<div dir="ltr"><div dir="ltr"><div dir="ltr"><div>It would probably be possible to get substantial gains on existing formats that have spatial indexing by making them more optimal/balanced however I see the following major issues:</div><div><br></div><div>* Not sure how by convention deal with non-updatable indexes (packing/sorting on hilbert or sort-tile-recursive seem inherently static) on formats that are assumed to be mutable</div><div>* GeoPackage cannot benefit from clustering on spatial index (so it will not be possible to "cloud" optimize)</div><div>* Shapefile has several other drawbacks as a format perhaps making it less interesting to invest into</div><div><br></div><div>I have wondered several times about these things in relation to PostgreSQL and PostGIS... first thought is why there is no way to mark a table static/immutable in PostgreSQL, it seems to be there would be several general optimization possibilities if it was known that a table cannot be changed, not the least that it would make it possible to construct similar packed R-trees as discussed above. Second thought is how the R-tree is constructed/implemented via gist index (I have not had the brain power/interest to dive into the source to understand it yet) - unless it can be static it should probably optimally be a R*-tree with topological split but I've got indications that it is not.</div><div><br></div><div>/Björn</div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">Den mån 25 feb. 2019 kl 11:06 skrev Darafei "Komяpa" Praliaskouski <<a href="mailto:me@komzpa.net">me@komzpa.net</a>>:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><div dir="ltr">Hi Björn,<div><br></div><div>Would it make sense to implement it not as a separate format, but as a special convention on top of any of already existing ones?<br>What happens if you sort by Hilbert curve a shapefile or geopackage, and tweak the internals to build indexes in flattish manner?</div><div><br></div><div>For example, here is a branch of Postgres where GiST index is being built in flat manner. \</div><div>It does not require any change on user's side, but gets you all the benefits by plainly building 10x faster.</div><div><a href="https://github.com/glukhovn/postgres/tree/gist_btree_build" target="_blank">https://github.com/glukhovn/postgres/tree/gist_btree_build</a> <br><br></div></div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sun, Feb 24, 2019 at 4:59 PM Björn Harrtell <<a href="mailto:bjorn.harrtell@gmail.com" target="_blank">bjorn.harrtell@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div>I've now implemented so that the traversal of the R-tree reads nodes on demand and as suspected it gives a significant boost:</div><div><br></div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_roads_free_1.fgb gis_osm_roads_free_1; done<br></div><div>real<span style="white-space:pre-wrap"> </span>0m0,378s (~ 20% improvement)<br></div><div><br></div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_buildings_a_free_1_single.fgb gis_osm_buildings_a_free_1_single; done</div><div dir="ltr">real<span style="white-space:pre-wrap"> </span>0m0,790s (~ 30% improvement)<br></div><div dir="ltr"><br></div><div>I'm now also faster on the reduced window (56 10.1 56.1) beating gpkg:</div><div><br></div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.1 56.1 gis_osm_buildings_a_free_1_single.gpkg gis_osm_buildings_a_free_1_single; done</div><div>real<span style="white-space:pre-wrap"> </span>0m0,305s<br></div><div><br></div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.1 56.1 gis_osm_buildings_a_free_1_single.fgb gis_osm_buildings_a_free_1_single; done</div><div dir="ltr">real<span style="white-space:pre-wrap"> </span>0m0,242s<br></div><div dir="ltr"><br></div><div>It still remains a TODO to further optimize I/O to read larger blocks if possible (applies both to R-tree nodes and the feature data itself) and I think that could prove a very significant performance boost potentially leaving any competition in the dust. :D</div><div><br></div><div>/Björn</div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">Den lör 9 feb. 2019 kl 00:08 skrev Björn Harrtell <<a href="mailto:bjorn.harrtell@gmail.com" target="_blank">bjorn.harrtell@gmail.com</a>>:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div>I've now implemented spatial filtering (at GDAL fork <a href="https://github.com/bjornharrtell/gdal/tree/flatgeobuf" target="_blank">https://github.com/bjornharrtell/gdal/tree/flatgeobuf</a>) and results are promising, for two test cases about 3,5-4 times faster than shapefile with the same content. I've used these command lines to measure total time over 10 iterations for each format and dataset:</div><div><br></div><div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_roads_free_1.shp gis_osm_roads_free_1; done</div><div>real<span style="white-space:pre-wrap"> </span>0m1,940s</div></div><div><br></div><div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_roads_free_1.gpkg gis_osm_roads_free_1; done</div><div>real<span style="white-space:pre-wrap"> </span>0m0,750s</div></div><div><br></div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_roads_free_1.fgb gis_osm_roads_free_1; done</div><div>real<span style="white-space:pre-wrap"> </span>0m0,461s</div><div><br></div><div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_buildings_a_free_1_single.shp gis_osm_buildings_a_free_1_single; done</div><div>real<span style="white-space:pre-wrap"> </span>0m4,381s</div></div><div><br></div><div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_buildings_a_free_1_single.gpkg gis_osm_buildings_a_free_1_single; done</div><div>real<span style="white-space:pre-wrap"> </span>0m1,624s</div></div><div><br></div><div><div>> time for i in {1..10}; do ogrinfo -qq -spat 10 56 10.2 56.2 gis_osm_buildings_a_free_1_single.fgb gis_osm_buildings_a_free_1_single; done</div><div>real<span style="white-space:pre-wrap"> </span>0m1,113s</div></div><div><br></div><div>The bounds 10 56 10.2 56.2 contains 21525 out of the 812547 total features for gis_osm_roads_free_1 and 57699 out of 2027890 features for gis_osm_buildings_a_free_1_single.</div><div><br></div><div>And there is definitely room for optimization. The implementation is naive and reads the whole R-tree in memory and does not yet read consecutive features (common case as the R-tree is balanced) in larger I/O blocks which I think can be significant. If I reduce the extent to 10 56 10.1 56.1 GPKG is actually fair bit faster which I think is due to it being able to read the index partially.</div><div><br></div><div>/Björn</div><div><br></div><div class="gmail_quote"><div dir="ltr" class="gmail_attr">Den tis 22 jan. 2019 kl 18:43 skrev Björn Harrtell <<a href="mailto:bjorn.harrtell@gmail.com" target="_blank">bjorn.harrtell@gmail.com</a>>:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div>After posting about my experimental format I realized that I lack numbers on the potential performance, so I tried to make some more or less scientific measuring. The results was disappointing, reaching similar performance as shapefile for full sequential reads and so I lost interest for a while. But recently I found out that my method of measuring was flawed - I measured the time to read into a new shapefile using ogr2ogr, but it turns out that can be quite unfair due to the string field length dynamic resize that ogr2ogr will do if the strings from input feature attributes are of variable length. I've now made some new measurements using ogrinfo and the undocumented flag -qq to get a full sequential read.</div><div><br></div><div>I've used the shapefiles "gis_osm_buildings_a_free_1" (exploded to single poly) and "gis_osm_roads_free_1" from Denmark at <a href="http://download.geofabrik.de/europe.html" target="_blank">http://download.geofabrik.de/europe.html</a> as the source, converted to respective format, then measured the time it takes to run ogrinfo -qq on them. Results (average over three runs):</div><div><br></div><div>## gis_osm_buildings_a_free_1 (2027890 Polygons)</div><div>* SHP: 3800 ms</div><div>* GPKG: 2700 ms</div><div>* FlatGeobuf: 2200 ms</div><div><div><br></div><div>## gis_osm_roads_free_1 (812547 LineStrings)</div><div>* SHP: 1600 ms</div><div>* GPKG: 1350 ms</div><div>* FlatGeobuf: 800 ms</div></div><div><br></div><div>With that my hope and interest is rekindled. I believe I can fare even better against the competition with with spatially filtered searches, will hopefully soon have some results on that too.</div><div><br></div><div>/Björn</div><div dir="ltr"><br><div class="gmail_quote"><div dir="ltr">Den sön 9 dec. 2018 kl 20:36 skrev Björn Harrtell <<a href="mailto:bjorn.harrtell@gmail.com" target="_blank">bjorn.harrtell@gmail.com</a>>:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div>Hi GDAL/OGR folks,</div><div><br></div><div>In my spare time I've been working on a vector file format called FlatGeobuf (tentatively). The main reason, besides curiosity and learning, I'm putting time into it is that I think shapefile still rules the read/query static data performance game, which is kind of sad, and probably one of the reasons it is still so widely popular. Also, the main competitor (GeoPackage) isn't suitable for streaming access (AFAIK) which shapefiles also handles surprisingly (?) well.</div><div><br></div><div>By using a performance focused write once binary encoding (flatbuffers), schema constraint and focusing on read/query performance by clustering on an optimal spatial index (Packed Hilbert R-Tree) I hope to be able to beat shapefile performance and at the same time be optimal for streaming/cloud access.</div><div><br></div><div>I think I'm starting to get somewhere, more details and source is at <a href="https://github.com/bjornharrtell/flatgeobuf" target="_blank">https://github.com/bjornharrtell/flatgeobuf</a> and I have an early proof of concept driver implementation at <a href="https://github.com/bjornharrtell/gdal/tree/flatgeobuf" target="_blank">https://github.com/bjornharrtell/gdal/tree/flatgeobuf</a> and results are already quite promising - it can do roundtrip read/write and is already quite a bit faster than shapefile. I also have implemented naive read only QGIS provider for experimental purposes.</div><div><br></div><div>Basically I'm fishing for interest in this effort, hoping that others will see potential in it even if it's "yet another format" and far from final/stable yet. Any feedback is welcome. As I see it, GDAL is a good place for a format specification and reference implementation to incubate.<br></div><div><br></div><div>Some additional food for thought that I've had during the experimentation:</div><div><br></div><div>1. The main in memory representations of geometry/coordinates seem to be either separate arrays per dimension (GDAL (partially?) and QGIS) or a single array with dimension as stride. I've chosen the latter as of yet but I'm still a bit undecided. There is of course a substantial involved in transforming between the two representations so the situation with two competing models is a bit unfortunate. I'm also not sure about which of these models are objectively "better" than the other?</div><div><br></div><div>2. One could get better space efficiency with protobuf instead of flatbuffers, but it has a performance cost. I have not gone far into investigating how much though and one could even reason about supporting both these encodings in a single format specification but it's taking it a bit far I think.</div><div><br></div><div>3. Is there a reason to support different packing strategies for the R-Tree or is Packed Hilbert a good compromise (besides it being beautifully simple to implement)?</div><div><br></div><div>4. FlatGeobuf is perhaps a too technical name, not catchy enough and has a bad/boring abbreviation. Other candidates I've considered are OpenGeoFile, OpenVectorFile or OpenSpatialFile but I'm undecided. Any ideas? :)</div><div><br></div><div>Regards,<br><br>Björn Harrtell</div></div>
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