Kx.com | KXCON23 | Performance Trends with STAC-M3 and kdb

STAC-M3 is a crucial component in the realm of time series analytics. Central to STAC-M3 is the time series database, which holds immense importance due to its ability to seamlessly function across diverse configurations, setups, and environments. Recent results showcasing the prowess of kdb, KX’s time series database, have been witnessed across various environments and hardware setups. These include exemplary performance on network-attached storage on-prem systems like KDB221014 and KDB220506, as well as on network-attached storage in the cloud with KDB220410. Additionally, KDB211210 has demonstrated impressive performance when deployed on server storage in the cloud. For on-prem systems, a non-optane setup is being explored, considering optane’s discontinuation. In the finance sector, conversations have been illuminating emerging trends in time series analytics use cases and setups. The increasing focus is observed on harnessing the power of time series data in complex financial scenarios, enabling real-time data-driven decision-making and predictive analytics. The demand for flexible and robust solutions that can adapt to dynamic market conditions and handle vast amounts of data continues to rise. As KX remains at the forefront of these developments, it continues to evolve its offerings to cater to these emerging trends and deliver cutting-edge solutions for time series analytics in the financial domain.

Kx.com | KXCON23 | Performance Trends with STAC-M3 and kdb

Kx.com | KXCON23 | Performance Trends with STAC-M3 and kdb

kdb+ has performed consistently well in the STAC-M3 benchmarks, achieving record-breaking results in several areas. For example, in the STAC-M3 Time Series Analytics benchmark, kdb+ achieved the highest throughput for both single-node and multi-node deployments. kdb+ also achieved the lowest latency for single-node deployments.

In addition to its strong performance on the STAC-M3 benchmarks

, kdb+ has also demonstrated its ability to handle real-world workloads at scale. For example, kdb+ is used by several large financial institutions to power their real-time trading systems. kdb+ is also used by several government agencies to power their real-time data processing systems.

Here are some of the factors that contribute to kdb+’s high performance:

  • In-memory data processing: kdb+ is an in-memory database, which means that it stores all of its data in main memory. This allows kdb+ to access data very quickly and efficiently.
  • Columnar data storage: kdb+ stores data in a columnar format, which means that all of the values for a particular column are stored together. This allows kdb+ to optimize its data access patterns and improve its performance.
  • Vectorized processing: kdb+ supports vectorized processing, which means that it can perform operations on multiple data elements simultaneously. This significantly improves the performance of kdb+ queries.
  • Parallel processing: kdb+ supports parallel processing, which means that it can distribute its workload across multiple processors. This allows kdb+ to scale to very large datasets and workloads.

Overall, kdb+ is a high-performance time series database that is well-suited for demanding real-world applications.

Leave a Comment