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https://issues.apache.org/jira/browse/DRILL-6385?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16547976#comment-16547976
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Aman Sinha commented on DRILL-6385:
-----------------------------------
The design discussions are getting lost in among all the GitHub comments. I am
not sure how to avoid this in the JIRA.. In any case, [~weijie] regarding the
previous comments about broadcast hash join, my point was that the BF does not
save network I/O on probe (since there is no network transfer) and it does not
save memory (since blocking operators won't let BF pass through). So, the
question is how much does the reduction in the row count help hash join probe
given that there is a CPU cost associated with creating and evaluating a BF ?
{noformat}
Suppose original row count from table is N
Let selectivity of BF is s1 (fraction between 0 to 1)
So, original Hash Join probe side cost Cost_Orig =
I/O cost of Scanning N rows + Probe cost of N rows
With BF, new Hash Join probe side cost Cost_BF_Probe =
I/O cost of scanning N rows + CPU cost of Bloom Filter on N rows + Probe
cost of (s1 * N) rows
With BF, assume hash join build side cost is Cost_BF_build
So total_HJ_BF_Cost = Cost_BF_Probe + Cost_BF_build
{noformat}
Whether or not the second total cost is cheaper than the original depends on
selectivity and the actual cost of creating/evaluation bloom filter. So, all
I was saying previously was that for broadcast HJ, the expected benefits would
be less. We would only find out through some performance runs.
> Support JPPD (Join Predicate Push Down)
> ---------------------------------------
>
> Key: DRILL-6385
> URL: https://issues.apache.org/jira/browse/DRILL-6385
> Project: Apache Drill
> Issue Type: New Feature
> Components: Server, Execution - Flow
> Affects Versions: 1.14.0
> Reporter: weijie.tong
> Assignee: weijie.tong
> Priority: Major
>
> This feature is to support the JPPD (Join Predicate Push Down). It will
> benefit the HashJoin ,Broadcast HashJoin performance by reducing the number
> of rows to send across the network ,the memory consumed. This feature is
> already supported by Impala which calls it RuntimeFilter
> ([https://www.cloudera.com/documentation/enterprise/5-9-x/topics/impala_runtime_filtering.html]).
> The first PR will try to push down a bloom filter of HashJoin node to
> Parquet’s scan node. The propose basic procedure is described as follow:
> # The HashJoin build side accumulate the equal join condition rows to
> construct a bloom filter. Then it sends out the bloom filter to the foreman
> node.
> # The foreman node accept the bloom filters passively from all the fragments
> that has the HashJoin operator. It then aggregates the bloom filters to form
> a global bloom filter.
> # The foreman node broadcasts the global bloom filter to all the probe side
> scan nodes which maybe already have send out partial data to the hash join
> nodes(currently the hash join node will prefetch one batch from both sides ).
> 4. The scan node accepts a global bloom filter from the foreman node.
> It will filter the rest rows satisfying the bloom filter.
>
> To implement above execution flow, some main new notion described as below:
> 1. RuntimeFilter
> It’s a filter container which may contain BloomFilter or MinMaxFilter.
> 2. RuntimeFilterReporter
> It wraps the logic to send hash join’s bloom filter to the foreman.The
> serialized bloom filter will be sent out through the data tunnel.This object
> will be instanced by the FragmentExecutor and passed to the
> FragmentContext.So the HashJoin operator can obtain it through the
> FragmentContext.
> 3. RuntimeFilterRequestHandler
> It is responsible to accept a SendRuntimeFilterRequest RPC to strip the
> actual BloomFilter from the network. It then translates this filter to the
> WorkerBee’s new interface registerRuntimeFilter.
> Another RPC type is BroadcastRuntimeFilterRequest. It will register the
> accepted global bloom filter to the WorkerBee by the registerRuntimeFilter
> method and then propagate to the FragmentContext through which the probe side
> scan node can fetch the aggregated bloom filter.
> 4.RuntimeFilterManager
> The foreman will instance a RuntimeFilterManager .It will indirectly get
> every RuntimeFilter by the WorkerBee. Once all the BloomFilters have been
> accepted and aggregated . It will broadcast the aggregated bloom filter to
> all the probe side scan nodes through the data tunnel by a
> BroadcastRuntimeFilterRequest RPC.
> 5. RuntimeFilterEnableOption
> A global option will be added to decide whether to enable this new feature.
>
> Welcome suggestion and advice from you.The related PR will be presented as
> soon as possible.
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