DreamDB Python SDK

Python bindings for the DreamDB multimodal versioned data lake — image + audio + text + embeddings + scalar metadata on content-addressed object storage, with vector and metadata filters for ML training pipelines.

Quick start

The SDK exposes read-only SqlSession.open(ref_name, backend) with query and explain. See SQL adapters for typed parameters, snapshot semantics and limits.

from pathlib import Path
from tempfile import TemporaryDirectory
import dreamdb as db

with TemporaryDirectory(prefix="dreamdb-quickstart-") as directory:
    backend = Path(directory).as_uri()
    schema = db.Schema().add_scalar_string("label")
    ds = db.Dataset.create("example", schema, backend=backend)
    ds.append_many([{"_anchor": 1, "label": "cat"}])
    reopened = db.Dataset.open("example", backend=backend)
    assert reopened.count() == 1

For trained vector indexes, use the versioned index guide. The feature examples cover Python 0.0.11, including entity keys, progressive geometry and structured arrays. Older packages do not necessarily expose these methods.

Build from source

Segmented videos with independent initialization (Python 0.0.15)

Schema.add_video_item("video_raw", codec="h265") declares optional VideoItems. Unlike a flat CMAF Track, each item owns its decoder initialization and relative fragment index. The application prepares the media (for example by stream-copy remuxing at source keyframes); DreamDB does not transcode or create previews.

schema = db.Schema().add_video_item("video_raw", codec="h265")
ds = db.Dataset.create("new-originals", schema, backend=backend)
# fragments: [(fragment_bytes, relative_start_ns, relative_end_ns), ...]
published = ds.publish_prepared_video_item(
    "video_raw", b"opaque-clip-key", absolute_start_ns, duration_ns,
    init_bytes, fragments,
)
reopened = db.Dataset.open("new-originals", backend=backend)
window = reopened.read_video_item_range("video_raw", b"opaque-clip-key", 0, duration_ns)

This API materializes one item's inputs in memory, copies them into native ownership and publishes through the existing core validation/CAS boundary. It is not streaming, zero-copy, or a browser compatibility guarantee. Reusing an item key replaces that item; concurrent publication can raise a CAS conflict and is not implicitly retried. Returned addresses identify item, Track and Manifest. These write/schema methods are included in Python 0.0.15; 0.0.14 does not expose them.

Building the bindings

pip install maturin
cd dreamdb-dataset-python
maturin develop --release

This produces the dreamdb package installed into the current virtualenv. Importing it gives the Schema and Dataset classes shown above.

Publisher identity

New Manifest publications record the Python distribution version, shared core build identity and operation in the existing writer tag. Optional dataset.set_application(name, revision) identifies your application on later publications; reopening a Dataset resets that optional pair. This does not label retained payloads or prove execution. See publisher provenance for limits, build configuration and historical/operator behavior.

Explicit ingest checkpoints

For long-running base scalar + VideoItem ingest, a caller may explicitly start a new provenance epoch while reusing the current data objects:

used, limit = dataset.ingest_lineage_usage()
plan = dataset.plan_ingest_checkpoint(backend, "ingest-epoch-001")
# Durably persist these exact bytes BEFORE apply (file flush + fsync in your
# application's checkpoint journal); never rebuild a plan after an uncertain PUT.
save_durably(plan)
tip = dataset.apply_ingest_checkpoint(plan, backend)

backend here is the same non-secret endpoint/bucket identity on both calls. Exclude concurrent GC. The operation pins the old tip with a create-only tag, publishes a frozen new root and conditionally advances the working Ref. After an uncertain result, reopen that Ref and apply the same saved plan. A third tip conflicts; there is no automatic merge. A successful application preserves current data, not its provenance chain: old history remains under the tag, and pre-checkpoint branches cannot automatically merge into the new epoch.

Embeddings and layers are rejected by the base-only planner. For cold IVF-cosine embeddings, including layers over VideoItem, use plan_indexed_ingest_checkpoint(backend, archive_tag) and persist/apply the plan the same way. It retains exact original source bindings as structural ancestry claims, preserves SI/compressor/sidecars and current roles, and emits lineage-v5. All readers/writers must support that format before opting in; older packages reject it. Merge/backfill/entity combinations and unknown dependency-bearing metadata remain refused. This does not automatically publish new SDK packages.

Indexed admission reads bounded Track roots (4 MiB each, 32 MiB aggregate), not all bucket payloads; oversized imported roots need explicit conversion. It does not certify imported payload completeness. Existing full-directory compaction is separate, not implicit checkpoint work. See spec 0030.

The usage result is a measurement, not a guarantee that the next append fits. No object deletion, GC, tag expiry, or larger protocol limit is implied. See spec 0029.