ca.uhn.cache.internal
Interface IParamSpaceConfig
- All Known Implementing Classes:
- ParamSpaceConfig
- public interface IParamSpaceConfig
Configuration data for a ParamSpace
.
- Version:
- $Revision: 1.1 $ updated on $Date: 2005/01/24 22:53:27 $ by $Author: bryan_tripp $
- Author:
- Bryan Tripp
getDimensions
public IDimension[] getDimensions()
- Returns:
- a list of dimensions according to which data are organized.
getChunkBoundaries
public IQueryParam[] getChunkBoundaries(IDimension theDimension)
- This method is only meaningful along dimensions for which isChunked()
returns true.
- Parameters:
theDimension
- a dimension in the space (must be one of those
returned by getDimensions()).
- Returns:
- the limits of each chunk along the given dimension. The
whole space must be covered by this list. Returns an empty array
if isChunked() returns false for the given dimension.
isChunked
public boolean isChunked(IDimension theDimension)
- True iff data are split into a known list of chunks along this dimension
(the usual case). If true, then getChunkBoundaries() must return a list
of the boundaries of chunks along this dimension.
Dimensions are not always chunked. For example a dimension may correspond
to a large, volatile list of values such as person IDs. It would not be
efficient to provide a complete list of (possibly millions of) chunk
boundaries for such dimensions, or to iterate through them to see which
ones are intersected by a query. For such dimensions it is assumed that
any valid IQueryParam corresponds to a chunk. For example in an employee ID
dimension, it might make sense to have each chunk encompass exactly one
ID, in which case this method would return false.
- Parameters:
theDimension
- one of the values returned by getDimensions().
- Returns:
- true iff data are split into a known list of chunks along this
dimension
getSaturationPoint
public IQueryParam getSaturationPoint(IDimension theDimension)
- For ordered dimensions, the distances between their members is important.
Distances (see IQueryParam.getDistance()) are normalized to between zero
and 1. The distance corresponding to 1 is configurable. For example
a time dimension may have a relevant range over the last week or the last
20 years. In the first case, a difference of a week would correspond to
a distance of 1, and in the second case a difference of 20 years would
correspond to 1.
- Parameters:
theDimension
- a dimension in this space
- Returns:
- the param corresponding to a distance of 1, from the origin that
is natural for that dimension (e.g. now for a time dimension, 'a' for
an alphabetical dimension). Null for non-ordered dimensions.
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