org.opennms.netmgt.timeseries.sampleread

Class LateAggregationParams.LateAggregationParamsBuilder

java.lang.Object

org.opennms.netmgt.timeseries.sampleread.LateAggregationParams.LateAggregationParamsBuilder

Enclosing class:

LateAggregationParams

public static class LateAggregationParams.LateAggregationParamsBuilder
extends Object

Constructor Summary

Constructors

Constructor and Description
LateAggregationParamsBuilder()

Method Summary

Modifier and Type	Method and Description
LateAggregationParams	build() Calculates the parameters to use for late aggregation.
LateAggregationParams.LateAggregationParamsBuilder	heartbeat(Long heartbeat)
LateAggregationParams.LateAggregationParamsBuilder	interval(Long interval)
LateAggregationParams.LateAggregationParamsBuilder	step(Long step)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LateAggregationParamsBuilder

public LateAggregationParamsBuilder()

Method Detail

step

public LateAggregationParams.LateAggregationParamsBuilder step(Long step)

interval

public LateAggregationParams.LateAggregationParamsBuilder interval(Long interval)

heartbeat

public LateAggregationParams.LateAggregationParamsBuilder heartbeat(Long heartbeat)

build

public LateAggregationParams build()

Calculates the parameters to use for late aggregation.

Since we're in the process of transitioning from an RRD-world, most queries won't contain a specified interval or heartbeat. For this reason, we need to derive sensible values that will allow users to visualize the data on the graphs without too many NaNs.

The given step size will be variable based on the time range and the pixel width of the graph, so we need to derive the interval and heartbeat accordingly.

Let S = step, I = interval and H = heartbeat, the constraints are as follows: 0 < S 0 < I 0 < H S = aI for some integer a >= 2 H = bI for some integer b >= 2

While achieving these constraints, we also want to optimize for: min(|S - S*|) where S* is the user supplied step and S is the effective step.