deker 1.1.1

Multidimensional arrays storage engine

Deker

Deker is pure Python implementation of petabyte-scale highly parallel data storage engine for multidimensional arrays.

Deker name comes from term dekeract, the 10-cube.

Deker was made with the following major goals in mind:

• provide intuitive interface for storing and accessing huge data arrays
• support arbitrary number of data dimensions
• be thread and process safe and as lean on RAM use as possible

Deker empowers users to store and access a wide range of data types, virtually anything that can be represented as arrays, like geospacial data, satellite images, machine learning models, sensors data, graphs, key-value pairs, tabular data, and more.

Deker does not limit your data complexity and size: it supports virtually unlimited number of data dimensions and provides under the hood mechanisms to partition huge amounts of data for scalability.

Features

• Open source under GPL 3.0
• Scalabale storage of huge virtual arrays via tiling
• Parallel processing of virtual array tiles
• Own locking mechanism enabling virtual arrays parallel read and write
• Array level metadata attributes
• Fancy data slicing using timestamps and named labels
• Support for industry standard NumPy, Xarray
• Storage level data compression and chunking (via HDF5)

Code and Documentation

Open source implementation of Deker storage engine is published at

• https://github.com/openweathermap/deker

API documentation and tutorials for the current release could be found at

• https://docs.deker.io

Quick Start

Dependencies

Minimal Python version for Deker is 3.9.

Deker depends on the following third-party packages:

• numpy >= 1.18
• attrs >= 23.1.0
• tqdm >= 4.64.1
• psutil >= 5.9.5
• h5py >= 3.8.0
• hdf5plugin >= 4.0.1

Also please not that for flexibility few internal Deker components are published as separate packages:

• deker-local-adapters
• deker-server-adapters
• deker-tools

Install

To install Deker run:

pip install deker

Please refer to documentation for advanced topics such as running on Apple silicone or using Xarray with Deker API.

First Steps

Now you can write simple script to jump into Deker development:

from deker import Client, ArraySchema, DimensionSchema, TimeDimensionSchema
from datetime import datetime, timedelta, timezone
import numpy as np

# Where all data will be kept
DEKER_URI = "file:///tmp/deker"

# Define 3-dimensional schema with to numeric and one time dimension
dimensions = [
   DimensionSchema(name="y", size=128),
   DimensionSchema(name="x", size=128),
   TimeDimensionSchema(
      name="forecast_dt",
      size=128,
      start_value=datetime.now(timezone.utc),
      step=timedelta(3),
   )
]

# Define array schema with float dtype and dimensions
array_schema = ArraySchema(dtype=float, dimensions=dimensions)

# Instantiate client using context manager
with Client(DEKER_URI) as client:
   # Create collection
   collection = client.create_collection("my_collection", array_schema)
   
   # Create array
   array = collection.create()
   
   # Write some data
   array[:].update(np.ones(shape=array.shape))
   
   # And read the data back
   data = array[:].read()