### Description

Our most common aggregations should be optimized for single-valued fields. In general, our aggregations look like this:

```java
    @Override
    public LeafBucketCollector getLeafCollector(LeafReaderContext ctx, LeafBucketCollector sub) throws IOException {
        if (valuesSource == null) {
            return LeafBucketCollector.NO_OP_COLLECTOR;
        }
        SortedNumericDocValues values = valuesSource.longValues(ctx);
        return new LeafBucketCollectorBase(sub, values) {
            @Override
            public void collect(int doc, long owningBucketOrd) throws IOException {
                if (values.advanceExact(doc)) {
                    int valuesCount = values.docValueCount();
                    for (int i = 0; i < valuesCount; ++i) {
                        long value = values.nextValue();
                        // do something with value
                    }
                }
            }
        };
    }
```

The idea consists of optimizing for single-valued fields by unwrapping the single-valued doc-value iterator:

```java
    @Override
    public LeafBucketCollector getLeafCollector(LeafReaderContext ctx, LeafBucketCollector sub) throws IOException {
        if (valuesSource == null) {
            return LeafBucketCollector.NO_OP_COLLECTOR;
        }
        SortedNumericDocValues values = valuesSource.longValues(ctx);
        NumericDocValues singleValues = DocValues.unwrapSingleton(values); // or FieldData.unwrapSingleton for doubles
        if (singleValues != null) {
            return new LeafBucketCollectorBase(sub, values) {
                @Override
                public void collect(int doc, long owningBucketOrd) throws IOException {
                    if (singleValues.advanceExact(doc)) {
                        long value = singleValues.longValue();
                        // do something with value
                    }
                }
            };
        } else {
            return new LeafBucketCollectorBase(sub, values) {
                @Override
                public void collect(int doc, long owningBucketOrd) throws IOException {
                    if (values.advanceExact(doc)) {
                        int valuesCount = values.docValueCount();
                        for (int i = 0; i < valuesCount; ++i) {
                            long value = values.nextValue();
                            // do something with value
                        }
                    }
                }
            };
        }
    }
```

The `terms` aggregation already does this, and e.g. `min` and `max` aggregations use similar tricks that also help optimize the single-valued case. But `date_histogram`, which is one of our most common aggregations, doesn't. Let's specialize the single-value case for date histograms too?
server/src/test/java/org/elasticsearch/search/aggregations/bucket/histogram/DateHistogramAggregatorTests.java
for (String date : dataset) {
                    long instant = asLong(date, fieldType);
                    document.add(new SortedNumericDocValuesField(AGGREGABLE_DATE, instant));
                    if (aggregableDateIsSearchable) {
                        document.add(new LongPoint(AGGREGABLE_DATE, instant));
                    }
                    document.add(new LongPoint(SEARCHABLE_DATE, instant));
                    indexWriter.addDocument(document);
                    document.clear();
                }
            }
            try (IndexReader indexReader = DirectoryReader.open(directory)) {
                IndexSearcher indexSearcher = newSearcher(indexReader, true, true);
                DateHistogramAggregationBuilder aggregationBuilder = new DateHistogramAggregationBuilder("_name");
                if (configure != null) {
                    configure.accept(aggregationBuilder);
                }
                InternalDateHistogram histogram = searchAndReduce(indexSearcher, query, aggregationBuilder, maxBucket, fieldType);
                verify.accept(histogram);
            }
        }
    }
    private static long asLong(String dateTime) {
        return DateFormatters.from(DateFieldMapper.DEFAULT_DATE_TIME_FORMATTER.parse(dateTime)).toInstant().toEpochMilli();
    }
    private static long asLong(String dateTime, DateFieldMapper.DateFieldType fieldType) {
        return fieldType.parse(dateTime);
    }
}