So far, I've only generated the graph for Europe(using Graphhopper). Even just Europe required 128GB of RAM and around 10 hours of computation time (the entire planet would likely need 384GB of RAM). I plan to add North America on a separate Docker container soon though. I started with Europe because I’m familiar with some of the bike trails here, which makes it easier for me to check if the routing makes sense.
> the entire planet would likely need 384GB of RAM
Unlikely. Even with turn costs enabled 256GB (or less) are sufficient. You could also try to disable CH as for bike often no long routes are required (you could disable them). Here we have written down a few more details: https://www.graphhopper.com/blog/2022/06/27/host-your-own-wo...
Hey karussell, I really appreciate all the hard work you’ve put into Graphhopper. I wouldn't be able to create this project without GH. I have a question about memory usage during the import stage (specifically in the OSM Reader's preprocessRelations function). I'm using a HashMap<Long, List<Long>> to map way IDs to OSM bike route relation IDs, which means allocating lots of arrays. Could this be causing me to run out of heap memory faster or am I off base here?
I thought I would be able to compute the graph with 64GB of ram but it kept crushing before CH and LM stage. After switching to a 128GB instance, it finally worked, hitting around 90GB at peak memory usage. For context, I was using 3 profiles - one with CH and two with LM, plus elevation data and used all of the tips from deploy.md
Maybe you already considered, but there are a number of collection libraries out there that are optimized for holding Java primitives and/or for very large sets of data, which could help you save significant memory. Eclipse Collections [0] and Fastutil [1] come to mind first, but there are many out there [2]
Thank you! I'm a total Java noob - actually, this is the first project where I've written any Java code (had to slightly modify the Graphhopper source code to suit my needs). Those libraries look very interesting. I'm saving this post for another battle with processing maany GBs of OSM data :D
We already use carrotsearch internally so you could replace the java util classes like HashMap and HashList with it to reduce memory usage a bit. But it won't help much. E.g. all data structures (in any standard library btw) do usually double their size at some point when their size increases and then copy from the old internal array to the new internal array, which means that you need roughly 3x the current size and if that happens roughly at the end of the import process you have a problem. For that reason we developed DataAccess (inmemory or MMAP possible) which is basically a large List but 1. increases only segment by segment and 2. allows more than 2 billion items (signed int).
Another trick for planet size data structure could be to use a List instead of the Map and the OSM ID as index. Because the memory overhead of a Map compared to a List is huge (and you could use DataAccess) and the OSM IDs for planet are nearly adjacent or at least have not that many gaps (as those gaps are refilled I think).
All these tricks (there are more!) are rather tricky&low level but necessary for memory efficiency. A simpler way for your use case could be to just use a database for that, like MapDB or sqlite. But this might be (a lot) slower compared to in-memory stuff.
I'm in Portland and I say that anyone that lives on the Eastside within 82nd and works within those bounds should just get a bike, it's sooo much faster to get around the city. We have infamously small city blocks so all those intersections make driving a car very slow through the city and for good reason as it's safer. If you hop on a bike and take a greenway that has all the stop signs facing perpendicular traffic you can zoom around the city no problem. It's the exact same amount of time (from door to desk) for me to drive to work as it is to bike to work (including the shower). It's just shy of 7 miles. No brainer.
Really cool. The main thing I wish is that the transitions between "slides" was slower and not just a quick crossfade. The current transition is a bit disorienting mean I have to look around again to see where I am relative to where I was. Looking forward to seeing something like this on a VR headset.
Davinci Resolve is miles better than Premiere. I don't do a lot of compositing, but I know more and more people are starting to use it over After Effects as well.
Resolve is better than Premiere on its own (hence why I list premiere as having competition) but the Fusion compositing is not a comparison for After Effects, but rather for something like Nuke.
While After Effects does some compositing (and it’s decent at it but poor in comparison to Nuke/Fusion), its’ stronghold is motion graphics. There’s very little other than Cavalry to compete with it.
And with that comes the benefit of Premiere: live updates to my edit when using After Effects.
How do you fix the routes? I just checked out the suggested route from my work to home and it doesn't stick to the bike greenways or streets with bikelanes. It actually suggested I take a very dangerous road with no bike traffic at all. How can I help improve that?
A lot of the routing comes down to pressure around the tag `bicycle`. If there is a road illegal to bikes, tag it `bicycle=no`. But if it is unsafe, the best way to fix it is to better document designated routes, as well as cycleways.
OSM's philosophy on routing is you should not try to fix routing by trying to translate your opinion that it is unsafe to ride on the street into to tags. Instead the routing algorithm should improve or the data should improve to the point where an alternative route can be suggested based on data.
Also curious. I was looking locally for some sort of meetup/show where I could see people builds, ask about cost, etc. Sadly I just missed an expo the previous weekend.
Audio that went along with the physical issue of the NY Times Magazine, also in digital form. I'm a big fan of audio tours so thought it was a cool format to travel the world.
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