Cache locality can have a major impact on performance. This post explores contemporary CPU cache algorithms, prefetching mechanisms, and other strategies to overcome the memory wall, plus an actual cache-aware experiment conducted on two CPUs.
Optimize Materialized View recomputes in Databricks. Learn to detect and fix full recomputes, improve incremental refreshes, and reduce costs with best practices.
Many applications which consume hexagon data need smoothing due to data. One common smoothing approach is based on k-ring. During Uber Open Summit 2018, engi...
Geospatial indexing, or Geocoding, is the process of indexing latitude-longitude pairs to small subdivisions of geographical space, and it is a technique that we data scientists often find ourselves using when faced with geospatial data.
Though the first popular geospatial indexing technique “Geohash” was invented as recently as 2008, indexing latitude-longitude pairs to manageable subdidivisions of space is hardly a new concept. Governments have been breaking up their land into states, provinces, counties, and postal codes for centuries for all sorts of applications, such as taking censuses and aggregating votes for elections.
Discover how Tailscale achieved over 10Gb/s throughput on Linux using advanced UDP segmentation and checksum optimizations. Explore benchmarks, results, and the innovations powering wireguard go's latest performance leap.
How does Uber find your ride in seconds? In Part 1, we explored why traditional GPS searches are slow and how H3 geospatial indexing solves this with a hexagonal grid system. Learn how H3 outperforms squares and triangles in handling millions of ride requests per second.
This session was recorded live at State of the Map US 2025 in Boston, Massachusetts. Hosted by OpenStreetMap US, the annual State of the Map US conference is...