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https://issues.apache.org/jira/browse/LUCENE-10427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17514145#comment-17514145
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Suhan Mao commented on LUCENE-10427:
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[~jpountz] Sorry for the late reply and thanks for your suggestion. I
understand computing rollup during index time is easy to implement, but there's
still some drawback that should be taken into consideration.
It will slow down the index performance because it need to take extra actions
compared to append-only action
1. invoke a term query to retrieve the all the fields
2. compute rollup logic and save to new fields
3. delete original doc
4. index new fields
I think the most concerned point is that you do not want change the lucene
merge semantics, so I come up with a new approach to do rollup.
# The index sorting refer to all dimension fields.
# The index is written as a normal one and we continuously append documents.
# Run a special rollup scheduler and do a merge sort on doc values files from
different segments and do rollup computation in a streaming way. It can be
implemented inside the lucene or we can do it externally.
# Append new calculated doc in the index and delete docs from original
segments.
# Normal segment merges can still work to recycle deleted docs/segments.
# There will be some interaction between rollup scheduler and merge scheduler
to make the whole process more smoothly.
What do you think?
> OLAP likewise rollup during segment merge process
> -------------------------------------------------
>
> Key: LUCENE-10427
> URL: https://issues.apache.org/jira/browse/LUCENE-10427
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Suhan Mao
> Priority: Major
>
> Currently, many OLAP engines support rollup feature like
> clickhouse(AggregateMergeTree)/druid.
> Rollup definition: [https://athena.ecs.csus.edu/~mei/olap/OLAPoperations.php]
> One of the way to do rollup is to merge the same dimension buckets into one
> and do sum()/min()/max() operation on metric fields during segment
> compact/merge process. This can significantly reduce the size of the data and
> speed up the query a lot.
>
> *Abstraction of how to do*
> # Define rollup logic: which is dimensions and metrics.
> # Rollup definition for each metric field: max/min/sum ...
> # index sorting should the the same as dimension fields.
> # We will do rollup calculation during segment merge just like other OLAP
> engine do.
>
> *Assume the scenario*
> We use ES to ingest realtime raw temperature data every minutes of each
> sensor device along with many dimension information. User may want to query
> the data like "what is the max temperature of some device within some/latest
> hour" or "what is the max temperature of some city within some/latest hour"
> In that way, we can define such fields and rollup definition:
> # event_hour(round to hour granularity)
> # device_id(dimension)
> # city_id(dimension)
> # temperature(metrics, max/min rollup logic)
> The raw data will periodically be rolled up to the hour granularity during
> segment merge process, which should save 60x storage ideally in the end.
>
> *How we do rollup in segment merge*
> bucket: docs should belong to the same bucket if the dimension values are all
> the same.
> # For docvalues merge, we send the normal mappedDocId if we encounter a new
> bucket in DocIDMerger.
> # Since the index sorting fields are the same with dimension fields. if we
> encounter more docs in the same bucket, We emit special mappedDocId from
> DocIDMerger .
> # In DocValuesConsumer.mergeNumericField, if we meet special mappedDocId, we
> do a rollup calculation on metric fields and fold the result value to the
> first doc in the bucket. The calculation just like a streaming merge sort
> rollup.
> # We discard all the special mappedDocId docs because the metrics is already
> folded to the first doc of in the bucket.
> # In BKD/posting structure, we discard all the special mappedDocId docs and
> only place the first doc id within a bucket in the BKD/posting data. It
> should be simple.
>
> *How to define the logic*
>
> {code:java}
> public class RollupMergeConfig {
> private List<String> dimensionNames;
> private List<RollupMergeAggregateField> aggregateFields;
> }
> public class RollupMergeAggregateField {
> private String name;
> private RollupMergeAggregateType aggregateType;
> }
> public enum RollupMergeAggregateType {
> COUNT,
> SUM,
> MIN,
> MAX,
> CARDINALITY // if data sketch is stored in binary doc values, we can do a
> union logic
> }{code}
>
>
> I have written the initial code in a basic level. I can submit the complete
> PR if you think this feature is good to try.
>
>
>
>
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