Expected memory usage for large-scale road networks

Hi, I've been using graph-tool for the last year or so for calculating
shortest-path trees on large-scale road networks. We used to do this in a
postgres database with the pgrouting extension, but were continually
confronted with unacceptable startup costs. The switch to a python module
using graph-tool has considerably sped up our routing queries, but we are
suffering from this service using too much memory. I have the feeling I
might be using graph-tool in a wrong way, but before I dive into that, it
would be good to know what is the expected memory footprint for my use case.

Take for example a road network with 30Mio edges and 31 Mio nodes (the
combined road network of Belgium, Netherland, France and Germany in OSM).
For this road network, I need to calculate shortest paths using different
edge weights (edge property map). What would be a very rough estimate how
much memory this would use ? For the network only + per edge-property-map.
In our setup, there would be one edge-property-map with edge weights per
country. We're currently seeing usage of over 50Gb easily, spiking even
higher when we're loading extra cost structures or networks. Is that
expected ? Or am I experiencing memory leaks somewhere ?

How I'm using graph-tool right now:

*1) loading network*
*nw = dataframe with edges info in the structure: startnode-id, endnode-id,
edge-id, country*

G = gt.Graph(directed=True)
G.ep["edge_id"] = G.new_edge_property("int")
G.ep["country_id"] = G.new_edge_property("int16_t")
eprops = [G.ep["edge_id"], G.ep["country_id"]]

n = G.add_edge_list(nw.to_numpy(), hashed=True, eprops=eprops)
G.vertex_properties["n"] = n

*2) loading edge costs: I'm using GraphViews*

*countries = list of country-codes*
edge_filter = np.in1d(G.ep["country_id"].a, [get_country_id(c) for c in
GV = gt.GraphView(G, efilt=edge_filter)

edges = GV.get_edges([GV.edge_index])
sources = G.vertex_properties["n"].a[edges[:,0]]
targets = G.vertex_properties["n"].a[edges[:,1]]
idxs = edges[:,2]

*db_costs = pandas dataframe with structure: source, target, cost*

sti = np.vstack((idxs,sources,targets)).T
sti_df = pd.DataFrame({'idx': sti[:, 0], 'source': sti[:, 1], 'target':
sti[:, 2]})
m = pd.merge(sti_df, db_costs, on=['source', 'target'], how='left',
sort=False)[['idx', 'c']]
wgts_list = m.sort_values(by=['idx']).T.iloc[1].to_numpy()
wgts_list = np.where(wgts_list==np.nan, np.iinfo(np.int32).max, wgts_list)

wgts = GV.new_edge_property("int32_t")
wgts.fa = wgts_list
wgts.fa = np.where(wgts.fa==-2147483648, np.iinfo(np.int32).max, wgts.fa)
GV.edge_properties[cs_ids_str] = wgts

GV2 = gt.GraphView(GV, efilt=wgts.fa != np.inf)

*3) I then use GV2 for calculating Dijkstra and such...*

I could of course work on an MWE of some sorts. But would be very nice to
get an estimate on mem footprint, and to see if I'm doing sth really silly
in the code above.


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Hi all. Anyone can provide me with some insights here ? I know it's quite
an open question here, and it might take some effort of course. Would
anyone be available/willing to do an actual code audit of the code that I
have ? This would be compensated of course. Feel free to contact me to

Kind regards

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Dear Mathias,

It is not reasonable to expect us to make this kind of evaluation just
from partial code. As with anything, we need a minimal working example
to be able to say something concrete.

I would recommend you to try to separate the pandas dataframe
manipulation from the graph-tool side in order to determine which is
consuming more memory.


Fair point. I managed to find some time to make a MWE. Here's the link to a
zip file containing both the input data (network and costs as csv dumps
from our postgres db) and the code:

To use: just run "python mwe_main.py". The last line calculates a shortest
path tree until 60mins for cost structure "49" (these are costs associated
with car travel, the specific id has no meaning). Because this is only
Belgium, mem usage when I run this is manageable (under 1Gb).

In general my question is: can this code be made more performant, both in
terms of mem and speed ? Am I right in using GraphViews for each cost
structure, where the view only contains the edges which have costs
associated with them ? Does this make gt.shortest_distance() faster ?

The most production-critical part of the code here is point_drivetimes().
Any gain there in calculation time would be very valuable. The loading of
the network and cost structure only need to happen once, so can be a bit

Thx for helping me !

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Anyone able to scan if I'm using the library correctly? Or any other tips ?
The MWE is provided through a download link.

Much appreciated!

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