datastore/utils/parser-memory-profiler

Parser Memory Profiler

A tool for profiling memory allocations in the ClickHouse SQL parser using jemalloc's heap profiling capabilities.

Overview

This utility helps analyze memory allocation patterns when parsing SQL queries. It's useful for:

• Identifying memory hotspots in the parser
• Comparing memory usage between different parser implementations
• Optimizing AST node allocations and data structures
• Regression testing for memory usage changes

Components

File	Description
run_profiler.sh	Bash script to batch-process multiple queries and generate JSON results
generate_report.py	Python script to generate an interactive HTML report for a single result set
generate_comparison_report.py	Python script to compare two result sets (before/after optimization)
rebuild_reports.py	Python script to regenerate reports from existing heap profile files
test_queries.txt	Sample set of 100 diverse SQL queries for testing

The C++ profiler binary source is located at src/Parsers/examples/parser_memory_profiler.cpp.

Prerequisites

1. Build the profiler binary

cd /path/to/clickhouse
mkdir -p build && cd build
cmake ..
ninja parser_memory_profiler

2. Install jeprof (from jemalloc)

jeprof is used to analyze heap profile files and generate detailed reports.

git clone https://github.com/jemalloc/jemalloc ~/github/jemalloc
cd ~/github/jemalloc
./autogen.sh
make
# Binary will be at ~/github/jemalloc/bin/jeprof

3. Install FlameGraph (for flame graphs)

git clone https://github.com/brendangregg/FlameGraph ~/FlameGraph

4. Install graphviz (for call graph SVGs)

# macOS
brew install graphviz

# Ubuntu/Debian
apt install graphviz

# RHEL/CentOS
yum install graphviz

Quick Start

Basic Usage

cd utils/parser-memory-profiler

# Run profiler on default test queries
./run_profiler.sh -b ../../build

# Generate HTML report
python3 generate_report.py -i profiler_output/results.json -o profiler_output/report.html

# Open the report
open profiler_output/report.html  # macOS
xdg-open profiler_output/report.html  # Linux

Custom Queries

Create a file with SQL queries (one per line, ending with semicolons):

-- my_queries.txt
SELECT * FROM users WHERE id = 1;
CREATE TABLE test (a Int64, b String) ENGINE = MergeTree() ORDER BY a;
INSERT INTO test VALUES (1, 'hello'), (2, 'world');

Run the profiler:

./run_profiler.sh -q my_queries.txt -o my_results -b ../../build
python3 generate_report.py -i my_results/results.json -o my_results/report.html

Comparing Two Versions

Profile both versions and generate a comparison report:

# Profile original version
git checkout main
cd build && ninja parser_memory_profiler && cd ..
./run_profiler.sh -o results_before -b ../build

# Profile optimized version
git checkout my-optimization-branch
cd build && ninja parser_memory_profiler && cd ..
./run_profiler.sh -o results_after -b ../build

# Generate comparison report
python3 generate_comparison_report.py \
    --before results_before/results.json \
    --after results_after/results.json \
    --output comparison.html \
    --label-before "Original" \
    --label-after "Optimized"

Output Files

After running run_profiler.sh, the output directory contains:

profiler_output/
├── profiles/           # jemalloc heap profile files
│   ├── query_1_before.*.heap
│   ├── query_1_after.*.heap
│   ├── query_2_before.*.heap
│   └── ...
├── results.json        # JSON with all profiling data
└── report.html         # Interactive HTML report (after generate_report.py)

Understanding the Reports

Single Report (generate_report.py)

The HTML report shows for each query:

• Query text with syntax highlighting
• Memory stats: bytes allocated before/after parsing, difference
• Profile Report (Bytes): jeprof text output showing allocation sites
• Profile Report (Objects): allocation counts by call site
• Call Graph: SVG visualization of allocation call tree
• Flame Graph: Interactive flame graph of allocations

Comparison Report (generate_comparison_report.py)

Shows side-by-side comparison:

• Summary: Total memory difference across all queries
• Per-query comparison: Before vs After memory usage
• Sortable table: Sort by memory change, query length, etc.
• Expandable details: Click rows to see detailed profiles

How It Works

1. parser_memory_profiler.cpp:

   • Initializes jemalloc profiling via mallctl()
   • Takes a heap dump before parsing
   • Parses the SQL query using ClickHouse's parser
   • Takes a heap dump after parsing
   • Reports memory statistics

2. run_profiler.sh:

   • Iterates through queries in the input file
   • Runs parser_memory_profiler with MALLOC_CONF=prof:true,prof_active:true,lg_prof_sample:0
   • Uses jeprof to analyze heap profile diffs
   • Generates text reports, collapsed stacks, and SVGs
   • Collects everything into results.json

3. generate_report.py:

   • Reads results.json
   • Creates a self-contained HTML file with embedded data
   • Uses iframes for SVG content to preserve interactivity

Environment Variables

Variable	Description
MALLOC_CONF	jemalloc configuration

Required settings for profiling:

MALLOC_CONF=prof:true,prof_active:true,lg_prof_sample:0

• prof:true - Enable profiling support
• prof_active:true - Activate profiling at startup
• lg_prof_sample:0 - Sample every allocation (log2(1) = 0)

Troubleshooting

"jemalloc profiling not enabled"

The jemalloc library must be built with profiling support. Check:

./parser_memory_profiler <<< "SELECT 1"

If it shows "config.prof: no", jemalloc was built without --enable-prof.

Empty jeprof reports

Increase the sampling rate or check that lg_prof_sample:0 is set. Higher values (like 19, the default) will miss small allocations.

Timeout errors

jeprof can be slow when analyzing large binaries. Increase the timeout:

python3 rebuild_reports.py profiler_output --timeout 300

"dot: command not found"

Install graphviz for call graph generation:

brew install graphviz  # macOS
apt install graphviz   # Linux

Tips for Memory Optimization

1. Focus on hotspots: Look at the top entries in the profile reports
2. Compare allocations vs objects: High object count with low bytes suggests small allocations
3. Check flame graphs: Wide bars indicate major allocation sources
4. Use comparison reports: Track improvements across iterations

Example Results

Typical findings from parser memory profiling:

• AST node allocations: std::make_shared overhead can be significant
• String copies: Identifier and literal strings are frequently copied
• Vector resizing: Pre-sizing vectors can reduce allocations
• Small object overhead: Many small allocations have per-allocation overhead

License

This tool is part of ClickHouse and is licensed under the Apache License 2.0.