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https://issues.apache.org/jira/browse/LUCENE-10427?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17506402#comment-17506402
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Adrien Grand commented on LUCENE-10427:
---------------------------------------
Thanks I understand better now. With the sidecar approach, could you compute
rollups at index time by performing updates instead of hooking into the merging
process? For instance if a user is adding a new sample, you could retrieve data
for the current <your-data-granularity-goes-here> bucket for the given
dimensions and update the min/max/sum values?
> 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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