recordstore

A versioned key→record store over a content-addressed bytes store — a thin database kernel between an immutable blob store (such as Ethereum Swarm) and an application that wants to think in records and versions rather than blobs and references.

from recordstore import RecordStore, MemoryBytesStore

blobs = MemoryBytesStore()
store = RecordStore(blobs)
store.put("users/alice", {"name": "Alice", "role": "admin"})
store.put("users/bob", {"name": "Bob"})
root = store.commit()          # one reference identifies this entire version

store.get("users/alice")       # {'name': 'Alice', 'role': 'admin'}
list(store.keys("users/"))     # ['users/alice', 'users/bob']

snapshot = RecordStore.at(root, blobs)   # frozen view of that version

Concurrent writers converge without a lock server — if the shared pointer moved under a commit, it three-way merges and retries:

from recordstore import MemoryPointer

pointer = MemoryPointer(root)                  # a shared "latest version" name
a = RecordStore(blobs, pointer=pointer)        # two writers open the same version
b = RecordStore(blobs, pointer=pointer)
a.put("users/carol", {"name": "Carol"}); a.commit(reconcile=True)
b.put("users/dave",  {"name": "Dave"});  b.commit(reconcile=True)   # folds in a's change
# the pointer now names a version containing both carol and dave

Why

Content-addressed stores give you immutable put(bytes) → ref / get(ref) → bytes and nothing else: no keys, no typed records, no transactions, no snapshots. recordstore adds exactly that missing layer and nothing more:

• Records instead of raw bytes — values are any JSON-compatible object, stored under string keys.
• Atomic, versioned commits — mutations are staged in memory; commit() lands all of them as one new root reference. A reader either sees all of a commit or none of it.
• Snapshot isolation — RecordStore.at(root, blobs) pins one root and sees a frozen, self-consistent dataset for arbitrarily long reads, with no locking: the whole dataset-at-a-version is one reference.
• Canonical roots — encodings are deterministic, so equal content produces an equal root reference, regardless of the insertion/deletion history that produced it. Versions are content-addressable, comparable with a string equality check, and cheap to diff.
• Three-way merge — RecordStore.merge(base, ours, theirs) reconciles two divergent versions; canonicity makes unchanged subtrees merge for free and equal edits conflict-free, with conflicts raised or settled by a resolver(key, base, ours, theirs) you supply. The diff is O(divergence), not O(dataset).
• Verifiable proofs — store.prove(key) produces a small, JSON-ready inclusion or absence proof against the committed root (absence is provable because the canonical encoding gives a key exactly one possible location); verify_proof(proof, root) checks it with no store access at all — hold the 32-byte root, verify any claim about the dataset.
• Version comparison — store.diff(other_root) answers "what changed between these two published versions?" directly: (key, mine, theirs) per differing key, the same O(divergence) structural walk, so equal roots read nothing at all.
• Local-first — local_first_store(path, api_url) stops the choice between disk and Swarm: commits land on local disk instantly (offline is the normal mode), a background worker pushes them to Swarm and confirms arrival peer-to-peer, sync() is the certainty barrier, and local disk is a budgeted working set — unpushed data is pinned, only Swarm-confirmed blobs evict, evicted reads heal by verified re-fetch. Shape the working set with pin(name, prefix) / fetch(prefix); collapse local history with squash_history(); a key whose bytes are temporarily unreachable raises RecordUnavailable, never a false KeyError.
• Multi-writer, no lock server — commit(reconcile=True) makes concurrent writers converge: if the pointer moved under you it three-way merges and retries instead of overwriting. Race-free in-process; best-effort across processes over a Swarm feed. A commutative resolver keeps 3+ writers order-independent.
• Structural sharing — versions are stored as a persistent (copy-on-write) compacted radix trie; a commit writes only the blobs along the changed paths, and unchanged subtrees are shared between versions.
• Concurrent I/O — items() (bulk read), get_many/put_many, and a pooled keep-alive HTTP session let BeeBytesStore parallelise round trips instead of paying one per record — the difference between usable and not on a high-latency link.

Install

pip install recordstore

# with the Bee (Swarm) bytes backend's HTTP dependency:
pip install "recordstore[bee]"

# with the Swarm feed pointer (adds swarm-bee for SOC/secp256k1 signing):
pip install "recordstore[feeds]"

# with postage_batch_id="auto" and batch-health reporting (adds swarmfs):
pip install "recordstore[stamps]"

Python ≥ 3.11. The core imports only the standard library; both extra dependencies are imported lazily — requests only by BeeBytesStore ([bee]), swarm-bee only by SwarmFeedPointer ([feeds]).

The pieces

| swarm_store(topic, ...) | assembles the two Swarm pieces into a store | the one place Swarm is chosen: BeeBytesStore blobs and a SwarmFeedPointer head | | local_first_store(path, api_url) | disk now, Swarm in the background | swarmfs LocalStore + journal (the reflog) + background push/confirm; sync(), sync_status(), pin/fetch, publish(pointer), squash_history() ([local] extra, swarmfs ≥ 0.7) |

Nothing above RecordStore ever sees a stored blob or a trie node — and nothing below it needs to know Swarm exists unless you asked for it:

from recordstore import swarm_store

store = swarm_store("my-notes", signer=key)   # publish: blobs + feed on Swarm
store = swarm_store("my-notes", owner=addr)   # follow someone else's feed

Documentation

• User guide — the tutorial: concepts, the canonicity contract, running against a real Bee node, versioning patterns, error handling, and current limitations.
• Reference — compact and definition-first: every export, signature, error, and extra in tables, pinned against the code by tests/test_reference.py. The right document to hand to an AI agent.

Testing

python3 -m pytest tests/                                 # unit + fuzz + boundary tests

BEE_API=http://<node>:1633 BEE_BATCH=<batchID> \
    python3 -m pytest tests/test_recordstore_bee.py -v   # bytes backend, live node

pip install "recordstore[feeds]"                         # needs swarm-bee
BEE_API=http://<node>:1633 BEE_BATCH=<batchID> \
    python3 -m pytest tests/test_recordstore_feed.py -v  # feed pointer, live node

The fuzz suite runs randomized put/delete histories against a plain-dict oracle and asserts the canonical-root property throughout. The Bee integration tests skip automatically unless BEE_API is set (the feed test also needs swarm-bee installed); against a real (non-dev) node always provide BEE_BATCH with a purchased postage batch id.

Background

This is a Python re-implementation of an old idea — content-addressed, canonical-root, versioned key-value storage — best known from Ethereum's Merkle Patricia Trie and from Noms/Dolt's "prolly trees." The value here is fit, not novelty: a much simpler canonical encoding than MPT (avoiding the exact bug class that once caused a chain split), a far smaller scope than Dolt/Irmin (no query language; a single three-way merge primitive, not a merge engine), and — as far as we could find — the first implementation of this pattern for Python with a Swarm/Bee backend. See the user guide's background section for the full comparison.

Status

Extracted from petfold/ontodag (July 2026) with history preserved; validated against a live Bee 2.8.1 light node on Gnosis mainnet (roundtrips, canonical roots on real BMT references, network retrievability). SwarmFeedPointer (owner-signed Swarm feed, over swarm-bee) landed in v0.4.0; three-way merge in v0.8.0; auto-reconciling commit(reconcile=True) in v0.9.0; a best-effort feed compare_and_set (cross-process reconcile) in v0.10.0. Known gaps — a Swarm feed has no atomic index claim, so exactly-simultaneous same-index writes can still race (in-process reconcile is race-free); one blob per record — are detailed in the user guide.