/**
* Returns a histogram that, for each field, approximates distribution of values in the group-by
* and aggregation fields.
*
* <p>Assumes that:
*
* <ul>
* <li>Distributions of values in group-by fields are independent with each other
* <li>Aggregate values in different groups are distinct
* <li>
* </ul>
*
* @param hist the input join distribution of field values
* @param groupFlds the fields to group by. Can be empty, which means that all records are in a
* single group.
* @param aggFns the aggregation functions. Optional, can be null.
* @return a histogram that, for each field, approximates distribution of values in the group-by
* and aggregation fields
*/
public static Histogram groupByHistogram(
Histogram hist, Set<String> groupFlds, Set<AggregationFn> aggFns) {
if (Double.compare(hist.recordsOutput(), 1.0) < 0) return new Histogram(hist.fields());
double dvProd = 1.0; // the maximal number of group
for (String fld : groupFlds) {
double dv = 0.0;
for (Bucket bkt : hist.buckets(fld)) dv += bkt.distinctValues();
dvProd *= dv;
}
double numGroups = Math.min(dvProd, hist.recordsOutput());
double gbReduction = numGroups / hist.recordsOutput();
Histogram gbHist = new Histogram(groupFlds);
for (String fld : groupFlds) {
for (Bucket bkt : hist.buckets(fld)) {
double newFreq = bkt.frequency() * gbReduction;
if (Double.compare(newFreq, 1.0) < 0) continue;
gbHist.addBucket(
fld,
new Bucket(bkt.valueRange(), newFreq, bkt.distinctValues(), bkt.valuePercentiles()));
}
}
if (aggFns != null) {
for (AggregationFn aggFn : aggFns) {
String argFld = aggFn.argumentFieldName();
String fld = aggFn.fieldName();
if (aggFn instanceof SumFn)
gbHist.addBucket(fld, sumBucket(hist.buckets(argFld), numGroups));
else if (aggFn instanceof AvgFn)
gbHist.addBucket(fld, avgBucket(hist.buckets(argFld), numGroups));
else if (aggFn instanceof CountFn)
gbHist.addBucket(fld, countBucket(hist.buckets(argFld), numGroups));
else if (aggFn instanceof DistinctCountFn)
gbHist.addBucket(fld, distinctCountBucket(hist.buckets(argFld), numGroups));
else if (aggFn instanceof MinFn)
gbHist.addBucket(fld, minBucket(hist.buckets(argFld), numGroups));
else if (aggFn instanceof MaxFn)
gbHist.addBucket(fld, maxBucket(hist.buckets(argFld), numGroups));
else throw new UnsupportedOperationException();
}
}
return syncHistogram(gbHist);
}
/**
* Creates a group-by plan for the underlying query. The grouping is determined by the specified
* collection of group fields, and the aggregation is computed by the specified collection of
* aggregation functions.
*
* @param p a plan for the underlying query
* @param groupFlds the fields to group by. Can be empty, which means that all records are in a
* single group.
* @param aggFns the aggregation functions. Optional, can be null.
* @param tx the calling transaction
*/
public GroupByPlan(Plan p, Set<String> groupFlds, Set<AggregationFn> aggFns, Transaction tx) {
schema = new Schema();
this.groupFlds = groupFlds;
if (!this.groupFlds.isEmpty()) {
for (String fld : groupFlds) schema.add(fld, p.schema());
// sort records by group-by fields with default direction
sp = new SortPlan(p, new ArrayList<String>(groupFlds), tx);
} else
// all records are in a single group, so p is already sorted
sp = p;
this.aggFns = aggFns;
if (aggFns != null)
for (AggregationFn fn : aggFns) {
Type t =
fn.isArgumentTypeDependent() ? p.schema().type(fn.argumentFieldName()) : fn.fieldType();
schema.addField(fn.fieldName(), t);
}
hist = groupByHistogram(p.histogram(), this.groupFlds, aggFns);
}