Alexey Timin

Before we dive into the importance of data reduction for edge computing, it is important to define both terms. Data reduction refers to the process of reducing the amount of data that needs to be transmitted or stored, while still maintaining the necessary information and level of accuracy. This can be achieved through techniques such as compression, aggregation, and filtering.

Initially, I chose to use C++ for the early editions of ReductStore because of my experience with the language. This allowed me to quickly create a functional time series database for binary data. However, as our platform expanded to include Windows and MacOS, I found myself struggling to manage the C++ infrastructure as the codebase grew. This made it difficult for me to focus on enhancing the product's functionality and unique features, as I had to ensure compatibility across multiple platforms while managing numerous dependencies.

The ReductStore Project hosts the free "cats" dataset, which contains about 10K photos of cats in JPEG format with eye coordinates, a month, and ears as labels. In this example, we can learn how to download the dataset from the ReductStore instance and draw the features using the OpenCV library.

Installing Dependencies​

First, we need to install the ReductStore Client SDK for Python to download the photos and labels. We also need the OpenCV Python library for drawing features and Pillow to display images in the Jupyter environment:

pip install reduct-py opencv-python Pillow

Getting Data​

To retrieve data we need the URL of the ReductStore instance, the bucket name, where we store our datasets and an API token with read access, so that we can connect to the database by using the Client class:

from reduct import Client, Bucket

HOST = "https://play.reduct.store"
API_TOKEN = "reductstore"
BUCKET = "datasets"

client = Client(HOST, api_token=API_TOKEN)

bucket: Bucket = await client.get_bucket(BUCKET)