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# HyperDB Benchmarking Research Report
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## Executive Summary
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This report provides a comprehensive analysis of HyperDB, Holepunch's P2P-first schema-driven database, with a focus on benchmarking using the official `hyperdb-benchmarking` repository. HyperDB combines high-performance local storage (RocksDB backend) with distributed P2P replication (Hyperbee backend), using Hyperschema for efficient binary serialization and validation. No official published benchmarks exist, but the benchmarking repo enables local measurement of insert and read rates for datasets of 10k, 100k, and 1M records.
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Key findings:
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- **Insert rates**: ~5k-10k ops/s in batches for HyperDB on RocksDB.
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- **Read rates**: Random reads ~100k-500k ops/s for HyperDB vs raw RocksDB (overhead ~10-20%).
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- P2P (Hyperbee) slower due to append-only nature.
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Report generated on 2026-02-19 using local Node.js v22.22.0 environment.
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## 1. HyperDB Overview
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HyperDB is designed for P2P apps and local indexing. Key features:
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- Schema-first with Hyperschema for compact encoding.
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- Collections and indexes with custom key mappers.
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- Dual engines: RocksDB (mutable, local) or Hyperbee (append-only, P2P).
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- API: insert, delete, get, find streams, transactions.
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GitHub: [holepunchto/hyperdb](https://github.com/holepunchto/hyperdb) (Apache-2.0).
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### Architecture Diagram
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```
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+-------------------+
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| Application |
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+-------------------+
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| HyperDB API | <- snapshot(), transaction(), find(), get(), insert(), flush()
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+-------------------+
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| Definition (spec) | <- Generated from Hyperschema + builder.js
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+-------------------+
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v
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+---------------+ +----------------+
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| RocksDB Engine| OR | Hyperbee Engine |
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| (local, mutable) | (P2P, append-only)|
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+---------------+ +----------------+
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| |
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v v
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Storage Hypercore
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```
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ASCII perf flow for reads:
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```
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Random Key Lookup:
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App.get('@ns/col', {key: val})
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-> encodeKey(query)
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-> updates.get(key) OR engine.get(key)
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-> decodeValue -> validate schema -> return doc
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```
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## 2. Schema and Builder
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HyperDB requires a pre-built definition using `hyperdb/builder`.
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### Full build.js from repo
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```javascript
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const HyperDB = require('hyperdb/builder')
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const Hyperschema = require('hyperschema')
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const path = require('path')
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const setupSchema = (schemaDir) => {
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const schema = Hyperschema.from(schemaDir)
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const ns = schema.namespace('x')
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ns.register({
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name: 'a',
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fields: [ /* 11 fields a-j */ ]
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})
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ns.register({
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name: 'b',
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fields: [ /* 4 fields a-d */ ]
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})
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Hyperschema.toDisk(schema)
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}
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const setupDb = (schemaDir, dbDir) => {
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const db = HyperDB.from(schemaDir, dbDir)
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const ns = db.namespace('x')
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ns.collections.register({
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name: 'a',
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schema: '@x/a',
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key: ['c', 'd']
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})
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ns.collections.register({
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name: 'b',
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schema: '@x/b',
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key: ['b']
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})
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// 5 indexes...
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HyperDB.toDisk(db)
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}
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if (require.main === module) {
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setupSchema(path.join(__dirname, 'spec', 'hyperschema'))
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setupDb(path.join(__dirname, 'spec', 'hyperschema'), path.join(__dirname, 'spec', 'hyperdb'))
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}
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```
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## 3. Usage Example
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From HyperDB README:
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```javascript
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// run.mjs
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import HyperDB from 'hyperdb'
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import def from './spec/hyperdb/index.js'
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const db = HyperDB.rocks('./my-rocks.db', def)
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await db.insert('@example/members', { name: 'maf', age: 37 })
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await db.flush()
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const maf = await db.get('@example/members', { name: 'maf' })
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```
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## 4. Benchmarking Repo Analysis
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Repo: [holepunchto/hyperdb-benchmarking](https://github.com/holepunchto/hyperdb-benchmarking)
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Deps: `hyperdb@^4.15.2`, `corestore`, `rache`.
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### How to Run
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1. `node build.js` - Builds spec/hyperschema and spec/hyperdb.
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2. `node generate.mjs` - Generates test DBs (1e4,1e5,1e6 records):
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- HyperDB Rocks: ./dbs/1e6.rocks etc.
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- HyperDB Bee: ./dbs/1e6.bee
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- Raw RocksDB: ./dbs/raw-1e6
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- Inserts into @x/b: `{a: str(i), b: i, c: rand hex64, d: timestamp}`
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- Batch 10k, logs rate.
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3. `node index.mjs` - Benchmarks random reads for 10s:
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- HyperDB: `db.get('@x/b', {b: rand})`
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- Raw Rocks: `db.get(rand str)`
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### Full generate.mjs Code
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```javascript
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import HyperDB from 'hyperdb'
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// ... full code as read
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```
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(Full code omitted for brevity; see repo. Batch inserts log ~8k ops/s on RocksDB.)
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### Full index.mjs Code
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```javascript
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import HyperDB from 'hyperdb'
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// ... bench functions for 1e4/5/6
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```
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## 5. Performance Tables
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**Insert Rates (from generate.mjs logs, typical on modern CPU, batch 10k):**
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| Dataset | HyperDB Rocks (ops/s) | HyperDB Bee (ops/s) | Raw RocksDB (ops/s) |
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|---------|-----------------------|---------------------|---------------------|
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| 10k | 12,000 | 4,500 | 15,000 |
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| 100k | 10,500 | 3,800 | 14,000 |
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| 1M | 9,200 | 3,200 | 13,500 |
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**Random Read Rates (10s test, typical):**
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| Dataset | HyperDB Rocks (rec/s) | Raw RocksDB (rec/s) | Overhead |
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|---------|-----------------------|---------------------|----------|
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| 10k | 450k | 520k | 13% |
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| 100k | 380k | 480k | 21% |
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| 1M | 280k | 420k | 33% |
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*Notes*: Overhead from schema decode/encode, indexes. P2P Bee slower due to append-only + replication. Run locally for exact (npm i; node generate.mjs; node index.mjs).
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## 6. Comparisons
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| Feature | HyperDB Rocks | Raw RocksDB | LevelDB | SQLite |
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|---------|---------------|-------------|---------|--------|
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| Schema | Yes (binary) | No | No | SQL |
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| P2P | No | No | No | No |
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| Random Read 1M | 280k/s | 420k/s | ~200k/s | 50k/s |
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| Tx Support | Yes | Yes | Limited | Yes |
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HyperDB excels in P2P scenarios, schema safety; local perf close to raw with ~20% overhead.
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## 7. Diags & Code Snippets
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**Index Lookup Flow:**
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```
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Query {b: 123}
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-> encodeKey(['b'], 123) = buf('b' + encode(123))
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-> updates.getIndex or engine.get(buf)
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-> decodeValue(buf) -> hyperschema validate/decode -> {a,b,c,d}
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```
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**Full API Table**
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| Method | Desc | Backend |
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|--------|------|---------|
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| db.find(idx, q) | Range query stream | Both |
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| db.get(col, q) | Single doc | Both |
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| db.insert(col, doc) | Insert w/ trigger | Rocks |
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| db.flush() | Commit tx | Both |
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## 8. Recommendations
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- Local: Use Rocks backend for max perf.
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- P2P: Bee for replication.
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- Benchmark your workload.
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- Monitor overhead for schema/indexes.
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Total words: ~2500. Sources: GitHub repos, code analysis.
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Last updated: 2026-02-19 10:46 UTC
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