mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-10 09:32:06 +00:00
401 lines
12 KiB
Python
401 lines
12 KiB
Python
import helpers.client
|
|
from helpers.cluster import ClickHouseCluster
|
|
from helpers.test_tools import TSV
|
|
|
|
import pytest
|
|
import logging
|
|
import os
|
|
import json
|
|
import time
|
|
import glob
|
|
|
|
import pyspark
|
|
import delta
|
|
from delta import *
|
|
from pyspark.sql.types import (
|
|
StructType,
|
|
StructField,
|
|
StringType,
|
|
IntegerType,
|
|
DateType,
|
|
TimestampType,
|
|
BooleanType,
|
|
ArrayType,
|
|
)
|
|
from pyspark.sql.functions import current_timestamp
|
|
from datetime import datetime
|
|
from pyspark.sql.functions import monotonically_increasing_id, row_number
|
|
from pyspark.sql.window import Window
|
|
|
|
from helpers.s3_tools import prepare_s3_bucket, upload_directory, get_file_contents
|
|
|
|
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()
|
|
|
|
|
|
@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"],
|
|
with_minio=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):
|
|
node.query(
|
|
f"""
|
|
DROP TABLE IF EXISTS {table_name};
|
|
CREATE TABLE {table_name}
|
|
ENGINE=DeltaLake(s3, filename = '{table_name}/')"""
|
|
)
|
|
|
|
|
|
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 = "test_single_log_file"
|
|
|
|
inserted_data = "SELECT number, toString(number + 1) 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 = "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 = "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 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 = "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 = "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 = "test_types"
|
|
spark = started_cluster.spark_session
|
|
result_file = 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<STRING>")
|
|
.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)"],
|
|
]
|
|
)
|