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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2022/10/11 19:39:16 UTC
[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #3769: Add benchmarks for testing row filtering
alamb commented on code in PR #3769:
URL: https://github.com/apache/arrow-datafusion/pull/3769#discussion_r992689413
##########
benchmarks/README.md:
##########
@@ -126,3 +126,37 @@ h2o groupby query 1 took 1669 ms
[1]: http://www.tpc.org/tpch/
[2]: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
+
+## Parquet filter pushdown benchmarks
+
+This is a set of benchmarks for testing and verifying performance of parquet filter pushdown. The queries are executed on
+a synthetic dataset generated during the benchmark execution and designed to simulate web server access logs.
+
+```base
+cargo run --release --bin parquet_filter_pushdown --query --path ./data --scale-factor 1.0
Review Comment:
When I tried this command, I got
```shell
alamb@aal-dev:~/arrow-datafusion$ cargo run --release --bin parquet_filter_pushdown --query --path ./data --scale-facto
error: Found argument '--query' which wasn't expected, or isn't valid in this context
If you tried to supply `--query` as a value rather than a flag, use `-- --query`
USAGE:
cargo run --release --bin [<NAME>]
```
Also, as @Ted-Jiang mentions, the `--query` argument is not correct and I get an error if it is included:
```
error: Found argument '--query' which wasn't expected, or isn't valid in this context
```
```suggestion
cargo run --release --bin parquet_filter_pushdown -- --path ./data --scale-factor 1.0
```
##########
benchmarks/src/bin/parquet_filter_pushdown.rs:
##########
@@ -0,0 +1,462 @@
+// 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.
+
+use arrow::array::{
+ Int32Builder, StringBuilder, StringDictionaryBuilder, TimestampNanosecondBuilder,
+ UInt16Builder,
+};
+use arrow::datatypes::{DataType, Field, Int32Type, Schema, SchemaRef, TimeUnit};
+use arrow::record_batch::RecordBatch;
+use arrow::util::pretty;
+use datafusion::common::Result;
+use datafusion::datasource::listing::{ListingTableUrl, PartitionedFile};
+use datafusion::datasource::object_store::ObjectStoreUrl;
+use datafusion::execution::context::ExecutionProps;
+use datafusion::logical_expr::{lit, or, Expr};
+use datafusion::logical_plan::ToDFSchema;
+use datafusion::physical_expr::create_physical_expr;
+use datafusion::physical_plan::collect;
+use datafusion::physical_plan::file_format::{
+ FileScanConfig, ParquetExec, ParquetScanOptions,
+};
+use datafusion::physical_plan::filter::FilterExec;
+use datafusion::prelude::{col, combine_filters, SessionConfig, SessionContext};
+use object_store::path::Path;
+use object_store::ObjectMeta;
+use parquet::arrow::ArrowWriter;
+use rand::rngs::StdRng;
+use rand::{Rng, SeedableRng};
+use std::fs::File;
+use std::ops::Range;
+use std::path::PathBuf;
+use std::sync::Arc;
+use std::time::Instant;
+use structopt::StructOpt;
+
+#[cfg(feature = "snmalloc")]
+#[global_allocator]
+static ALLOC: snmalloc_rs::SnMalloc = snmalloc_rs::SnMalloc;
+
+#[derive(Debug, StructOpt)]
+#[structopt(name = "Benchmarks", about = "Apache Arrow Rust Benchmarks.")]
+struct Opt {
+ /// Activate debug mode to see query results
+ #[structopt(short, long)]
+ debug: bool,
+
+ /// Number of iterations of each test run
+ #[structopt(short = "i", long = "iterations", default_value = "3")]
+ iterations: usize,
+
+ /// Number of partitions to process in parallel
+ #[structopt(long = "partitions", default_value = "2")]
+ partitions: usize,
+
+ /// Path to folder where access log file will be generated
+ #[structopt(parse(from_os_str), required = true, short = "p", long = "path")]
+ path: PathBuf,
+
+ /// Batch size when reading Parquet files
+ #[structopt(short = "s", long = "batch-size", default_value = "8192")]
+ batch_size: usize,
+
+ /// Total size of generated dataset. The default scale factor of 1.0 will generate a roughly 1GB parquet file
+ #[structopt(short = "s", long = "scale-factor", default_value = "1.0")]
+ scale_factor: f32,
+}
+
+#[tokio::main]
+async fn main() -> Result<()> {
+ let opt: Opt = Opt::from_args();
+ println!("Running benchmarks with the following options: {:?}", opt);
+
+ let config = SessionConfig::new()
+ .with_target_partitions(opt.partitions)
+ .with_batch_size(opt.batch_size);
+ let mut ctx = SessionContext::with_config(config);
+
+ let path = opt.path.join("logs.parquet");
+
+ let (object_store_url, object_meta) = gen_data(path, opt.scale_factor)?;
+
+ run_benchmarks(
+ &mut ctx,
+ object_store_url.clone(),
+ object_meta.clone(),
+ opt.iterations,
+ opt.debug,
+ )
+ .await?;
+
+ Ok(())
+}
+
+async fn run_benchmarks(
+ ctx: &mut SessionContext,
+ object_store_url: ObjectStoreUrl,
+ object_meta: ObjectMeta,
+ iterations: usize,
+ debug: bool,
+) -> Result<()> {
+ let scan_options_matrix = vec![
+ ParquetScanOptions::default(),
+ ParquetScanOptions::default()
+ .with_page_index(true)
+ .with_pushdown_filters(true)
+ .with_reorder_predicates(true),
+ ParquetScanOptions::default()
+ .with_page_index(true)
+ .with_pushdown_filters(true)
+ .with_reorder_predicates(false),
+ ];
+
+ let filter_matrix = vec![
+ // Selective-ish filter
+ col("request_method").eq(lit("GET")),
+ // Non-selective filter
+ col("request_method").not_eq(lit("GET")),
+ // Basic conjunction
+ col("request_method")
+ .eq(lit("POST"))
+ .and(col("response_status").eq(lit(503_u16))),
+ // Nested filters
+ col("request_method").eq(lit("POST")).and(or(
+ col("response_status").eq(lit(503_u16)),
+ col("response_status").eq(lit(403_u16)),
+ )),
+ // Many filters
+ combine_filters(&[
+ col("request_method").not_eq(lit("GET")),
+ col("response_status").eq(lit(400_u16)),
+ // TODO this fails in the FilterExec with Error: Internal("The type of Dictionary(Int32, Utf8) = Utf8 of binary physical should be same")
+ // col("service").eq(lit("backend")),
+ ])
+ .unwrap(),
+ // Filter everything
+ col("response_status").eq(lit(429_u16)),
+ // Filter nothing
+ col("response_status").gt(lit(0_u16)),
+ ];
+
+ for filter_expr in &filter_matrix {
+ println!("Executing with filter '{}'", filter_expr);
+ for scan_options in &scan_options_matrix {
+ println!("Using scan options {:?}", scan_options);
+ for i in 0..iterations {
+ let start = Instant::now();
+ let rows = exec_scan(
+ ctx,
+ object_store_url.clone(),
+ object_meta.clone(),
+ filter_expr.clone(),
+ scan_options.clone(),
+ debug,
+ )
+ .await?;
+ println!(
+ "Iteration {} returned {} rows in {} ms",
+ i,
+ rows,
+ start.elapsed().as_millis()
+ );
+ }
+ }
+ println!("\n");
+ }
+ Ok(())
+}
+
+async fn exec_scan(
+ ctx: &SessionContext,
+ object_store_url: ObjectStoreUrl,
+ object_meta: ObjectMeta,
+ filter: Expr,
+ scan_options: ParquetScanOptions,
+ debug: bool,
+) -> Result<usize> {
+ let schema = BatchBuilder::schema();
+ let scan_config = FileScanConfig {
+ object_store_url,
+ file_schema: schema.clone(),
+ file_groups: vec![vec![PartitionedFile {
+ object_meta,
+ partition_values: vec![],
+ range: None,
+ extensions: None,
+ }]],
+ statistics: Default::default(),
+ projection: None,
+ limit: None,
+ table_partition_cols: vec![],
+ };
+
+ let df_schema = schema.clone().to_dfschema()?;
+
+ let physical_filter_expr = create_physical_expr(
+ &filter,
+ &df_schema,
+ schema.as_ref(),
+ &ExecutionProps::default(),
+ )?;
+
+ let parquet_exec = Arc::new(
+ ParquetExec::new(scan_config, Some(filter), None).with_scan_options(scan_options),
+ );
+
+ let exec = Arc::new(FilterExec::try_new(physical_filter_expr, parquet_exec)?);
+
+ let task_ctx = ctx.task_ctx();
+ let result = collect(exec, task_ctx).await?;
+
+ if debug {
+ pretty::print_batches(&result)?;
+ }
+ Ok(result.iter().map(|b| b.num_rows()).sum())
+}
+
+fn gen_data(path: PathBuf, scale_factor: f32) -> Result<(ObjectStoreUrl, ObjectMeta)> {
+ let generator = Generator::new();
+
+ let file = File::create(&path).unwrap();
+ let mut writer = ArrowWriter::try_new(file, generator.schema.clone(), None).unwrap();
Review Comment:
I wonder if we should make the properties used here explicit?
Like maybe explicitly set what type of statistics are created as well as potentially setting ZSTD compression
https://docs.rs/parquet/24.0.0/parquet/file/properties/struct.WriterPropertiesBuilder.html
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