Datehistogram improvement #170
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Signed-off-by: bowenlan-amzn <[email protected]>
Signed-off-by: bowenlan-amzn <[email protected]>
jainankitk
reviewed
Nov 17, 2023
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| while (i < targetBuckets) { | ||
| // Calculate the lower bucket bound | ||
| final byte[] lower = new byte[8]; | ||
| NumericUtils.longToSortableBytes(Math.max(roundedLow, low), lower, 0); | ||
| // Calculate the upper bucket bound | ||
| final byte[] upper = new byte[8]; | ||
| roundedLow = preparedRounding.round(roundedLow + interval); | ||
| // Subtract -1 if the minimum is roundedLow as roundedLow itself | ||
| // is included in the next bucket | ||
| NumericUtils.longToSortableBytes(Math.min(roundedLow - 1, high), upper, 0); | ||
|
|
||
| filters[i++] = context.searcher().createWeight(new PointRangeQuery(field, lower, upper, 1) { |
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This logic has problem since we are focused on creating the required number of target buckets, instead we should limit to the upper and lower bounds. For example, I see the following response:
% curl -s -X GET "localhost:9200/nyc_taxis/_search?pretty" -H 'Content-Type: application/json' -d'{"size": 0,"query": {"range": {"dropoff_datetime": {"gte": "2015-01-01 01:04:06","lt": "2016-01-01 00:00:00"}}},"aggs": {"dropoffs_over_time": {"auto_date_histogram": {"field": "dropoff_datetime","buckets": "4"}}}}'
{
"took" : 4295,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 100,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"dropoffs_over_time" : {
"buckets" : [
{
"key_as_string" : "2015-01-01 00:00:00",
"key" : 1420070400000,
"doc_count" : 100
},
{
"key_as_string" : "2016-01-01 00:00:00",
"key" : 1451606400000,
"doc_count" : 0
},
{
"key_as_string" : "2017-01-01 00:00:00",
"key" : 1483228800000,
"doc_count" : 0
}
],
"interval" : "1y"
}
}
}
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| roundingInfosLoop: do { | ||
| RoundingInfo curRoundingInfo = roundingInfos[roundingIdx]; | ||
| for (int curInnerInterval: curRoundingInfo.innerIntervals) { | ||
| if (bestDuration <= curInnerInterval * curRoundingInfo.roughEstimateDurationMillis) { | ||
| interval = curInnerInterval * curRoundingInfo.roughEstimateDurationMillis; | ||
| break roundingInfosLoop; | ||
| } | ||
| } | ||
| roundingIdx++; |
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Probably need to move the preparedRounding as well to the next index? Else it is always staying at 0 (second level)
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| for (i = 0; i < filters.length; i++) { | ||
| long bucketOrd = bucketOrds.add( | ||
| owningBucketOrd, | ||
| preparedRounding.round(NumericUtils.sortableBytesToLong(((PointRangeQuery) filters[i].getQuery()).getLowerPoint(), 0)) | ||
| ); | ||
| if (bucketOrd < 0) { // already seen | ||
| bucketOrd = -1 - bucketOrd; | ||
| } | ||
| incrementBucketDocCount(bucketOrd, counts[i]); | ||
| } | ||
| throw new CollectionTerminatedException(); |
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We can be aggressive during bucket creation, and invoke the logic to merge buckets if it exceeds the target bucket count:
do {
try (LongKeyedBucketOrds oldOrds = bucketOrds) {
preparedRounding = prepareRounding(++roundingIdx);
long[] mergeMap = new long[Math.toIntExact(oldOrds.size())];
bucketOrds = new LongKeyedBucketOrds.FromSingle(context.bigArrays());
LongKeyedBucketOrds.BucketOrdsEnum ordsEnum = oldOrds.ordsEnum(0);
while (ordsEnum.next()) {
long oldKey = ordsEnum.value();
long newKey = preparedRounding.round(oldKey);
long newBucketOrd = bucketOrds.add(0, newKey);
mergeMap[(int) ordsEnum.ord()] = newBucketOrd >= 0 ? newBucketOrd : -1 - newBucketOrd;
}
merge(mergeMap, bucketOrds.size());
}
} while (roundingIdx < roundingInfos.length - 1
&& (bucketOrds.size() > targetBuckets * roundingInfos[roundingIdx].getMaximumInnerInterval()
|| max - min > targetBuckets * roundingInfos[roundingIdx].getMaximumRoughEstimateDurationMillis()));
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Description
[Describe what this change achieves]
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Resolves #[Issue number to be closed when this PR is merged]
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