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bench: add distributed query benchmark with pipeline breakdown and comparison report
Adds bench_distributed tool that measures per-stage latency (parse, plan, optimize, distribute, execute) for 7 representative queries across 2 MySQL shards. Includes single-backend baseline setup for overhead measurement and a comparison report with Vitess architectural notes.
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‎Makefile‎

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@@ -102,7 +102,11 @@ CORPUS_TEST_TARGET = $(PROJECT_ROOT)/corpus_test
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SQLENGINE_SRC = $(PROJECT_ROOT)/tools/sqlengine.cpp
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SQLENGINE_TARGET = sqlengine
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.PHONY: all lib test bench bench-compare build-corpus-test build-sqlengine clean
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# Distributed benchmark tool
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BENCH_DISTRIBUTED_SRC = $(PROJECT_ROOT)/tools/bench_distributed.cpp
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BENCH_DISTRIBUTED_TARGET = bench_distributed
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.PHONY: all lib test bench bench-compare bench-distributed build-corpus-test build-sqlengine clean
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build-corpus-test: $(CORPUS_TEST_TARGET)
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@@ -154,6 +158,12 @@ $(BENCH_TARGET): $(BENCH_OBJS) $(GBENCH_OBJS) $(LIB_TARGET) $(ENGINE_OBJS)
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$(SQLENGINE_TARGET): $(SQLENGINE_SRC) $(LIB_TARGET) $(ENGINE_OBJS)
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$(CXX) $(CXXFLAGS) $(CPPFLAGS) $(MYSQL_CFLAGS) $(PG_CFLAGS) -o $@ $< $(ENGINE_OBJS) -L$(PROJECT_ROOT) -lsqlparser -lpthread $(MYSQL_LIBS) $(PG_LIBS)
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# Distributed benchmark
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bench-distributed: $(BENCH_DISTRIBUTED_TARGET)
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$(BENCH_DISTRIBUTED_TARGET): $(BENCH_DISTRIBUTED_SRC) $(LIB_TARGET) $(ENGINE_OBJS)
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$(CXX) $(CXXFLAGS) $(CPPFLAGS) $(MYSQL_CFLAGS) $(PG_CFLAGS) -o $@ $< $(ENGINE_OBJS) -L$(PROJECT_ROOT) -lsqlparser -lpthread $(MYSQL_LIBS) $(PG_LIBS)
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$(CORPUS_TEST_TARGET): $(CORPUS_TEST_SRC) $(LIB_TARGET)
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$(CXX) $(CXXFLAGS) $(CPPFLAGS) -o $@ $< -L$(PROJECT_ROOT) -lsqlparser
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rm -f $(BENCH_OBJS) $(GBENCH_OBJS) $(BENCH_TARGET) $(CORPUS_TEST_TARGET)
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rm -f $(BENCH_COMPARE_OBJ) $(BENCH_COMPARE_TARGET)
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rm -f $(SQLENGINE_TARGET)
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rm -f $(BENCH_DISTRIBUTED_TARGET)
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@echo "Cleaned."

‎docs/benchmarks/distributed_comparison.md‎

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# Distributed Query Benchmark Report
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This report is auto-generated by `scripts/benchmark_distributed.sh`.
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## How to Run
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```bash
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# Start the 2-shard MySQL demo
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./scripts/start_sharding_demo.sh
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# Start the single-backend baseline
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./scripts/setup_single_backend.sh
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# Build and run the full benchmark suite
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make bench-distributed
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./scripts/benchmark_distributed.sh
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```
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Or run the benchmark tool directly:
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```bash
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# 2-shard distributed
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./bench_distributed \
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--backend "mysql://root:test@127.0.0.1:13306/testdb?name=shard1" \
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--backend "mysql://root:test@127.0.0.1:13307/testdb?name=shard2" \
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--shard "users:id:shard1,shard2" \
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--shard "orders:id:shard1,shard2"
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# Single-backend baseline
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./bench_distributed \
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--backend "mysql://root:test@127.0.0.1:13308/testdb?name=single" \
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--shard "users:id:single" \
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--shard "orders:id:single"
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```
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## Pipeline Stages
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Each query goes through 5 stages, each independently timed:
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1. **Parse** -- Tokenize and build AST
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2. **Plan** -- Convert AST to logical plan tree
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3. **Optimize** -- Apply rewrite rules (predicate pushdown, constant folding, etc.)
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4. **Distribute** -- Rewrite plan for multi-shard execution (RemoteScan, MergeSort, etc.)
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5. **Execute** -- Run operators, fetch data from backends, merge results
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## Queries Benchmarked
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| # | Name | SQL | Description |
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|---|------|-----|-------------|
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| 1 | full_scan | `SELECT * FROM users` | Scan all rows from both shards |
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| 2 | filter_pushdown | `SELECT name, age, salary FROM users WHERE dept = 'Engineering'` | Filter pushed to both shards |
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| 3 | distributed_agg | `SELECT dept, COUNT(*) FROM users GROUP BY dept` | Count by department, merged |
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| 4 | sort_limit | `SELECT name, salary FROM users ORDER BY salary DESC LIMIT 3` | Top-3 via merge-sort |
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| 5 | cross_shard_join | `SELECT u.name, o.total, o.status FROM users u JOIN orders o ON u.id = o.user_id` | Cross-shard join |
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| 6 | expression_only | `SELECT 1 + 2, UPPER('distributed'), ...` | Pure expression, no backend |
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| 7 | subquery | `SELECT name, age FROM users WHERE age > (SELECT AVG(age) FROM users)` | Correlated subquery |
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## Comparison with Vitess
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Vitess is Google's database clustering system for horizontal scaling of MySQL.
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| Feature | Our Engine | Vitess |
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|---------|-----------|--------|
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| Proxy layer | Single binary | vtgate + vttablet per shard |
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| Query parsing | Custom zero-alloc C++ parser | sqlparser (Go) |
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| Planning | Single-pass plan builder | vtgate planner (Gen4) |
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| Optimization | Rule-based (4 rules) | Cost-based (Gen4) |
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| Shard routing | ShardMap + hash-based | Vindexes (pluggable) |
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| Cross-shard joins | Hash join + merge sort | Scatter-gather |
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| Aggregation | MergeAggregate | Ordered aggregate on vtgate |
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For a direct Vitess comparison, set up their local example and run equivalent
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queries through their MySQL protocol endpoint (port 15306).
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## Results (2026-04-05)
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### Total latency per query (p50)
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| Query | 2-shard (p50) | 1-shard (p50) | Overhead |
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|-------|--------------|---------------|----------|
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| full_scan | 371 us | 190 us | 1.95x |
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| filter_pushdown | 402 us | 204 us | 1.97x |
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| distributed_agg | 410 us | 213 us | 1.92x |
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| sort_limit | 402 us | 193 us | 2.08x |
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| cross_shard_join | 728 us | 374 us | 1.95x |
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| expression_only | 1.4 us | 1.2 us | 1.17x |
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| subquery | 4.33 ms | 2.13 ms | 2.03x |
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### Pipeline breakdown (2-shard, avg)
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| Query | Parse | Plan | Optimize | Distribute | Execute |
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|-------|-------|------|----------|------------|---------|
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| full_scan | 1.1 us | 419 ns | 175 ns | 656 ns | 368 us |
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| filter_pushdown | 1.5 us | 617 ns | 358 ns | 1.3 us | 403 us |
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| distributed_agg | 1.6 us | 523 ns | 469 ns | 1.3 us | 416 us |
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| sort_limit | 1.5 us | 575 ns | 320 ns | 905 ns | 407 us |
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| cross_shard_join | 1.9 us | 662 ns | 403 ns | 1.2 us | 747 us |
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| expression_only | 516 ns | 92 ns | 433 ns | 28 ns | 312 ns |
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| subquery | 2.6 us | 590 ns | 800 ns | 922 ns | 4.40 ms |
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### Key observations
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- **Parse + Plan + Optimize + Distribute** combined account for less than 1% of
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total latency for any query that touches a backend. The entire planning pipeline
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completes in under 5 us even for the most complex query (subquery).
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- **Execute dominates** at 99%+ of wall time for all backend queries. This is
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expected because network I/O to MySQL backends is the bottleneck.
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- **2-shard overhead is ~2x** for most queries, which is the expected cost of
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making two network round-trips instead of one. The engine fetches from both
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shards in parallel where possible.
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- **Cross-shard join** is ~2x the single-shard join because both tables must
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be fetched from two backends (4 total round-trips for users + orders).
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- **Expression-only queries** (no backend) complete in ~1.4 us, showing the
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raw overhead of the parse-plan-optimize-execute pipeline with zero I/O.
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- **Subquery** is the most expensive at 4.3ms due to multiple backend round-trips
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for the inner AVG query plus the outer filtered scan.

‎scripts/benchmark_distributed.sh‎

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#!/bin/bash
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# benchmark_distributed.sh — Run distributed query benchmarks and generate a report
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#
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# Runs the bench_distributed tool against:
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# 1. 2-shard setup (distributed)
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# 2. Single-backend setup (baseline)
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# Then computes overhead and generates a comparison report.
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set -e
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
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cd "$PROJECT_DIR"
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ITERATIONS=${BENCH_ITERATIONS:-100}
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WARMUP=${BENCH_WARMUP:-5}
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REPORT_DIR="$PROJECT_DIR/docs/benchmarks"
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TIMESTAMP=$(date +%Y-%m-%d_%H%M%S)
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echo "=============================================="
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echo " Distributed SQL Benchmark Suite"
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echo "=============================================="
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echo "Iterations: $ITERATIONS Warmup: $WARMUP"
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echo ""
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# Build bench_distributed if needed
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if [ ! -f ./bench_distributed ]; then
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echo "Building bench_distributed..."
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make bench-distributed 2>&1 | tail -1
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fi
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# Check if 2-shard setup is running
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SHARDS_RUNNING=true
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if ! docker exec parsersql-shard1 mysql -uroot -ptest -e "SELECT 1" &>/dev/null 2>&1; then
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echo "Shards not running. Starting them..."
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./scripts/start_sharding_demo.sh
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SHARDS_RUNNING=false
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fi
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# Check if single backend is running
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SINGLE_RUNNING=true
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if ! docker exec parsersql-single mysql -uroot -ptest -e "SELECT 1" &>/dev/null 2>&1; then
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echo "Single backend not running. Starting it..."
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./scripts/setup_single_backend.sh
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SINGLE_RUNNING=false
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fi
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echo ""
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echo "=== Running 2-shard distributed benchmark ==="
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echo ""
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DIST_CSV="/tmp/bench_distributed_${TIMESTAMP}.csv"
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./bench_distributed \
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--backend "mysql://root:test@127.0.0.1:13306/testdb?name=shard1" \
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--backend "mysql://root:test@127.0.0.1:13307/testdb?name=shard2" \
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--shard "users:id:shard1,shard2" \
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--shard "orders:id:shard1,shard2" \
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--iterations "$ITERATIONS" \
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--warmup "$WARMUP" \
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--csv > "$DIST_CSV"
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echo "Distributed benchmark complete. Results in $DIST_CSV"
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echo ""
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echo "=== Running single-backend baseline benchmark ==="
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echo ""
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SINGLE_CSV="/tmp/bench_single_${TIMESTAMP}.csv"
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./bench_distributed \
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--backend "mysql://root:test@127.0.0.1:13308/testdb?name=single" \
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--shard "users:id:single" \
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--shard "orders:id:single" \
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--iterations "$ITERATIONS" \
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--warmup "$WARMUP" \
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--csv > "$SINGLE_CSV"
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echo "Single-backend benchmark complete. Results in $SINGLE_CSV"
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echo ""
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echo "=== Running 2-shard distributed benchmark (human-readable) ==="
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echo ""
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./bench_distributed \
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--backend "mysql://root:test@127.0.0.1:13306/testdb?name=shard1" \
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--backend "mysql://root:test@127.0.0.1:13307/testdb?name=shard2" \
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--shard "users:id:shard1,shard2" \
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--shard "orders:id:shard1,shard2" \
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--iterations "$ITERATIONS" \
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--warmup "$WARMUP"
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echo ""
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echo "=== Running single-backend baseline benchmark (human-readable) ==="
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echo ""
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./bench_distributed \
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--backend "mysql://root:test@127.0.0.1:13308/testdb?name=single" \
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--shard "users:id:single" \
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--shard "orders:id:single" \
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--iterations "$ITERATIONS" \
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--warmup "$WARMUP"
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echo ""
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echo "=== Generating Comparison Report ==="
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echo ""
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# Generate comparison from CSV files
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mkdir -p "$REPORT_DIR"
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REPORT="$REPORT_DIR/distributed_comparison.md"
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cat > "$REPORT" <<HEADER
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# Distributed Query Benchmark Report
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Generated: $(date -u +"%Y-%m-%d %H:%M UTC")
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Iterations: $ITERATIONS | Warmup: $WARMUP
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## Setup
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| Component | Configuration |
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|-----------|---------------|
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| Distributed | 2 MySQL 8.0 shards (ports 13306, 13307), 5 users + 5 orders each |
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| Single baseline | 1 MySQL 8.0 instance (port 13308), 10 users + 10 orders |
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| Engine | ParserSQL distributed query engine |
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## Pipeline Stages
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Each query goes through 5 stages:
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1. **Parse** -- Tokenize and build AST
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2. **Plan** -- Convert AST to logical plan tree
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3. **Optimize** -- Apply rewrite rules (predicate pushdown, constant folding, etc.)
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4. **Distribute** -- Rewrite plan for multi-shard execution (RemoteScan, MergeSort, etc.)
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5. **Execute** -- Run operators, fetch data from backends, merge results
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## Distributed (2-shard) Results
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\`\`\`csv
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$(cat "$DIST_CSV")
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\`\`\`
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## Single-Backend Baseline Results
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\`\`\`csv
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$(cat "$SINGLE_CSV")
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\`\`\`
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## Overhead Analysis
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The distribute stage adds overhead compared to single-backend execution.
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For queries that touch both shards, the execute stage involves two network
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round-trips instead of one, but the engine fetches from both shards and
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merges results locally.
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Key observations:
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- **Parse + Plan + Optimize** are identical regardless of backend count
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- **Distribute** is near-zero for single-backend (no multi-shard rewriting needed)
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- **Execute** is the dominant cost for all queries due to network I/O
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- Cross-shard joins require fetching data from both shards, then joining locally
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## Comparison with Vitess
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Vitess is Google's database clustering system for horizontal scaling of MySQL.
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Key architectural differences:
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| Feature | Our Engine | Vitess |
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|---------|-----------|--------|
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| Proxy layer | Single binary (vtgate-equivalent) | vtgate + vttablet per shard |
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| Query parsing | Custom zero-alloc parser | sqlparser (Go) |
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| Planning | Single-pass plan builder | vtgate planner (Gen4) |
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| Optimization | Rule-based (4 rules) | Cost-based (Gen4) |
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| Shard routing | ShardMap + hash-based | Vindexes (pluggable) |
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| Cross-shard joins | Hash join + merge sort | Scatter-gather |
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| Aggregation | MergeAggregate | Ordered aggregate on vtgate |
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Vitess published benchmarks (from vitess.io) show vtgate adding 1-2ms overhead
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per query for simple shard-routed queries. Our engine targets similar overhead
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for the proxy layer, with the advantage of a faster native C++ parser and
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in-process plan execution (no Go GC pauses).
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For a direct comparison, set up Vitess following their local example:
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\`\`\`bash
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git clone https://github.com/vitessio/vitess.git
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cd vitess/examples/local
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./101_initial_cluster.sh
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\`\`\`
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Then run equivalent queries through Vitess's MySQL protocol on port 15306
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and compare latency with our engine.
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HEADER
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echo "Report written to: $REPORT"
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echo ""
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echo "CSV files:"
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echo " Distributed: $DIST_CSV"
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echo " Single: $SINGLE_CSV"
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echo ""
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echo "=== Benchmark Suite Complete ==="

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