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309 lines
9.9 KiB
Python
309 lines
9.9 KiB
Python
import helpers.client
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from helpers.cluster import ClickHouseCluster
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from helpers.test_tools import TSV
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import pyspark
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import logging
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import os
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import json
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import pytest
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import time
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from pyspark.sql.types import (
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StructType,
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StructField,
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StringType,
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IntegerType,
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DateType,
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TimestampType,
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BooleanType,
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ArrayType,
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)
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from pyspark.sql.functions import current_timestamp
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from datetime import datetime
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from pyspark.sql.functions import monotonically_increasing_id, row_number
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from pyspark.sql.window import Window
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from pyspark.sql.readwriter import DataFrameWriter, DataFrameWriterV2
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from helpers.s3_tools import prepare_s3_bucket, upload_directory, get_file_contents
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SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
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USER_FILES_PATH = os.path.join(SCRIPT_DIR, "./_instances/node1/database/user_files")
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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__)
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cluster.add_instance(
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"node1",
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main_configs=["configs/config.d/named_collections.xml"],
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with_minio=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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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 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.jars.packages",
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"org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.1.0",
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)
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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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.master("local")
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)
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return builder.master("local").getOrCreate()
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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 create_iceberg_table(node, table_name):
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node.query(
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f"""
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DROP TABLE IF EXISTS {table_name};
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CREATE TABLE {table_name}
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ENGINE=Iceberg(s3, filename = 'iceberg_data/default/{table_name}/')"""
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)
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def create_initial_data_file(node, query, table_name, compression_method="none"):
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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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result_path = f"{USER_FILES_PATH}/{table_name}.parquet"
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return result_path
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@pytest.mark.parametrize("format_version", ["1", "2"])
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def test_single_iceberg_file(started_cluster, format_version):
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instance = started_cluster.instances["node1"]
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minio_client = started_cluster.minio_client
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bucket = started_cluster.minio_bucket
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spark = get_spark()
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TABLE_NAME = "test_single_iceberg_file_" + format_version
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inserted_data = "SELECT number, toString(number) FROM numbers(100)"
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parquet_data_path = create_initial_data_file(instance, inserted_data, TABLE_NAME)
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write_iceberg_from_file(
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spark, parquet_data_path, TABLE_NAME, format_version=format_version
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)
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files = upload_directory(
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minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}/", ""
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)
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create_iceberg_table(instance, TABLE_NAME)
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assert instance.query(f"SELECT * FROM {TABLE_NAME}") == instance.query(
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inserted_data
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)
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@pytest.mark.parametrize("format_version", ["1", "2"])
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def test_partition_by(started_cluster, format_version):
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instance = started_cluster.instances["node1"]
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minio_client = started_cluster.minio_client
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bucket = started_cluster.minio_bucket
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spark = get_spark()
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TABLE_NAME = "test_partition_by_" + format_version
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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 = upload_directory(
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minio_client, bucket, 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(instance, TABLE_NAME)
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 10
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@pytest.mark.parametrize("format_version", ["1", "2"])
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def test_multiple_iceberg_files(started_cluster, format_version):
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instance = started_cluster.instances["node1"]
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minio_client = started_cluster.minio_client
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bucket = started_cluster.minio_bucket
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spark = get_spark()
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TABLE_NAME = "test_multiple_iceberg_files_" + format_version
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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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)
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files = upload_directory(
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minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}", ""
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)
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# ['/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',
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# '/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(instance, TABLE_NAME)
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
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write_iceberg_from_df(
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spark,
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generate_data(spark, 100, 200),
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TABLE_NAME,
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mode="append",
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format_version=format_version,
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)
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files = upload_directory(
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minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}", ""
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)
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assert len(files) == 9
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 200
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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)"
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)
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@pytest.mark.parametrize("format_version", ["1", "2"])
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def test_types(started_cluster, format_version):
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instance = started_cluster.instances["node1"]
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minio_client = started_cluster.minio_client
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bucket = started_cluster.minio_bucket
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spark = get_spark()
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TABLE_NAME = "test_types_" + format_version
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data = [
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(
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123,
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"string",
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datetime.strptime("2000-01-01", "%Y-%m-%d"),
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["str1", "str2"],
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True,
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)
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]
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schema = StructType(
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[
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StructField("a", IntegerType()),
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StructField("b", StringType()),
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StructField("c", DateType()),
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StructField("d", ArrayType(StringType())),
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StructField("e", BooleanType()),
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]
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)
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df = spark.createDataFrame(data=data, schema=schema)
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df.printSchema()
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write_iceberg_from_df(
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spark, df, TABLE_NAME, mode="overwrite", format_version=format_version
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)
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upload_directory(minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}", "")
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create_iceberg_table(instance, TABLE_NAME)
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 1
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assert (
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instance.query(f"SELECT a, b, c, d, e FROM {TABLE_NAME}").strip()
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== "123\tstring\t2000-01-01\t['str1','str2']\ttrue"
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)
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table_function = f"iceberg(s3, filename='iceberg_data/default/{TABLE_NAME}/')"
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assert (
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instance.query(f"SELECT a, b, c, d, e FROM {table_function}").strip()
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== "123\tstring\t2000-01-01\t['str1','str2']\ttrue"
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)
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assert instance.query(f"DESCRIBE {table_function} FORMAT TSV") == TSV(
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[
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["a", "Nullable(Int32)"],
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["b", "Nullable(String)"],
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["c", "Nullable(Date32)"],
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["d", "Array(Nullable(String))"],
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["e", "Nullable(Bool)"],
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]
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)
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