I just saw the
HatBox spatial extension to Derby and H2. (Cute name - Derby, Hat
BOX - get it?)
And of course HatBox uses
JTS!
This is great - H2 is a fantastic pure Java database, which has been crying out for spatial support. (Perhaps if this extension proves its worth it will be merged into the main H2 codebase at some point?)
However, HatBox is still a "user-space extension" - which means that it has not enhanced the SQL evaluation engine itself with knowledge about spatial indexes and when to use them. So to utilize spatial indexing you have to explicitly join to the spatial index table, which results in ugly SQL like this:
select ID, GEOM from T1 as t
inner join
HATBOX_MBR_INTERSECTS_ENV('PUBLIC','T1',145.05,145.25,-37.25,-37.05) as i
on t.ID = i.HATBOX_JOIN_ID
This is the same approach used in Sqlite and ESRI SDE and other spatial extensions which operate in user space rather than DB engine space (Mike Butler of SDBE fame used to call this "staying above the blood-brain barrier" 8^). Basically you are adding the index filter condition which for scalar indexes the SQL engine adds automatically.
In contrast, the "big boys" like PostGIS, Oracle, SQL Server, DB2, Informix, etc have actually extended their database engine to handle spatial datatypes and indexes. Most of these systems actually have provided a general extensibility mechanism which allows a clean separation between the engine core and the new datatypes. PostgreSQL is probably the one which takes this to the ultimate extent.
User-space spatial extensions are for a first approach, but it would be really nice to be able to play with the big boys and incorporate knowledge of spatial indexes and functions directly into the database engine. This should be easierto do in Java than in C - are you listening, H2?
