minidb

A MongoDB-style document database, written from scratch.

You asked for a database of your own that behaves like MongoDB. This is one: a document store with a real query engine, its own index structures, its own aggregation pipeline and schema validation -- built on top of SQLite for durability, but with none of MongoDB's behaviour handed to you for free.

The API is deliberately shaped like PyMongo's, so application code written against minidb keeps working if you ever point it at a real MongoDB server.

from minidb import Database

db = Database("app.db")
db.create_collection("users", validator={"required": ["name", "email"]})

db.users.insert_one({"name": "Ada", "email": "ada@example.com", "role": "admin"})
db.users.find_one({"email": "ada@example.com"})
db.users.update_one({"name": "Ada"}, {"$inc": {"logins": 1}})
db.users.create_index("email", unique=True)

---

Is it really MongoDB-compatible?

Rather than guess, minidb is tested against a real MongoDB server. Every filter, update and pipeline in tests/test_parity.py is executed against both databases and the results compared:

$ pytest tests/ -q
163 passed

That suite is what pinned down the subtle parts of MongoDB's behaviour -- for example, that range operators type bracket (a null field never matches {"age": {"$lt": 36}}), that $min/$max ignore nulls, and that $addToSet ordering is deliberately unspecified.

If you have MongoDB running locally, the parity tests use it. Without it, they skip and the rest of the suite still runs.

---

Getting started

pip install minidb-db            # from PyPI
pip install -e ".[dev]"           # from a clone, with the test tools

Then:

python -m app.demo          # a guided tour of the example app
python -m bench.benchmark   # what indexes buy you
pytest tests/ -q            # the test suite
minidb-ui app.db            # browse the database in your browser

The import name is minidb, whatever the package is called:

from minidb import Database

db = Database("app.db")
db.users.insert_one({"name": "Ada", "email": "ada@example.com"})

The package has no runtime dependencies -- it is pure Python on top of the standard library's sqlite3.

---

What is in the box

Area	What works
Documents	JSON documents with ObjectId, datetime, Decimal, UUID and bytes support
Queries	$eq $ne $gt $gte $lt $lte $in $nin $exists $regex $options $all $size $elemMatch $not $type $mod, plus $and $or $nor
Paths	Dotted paths ("address.city"), implicit traversal into arrays
Updates	$set $unset $inc $mul $min $max $push $addToSet $pop $pull $pullAll $rename $setOnInsert $currentDate, arrayFilters, upserts, replacements
Aggregation	$match $project $addFields/$set $unset $group $sort $limit $skip $count $unwind $replaceRoot $sortByCount and ~30 expressions
Schemas	Per-collection validators: types, required, enum, pattern, numeric bounds, array rules, additionalProperties, and default values
Indexes	Single-field and compound indexes (unique or not), multikey (array) indexes, order-preserving keys that serve equality and range lookups
Writes	Transactions with rollback, unique-constraint enforcement, ObjectId generation
Extras	explain() to see the query plan, distinct(), count_documents(), cursors with sort/skip/limit

---

How it works

            your app
               |
        minidb/collection.py     the API (PyMongo shaped)
               |
    +----------+-----------+-------------------+
    |          |           |                   |
 query.py  update.py  aggregation.py    validation.py
    |          |           |                   |
    +----------+-----------+-------------------+
               |
          index.py         the planner + the key encoding
               |
         engine.py          SQLite: documents, index tables, transactions
               |
           SQLite file

Storage. Documents are stored as JSON text, one row per document, keyed by (collection, id). SQLite gives durability, atomicity and crash safety for free -- but every decision that defines how the database behaves lives in this project, not in SQLite.

Indexes. minidb keeps its own index tables (_minidb_index_entries), keyed by (collection, index, encoded_key, document_id) and stored WITHOUT ROWID, so SQLite's B-tree does the work. The encoding is order preserving: a value is mapped onto bytes whose byte order matches MongoDB's BSON comparison order, which is what lets one index serve both equality lookups and range scans. Each component of a key is self-delimiting, so a compound key's components are compared one after the other.

The planner. index.py looks at the filter, finds an indexed field it can use, and hands the engine a plan (eq, prefix or range). The engine then returns a candidate set -- always a superset of the matching documents -- and the full filter is applied in Python afterwards. That last step is what keeps the answers correct: an index may be an approximation, but it can never change the result.

Type ordering. MongoDB compares values of different types using a fixed order (null < numbers < strings < objects < arrays < ... < dates). minidb reproduces that in comparison.py, which is why {"a": {"$gt": 5}} behaves the same in both.

---

Using it

Reading

db.users.find_one({"email": "ada@example.com"})
db.users.find({"age": {"$gte": 30}}, sort=[("age", -1)], limit=10)
db.users.find({"address.city": "London"}).sort("name").skip(20)

db.users.find_one({"name": "Ada"}, {"name": 1, "email": 1, "_id": 0})

Writing

db.users.insert_one({"name": "Ada", "email": "ada@example.com"})
db.users.insert_many([...])

db.users.update_one({"_id": oid}, {"$set": {"role": "admin"}, "$inc": {"logins": 1}})
db.users.update_many({"role": "viewer"}, {"$set": {"active": False}})
db.users.replace_one({"_id": oid}, {"name": "Ada", "email": "a@b.com"})

db.users.update_one(
    {"_id": oid},
    {"$set": {"items.$[i].done": True}},
    array_filters=[{"i.qty": {"$gte": 2}}],
)

Aggregating

list(db.tasks.aggregate([
    {"$match": {"project_id": project_id}},
    {"$group": {"_id": "$assignee_id", "hours": {"$sum": "$estimate_hours"}}},
    {"$sort": {"hours": -1}},
]))

Schemas

db.create_collection("users", validator={
    "required": ["name", "email"],
    "additionalProperties": False,
    "properties": {
        "name": {"type": "string", "minLength": 1},
        "email": {"type": "string", "format": "email"},
        "role": {"type": "string", "enum": ["admin", "member"], "default": "member"},
    },
})

A document that breaks the schema is rejected on write, with a message pointing at the offending field. default values are filled in for you.

Indexes

db.users.create_index("email", unique=True)
db.tasks.create_index([("project_id", 1), ("status", 1)])   # compound
db.users.list_indexes()
db.users.explain({"email": "ada@example.com"})

{'strategy': 'IXSCAN', 'index': 'email_1', 'documentsExamined': 1, 'totalDocuments': 20000}

A compound index covers several fields. Its key is the concatenation of self-delimiting component keys, so the components are compared one after the other -- exactly like MongoDB, whose compound keys are compared component by component. That is what lets one index answer:

Query	What the index does
{"a": 1, "b": 2}	exact key lookup
{"a": 1}	prefix scan
{"a": 1, "b": {"$gt": 5}}	one contiguous range of keys
{"b": 2}	not usable -- a comes first, so the index cannot help

On 20,000 documents, a compound index turned a 55.9 ms scan into a 0.01 ms lookup.

---

The web UI

Every database deserves a way to look at it. minidb ships with one -- a browser app in the spirit of MongoDB Compass:

minidb-ui app.db            # or: python -m minidb.webui app.db

No data yet? Seed a sample database first:

python -c "from minidb import Database; from minidb.demo import seed; seed(Database('demo.db'))"
minidb-ui demo.db

It opens on http://127.0.0.1:5433 and gives you:

• Browse -- every collection with its document count, its indexes, and a filter/sort/limit toolbar
• Query -- a filter, a projection, a sort, a limit and a skip
• Aggregate -- a whole aggregation pipeline
• Insert -- a document, validated by the collection's schema
• Info -- statistics (documents, data size, indexes, fields), the schema inferred from the data (every field, its type, and how often it is present), the index list and the collection's validator
• Explain -- the query plan for the current filter: which index was used, how many documents were examined, how many matched
• Document detail -- click any document for highlighted JSON, and delete it from there

Deep links work too: ?collection=users&tab=info opens straight to that collection and tab.

The server is built on http.server from the standard library, so it has no dependencies of its own. It binds to localhost and has no authentication: it is a local development tool, not something to expose to a network.

Which port?

minidb itself has no port. It is an embedded database, like SQLite: it lives inside your Python process and is reached through function calls, not through a socket. There is no server to connect to, and no port to open.

The only network port is the GUI's, and it defaults to 5433:

minidb-ui app.db --port 9000     # change it
minidb-ui app.db --host 0.0.0.0  # bind elsewhere (see the warning above)

---

The example application

app/ is a small but complete backend built on the database:

File	What it holds
schemas/collections.py	The schemas for users, projects, tasks, notes
app/database.py	Opening the database, creating collections and indexes
app/repositories.py	The access layer the application actually uses
app/demo.py	A runnable tour (python -m app.demo)

Keeping every database call inside the repositories means the storage engine can be swapped later without touching the rest of the app -- swap minidb.Database for pymongo.MongoClient and the repositories keep working.

---

Differences from MongoDB (the honest list)

minidb is a from-scratch implementation, so some things are missing or behave differently. Everything here is a deliberate limitation, not a bug:

Not implemented

• Descending index directions -- compound indexes are ascending only, and create_index says so rather than quietly ignoring the direction.
• $lookup, $facet, $out, $merge, $graphLookup, $unionWith.
• The positional $ operator in updates ("items.$.qty"). arrayFilters ("items.$[i].qty") do work.
• Change streams, geospatial queries, full-text search, map/reduce.
• A network protocol: minidb is an embedded library, not a server.
• Replication, sharding, and transactions spanning collections.

Different on purpose

• find() returns documents in insertion order, and sorting ties keep insertion order. MongoDB does not promise an order for ties.
• $addToSet keeps insertion order; MongoDB's order is unspecified.
• additionalProperties: False exempts _id, which minidb adds itself.
• The _id index is always present; there is no way to drop it.
• A document with several very large array fields in one compound index needs too many key combinations to index completely. minidb keeps the document findable through an overflow table rather than hiding it.

---

Continuous integration

Every push runs the suite on Python 3.11, 3.12 and 3.13 -- against a real MongoDB service, so the parity tests actually run rather than skipping themselves. The browser code is checked under node, and the package is built, checked with twine and installed into a clean environment.

Releasing to PyPI is one tag away, once the repository is registered as a trusted publisher on PyPI:

git tag v0.1.0 && git push origin v0.1.0

---

Where to take it next

The interesting problems in a database are all still open here:

1. Descending index directions -- a compound key whose components can each be scanned backwards, and a planner that uses them for reverse sorts.
2. A real storage engine -- replace the SQLite file with a write-ahead log, free-space reuse and page-level compaction.
3. Concurrency -- MVCC with snapshot isolation instead of a single write lock.
4. A server -- a wire protocol and a client library, so minidb can be used from another language.
5. A better planner -- cost estimates, index intersection, and choosing between several usable indexes.
6. Full-text search -- an inverted index over tokenised documents.

---

Layout

minidb/          the database
  collection.py    the API
  query.py         filter semantics
  update.py        update operators
  aggregation.py   the pipeline
  validation.py    schemas
  schema.py        inferring a schema from the data
  index.py         key encoding + planner
  engine.py        SQLite storage layer
  comparison.py    BSON value ordering
  bson.py          document codec
  objectid.py      ObjectId
  database.py      the Database object
  demo.py          sample data
  webui/           the web UI (stdlib only)
    server.py        the HTTP API
    static/          the browser app
schemas/         the example app's schemas
app/             the example app
bench/           benchmarks
docs/            the screenshot
tests/
  test_minidb.py    unit tests
  test_parity.py    minidb vs. real MongoDB
  test_webui.py     the web UI's API
  frontend_smoke.mjs  the browser code, in a DOM shim