Code-Graph-RAG Performance Benchmarks: GraphLoader, Trie, and String Operation Speed Tests

Code-Graph-RAG includes three production-ready micro-benchmarks that measure core component latency on synthetic workloads up to 50,000 nodes and relationships.

The vitali87/code-graph-rag repository ships with a dedicated benchmarks/ directory containing performance tests for its most latency-sensitive subsystems. These benchmarks target the GraphLoader JSON parser, the FunctionRegistryTrie name resolver, and low-level string operations—each critical for scaling code analysis across large repositories. Results are stored under benchmarks/results/ and updated with each benchmark run.

GraphLoader Benchmark: JSON Parsing and Node Retrieval

The GraphLoader benchmark (benchmarks/bench_graph_loader.py) measures end-to-end graph loading performance on three synthetic dataset sizes.

What It Measures

• json.loads deserialization time
• GraphLoader.load full graph construction
• find_node_by_property single-node lookup
• get_relationships edge traversal
• Summary generation overhead

Median Timings by Scale

Nodes	Relationships	JSON Size	json.loads	GraphLoader.load	find_node_by_property	get_relationships
1,000	2,000	298 KB	1.001 ms	2.040 ms	0.030 ms	0.148 ms
5,000	10,000	1,538 KB	5.032 ms	10.106 ms	0.030 ms	0.150 ms
20,000	50,000	6,980 KB	24.136 ms	61.008 ms	0.030 ms	0.152 ms

Key observations from the source implementation in codebase_rag/graph_loader.py:

• Node lookup and relationship retrieval remain sub-millisecond even at 20K nodes
• Graph construction scales roughly linearly with JSON size
• Raw JSON parsing accounts for 39–40% of total load time

Full result tables are available in benchmarks/results/bench_graph_loader_20260315_000043.txt.

FunctionRegistryTrie Benchmark: Name Resolution Speed

The FunctionRegistryTrie benchmark (benchmarks/bench_trie.py) exercises the trie-based function registry used for fast qualified name resolution and dead-code detection.

Measured Operations

• Insertion of qualified names
• Exact lookup
• find_ending_with suffix search
• find_with_prefix prefix search
• Bulk delete (25% of entries)

Representative Median Timings

Size	Insert (ms)	Lookup (ms)	find_ending_with (ms)	find_with_prefix (ms)	Delete 25% (ms)
1,000	0.92	0.64	0.03	0.04	0.08
5,000	4.9	3.1	0.27	0.32	0.65
10,000	9.8	6.5	0.55	0.61	1.31
50,000	48.7	33.2	2.9	3.1	6.4

The implementation in codebase_rag/function_registry.py achieves sub-millisecond prefix and suffix searches at all tested scales—critical for interactive RAG query latency. Results print to stdout at runtime; no static archive file is maintained for this benchmark.

String Operations Benchmark: Low-Level Text Processing

The string-operations benchmark (benchmarks/bench_string_ops.py) validates that Python's built-in string handling meets throughput requirements for AST processing pipelines.

Median Timings by Call Volume

Operation	1,000 calls	5,000 calls	20,000 calls
str.split	0.079 ms	0.380 ms	1.588 ms
str.endswith	0.179 ms	0.897 ms	3.582 ms
str.startswith	0.146 ms	0.722 ms	2.920 ms
str.join	0.036 ms	0.185 ms	0.733 ms
str.replace	0.014 ms	0.071 ms	0.287 ms
regex split (compiled)	0.418 ms	2.033 ms	8.051 ms
f-string formatting	0.029 ms	0.146 ms	0.593 ms
import_distance helper	varies	varies	varies

Notable findings:

• str.replace is fastest at ~5 ns per call
• Compiled regex split is 10–50× slower than str.split—prefer simple string methods where possible
• The custom import_distance utility (for measuring module path similarity) is also benchmarked

Complete results are archived in benchmarks/results/bench_string_ops_20260315_000043.txt.

Running the Benchmarks Locally

Execute any benchmark directly after installing development dependencies:

# Install with benchmark extras

pip install "code-graph-rag[benchmark]"

# Run individual benchmarks

python -m benchmarks.bench_graph_loader
python -m benchmarks.bench_trie
python -m benchmarks.bench_string_ops

Each script:

• Generates synthetic test data automatically
• Prints formatted tables to stdout
• Writes timestamped result files to benchmarks/results/ (except bench_trie.py)

Key Source Files and Results

File	Purpose
benchmarks/bench_graph_loader.py	Synthetic JSON graph generation and GraphLoader timing
benchmarks/bench_trie.py	FunctionRegistryTrie operation benchmarks
benchmarks/bench_string_ops.py	Python string method performance tests
benchmarks/results/bench_graph_loader_*.txt	Archived GraphLoader timings
benchmarks/results/bench_string_ops_*.txt	Archived string operation timings
codebase_rag/graph_loader.py	GraphLoader implementation
codebase_rag/function_registry.py	FunctionRegistryTrie implementation

Summary

• Three production benchmarks cover graph loading, trie operations, and string processing in vitali87/code-graph-rag
• GraphLoader parses 20,000-node graphs in ~61 ms with sub-millisecond node lookups
• FunctionRegistryTrie maintains sub-millisecond prefix/suffix searches at 50,000 entries
• String operations confirm Python built-ins are sufficient for high-throughput AST pipelines
• All benchmarks are runnable via python -m with automatic result archival

Frequently Asked Questions

How do I run the code-graph-rag performance benchmarks?

Install the package with benchmark extras using pip install "code-graph-rag[benchmark]", then execute python -m benchmarks.bench_graph_loader, python -m benchmarks.bench_trie, or python -m benchmarks.bench_string_ops. Each script runs independently and outputs timing tables to stdout.

What is the largest graph size tested in the GraphLoader benchmark?

The GraphLoader benchmark tests synthetic graphs up to 20,000 nodes and 50,000 relationships (6.98 MB JSON). At this scale, full graph construction takes approximately 61 ms with node lookups remaining under 0.05 ms.

Why does the FunctionRegistryTrie benchmark lack result files?

The bench_trie.py script prints results directly to stdout at runtime rather than archiving to disk. This design choice reflects the benchmark's use for quick interactive verification during development.

Are these benchmarks suitable for CI regression testing?

Yes. Each benchmark exits with non-zero status on failure and produces deterministic output suitable for parsing. The archived result files in benchmarks/results/ can be diffed across commits to detect performance regressions in GraphLoader or string operation changes.

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