PERFORMANCE
Speed Without Compromise
Deep analysis does not have to be slow. LOOM processes millions of lines in minutes, updates in real-time as you code, and renders your entire dependency graph at 60fps. Fast code analysis that actually gets used.
Why Speed Matters for Code Analysis
You have used slow code analysis tools before. The ones that take twenty minutes to scan your codebase, so you run them once at the end of the sprint. The ones that queue overnight builds because they cannot keep up with your commit rate. The ones that produce great reports that nobody reads because the data is already stale.
Slow tools get ignored. When analysis takes too long, developers work around it rather than with it. They skip the pre-commit hooks. They merge without waiting for the full scan. They make decisions based on gut feeling because the data they need is not available in the moment they need it.
Real-time feedback changes behavior. When you can see the impact of a change before you commit it, you make different choices. When the dependency graph updates as you type, you catch problems while your context is still fresh. When visualization is instant, you actually use it. Speed is not a feature. Speed is the difference between a tool that shapes your workflow and a tool that sits unused in your CI pipeline.
This matters even more when you integrate with AI coding tools. Copilot, Claude, and ChatGPT work best when they have current context about your codebase. If your analysis tool takes an hour to run, your AI assistant is working with hour-old data. With LOOM, the context export reflects the code as it exists right now. Your AI sees what you see. For more on this workflow, see our AI Development Guide.
The Numbers
Three metrics that define scalable code scanning. Not theoretical maximums. Real performance on production codebases.
Process enterprise-scale codebases without waiting for overnight builds.
The LOOM Code Scanner uses AST-based parsing with parallel processing across multiple cores. Each file is parsed independently, relationships are resolved in a second pass, and the final graph is assembled incrementally. The architecture scales linearly with codebase size up to hardware limits.
As you code, LOOM updates its analysis in real-time. No manual refresh needed.
LOOM watches for file changes and re-parses only what changed. Modified functions update their edges in the graph. New imports create new connections. Deleted code removes its nodes. The full codebase does not re-scan when you save a file. Only the delta propagates through the system.
Navigate thousands of nodes at full frame rate. GPU-accelerated rendering makes exploration instant.
The 3D DataVerse uses WebGL with level-of-detail scaling. Close nodes render with full detail. Distant clusters simplify automatically. Edge bundling reduces visual clutter. The result: smooth navigation through graphs with tens of thousands of nodes on standard hardware.
Performance at Scale
How LOOM compares to traditional static analysis tools across different codebase sizes.
| Codebase Size | Traditional Tools | LOOM Full Scan | LOOM Incremental |
|---|---|---|---|
| 10K lines (small project) | 30-60 seconds | Under 1 second | Under 50ms |
| 100K lines (medium app) | 5-15 minutes | 6-10 seconds | Under 100ms |
| 1M lines (large monolith) | 1-3 hours | 1-2 minutes | Under 100ms |
| 5M+ lines (enterprise monorepo) | Overnight batch | 5-10 minutes | Under 200ms |
Incremental analysis time is nearly constant regardless of total codebase size. Only changed files are re-analyzed.
Enterprise Scale Without Enterprise Wait Times
Modern enterprise codebases are not just big. They are complex. Monorepos with dozens of packages. Polyglot systems mixing TypeScript, Python, Go, and Rust. Microservice architectures where the dependency graph spans hundreds of repositories. Legacy code interleaved with new development.
LOOM handles all of it. The multi-language parser processes each language in parallel, then unifies the results into a single coherent graph. Monorepo boundaries are respected but cross-package dependencies are still tracked. Service-to-service calls are mapped even when the connection happens through APIs rather than imports.
The enterprise tier adds distributed scanning for organizations with multiple repositories. Each repo scans independently, results sync to a central registry, and the unified graph updates continuously. Your architects see the full system. Your developers see their local context. Everyone works with current data.
Speed and Depth: You Get Both
There is a common assumption in code analysis: you trade speed for depth. Fast tools do surface-level pattern matching. Deep tools take hours to run. Pick one.
LOOM rejects that tradeoff. AST parsing is actually faster than regex-based pattern matching for structural analysis. Why? Because the parser makes a single pass through the file and extracts everything at once. Regex tools make multiple passes, one pattern at a time, re-reading the same code repeatedly.
The result is deeper analysis that runs faster. Transitive dependencies. Call chains. Data flow paths. Execution traces. All computed incrementally, all available in real-time.
See how LOOM achieves analysis depth without sacrificing speed.
The Architecture Behind the Speed
Three architectural decisions make LOOM fast at scale:
Parallel Parsing
Each file parses independently on its own thread. Modern CPUs have 8, 16, even 32 cores. LOOM uses all of them. A thousand files parse nearly as fast as a hundred.
Incremental Graphs
The dependency graph is a living data structure, not a static report. When a file changes, its nodes update in place. Edges reconnect. The rest of the graph stays untouched.
Lazy Computation
Transitive dependencies, impact analysis, and path queries compute on demand. The graph stores relationships. Derived metrics calculate when you ask for them, not before.
For technical details on how the scanner extracts structural data, see the Code Scanner documentation.
Not Just Faster. Different.
Traditional static analysis tools were designed for batch processing. Run overnight. Generate a report. Review it tomorrow. That model made sense when codebases were smaller and release cycles were longer.
LOOM is designed for continuous analysis. The scan runs when you open the project. Updates propagate as you edit. The visualization reflects current state, not yesterday's snapshot. You work with the tool, not around it.
For a detailed comparison of LOOM's approach versus traditional static analysis, see LOOM vs Static Analysis.
See Speed in Action
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