Skip to main content

Histogramming as a Service (HistServ)

PyPI version Conda-Forge PyPI platforms

Install from PyPI

pip install histserv

Install from conda-forge

conda install -c conda-forge histserv

Quickstart

Start the async gRPC server (or just ./example/start_server.sh):

histserv --port 50051
# 2026-03-26 11:58:00.643 INFO:histserv:server (listening at [::]:50051) started with port=50051, prune_after=24.00 h, prune_interval=5.00 min, stats_interval=5.00 s

Then run:

from hist import Hist
from histserv import Client
import numpy as np


# initialize hist locally
H_local = Hist.new.Reg(30, -3, 3, name="x", label="x-axis").Double()

with Client(address="[::]:50051") as client:
    # initialize it on the server and receive a remote hist to interact with it
    H_remote = client.init(H_local)
    # fill the remote hist; the client pre-bins locally and sends dense storage
    H_remote.fill(x=np.random.normal(size=1000))
    # retrieve it back as a ChunkedHist, drop it from the server, and materialize it locally
    H_snapshot = H_remote.snapshot(delete_from_server=True)
    print(H_snapshot.to_hist())


# local hist hasn't been filled
assert np.all(H_local.view(True) == 0)

Output in ipython:

┌────────────────────────────────────────────────────────────────────────────┐
[-inf,   -3) 1  │▋                                                           │
[  -3, -2.8) 0  │                                                            │
[-2.8, -2.6) 1  │▋                                                           │
[-2.6, -2.4) 1  │▋                                                           │
[-2.4, -2.2) 6  │████                                                        │
[-2.2,   -2) 11 │███████▍                                                    │
[  -2, -1.8) 12 │████████                                                    │
[-1.8, -1.6) 20 │█████████████▍                                              │
[-1.6, -1.4) 19 │████████████▊                                               │
[-1.4, -1.2) 33 │██████████████████████▏                                     │
[-1.2,   -1) 50 │█████████████████████████████████▌                          │
[  -1, -0.8) 70 │██████████████████████████████████████████████▉             │
[-0.8, -0.6) 49 │████████████████████████████████▉                           │
[-0.6, -0.4) 88 │███████████████████████████████████████████████████████████ │
[-0.4, -0.2) 63 │██████████████████████████████████████████▎                 │
[-0.2,    0) 65 │███████████████████████████████████████████▋                │
[   0,  0.2) 85 │█████████████████████████████████████████████████████████   │
[ 0.2,  0.4) 77 │███████████████████████████████████████████████████▋        │
[ 0.4,  0.6) 65 │███████████████████████████████████████████▋                │
[ 0.6,  0.8) 61 │████████████████████████████████████████▉                   │
[ 0.8,    1) 63 │██████████████████████████████████████████▎                 │
[   1,  1.2) 45 │██████████████████████████████▏                             │
[ 1.2,  1.4) 36 │████████████████████████▏                                   │
[ 1.4,  1.6) 32 │█████████████████████▌                                      │
[ 1.6,  1.8) 15 │██████████                                                  │
[ 1.8,    2) 11 │███████▍                                                    │
[   2,  2.2) 10 │██████▊                                                     │
[ 2.2,  2.4) 3  │██                                                          │
[ 2.4,  2.6) 5  │███▍                                                        │
[ 2.6,  2.8) 2  │█▍                                                          │
[ 2.8,    3) 1  │▋                                                           │
[   3,  inf) 0  │                                                            │
└────────────────────────────────────────────────────────────────────────────┘

Dashboard

See more information about setting up the histserv dashboard in the corresponding dashboard README.

Examples

See example/ for more examples.

Run example client:

python example/client.py
# Remote hist initialized: RemoteHist(hist_id='52c77c93da8146f2a72c53af269d1ab5', address='[::]:50051', token=None)
# Remote hist fill succeeded.
# Snapshotting current hist: ChunkedHist(...)
# Remote hist fill succeeded.
# Remote hist fill succeeded.
# Remote hist flushed successfully to hist.h5.

Or check out how to use remote histogram filling with an example coffea Processor in example/coffea_processor.py.

Useful client methods on RemoteHist:

  • fill(...)
  • fill_many([...])
  • snapshot(delete_from_server=False)
  • reset()
  • exists()
  • get_connection_info()
  • RemoteHist.from_connection_info(...)
  • flush(destination="hist.h5")
  • delete()

Current supported types

Axis support:

  • hist.axis.Regular without transforms
  • hist.axis.Boolean
  • hist.axis.Variable
  • hist.axis.Integer
  • hist.axis.IntCategory
  • hist.axis.StrCategory

Storage support:

  • hist.storage.Double
  • hist.storage.Int64
  • boost_histogram.storage.AtomicInt64
  • hist.storage.Weight
  • boost_histogram.storage.Unlimited

Unsupported today:

  • transformed hist.axis.Regular
  • boost_histogram.storage.Mean
  • boost_histogram.storage.WeightedMean
  • overflow bins on IntCategory/StrCategory chunk axes are not preserved across from_hist()/to_hist() (categorical overflow is silently dropped)

Notes:

  • Growable categorical axes (IntCategory, StrCategory) are treated as chunk keys rather than dense axes.
  • Histograms with one or more growable categorical axes are supported; the categorical values must be provided as scalars when filling.
  • On the wire, fills are sent as dense per-chunk payloads rather than as generic Python objects.
  • RemoteHist.snapshot() returns a ChunkedHist; call .to_hist() to materialize a local hist.Hist.
  • fill_many(...) is useful for bundling several fills into one gRPC request.
  • Dense ndarray transport is generic over NumPy dtypes, but object arrays are not supported on the wire.

Developer Info

Install

uv sync --dev

Test

python -m pytest -q
uvx ty check src

Protobuf Codegen

python -m grpc_tools.protoc -Isrc/histserv/protos --python_out=src/histserv/protos --pyi_out=src/histserv/protos --grpc_python_out=src/histserv/protos src/histserv/protos/hist.proto

After regeneration, ensure src/histserv/protos/hist_pb2_grpc.py keeps the package-relative import:

from . import hist_pb2 as hist__pb2

Metadata

Release files for histserv 0.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for histserv 0.2.1
File Size Uploaded Provenance Actions
histserv-0.2.1.tar.gz 222.1 kB With provenance Details for histserv-0.2.1.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for histserv 0.2.1
File Interpreter ABI Platform Actions
histserv-0.2.1-py3-none-any.whl Python 3 none any Details for histserv-0.2.1-py3-none-any.whl

Total release size: 323.4 kB

Release files / histserv-0.2.1.tar.gz

Download URL histserv-0.2.1.tar.gz
Size 222.1 kB
Tags Source
SHA-256 checksum
How to use checksums
a00c59a36ab68cf2f32d299b56d0df0ca8a8d1cbeb6cd859464d98f8ecb48651
BLAKE2b-256 checksum
How to use checksums
195a986f21084226be9be3e4b83ca1c1f7e802b7e8e52c55ba3681b27e87a54b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 31, 2026.

Transparency log

Release files / histserv-0.2.1-py3-none-any.whl

Download URL histserv-0.2.1-py3-none-any.whl
Size 101.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
f971c8c3f84db8809a91231fc5ff4dddd605613102cdf94fb5cf97fb8b4205d5
BLAKE2b-256 checksum
How to use checksums
001df681722f2a358b74ab29ddaca04eca925db3af049696bb49b1600146f894
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 31, 2026.

Transparency log

Security for histserv 0.2.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for histserv-0.2.1.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jul 31, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page