Examples with ApproximateHistogramFoldingAggregatorFactory org.apache.druid.query.aggregation.histogram.ApproximateHistogramFoldingAggregatorFactory used on opensource projects

Example 1 with ApproximateHistogramFoldingAggregatorFactory

use of org.apache.druid.query.aggregation.histogram.ApproximateHistogramFoldingAggregatorFactory in project druid by druid-io.

the class QuantileSqlAggregator method toDruidAggregation.

@Nullable
@Override
public Aggregation toDruidAggregation(final PlannerContext plannerContext, final RowSignature rowSignature, final VirtualColumnRegistry virtualColumnRegistry, final RexBuilder rexBuilder, final String name, final AggregateCall aggregateCall, final Project project, final List<Aggregation> existingAggregations, final boolean finalizeAggregations) {
    final DruidExpression input = Aggregations.toDruidExpressionForNumericAggregator(plannerContext, rowSignature, Expressions.fromFieldAccess(rowSignature, project, aggregateCall.getArgList().get(0)));
    if (input == null) {
        return null;
    }
    final AggregatorFactory aggregatorFactory;
    final String histogramName = StringUtils.format("%s:agg", name);
    final RexNode probabilityArg = Expressions.fromFieldAccess(rowSignature, project, aggregateCall.getArgList().get(1));
    if (!probabilityArg.isA(SqlKind.LITERAL)) {
        // Probability must be a literal in order to plan.
        return null;
    }
    final float probability = ((Number) RexLiteral.value(probabilityArg)).floatValue();
    final int resolution;
    if (aggregateCall.getArgList().size() >= 3) {
        final RexNode resolutionArg = Expressions.fromFieldAccess(rowSignature, project, aggregateCall.getArgList().get(2));
        if (!resolutionArg.isA(SqlKind.LITERAL)) {
            // Resolution must be a literal in order to plan.
            return null;
        }
        resolution = ((Number) RexLiteral.value(resolutionArg)).intValue();
    } else {
        resolution = ApproximateHistogram.DEFAULT_HISTOGRAM_SIZE;
    }
    final int numBuckets = ApproximateHistogram.DEFAULT_BUCKET_SIZE;
    final float lowerLimit = Float.NEGATIVE_INFINITY;
    final float upperLimit = Float.POSITIVE_INFINITY;
    // Look for existing matching aggregatorFactory.
    for (final Aggregation existing : existingAggregations) {
        for (AggregatorFactory factory : existing.getAggregatorFactories()) {
            if (factory instanceof ApproximateHistogramAggregatorFactory) {
                final ApproximateHistogramAggregatorFactory theFactory = (ApproximateHistogramAggregatorFactory) factory;
                // Check input for equivalence.
                final boolean inputMatches;
                final DruidExpression virtualInput = virtualColumnRegistry.findVirtualColumnExpressions(theFactory.requiredFields()).stream().findFirst().orElse(null);
                if (virtualInput == null) {
                    inputMatches = input.isDirectColumnAccess() && input.getDirectColumn().equals(theFactory.getFieldName());
                } else {
                    inputMatches = virtualInput.equals(input);
                }
                final boolean matches = inputMatches && theFactory.getResolution() == resolution && theFactory.getNumBuckets() == numBuckets && theFactory.getLowerLimit() == lowerLimit && theFactory.getUpperLimit() == upperLimit;
                if (matches) {
                    // Found existing one. Use this.
                    return Aggregation.create(ImmutableList.of(), new QuantilePostAggregator(name, factory.getName(), probability));
                }
            }
        }
    }
    // No existing match found. Create a new one.
    if (input.isDirectColumnAccess()) {
        if (rowSignature.getColumnType(input.getDirectColumn()).map(type -> type.is(ValueType.COMPLEX)).orElse(false)) {
            aggregatorFactory = new ApproximateHistogramFoldingAggregatorFactory(histogramName, input.getDirectColumn(), resolution, numBuckets, lowerLimit, upperLimit, false);
        } else {
            aggregatorFactory = new ApproximateHistogramAggregatorFactory(histogramName, input.getDirectColumn(), resolution, numBuckets, lowerLimit, upperLimit, false);
        }
    } else {
        final String virtualColumnName = virtualColumnRegistry.getOrCreateVirtualColumnForExpression(input, ColumnType.FLOAT);
        aggregatorFactory = new ApproximateHistogramAggregatorFactory(histogramName, virtualColumnName, resolution, numBuckets, lowerLimit, upperLimit, false);
    }
    return Aggregation.create(ImmutableList.of(aggregatorFactory), new QuantilePostAggregator(name, histogramName, probability));
}