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.
Best,
Tiago
Am 22.06.21 um 09:24 schrieb Mathias Versichele:
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 discuss.
Kind regards
On Tue, Jun 15, 2021 at 8:45 PM Mathias Versichele
<mathias.versich...@gmail.com <mailto:mathias.versich...@gmail.com>> wrote:
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 countries])*
*
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.
Thx!
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Tiago de Paula Peixoto <ti...@skewed.de>
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