Add Result Cache executor node (take 2)

Here we add a new executor node type named "Result Cache".  The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins.  This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again.  Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.

For certain data sets, this can significantly improve the performance of
joins.  The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join.  In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch.  Merge joins would have to
skip over all of the unmatched rows.  If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join.  The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large.  Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join.  This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does.  The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables.  Smaller hash tables generally perform better.

The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size.  We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.

For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node.  We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be.  Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.

For now, the planner will only consider using a result cache for
parameterized nested loop joins.  This works for both normal joins and
also for LATERAL type joins to subqueries.  It is possible to use this new
node for other uses in the future.  For example, to cache results from
correlated subqueries.  However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio.  Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.

The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations.  With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be.   In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%.  Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join.   However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values.  If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join.  Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature.  Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.

For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache.  However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default.  There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression.  Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default.  It remains to be seen if we'll
maintain that setting for the release.

Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch.  Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people.  If there's some consensus on a better name, then we can
change it before the release.  Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.

Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu, Hou Zhijie
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com

1 files changed, 78 insertions, 53 deletions

diff --git a/src/test/regress/expected/join.out b/src/test/regress/expected/join.out
index 04e802d421..86fd3907c5 100644
--- a/src/test/regress/expected/join.out
+++ b/src/test/regress/expected/join.out
@@ -2536,6 +2536,7 @@ reset enable_nestloop;
 --
 set work_mem to '64kB';
 set enable_mergejoin to off;
+set enable_resultcache to off;
 explain (costs off)
 select count(*) from tenk1 a, tenk1 b
   where a.hundred = b.thousand and (b.fivethous % 10) < 10;
@@ -2559,6 +2560,7 @@ select count(*) from tenk1 a, tenk1 b
 
 reset work_mem;
 reset enable_mergejoin;
+reset enable_resultcache;
 --
 -- regression test for 8.2 bug with improper re-ordering of left joins
 --
@@ -3663,8 +3665,8 @@ select * from tenk1 t1 left join
   (tenk1 t2 join tenk1 t3 on t2.thousand = t3.unique2)
   on t1.hundred = t2.hundred and t1.ten = t3.ten
 where t1.unique1 = 1;
-                       QUERY PLAN                       
---------------------------------------------------------
+                          QUERY PLAN                          
+--------------------------------------------------------------
  Nested Loop Left Join
    ->  Index Scan using tenk1_unique1 on tenk1 t1
          Index Cond: (unique1 = 1)
@@ -3674,17 +3676,19 @@ where t1.unique1 = 1;
                Recheck Cond: (t1.hundred = hundred)
                ->  Bitmap Index Scan on tenk1_hundred
                      Index Cond: (hundred = t1.hundred)
-         ->  Index Scan using tenk1_unique2 on tenk1 t3
-               Index Cond: (unique2 = t2.thousand)
-(11 rows)
+         ->  Result Cache
+               Cache Key: t2.thousand
+               ->  Index Scan using tenk1_unique2 on tenk1 t3
+                     Index Cond: (unique2 = t2.thousand)
+(13 rows)
 
 explain (costs off)
 select * from tenk1 t1 left join
   (tenk1 t2 join tenk1 t3 on t2.thousand = t3.unique2)
   on t1.hundred = t2.hundred and t1.ten + t2.ten = t3.ten
 where t1.unique1 = 1;
-                       QUERY PLAN                       
---------------------------------------------------------
+                          QUERY PLAN                          
+--------------------------------------------------------------
  Nested Loop Left Join
    ->  Index Scan using tenk1_unique1 on tenk1 t1
          Index Cond: (unique1 = 1)
@@ -3694,9 +3698,11 @@ where t1.unique1 = 1;
                Recheck Cond: (t1.hundred = hundred)
                ->  Bitmap Index Scan on tenk1_hundred
                      Index Cond: (hundred = t1.hundred)
-         ->  Index Scan using tenk1_unique2 on tenk1 t3
-               Index Cond: (unique2 = t2.thousand)
-(11 rows)
+         ->  Result Cache
+               Cache Key: t2.thousand
+               ->  Index Scan using tenk1_unique2 on tenk1 t3
+                     Index Cond: (unique2 = t2.thousand)
+(13 rows)
 
 explain (costs off)
 select count(*) from
@@ -4210,8 +4216,8 @@ where t1.f1 = ss.f1;
                     QUERY PLAN                    
 --------------------------------------------------
  Nested Loop
-   Output: t1.f1, i8.q1, i8.q2, (i8.q1), t2.f1
-   Join Filter: (t1.f1 = t2.f1)
+   Output: t1.f1, i8.q1, i8.q2, q1, f1
+   Join Filter: (t1.f1 = f1)
    ->  Nested Loop Left Join
          Output: t1.f1, i8.q1, i8.q2
          ->  Seq Scan on public.text_tbl t1
@@ -4221,11 +4227,14 @@ where t1.f1 = ss.f1;
                ->  Seq Scan on public.int8_tbl i8
                      Output: i8.q1, i8.q2
                      Filter: (i8.q2 = 123)
-   ->  Limit
-         Output: (i8.q1), t2.f1
-         ->  Seq Scan on public.text_tbl t2
-               Output: i8.q1, t2.f1
-(16 rows)
+   ->  Result Cache
+         Output: q1, f1
+         Cache Key: i8.q1
+         ->  Limit
+               Output: (i8.q1), t2.f1
+               ->  Seq Scan on public.text_tbl t2
+                     Output: i8.q1, t2.f1
+(19 rows)
 
 select * from
   text_tbl t1
@@ -4246,13 +4255,13 @@ select * from
   lateral (select i8.q1, t2.f1 from text_tbl t2 limit 1) as ss1,
   lateral (select ss1.* from text_tbl t3 limit 1) as ss2
 where t1.f1 = ss2.f1;
-                            QUERY PLAN                             
--------------------------------------------------------------------
+                       QUERY PLAN                       
+--------------------------------------------------------
  Nested Loop
-   Output: t1.f1, i8.q1, i8.q2, (i8.q1), t2.f1, ((i8.q1)), (t2.f1)
-   Join Filter: (t1.f1 = (t2.f1))
+   Output: t1.f1, i8.q1, i8.q2, q1, f1, q1, f1
+   Join Filter: (t1.f1 = f1)
    ->  Nested Loop
-         Output: t1.f1, i8.q1, i8.q2, (i8.q1), t2.f1
+         Output: t1.f1, i8.q1, i8.q2, q1, f1
          ->  Nested Loop Left Join
                Output: t1.f1, i8.q1, i8.q2
                ->  Seq Scan on public.text_tbl t1
@@ -4262,15 +4271,21 @@ where t1.f1 = ss2.f1;
                      ->  Seq Scan on public.int8_tbl i8
                            Output: i8.q1, i8.q2
                            Filter: (i8.q2 = 123)
+         ->  Result Cache
+               Output: q1, f1
+               Cache Key: i8.q1
+               ->  Limit
+                     Output: (i8.q1), t2.f1
+                     ->  Seq Scan on public.text_tbl t2
+                           Output: i8.q1, t2.f1
+   ->  Result Cache
+         Output: q1, f1
+         Cache Key: q1, f1
          ->  Limit
-               Output: (i8.q1), t2.f1
-               ->  Seq Scan on public.text_tbl t2
-                     Output: i8.q1, t2.f1
-   ->  Limit
-         Output: ((i8.q1)), (t2.f1)
-         ->  Seq Scan on public.text_tbl t3
-               Output: (i8.q1), t2.f1
-(22 rows)
+               Output: (q1), (f1)
+               ->  Seq Scan on public.text_tbl t3
+                     Output: q1, f1
+(28 rows)
 
 select * from
   text_tbl t1
@@ -4316,14 +4331,17 @@ where tt1.f1 = ss1.c0;
                      ->  Seq Scan on public.text_tbl tt4
                            Output: tt4.f1
                            Filter: (tt4.f1 = 'foo'::text)
-   ->  Subquery Scan on ss1
+   ->  Result Cache
          Output: ss1.c0
-         Filter: (ss1.c0 = 'foo'::text)
-         ->  Limit
-               Output: (tt4.f1)
-               ->  Seq Scan on public.text_tbl tt5
-                     Output: tt4.f1
-(29 rows)
+         Cache Key: tt4.f1
+         ->  Subquery Scan on ss1
+               Output: ss1.c0
+               Filter: (ss1.c0 = 'foo'::text)
+               ->  Limit
+                     Output: (tt4.f1)
+                     ->  Seq Scan on public.text_tbl tt5
+                           Output: tt4.f1
+(32 rows)
 
 select 1 from
   text_tbl as tt1
@@ -4997,34 +5015,40 @@ select count(*) from tenk1 a, lateral generate_series(1,two) g;
 
 explain (costs off)
   select count(*) from tenk1 a, lateral generate_series(1,two) g;
-                   QUERY PLAN                   
-------------------------------------------------
+                      QUERY PLAN                      
+------------------------------------------------------
  Aggregate
    ->  Nested Loop
          ->  Seq Scan on tenk1 a
-         ->  Function Scan on generate_series g
-(4 rows)
+         ->  Result Cache
+               Cache Key: a.two
+               ->  Function Scan on generate_series g
+(6 rows)
 
 explain (costs off)
   select count(*) from tenk1 a cross join lateral generate_series(1,two) g;
-                   QUERY PLAN                   
-------------------------------------------------
+                      QUERY PLAN                      
+------------------------------------------------------
  Aggregate
    ->  Nested Loop
          ->  Seq Scan on tenk1 a
-         ->  Function Scan on generate_series g
-(4 rows)
+         ->  Result Cache
+               Cache Key: a.two
+               ->  Function Scan on generate_series g
+(6 rows)
 
 -- don't need the explicit LATERAL keyword for functions
 explain (costs off)
   select count(*) from tenk1 a, generate_series(1,two) g;
-                   QUERY PLAN                   
-------------------------------------------------
+                      QUERY PLAN                      
+------------------------------------------------------
  Aggregate
    ->  Nested Loop
          ->  Seq Scan on tenk1 a
-         ->  Function Scan on generate_series g
-(4 rows)
+         ->  Result Cache
+               Cache Key: a.two
+               ->  Function Scan on generate_series g
+(6 rows)
 
 -- lateral with UNION ALL subselect
 explain (costs off)
@@ -5079,14 +5103,15 @@ explain (costs off)
                             QUERY PLAN                            
 ------------------------------------------------------------------
  Aggregate
-   ->  Hash Join
-         Hash Cond: ("*VALUES*".column1 = b.unique2)
+   ->  Nested Loop
          ->  Nested Loop
                ->  Index Only Scan using tenk1_unique1 on tenk1 a
                ->  Values Scan on "*VALUES*"
-         ->  Hash
+         ->  Result Cache
+               Cache Key: "*VALUES*".column1
                ->  Index Only Scan using tenk1_unique2 on tenk1 b
-(8 rows)
+                     Index Cond: (unique2 = "*VALUES*".column1)
+(9 rows)
 
 select count(*) from tenk1 a,
   tenk1 b join lateral (values(a.unique1),(-1)) ss(x) on b.unique2 = ss.x;