feat: add read throughput micro-benchmark for ArrowScan configurations

Author: sumedhsakdeo

tests/benchmark/test_read_benchmark.py

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+# 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.
+"""Read throughput micro-benchmark for ArrowScan configurations.
+
+Measures records/sec and peak Arrow memory across streaming, concurrent_files,
+and batch_size configurations introduced for issue #3036.
+
+Memory is measured using pa.total_allocated_bytes() which tracks PyArrow's C++
+memory pool (Arrow buffers, Parquet decompression), not Python heap allocations.
+
+Run with: uv run pytest tests/benchmark/test_read_benchmark.py -v -s -m benchmark
+"""
+
+import gc
+import statistics
+import timeit
+from datetime import datetime, timezone
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pytest
+
+from pyiceberg.catalog.sql import SqlCatalog
+from pyiceberg.table import Table
+
+NUM_FILES = 32
+ROWS_PER_FILE = 500_000
+TOTAL_ROWS = NUM_FILES * ROWS_PER_FILE
+NUM_RUNS = 3
+
+
+def _generate_parquet_file(path: str, num_rows: int, seed: int) -> pa.Schema:
+    """Write a synthetic Parquet file and return its schema."""
+    table = pa.table(
+        {
+            "id": pa.array(range(seed, seed + num_rows), type=pa.int64()),
+            "value": pa.array([float(i) * 0.1 for i in range(num_rows)], type=pa.float64()),
+            "label": pa.array([f"row_{i}" for i in range(num_rows)], type=pa.string()),
+            "flag": pa.array([i % 2 == 0 for i in range(num_rows)], type=pa.bool_()),
+            "ts": pa.array([datetime.now(timezone.utc)] * num_rows, type=pa.timestamp("us", tz="UTC")),
+        }
+    )
+    pq.write_table(table, path)
+    return table.schema
+
+
+@pytest.fixture(scope="session")
+def benchmark_table(tmp_path_factory: pytest.TempPathFactory) -> Table:
+    """Create a catalog and table with synthetic Parquet files for benchmarking."""
+    warehouse_path = str(tmp_path_factory.mktemp("benchmark_warehouse"))
+    catalog = SqlCatalog(
+        "benchmark",
+        uri=f"sqlite:///{warehouse_path}/pyiceberg_catalog.db",
+        warehouse=f"file://{warehouse_path}",
+    )
+    catalog.create_namespace("default")
+
+    # Generate files and append to table
+    table = None
+    for i in range(NUM_FILES):
+        file_path = f"{warehouse_path}/data_{i}.parquet"
+        _generate_parquet_file(file_path, ROWS_PER_FILE, seed=i * ROWS_PER_FILE)
+
+        file_table = pq.read_table(file_path)
+        if table is None:
+            table = catalog.create_table("default.benchmark_read", schema=file_table.schema)
+        table.append(file_table)
+
+    return table
+
+
+@pytest.mark.benchmark
+@pytest.mark.parametrize(
+    "streaming,concurrent_files,batch_size",
+    [
+        pytest.param(False, 1, None, id="default"),
+        pytest.param(True, 1, None, id="streaming-cf1"),
+        pytest.param(True, 2, None, id="streaming-cf2"),
+        pytest.param(True, 4, None, id="streaming-cf4"),
+        pytest.param(True, 8, None, id="streaming-cf8"),
+        pytest.param(True, 16, None, id="streaming-cf16"),
+    ],
+)
+def test_read_throughput(
+    benchmark_table: Table,
+    streaming: bool,
+    concurrent_files: int,
+    batch_size: int | None,
+) -> None:
+    """Measure records/sec and peak Arrow memory for a scan configuration."""
+    effective_batch_size = batch_size or 131_072  # PyArrow default
+    if streaming:
+        config_str = f"streaming=True, concurrent_files={concurrent_files}, batch_size={effective_batch_size}"
+    else:
+        config_str = f"streaming=False (executor.map, all files parallel), batch_size={effective_batch_size}"
+    print(f"\n--- ArrowScan Read Throughput Benchmark ---")
+    print(f"Config: {config_str}")
+    print(f"  Files: {NUM_FILES}, Rows per file: {ROWS_PER_FILE}, Total rows: {TOTAL_ROWS}")
+
+    elapsed_times: list[float] = []
+    throughputs: list[float] = []
+    peak_memories: list[int] = []
+
+    for run in range(NUM_RUNS):
+        # Measure throughput
+        gc.collect()
+        pa.default_memory_pool().release_unused()
+        baseline_mem = pa.total_allocated_bytes()
+        peak_mem = baseline_mem
+
+        start = timeit.default_timer()
+        total_rows = 0
+        for batch in benchmark_table.scan().to_arrow_batch_reader(
+            batch_size=batch_size,
+            streaming=streaming,
+            concurrent_files=concurrent_files,
+        ):
+            total_rows += len(batch)
+            current_mem = pa.total_allocated_bytes()
+            if current_mem > peak_mem:
+                peak_mem = current_mem
+        elapsed = timeit.default_timer() - start
+
+        peak_above_baseline = peak_mem - baseline_mem
+        rows_per_sec = total_rows / elapsed if elapsed > 0 else 0
+        elapsed_times.append(elapsed)
+        throughputs.append(rows_per_sec)
+        peak_memories.append(peak_above_baseline)
+
+        print(
+            f"  Run {run + 1}: {elapsed:.2f}s, {rows_per_sec:,.0f} rows/s, "
+            f"peak arrow mem: {peak_above_baseline / (1024 * 1024):.1f} MB"
+        )
+
+        assert total_rows == TOTAL_ROWS, f"Expected {TOTAL_ROWS} rows, got {total_rows}"
+
+    mean_elapsed = statistics.mean(elapsed_times)
+    stdev_elapsed = statistics.stdev(elapsed_times) if len(elapsed_times) > 1 else 0.0
+    mean_throughput = statistics.mean(throughputs)
+    mean_peak_mem = statistics.mean(peak_memories)
+
+    print(
+        f"  Mean: {mean_elapsed:.2f}s ± {stdev_elapsed:.2f}s, {mean_throughput:,.0f} rows/s, "
+        f"peak arrow mem: {mean_peak_mem / (1024 * 1024):.1f} MB"
+    )