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)
2 maintainers
Metadata
2 maintainers
Release files for arrakis 0.20.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| arrakis-0.20.0.tar.gz | 176.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| arrakis-0.20.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 341.6 kB
Release files / arrakis-0.20.0.tar.gz
| Download URL | arrakis-0.20.0.tar.gz |
|---|---|
| Size | 176.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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BLAKE2b-256 checksum How to use checksums |
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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-0.20.0-py3-none-any.whl
| Download URL | arrakis-0.20.0-py3-none-any.whl |
|---|---|
| Size | 164.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
Hatch/1.16.5 cpython/3.13.12 HTTPX/0.28.1
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