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import timeit |
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from os.path import join |
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from linetimer import CodeTimer, linetimer |
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from pandas.core.frame import DataFrame as PandasDF |
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from pyspark.sql import DataFrame as SparkDF |
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from pyspark.sql import SparkSession |
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from common_utils import ( |
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ANSWERS_BASE_DIR, |
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DATASET_BASE_DIR, |
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LOG_TIMINGS, |
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SHOW_RESULTS, |
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SPARK_LOG_LEVEL, |
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append_row, |
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on_second_call, |
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) |
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print("SPARK_LOG_LEVEL:", SPARK_LOG_LEVEL) |
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def get_or_create_spark() -> SparkSession: |
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spark = ( |
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SparkSession.builder.appName("spark_queries").master("local[*]").getOrCreate() |
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) |
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spark.sparkContext.setLogLevel(SPARK_LOG_LEVEL) |
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return spark |
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def __read_parquet_ds(path: str, table_name: str) -> SparkDF: |
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df = get_or_create_spark().read.parquet(path) |
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df.createOrReplaceTempView(table_name) |
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return df |
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def get_query_answer(query: int, base_dir: str = ANSWERS_BASE_DIR) -> PandasDF: |
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import pandas as pd |
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answer_df = pd.read_csv( |
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join(base_dir, f"q{query}.out"), |
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sep="|", |
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parse_dates=True, |
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) |
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return answer_df.rename(columns=lambda x: x.strip()) |
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def test_results(q_num: int, result_df: PandasDF): |
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import pandas as pd |
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with CodeTimer(name=f"Testing result of Spark Query {q_num}", unit="s"): |
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answer = get_query_answer(q_num) |
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for c, t in answer.dtypes.items(): |
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s1 = result_df[c] |
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s2 = answer[c] |
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if t.name == "object": |
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s1 = s1.astype("string").apply(lambda x: x.strip()) |
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s2 = s2.astype("string").apply(lambda x: x.strip()) |
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elif t.name.startswith("int"): |
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s1 = s1.astype("int64") |
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s2 = s2.astype("int64") |
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pd.testing.assert_series_equal(left=s1, right=s2, check_index=False) |
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@on_second_call |
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def get_line_item_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "lineitem.parquet"), "lineitem") |
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@on_second_call |
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def get_orders_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "orders.parquet"), "orders") |
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@on_second_call |
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def get_customer_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "customer.parquet"), "customer") |
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@on_second_call |
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def get_region_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "region.parquet"), "region") |
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@on_second_call |
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def get_nation_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "nation.parquet"), "nation") |
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@on_second_call |
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def get_supplier_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "supplier.parquet"), "supplier") |
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@on_second_call |
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def get_part_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "part.parquet"), "part") |
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@on_second_call |
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def get_part_supp_ds(base_dir: str = DATASET_BASE_DIR) -> SparkDF: |
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return __read_parquet_ds(join(base_dir, "partsupp.parquet"), "partsupp") |
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def drop_temp_view(): |
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spark = get_or_create_spark() |
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[ |
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spark.catalog.dropTempView(t.name) |
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for t in spark.catalog.listTables() |
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if t.isTemporary |
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] |
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def run_query(q_num: int, result: SparkDF): |
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@linetimer(name=f"Overall execution of Spark Query {q_num}", unit="s") |
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def run(): |
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with CodeTimer(name=f"Get result of Spark Query {q_num}", unit="s"): |
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t0 = timeit.default_timer() |
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pdf = result.toPandas() |
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secs = timeit.default_timer() - t0 |
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if LOG_TIMINGS: |
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append_row( |
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solution="spark", |
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version=get_or_create_spark().version, |
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q=f"q{q_num}", |
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secs=secs, |
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) |
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else: |
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test_results(q_num, pdf) |
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if SHOW_RESULTS: |
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print(pdf) |
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run() |
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