Geographic Information Systems (GIS) is an interesting area of exploration because it poses two significant challenges: latency at scale and modeling spatial locality. Spatial locality in GIS data is tricky. This article, based on chapter 8 from HBase in Action, explains an algorithm called the geohash, which is a solution to this problem.
Let's say you're visiting NYC and need an Internet connection. Where's the nearest Wi-Fi hotspot? How might an HBase application help you answer that question? What kind of design decisions go into solving that problem, and how can it be solved in a scalable way?
You know you want fast access to a relevant subset of the data. To achieve that, let's start with two simple and related goals:
1. You want to store location points on disk such that points close to each other in space are close to each other on disk.
2. You want to retrieve as few points as possible when responding to a query.
Let's say you're visiting NYC and need an Internet connection. Where's the nearest Wi-Fi hotspot? How might an HBase application help you answer that question? What kind of design decisions go into solving that problem, and how can it be solved in a scalable way?
You know you want fast access to a relevant subset of the data. To achieve that, let's start with two simple and related goals:
1. You want to store location points on disk such that points close to each other in space are close to each other on disk.
2. You want to retrieve as few points as possible when responding to a query.
来自 “ ITPUB博客 ” ，链接：https://blog.itpub.net/301743/viewspace-748961/ ，如需转载，请注明出处，否则将追究法律责任。
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