Skip to main content

Python bindings for Datum stream blueprints

Project description

datum-stream

Python bindings for Datum stream blueprints.

Datum mirrors the Source -> Flow -> Sink and GraphDSL vocabulary from the Rust crate while keeping Python execution explicit: building a pipeline creates an immutable blueprint, and work starts only when a runnable graph is materialized with a Runtime.

Install

python -m pip install datum-stream

The package requires Python 3.13 or newer and depends on PyArrow. User Python code always runs through Arrow UDF batches: integer-stream map, filter, and flat_map wrap callables into single-column Arrow batches, while map_batches is the vectorized RecordBatch -> RecordBatch tier.

For scalar integer hot paths, use the named kernels such as map_add, map_multiply, and filter_greater_than. For Arrow batch streams, use typed col() expressions for lowerable work and map_batches for arbitrary Python. Terminal arithmetic is named too: Sink.fold() / Sink.fold_sum() sum values, and Sink.fold_product() multiplies them. Graph partitions select the typed PartitionStrategy.MODULO constant.

The package ships PEP 561 stubs (py.typed) with generic Source, Flow, Sink, RunnableGraph, Inlet, and Outlet types. Public construction is always strict: graph wiring and declared Arrow schemas are validated during builder calls, and empty Arrow inputs require an explicit schema=....

Example

import datum

with datum.Runtime() as runtime:
    graph = (
        datum.Source.range(1, 5)
        .map_add(1)
        .to_mat(datum.Sink.fold_product())
    )
    completion = graph.run(runtime)
    assert completion.wait() == 120

Scope

The current Python surface covers integer linear streams, a focused GraphDSL surface (Broadcast, Balance, Merge, Partition, Zip, Concat, and Interleave), Arrow batch UDFs, and Datum Connect for trusted client/server execution. Connect can run linear plans, supported junction graphs, tuple FlowShape results, direct ZipShape runs, and Merge/Concat/Interleave FanInShape runs. Local Connect defaults to arrow-ipc; remote Arrow payloads should prefer arrow-ipc-zstd when compression is wanted. Those wire-format defaults were chosen by measurement.

For the broader guide, see the Datum Python docs in docs/guides/python.

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

datum_stream-0.10.9.tar.gz (745.1 kB view details)

Uploaded Source

Built Distributions

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

datum_stream-0.10.9-cp313-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.4 MB view details)

Uploaded CPython 3.13+manylinux: glibc 2.17+ x86-64

datum_stream-0.10.9-cp313-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (7.5 MB view details)

Uploaded CPython 3.13+manylinux: glibc 2.17+ ARM64

File details

Details for the file datum_stream-0.10.9.tar.gz.

File metadata

  • Download URL: datum_stream-0.10.9.tar.gz
  • Upload date:
  • Size: 745.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for datum_stream-0.10.9.tar.gz
Algorithm Hash digest
SHA256 879f38c75f11d1ebfa2eb0bc5d440d0c3d8fe608710fabd762aeaf9096b1c84f
MD5 58d2d9d347fe3445884100072b923d19
BLAKE2b-256 377e29ef3f2a9c9064a5a32faf656493ec993c0b9951ed68b198d81ec952f6b9

See more details on using hashes here.

File details

Details for the file datum_stream-0.10.9-cp313-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for datum_stream-0.10.9-cp313-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0572c130b4c86e9c100eafd3c9c1fc29a3aeac94790a5495d29ff9333e19b2c9
MD5 757b72770baec5af70cd94515313d962
BLAKE2b-256 f9f9ea88bc1896272a8f8d55ab4687bd2443a0c763e1f8c0b99116a456af689e

See more details on using hashes here.

File details

Details for the file datum_stream-0.10.9-cp313-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for datum_stream-0.10.9-cp313-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d0135424caa93071284ac04b5526a8f0eb0b2ac56d4cbeb37f44fde2e2f90702
MD5 6596130f8ea7fd36bd52c2e4b3f774d8
BLAKE2b-256 50e09d8349380c0bb43c93e4196ce1c935a643420c59eea094db6ec499aa07c3

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