Obtaining interactive performance from a spatial database can be challenging. The fundamental goal of the database components in LuciadLightspeed is to be able to work with arbitrarily large databases. A database model will therefore not contain copies of all of its elements on the client side. Rather, they are retrieved from the database whenever they are needed. This implies unavoidable communication between the database and the client, which is generally far slower than just working with locally stored elements. There is an overhead due to sending and executing spatial SQL queries, and retrieving and decoding the resulting geometries and their features.
LuciadLightspeed attempts to reduce the effects of the overhead by taking a
two-step approach, internally. Model elements are typically requested for a
given rectangular area, by means of the method
In a first step, the database model retrieves the element identifiers (primary features) of the relevant elements in the database. The identifiers are requested with a spatial query.
In a second step, the database model retrieves the actual geometries and features that are not cached. They are requested with a query based on the primary feature of the spatial table. Retrieved and decoded model elements are cached whenever they are retrieved.
The most important factor in database performance is having the proper indexes. In the context of the database LuciadLightspeed component, two indexes are essential:
The spatial index on the geometry column of the spatial table.
The index on the primary key of the spatial table.
You can set the cache size on the client side:
setMaxCacheSizeon the database model decoder
For the model decoders which work with properties, use the
The cache size is expressed as a number of model elements. Memory permitting, it should be as large as the number of objects in the spatial database.
If the cache is sufficiently large, it may already contain all required elements after only a few queries. The second step in the two-step approach then is not necessary and the database model skips it. This maximally reduces the amount of geometries and features that have to be read from disk.
On the other hand, it is also possible to set the cache size to 0. In that case, all relevant geometries and features have to be read from disk for every query. The first step in the two-step approach then is not necessary, and the database model skips it. This maximally reduces the number of queries that have to be made to the database, which is useful if the round-trip time is high compared to the bandwidth.
At the other end of the spectrum, if all the elements of the database model
fit in memory, it is possible to just create a new local model, like
TLcd2DBoundsIndexedModel and copy all
database model elements over. This results in a single large query upfront.
The database model can then be disposed off, not requiring any further queries
while accessing the copied model. It is the fastest approach, provided that
the spatial database is sufficiently small.