Performance Considerations¶

Advanced regex features can impact performance. Understanding these implications helps you write efficient searches.

Multiline Mode Performance¶

Memory usage: - Multiline mode reads entire files into memory - Cannot use memory mapping - Large files can consume significant RAM

Automatic optimization: - Ripgrep detects when patterns don't actually need multiline mode - Avoids memory penalty when possible

Recommendations: - Use multiline mode only when actually matching across lines - Test on large files if memory is a concern - Consider line-based alternatives when possible

PCRE2 Engine Performance¶

Generally slower than default engine: - Backtracking regex implementation vs finite automata - More complex matching algorithm - Less optimized for large-scale text search

Backtracking Complexity: PCRE2's backtracking algorithm can exhibit exponential time complexity on certain "pathological" patterns, especially those with: - Nested quantifiers (e.g., (a+)+) - Complex alternations with overlapping possibilities - Patterns that cause extensive backtracking on non-matches

The default engine uses finite automata which guarantees linear time complexity regardless of pattern complexity. This makes it much more predictable and safer for untrusted input.

When PCRE2 is worth it: - Need lookaround or backreferences - Pattern complexity requires PCRE2 features - Search space is limited (specific files/directories) - You've tested the pattern on representative data and performance is acceptable

Recommendation: Use default engine unless you need PCRE2-specific features. When using PCRE2, test patterns on representative data to ensure acceptable performance, especially before using in production scripts or on large codebases.

flowchart LR
    Start[Writing Regex Pattern] --> NeedFeature{"Need lookaround
or backreferences?"}

    NeedFeature -->|No| Default[Use Default Engine]
    NeedFeature -->|Yes| TestSize{"Limited
search space?"}

    TestSize -->|Yes| TestPattern["Test Pattern
on Real Data"]
    TestSize -->|No| Rethink[Reconsider Approach]

    TestPattern --> Acceptable{"Performance
acceptable?"}
    Acceptable -->|Yes| UsePCRE2["Use PCRE2 Engine
with -P flag"]
    Acceptable -->|No| Simplify["Simplify Pattern
or Split Search"]

    Rethink --> Alternative["Find Alternative
Approach"]

    Default --> Fast["Fast Linear Time
SIMD Optimized"]
    UsePCRE2 --> Slower["Slower Backtracking
Test Thoroughly"]

    style Default fill:#e8f5e9
    style UsePCRE2 fill:#fff3e0
    style Fast fill:#e8f5e9
    style Slower fill:#ffebee

Figure: Decision flow for choosing between default engine and PCRE2 based on feature requirements and performance characteristics.

Backreferences and Lookaround¶

These features prevent some optimizations: - Backreferences require backtracking - Lookaround can be expensive for complex patterns - May scan more text than simple patterns

Use sparingly for best performance.

Parallelism and Threading¶

Ripgrep uses parallel search by default for maximum performance:

Thread Control: - Automatically selects thread count using heuristics (typically matches CPU core count) with work-stealing scheduler - Control threads with -j/--threads N flag - Single-threaded mode: --threads 1

# Use 4 threads
rg --threads 4 'pattern'

# Single-threaded for deterministic output
rg --threads 1 'pattern'

flowchart LR
    Start[Files to Search] --> Distribute["Thread Scheduler
    Work Stealing"]

    Distribute --> T1["Thread 1
    File Subset"]
    Distribute --> T2["Thread 2
    File Subset"]
    Distribute --> T3["Thread 3
    File Subset"]
    Distribute --> TN["Thread N
    File Subset"]

    T1 --> Search1[Search Files]
    T2 --> Search2[Search Files]
    T3 --> Search3[Search Files]
    TN --> SearchN[Search Files]

    Search1 --> Collect[Collect Results]
    Search2 --> Collect
    Search3 --> Collect
    SearchN --> Collect

    Collect --> Output[Output Matches]

    style Distribute fill:#e1f5ff
    style T1 fill:#fff3e0
    style T2 fill:#fff3e0
    style T3 fill:#fff3e0
    style TN fill:#fff3e0
    style Collect fill:#e8f5e9

Figure: Parallel search architecture showing how ripgrep distributes files across threads with work-stealing scheduler for optimal load balancing.

When to use single-threaded mode: - Need deterministic output order - Running in constrained environments - Debugging search behavior - Benchmarking without parallelism variance

I/O Strategies¶

Ripgrep automatically selects the best I/O strategy based on your search:

Memory Mapping vs Buffered Reading:

• Memory mapping (mmap): Used for single file searches
  • Maps file directly into memory
  • Faster for large files
  • Lower memory overhead
  • Note: Disabled by default on macOS due to platform-specific performance characteristics
• Buffered reading: Used for directory searches
  • Reads files incrementally
  • Better for many small files
  • More predictable memory usage

Manual Control:

# Force memory mapping
rg --mmap 'pattern'

# Force buffered reading
rg --no-mmap 'pattern'

flowchart TD
    Start[Search Request] --> Type{Search Type?}

    Type -->|Single File| Large{Large File?}
    Type -->|Directory| ManyFiles[Many Files to Scan]

    Large -->|Yes| MMap["Memory Mapping
mmap"]
    Large -->|No| MMap

    ManyFiles --> Buffered["Buffered Reading
Incremental"]

    MMap --> MMAPBenefits["✓ Maps file to memory
✓ Faster for large files
✓ Lower memory overhead"]

    Buffered --> BufferedBenefits["✓ Reads incrementally
✓ Better for many small files
✓ Predictable memory"]

    MMAPBenefits --> Result[Search Results]
    BufferedBenefits --> Result

    style MMap fill:#e1f5ff
    style Buffered fill:#fff3e0
    style MMAPBenefits fill:#e1f5ff
    style BufferedBenefits fill:#fff3e0

Figure: I/O strategy selection showing how ripgrep automatically chooses between memory mapping and buffered reading based on search type.

Performance Testing¶

Use --stats flag to see performance metrics:

rg --stats -U 'pattern'

This shows: - Files searched - Searches with match - Bytes searched - Matches found - Search time

4 files searched
2 searches with match
15432 bytes searched
8 matches found
0.012s elapsed

Performance Tips¶

Quick Performance Wins¶

Search Optimization¶

1. Prefer default engine when possible - uses SIMD acceleration and finite automata
2. Use literal search (-F) for plain strings - much faster than regex
3. Avoid multiline unless necessary
4. Limit search scope with file type filters (-t) - see File Filtering for details

Performance Tuning Flags¶

Skip large files:

# Skip files larger than 10MB
rg --max-filesize 10M 'pattern'  # (1)!

1. Ignores files larger than 10MB. Useful for avoiding slow searches through large binary files or logs.

Handle long lines:

# Set maximum line length to process
rg --max-columns 500 'pattern'  # (1)!

1. Ignores lines longer than 500 characters. Prevents slow regex matching on extremely long lines like minified code.

Stop after N matches:

# Stop searching after finding 100 matches
rg --max-count 100 'pattern'  # (1)!

1. Stops after finding 100 matches. Useful for quick verification or when you only need a few examples.

Avoid crossing filesystem boundaries:

# Stay on one filesystem
rg --one-file-system 'pattern'  # (1)!

1. Prevents searching across mount points. Avoids accidentally searching network drives or external disks.

Testing and Profiling¶

• Test with --stats on representative data
• Profile complex patterns before using in production scripts
• Use time command for comparing different approaches
• Test on your actual datasets, not toy examples

Regex Limits¶

Ripgrep has configurable limits to prevent excessive memory use and compilation time.

Regex Size Limit¶

Controls the maximum size of compiled regex (default: 10M):

# Increase for extremely complex patterns
rg --regex-size-limit 100M 'very_complex_pattern'  # (1)!

1. Sets max compiled regex size to 100MB (default: 10M). Use when searching with large alternations or auto-generated patterns.

When you might need this: - Very large alternations (pattern1|pattern2|...|pattern1000) - Extremely complex nested patterns - Auto-generated regexes

Error message:

error: compiled regex exceeds size limit

DFA Size Limit¶

Controls DFA (deterministic finite automaton) size for the default engine:

# Increase DFA size limit
rg --dfa-size-limit 100M 'pattern'  # (1)!

1. Sets max DFA size to 100MB (default: 10M). Needed for patterns with many states or large character class combinations.

When you might need this: - Complex patterns with many possible states - Large character class combinations

Automatic Limit Suggestions¶

When you hit a limit, ripgrep's error message suggests the appropriate flag to increase it:

error: regex compiled too large
help: use --regex-size-limit to increase the limit

When to Increase Limits vs Simplify Patterns¶

Hitting regex limits is often a sign that your pattern needs simplification, but sometimes large patterns are legitimate. Here's how to decide:

flowchart TD
    Start[Regex Limit Error] --> Type{"Pattern
    Type?"}

    Type -->|Auto-generated| Increase1["Increase Limit
    Legitimate Use"]
    Type -->|Large Alternations| Check{"Truly Need
    All Cases?"}
    Type -->|Deeply Nested| Simplify1["Refactor Pattern
    Reduce Nesting"]
    Type -->|Unclear| Review["Review Design
    Understand Complexity"]

    Check -->|Yes| Increase2["Increase Limit
    Document Why"]
    Check -->|No| Multiple["Use Multiple
    Simpler Searches"]

    Review --> Clear{"Complexity
    Justified?"}
    Clear -->|Yes| Increase3[Increase Limit]
    Clear -->|No| Simplify2["Simplify Pattern
    Or Split Search"]

    Increase1 --> Test[Test Performance]
    Increase2 --> Test
    Increase3 --> Test

    Multiple --> Done[Efficient Search]
    Simplify1 --> Done
    Simplify2 --> Done
    Test --> Acceptable{"Performance
    OK?"}
    Acceptable -->|Yes| Done
    Acceptable -->|No| Rethink["Rethink Approach
    Consider Alternatives"]

    style Increase1 fill:#fff3e0
    style Increase2 fill:#fff3e0
    style Increase3 fill:#fff3e0
    style Simplify1 fill:#e8f5e9
    style Simplify2 fill:#e8f5e9
    style Multiple fill:#e8f5e9
    style Done fill:#e8f5e9

Figure: Decision flow for handling regex limit errors - when to increase limits versus simplifying patterns based on use case and pattern characteristics.

Increase limits for: - Auto-generated patterns: Patterns produced by tools or scripts (e.g., generated from configuration) - Large alternations: Legitimate need to match many alternatives (word1|word2|...|word1000) - Comprehensive matching: Domain-specific patterns covering many cases (e.g., all valid email formats, file extensions) - One-time searches: Complex ad-hoc queries that won't be reused

Simplify patterns instead for: - Deeply nested groups: Patterns with excessive nesting usually can be refactored - Repeated similar logic: Extract common patterns or use multiple simpler searches - Unclear complexity: If you can't explain why the pattern is complex, it's probably poorly designed - Production scripts: Patterns used repeatedly should be simple and maintainable

Simplification strategies:

# Instead of huge alternation, use multiple searches
rg -e 'pattern1' -e 'pattern2' -e 'pattern3'

# Instead of complex nested groups, break into stages
rg 'simple_pattern1' | rg 'simple_pattern2'

# Use character classes instead of alternations
# Bad: (a|b|c|d|e)
# Good: [a-e]

Rule of thumb: If increasing the limit solves a one-time problem, that's fine. If you're hitting limits regularly, invest time in understanding and simplifying your patterns.