Maksim Timonin created IGNITE-24688:
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             Summary: Add FILTER_CORRELATE rule to HEP push down list
                 Key: IGNITE-24688
                 URL: https://issues.apache.org/jira/browse/IGNITE-24688
             Project: Ignite
          Issue Type: Improvement
            Reporter: Maksim Timonin
            Assignee: Maksim Timonin


 

In the plan below Filter rule is under IgniteTableScan. This leads to memory 
overhead. Let's fix it by adding new filter push down rule - Calcite's 
FILTER_CORRELATE rule

 
{code:java}
IgniteColocatedSortAggregate(group=[{0}], ORDER_COUNT=[COUNT()], collation=[[0 
ASC-nulls-first]]): rowcount = 187500.0, cumulative cost = IgniteCost 
[rowCount=4.500912075E13, cpu=3.60073862443905E13, memory=1.08021942000009E14, 
io=0.0, network=1.08021936E14], id = 2193
  IgniteProject(O_ORDERPRIORITY=[$7]): rowcount = 375000.0, cumulative cost = 
IgniteCost [rowCount=4.5009120375E13, cpu=3.60073858693905E13, 
memory=1.08021942E14, io=0.0, network=1.08021936E14], id = 2192
    IgniteFilter(condition=[AND(>=($6, 1998-01-01), <($6, +(1998-01-01, 
3:INTERVAL MONTH)))]): rowcount = 375000.0, cumulative cost = IgniteCost 
[rowCount=4.500912E13, cpu=3.60073854943905E13, memory=1.08021942E14, io=0.0, 
network=1.08021936E14], id = 2191
      IgniteCorrelatedNestedLoopJoin(condition=[true], joinType=[inner], 
variablesSet=[[$cor0]], variablesSet=[[0]], correlationVariables=[[$cor0]]): 
rowcount = 1500000.0, cumulative cost = IgniteCost [rowCount=4.50091185E13, 
cpu=3.60073794943905E13, memory=1.08021942E14, io=0.0, network=1.08021936E14], 
id = 2190
        IgniteExchange(distribution=[single]): rowcount = 1500000.0, cumulative 
cost = IgniteCost [rowCount=4500000.0, cpu=6.849439049732597E7, memory=6.6E7, 
io=0.0, network=6.6E7], id = 2185
          IgniteSort(sort0=[$7], dir0=[ASC-nulls-first]): rowcount = 1500000.0, 
cumulative cost = IgniteCost [rowCount=3000000.0, cpu=6.699439049732597E7, 
memory=6.6E7, io=0.0, network=0.0], id = 2184
            IgniteTableScan(table=[[PUBLIC, ORDERS]]): rowcount = 1500000.0, 
cumulative cost = IgniteCost [rowCount=1500000.0, cpu=1500000.0, memory=0.0, 
io=0.0, network=0.0], id = 172
        IgniteColocatedHashAggregate(group=[{0}]): rowcount = 1.0, cumulative 
cost = IgniteCost [rowCount=3.0006075E7, cpu=2.400487E7, memory=7.2014584E7, 
io=0.0, network=7.201458E7], id = 2189
          IgniteProject(i=[true]): rowcount = 6001215.0, cumulative cost = 
IgniteCost [rowCount=2.400486E7, cpu=1.8003655E7, memory=7.201458E7, io=0.0, 
network=7.201458E7], id = 2188
            IgniteHashIndexSpool(readType=[LAZY], writeType=[EAGER], 
searchRow=[[$cor0.O_ORDERKEY, null, null]], condition=[AND(=($0, 
$cor0.O_ORDERKEY), <($1, $2))], allowNulls=[false]): rowcount = 6001215.0, 
cumulative cost = IgniteCost [rowCount=1.8003645E7, cpu=1.200244E7, 
memory=7.201458E7, io=0.0, network=7.201458E7], id = 2187
              IgniteExchange(distribution=[single]): rowcount = 6001215.0, 
cumulative cost = IgniteCost [rowCount=1.200243E7, cpu=1.200243E7, memory=0.0, 
io=0.0, network=7.201458E7], id = 2186
                IgniteTableScan(table=[[PUBLIC, LINEITEM]], 
requiredColumns=[{2, 13, 14}]): rowcount = 6001215.0, cumulative cost = 
IgniteCost [rowCount=6001215.0, cpu=6001215.0, memory=0.0, io=0.0, 
network=0.0], id = 224
 {code}
 

 



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