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https://issues.apache.org/jira/browse/CALCITE-299?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16811737#comment-16811737
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Haisheng Yuan commented on CALCITE-299:
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[~julianhyde] Is this still an issue?
> Divide the join graph into partitions that can be optimized separately
> ----------------------------------------------------------------------
>
> Key: CALCITE-299
> URL: https://issues.apache.org/jira/browse/CALCITE-299
> Project: Calcite
> Issue Type: Bug
> Reporter: GitHub Import
> Priority: Major
> Labels: github-import
>
> Currently Optiq's join optimization is exhaustive. The search space increases
> combinatorially, so for a query with more than about 8 joins, this is
> impractical. I propose a "divide and conquer" approach that tames the search
> space but doesn't cut out the important parts of the search space.
> First, there is a planning phase that gathers together all joins into a
> single mega relational expression, MultiJoinRel.
> Then an algorithm builds a join graph, and finds a sub-graph (tree) that
> minimizes some cost function. Then divide that sub-graph into clusters; the
> nodes in a cluster are tightly connected, and the links between clusters are
> relatively loosely connected.
> The output of this algorithm is the graph, partitioned into moderately sized
> groups of nodes (say 5 - 7 maximum). Typically, in a multi-star query, each
> group of nodes would have a "fact table" at its center, surrounded by
> "dimension tables" connected by many-to-one relationships. (Fact table and
> dimension table are relative terms.)
> We put in place fire walls (maybe a special kind of RelNode) to prevent join
> transformation rules from working across clusters. Then we invoke the
> conventional planning process.
> ---------------- Imported from GitHub ----------------
> Url: https://github.com/julianhyde/optiq/issues/299
> Created by: [julianhyde|https://github.com/julianhyde]
> Labels:
> Created at: Wed Jun 11 23:24:08 CEST 2014
> State: open
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