arrakis-python

Arrakis Python client library

Python client library for the Arrakis low-latency timeseries data distribution platform. Query, stream, and publish timeseries data.

Resources

Installation

With pip:

pip install arrakis

With conda:

conda install -c conda-forge arrakis-python

Where to Start

Tutorial — New to arrakis? Fetch your first timeseries data step by step.
User Guide — Fetching, streaming, channel discovery, publishing, the CLI, and more.
Background — How the system works: Arrow Flight, Kafka, multiplexing, and the data model.
API Reference — Auto-generated documentation from source code.

Features

Stream live and historical timeseries data
Describe channel metadata
Search for channels matching a set of conditions
Publish timeseries data
Command-line interface for all operations

Quickstart

Fetch timeseries

import arrakis

start = 1187000000
end = 1187001000
channels = [
    "H1:CAL-DELTAL_EXTERNAL_DQ",
    "H1:LSC-POP_A_LF_OUT_DQ",
]

block = arrakis.fetch(channels, start, end)
for channel, series in block.items():
    print(channel, series)

where block is a [arrakis.block.SeriesBlock][] and series is a [arrakis.block.Series][].

Stream timeseries

1. Live data

import arrakis

channels = [
    "H1:CAL-DELTAL_EXTERNAL_DQ",
    "H1:LSC-POP_A_LF_OUT_DQ",
]

for block in arrakis.stream(channels):
	print(block)

2. Historical data

import arrakis

start = 1187000000
end = 1187001000
channels = [
    "H1:CAL-DELTAL_EXTERNAL_DQ",
    "H1:LSC-POP_A_LF_OUT_DQ",
]

for block in arrakis.stream(channels, start, end):
    print(block)

Describe metadata

import arrakis

channels = [
    "H1:CAL-DELTAL_EXTERNAL_DQ",
    "H1:LSC-POP_A_LF_OUT_DQ",
]

metadata = arrakis.describe(channels)

where metadata is a dictionary mapping channel names to [arrakis.channel.Channel][].

Find channels

import arrakis

for channel in arrakis.find("H1:LSC-*"):
    print(channel)

where channel is a [arrakis.channel.Channel][].

Count channels

import arrakis

count = arrakis.count("H1:LSC-*")

Publish timeseries

from arrakis import Channel, Publisher, SeriesBlock, Time
import numpy

# admin-assigned ID
publisher_id = "my_producer"

# define channel metadata
metadata = {
    "H1:FKE-TEST_CHANNEL1": Channel(
        "H1:FKE-TEST_CHANNEL1",
        data_type=numpy.float64,
        sample_rate=64,
    ),
    "H1:FKE-TEST_CHANNEL2": Channel(
        "H1:FKE-TEST_CHANNEL2",
        data_type=numpy.int32,
        sample_rate=32,
    ),
}

publisher = Publisher(publisher_id)
publisher.register()

with publisher:
    # create block to publish
    series = {
        "H1:FKE-TEST_CHANNEL1": numpy.array([0.1, 0.2, 0.3, 0.4], dtype=numpy.float64),
        "H1:FKE-TEST_CHANNEL2": numpy.array([1, 2], dtype=numpy.int32),
    }
    block = SeriesBlock(
        1234567890 * Time.SECONDS,  # time in nanoseconds for first sample
        series,                     # the data to publish
        metadata,                   # the channel metadata
    )

    # publish timeseries
    publisher.publish(block)

Metadata

Release files for arrakis 0.21.0

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

Source distribution (sdist)

Source distribution for arrakis 0.21.0
File	Size	Uploaded
arrakis-0.21.0.tar.gz	178.7 kB	Sep 21, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for arrakis 0.21.0
File	Interpreter	ABI	Platform	Reset
arrakis-0.21.0-py3-none-any.whl 166.6 kB Sep 21, 2026	Python 3	none	any	Details

Total release size: 345.3 kB

Release files / arrakis-0.21.0.tar.gz

Download URL	arrakis-0.21.0.tar.gz
Size	178.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`1c278e36db82d3b09f37f7a91bae0aeae0df6c2d2840ac8bbb5097f03d39fa4f`
BLAKE2b-256 checksum How to use checksums	`5420aee19f65906fd21065836a8dbf2d4ba37304f676c3a30ad0afd8dfcc8b62`
Upload date	Sep 21, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Hatch/1.16.5 cpython/3.13.12 HTTPX/0.28.1`

Release files / arrakis-0.21.0-py3-none-any.whl

Download URL	arrakis-0.21.0-py3-none-any.whl
Size	166.6 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`cbba173fcae092c2eab0cf23cca75bdc0ab5987adbb0692f99aad5294a230c13`
BLAKE2b-256 checksum How to use checksums	`3f22c1e424a4b350c7764752ccd403fb58df67693655924b307cfeb991312889`
Upload date	Sep 21, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`Hatch/1.16.5 cpython/3.13.12 HTTPX/0.28.1`