
generate_data(spark, 0, 100),
TABLE_NAME,
mode=mode,
format_version=format_version,
)
files = default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
logging.info(f"Adding another dataframe. result files: {files}")
return files
files = add_df(mode="overwrite")
for i in range(1, len(started_cluster.instances)):
files = add_df(mode="append")
logging.info(f"Setup complete. files: {files}")
assert len(files) == 5 + 4 * (len(started_cluster.instances) - 1)
clusters = instance.query(f"SELECT * FROM system.clusters")
logging.info(f"Clusters setup: {clusters}")
# Regular Query only node1
table_function_expr = get_creation_expression(
storage_type, TABLE_NAME, started_cluster, table_function=True
)
select_regular = (
instance.query(f"SELECT * FROM {table_function_expr}").strip().split()
)
# Cluster Query with node1 as coordinator
table_function_expr_cluster = get_creation_expression(
storage_type,
TABLE_NAME,
started_cluster,
table_function=True,
run_on_cluster=True,
)
select_cluster = (
instance.query(f"SELECT * FROM {table_function_expr_cluster}").strip().split()
)
# Simple size check
assert len(select_regular) == 600
assert len(select_cluster) == 600
# Actual check
assert select_cluster == select_regular
# Check query_log
for replica in started_cluster.instances.values():
replica.query("SYSTEM FLUSH LOGS")
for node_name, replica in started_cluster.instances.items():
cluster_secondary_queries = (
replica.query(
f"""
SELECT query, type, is_initial_query, read_rows, read_bytes FROM system.query_log
WHERE
type = 'QueryStart' AND
positionCaseInsensitive(query, '{storage_type}Cluster') != 0 AND
position(query, '{TABLE_NAME}') != 0 AND
position(query, 'system.query_log') = 0 AND
NOT is_initial_query
"""
)
.strip()
.split("\n")
)
logging.info(
f"[{node_name}] cluster_secondary_queries: {cluster_secondary_queries}"
)
assert len(cluster_secondary_queries) == 1
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_delete_files(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_delete_files_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
write_iceberg_from_df(
spark,
generate_data(spark, 0, 100),
TABLE_NAME,
mode="overwrite",
format_version=format_version,
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
spark.sql(f"DELETE FROM {TABLE_NAME} WHERE a >= 0")
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
"",
)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 0
assert instance.contains_in_log("Processing delete file for path")
write_iceberg_from_df(
spark,
generate_data(spark, 100, 200),
TABLE_NAME,
mode="upsert",
format_version=format_version,
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
"",
)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
spark.sql(f"DELETE FROM {TABLE_NAME} WHERE a >= 150")
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
"",
)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 50
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
@pytest.mark.parametrize("is_table_function", [False, True])
def test_evolved_schema_simple(
started_cluster, format_version, storage_type, is_table_function
):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_evolved_schema_simple_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
def execute_spark_query(query: str):
spark.sql(query)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
return
execute_spark_query(
f"""
DROP TABLE IF EXISTS {TABLE_NAME};
"""
)
execute_spark_query(
f"""
CREATE TABLE IF NOT EXISTS {TABLE_NAME} (
a int NOT NULL,
b float,
c decimal(9,2) NOT NULL,
d array<int>
)
USING iceberg
OPTIONS ('format-version'='{format_version}')
"""
)
table_creation_expression = get_creation_expression(
storage_type,
TABLE_NAME,
started_cluster,
table_function=is_table_function,
allow_dynamic_metadata_for_data_lakes=True,
)
table_select_expression = (
TABLE_NAME if not is_table_function else table_creation_expression
)
if not is_table_function:
instance.query(table_creation_expression)
check_schema_and_data(
instance,
table_select_expression,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (4, NULL, 7.12, ARRAY(5, 6, 7));
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "\\N", "7.12", "[5,6,7]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN b TYPE double;
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["a", "Int32"],
["b", "Nullable(Float64)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "\\N", "7.12", "[5,6,7]"]],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (7, 5.0, 18.1, ARRAY(6, 7, 9));
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["a", "Int32"],
["b", "Nullable(Float64)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "\\N", "7.12", "[5,6,7]"], ["7", "5", "18.1", "[6,7,9]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN d FIRST;
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["a", "Int32"],
["b", "Nullable(Float64)"],
["c", "Decimal(9, 2)"],
],
[["[5,6,7]", "4", "\\N", "7.12"], ["[6,7,9]", "7", "5", "18.1"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN b AFTER d;
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Int32"],
["c", "Decimal(9, 2)"],
],
[["[5,6,7]", "\\N", "4", "7.12"], ["[6,7,9]", "5", "7", "18.1"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME}
ADD COLUMNS (
e string
);
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Int32"],
["c", "Decimal(9, 2)"],
["e", "Nullable(String)"],
],
[
["[5,6,7]", "\\N", "4", "7.12", "\\N"],
["[6,7,9]", "5", "7", "18.1", "\\N"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN c TYPE decimal(12, 2);
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Int32"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["[5,6,7]", "\\N", "4", "7.12", "\\N"],
["[6,7,9]", "5", "7", "18.1", "\\N"],
],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (ARRAY(5, 6, 7), 3, -30, 7.12, 'AAA');
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Int32"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["[5,6,7]", "3", "-30", "7.12", "AAA"],
["[5,6,7]", "\\N", "4", "7.12", "\\N"],
["[6,7,9]", "5", "7", "18.1", "\\N"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN a TYPE BIGINT;
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Int64"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["[5,6,7]", "3", "-30", "7.12", "AAA"],
["[5,6,7]", "\\N", "4", "7.12", "\\N"],
["[6,7,9]", "5", "7", "18.1", "\\N"],
],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (ARRAY(), 3.0, 12, -9.13, 'BBB');
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Int64"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["[]", "3", "12", "-9.13", "BBB"],
["[5,6,7]", "3", "-30", "7.12", "AAA"],
["[5,6,7]", "\\N", "4", "7.12", "\\N"],
["[6,7,9]", "5", "7", "18.1", "\\N"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN a DROP NOT NULL;
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Nullable(Int64)"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["[]", "3", "12", "-9.13", "BBB"],
["[5,6,7]", "3", "-30", "7.12", "AAA"],
["[5,6,7]", "\\N", "4", "7.12", "\\N"],
["[6,7,9]", "5", "7", "18.1", "\\N"],
],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (NULL, 3.4, NULL, -9.13, NULL);
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["d", "Array(Nullable(Int32))"],
["b", "Nullable(Float64)"],
["a", "Nullable(Int64)"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["[]", "3", "12", "-9.13", "BBB"],
["[]", "3.4", "\\N", "-9.13", "\\N"],
["[5,6,7]", "3", "-30", "7.12", "AAA"],
["[5,6,7]", "\\N", "4", "7.12", "\\N"],
["[6,7,9]", "5", "7", "18.1", "\\N"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} DROP COLUMN d;
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["b", "Nullable(Float64)"],
["a", "Nullable(Int64)"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["3", "-30", "7.12", "AAA"],
["3", "12", "-9.13", "BBB"],
["3.4", "\\N", "-9.13", "\\N"],
["5", "7", "18.1", "\\N"],
["\\N", "4", "7.12", "\\N"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} RENAME COLUMN a TO f;
"""
)
check_schema_and_data(
instance,
table_select_expression,
[
["b", "Nullable(Float64)"],
["f", "Nullable(Int64)"],
["c", "Decimal(12, 2)"],
["e", "Nullable(String)"],
],
[
["3", "-30", "7.12", "AAA"],
["3", "12", "-9.13", "BBB"],
["3.4", "\\N", "-9.13", "\\N"],
["5", "7", "18.1", "\\N"],
["\\N", "4", "7.12", "\\N"],
],
)
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_not_evolved_schema(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_evolved_schema_simple_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
def execute_spark_query(query: str):
spark.sql(query)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
return
execute_spark_query(
f"""
DROP TABLE IF EXISTS {TABLE_NAME};
"""
)
execute_spark_query(
f"""
CREATE TABLE IF NOT EXISTS {TABLE_NAME} (
a int NOT NULL,
b float,
c decimal(9,2) NOT NULL,
d array<int>
)
USING iceberg
OPTIONS ('format-version'='{format_version}')
"""
)
instance.query(
get_creation_expression(
storage_type,
TABLE_NAME,
started_cluster,
table_function=False,
allow_dynamic_metadata_for_data_lakes=False,
)
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (4, 3.0, 7.12, ARRAY(5, 6, 7));
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "3", "7.12", "[5,6,7]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN b TYPE double;
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "3", "7.12", "[5,6,7]"]],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (7, 5.0, 18.1, ARRAY(6, 7, 9));
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "3", "7.12", "[5,6,7]"], ["7", "5", "18.1", "[6,7,9]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN d FIRST;
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "3", "7.12", "[5,6,7]"], ["7", "5", "18.1", "[6,7,9]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN b AFTER d;
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "3", "7.12", "[5,6,7]"], ["7", "5", "18.1", "[6,7,9]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME}
ADD COLUMNS (
e string
);
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "3", "7.12", "[5,6,7]"], ["7", "5", "18.1", "[6,7,9]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN c TYPE decimal(12, 2);
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[["4", "3", "7.12", "[5,6,7]"], ["7", "5", "18.1", "[6,7,9]"]],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (ARRAY(5, 6, 7), 3, -30, 7.12, 'AAA');
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[
["-30", "3", "7.12", "[5,6,7]"],
["4", "3", "7.12", "[5,6,7]"],
["7", "5", "18.1", "[6,7,9]"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN a TYPE BIGINT;
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[
["-30", "3", "7.12", "[5,6,7]"],
["4", "3", "7.12", "[5,6,7]"],
["7", "5", "18.1", "[6,7,9]"],
],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (ARRAY(), 3.0, 12, -9.13, 'BBB');
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[
["-30", "3", "7.12", "[5,6,7]"],
["4", "3", "7.12", "[5,6,7]"],
["7", "5", "18.1", "[6,7,9]"],
["12", "3", "-9.13", "[]"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN a DROP NOT NULL;
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[
["-30", "3", "7.12", "[5,6,7]"],
["4", "3", "7.12", "[5,6,7]"],
["7", "5", "18.1", "[6,7,9]"],
["12", "3", "-9.13", "[]"],
],
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (NULL, 3.4, NULL, -9.13, NULL);
"""
)
check_schema_and_data(
instance,
TABLE_NAME,
[
["a", "Int32"],
["b", "Nullable(Float32)"],
["c", "Decimal(9, 2)"],
["d", "Array(Nullable(Int32))"],
],
[
["-30", "3", "7.12", "[5,6,7]"],
["0", "3.4", "-9.13", "[]"],
["4", "3", "7.12", "[5,6,7]"],
["7", "5", "18.1", "[6,7,9]"],
["12", "3", "-9.13", "[]"],
],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} DROP COLUMN d;
"""
)
error = instance.query_and_get_error(f"SELECT * FROM {TABLE_NAME} ORDER BY ALL")
assert "Not found column" in error
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "local"])
def test_evolved_schema_complex(started_cluster, format_version, storage_type):
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_evolved_schema_complex_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
def execute_spark_query(query: str):
spark.sql(query)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
return
execute_spark_query(
f"""
DROP TABLE IF EXISTS {TABLE_NAME};
"""
)
execute_spark_query(
f"""
CREATE TABLE {TABLE_NAME} (
address STRUCT<
house_number : DOUBLE,
city: STRING,
zip: INT
>,
animals ARRAY<INT>
)
USING iceberg
OPTIONS ('format-version'='{format_version}')
"""
)
execute_spark_query(
f"""
INSERT INTO {TABLE_NAME} VALUES (named_struct('house_number', 3, 'city', 'Singapore', 'zip', 12345), ARRAY(4, 7));
"""
)
table_function = get_creation_expression(
storage_type, TABLE_NAME, started_cluster, table_function=True
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ADD COLUMNS ( address.appartment INT );
"""
)
error = instance.query_and_get_error(f"SELECT * FROM {table_function} ORDER BY ALL")
assert "UNSUPPORTED_METHOD" in error
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} DROP COLUMN address.appartment;
"""
)
check_schema_and_data(
instance,
table_function,
[
[
"address",
"Tuple(\\n house_number Nullable(Float64),\\n city Nullable(String),\\n zip Nullable(Int32))",
],
["animals", "Array(Nullable(Int32))"],
],
[["(3,'Singapore',12345)", "[4,7]"]],
)
execute_spark_query(
f"""
ALTER TABLE {TABLE_NAME} ALTER COLUMN animals.element TYPE BIGINT
"""
)
error = instance.query_and_get_error(f"SELECT * FROM {table_function} ORDER BY ALL")
assert "UNSUPPORTED_METHOD" in error
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_row_based_deletes(started_cluster, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = "test_row_based_deletes_" + storage_type + "_" + get_uuid_str()
spark.sql(
f"CREATE TABLE {TABLE_NAME} (id bigint, data string) USING iceberg TBLPROPERTIES ('format-version' = '2', 'write.update.mode'='merge-on-read', 'write.delete.mode'='merge-on-read', 'write.merge.mode'='merge-on-read')"
)
spark.sql(
f"INSERT INTO {TABLE_NAME} select id, char(id + ascii('a')) from range(100)"
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
spark.sql(f"DELETE FROM {TABLE_NAME} WHERE id < 10")
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
"",
)
error = instance.query_and_get_error(f"SELECT * FROM {TABLE_NAME}")
assert "UNSUPPORTED_METHOD" in error
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_schema_inference(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
for format in ["Parquet", "ORC", "Avro"]:
TABLE_NAME = (
"test_schema_inference_"
+ format
+ "_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
# Types time, timestamptz, fixed are not supported in Spark.
spark.sql(
f"CREATE TABLE {TABLE_NAME} (intC int, longC long, floatC float, doubleC double, decimalC1 decimal(10, 3), decimalC2 decimal(20, 10), decimalC3 decimal(38, 30), dateC date, timestampC timestamp, stringC string, binaryC binary, arrayC1 array<int>, mapC1 map<string, string>, structC1 struct<field1: int, field2: string>, complexC array<struct<field1: map<string, array<map<string, int>>>, field2: struct<field3: int, field4: string>>>) USING iceberg TBLPROPERTIES ('format-version' = '{format_version}', 'write.format.default' = '{format}')"
)
spark.sql(
f"insert into {TABLE_NAME} select 42, 4242, 42.42, 4242.4242, decimal(42.42), decimal(42.42), decimal(42.42), date('2020-01-01'), timestamp('2020-01-01 20:00:00'), 'hello', binary('hello'), array(1,2,3), map('key', 'value'), struct(42, 'hello'), array(struct(map('key', array(map('key', 42))), struct(42, 'hello')))"
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(
storage_type, instance, TABLE_NAME, started_cluster, format=format
)
res = instance.query(
f"DESC {TABLE_NAME} FORMAT TSVRaw", settings={"print_pretty_type_names": 0}
)
expected = TSV(
[
["intC", "Nullable(Int32)"],
["longC", "Nullable(Int64)"],
["floatC", "Nullable(Float32)"],
["doubleC", "Nullable(Float64)"],
["decimalC1", "Nullable(Decimal(10, 3))"],
["decimalC2", "Nullable(Decimal(20, 10))"],
["decimalC3", "Nullable(Decimal(38, 30))"],
["dateC", "Nullable(Date)"],
["timestampC", "Nullable(DateTime64(6, 'UTC'))"],
["stringC", "Nullable(String)"],
["binaryC", "Nullable(String)"],
["arrayC1", "Array(Nullable(Int32))"],
["mapC1", "Map(String, Nullable(String))"],
["structC1", "Tuple(field1 Nullable(Int32), field2 Nullable(String))"],
[
"complexC",
"Array(Tuple(field1 Map(String, Array(Map(String, Nullable(Int32)))), field2 Tuple(field3 Nullable(Int32), field4 Nullable(String))))",
],
]
)
assert res == expected
# Check that we can parse data
instance.query(f"SELECT * FROM {TABLE_NAME}")
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_metadata_file_selection(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_metadata_selection_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
spark.sql(
f"CREATE TABLE {TABLE_NAME} (id bigint, data string) USING iceberg TBLPROPERTIES ('format-version' = '2', 'write.update.mode'='merge-on-read', 'write.delete.mode'='merge-on-read', 'write.merge.mode'='merge-on-read')"
)
for i in range(50):
spark.sql(
f"INSERT INTO {TABLE_NAME} select id, char(id + ascii('a')) from range(10)"
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 500
@pytest.mark.parametrize("format_version", ["1", "2"])
@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
def test_metadata_file_format_with_uuid(started_cluster, format_version, storage_type):
if is_arm() and storage_type == "hdfs":
pytest.skip("Disabled test IcebergHDFS for aarch64")
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = (
"test_metadata_selection_with_uuid_"
+ format_version
+ "_"
+ storage_type
+ "_"
+ get_uuid_str()
)
spark.sql(
f"CREATE TABLE {TABLE_NAME} (id bigint, data string) USING iceberg TBLPROPERTIES ('format-version' = '2', 'write.update.mode'='merge-on-read', 'write.delete.mode'='merge-on-read', 'write.merge.mode'='merge-on-read')"
)
for i in range(50):
spark.sql(
f"INSERT INTO {TABLE_NAME} select id, char(id + ascii('a')) from range(10)"
)
for i in range(50):
os.rename(
f"/iceberg_data/default/{TABLE_NAME}/metadata/v{i + 1}.metadata.json",
f"/iceberg_data/default/{TABLE_NAME}/metadata/{str(i).zfill(5)}-{get_uuid_str()}.metadata.json",
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 500
def test_restart_broken_s3(started_cluster):
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = "test_restart_broken_table_function_s3" + "_" + get_uuid_str()
minio_client = started_cluster.minio_client
bucket = "broken2"
if not minio_client.bucket_exists(bucket):
minio_client.make_bucket(bucket)
write_iceberg_from_df(
spark,
generate_data(spark, 0, 100),
TABLE_NAME,
mode="overwrite",
format_version="1",
)
files = default_upload_directory(
started_cluster,
"s3",
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
bucket=bucket,
)
create_iceberg_table("s3", instance, TABLE_NAME, started_cluster, bucket=bucket)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
s3_objects = list_s3_objects(minio_client, bucket, prefix="")
assert (
len(
list(
minio_client.remove_objects(
bucket,
[DeleteObject(obj) for obj in s3_objects],
)
)
)
== 0
)
minio_client.remove_bucket(bucket)
instance.restart_clickhouse()
assert "NoSuchBucket" in instance.query_and_get_error(
f"SELECT count() FROM {TABLE_NAME}"
)
minio_client.make_bucket(bucket)
files = default_upload_directory(
started_cluster,
"s3",
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
bucket=bucket,
)
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
@pytest.mark.parametrize("storage_type", ["s3"])
def test_filesystem_cache(started_cluster, storage_type):
instance = started_cluster.instances["node1"]
spark = started_cluster.spark_session
TABLE_NAME = "test_filesystem_cache_" + storage_type + "_" + get_uuid_str()
write_iceberg_from_df(
spark,
generate_data(spark, 0, 10),
TABLE_NAME,
mode="overwrite",
format_version="1",
partition_by="a",
)
default_upload_directory(
started_cluster,
storage_type,
f"/iceberg_data/default/{TABLE_NAME}/",
f"/iceberg_data/default/{TABLE_NAME}/",
)
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
query_id = f"{TABLE_NAME}-{uuid.uuid4()}"
instance.query(
f"SELECT * FROM {TABLE_NAME} SETTINGS filesystem_cache_name = 'cache1'",
query_id=query_id,
)
instance.query("SYSTEM FLUSH LOGS")
written_to_cache_first_select = int(
instance.query(
f"SELECT ProfileEvents['CachedReadBufferCacheWriteBytes'] FROM system.query_log WHERE query_id = '{query_id}' AND type = 'QueryFinish'"
)
)
read_from_cache_first_select = int(
instance.query(
f"SELECT ProfileEvents['CachedReadBufferReadFromCacheBytes'] FROM system.query_log WHERE query_id = '{query_id}' AND type = 'QueryFinish'"
)
)
assert 0 < int(
instance.query(
f"SELECT ProfileEvents['S3GetObject'] FROM system.query_log WHERE query_id = '{query_id}' AND type = 'QueryFinish'"
)
)
query_id = f"{TABLE_NAME}-{uuid.uuid4()}"
instance.query(
f"SELECT * FROM {TABLE_NAME} SETTINGS filesystem_cache_name = 'cache1'",
query_id=query_id,
)
instance.query("SYSTEM FLUSH LOGS")
read_from_cache_second_select = int(
instance.query(
f"SELECT ProfileEvents['CachedReadBufferReadFromCacheBytes'] FROM system.query_log WHERE query_id = '{query_id}' AND type = 'QueryFinish'"
)
)
assert (
read_from_cache_second_select
== read_from_cache_first_select + written_to_cache_first_select
)
assert 0 == int(
instance.query(
f"SELECT ProfileEvents['S3GetObject'] FROM system.query_log WHERE query_id = '{query_id}' AND type = 'QueryFinish'"
)
)
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