mirror of
https://github.com/ClickHouse/ClickHouse.git
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315 lines
9.6 KiB
Python
315 lines
9.6 KiB
Python
import logging
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import pytest
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import os
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import json
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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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from helpers.s3_tools import prepare_s3_bucket, upload_directory, get_file_contents
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import pyspark
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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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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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"org.apache.spark.sql.hudi.catalog.HoodieCatalog",
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)
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.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
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.config(
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"spark.sql.catalog.local", "org.apache.spark.sql.hudi.catalog.HoodieCatalog"
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)
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.config("spark.driver.memory", "20g")
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.master("local")
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)
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return builder.getOrCreate()
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@pytest.fixture(scope="module")
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def started_cluster():
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cluster = ClickHouseCluster(__file__, with_spark=True)
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try:
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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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cluster.spark_session = get_spark()
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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_hudi_from_df(spark, table_name, df, result_path, mode="overwrite"):
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if mode is "overwrite":
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hudi_write_mode = "insert_overwrite"
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else:
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hudi_write_mode = "upsert"
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df.write.mode(mode).option("compression", "none").option(
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"compression", "none"
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).format("hudi").option("hoodie.table.name", table_name).option(
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"hoodie.datasource.write.partitionpath.field", "partitionpath"
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).option(
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"hoodie.datasource.write.table.name", table_name
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).option(
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"hoodie.datasource.write.operation", hudi_write_mode
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).option(
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"hoodie.datasource.compaction.async.enable", "true"
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).option(
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"hoodie.compact.inline", "false"
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).option(
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"hoodie.compact.inline.max.delta.commits", "10"
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).option(
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"hoodie.parquet.compression.codec", "snappy"
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).option(
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"hoodie.hfile.compression.algorithm", "uncompressed"
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).option(
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"hoodie.datasource.write.recordkey.field", "a"
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).option(
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"hoodie.datasource.write.precombine.field", "a"
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).save(
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result_path
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)
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def write_hudi_from_file(spark, table_name, path, result_path):
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spark.conf.set("spark.sql.debug.maxToStringFields", 100000)
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df = spark.read.load(f"file://{path}")
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write_hudi_from_df(spark, table_name, df, result_path)
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def generate_data(spark, start, end, append=1):
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a = spark.range(start, end, 1).toDF("a")
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b = spark.range(start + append, end + append, 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_hudi_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=Hudi(s3, filename = '{table_name}/')"""
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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 test_single_hudi_file(started_cluster):
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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 = "test_single_hudi_file"
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inserted_data = "SELECT number as a, toString(number) as b FROM numbers(100)"
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parquet_data_path = create_initial_data_file(
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started_cluster, instance, inserted_data, TABLE_NAME
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)
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write_hudi_from_file(spark, TABLE_NAME, parquet_data_path, f"/{TABLE_NAME}")
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files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "")
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assert len(files) == 1
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assert files[0].endswith(".parquet")
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create_hudi_table(instance, TABLE_NAME)
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assert instance.query(f"SELECT a, b FROM {TABLE_NAME}") == instance.query(
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inserted_data
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)
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def test_multiple_hudi_files(started_cluster):
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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 = "test_multiple_hudi_files"
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write_hudi_from_df(
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spark, TABLE_NAME, generate_data(spark, 0, 100), f"/{TABLE_NAME}"
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)
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files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "")
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assert len(files) == 1
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create_hudi_table(instance, TABLE_NAME)
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 100
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write_hudi_from_df(
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spark,
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TABLE_NAME,
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generate_data(spark, 100, 200),
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f"/{TABLE_NAME}",
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mode="append",
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)
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files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "")
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assert len(files) == 2
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 200
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assert instance.query(
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f"SELECT a, b FROM {TABLE_NAME} ORDER BY 1"
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) == instance.query("SELECT number, toString(number + 1) FROM numbers(200)")
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write_hudi_from_df(
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spark,
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TABLE_NAME,
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generate_data(spark, 100, 300),
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f"/{TABLE_NAME}",
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mode="append",
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)
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files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "")
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assert len(files) == 3
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 300
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assert instance.query(
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f"SELECT a, b FROM {TABLE_NAME} ORDER BY 1"
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) == instance.query("SELECT number, toString(number + 1) FROM numbers(300)")
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assert int(instance.query(f"SELECT b FROM {TABLE_NAME} WHERE a = 100")) == 101
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write_hudi_from_df(
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spark,
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TABLE_NAME,
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generate_data(spark, 100, 101, append=0),
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f"/{TABLE_NAME}",
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mode="append",
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)
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files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "")
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 300
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assert int(instance.query(f"SELECT b FROM {TABLE_NAME} WHERE a = 100")) == 100
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write_hudi_from_df(
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spark,
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TABLE_NAME,
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generate_data(spark, 100, 1000000, append=0),
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f"/{TABLE_NAME}",
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mode="append",
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)
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files = upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "")
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assert int(instance.query(f"SELECT count() FROM {TABLE_NAME}")) == 1000000
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def test_types(started_cluster):
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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 = "test_types"
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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_hudi_from_df(spark, TABLE_NAME, df, f"/{TABLE_NAME}", mode="overwrite")
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upload_directory(minio_client, bucket, f"/{TABLE_NAME}", "")
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create_hudi_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"hudi(s3, filename='{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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["_hoodie_commit_time", "Nullable(String)"],
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["_hoodie_commit_seqno", "Nullable(String)"],
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["_hoodie_record_key", "Nullable(String)"],
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["_hoodie_partition_path", "Nullable(String)"],
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["_hoodie_file_name", "Nullable(String)"],
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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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