ClickHouse/tests/integration/test_storage_iceberg/test.py

import helpers.client
from helpers.cluster import ClickHouseCluster
from helpers.test_tools import TSV

import pyspark
import logging
import os
import json
import pytest
import time

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 pyspark.sql.readwriter import DataFrameWriter, DataFrameWriterV2

from helpers.s3_tools import prepare_s3_bucket, upload_directory, get_file_contents

SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
USER_FILES_PATH = os.path.join(SCRIPT_DIR, "./_instances/node1/database/user_files")


@pytest.fixture(scope="module")
def started_cluster():
    try:
        cluster = ClickHouseCluster(__file__)
        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)
        logging.info("S3 bucket created")

        yield cluster

    finally:
        cluster.shutdown()


def run_query(instance, query, stdin=None, settings=None):
    # type: (ClickHouseInstance, str, object, dict) -> str

    logging.info("Running query '{}'...".format(query))
    result = instance.query(query, stdin=stdin, settings=settings)
    logging.info("Query finished")

    return result


def get_spark():
    builder = (
        pyspark.sql.SparkSession.builder.appName("spark_test")
        .config(
            "spark.jars.packages",
            "org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.1.0",
        )
        .config(
            "spark.sql.catalog.spark_catalog",
            "org.apache.iceberg.spark.SparkSessionCatalog",
        )
        .config("spark.sql.catalog.local", "org.apache.iceberg.spark.SparkCatalog")
        .config("spark.sql.catalog.spark_catalog.type", "hadoop")
        .config("spark.sql.catalog.spark_catalog.warehouse", "/iceberg_data")
        .master("local")
    )
    return builder.master("local").getOrCreate()


def write_iceberg_from_file(
    spark, path, table_name, mode="overwrite", format_version="1"
):
    if mode == "overwrite":
        spark.read.load(f"file://{path}").writeTo(table_name).tableProperty(
            "format-version", format_version
        ).using("iceberg").create()
    else:
        spark.read.load(f"file://{path}").writeTo(table_name).append()


def write_iceberg_from_df(spark, df, table_name, mode="overwrite", format_version="1"):
    if mode == "overwrite":
        df.writeTo(table_name).tableProperty("format-version", format_version).using(
            "iceberg"
        ).create()
    else:
        df.writeTo(table_name).append()


def generate_data(spark, start, end):
    a = spark.range(start, end, 1).toDF("a")
    b = spark.range(start + 1, end + 1, 1).toDF("b")
    b = b.withColumn("b", b["b"].cast(StringType()))

    a = a.withColumn(
        "row_index", row_number().over(Window.orderBy(monotonically_increasing_id()))
    )
    b = b.withColumn(
        "row_index", row_number().over(Window.orderBy(monotonically_increasing_id()))
    )

    df = a.join(b, on=["row_index"]).drop("row_index")
    return df


def create_iceberg_table(node, table_name):
    node.query(
        f"""
        DROP TABLE IF EXISTS {table_name};
        CREATE TABLE {table_name}
        ENGINE=Iceberg(s3, filename = 'iceberg_data/default/{table_name}/')"""
    )


def create_initial_data_file(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"""
    )
    result_path = f"{USER_FILES_PATH}/{table_name}.parquet"
    return result_path


@pytest.mark.parametrize("format_version", ["1", "2"])
def test_single_iceberg_file(started_cluster, format_version):
    instance = started_cluster.instances["node1"]
    minio_client = started_cluster.minio_client
    bucket = started_cluster.minio_bucket
    spark = get_spark()
    TABLE_NAME = "test_single_iceberg_file_" + format_version

    inserted_data = "SELECT number, toString(number) FROM numbers(100)"
    parquet_data_path = create_initial_data_file(instance, inserted_data, TABLE_NAME)
    write_iceberg_from_file(
        spark, parquet_data_path, TABLE_NAME, format_version=format_version
    )

    files = upload_directory(
        minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}/", ""
    )

    create_iceberg_table(instance, TABLE_NAME)
    assert instance.query(f"SELECT * FROM {TABLE_NAME}") == instance.query(
        inserted_data
    )


@pytest.mark.parametrize("format_version", ["1", "2"])
def test_multiple_iceberg_files(started_cluster, format_version):
    instance = started_cluster.instances["node1"]
    minio_client = started_cluster.minio_client
    bucket = started_cluster.minio_bucket
    spark = get_spark()
    TABLE_NAME = "test_multiple_iceberg_files_" + format_version

    write_iceberg_from_df(
        spark, generate_data(spark, 0, 100), TABLE_NAME, mode="overwrite", format_version=format_version,
    )

    files = upload_directory(
        minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}", ""
    )
    # ['/iceberg_data/default/test_multiple_iceberg_files/data/00000-1-35302d56-f1ed-494e-a85b-fbf85c05ab39-00001.parquet',
    # '/iceberg_data/default/test_multiple_iceberg_files/metadata/version-hint.text',
    # '/iceberg_data/default/test_multiple_iceberg_files/metadata/3127466b-299d-48ca-a367-6b9b1df1e78c-m0.avro',
    # '/iceberg_data/default/test_multiple_iceberg_files/metadata/snap-5220855582621066285-1-3127466b-299d-48ca-a367-6b9b1df1e78c.avro',
    # '/iceberg_data/default/test_multiple_iceberg_files/metadata/v1.metadata.json']
    assert len(files) == 5

    create_iceberg_table(instance, TABLE_NAME)
    assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100

    write_iceberg_from_df(
        spark, generate_data(spark, 100, 200), TABLE_NAME, mode="append", format_version=format_version
    )
    files = upload_directory(
        minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}", ""
    )
    assert len(files) == 9

    assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 200
    assert instance.query(f"SELECT * FROM {TABLE_NAME} ORDER BY 1") == instance.query(
        "SELECT number, toString(number + 1) FROM numbers(200)"
    )


@pytest.mark.parametrize("format_version", ["1", "2"])
def test_types(started_cluster, format_version):
    instance = started_cluster.instances["node1"]
    minio_client = started_cluster.minio_client
    bucket = started_cluster.minio_bucket
    spark = get_spark()
    TABLE_NAME = "test_types_" + format_version

    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)

    upload_directory(minio_client, bucket, f"/iceberg_data/default/{TABLE_NAME}", "")

    create_iceberg_table(instance, TABLE_NAME)
    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 = f"iceberg(s3, filename='iceberg_data/default/{TABLE_NAME}/')"
    assert (
        instance.query(f"SELECT a, b, c, d, e 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)"],
        ]
    )