mirror of
https://github.com/ClickHouse/ClickHouse.git
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1088 lines
36 KiB
Python
1088 lines
36 KiB
Python
import glob
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import json
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import logging
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import os
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import time
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import uuid
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from datetime import datetime
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import pyspark
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import pytest
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from azure.storage.blob import BlobServiceClient
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from minio.deleteobjects import DeleteObject
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from pyspark.sql.functions import (
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current_timestamp,
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monotonically_increasing_id,
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row_number,
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)
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from pyspark.sql.readwriter import DataFrameWriter, DataFrameWriterV2
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from pyspark.sql.types import (
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ArrayType,
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BooleanType,
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DateType,
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IntegerType,
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StringType,
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StructField,
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StructType,
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TimestampType,
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)
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from pyspark.sql.window import Window
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import helpers.client
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from helpers.cluster import ClickHouseCluster, ClickHouseInstance, is_arm
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from helpers.s3_tools import (
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AzureUploader,
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HDFSUploader,
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LocalUploader,
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S3Uploader,
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get_file_contents,
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list_s3_objects,
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prepare_s3_bucket,
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)
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from helpers.test_tools import TSV
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SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
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def get_spark():
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builder = (
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pyspark.sql.SparkSession.builder.appName("spark_test")
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.config(
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"spark.sql.catalog.spark_catalog",
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"org.apache.iceberg.spark.SparkSessionCatalog",
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)
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.config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")
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.config("spark.sql.catalog.spark_catalog.type", "hadoop")
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.config("spark.sql.catalog.spark_catalog.warehouse", "/iceberg_data")
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.config(
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"spark.sql.extensions",
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"org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions",
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)
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.master("local")
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)
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return builder.master("local").getOrCreate()
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@pytest.fixture(scope="module")
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def started_cluster():
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try:
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cluster = ClickHouseCluster(__file__, with_spark=True)
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with_hdfs = True
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if is_arm():
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with_hdfs = False
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cluster.add_instance(
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"node1",
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main_configs=[
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"configs/config.d/query_log.xml",
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"configs/config.d/cluster.xml",
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"configs/config.d/named_collections.xml",
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"configs/config.d/filesystem_caches.xml",
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],
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user_configs=["configs/users.d/users.xml"],
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with_minio=True,
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with_azurite=True,
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with_hdfs=with_hdfs,
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stay_alive=True,
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)
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cluster.add_instance(
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"node2",
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main_configs=[
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"configs/config.d/query_log.xml",
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"configs/config.d/cluster.xml",
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"configs/config.d/named_collections.xml",
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"configs/config.d/filesystem_caches.xml",
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],
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user_configs=["configs/users.d/users.xml"],
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stay_alive=True,
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)
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cluster.add_instance(
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"node3",
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main_configs=[
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"configs/config.d/query_log.xml",
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"configs/config.d/cluster.xml",
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"configs/config.d/named_collections.xml",
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"configs/config.d/filesystem_caches.xml",
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],
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user_configs=["configs/users.d/users.xml"],
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stay_alive=True,
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)
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logging.info("Starting cluster...")
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cluster.start()
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prepare_s3_bucket(cluster)
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logging.info("S3 bucket created")
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cluster.spark_session = get_spark()
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cluster.default_s3_uploader = S3Uploader(
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cluster.minio_client, cluster.minio_bucket
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)
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cluster.azure_container_name = "mycontainer"
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cluster.blob_service_client = cluster.blob_service_client
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container_client = cluster.blob_service_client.create_container(
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cluster.azure_container_name
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)
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cluster.container_client = container_client
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cluster.default_azure_uploader = AzureUploader(
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cluster.blob_service_client, cluster.azure_container_name
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)
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cluster.default_hdfs_uploader = HDFSUploader(cluster)
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cluster.default_local_uploader = LocalUploader(cluster.instances["node1"])
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yield cluster
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finally:
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cluster.shutdown()
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def run_query(instance, query, stdin=None, settings=None):
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# type: (ClickHouseInstance, str, object, dict) -> str
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logging.info("Running query '{}'...".format(query))
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result = instance.query(query, stdin=stdin, settings=settings)
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logging.info("Query finished")
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return result
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def write_iceberg_from_file(
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spark, path, table_name, mode="overwrite", format_version="1", partition_by=None
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):
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if mode == "overwrite":
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if partition_by is None:
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spark.read.load(f"file://{path}").writeTo(table_name).tableProperty(
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"format-version", format_version
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).using("iceberg").create()
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else:
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spark.read.load(f"file://{path}").writeTo(table_name).partitionedBy(
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partition_by
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).tableProperty("format-version", format_version).using("iceberg").create()
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else:
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spark.read.load(f"file://{path}").writeTo(table_name).append()
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def write_iceberg_from_df(
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spark, df, table_name, mode="overwrite", format_version="1", partition_by=None
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):
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if mode == "overwrite":
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if partition_by is None:
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df.writeTo(table_name).tableProperty(
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"format-version", format_version
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).using("iceberg").create()
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else:
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df.writeTo(table_name).tableProperty(
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"format-version", format_version
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).partitionedBy(partition_by).using("iceberg").create()
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else:
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df.writeTo(table_name).append()
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def generate_data(spark, start, end):
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a = spark.range(start, end, 1).toDF("a")
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b = spark.range(start + 1, end + 1, 1).toDF("b")
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b = b.withColumn("b", b["b"].cast(StringType()))
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a = a.withColumn(
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"row_index", row_number().over(Window.orderBy(monotonically_increasing_id()))
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)
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b = b.withColumn(
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"row_index", row_number().over(Window.orderBy(monotonically_increasing_id()))
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)
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df = a.join(b, on=["row_index"]).drop("row_index")
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return df
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def get_creation_expression(
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storage_type,
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table_name,
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cluster,
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format="Parquet",
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table_function=False,
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run_on_cluster=False,
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**kwargs,
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):
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if storage_type == "s3":
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if "bucket" in kwargs:
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bucket = kwargs["bucket"]
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else:
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bucket = cluster.minio_bucket
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if run_on_cluster:
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assert table_function
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return f"icebergS3Cluster('cluster_simple', s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"
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else:
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if table_function:
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return f"icebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"
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else:
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return f"""
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DROP TABLE IF EXISTS {table_name};
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CREATE TABLE {table_name}
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ENGINE=IcebergS3(s3, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'http://minio1:9001/{bucket}/')"""
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elif storage_type == "azure":
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if run_on_cluster:
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assert table_function
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return f"""
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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})
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"""
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else:
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if table_function:
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return f"""
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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})
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"""
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else:
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return f"""
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DROP TABLE IF EXISTS {table_name};
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CREATE TABLE {table_name}
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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})"""
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elif storage_type == "hdfs":
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if run_on_cluster:
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assert table_function
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return f"""
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icebergHDFSCluster('cluster_simple', hdfs, filename= 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')
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"""
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else:
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if table_function:
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return f"""
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icebergHDFS(hdfs, filename= 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/')
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"""
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else:
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return f"""
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DROP TABLE IF EXISTS {table_name};
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CREATE TABLE {table_name}
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ENGINE=IcebergHDFS(hdfs, filename = 'iceberg_data/default/{table_name}/', format={format}, url = 'hdfs://hdfs1:9000/');"""
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elif storage_type == "local":
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assert not run_on_cluster
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if table_function:
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return f"""
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icebergLocal(local, path = '/iceberg_data/default/{table_name}/', format={format})
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"""
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else:
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return f"""
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DROP TABLE IF EXISTS {table_name};
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CREATE TABLE {table_name}
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ENGINE=IcebergLocal(local, path = '/iceberg_data/default/{table_name}/', format={format});"""
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else:
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raise Exception(f"Unknown iceberg storage type: {storage_type}")
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def get_uuid_str():
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return str(uuid.uuid4()).replace("-", "_")
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def create_iceberg_table(
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storage_type,
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node,
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table_name,
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cluster,
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format="Parquet",
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**kwargs,
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):
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node.query(
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get_creation_expression(storage_type, table_name, cluster, format, **kwargs)
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)
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def create_initial_data_file(
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cluster, node, query, table_name, compression_method="none"
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):
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node.query(
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f"""
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INSERT INTO TABLE FUNCTION
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file('{table_name}.parquet')
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SETTINGS
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output_format_parquet_compression_method='{compression_method}',
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s3_truncate_on_insert=1 {query}
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FORMAT Parquet"""
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)
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user_files_path = os.path.join(
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SCRIPT_DIR, f"{cluster.instances_dir_name}/node1/database/user_files"
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)
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result_path = f"{user_files_path}/{table_name}.parquet"
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return result_path
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def default_upload_directory(
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started_cluster, storage_type, local_path, remote_path, **kwargs
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):
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if storage_type == "local":
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return started_cluster.default_local_uploader.upload_directory(
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local_path, remote_path, **kwargs
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)
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elif storage_type == "hdfs":
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return started_cluster.default_hdfs_uploader.upload_directory(
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local_path, remote_path, **kwargs
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)
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elif storage_type == "s3":
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print(kwargs)
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return started_cluster.default_s3_uploader.upload_directory(
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local_path, remote_path, **kwargs
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)
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elif storage_type == "azure":
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return started_cluster.default_azure_uploader.upload_directory(
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local_path, remote_path, **kwargs
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)
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else:
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raise Exception(f"Unknown iceberg storage type: {storage_type}")
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@pytest.mark.parametrize("format_version", ["1", "2"])
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@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
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def test_single_iceberg_file(started_cluster, format_version, storage_type):
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if is_arm() and storage_type == "hdfs":
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pytest.skip("Disabled test IcebergHDFS for aarch64")
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instance = started_cluster.instances["node1"]
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spark = started_cluster.spark_session
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TABLE_NAME = (
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"test_single_iceberg_file_"
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+ format_version
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+ "_"
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+ storage_type
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+ "_"
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+ get_uuid_str()
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)
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write_iceberg_from_df(spark, generate_data(spark, 0, 100), TABLE_NAME)
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files = default_upload_directory(
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started_cluster,
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storage_type,
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f"/iceberg_data/default/{TABLE_NAME}/",
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f"/iceberg_data/default/{TABLE_NAME}/",
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)
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create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
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assert instance.query(f"SELECT * FROM {TABLE_NAME}") == instance.query(
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"SELECT number, toString(number + 1) FROM numbers(100)"
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)
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@pytest.mark.parametrize("format_version", ["1", "2"])
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@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
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def test_partition_by(started_cluster, format_version, storage_type):
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if is_arm() and storage_type == "hdfs":
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pytest.skip("Disabled test IcebergHDFS for aarch64")
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instance = started_cluster.instances["node1"]
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spark = started_cluster.spark_session
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TABLE_NAME = (
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"test_partition_by_"
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+ format_version
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+ "_"
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+ storage_type
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+ "_"
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+ get_uuid_str()
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)
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write_iceberg_from_df(
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spark,
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generate_data(spark, 0, 10),
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TABLE_NAME,
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mode="overwrite",
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format_version=format_version,
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partition_by="a",
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)
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files = default_upload_directory(
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started_cluster,
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storage_type,
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f"/iceberg_data/default/{TABLE_NAME}/",
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f"/iceberg_data/default/{TABLE_NAME}/",
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)
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assert len(files) == 14 # 10 partitiions + 4 metadata files
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create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 10
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|
|
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@pytest.mark.parametrize("format_version", ["1", "2"])
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@pytest.mark.parametrize("storage_type", ["s3", "azure", "hdfs", "local"])
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def test_multiple_iceberg_files(started_cluster, format_version, storage_type):
|
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if is_arm() and storage_type == "hdfs":
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pytest.skip("Disabled test IcebergHDFS for aarch64")
|
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instance = started_cluster.instances["node1"]
|
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spark = started_cluster.spark_session
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minio_client = started_cluster.minio_client
|
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bucket = started_cluster.minio_bucket
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TABLE_NAME = (
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"test_multiple_iceberg_files_"
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+ format_version
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+ "_"
|
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+ storage_type
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+ "_"
|
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+ get_uuid_str()
|
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)
|
|
|
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write_iceberg_from_df(
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spark,
|
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generate_data(spark, 0, 100),
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TABLE_NAME,
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mode="overwrite",
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format_version=format_version,
|
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)
|
|
|
|
files = default_upload_directory(
|
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started_cluster,
|
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storage_type,
|
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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',
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# '/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',
|
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# '/iceberg_data/default/test_multiple_iceberg_files/metadata/snap-5220855582621066285-1-3127466b-299d-48ca-a367-6b9b1df1e78c.avro',
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# '/iceberg_data/default/test_multiple_iceberg_files/metadata/v1.metadata.json']
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assert len(files) == 5
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create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
|
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
|
|
|
|
write_iceberg_from_df(
|
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spark,
|
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generate_data(spark, 100, 200),
|
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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(
|
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"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 = (
|
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"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
|
|
minio_client = started_cluster.minio_client
|
|
bucket = started_cluster.minio_bucket
|
|
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,
|
|
)
|
|
|
|
files = 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")
|
|
files = 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,
|
|
)
|
|
|
|
files = 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")
|
|
files = 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"])
|
|
def test_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
|
|
minio_client = started_cluster.minio_client
|
|
bucket = started_cluster.minio_bucket
|
|
TABLE_NAME = (
|
|
"test_evolved_schema_"
|
|
+ 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}/",
|
|
)
|
|
|
|
create_iceberg_table(storage_type, instance, TABLE_NAME, started_cluster)
|
|
|
|
assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
|
|
|
|
expected_data = instance.query(f"SELECT * FROM {TABLE_NAME} order by a, b")
|
|
|
|
spark.sql(f"ALTER TABLE {TABLE_NAME} ADD COLUMNS (x bigint)")
|
|
files = 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
|
|
|
|
data = instance.query(
|
|
f"SELECT * FROM {TABLE_NAME} SETTINGS iceberg_engine_ignore_schema_evolution=1"
|
|
)
|
|
assert data == expected_data
|
|
|
|
|
|
@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
|
|
minio_client = started_cluster.minio_client
|
|
bucket = started_cluster.minio_bucket
|
|
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)"
|
|
)
|
|
|
|
files = 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")
|
|
files = 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')))"
|
|
)
|
|
files = 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)"
|
|
)
|
|
|
|
files = 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",
|
|
)
|
|
|
|
files = 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")
|
|
|
|
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'"
|
|
)
|
|
)
|