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Jinja Template Engine Profiler

This tool helps identify performance bottlenecks in the Jinja template engine implementation.

Quick Start

Use the Makefile targets to run common profiling scenarios:

# Profile the complex_template with all profiling types
make profile-complex

# Profile the nested_loops template with all profiling types
make profile-nested-loops 

# Profile all templates from the benchmark suite
make profile-all

# Run custom profiling
make profile ARGS="--template conditional --cpu --iterations 5000"

Manual Usage

You can also run the profile script directly:

./cmd/profile/profile.sh --template template_name [options]

Options

• --template NAME: Profile a specific template from templates.json
• --iterations N: Number of iterations to run (default: 10000)
• --cpu: Enable CPU profiling
• --mem: Enable memory profiling
• --block: Enable block profiling
• --all: Enable all profiling types
• --all-templates: Run profiling for all templates
• --help: Show help message

Analyzing Results

Profiling results are saved to the profile_results/ directory. For each template, you'll find:

• template.txt: The template that was profiled
• context.json: The context data used
• profile_output.txt: Text output from the profiling run
• cpu.prof, mem.prof, block.prof: Profile data files (if enabled)

Visualizing with pprof

To visualize the results in a web browser:

go tool pprof -http=:8080 profile_results/template_name/cpu.prof

This opens a web UI at http://localhost:8080 where you can explore:

• Top functions (CPU time, memory allocations)
• Flame graphs
• Call graphs
• Source code view with hotspots

Text-based Analysis

For text-based analysis:

go tool pprof profile_results/template_name/cpu.prof

# At the pprof prompt, try these commands:
(pprof) top10                # Show top 10 CPU-consuming functions
(pprof) list TemplateString  # Show source with time spent in the TemplateString function
(pprof) list Parser.ParseNext # Show source with time spent in the ParseNext function

Common Performance Issues

When analyzing profiles, look for:

1. Excessive Allocations: Check memory profiles for functions making many allocations.
2. String Concatenation: Look for time spent in string handling operations.
3. Recursive Parser Calls: Check for deep call stacks in parsing functions.
4. Repeated Template Parsing: Templates should ideally be parsed once and cached.
5. Expression Evaluation: Check for inefficient expression evaluation.

Optimization Opportunities

Based on profiling results, consider these optimization strategies:

1. Cached templates: Implement template caching to avoid repeated parsing.
2. Optimized parsing: Identify and optimize hotspots in the parser.
3. Reduced allocations: Reuse buffers and preallocate when possible.
4. Switch to token-based parsing: For complex templates, a tokenization step before parsing may be more efficient.
5. Use sync.Pool: For frequently allocated objects (nodes, parsers, etc.).
6. Context optimizations: Optimize variable lookups in context maps.