Skip to main content

NEDB — a versioned, self-compressing, time-traveling embedded database (replay-protected, idempotent, relational, searchable) with durable AOF persistence and a server daemon (nedbd).

Project description

NEDB

A versioned, self-compressing, time-traveling embedded database.

Replay-protected · idempotent · relational · filterable · sortable · searchable · provable. One Rust core → ships to PyPI and npm from a single source.

Website & docs → eth-interchained.github.io/nedb


Why NEDB

Redis is fast because it's in-memory and simple — but relations are hand-rolled, history is gone the moment you overwrite, and every call pays a network hop. NEDB keeps the speed and adds the things real systems actually need:

  • Faster-than-Redis latency where it's honest to claim it — NEDB runs embedded, in-process, so point reads pay no socket hop. The networked server (nedbd, RESP-compatible) competes on the Rust core's merits.
  • Replay protection + idempotency in the core, not the app. Every write carries a strictly-monotonic per-client nonce and an optional idempotency key. Retries are no-ops; stale/out-of-order ops are rejected. This is built into one hash-chained, append-only log.
  • Time-travel. Read the database exactly as it existed at any past sequence — AS OF seq. Debugging, audit, MVCC snapshots, and deterministic replay all fall out of the same log.
  • Durable persistence, Redis-style. Point a database at a path and every op is appended to the hash-chained log on disk (and fsync'd); it reloads by replaying that log on open. It's exactly Redis's AOF model — except the append-only log is the same tamper-evident chain the engine already trusts, so verify() and AS OF hold across restarts and the log is never rewritten.
  • First-class relations. Adjacency-list graph edges with O(1) traversal — and the graph time-travels too.
  • Filter / sort / search. Equality, ordered, and full-text inverted indexes, maintained incrementally.
  • git-style files with maximum compression. Content-defined chunking + content-addressed dedup + temperature tiers (fast warm codec, max-ratio cold archival). Every file version has a Merkle root you can anchor on-chain.

The keystone: one nonce-enforced append-only log is the substrate for idempotency, replay protection, crash recovery, MVCC, and time-travel — simultaneously.


Quickstart (Python reference engine — runs today, zero build)

git clone https://github.com/Eth-Interchained/nedb && cd nedb
pip install -e .                 # pure-Python reference; no toolchain needed
python3 examples/demo.py         # see every feature
python3 tests/test_nedb.py       # 11/11 invariants
from nedb import NEDB

db = NEDB("./mydata")            # durable: append-only log on disk, reloads on open
# db = NEDB()                    # (no path = purely in-memory)
db.create_index("users", "status", "eq")
db.create_index("users", "age", "ordered")
db.create_index("users", "bio", "search")

db.put("users", "alice", {"name": "Alice", "age": 31, "status": "active",
                          "city": "Austin", "bio": "rust systems hacker"})

# Idempotent, replay-protected write (safe to retry forever):
db.put("orders", "o1", {"total": 42}, client="checkout", nonce=7, idem="charge-o1")

# NQL — filter + sort
db.query('FROM users WHERE age >= 25 AND status = "active" ORDER BY age DESC')

# Full-text search
db.query('FROM users SEARCH "rust"')

# Relations + graph traversal
db.link("users:alice", "follows", "users:bob")
db.q("users").where("_id", "=", "alice").traverse("follows").run()

# Time-travel
s = db.seq
db.put("users", "alice", {"name": "Alice", "city": "Lisbon", "age": 31, "status": "active"})
db.get("users", "alice", as_of=s)["city"]      # -> "Austin"

# git-style files with Cascade compression + provable history
v1 = db.put_file("notes.txt", open("notes.txt","rb").read())
db.file_root("notes.txt", v1)                  # Merkle root — anchorable on ITC

# Durable + provable across restarts
db.close()
db = NEDB("./mydata")                          # replays the log on open
assert db.verify()                             # the hash chain is intact
db.get("users", "alice", as_of=s)["city"]      # AS OF still works -> "Austin"

Persistence

NEDB persists the way Redis does — by writing the operations, not by dumping pages — because the engine's whole thesis is that state is a pure function of the log.

  • NEDB(path) opens a durable database in a directory. Every op is appended to log.aof (one JSON line) and fsync'd; index configuration is snapshotted to meta.json. On open, NEDB replays the log to rebuild state.
  • NEDB() with no path is in-memory (unchanged).
  • The append-only log is the same hash-chained, tamper-evident chain that powers idempotency, replay protection, and time-travel — so verify(), AS OF, relations, and the anchorable head all survive a restart. The log is never rewritten, so the chain (and its commitment) stays provable.
db = NEDB("./mydata")
db.put("users", "alice", {"name": "Alice", "status": "active"})
db.close()                       # flush + fsync

again = NEDB("./mydata")         # replays log.aof
assert again.verify()            # chain intact across the restart
again.get("users", "alice")      # -> {"name": "Alice", ...}

Snapshotting (an RDB-style fast-load checkpoint that keeps the AOF intact) and Rust-core parity are tracked on the roadmap.


nedbd — run NEDB as a server

For client/server setups (multiple apps, a remote admin UI like NEDB Studio, or just keeping the database in its own process), pip install nedb-engine ships a daemon. It runs the engine as a long-lived process and serves an HTTP/JSON API; each named database is a durable NEDB(path) held open in memory. Connect to it the way you'd connect to Redis or Postgres — over a URL.

nedbd                       # http://127.0.0.1:7070, data in ./nedb-data
# config via env: NEDBD_HOST, NEDBD_PORT, NEDBD_DATA, NEDBD_TOKEN (optional bearer auth)
# create a database (optionally seeded with indexes / rows / links)
curl -X POST localhost:7070/v1/databases -d '{"name":"shop","init":{
  "indexes":[["users","status","eq"]],
  "seed":{"users":[{"id":"u1","name":"Ada","status":"active"}]}}}'

# query it (real NQL, real engine)
curl -X POST localhost:7070/v1/databases/shop/query -d '{"nql":"FROM users WHERE status = \"active\""}'

# write, verify, time-travel — all server-side on the durable log
curl -X POST localhost:7070/v1/databases/shop/put   -d '{"coll":"users","id":"u2","doc":{"name":"Bo"}}'
curl       localhost:7070/v1/databases/shop/verify

API: GET /health · GET|POST /v1/databases · GET|DELETE /v1/databases/<name> · POST …/query · POST …/put · POST …/index · POST …/link · DELETE …/rows/<coll>/<id> · GET …/verify · GET …/log. Databases persist across daemon restarts (the engine replays its append-only log on open).


NQL — the NEDB Query Language

One small grammar; the Rust parser is the single source of truth so Python and Node share identical semantics. A fluent builder compiles to the same plan.

FROM <collection>
  [ AS OF <seq> ]
  [ WHERE <field> <op> <value> (AND ...)* ]      op ∈ = != < <= > >=
  [ SEARCH "<text>" ]
  [ ORDER BY <field> [ASC|DESC] ]
  [ TRAVERSE <relation> ]
  [ LIMIT <n> ]

What's measured (v0.4.1 · pure Python · Linux x86_64)

Numbers from python3 bench/benchmarks.py — reproducible, not cherry-picked. Full results in bench/RESULTS.md.

Operation Throughput Latency
GET (embedded, in-process) 1.30M/s 0.77 µs
GET AS OF (time-travel) 997K/s 1.00 µs
PUT (logged, no index) 63.7K/s 15.7 µs
PUT durable (AOF + fsync) 7.0K/s 143 µs
QUERY: eq filter, eq index 1.42M/s 0.71 µs
QUERY: eq filter, no index (scan) 515K/s 1.94 µs
QUERY: SEARCH (inverted index) 467K/s 2.14 µs
SQL SELECT → NQL (adapter) 1.70M/s 0.59 µs
AutoIndexDB wrapper overhead ~0% 0.54 µs
File compression — warm 39.9×
File compression — cold (LZMA) 88.9×
Cross-version dedup 20 of 22 chunks

The reference engine proves the architecture. Run python3 bench/benchmarks.py --redis to compare against Redis TCP on your own machine. The Rust core (rust/) is the future speed target.


Architecture

            ┌──────────────────────────────────────────────┐
  put/del → │  OpLog  (append-only · BLAKE3 hash chain ·    │ ← single source of truth
  link      │          per-client nonce · idempotency keys) │
            └───────────────┬──────────────────────────────┘
            deterministic fold │ (state = pure function of the log)
        ┌──────────────┬───────┴────────┬───────────────────┐
        ▼              ▼                ▼                   ▼
   MVCC store     Relations         Indexes            BlobStore (Cascade)
   (time-travel)  (graph, AS OF)    eq/ordered/search  CDC+dedup+tiers, Merkle roots

PyPI ships a universal pure-Python wheel (pip install nedb-engine works on every platform/Python, and includes the nedbd server) — the engine, persistence, and daemon are all pure Python. npm ships napi-rs native addons. Native PyO3 acceleration for PyPI is additive/roadmap (the public API is identical with or without it). A RESP-compatible nedbd wire protocol and a WASM build are also on the roadmap.

Full design: docs/SPEC.md.


Repo layout

nedb/            pure-Python reference engine (this is what `pip install` ships today)
rust/            production core — nedb-core + nedb-py (PyO3) + nedb-node (napi-rs)
examples/demo.py end-to-end walkthrough
tests/           invariant tests
bench/           embedded micro-bench + Redis head-to-head harness
docs/SPEC.md     architecture specification
.github/         release CI → PyPI + npm on tag

Roadmap

  • Reference engine: log, MVCC, relations, indexes, NQL, Cascade, Merkle
  • Durable persistence: append-only log (AOF) on disk + replay-on-open; verify() / AS OF survive restarts
  • RDB-style snapshot checkpoint (fast load) that keeps the AOF chain intact
  • Rust core parity (persistence in nedb._native) + criterion benches + cargo test
  • Universal pure-Python wheel + sdist on PyPI (installs everywhere; ships the nedbd command); napi-rs binaries on npm
  • Additive native PyO3 acceleration wheels for PyPI (optional speed; same API)
  • nedbd server: HTTP/JSON daemon — durable, multi-database; pip install ships the nedbd command
  • nedbd: RESP-compatible wire protocol + native protocol
  • Similarity-picked deltas + schema-aware columnar transforms
  • On-chain (ITC) root anchoring; WASM build

NEDB Studio

The agentic, prompt-to-database GUI for NEDB — natural language → schema, NQL, seed data, and Python/Node snippets — lives in its own repo: Eth-Interchained/nedb-studio (Portal-powered, GPLv3).

License

Apache-2.0 · © INTERCHAINED, LLC — interchained.org. Built with AiAssist.


Authors

Built by Mark Allen Evans Jr. (INTERCHAINED, LLC) with Claude Sonnet 4.6 on Hyperagent.

"Take one idea, turn it into an LP, then an app, then a system, then a platform, then infrastructure that is irreplaceable."

Built with Hyperagent

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nedb_engine-0.8.2.tar.gz (62.0 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

nedb_engine-0.8.2-py3-none-any.whl (70.9 kB view details)

Uploaded Python 3

nedb_engine-0.8.2-cp38-abi3-win_amd64.whl (347.7 kB view details)

Uploaded CPython 3.8+Windows x86-64

nedb_engine-0.8.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (464.1 kB view details)

Uploaded CPython 3.8+manylinux: glibc 2.17+ x86-64

nedb_engine-0.8.2-cp38-abi3-macosx_11_0_arm64.whl (424.2 kB view details)

Uploaded CPython 3.8+macOS 11.0+ ARM64

File details

Details for the file nedb_engine-0.8.2.tar.gz.

File metadata

  • Download URL: nedb_engine-0.8.2.tar.gz
  • Upload date:
  • Size: 62.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.15

File hashes

Hashes for nedb_engine-0.8.2.tar.gz
Algorithm Hash digest
SHA256 3682c311c0a9fe4b1aa0594fa621cf037ea782e938d138a40b2dc4cf760f48c9
MD5 a131992f47838e574da28a9275a9b390
BLAKE2b-256 8e1ef3e0320adafbac3bd609c9e7e5c923e8685483bc7ad8065f68894e00a67f

See more details on using hashes here.

File details

Details for the file nedb_engine-0.8.2-py3-none-any.whl.

File metadata

  • Download URL: nedb_engine-0.8.2-py3-none-any.whl
  • Upload date:
  • Size: 70.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for nedb_engine-0.8.2-py3-none-any.whl
Algorithm Hash digest
SHA256 d1fa05e175dc845fd21b019ccd6f07a4a21660e0d639312a2276e2ca83622367
MD5 ebcc8a091cc19fd32a2b908b2337397a
BLAKE2b-256 f57e2524db70cec78c98b96ae6dc7c394adafd75fb98592f55b25d57b6d0f8bc

See more details on using hashes here.

File details

Details for the file nedb_engine-0.8.2-cp38-abi3-win_amd64.whl.

File metadata

  • Download URL: nedb_engine-0.8.2-cp38-abi3-win_amd64.whl
  • Upload date:
  • Size: 347.7 kB
  • Tags: CPython 3.8+, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.15

File hashes

Hashes for nedb_engine-0.8.2-cp38-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 2817daea4c46b993e955d8a85b60e3de905b7b9ad8a4a4ff548fffc52e63597c
MD5 05be6e739c9d39d3453463c4b551a9b9
BLAKE2b-256 4eb41b1a42f6defdb162d0b2281a68be3844c1c2f0f883d0864a9c1d271b26a4

See more details on using hashes here.

File details

Details for the file nedb_engine-0.8.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nedb_engine-0.8.2-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 15fbc713cc264565e868751e12d580e79aa9d729109bf0358bb76874db4d23b0
MD5 9a1e260e1a5a91a71a0f3662e9351ec7
BLAKE2b-256 8a4e7f860d2b0401e935a53f66fdf083cd9b80bd443b5dae51ffb5c7cfc5d66e

See more details on using hashes here.

File details

Details for the file nedb_engine-0.8.2-cp38-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for nedb_engine-0.8.2-cp38-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6c062f17e2a34d5f6e99ce418a452a66df4747d9a8d8ea06376208af49c88a86
MD5 6853c0e6d730ccb823c82b410d879c21
BLAKE2b-256 a3d7b96567884a4d21286352ea2e6bfc7ae302698676b5efbadf9d839c9e93d6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page