Similarly, other operations such as matrix-vector multiplication, matrix-matrix multiplication, and elementwise operations can also be expressed as matrix calculations, allowing for their efficient computation.
This can be computed much faster and more efficiently using optimized matrix algorithms. Instead of performing the operations individually, we can represent the arrays as matrices and perform the calculation as a matrix-matrix addition. This is in contrast to conventional distributed analytic databases which process data on a row-by-row basis, which is usually much slower and requires more computational resources.įor example, consider a set of scalar operations that add two arrays of numbers elementwise. In addition to improving query performance, this vectorized approach can also reduce the amount of compute and data engineering required, making them more efficient and cost-effective. This allows the query engine to process multiple data elements simultaneously, resulting in faster query execution and improved performance. In a vectorized query engine, data is stored in fixed-size blocks called vectors, and query operations are performed on these vectors in parallel, rather than on individual data elements. This approach allows for significant performance gains, particularly when working with massive and fast-moving datasets. This technology is based on the concept of vector processing, which involves performing mathematical operations on arrays of data, rather than on individual elements. Vectorization has emerged as the next key accelerator for analytic databases in recent years, enabling them to process extreme amounts of data at high speeds on significantly less hardware. Several advancements have dramatically accelerated the performance of analytic databases this century, including distributed systems, columnar storage, and in-memory computing. In this feature, Kinetica Co-Founder and CEO Nima Negahban offers a comprehensive database vectorization meaning resource with definition and commentary.
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