The GeoGig team is proud to announce geogig 1.1-beta1 has been released for testing.
GeoGig 1.1-beta1 Release Notes
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April 18, 2017.
This release introduces the long awaited spatial indexing capabilities to GeoGig.
GeoGig's spatial index can index the whole history of a repository's datasets. It was
developed from scratch to take advantage of geogig's core design principle to efficiently
store large amounts of features and quickly compute the differences between any two
snapshots, the Merkle Tree (a.k.a. Hash Tree), that allows us to create a DAG (Directed
Acyclic Graph) where each data snapshot is an entry point to the graph, while they share
all but what changes internally.
To the best of our knowledge, this is the first spatial index that can effectively
work on such a graph of data.
When an index is created for a feature tree (i.e. layer), a new tree is created mirroring
the canonical tree contents, but using a quad-tree clustering strategy for its non terminal
nodes.
Spatial indexing capabilities:
* Indexes are automatically updated on commits. Create an index for a layer, and any operation
that creates a new commit will trigger the update of the index (i.e. commit, rebase, merge, etc).
* Materialized attributes: the spatial index can materialize any spatial or non spatial attribute
of the FeatureType being indexed, in order to speed up attribute queries as well as spatial ones.
For example, you might want to include the attributes needed by the Styled Layer Descriptor to properly
filter out Features for rendering on a WMS; or include the time/elevation attributes to speed up
queries on time series in GeoServer.
* Spatial indexing command set: the geogig command line and its WEB API include a full set of
commands to manage the indexes. Run `geogig index --help` or browse the online documentation at
Other improvements in this release:
* Better support for foreign CRS definitions. Importing from datasets that define its coordinate
reference system in non OGC WKT formats (e.g. most shapefiles) are now correctly matches to their
corresponding CRS from the EPSG database, providing extra CRS metadata such as area of validity.
* Better PostgreSQL backend caching. The PostgreSQL backend's internal cache uses an improved
serialization format that's a lot faster to decode, while still allowing to control the cache
size by storing serialized objects instead of Java objects.
