What is Caterva2?​

Caterva2 is a Free/Open Source distributed system written in Python meant for sharing Blosc2 datasets (either native or converted on-the-fly from HDF5) among different hosts. It uses a publish–subscribe messaging pattern where the data of a publisher can be replicated by an unlimited amount of subscribers. Also, every subscriber exposes a REST interface that allows clients to access the datasets.

Let's suppose that we have a large dataset that we want to share with a group of people. Instead of sending the data to each person individually, we can use Caterva2 to publish the data once and allow multiple subscribers to access it. This way, we can save time and resources by avoiding the need to send the data to each person separately.

Lazy expressions in Caterva2​

Besides the data sharing utility, Caterva2 can also perform operations on the data via Python-Blosc2 v3. These operations range from arithmetic expressions to reductions, filters and broadcasting, and are performed lazily, that is, only when a part of the result is needed.

What expressions are?​

The forthcoming version of Blosc2 will bring a powerful tool for performing mathematical operations on pre-compressed arrays, that is, on arrays whose data has been reduced in size using compression techniques. This functionality provides a flexible and efficient way to perform a wide range of operations, such as addition, subtraction, multiplication and other mathematical functions, directly on compressed arrays. This approach saves time and resources, especially when working with large data sets.

An example of expression computation in Blosc2 might be:

dtype = np.float64
shape = [30_000, 4_000]
size = shape[0] * shape[1]
a = np.linspace(0, 10, num=size, dtype=dtype).reshape(shape)
b = np.linspace(0, 10, num=size, dtype=dtype).reshape(shape)
c = np.linspace(0, 10, num=size, dtype=dtype).reshape(shape)

#  Convert numpy arrays to Blosc2 arrays
a1 = blosc2.asarray(a, cparams=cparams)
b1 = blosc2.asarray(b, cparams=cparams)
c1 = blosc2.asarray(c, cparams=cparams)

# Perform the mathematical operation
expr = a1 + b1 * c1  # LazyExpr expression
expr += 2  # expressions can be modified
output = expr.compute(cparams=cparams)  # compute! (output is compressed too)

Compressed arrays ( a1, b1, c1) are created from existing numpy arrays ( a, b, c) using Blosc2, then mathematical operations are performed on these compressed arrays using general algebraic expressions. The computation of these expressions is lazy, in that they are not evaluated immediately, but are meant to be evaluated later. Finally, the resulting expression is actually computed (via .compute()) and the desired output (compressed as well) is obtained.

How it works​

Dear valued community,

Two years ago, ironArray embarked on an ambitious journey with the launch of our groundbreaking ironArray product, designed to revolutionize computations with compressed data. While our aspirations were high, we faced challenges in gaining traction and failed to meet our sales targets.

However, every setback is an opportunity for growth and transformation. Today, we are thrilled to announce a strategic shift in our business focus towards consulting services, leveraging the power of compression for big data, specifically through the acclaimed Blosc compressor.