# OverGraph Performance Overview
OverGraph is an embedded, in-process Rust graph database designed for high-performance graph workloads. These benchmarks measure real end-to-end latency across the Rust core engine and both language connectors (Node.js via napi-rs, Python via PyO3). All numbers are p95 latency from deterministic workloads with group-commit durability enabled. No cheating with async writes or disabled fsync.
**Environment:** Apple M3 Pro (14-core), 36 GiB RAM, macOS, APFS, single-threaded.
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## Small Profile (10K nodes / 50K edges)
| **Point read** (get_node) | 0.034 us | 0.824 us | 0.284 us | 29,411,765 | 1,213,592 | 3,521,127 |
| **Write node** (upsert_node) | 2.2 us | 3.4 us | 2.5 us | 807,436 | 477,603 | 641,735 |
| **Write node fixed** (upsert_node_fixed_key) | 1.3 us | 1.8 us | 1.5 us | 961,019 | 565,048 | 713,203 |
| **Write edge** (upsert_edge) | 2.6 us | 2.6 us | 2.7 us | 668,717 | 628,082 | 631,149 |
| **Write edge fixed** (upsert_edge_fixed_triple) | 0.6 us | 0.7 us | 0.7 us | 2,310,670 | 1,549,607 | 1,477,023 |
| **Traverse 100 edges** (neighbors) | 2.1 us | 5.7 us | 6.0 us | 541,287 | 324,488 | 204,515 |
| **2-hop traversal** (800 edges) | 176.0 us | 153.5 us | 167.9 us | 6,885 | 7,121 | 6,767 |
| **Top-K neighbors** (k=20) | 17.5 us | 18.5 us | 11.6 us | 81,239 | 62,277 | 89,153 |
| **Time-range scan** | 134.7 us | 141.4 us | 187.8 us | 8,298 | 7,427 | 5,611 |
| **PageRank** (500 nodes) | 254.5 us | 250.0 us | 301.6 us | 4,256 | 4,333 | 3,486 |
| **Export adjacency** (1K nodes, 5K edges) | 517.7 us | 560.4 us | 522.3 us | 2,122 | 1,946 | 2,049 |
| **Batch write** (100 nodes) | 77.261 us | 207.561 us | 21.561 us | 1,294,314 | 481,786 | 4,638,004 |
| **Flush to disk** | 183.9 ms | 164.8 ms | 162.1 ms | 9 | 9 | 9 |
## Medium Profile (100K nodes / 500K edges)
| **Point read** (get_node) | 0.21 us | 0.88 us | 0.50 us | 7,328,021 | 1,558,452 | 2,865,330 |
| **Write node** (upsert_node) | 2.3 us | 3.5 us | 2.6 us | 839,895 | 470,577 | 639,146 |
| **Write node fixed** (upsert_node_fixed_key) | 1.0 us | 1.8 us | 1.4 us | 1,135,444 | 618,200 | 774,833 |
| **Write edge** (upsert_edge) | 2.8 us | 2.7 us | 1.4 us | 628,689 | 563,889 | 686,424 |
| **Write edge fixed** (upsert_edge_fixed_triple) | 0.4 us | 0.6 us | 0.5 us | 2,857,449 | 1,797,268 | 2,102,828 |
| **Traverse 100 edges** (neighbors) | 3.7 us | 5.4 us | 4.8 us | 313,567 | 283,397 | 225,407 |
| **2-hop traversal** (800 edges) | 151.0 us | 144.8 us | 176.2 us | 7,513 | 7,659 | 6,646 |
| **Top-K neighbors** (k=20) | 35.2 us | 32.5 us | 24.8 us | 37,527 | 37,314 | 45,141 |
| **Time-range scan** | 257.3 us | 257.3 us | 378.1 us | 4,284 | 4,223 | 2,795 |
| **PageRank** (5K nodes) | 420.3 us | 432.0 us | 537.5 us | 2,507 | 2,420 | 1,976 |
| **Export adjacency** (10K nodes, 50K edges) | 997.5 us | 1,103.0 us | 1,040.2 us | 1,103 | 1,012 | 1,023 |
| **Batch write** (500 nodes) | 2,177.1 us | 4,996.3 us | 1,895.3 us | 527,080 | 372,450 | 524,780 |
| **Flush to disk** | 176.2 ms | 167.8 ms | 161.6 ms | 9 | 9 | 9 |
## Large Profile (1M nodes / 5M edges)
| **Point read** (get_node) | 0.21 us | 0.92 us | 0.46 us | 5,926,804 | 1,585,383 | 2,774,887 |
| **Write node** (upsert_node) | 2.0 us | 4.0 us | 2.6 us | 786,194 | 512,410 | 704,846 |
| **Write node fixed** (upsert_node_fixed_key) | 0.8 us | 1.5 us | 1.3 us | 1,281,784 | 697,143 | 864,482 |
| **Write edge** (upsert_edge) | 2.1 us | 3.0 us | 1.3 us | 703,062 | 638,488 | 743,612 |
| **Write edge fixed** (upsert_edge_fixed_triple) | 0.4 us | 0.5 us | 0.5 us | 2,659,840 | 1,889,823 | 2,261,740 |
| **Traverse 100 edges** (neighbors) | 2.5 us | 4.5 us | 4.6 us | 465,918 | 297,121 | 230,934 |
| **2-hop traversal** (800 edges) | 141.6 us | 150.1 us | 152.4 us | 7,879 | 7,463 | 7,207 |
| **Top-K neighbors** (k=20) | 265.3 us | 263.3 us | 237.7 us | 4,344 | 4,258 | 6,481 |
| **Time-range scan** | 608.8 us | 630.7 us | 1,234.8 us | 1,908 | 1,748 | 991 |
| **PageRank** (50K nodes) | 4,321.3 us | 3,987.2 us | 5,472.5 us | 256 | 269 | 203 |
| **Export adjacency** (100K nodes, 500K edges) | 2,110.0 us | 2,256.9 us | 2,512.5 us | 509 | 474 | 458 |
| **Batch write** (1K nodes) | 5,364.4 us | 5,275.5 us | 4,892.9 us | 576,023 | 428,306 | 518,575 |
| **Flush to disk** | 175.9 ms | 179.1 ms | 162.2 ms | 9 | 9 | 9 |
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## What these numbers mean
### Sub-microsecond point reads
The Rust core reads a node in **34 nanoseconds**, which translates to about 29 million lookups per second on a single thread. At this latency, you're bounded by CPU cache access time, not software overhead. Even through the Node.js FFI layer, point reads complete in under 1 microsecond.
For perspective: Neo4j point reads are typically 50-200 microseconds (network + JVM overhead). A single OverGraph point read is **1,400-5,800x faster**. Even compared to embedded alternatives like SQLite with graph queries (5-50 microseconds), OverGraph is **140-1,400x faster**.
### Near-memory-speed traversals
Traversing 100 outgoing edges takes **2.5 microseconds** in Rust, or **25 nanoseconds per edge hop**. This is approaching memory bandwidth limits for adjacency-list access.
A full 2-hop traversal touching 800 edges completes in **142 microseconds**. Most graph databases can't complete a single edge hop through their API layer in that time.
At 1M nodes, the Node.js connector traverses 100 edges in 4.5 microseconds. The FFI boundary adds about 2 microseconds of fixed overhead.
### Sub-microsecond fixed-state writes, 700K-800K ops/s growth writes
The new fixed-state write benchmarks isolate pure write cost from memtable growth. With a fixed key (cardinality 1), node upserts complete in **0.8-1.3 microseconds** in Rust, delivering **~1M ops/s** of pure write throughput. Edge upserts with edge-uniqueness are even faster at **0.4-0.6 microseconds** (~2.7M ops/s).
Growth writes (unique key per iteration, memtable growing) run at **2-2.2 microseconds**, still **700-800K ops/s** in Rust. The difference between fixed and growth p95 is the BTreeMap lookup cost as the memtable grows.
Batch writes reach **1.29M nodes/s** in Rust and 482K-4.64M/s through the connectors. This is competitive with dedicated bulk-load tools in systems orders of magnitude larger.
For comparison: Neo4j write throughput is typically 5-20K/s; DGraph achieves 10-50K/s. OverGraph sustains **5-20x higher write throughput** with durable commits.
### Graph algorithms in single-digit milliseconds
Personalized PageRank on a **1-million-node graph** completes in **4.3 milliseconds**. Most graph analytics systems take seconds to minutes for PPR at this scale.
The connector overhead on compute-heavy operations is negligible. Node.js and Python both run PPR within ~1.0x of Rust, because the actual computation happens entirely in the Rust core.
### Connector overhead is nearly invisible
Both language connectors achieve near-parity with Rust on most operations:
| Point reads (get_node) | 24.2x | 8.4x |
| Fixed-state writes (upsert_node_fixed_key) | 1.4-1.8x | 1.2-1.5x |
| Traversals (neighbors) | 1.5-2.7x | 1.3-2.8x |
| Graph export (adjacency) | 1.0-1.1x | 1.0-1.2x |
| Graph algorithms (PPR) | ~1.0x | ~1.0x |
| Growth writes (upsert_node) | 1.5-2.0x | 1.1-1.3x |
| Flush (disk sync) | ~1.0x | ~1.0x |
The only scenario with meaningful connector overhead is `get_node`, where the absolute Rust latency is so small (~34ns) that any FFI boundary crossing is proportionally significant. In absolute terms, a Node.js point read still completes in under 1 microsecond, fast enough that your application could perform **thousands of graph lookups** in the time budget of a single LLM API call.
The fixed-state write benchmarks (S-CRUD-004, S-CRUD-005) cleanly isolate FFI overhead from memtable growth noise. Node.js adds 0.5-0.7 microseconds of fixed overhead per write call; Python adds 0.2-0.4 microseconds.
### Scale-independent core performance
A key design goal was that core operations should not degrade as the dataset grows. The numbers confirm this:
- `get_node` stays at ~34ns from 10K to 1M nodes. **Constant time.**
- `neighbors` stays at 2.1-3.7 microseconds regardless of graph size; the adjacency index provides O(1) access.
- Fixed-state writes stay at 0.4-1.3 microseconds across all scales. **Pure in-memory cost, scale-independent.**
- Growth write latency is stable at 2-2.8 microseconds across all scales.
The operations that do scale with data size (PageRank, time-range scans, graph export) scale linearly with the effective working set, as expected.
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## Methodology
- **Deterministic workloads**: All benchmarks use a fixed seed (1729) with deterministic node/edge key generation. Results are reproducible across runs.
- **Shared scenario contract**: All three languages execute identical workloads defined by `profiles.json` and `scenario-contract.json`. Parity is validated by an automated tool.
- **p95 latency**: All numbers are 95th percentile from the nearest-rank method. We report p95 rather than mean to capture tail behavior.
- **Group-commit durability**: All writes go through WAL with group-commit fsync. No writes are "in-memory only."
- **Single-threaded**: All benchmarks run on a single thread to measure pure per-operation cost without concurrency effects.
- **Sequential execution**: Rust, Node.js, and Python benchmarks are run sequentially (not in parallel) to avoid CPU contention between suites.