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https://issues.apache.org/jira/browse/HIVE-23880?focusedWorklogId=462171&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-462171
]
ASF GitHub Bot logged work on HIVE-23880:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 22/Jul/20 17:30
Start Date: 22/Jul/20 17:30
Worklog Time Spent: 10m
Work Description: abstractdog commented on a change in pull request #1280:
URL: https://github.com/apache/hive/pull/1280#discussion_r458962698
##########
File path: common/src/java/org/apache/hadoop/hive/conf/HiveConf.java
##########
@@ -4285,6 +4285,8 @@ private static void populateLlapDaemonVarsSet(Set<String>
llapDaemonVarsSetLocal
"Bloom filter should be of at max certain size to be effective"),
TEZ_BLOOM_FILTER_FACTOR("hive.tez.bloom.filter.factor", (float) 1.0,
"Bloom filter should be a multiple of this factor with nDV"),
+ TEZ_BLOOM_FILTER_MERGE_THREADS("hive.tez.bloom.filter.merge.threads", 8,
Review comment:
agree, you'll see that the new version of PR will default to 0 which
causes automatic thread number calculation
but I would leave this option as an "expert setting"...e.g.: explicitly
setting a value for testing purposes, or setting -1 for sanity check (it will
fail with negative values, so you can double-check if bloom filter merge really
happens in your query)
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Issue Time Tracking
-------------------
Worklog Id: (was: 462171)
Time Spent: 2h 40m (was: 2.5h)
> Bloom filters can be merged in a parallel way in VectorUDAFBloomFilterMerge
> ---------------------------------------------------------------------------
>
> Key: HIVE-23880
> URL: https://issues.apache.org/jira/browse/HIVE-23880
> Project: Hive
> Issue Type: Improvement
> Reporter: László Bodor
> Assignee: László Bodor
> Priority: Major
> Labels: pull-request-available
> Attachments: lipwig-output3605036885489193068.svg
>
> Time Spent: 2h 40m
> Remaining Estimate: 0h
>
> Merging bloom filters in semijoin reduction can become the main bottleneck in
> case of large number of source mapper tasks (~1000, Map 1 in below example)
> and a large amount of expected entries (50M) in bloom filters.
> For example in TPCDS Q93:
> {code}
> select /*+ semi(store_returns, sr_item_sk, store_sales, 70000000)*/
> ss_customer_sk
> ,sum(act_sales) sumsales
> from (select ss_item_sk
> ,ss_ticket_number
> ,ss_customer_sk
> ,case when sr_return_quantity is not null then
> (ss_quantity-sr_return_quantity)*ss_sales_price
> else
> (ss_quantity*ss_sales_price) end act_sales
> from store_sales left outer join store_returns on (sr_item_sk =
> ss_item_sk
> and
> sr_ticket_number = ss_ticket_number)
> ,reason
> where sr_reason_sk = r_reason_sk
> and r_reason_desc = 'reason 66') t
> group by ss_customer_sk
> order by sumsales, ss_customer_sk
> limit 100;
> {code}
> On 10TB-30TB scale there is a chance that from 3-4 mins of query runtime 1-2
> mins are spent with merging bloom filters (Reducer 2), as in:
> [^lipwig-output3605036885489193068.svg]
> {code}
> ----------------------------------------------------------------------------------------------
> VERTICES MODE STATUS TOTAL COMPLETED RUNNING PENDING
> FAILED KILLED
> ----------------------------------------------------------------------------------------------
> Map 3 .......... llap SUCCEEDED 1 1 0 0
> 0 0
> Map 1 .......... llap SUCCEEDED 1263 1263 0 0
> 0 0
> Reducer 2 llap RUNNING 1 0 1 0
> 0 0
> Map 4 llap RUNNING 6154 0 207 5947
> 0 0
> Reducer 5 llap INITED 43 0 0 43
> 0 0
> Reducer 6 llap INITED 1 0 0 1
> 0 0
> ----------------------------------------------------------------------------------------------
> VERTICES: 02/06 [====>>----------------------] 16% ELAPSED TIME: 149.98 s
> ----------------------------------------------------------------------------------------------
> {code}
> For example, 70M entries in bloom filter leads to a 436 465 696 bits, so
> merging 1263 bloom filters means running ~ 1263 * 436 465 696 bitwise OR
> operation, which is very hot codepath, but can be parallelized.
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