import glob import json import logging import os import random import string import time import uuid from datetime import datetime import delta import pyarrow as pa import pyarrow.parquet as pq import pyspark import pytest from delta import * from deltalake.writer import write_deltalake from minio.deleteobjects import DeleteObject from pyspark.sql.functions import ( current_timestamp, monotonically_increasing_id, row_number, ) 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 from helpers.s3_tools import ( get_file_contents, list_s3_objects, prepare_s3_bucket, upload_directory, ) 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.extensions", "io.delta.sql.DeltaSparkSessionExtension") .config( "spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog", ) .master("local") ) return builder.master("local").getOrCreate() def randomize_table_name(table_name, random_suffix_length=10): letters = string.ascii_letters + string.digits return f"{table_name}{''.join(random.choice(letters) for _ in range(random_suffix_length))}" @pytest.fixture(scope="module") def started_cluster(): try: cluster = ClickHouseCluster(__file__, with_spark=True) cluster.add_instance( "node1", main_configs=[ "configs/config.d/named_collections.xml", "configs/config.d/filesystem_caches.xml", ], user_configs=["configs/users.d/users.xml"], with_minio=True, stay_alive=True, ) logging.info("Starting cluster...") cluster.start() prepare_s3_bucket(cluster) cluster.spark_session = get_spark() yield cluster finally: cluster.shutdown() def write_delta_from_file(spark, path, result_path, mode="overwrite"): spark.read.load(path).write.mode(mode).option("compression", "none").format( "delta" ).option("delta.columnMapping.mode", "name").save(result_path) def write_delta_from_df(spark, df, result_path, mode="overwrite", partition_by=None): if partition_by is None: df.write.mode(mode).option("compression", "none").format("delta").option( "delta.columnMapping.mode", "name" ).save(result_path) else: df.write.mode(mode).option("compression", "none").format("delta").option( "delta.columnMapping.mode", "name" ).partitionBy("a").save(result_path) 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_delta_metadata(delta_metadata_file): jsons = [json.loads(x) for x in delta_metadata_file.splitlines()] combined_json = {} for d in jsons: combined_json.update(d) return combined_json def create_delta_table(node, table_name, bucket="root"): node.query( f""" DROP TABLE IF EXISTS {table_name}; CREATE TABLE {table_name} ENGINE=DeltaLake(s3, filename = '{table_name}/', url = 'http://minio1:9001/{bucket}/')""" ) 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 test_single_log_file(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket TABLE_NAME = randomize_table_name("test_single_log_file") inserted_data = "SELECT number as a, toString(number + 1) as b FROM numbers(100)" parquet_data_path = create_initial_data_file( started_cluster, instance, inserted_data, TABLE_NAME ) write_delta_from_file(spark, parquet_data_path, f"/{TABLE_NAME}") files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert len(files) == 2 # 1 metadata files + 1 data file create_delta_table(instance, TABLE_NAME) assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100 assert instance.query(f"SELECT * FROM {TABLE_NAME}") == instance.query( inserted_data ) def test_partition_by(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket TABLE_NAME = randomize_table_name("test_partition_by") write_delta_from_df( spark, generate_data(spark, 0, 10), f"/{TABLE_NAME}", mode="overwrite", partition_by="a", ) files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert len(files) == 11 # 10 partitions and 1 metadata file create_delta_table(instance, TABLE_NAME) assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 10 def test_checkpoint(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket TABLE_NAME = randomize_table_name("test_checkpoint") write_delta_from_df( spark, generate_data(spark, 0, 1), f"/{TABLE_NAME}", mode="overwrite", ) for i in range(1, 25): write_delta_from_df( spark, generate_data(spark, i, i + 1), f"/{TABLE_NAME}", mode="append", ) files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") # 25 data files # 25 metadata files # 1 last_metadata file # 2 checkpoints assert len(files) == 25 * 2 + 3 ok = False for file in files: if file.endswith("last_checkpoint"): ok = True assert ok create_delta_table(instance, TABLE_NAME) assert ( int( instance.query( f"SELECT count() FROM {TABLE_NAME} SETTINGS input_format_parquet_allow_missing_columns=1" ) ) == 25 ) table = DeltaTable.forPath(spark, f"/{TABLE_NAME}") table.delete("a < 10") files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 15 for i in range(0, 5): write_delta_from_df( spark, generate_data(spark, i, i + 1), f"/{TABLE_NAME}", mode="append", ) # + 1 metadata files (for delete) # + 5 data files # + 5 metadata files # + 1 checkpoint file # + 1 ? files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert len(files) == 53 + 1 + 5 * 2 + 1 + 1 assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 20 assert ( instance.query(f"SELECT * FROM {TABLE_NAME} ORDER BY 1").strip() == instance.query( "SELECT * FROM (" "SELECT number, toString(number + 1) FROM numbers(5) " "UNION ALL SELECT number, toString(number + 1) FROM numbers(10, 15) " ") ORDER BY 1" ).strip() ) def test_multiple_log_files(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket TABLE_NAME = randomize_table_name("test_multiple_log_files") write_delta_from_df( spark, generate_data(spark, 0, 100), f"/{TABLE_NAME}", mode="overwrite" ) files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert len(files) == 2 # 1 metadata files + 1 data file s3_objects = list( minio_client.list_objects(bucket, f"{TABLE_NAME}/_delta_log/", recursive=True) ) assert len(s3_objects) == 1 create_delta_table(instance, TABLE_NAME) assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100 write_delta_from_df( spark, generate_data(spark, 100, 200), f"/{TABLE_NAME}", mode="append" ) files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert len(files) == 4 # 2 metadata files + 2 data files s3_objects = list( minio_client.list_objects(bucket, f"{TABLE_NAME}/_delta_log/", recursive=True) ) assert len(s3_objects) == 2 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)" ) def test_metadata(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket TABLE_NAME = randomize_table_name("test_metadata") parquet_data_path = create_initial_data_file( started_cluster, instance, "SELECT number, toString(number) FROM numbers(100)", TABLE_NAME, ) write_delta_from_file(spark, parquet_data_path, f"/{TABLE_NAME}") upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") data = get_file_contents( minio_client, bucket, f"/{TABLE_NAME}/_delta_log/00000000000000000000.json", ) delta_metadata = get_delta_metadata(data) stats = json.loads(delta_metadata["add"]["stats"]) assert stats["numRecords"] == 100 assert next(iter(stats["minValues"].values())) == 0 assert next(iter(stats["maxValues"].values())) == 99 create_delta_table(instance, TABLE_NAME) assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100 def test_types(started_cluster): TABLE_NAME = randomize_table_name("test_types") spark = started_cluster.spark_session result_file = randomize_table_name(f"{TABLE_NAME}_result_2") delta_table = ( DeltaTable.create(spark) .tableName(TABLE_NAME) .location(f"/{result_file}") .addColumn("a", "INT") .addColumn("b", "STRING") .addColumn("c", "DATE") .addColumn("d", "ARRAY") .addColumn("e", "BOOLEAN") .execute() ) 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() df.write.mode("append").format("delta").saveAsTable(TABLE_NAME) minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket upload_directory(minio_client, bucket, f"/{result_file}", "") instance = started_cluster.instances["node1"] instance.query( f""" DROP TABLE IF EXISTS {TABLE_NAME}; CREATE TABLE {TABLE_NAME} ENGINE=DeltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123')""" ) assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 1 assert ( instance.query(f"SELECT * FROM {TABLE_NAME}").strip() == "123\tstring\t2000-01-01\t['str1','str2']\ttrue" ) table_function = f"deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123')" assert ( instance.query(f"SELECT * FROM {table_function}").strip() == "123\tstring\t2000-01-01\t['str1','str2']\ttrue" ) assert instance.query(f"DESCRIBE {table_function} FORMAT TSV") == TSV( [ ["a", "Nullable(Int32)"], ["b", "Nullable(String)"], ["c", "Nullable(Date32)"], ["d", "Array(Nullable(String))"], ["e", "Nullable(Bool)"], ] ) def test_restart_broken(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = "broken" TABLE_NAME = randomize_table_name("test_restart_broken") if not minio_client.bucket_exists(bucket): minio_client.make_bucket(bucket) parquet_data_path = create_initial_data_file( started_cluster, instance, "SELECT number, toString(number) FROM numbers(100)", TABLE_NAME, ) write_delta_from_file(spark, parquet_data_path, f"/{TABLE_NAME}") upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") create_delta_table(instance, TABLE_NAME, 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}" ) s3_disk_no_key_errors_metric_value = int( instance.query( """ SELECT value FROM system.metrics WHERE metric = 'DiskS3NoSuchKeyErrors' """ ).strip() ) assert s3_disk_no_key_errors_metric_value == 0 minio_client.make_bucket(bucket) upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100 def test_restart_broken_table_function(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = "broken2" TABLE_NAME = randomize_table_name("test_restart_broken_table_function") if not minio_client.bucket_exists(bucket): minio_client.make_bucket(bucket) parquet_data_path = create_initial_data_file( started_cluster, instance, "SELECT number, toString(number) FROM numbers(100)", TABLE_NAME, ) write_delta_from_file(spark, parquet_data_path, f"/{TABLE_NAME}") upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") instance.query( f""" DROP TABLE IF EXISTS {TABLE_NAME}; CREATE TABLE {TABLE_NAME} AS deltaLake(s3, filename = '{TABLE_NAME}/', url = 'http://minio1:9001/{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) upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100 def test_partition_columns(started_cluster): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket TABLE_NAME = randomize_table_name("test_partition_columns") result_file = f"{TABLE_NAME}" partition_columns = ["b", "c", "d", "e"] delta_table = ( DeltaTable.create(spark) .tableName(TABLE_NAME) .location(f"/{result_file}") .addColumn("a", "INT") .addColumn("b", "STRING") .addColumn("c", "DATE") .addColumn("d", "INT") .addColumn("e", "BOOLEAN") .partitionedBy(partition_columns) .execute() ) num_rows = 9 schema = StructType( [ StructField("a", IntegerType()), StructField("b", StringType()), StructField("c", DateType()), StructField("d", IntegerType()), StructField("e", BooleanType()), ] ) for i in range(1, num_rows + 1): data = [ ( i, "test" + str(i), datetime.strptime(f"2000-01-0{i}", "%Y-%m-%d"), i, False if i % 2 == 0 else True, ) ] df = spark.createDataFrame(data=data, schema=schema) df.printSchema() df.write.mode("append").format("delta").partitionBy(partition_columns).save( f"/{TABLE_NAME}" ) minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") assert len(files) > 0 print(f"Uploaded files: {files}") result = instance.query( f"describe table deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123')" ).strip() assert ( result == "a\tNullable(Int32)\t\t\t\t\t\nb\tNullable(String)\t\t\t\t\t\nc\tNullable(Date32)\t\t\t\t\t\nd\tNullable(Int32)\t\t\t\t\t\ne\tNullable(Bool)" ) result = int( instance.query( f"""SELECT count() FROM deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123') """ ) ) assert result == num_rows result = int( instance.query( f"""SELECT count() FROM deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123') WHERE c == toDateTime('2000/01/05') """ ) ) assert result == 1 instance.query( f""" DROP TABLE IF EXISTS {TABLE_NAME}; CREATE TABLE {TABLE_NAME} (a Nullable(Int32), b Nullable(String), c Nullable(Date32), d Nullable(Int32), e Nullable(Bool)) ENGINE=DeltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123')""" ) assert ( """1 test1 2000-01-01 1 true 2 test2 2000-01-02 2 false 3 test3 2000-01-03 3 true 4 test4 2000-01-04 4 false 5 test5 2000-01-05 5 true 6 test6 2000-01-06 6 false 7 test7 2000-01-07 7 true 8 test8 2000-01-08 8 false 9 test9 2000-01-09 9 true""" == instance.query(f"SELECT * FROM {TABLE_NAME} ORDER BY b").strip() ) assert ( int( instance.query( f"SELECT count() FROM {TABLE_NAME} WHERE c == toDateTime('2000/01/05')" ) ) == 1 ) # Subset of columns should work. instance.query( f""" DROP TABLE IF EXISTS {TABLE_NAME}; CREATE TABLE {TABLE_NAME} (b Nullable(String), c Nullable(Date32), d Nullable(Int32)) ENGINE=DeltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123')""" ) assert ( """test1 2000-01-01 1 test2 2000-01-02 2 test3 2000-01-03 3 test4 2000-01-04 4 test5 2000-01-05 5 test6 2000-01-06 6 test7 2000-01-07 7 test8 2000-01-08 8 test9 2000-01-09 9""" == instance.query(f"SELECT * FROM {TABLE_NAME} ORDER BY b").strip() ) for i in range(num_rows + 1, 2 * num_rows + 1): data = [ ( i, "test" + str(i), datetime.strptime(f"2000-01-{i}", "%Y-%m-%d"), i, False if i % 2 == 0 else True, ) ] df = spark.createDataFrame(data=data, schema=schema) df.printSchema() df.write.mode("append").format("delta").partitionBy(partition_columns).save( f"/{TABLE_NAME}" ) files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") ok = False for file in files: if file.endswith("last_checkpoint"): ok = True assert ok result = int( instance.query( f"""SELECT count() FROM deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123') """ ) ) assert result == num_rows * 2 assert ( """1 test1 2000-01-01 1 true 2 test2 2000-01-02 2 false 3 test3 2000-01-03 3 true 4 test4 2000-01-04 4 false 5 test5 2000-01-05 5 true 6 test6 2000-01-06 6 false 7 test7 2000-01-07 7 true 8 test8 2000-01-08 8 false 9 test9 2000-01-09 9 true 10 test10 2000-01-10 10 false 11 test11 2000-01-11 11 true 12 test12 2000-01-12 12 false 13 test13 2000-01-13 13 true 14 test14 2000-01-14 14 false 15 test15 2000-01-15 15 true 16 test16 2000-01-16 16 false 17 test17 2000-01-17 17 true 18 test18 2000-01-18 18 false""" == instance.query( f""" SELECT * FROM deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/{bucket}/{result_file}/', 'minio', 'minio123') ORDER BY c """ ).strip() ) assert ( int( instance.query( f"SELECT count() FROM {TABLE_NAME} WHERE c == toDateTime('2000/01/15')" ) ) == 1 ) def test_complex_types(started_cluster): node = started_cluster.instances["node1"] minio_client = started_cluster.minio_client bucket = started_cluster.minio_bucket schema = pa.schema( [ ("id", pa.int32()), ("name", pa.string()), ( "address", pa.struct( [ ("street", pa.string()), ("city", pa.string()), ("state", pa.string()), ] ), ), ("interests", pa.list_(pa.string())), ( "metadata", pa.map_( pa.string(), pa.string() ), # Map with string keys and string values ), ] ) # Create sample data data = [ pa.array([1, 2, 3], type=pa.int32()), pa.array(["John Doe", "Jane Smith", "Jake Johnson"], type=pa.string()), pa.array( [ {"street": "123 Elm St", "city": "Springfield", "state": "IL"}, {"street": "456 Maple St", "city": "Shelbyville", "state": "IL"}, {"street": "789 Oak St", "city": "Ogdenville", "state": "IL"}, ], type=schema.field("address").type, ), pa.array( [ pa.array(["dancing", "coding", "hiking"]), pa.array(["dancing", "coding", "hiking"]), pa.array(["dancing", "coding", "hiking"]), ], type=schema.field("interests").type, ), pa.array( [ {"key1": "value1", "key2": "value2"}, {"key1": "value3", "key2": "value4"}, {"key1": "value5", "key2": "value6"}, ], type=schema.field("metadata").type, ), ] endpoint_url = f"http://{started_cluster.minio_ip}:{started_cluster.minio_port}" aws_access_key_id = "minio" aws_secret_access_key = "minio123" table_name = randomize_table_name("test_complex_types") storage_options = { "AWS_ENDPOINT_URL": endpoint_url, "AWS_ACCESS_KEY_ID": aws_access_key_id, "AWS_SECRET_ACCESS_KEY": aws_secret_access_key, "AWS_ALLOW_HTTP": "true", "AWS_S3_ALLOW_UNSAFE_RENAME": "true", } path = f"s3://root/{table_name}" table = pa.Table.from_arrays(data, schema=schema) write_deltalake(path, table, storage_options=storage_options) assert "1\n2\n3\n" in node.query( f"SELECT id FROM deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/root/{table_name}' , 'minio', 'minio123')" ) assert ( "('123 Elm St','Springfield','IL')\n('456 Maple St','Shelbyville','IL')\n('789 Oak St','Ogdenville','IL')" in node.query( f"SELECT address FROM deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/root/{table_name}' , 'minio', 'minio123')" ) ) assert ( "{'key1':'value1','key2':'value2'}\n{'key1':'value3','key2':'value4'}\n{'key1':'value5','key2':'value6'}" in node.query( f"SELECT metadata FROM deltaLake('http://{started_cluster.minio_ip}:{started_cluster.minio_port}/root/{table_name}' , 'minio', 'minio123')" ) ) @pytest.mark.parametrize("storage_type", ["s3"]) def test_filesystem_cache(started_cluster, storage_type): instance = started_cluster.instances["node1"] spark = started_cluster.spark_session minio_client = started_cluster.minio_client TABLE_NAME = randomize_table_name("test_filesystem_cache") bucket = started_cluster.minio_bucket if not minio_client.bucket_exists(bucket): minio_client.make_bucket(bucket) parquet_data_path = create_initial_data_file( started_cluster, instance, "SELECT number, toString(number) FROM numbers(100)", TABLE_NAME, ) write_delta_from_file(spark, parquet_data_path, f"/{TABLE_NAME}") upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "") create_delta_table(instance, TABLE_NAME, bucket=bucket) 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") count = int( instance.query( f"SELECT ProfileEvents['CachedReadBufferCacheWriteBytes'] 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") assert count == 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'" ) )