The last significant features we shipped dealt with out-of-order data ingestion, and we focused our efforts on hitting the highest write-throughput that we could achieve for that release. Our latest feature highlight adds space as a new dimension that our database can manage and allows users to work with data sets that have spatial and time components.
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The latest QuestDB release introduces support for geospatial data via the addition of geohash types. Geohashes encode geographic areas as base-32 strings, and native support for this type allows for fast and efficient querying and storage of geodata. Also included are helper functions for rounding timestamps, performance improvements for existing functions, alongside other fixes and features. Here's the full roundup of changes that have just landed!
We've published the latest QuestDB release, and it focuses on community-driven
topics raised with us recently by our users. The features included are
performance improvements, increased parallelization of existing code, and
calendar alignment for
SAMPLE BY queries. Also included is the introduction of
a framework for exposing Prometheus metrics by our community member Piotr
Rżysko. Here's the full roundup of changes that have just landed!
We've just published an alpha version for the upcoming 6.0 major release and it includes long-awaited support for ingesting out-of-order records on-the-fly, a complete overhaul of the InfluxDB Line Protocol subsystem, and multiple fixes which provide stability improvements. Here's a roundup of changes that have just landed in the latest and greatest version!
We've just released software version 5.0.6 and it comes with plenty of additional features and functionality, a full refactoring of PostgreSQL wire support, and multiple fixes to improve the stability of the system. Here's a roundup of recent changes that have just landed.
SIMD instructions are specific CPU instruction sets for arithmetic calculations that use synthetic parallelization. This approach allows us to perform the same calculations and operations on numerous data points simultaneously. This post describes how SIMD works with typical operation performance and describes additional optimizations we managed to achieve.