Vectorizationthe technology behind real-time analytics at scale
What is Vectorization?
Most databases have evolved with the CPU
The CPU has been the core of the computer for decades. Database systems have evolved alongside using sequential processing to perform calculations.
Take this example of an array of numbers. To add five to each number and place them into a new array, a CPU will rapidly work through the list.
But this sequential process has its limits.
What if you could do 1000 instructions at once?
GPUs which typically have thousands of cores were designed to speed up drawing of graphics on a screen. Instead of rendering a pixel at a time, a GPU could render a whole screen in one go – a technique known as single-instruction, multiple data (SIMD).
It turns out this same capability is well suited to performing repeated similar instructions on data in parallel. With Intel's Advanced Vector Extensions (AVX) making it into CPUs in the data center, the path is now wide open to leverage vectorized compute in the cloud for analytics workloads.
How does Kinetica harness vectorization?
Kinetica was designed from the ground up to leverage the vectorization capabilities of GPUs and modern CPUs. Analytical functions in Kinetica have all been written from scratch to take advantage of vectorization.
Vectorization unleashes significant performance improvements – particularly on spatial and temporal queries at scale. Aggregations, predicate joins, windowing functions, graph solvers all operate far more efficiently.
Vectorization Gives You Freedom
Simpler Data Structures
Brute force vectorized compute means there is less need to think through schemas before data can be explored.
Simpler data structures means less to index. Combined with Kinetica's lockless, distributed architecture, data is available for query immediately after it lands.
Linear Scale Out
With less to index, the database scales in proportion to the size of the data. This leads to a smaller and more predictable scale-out footprin.t
Spend less time engineering schemas, and more time using your data. Business analysts have more flexibility and freedom for ad-hoc data discovery projects.
Better Performance with Less Infrastructure.
With vectorized algorithms and reduced need for supporting data structures, Kinetica enables you to do more with fewer resources and less work than comparable systems.
Large US Financial Institution700-node spark cluster running queries in hours took seconds on 16 nodes of Kinetica
Top US RetailerConsolidated 100 nodes of Cassandra(NoSQL) and Spark into 8 Kinetica nodes
Large PharmaIdentical performance between a 88-node Impala cluster and a 6-node Kinetica Intel cluster in Azure
Vectorization: The New Era of Big Data Parallelism
Every five to 10 years, an engineering breakthrough emerges that disrupts database software for the better. Vectorization is the newest breakthrough gaining momentum towards widespread adoption. Early adopters are using fully vectorized databases to foster new applications and reap lower costs.
Learn more about vectorization in this white paper.Download the White Paper