zhuqi-lucas commented on code in PR #19042: URL: https://github.com/apache/datafusion/pull/19042#discussion_r2581523202
########## benchmarks/sort_clickbench.py: ########## @@ -0,0 +1,253 @@ +#!/usr/bin/env python3 + +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +#!/usr/bin/env python3 +""" +Sort ClickBench data by EventTime for reverse scan benchmark. +Enhanced version with configurable row group size and optimization options. +""" + +import sys +import argparse +from pathlib import Path + +try: + import pyarrow.parquet as pq + import pyarrow.compute as pc +except ImportError: + print("Error: pyarrow is not installed.") + print("Please install it with: pip install pyarrow") + sys.exit(1) + + +def sort_clickbench_data( + input_path: str, + output_path: str, + row_group_size: int = 1024 * 1024, # 1M rows default + compression: str = 'snappy', + verify: bool = True +): + """Sort parquet file by EventTime column with optimized settings.""" + + input_file = Path(input_path) + output_file = Path(output_path) + + if not input_file.exists(): + print(f"Error: Input file not found: {input_file}") + sys.exit(1) + + if output_file.exists(): + print(f"Sorted file already exists: {output_file}") + if verify: + verify_sorted_file(output_file) + return + + try: + print(f"Reading {input_file.name}...") + table = pq.read_table(str(input_file)) + + print(f"Original table has {len(table):,} rows") + print("Sorting by EventTime...") + + # Sort the table by EventTime + sorted_indices = pc.sort_indices(table, sort_keys=[("EventTime", "ascending")]) + sorted_table = pc.take(table, sorted_indices) + + print(f"Sorted table has {len(sorted_table):,} rows") + + # Verify sort + event_times = sorted_table.column('EventTime').to_pylist() + if event_times and verify: + print(f"First EventTime: {event_times[0]}") + print(f"Last EventTime: {event_times[-1]}") + # Verify ascending order + is_sorted = all(event_times[i] <= event_times[i+1] for i in range(min(1000, len(event_times)-1))) + print(f"Sort verification (first 1000 rows): {'✓ PASS' if is_sorted else '✗ FAIL'}") + + print(f"Writing sorted file to {output_file}...") + print(f" Row group size: {row_group_size:,} rows") + print(f" Compression: {compression}") + + # Write sorted table with optimized settings + pq.write_table( + sorted_table, + str(output_file), + compression=compression, + use_dictionary=True, + write_statistics=True, + # Optimize row group size for better performance + row_group_size=row_group_size, + # Set data page size (1MB is good for most cases) + data_page_size=1024 * 1024, + # Use v2 data page format for better compression + use_deprecated_int96_timestamps=False, + coerce_timestamps='us', # Use microsecond precision + # Batch size for writing + write_batch_size=min(row_group_size, 1024 * 64), + # Enable compression for all columns + compression_level=None, # Use default compression level + ) + + # Report results + input_size_mb = input_file.stat().st_size / (1024**2) + output_size_mb = output_file.stat().st_size / (1024**2) + + # Read metadata to verify row groups + parquet_file = pq.ParquetFile(str(output_file)) + num_row_groups = parquet_file.num_row_groups + + print(f"\n✓ Successfully created sorted file!") + print(f" Input: {input_size_mb:.1f} MB") + print(f" Output: {output_size_mb:.1f} MB") + print(f" Compression ratio: {input_size_mb/output_size_mb:.2f}x") + print(f"\nRow Group Statistics:") + print(f" Total row groups: {num_row_groups}") + print(f" Total rows: {len(sorted_table):,}") + + # Show row group details + for i in range(min(3, num_row_groups)): + rg_metadata = parquet_file.metadata.row_group(i) + print(f" Row group {i}: {rg_metadata.num_rows:,} rows, {rg_metadata.total_byte_size / 1024**2:.1f} MB") + + if num_row_groups > 3: + print(f" ... and {num_row_groups - 3} more row groups") + + avg_rows_per_group = len(sorted_table) / num_row_groups if num_row_groups > 0 else 0 + print(f" Average rows per group: {avg_rows_per_group:,.0f}") + + except Exception as e: + print(f"Error: {e}") + import traceback + traceback.print_exc() + sys.exit(1) + + +def verify_sorted_file(file_path: Path): + """Verify that a parquet file is sorted by EventTime.""" + try: + print(f"Verifying sorted file: {file_path}") + parquet_file = pq.ParquetFile(str(file_path)) + + num_row_groups = parquet_file.num_row_groups + file_size_mb = file_path.stat().st_size / (1024**2) + + print(f" File size: {file_size_mb:.1f} MB") + print(f" Row groups: {num_row_groups}") + + # Read first and last row group to verify sort + first_rg = parquet_file.read_row_group(0) + last_rg = parquet_file.read_row_group(num_row_groups - 1) + + first_time = first_rg.column('EventTime')[0].as_py() + last_time = last_rg.column('EventTime')[-1].as_py() + + print(f" First EventTime: {first_time}") + print(f" Last EventTime: {last_time}") + print(f" Sorted: {'✓ YES' if first_time <= last_time else '✗ NO'}") + + except Exception as e: + print(f"Error during verification: {e}") + + +def main(): + parser = argparse.ArgumentParser( + description='Sort ClickBench parquet file by EventTime', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Basic usage (1M rows per group) Review Comment: Yes. -- This is an automated message from the Apache Git Service. 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