s2geometry | Computational geometry and spatial indexing on the sphere | 3D Animation library

by google C++ Version: v0.10.0 License: Apache-2.0

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kandi X-RAY | s2geometry Summary

s2geometry is a C++ library typically used in User Interface, 3D Animation applications. s2geometry has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

This is a package for manipulating geometric shapes. Unlike many geometry libraries, S2 is primarily designed to work with spherical geometry, i.e., shapes drawn on a sphere rather than on a planar 2D map. This makes it especially suitable for working with geographic data. If you want to learn more about the library, start by reading the overview and quick start document, then read the introduction to the basic types. S2 documentation can be found on s2geometry.io.

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Quality

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Support

s2geometry has a medium active ecosystem.

It has 1993 star(s) with 280 fork(s). There are 70 watchers for this library.

It had no major release in the last 12 months.

There are 48 open issues and 121 have been closed. On average issues are closed in 165 days. There are 16 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of s2geometry is v0.10.0

Quality

s2geometry has no bugs reported.

Security

s2geometry has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

s2geometry is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

s2geometry releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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s2geometry Key Features

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s2geometry Examples and Code Snippets

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Community Discussions

Trending Discussions on s2geometry

Cloud Run - Requests latency

Possible to find K closest points using S2 library (and is it effective)?

QUESTION

Cloud Run - Requests latency

Asked 2020-Sep-25 at 07:27

I am trying to use Cloud Run to run a microservice connected to Firestore. The microservice creates objects based on s2geometry to create multiple geographical zones with specific attributes and thus help localizing users to send them information according to the zone I locate them in.

I used Python 3.7 and FastAPI to create the microservice and the routes to communicate with it.

The microservice runs smoothly on my local machine and on Compute Engines as most of my routes takes less than 150 ms to answer when I test them. However I have a latency issue when I deploy it with Cloud Run. From time to time the microservice takes a really long time to answer (up to 15 mins) and I can't pin point what exactly causes it.

Here is a screen shot where we can see the Request Count and the Request Latency :

Request Count and Request Latency

There are no real correlations between the requests latency and the number of requests or at least no trivial ones. I also looked at the memory usage of the service and the memory usage is at 30% at most. The CPU usage however some times hit 100% but not necessarily when requests are slow.

Finally when I explored the Trace List and compared requests that have high latency I noticed the following difference

Trace of slow request
Trace of fast request

Fast requests seem to call themselves whereas slow requests don't and I do not know why.

For now we do not really have a lot of users so I thought that it could be a cold start issue but slow requests are not necessarily the first ones.

Now, to be honest I don't know what's going on here and what Cloud Run does (or what I did wrong) and I also find it pretty difficult to find a thorough explanation on how Cloud Run actually works so if you have one (other than the google one) I would gladly dive into it.

Thank your very much for you help

...

ANSWER

Answered 2020-Sep-25 at 07:27

After different experiments it seems that it was a cold start issue. Cloud Run container are stoped after a certain time if they are not begin used and as we did not have a lot of traffic the container had to boot every time a user wanted to access the app.

Solution :

I created a Cloud Function that sends a request to the container when triggered and then created a Cloud Scheduler job that runs the function every minute.

Note :

If different revisions are routed to your service you need to create a Cloud Scheduler job for each of the revision. To do so you have to create a Revision URL (tag) for each of the routed revision (currently beta).

Source https://stackoverflow.com/questions/64006589

QUESTION

Possible to find K closest points using S2 library (and is it effective)?

Asked 2019-Sep-10 at 16:29

Background

Hey everyone, I recently discovered Google's open source S2 library

https://github.com/google/s2geometry

I am currently programming an application that requires finding K closest points, given an original target point. Currently I am using PostgreSQL with geospatial indexes on columns containing latitude/longitude values to achieve this - However, I am looking for alternatives when S2 caught my eye.

Questions

I am rather unfamiliar with the library, and I have some questions regarding it.

1) Is it possible to find K closest points using the S2 library

2) How fast would the query in S2 be vs Geospatial indexes (superior/inferior/same/etc)

...

ANSWER

Answered 2019-Sep-10 at 16:29

Google's S2 library is a form of geohashing. It can be used to optimize your geo lookups significantly since it's just a hash/id lookup.

One method of indexing could be:

Index all your points that you care about on a fairly large S2 cell level. You should evaluate your points and see what level works for you based on this chart.
On retrieval, convert your search point to an S2 cell at that level, and then pull all candidate points based on that.
(Optional depending on the accuracy you care about) Calculate distance between candidate points and search point and sort

There are some trade-offs with this performance gain:

Indexing S2-cells on your points means slightly more storage (64-bit integers per id)
You may miss points outside of the S2 cell that you queried by. You could index on multiple levels of S2 to ensure you retrieve enough points. Depending on the density of your points, this might not be an issue.
Retrieving by S2 cell IDs won't actually give you the distance between points - you'll have to calculate that yourself

Here's a code example from the Node S2 library:

Source https://stackoverflow.com/questions/56823409

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install s2geometry

You may either download the source as a ZIP archive, or clone the git repository.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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