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From "Mostafa Mokhtar (JIRA)" <j...@apache.org>
Subject [jira] [Created] (HIVE-8044) CBO : Container size and hash table size should be taken into account before deciding to do a MapJoin
Date Wed, 10 Sep 2014 20:49:34 GMT
Mostafa Mokhtar created HIVE-8044:
-------------------------------------

             Summary: CBO : Container size and hash table size should be taken into account
before deciding to do a MapJoin
                 Key: HIVE-8044
                 URL: https://issues.apache.org/jira/browse/HIVE-8044
             Project: Hive
          Issue Type: Bug
          Components: CBO
    Affects Versions: 0.14.0, 0.13.1
            Reporter: Mostafa Mokhtar
            Assignee: Harish Butani
             Fix For: 0.14.0


Currently CBO uses NDV not join selectivity in computeInnerJoinSelectivity which results in
in-accurate estimate number of rows.

I looked at the plan for TPC-DS Q17 after the latest set of changes and I am concerned that
the estimate of rows for the join of store_sales and store_returns is so low, as you can see
the estimate is 8461 rows for joining 1.2795706667449066E8 with 1.2922108035889767E7.

{code}
    HiveJoinRel(condition=[AND(=($130, $3), =($129, $15))], joinType=[inner]): rowcount =
1079.1345153548855, cumulative cost = {8.271845957931738E10 rows, 0.0 cpu, 0.0 io}, id = 517
                  HiveJoinRel(condition=[=($0, $38)], joinType=[inner]): rowcount = 6.669190301841249E7,
cumulative cost = {4.300510912631623E10 rows, 0.0 cpu, 0.0 io}, id = 402
                    HiveTableScanRel(table=[[catalog_sales]]): rowcount = 4.3005109025E10,
cumulative cost = {0}, id = 2
                    HiveFilterRel(condition=[in($15, '2000Q1', '2000Q2', '2000Q3')]): rowcount
= 101.31622746185853, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 181
                      HiveTableScanRel(table=[[d3]]): rowcount = 73049.0, cumulative cost
= {0}, id = 3
                  HiveJoinRel(condition=[AND(AND(=($3, $61), =($2, $60)), =($9, $67))], joinType=[inner]):
rowcount = 8461.27236667537, cumulative cost = {8.26517592150266E10 rows, 0.0 cpu, 0.0 io},
id = 515
                    HiveJoinRel(condition=[=($27, $0)], joinType=[inner]): rowcount = 1.2795706667449066E8,
cumulative cost = {8.251088004031622E10 rows, 0.0 cpu, 0.0 io}, id = 417
                      HiveTableScanRel(table=[[store_sales]]): rowcount = 8.2510879939E10,
cumulative cost = {0}, id = 5
                      HiveFilterRel(condition=[=($15, '2000Q1')]): rowcount = 101.31622746185853,
cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 173
                        HiveTableScanRel(table=[[d1]]): rowcount = 73049.0, cumulative cost
= {0}, id = 0
                    HiveJoinRel(condition=[=($0, $24)], joinType=[inner]): rowcount = 1.2922108035889767E7,
cumulative cost = {8.332595810316228E9 rows, 0.0 cpu, 0.0 io}, id = 424
                      HiveTableScanRel(table=[[store_returns]]): rowcount = 8.332595709E9,
cumulative cost = {0}, id = 7
                      HiveFilterRel(condition=[in($15, '2000Q1', '2000Q2', '2000Q3')]): rowcount
= 101.31622746185853, cumulative cost = {0.0 rows, 0.0 cpu, 0.0 io}, id = 177
                        HiveTableScanRel(table=[[d2]]): rowcount = 73049.0, cumulative cost
= {0}, id = 1
{code}



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