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https://issues.apache.org/jira/browse/IGNITE-8732?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17083787#comment-17083787
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Stanislav Lukyanov commented on IGNITE-8732:
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I'm now working on a draft of the approach #2 - treat REPLICATED as PARTITIONED.
It seems to be doable relatively cheap. The limitation with this approach is
that it requires the primary partitions of REPLICATED and PARTITIONED caches to
be collocated, i.e. the REPLICATED cache needs to have the same affinity key
and affinity function.
> SQL: REPLICATED cache cannot be left-joined to PARTITIONED
> ----------------------------------------------------------
>
> Key: IGNITE-8732
> URL: https://issues.apache.org/jira/browse/IGNITE-8732
> Project: Ignite
> Issue Type: Improvement
> Components: sql
> Affects Versions: 2.5
> Reporter: Vladimir Ozerov
> Priority: Major
> Labels: sql-engine
>
> *Steps to reproduce*
> # Run
> {{org.apache.ignite.sqltests.ReplicatedSqlTest#testLeftJoinReplicatedPartitioned}}
> # Observe that we have 2x results on 2-node cluster
> *Root Cause*
> {{left LEFT JOIN right ON cond}} operation assumes full scan of of a left
> expression. Currently we perform this scan on every node and then simply
> merge results on reducer. Two nodes, two scans of {{REPLICATED}} cache, 2x
> results.
> *Potential Solutions*
> We may consider several solutions. Deeper analysis is required to understand
> which is the right one.
> # Perform deduplication on reducer - this most prospective and general
> technique, described in more details below
> # Treat {{REPLICATED}} cache as {{PARTITIONED}}. Essentially, we just need to
> pass proper backup filter. But what if {{REPLICATED}} cache spans more nodes
> than {{PARTITIONED}}? We cannot rely on primary/backup in this case
> # Implement additional execution phase as follows:
> {code}
> SELECT left.cols, right.cols FROM left INNER JOIN right ON cond;
> // Get "inner join" part
> UNION
> UNICAST SELECT left.cols, [NULL].cols FROM left WHERE left.id NOT IN ([ids
> from the first phase]) // Get "outer join" part
> {code}
> *Reducer Deduplication*
> The idea is to get all data locally and then perform final deduplication.
> This may incur high network overhead, because of lot of duplicated left parts
> would be transferred. However, this could be optimized greatly with the
> following techniques applied one after another
> # Semi-jions: {{left}} is {{joined}} on mapper node, but instead of sending
> {{(left, right)}} relation, we send {{(left) + (right)}}
> # In case {{left}} part is known to be idempotent (i.e. it produces the same
> result set on all nodes), only one node will send {{(left) + (right)}}, other
> nodes will send {{(right)}} only
> # Merge {{left}} results with if needed (i.e. if idempotence-related opto was
> not applicable)
> # Join {{left}} and {{right}} parts on reducer
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