Add Result Cache executor node

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 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

Add Result Cache executor node
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 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
b6002a79 · David Rowley · 6ec578e6 · b6002a79 · b6002a79 · b6002a79
Commit b6002a79 authored Apr 01, 2021 by David Rowley
45 changed files
--- a/contrib/postgres_fdw/expected/postgres_fdw.out
+++ b/contrib/postgres_fdw/expected/postgres_fdw.out
@@ -1602,6 +1602,7 @@ SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 LEFT JOIN ft2 t2 ON (t1.c1 = t2.c1) FULL
 20 |  0 | AAA020
 (10 rows)

+SET enable_resultcache TO off;
 -- right outer join + left outer join
 EXPLAIN (VERBOSE, COSTS OFF)
 SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 RIGHT JOIN ft2 t2 ON (t1.c1 = t2.c1) LEFT JOIN ft4 t3 ON (t2.c1 = t3.c1) OFFSET 10 LIMIT 10;
@@ -1628,6 +1629,7 @@ SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 RIGHT JOIN ft2 t2 ON (t1.c1 = t2.c1) LEFT
 20 |  0 | AAA020
 (10 rows)

+RESET enable_resultcache;
 -- left outer join + right outer join
 EXPLAIN (VERBOSE, COSTS OFF)
 SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 LEFT JOIN ft2 t2 ON (t1.c1 = t2.c1) RIGHT JOIN ft4 t3 ON (t2.c1 = t3.c1) OFFSET 10 LIMIT 10;
@@ -2140,13 +2142,16 @@ SELECT t1c1, avg(t1c1 + t2c1) FROM (SELECT t1.c1, t2.c1 FROM ft1 t1 JOIN ft2 t2
 EXPLAIN (VERBOSE, COSTS OFF)
 SELECT t1."C 1" FROM "S 1"."T 1" t1, LATERAL (SELECT DISTINCT t2.c1, t3.c1 FROM ft1 t2, ft2 t3 WHERE t2.c1 = t3.c1 AND t2.c2 = t1.c2) q ORDER BY t1."C 1" OFFSET 10 LIMIT 10;
                                                                                   QUERY PLAN                                                                                   
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Limit
   Output: t1."C 1"
   ->  Nested Loop
         Output: t1."C 1"
         ->  Index Scan using t1_pkey on "S 1"."T 1" t1
               Output: t1."C 1", t1.c2, t1.c3, t1.c4, t1.c5, t1.c6, t1.c7, t1.c8
+         ->  Result Cache
+               Cache Key: t1.c2
+               ->  Subquery Scan on q
                     ->  HashAggregate
                           Output: t2.c1, t3.c1
                           Group Key: t2.c1, t3.c1
@@ -2154,7 +2159,7 @@ SELECT t1."C 1" FROM "S 1"."T 1" t1, LATERAL (SELECT DISTINCT t2.c1, t3.c1 FROM
                                 Output: t2.c1, t3.c1
                                 Relations: (public.ft1 t2) INNER JOIN (public.ft2 t3)
                                 Remote SQL: SELECT r1."C 1", r2."C 1" FROM ("S 1"."T 1" r1 INNER JOIN "S 1"."T 1" r2 ON (((r1."C 1" = r2."C 1")) AND ((r1.c2 = $1::integer))))
-(13 rows)
+(16 rows)

 SELECT t1."C 1" FROM "S 1"."T 1" t1, LATERAL (SELECT DISTINCT t2.c1, t3.c1 FROM ft1 t2, ft2 t3 WHERE t2.c1 = t3.c1 AND t2.c2 = t1.c2) q ORDER BY t1."C 1" OFFSET 10 LIMIT 10;
 C 1 

--- a/contrib/postgres_fdw/sql/postgres_fdw.sql
+++ b/contrib/postgres_fdw/sql/postgres_fdw.sql
@@ -502,10 +502,12 @@ SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 FULL JOIN ft2 t2 ON (t1.c1 = t2.c1) LEFT
 EXPLAIN (VERBOSE, COSTS OFF)
 SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 LEFT JOIN ft2 t2 ON (t1.c1 = t2.c1) FULL JOIN ft4 t3 ON (t2.c1 = t3.c1) OFFSET 10 LIMIT 10;
 SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 LEFT JOIN ft2 t2 ON (t1.c1 = t2.c1) FULL JOIN ft4 t3 ON (t2.c1 = t3.c1) OFFSET 10 LIMIT 10;
+SET enable_resultcache TO off;
 -- right outer join + left outer join
 EXPLAIN (VERBOSE, COSTS OFF)
 SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 RIGHT JOIN ft2 t2 ON (t1.c1 = t2.c1) LEFT JOIN ft4 t3 ON (t2.c1 = t3.c1) OFFSET 10 LIMIT 10;
 SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 RIGHT JOIN ft2 t2 ON (t1.c1 = t2.c1) LEFT JOIN ft4 t3 ON (t2.c1 = t3.c1) OFFSET 10 LIMIT 10;
+RESET enable_resultcache;
 -- left outer join + right outer join
 EXPLAIN (VERBOSE, COSTS OFF)
 SELECT t1.c1, t2.c2, t3.c3 FROM ft2 t1 LEFT JOIN ft2 t2 ON (t1.c1 = t2.c1) RIGHT JOIN ft4 t3 ON (t2.c1 = t3.c1) OFFSET 10 LIMIT 10;

--- a/doc/src/sgml/config.sgml
+++ b/doc/src/sgml/config.sgml
@@ -1770,8 +1770,9 @@ include_dir 'conf.d'
        fact in mind when choosing the value.  Sort operations are used
        for <literal>ORDER BY</literal>, <literal>DISTINCT</literal>,
        and merge joins.
-        Hash tables are used in hash joins, hash-based aggregation, and
-        hash-based processing of <literal>IN</literal> subqueries.
+        Hash tables are used in hash joins, hash-based aggregation, result
+        cache nodes and hash-based processing of <literal>IN</literal>
+        subqueries.
       </para>
       <para>
        Hash-based operations are generally more sensitive to memory
@@ -4925,6 +4926,25 @@ ANY <replaceable class="parameter">num_sync</replaceable> ( <replaceable class="
      </listitem>
     </varlistentry>

+     <varlistentry id="guc-enable-resultcache" xreflabel="enable_resultcache">
+      <term><varname>enable_resultcache</varname> (<type>boolean</type>)
+      <indexterm>
+       <primary><varname>enable_resultcache</varname> configuration parameter</primary>
+      </indexterm>
+      </term>
+      <listitem>
+       <para>
+        Enables or disables the query planner's use of result cache plans for
+        caching results from parameterized scans inside nested-loop joins.
+        This plan type allows scans to the underlying plans to be skipped when
+        the results for the current parameters are already in the cache.  Less
+        commonly looked up results may be evicted from the cache when more
+        space is required for new entries. The default is
+        <literal>on</literal>.
+       </para>
+      </listitem>
+     </varlistentry>
+
     <varlistentry id="guc-enable-mergejoin" xreflabel="enable_mergejoin">
      <term><varname>enable_mergejoin</varname> (<type>boolean</type>)
      <indexterm>

--- a/src/backend/commands/explain.c
+++ b/src/backend/commands/explain.c
@@ -108,6 +108,8 @@ static void show_sort_info(SortState *sortstate, ExplainState *es);
 static void show_incremental_sort_info(IncrementalSortState *incrsortstate,
 									   ExplainState *es);
 static void show_hash_info(HashState *hashstate, ExplainState *es);
+static void show_resultcache_info(ResultCacheState *rcstate, List *ancestors,
+								  ExplainState *es);
 static void show_hashagg_info(AggState *hashstate, ExplainState *es);
 static void show_tidbitmap_info(BitmapHeapScanState *planstate,
 								ExplainState *es);
@@ -1284,6 +1286,9 @@ ExplainNode(PlanState *planstate, List *ancestors,
 		case T_Material:
 			pname = sname = "Materialize";
 			break;
+		case T_ResultCache:
+			pname = sname = "Result Cache";
+			break;
 		case T_Sort:
 			pname = sname = "Sort";
 			break;
@@ -1996,6 +2001,10 @@ ExplainNode(PlanState *planstate, List *ancestors,
 		case T_Hash:
 			show_hash_info(castNode(HashState, planstate), es);
 			break;
+		case T_ResultCache:
+			show_resultcache_info(castNode(ResultCacheState, planstate),
+								  ancestors, es);
+			break;
 		default:
 			break;
 	}
@@ -3063,6 +3072,137 @@ show_hash_info(HashState *hashstate, ExplainState *es)
 	}
 }

+/*
+ * Show information on result cache hits/misses/evictions and memory usage.
+ */
+static void
+show_resultcache_info(ResultCacheState *rcstate, List *ancestors, ExplainState *es)
+{
+	Plan	   *plan = ((PlanState *) rcstate)->plan;
+	ListCell   *lc;
+	List	   *context;
+	StringInfoData keystr;
+	char	   *seperator = "";
+	bool		useprefix;
+	int64		memPeakKb;
+
+	initStringInfo(&keystr);
+
+	/*
+	 * It's hard to imagine having a result cache with fewer than 2 RTEs, but
+	 * let's just keep the same useprefix logic as elsewhere in this file.
+	 */
+	useprefix = list_length(es->rtable) > 1 || es->verbose;
+
+	/* Set up deparsing context */
+	context = set_deparse_context_plan(es->deparse_cxt,
+									   plan,
+									   ancestors);
+
+	foreach(lc, ((ResultCache *) plan)->param_exprs)
+	{
+		Node	   *expr = (Node *) lfirst(lc);
+
+		appendStringInfoString(&keystr, seperator);
+
+		appendStringInfoString(&keystr, deparse_expression(expr, context,
+														   useprefix, false));
+		seperator = ", ";
+	}
+
+	if (es->format != EXPLAIN_FORMAT_TEXT)
+	{
+		ExplainPropertyText("Cache Key", keystr.data, es);
+	}
+	else
+	{
+		ExplainIndentText(es);
+		appendStringInfo(es->str, "Cache Key: %s\n", keystr.data);
+	}
+
+	pfree(keystr.data);
+
+	if (!es->analyze)
+		return;
+
+	/*
+	 * mem_peak is only set when we freed memory, so we must use mem_used when
+	 * mem_peak is 0.
+	 */
+	if (rcstate->stats.mem_peak > 0)
+		memPeakKb = (rcstate->stats.mem_peak + 1023) / 1024;
+	else
+		memPeakKb = (rcstate->mem_used + 1023) / 1024;
+
+	if (es->format != EXPLAIN_FORMAT_TEXT)
+	{
+		ExplainPropertyInteger("Cache Hits", NULL, rcstate->stats.cache_hits, es);
+		ExplainPropertyInteger("Cache Misses", NULL, rcstate->stats.cache_misses, es);
+		ExplainPropertyInteger("Cache Evictions", NULL, rcstate->stats.cache_evictions, es);
+		ExplainPropertyInteger("Cache Overflows", NULL, rcstate->stats.cache_overflows, es);
+		ExplainPropertyInteger("Peak Memory Usage", "kB", memPeakKb, es);
+	}
+	else
+	{
+		ExplainIndentText(es);
+		appendStringInfo(es->str,
+						 "Hits: " UINT64_FORMAT "  Misses: " UINT64_FORMAT "  Evictions: " UINT64_FORMAT "  Overflows: " UINT64_FORMAT "  Memory Usage: " INT64_FORMAT "kB\n",
+						 rcstate->stats.cache_hits,
+						 rcstate->stats.cache_misses,
+						 rcstate->stats.cache_evictions,
+						 rcstate->stats.cache_overflows,
+						 memPeakKb);
+	}
+
+	if (rcstate->shared_info == NULL)
+		return;
+
+	/* Show details from parallel workers */
+	for (int n = 0; n < rcstate->shared_info->num_workers; n++)
+	{
+		ResultCacheInstrumentation *si;
+
+		si = &rcstate->shared_info->sinstrument[n];
+
+		if (es->workers_state)
+			ExplainOpenWorker(n, es);
+
+		/*
+		 * Since the worker's ResultCacheState.mem_used field is unavailable
+		 * to us, ExecEndResultCache will have set the
+		 * ResultCacheInstrumentation.mem_peak field for us.  No need to do
+		 * the zero checks like we did for the serial case above.
+		 */
+		memPeakKb = (si->mem_peak + 1023) / 1024;
+
+		if (es->format == EXPLAIN_FORMAT_TEXT)
+		{
+			ExplainIndentText(es);
+			appendStringInfo(es->str,
+							 "Hits: " UINT64_FORMAT "  Misses: " UINT64_FORMAT "  Evictions: " UINT64_FORMAT "  Overflows: " UINT64_FORMAT "  Memory Usage: " INT64_FORMAT "kB\n",
+							 si->cache_hits, si->cache_misses,
+							 si->cache_evictions, si->cache_overflows,
+							 memPeakKb);
+		}
+		else
+		{
+			ExplainPropertyInteger("Cache Hits", NULL,
+								   si->cache_hits, es);
+			ExplainPropertyInteger("Cache Misses", NULL,
+								   si->cache_misses, es);
+			ExplainPropertyInteger("Cache Evictions", NULL,
+								   si->cache_evictions, es);
+			ExplainPropertyInteger("Cache Overflows", NULL,
+								   si->cache_overflows, es);
+			ExplainPropertyInteger("Peak Memory Usage", "kB", memPeakKb,
+								   es);
+		}
+
+		if (es->workers_state)
+			ExplainCloseWorker(n, es);
+	}
+}
+
 /*
 * Show information on hash aggregate memory usage and batches.
 */

--- a/src/backend/executor/Makefile
+++ b/src/backend/executor/Makefile
@@ -61,6 +61,7 @@ OBJS = \
 	nodeProjectSet.o \
 	nodeRecursiveunion.o \
 	nodeResult.o \
+	nodeResultCache.o \
 	nodeSamplescan.o \
 	nodeSeqscan.o \
 	nodeSetOp.o \

--- a/src/backend/executor/execAmi.c
+++ b/src/backend/executor/execAmi.c
@@ -44,6 +44,7 @@
 #include "executor/nodeProjectSet.h"
 #include "executor/nodeRecursiveunion.h"
 #include "executor/nodeResult.h"
+#include "executor/nodeResultCache.h"
 #include "executor/nodeSamplescan.h"
 #include "executor/nodeSeqscan.h"
 #include "executor/nodeSetOp.h"
@@ -254,6 +255,10 @@ ExecReScan(PlanState *node)
 			ExecReScanMaterial((MaterialState *) node);
 			break;

+		case T_ResultCacheState:
+			ExecReScanResultCache((ResultCacheState *) node);
+			break;
+
 		case T_SortState:
 			ExecReScanSort((SortState *) node);
 			break;

--- a/src/backend/executor/execExpr.c
+++ b/src/backend/executor/execExpr.c
@@ -3696,3 +3696,137 @@ ExecBuildGroupingEqual(TupleDesc ldesc, TupleDesc rdesc,

 	return state;
 }
+
+/*
+ * Build equality expression that can be evaluated using ExecQual(), returning
+ * true if the expression context's inner/outer tuples are equal.  Datums in
+ * the inner/outer slots are assumed to be in the same order and quantity as
+ * the 'eqfunctions' parameter.  NULLs are treated as equal.
+ *
+ * desc: tuple descriptor of the to-be-compared tuples
+ * lops: the slot ops for the inner tuple slots
+ * rops: the slot ops for the outer tuple slots
+ * eqFunctions: array of function oids of the equality functions to use
+ * this must be the same length as the 'param_exprs' list.
+ * collations: collation Oids to use for equality comparison. Must be the
+ * same length as the 'param_exprs' list.
+ * parent: parent executor node
+ */
+ExprState *
+ExecBuildParamSetEqual(TupleDesc desc,
+					   const TupleTableSlotOps *lops,
+					   const TupleTableSlotOps *rops,
+					   const Oid *eqfunctions,
+					   const Oid *collations,
+					   const List *param_exprs,
+					   PlanState *parent)
+{
+	ExprState  *state = makeNode(ExprState);
+	ExprEvalStep scratch = {0};
+	int			maxatt = list_length(param_exprs);
+	List	   *adjust_jumps = NIL;
+	ListCell   *lc;
+
+	state->expr = NULL;
+	state->flags = EEO_FLAG_IS_QUAL;
+	state->parent = parent;
+
+	scratch.resvalue = &state->resvalue;
+	scratch.resnull = &state->resnull;
+
+	/* push deform steps */
+	scratch.opcode = EEOP_INNER_FETCHSOME;
+	scratch.d.fetch.last_var = maxatt;
+	scratch.d.fetch.fixed = false;
+	scratch.d.fetch.known_desc = desc;
+	scratch.d.fetch.kind = lops;
+	if (ExecComputeSlotInfo(state, &scratch))
+		ExprEvalPushStep(state, &scratch);
+
+	scratch.opcode = EEOP_OUTER_FETCHSOME;
+	scratch.d.fetch.last_var = maxatt;
+	scratch.d.fetch.fixed = false;
+	scratch.d.fetch.known_desc = desc;
+	scratch.d.fetch.kind = rops;
+	if (ExecComputeSlotInfo(state, &scratch))
+		ExprEvalPushStep(state, &scratch);
+
+	for (int attno = 0; attno < maxatt; attno++)
+	{
+		Form_pg_attribute att = TupleDescAttr(desc, attno);
+		Oid			foid = eqfunctions[attno];
+		Oid			collid = collations[attno];
+		FmgrInfo   *finfo;
+		FunctionCallInfo fcinfo;
+		AclResult	aclresult;
+
+		/* Check permission to call function */
+		aclresult = pg_proc_aclcheck(foid, GetUserId(), ACL_EXECUTE);
+		if (aclresult != ACLCHECK_OK)
+			aclcheck_error(aclresult, OBJECT_FUNCTION, get_func_name(foid));
+
+		InvokeFunctionExecuteHook(foid);
+
+		/* Set up the primary fmgr lookup information */
+		finfo = palloc0(sizeof(FmgrInfo));
+		fcinfo = palloc0(SizeForFunctionCallInfo(2));
+		fmgr_info(foid, finfo);
+		fmgr_info_set_expr(NULL, finfo);
+		InitFunctionCallInfoData(*fcinfo, finfo, 2,
+								 collid, NULL, NULL);
+
+		/* left arg */
+		scratch.opcode = EEOP_INNER_VAR;
+		scratch.d.var.attnum = attno;
+		scratch.d.var.vartype = att->atttypid;
+		scratch.resvalue = &fcinfo->args[0].value;
+		scratch.resnull = &fcinfo->args[0].isnull;
+		ExprEvalPushStep(state, &scratch);
+
+		/* right arg */
+		scratch.opcode = EEOP_OUTER_VAR;
+		scratch.d.var.attnum = attno;
+		scratch.d.var.vartype = att->atttypid;
+		scratch.resvalue = &fcinfo->args[1].value;
+		scratch.resnull = &fcinfo->args[1].isnull;
+		ExprEvalPushStep(state, &scratch);
+
+		/* evaluate distinctness */
+		scratch.opcode = EEOP_NOT_DISTINCT;
+		scratch.d.func.finfo = finfo;
+		scratch.d.func.fcinfo_data = fcinfo;
+		scratch.d.func.fn_addr = finfo->fn_addr;
+		scratch.d.func.nargs = 2;
+		scratch.resvalue = &state->resvalue;
+		scratch.resnull = &state->resnull;
+		ExprEvalPushStep(state, &scratch);
+
+		/* then emit EEOP_QUAL to detect if result is false (or null) */
+		scratch.opcode = EEOP_QUAL;
+		scratch.d.qualexpr.jumpdone = -1;
+		scratch.resvalue = &state->resvalue;
+		scratch.resnull = &state->resnull;
+		ExprEvalPushStep(state, &scratch);
+		adjust_jumps = lappend_int(adjust_jumps,
+								   state->steps_len - 1);
+	}
+
+	/* adjust jump targets */
+	foreach(lc, adjust_jumps)
+	{
+		ExprEvalStep *as = &state->steps[lfirst_int(lc)];
+
+		Assert(as->opcode == EEOP_QUAL);
+		Assert(as->d.qualexpr.jumpdone == -1);
+		as->d.qualexpr.jumpdone = state->steps_len;
+	}
+
+	scratch.resvalue = NULL;
+	scratch.resnull = NULL;
+	scratch.opcode = EEOP_DONE;
+	ExprEvalPushStep(state, &scratch);
+
+	ExecReadyExpr(state);
+
+	return state;
+}
--- a/src/backend/executor/execParallel.c
+++ b/src/backend/executor/execParallel.c
@@ -35,6 +35,7 @@
 #include "executor/nodeIncrementalSort.h"
 #include "executor/nodeIndexonlyscan.h"
 #include "executor/nodeIndexscan.h"
+#include "executor/nodeResultCache.h"
 #include "executor/nodeSeqscan.h"
 #include "executor/nodeSort.h"
 #include "executor/nodeSubplan.h"
@@ -292,6 +293,10 @@ ExecParallelEstimate(PlanState *planstate, ExecParallelEstimateContext *e)
 			/* even when not parallel-aware, for EXPLAIN ANALYZE */
 			ExecAggEstimate((AggState *) planstate, e->pcxt);
 			break;
+		case T_ResultCacheState:
+			/* even when not parallel-aware, for EXPLAIN ANALYZE */
+			ExecResultCacheEstimate((ResultCacheState *) planstate, e->pcxt);
+			break;
 		default:
 			break;
 	}
@@ -512,6 +517,10 @@ ExecParallelInitializeDSM(PlanState *planstate,
 			/* even when not parallel-aware, for EXPLAIN ANALYZE */
 			ExecAggInitializeDSM((AggState *) planstate, d->pcxt);
 			break;
+		case T_ResultCacheState:
+			/* even when not parallel-aware, for EXPLAIN ANALYZE */
+			ExecResultCacheInitializeDSM((ResultCacheState *) planstate, d->pcxt);
+			break;
 		default:
 			break;
 	}
@@ -988,6 +997,7 @@ ExecParallelReInitializeDSM(PlanState *planstate,
 		case T_HashState:
 		case T_SortState:
 		case T_IncrementalSortState:
+		case T_ResultCacheState:
 			/* these nodes have DSM state, but no reinitialization is required */
 			break;

@@ -1057,6 +1067,9 @@ ExecParallelRetrieveInstrumentation(PlanState *planstate,
 		case T_AggState:
 			ExecAggRetrieveInstrumentation((AggState *) planstate);
 			break;
+		case T_ResultCacheState:
+			ExecResultCacheRetrieveInstrumentation((ResultCacheState *) planstate);
+			break;
 		default:
 			break;
 	}
@@ -1349,6 +1362,11 @@ ExecParallelInitializeWorker(PlanState *planstate, ParallelWorkerContext *pwcxt)
 			/* even when not parallel-aware, for EXPLAIN ANALYZE */
 			ExecAggInitializeWorker((AggState *) planstate, pwcxt);
 			break;
+		case T_ResultCacheState:
+			/* even when not parallel-aware, for EXPLAIN ANALYZE */
+			ExecResultCacheInitializeWorker((ResultCacheState *) planstate,
+											pwcxt);
+			break;
 		default:
 			break;
 	}

--- a/src/backend/executor/execProcnode.c
+++ b/src/backend/executor/execProcnode.c
@@ -102,6 +102,7 @@
 #include "executor/nodeProjectSet.h"
 #include "executor/nodeRecursiveunion.h"
 #include "executor/nodeResult.h"
+#include "executor/nodeResultCache.h"
 #include "executor/nodeSamplescan.h"
 #include "executor/nodeSeqscan.h"
 #include "executor/nodeSetOp.h"
@@ -325,6 +326,11 @@ ExecInitNode(Plan *node, EState *estate, int eflags)
 														   estate, eflags);
 			break;

+		case T_ResultCache:
+			result = (PlanState *) ExecInitResultCache((ResultCache *) node,
+													   estate, eflags);
+			break;
+
 		case T_Group:
 			result = (PlanState *) ExecInitGroup((Group *) node,
 												 estate, eflags);
@@ -713,6 +719,10 @@ ExecEndNode(PlanState *node)
 			ExecEndIncrementalSort((IncrementalSortState *) node);
 			break;

+		case T_ResultCacheState:
+			ExecEndResultCache((ResultCacheState *) node);
+			break;
+
 		case T_GroupState:
 			ExecEndGroup((GroupState *) node);
 			break;

--- a/src/backend/executor/nodeResultCache.c
+++ b/src/backend/executor/nodeResultCache.c
+/*-------------------------------------------------------------------------
+ *
+ * nodeResultCache.c
+ *	  Routines to handle caching of results from parameterized nodes
+ *
+ * Portions Copyright (c) 2021, PostgreSQL Global Development Group
+ * Portions Copyright (c) 1994, Regents of the University of California
+ *
+ *
+ * IDENTIFICATION
+ *	  src/backend/executor/nodeResultCache.c
+ *
+ * ResultCache nodes are intended to sit above parameterized nodes in the plan
+ * tree in order to cache results from them.  The intention here is that a
+ * repeat scan with a parameter value that has already been seen by the node
+ * can fetch tuples from the cache rather than having to re-scan the outer
+ * node all over again.  The query planner may choose to make use of one of
+ * these when it thinks rescans for previously seen values are likely enough
+ * to warrant adding the additional node.
+ *
+ * The method of cache we use is a hash table.  When the cache fills, we never
+ * spill tuples to disk, instead, we choose to evict the least recently used
+ * cache entry from the cache.  We remember the least recently used entry by
+ * always pushing new entries and entries we look for onto the tail of a
+ * doubly linked list.  This means that older items always bubble to the top
+ * of this LRU list.
+ *
+ * Sometimes our callers won't run their scans to completion. For example a
+ * semi-join only needs to run until it finds a matching tuple, and once it
+ * does, the join operator skips to the next outer tuple and does not execute
+ * the inner side again on that scan.  Because of this, we must keep track of
+ * when a cache entry is complete, and by default, we know it is when we run
+ * out of tuples to read during the scan.  However, there are cases where we
+ * can mark the cache entry as complete without exhausting the scan of all
+ * tuples.  One case is unique joins, where the join operator knows that there
+ * will only be at most one match for any given outer tuple.  In order to
+ * support such cases we allow the "singlerow" option to be set for the cache.
+ * This option marks the cache entry as complete after we read the first tuple
+ * from the subnode.
+ *
+ * It's possible when we're filling the cache for a given set of parameters
+ * that we're unable to free enough memory to store any more tuples.  If this
+ * happens then we'll have already evicted all other cache entries.  When
+ * caching another tuple would cause us to exceed our memory budget, we must
+ * free the entry that we're currently populating and move the state machine
+ * into RC_CACHE_BYPASS_MODE.  This means that we'll not attempt to cache any
+ * further tuples for this particular scan.  We don't have the memory for it.
+ * The state machine will be reset again on the next rescan.  If the memory
+ * requirements to cache the next parameter's tuples are less demanding, then
+ * that may allow us to start putting useful entries back into the cache
+ * again.
+ *
+ *
+ * INTERFACE ROUTINES
+ *		ExecResultCache			- lookup cache, exec subplan when not found
+ *		ExecInitResultCache		- initialize node and subnodes
+ *		ExecEndResultCache		- shutdown node and subnodes
+ *		ExecReScanResultCache	- rescan the result cache
+ *
+ *		ExecResultCacheEstimate		estimates DSM space needed for parallel plan
+ *		ExecResultCacheInitializeDSM initialize DSM for parallel plan
+ *		ExecResultCacheInitializeWorker attach to DSM info in parallel worker
+ *		ExecResultCacheRetrieveInstrumentation get instrumentation from worker
+ *-------------------------------------------------------------------------
+ */
+
+#include "postgres.h"
+
+#include "access/parallel.h"
+#include "common/hashfn.h"
+#include "executor/executor.h"
+#include "executor/nodeResultCache.h"
+#include "lib/ilist.h"
+#include "miscadmin.h"
+#include "utils/lsyscache.h"
+
+/* States of the ExecResultCache state machine */
+#define RC_CACHE_LOOKUP				1	/* Attempt to perform a cache lookup */
+#define RC_CACHE_FETCH_NEXT_TUPLE	2	/* Get another tuple from the cache */
+#define RC_FILLING_CACHE			3	/* Read outer node to fill cache */
+#define RC_CACHE_BYPASS_MODE		4	/* Bypass mode.  Just read from our
+										 * subplan without caching anything */
+#define RC_END_OF_SCAN				5	/* Ready for rescan */
+
+
+/* Helper macros for memory accounting */
+#define EMPTY_ENTRY_MEMORY_BYTES(e)		(sizeof(ResultCacheEntry) + \
+										 sizeof(ResultCacheKey) + \
+										 (e)->key->params->t_len);
+#define CACHE_TUPLE_BYTES(t)			(sizeof(ResultCacheTuple) + \
+										 (t)->mintuple->t_len)
+
+ /* ResultCacheTuple Stores an individually cached tuple */
+typedef struct ResultCacheTuple
+{
+	MinimalTuple mintuple;		/* Cached tuple */
+	struct ResultCacheTuple *next;	/* The next tuple with the same parameter
+									 * values or NULL if it's the last one */
+} ResultCacheTuple;
+
+/*
+ * ResultCacheKey
+ * The hash table key for cached entries plus the LRU list link
+ */
+typedef struct ResultCacheKey
+{
+	MinimalTuple params;
+	dlist_node	lru_node;		/* Pointer to next/prev key in LRU list */
+} ResultCacheKey;
+
+/*
+ * ResultCacheEntry
+ *		The data struct that the cache hash table stores
+ */
+typedef struct ResultCacheEntry
+{
+	ResultCacheKey *key;		/* Hash key for hash table lookups */
+	ResultCacheTuple *tuplehead;	/* Pointer to the first tuple or NULL if
+									 * no tuples are cached for this entry */
+	uint32		hash;			/* Hash value (cached) */
+	char		status;			/* Hash status */
+	bool		complete;		/* Did we read the outer plan to completion? */
+} ResultCacheEntry;
+
+
+#define SH_PREFIX resultcache
+#define SH_ELEMENT_TYPE ResultCacheEntry
+#define SH_KEY_TYPE ResultCacheKey *
+#define SH_SCOPE static inline
+#define SH_DECLARE
+#include "lib/simplehash.h"
+
+static uint32 ResultCacheHash_hash(struct resultcache_hash *tb,
+								   const ResultCacheKey *key);
+static int	ResultCacheHash_equal(struct resultcache_hash *tb,
+								  const ResultCacheKey *params1,
+								  const ResultCacheKey *params2);
+
+#define SH_PREFIX resultcache
+#define SH_ELEMENT_TYPE ResultCacheEntry
+#define SH_KEY_TYPE ResultCacheKey *
+#define SH_KEY key
+#define SH_HASH_KEY(tb, key) ResultCacheHash_hash(tb, key)
+#define SH_EQUAL(tb, a, b) (ResultCacheHash_equal(tb, a, b) == 0)
+#define SH_SCOPE static inline
+#define SH_STORE_HASH
+#define SH_GET_HASH(tb, a) a->hash
+#define SH_DEFINE
+#include "lib/simplehash.h"
+
+/*
+ * ResultCacheHash_hash
+ *		Hash function for simplehash hashtable.  'key' is unused here as we
+ *		require that all table lookups first populate the ResultCacheState's
+ *		probeslot with the key values to be looked up.
+ */
+static uint32
+ResultCacheHash_hash(struct resultcache_hash *tb, const ResultCacheKey *key)
+{
+	ResultCacheState *rcstate = (ResultCacheState *) tb->private_data;
+	TupleTableSlot *pslot = rcstate->probeslot;
+	uint32		hashkey = 0;
+	int			numkeys = rcstate->nkeys;
+	FmgrInfo   *hashfunctions = rcstate->hashfunctions;
+	Oid		   *collations = rcstate->collations;
+
+	for (int i = 0; i < numkeys; i++)
+	{
+		/* rotate hashkey left 1 bit at each step */
+		hashkey = (hashkey << 1) | ((hashkey & 0x80000000) ? 1 : 0);
+
+		if (!pslot->tts_isnull[i])	/* treat nulls as having hash key 0 */
+		{
+			uint32		hkey;
+
+			hkey = DatumGetUInt32(FunctionCall1Coll(&hashfunctions[i],
+													collations[i], pslot->tts_values[i]));
+			hashkey ^= hkey;
+		}
+	}
+
+	return murmurhash32(hashkey);
+}
+
+/*
+ * ResultCacheHash_equal
+ *		Equality function for confirming hash value matches during a hash
+ *		table lookup.  'key2' is never used.  Instead the ResultCacheState's
+ *		probeslot is always populated with details of what's being looked up.
+ */
+static int
+ResultCacheHash_equal(struct resultcache_hash *tb, const ResultCacheKey *key1,
+					  const ResultCacheKey *key2)
+{
+	ResultCacheState *rcstate = (ResultCacheState *) tb->private_data;
+	ExprContext *econtext = rcstate->ss.ps.ps_ExprContext;
+	TupleTableSlot *tslot = rcstate->tableslot;
+	TupleTableSlot *pslot = rcstate->probeslot;
+
+	/* probeslot should have already been prepared by prepare_probe_slot() */
+
+	ExecStoreMinimalTuple(key1->params, tslot, false);
+
+	econtext->ecxt_innertuple = tslot;
+	econtext->ecxt_outertuple = pslot;
+	return !ExecQualAndReset(rcstate->cache_eq_expr, econtext);
+}
+
+/*
+ * Initialize the hash table to empty.
+ */
+static void
+build_hash_table(ResultCacheState *rcstate, uint32 size)
+{
+	/* Make a guess at a good size when we're not given a valid size. */
+	if (size == 0)
+		size = 1024;
+
+	/* resultcache_create will convert the size to a power of 2 */
+	rcstate->hashtable = resultcache_create(rcstate->tableContext, size,
+											rcstate);
+}
+
+/*
+ * prepare_probe_slot
+ *		Populate rcstate's probeslot with the values from the tuple stored
+ *		in 'key'.  If 'key' is NULL, then perform the population by evaluating
+ *		rcstate's param_exprs.
+ */
+static inline void
+prepare_probe_slot(ResultCacheState *rcstate, ResultCacheKey *key)
+{
+	TupleTableSlot *pslot = rcstate->probeslot;
+	TupleTableSlot *tslot = rcstate->tableslot;
+	int			numKeys = rcstate->nkeys;
+
+	ExecClearTuple(pslot);
+
+	if (key == NULL)
+	{
+		/* Set the probeslot's values based on the current parameter values */
+		for (int i = 0; i < numKeys; i++)
+			pslot->tts_values[i] = ExecEvalExpr(rcstate->param_exprs[i],
+												rcstate->ss.ps.ps_ExprContext,
+												&pslot->tts_isnull[i]);
+	}
+	else
+	{
+		/* Process the key's MinimalTuple and store the values in probeslot */
+		ExecStoreMinimalTuple(key->params, tslot, false);
+		slot_getallattrs(tslot);
+		memcpy(pslot->tts_values, tslot->tts_values, sizeof(Datum) * numKeys);
+		memcpy(pslot->tts_isnull, tslot->tts_isnull, sizeof(bool) * numKeys);
+	}
+
+	ExecStoreVirtualTuple(pslot);
+}
+
+/*
+ * entry_purge_tuples
+ *		Remove all tuples from the cache entry pointed to by 'entry'.  This
+ *		leaves an empty cache entry.  Also, update the memory accounting to
+ *		reflect the removal of the tuples.
+ */
+static inline void
+entry_purge_tuples(ResultCacheState *rcstate, ResultCacheEntry *entry)
+{
+	ResultCacheTuple *tuple = entry->tuplehead;
+	uint64		freed_mem = 0;
+
+	while (tuple != NULL)
+	{
+		ResultCacheTuple *next = tuple->next;
+
+		freed_mem += CACHE_TUPLE_BYTES(tuple);
+
+		/* Free memory used for this tuple */
+		pfree(tuple->mintuple);
+		pfree(tuple);
+
+		tuple = next;
+	}
+
+	entry->complete = false;
+	entry->tuplehead = NULL;
+
+	/* Update the memory accounting */
+	rcstate->mem_used -= freed_mem;
+
+	Assert(rcstate->mem_used >= 0);
+}
+
+/*
+ * remove_cache_entry
+ *		Remove 'entry' from the cache and free memory used by it.
+ */
+static void
+remove_cache_entry(ResultCacheState *rcstate, ResultCacheEntry *entry)
+{
+	ResultCacheKey *key = entry->key;
+
+	dlist_delete(&entry->key->lru_node);
+
+#ifdef USE_ASSERT_CHECKING
+	/*
+	 * Validate the memory accounting code is correct in assert builds. XXX is
+	 * this too expensive for USE_ASSERT_CHECKING?
+	 */
+	{
+		int			i,
+					count;
+		uint64		mem = 0;
+
+		count = 0;
+		for (i = 0; i < rcstate->hashtable->size; i++)
+		{
+			ResultCacheEntry *entry = &rcstate->hashtable->data[i];
+
+			if (entry->status == resultcache_SH_IN_USE)
+			{
+				ResultCacheTuple *tuple = entry->tuplehead;
+
+				mem += EMPTY_ENTRY_MEMORY_BYTES(entry);
+				while (tuple != NULL)
+				{
+					mem += CACHE_TUPLE_BYTES(tuple);
+					tuple = tuple->next;
+				}
+				count++;
+			}
+		}
+
+		Assert(count == rcstate->hashtable->members);
+		Assert(mem == rcstate->mem_used);
+	}
+#endif
+
+	/* Remove all of the tuples from this entry */
+	entry_purge_tuples(rcstate, entry);
+
+	/*
+	 * Update memory accounting. entry_purge_tuples should have already
+	 * subtracted the memory used for each cached tuple.  Here we just update
+	 * the amount used by the entry itself.
+	 */
+	rcstate->mem_used -= EMPTY_ENTRY_MEMORY_BYTES(entry);
+
+	Assert(rcstate->mem_used >= 0);
+
+	/* Remove the entry from the cache */
+	resultcache_delete_item(rcstate->hashtable, entry);
+
+	pfree(key->params);
+	pfree(key);
+}
+
+/*
+ * cache_reduce_memory
+ *		Evict older and less recently used items from the cache in order to
+ *		reduce the memory consumption back to something below the
+ *		ResultCacheState's mem_limit.
+ *
+ * 'specialkey', if not NULL, causes the function to return false if the entry
+ * which the key belongs to is removed from the cache.
+ */
+static bool
+cache_reduce_memory(ResultCacheState *rcstate, ResultCacheKey *specialkey)
+{
+	bool		specialkey_intact = true;	/* for now */
+	dlist_mutable_iter iter;
+	uint64		evictions = 0;
+
+	/* Update peak memory usage */
+	if (rcstate->mem_used > rcstate->stats.mem_peak)
+		rcstate->stats.mem_peak = rcstate->mem_used;
+
+	/* We expect only to be called when we've gone over budget on memory */
+	Assert(rcstate->mem_used > rcstate->mem_limit);
+
+	/* Start the eviction process starting at the head of the LRU list. */
+	dlist_foreach_modify(iter, &rcstate->lru_list)
+	{
+		ResultCacheKey *key = dlist_container(ResultCacheKey, lru_node,
+											  iter.cur);
+		ResultCacheEntry *entry;
+
+		/*
+		 * Populate the hash probe slot in preparation for looking up this LRU
+		 * entry.
+		 */
+		prepare_probe_slot(rcstate, key);
+
+		/*
+		 * Ideally the LRU list pointers would be stored in the entry itself
+		 * rather than in the key.  Unfortunately, we can't do that as the
+		 * simplehash.h code may resize the table and allocate new memory for
+		 * entries which would result in those pointers pointing to the old
+		 * buckets.  However, it's fine to use the key to store this as that's
+		 * only referenced by a pointer in the entry, which of course follows
+		 * the entry whenever the hash table is resized.  Since we only have a
+		 * pointer to the key here, we must perform a hash table lookup to
+		 * find the entry that the key belongs to.
+		 */
+		entry = resultcache_lookup(rcstate->hashtable, NULL);
+
+		/* A good spot to check for corruption of the table and LRU list. */
+		Assert(entry != NULL);
+		Assert(entry->key == key);
+
+		/*
+		 * If we're being called to free memory while the cache is being
+		 * populated with new tuples, then we'd better take some care as we
+		 * could end up freeing the entry which 'specialkey' belongs to.
+		 * Generally callers will pass 'specialkey' as the key for the cache
+		 * entry which is currently being populated, so we must set
+		 * 'specialkey_intact' to false to inform the caller the specialkey
+		 * entry has been removed.
+		 */
+		if (key == specialkey)
+			specialkey_intact = false;
+
+		/*
+		 * Finally remove the entry.  This will remove from the LRU list too.
+		 */
+		remove_cache_entry(rcstate, entry);
+
+		evictions++;
+
+		/* Exit if we've freed enough memory */
+		if (rcstate->mem_used <= rcstate->mem_limit)
+			break;
+	}
+
+	rcstate->stats.cache_evictions += evictions;	/* Update Stats */
+
+	return specialkey_intact;
+}
+
+/*
+ * cache_lookup
+ *		Perform a lookup to see if we've already cached results based on the
+ *		scan's current parameters.  If we find an existing entry we move it to
+ *		the end of the LRU list, set *found to true then return it.  If we
+ *		don't find an entry then we create a new one and add it to the end of
+ *		the LRU list.  We also update cache memory accounting and remove older
+ *		entries if we go over the memory budget.  If we managed to free enough
+ *		memory we return the new entry, else we return NULL.
+ *
+ * Callers can assume we'll never return NULL when *found is true.
+ */
+static ResultCacheEntry *
+cache_lookup(ResultCacheState *rcstate, bool *found)
+{
+	ResultCacheKey *key;
+	ResultCacheEntry *entry;
+	MemoryContext oldcontext;
+
+	/* prepare the probe slot with the current scan parameters */
+	prepare_probe_slot(rcstate, NULL);
+
+	/*
+	 * Add the new entry to the cache.  No need to pass a valid key since the
+	 * hash function uses rcstate's probeslot, which we populated above.
+	 */
+	entry = resultcache_insert(rcstate->hashtable, NULL, found);
+
+	if (*found)
+	{
+		/*
+		 * Move existing entry to the tail of the LRU list to mark it as the
+		 * most recently used item.
+		 */
+		dlist_move_tail(&rcstate->lru_list, &entry->key->lru_node);
+
+		return entry;
+	}
+
+	oldcontext = MemoryContextSwitchTo(rcstate->tableContext);
+
+	/* Allocate a new key */
+	entry->key = key = (ResultCacheKey *) palloc(sizeof(ResultCacheKey));
+	key->params = ExecCopySlotMinimalTuple(rcstate->probeslot);
+
+	/* Update the total cache memory utilization */
+	rcstate->mem_used += EMPTY_ENTRY_MEMORY_BYTES(entry);
+
+	/* Initialize this entry */
+	entry->complete = false;
+	entry->tuplehead = NULL;
+
+	/*
+	 * Since this is the most recently used entry, push this entry onto the
+	 * end of the LRU list.
+	 */
+	dlist_push_tail(&rcstate->lru_list, &entry->key->lru_node);
+
+	rcstate->last_tuple = NULL;
+
+	MemoryContextSwitchTo(oldcontext);
+
+	/*
+	 * If we've gone over our memory budget, then we'll free up some space in
+	 * the cache.
+	 */
+	if (rcstate->mem_used > rcstate->mem_limit)
+	{
+		/*
+		 * Try to free up some memory.  It's highly unlikely that we'll fail
+		 * to do so here since the entry we've just added is yet to contain
+		 * any tuples and we're able to remove any other entry to reduce the
+		 * memory consumption.
+		 */
+		if (unlikely(!cache_reduce_memory(rcstate, key)))
+			return NULL;
+
+		/*
+		 * The process of removing entries from the cache may have caused the
+		 * code in simplehash.h to shuffle elements to earlier buckets in the
+		 * hash table.  If it has, we'll need to find the entry again by
+		 * performing a lookup.  Fortunately, we can detect if this has
+		 * happened by seeing if the entry is still in use and that the key
+		 * pointer matches our expected key.
+		 */
+		if (entry->status != resultcache_SH_IN_USE || entry->key != key)
+		{
+			/*
+			 * We need to repopulate the probeslot as lookups performed during
+			 * the cache evictions above will have stored some other key.
+			 */
+			prepare_probe_slot(rcstate, key);
+
+			/* Re-find the newly added entry */
+			entry = resultcache_lookup(rcstate->hashtable, NULL);
+			Assert(entry != NULL);
+		}
+	}
+
+	return entry;
+}
+
+/*
+ * cache_store_tuple
+ *		Add the tuple stored in 'slot' to the rcstate's current cache entry.
+ *		The cache entry must have already been made with cache_lookup().
+ *		rcstate's last_tuple field must point to the tail of rcstate->entry's
+ *		list of tuples.
+ */
+static bool
+cache_store_tuple(ResultCacheState *rcstate, TupleTableSlot *slot)
+{
+	ResultCacheTuple *tuple;
+	ResultCacheEntry *entry = rcstate->entry;
+	MemoryContext oldcontext;
+
+	Assert(slot != NULL);
+	Assert(entry != NULL);
+
+	oldcontext = MemoryContextSwitchTo(rcstate->tableContext);
+
+	tuple = (ResultCacheTuple *) palloc(sizeof(ResultCacheTuple));
+	tuple->mintuple = ExecCopySlotMinimalTuple(slot);
+	tuple->next = NULL;
+
+	/* Account for the memory we just consumed */
+	rcstate->mem_used += CACHE_TUPLE_BYTES(tuple);
+
+	if (entry->tuplehead == NULL)
+	{
+		/*
+		 * This is the first tuple for this entry, so just point the list head
+		 * to it.
+		 */
+		entry->tuplehead = tuple;
+	}
+	else
+	{
+		/* push this tuple onto the tail of the list */
+		rcstate->last_tuple->next = tuple;
+	}
+
+	rcstate->last_tuple = tuple;
+	MemoryContextSwitchTo(oldcontext);
+
+	/*
+	 * If we've gone over our memory budget then free up some space in the
+	 * cache.
+	 */
+	if (rcstate->mem_used > rcstate->mem_limit)
+	{
+		ResultCacheKey *key = entry->key;
+
+		if (!cache_reduce_memory(rcstate, key))
+			return false;
+
+		/*
+		 * The process of removing entries from the cache may have caused the
+		 * code in simplehash.h to shuffle elements to earlier buckets in the
+		 * hash table.  If it has, we'll need to find the entry again by
+		 * performing a lookup.  Fortunately, we can detect if this has
+		 * happened by seeing if the entry is still in use and that the key
+		 * pointer matches our expected key.
+		 */
+		if (entry->status != resultcache_SH_IN_USE || entry->key != key)
+		{
+			/*
+			 * We need to repopulate the probeslot as lookups performed during
+			 * the cache evictions above will have stored some other key.
+			 */
+			prepare_probe_slot(rcstate, key);
+
+			/* Re-find the entry */
+			rcstate->entry = entry = resultcache_lookup(rcstate->hashtable,
+														NULL);
+			Assert(entry != NULL);
+		}
+	}
+
+	return true;
+}
+
+static TupleTableSlot *
+ExecResultCache(PlanState *pstate)
+{
+	ResultCacheState *node = castNode(ResultCacheState, pstate);
+	PlanState  *outerNode;
+	TupleTableSlot *slot;
+
+	switch (node->rc_status)
+	{
+		case RC_CACHE_LOOKUP:
+			{
+				ResultCacheEntry *entry;
+				TupleTableSlot *outerslot;
+				bool		found;
+
+				Assert(node->entry == NULL);
+
+				/*
+				 * We're only ever in this state for the first call of the
+				 * scan.  Here we have a look to see if we've already seen the
+				 * current parameters before and if we have already cached a
+				 * complete set of records that the outer plan will return for
+				 * these parameters.
+				 *
+				 * When we find a valid cache entry, we'll return the first
+				 * tuple from it. If not found, we'll create a cache entry and
+				 * then try to fetch a tuple from the outer scan.  If we find
+				 * one there, we'll try to cache it.
+				 */
+
+				/* see if we've got anything cached for the current parameters */
+				entry = cache_lookup(node, &found);
+
+				if (found && entry->complete)
+				{
+					node->stats.cache_hits += 1;	/* stats update */
+
+					/*
+					 * Set last_tuple and entry so that the state
+					 * RC_CACHE_FETCH_NEXT_TUPLE can easily find the next
+					 * tuple for these parameters.
+					 */
+					node->last_tuple = entry->tuplehead;
+					node->entry = entry;
+
+					/* Fetch the first cached tuple, if there is one */
+					if (entry->tuplehead)
+					{
+						node->rc_status = RC_CACHE_FETCH_NEXT_TUPLE;
+
+						slot = node->ss.ps.ps_ResultTupleSlot;
+						ExecStoreMinimalTuple(entry->tuplehead->mintuple,
+											  slot, false);
+
+						return slot;
+					}
+
+					/* The cache entry is void of any tuples. */
+					node->rc_status = RC_END_OF_SCAN;
+					return NULL;
+				}
+
+				/* Handle cache miss */
+				node->stats.cache_misses += 1;	/* stats update */
+
+				if (found)
+				{
+					/*
+					 * A cache entry was found, but the scan for that entry
+					 * did not run to completion.  We'll just remove all
+					 * tuples and start again.  It might be tempting to
+					 * continue where we left off, but there's no guarantee
+					 * the outer node will produce the tuples in the same
+					 * order as it did last time.
+					 */
+					entry_purge_tuples(node, entry);
+				}
+
+				/* Scan the outer node for a tuple to cache */
+				outerNode = outerPlanState(node);
+				outerslot = ExecProcNode(outerNode);
+				if (TupIsNull(outerslot))
+				{
+					/*
+					 * cache_lookup may have returned NULL due to failure to
+					 * free enough cache space, so ensure we don't do anything
+					 * here that assumes it worked. There's no need to go into
+					 * bypass mode here as we're setting rc_status to end of
+					 * scan.
+					 */
+					if (likely(entry))
+						entry->complete = true;
+
+					node->rc_status = RC_END_OF_SCAN;
+					return NULL;
+				}
+
+				node->entry = entry;
+
+				/*
+				 * If we failed to create the entry or failed to store the
+				 * tuple in the entry, then go into bypass mode.
+				 */
+				if (unlikely(entry == NULL ||
+					!cache_store_tuple(node, outerslot)))
+				{
+					node->stats.cache_overflows += 1;	/* stats update */
+
+					node->rc_status = RC_CACHE_BYPASS_MODE;
+
+					/*
+					 * No need to clear out last_tuple as we'll stay in bypass
+					 * mode until the end of the scan.
+					 */
+				}
+				else
+				{
+					/*
+					 * If we only expect a single row from this scan then we
+					 * can mark that we're not expecting more.  This allows
+					 * cache lookups to work even when the scan has not been
+					 * executed to completion.
+					 */
+					entry->complete = node->singlerow;
+					node->rc_status = RC_FILLING_CACHE;
+				}
+
+				slot = node->ss.ps.ps_ResultTupleSlot;
+				ExecCopySlot(slot, outerslot);
+				return slot;
+			}
+
+		case RC_CACHE_FETCH_NEXT_TUPLE:
+			{
+				/* We shouldn't be in this state if these are not set */
+				Assert(node->entry != NULL);
+				Assert(node->last_tuple != NULL);
+
+				/* Skip to the next tuple to output */
+				node->last_tuple = node->last_tuple->next;
+
+				/* No more tuples in the cache */
+				if (node->last_tuple == NULL)
+				{
+					node->rc_status = RC_END_OF_SCAN;
+					return NULL;
+				}
+
+				slot = node->ss.ps.ps_ResultTupleSlot;
+				ExecStoreMinimalTuple(node->last_tuple->mintuple, slot,
+									  false);
+
+				return slot;
+			}
+
+		case RC_FILLING_CACHE:
+			{
+				TupleTableSlot *outerslot;
+				ResultCacheEntry *entry = node->entry;
+
+				/* entry should already have been set by RC_CACHE_LOOKUP */
+				Assert(entry != NULL);
+
+				/*
+				 * When in the RC_FILLING_CACHE state, we've just had a cache
+				 * miss and are populating the cache with the current scan
+				 * tuples.
+				 */
+				outerNode = outerPlanState(node);
+				outerslot = ExecProcNode(outerNode);
+				if (TupIsNull(outerslot))
+				{
+					/* No more tuples.  Mark it as complete */
+					entry->complete = true;
+					node->rc_status = RC_END_OF_SCAN;
+					return NULL;
+				}
+
+				/*
+				 * Validate if the planner properly set the singlerow flag.
+				 * It should only set that if each cache entry can, at most,
+				 * return 1 row.  XXX maybe this should be an Assert?
+				 */
+				if (unlikely(entry->complete))
+					elog(ERROR, "cache entry already complete");
+
+				/* Record the tuple in the current cache entry */
+				if (unlikely(!cache_store_tuple(node, outerslot)))
+				{
+					/* Couldn't store it?  Handle overflow */
+					node->stats.cache_overflows += 1;	/* stats update */
+
+					node->rc_status = RC_CACHE_BYPASS_MODE;
+
+					/*
+					 * No need to clear out entry or last_tuple as we'll stay
+					 * in bypass mode until the end of the scan.
+					 */
+				}
+
+				slot = node->ss.ps.ps_ResultTupleSlot;
+				ExecCopySlot(slot, outerslot);
+				return slot;
+			}
+
+		case RC_CACHE_BYPASS_MODE:
+			{
+				TupleTableSlot *outerslot;
+
+				/*
+				 * When in bypass mode we just continue to read tuples without
+				 * caching.  We need to wait until the next rescan before we
+				 * can come out of this mode.
+				 */
+				outerNode = outerPlanState(node);
+				outerslot = ExecProcNode(outerNode);
+				if (TupIsNull(outerslot))
+				{
+					node->rc_status = RC_END_OF_SCAN;
+					return NULL;
+				}
+
+				slot = node->ss.ps.ps_ResultTupleSlot;
+				ExecCopySlot(slot, outerslot);
+				return slot;
+			}
+
+		case RC_END_OF_SCAN:
+
+			/*
+			 * We've already returned NULL for this scan, but just in case
+			 * something calls us again by mistake.
+			 */
+			return NULL;
+
+		default:
+			elog(ERROR, "unrecognized resultcache state: %d",
+				 (int) node->rc_status);
+			return NULL;
+	}							/* switch */
+}
+
+ResultCacheState *
+ExecInitResultCache(ResultCache *node, EState *estate, int eflags)
+{
+	ResultCacheState *rcstate = makeNode(ResultCacheState);
+	Plan	   *outerNode;
+	int			i;
+	int			nkeys;
+	Oid		   *eqfuncoids;
+
+	/* check for unsupported flags */
+	Assert(!(eflags & (EXEC_FLAG_BACKWARD | EXEC_FLAG_MARK)));
+
+	rcstate->ss.ps.plan = (Plan *) node;
+	rcstate->ss.ps.state = estate;
+	rcstate->ss.ps.ExecProcNode = ExecResultCache;
+
+	/*
+	 * Miscellaneous initialization
+	 *
+	 * create expression context for node
+	 */
+	ExecAssignExprContext(estate, &rcstate->ss.ps);
+
+	outerNode = outerPlan(node);
+	outerPlanState(rcstate) = ExecInitNode(outerNode, estate, eflags);
+
+	/*
+	 * Initialize return slot and type. No need to initialize projection info
+	 * because this node doesn't do projections.
+	 */
+	ExecInitResultTupleSlotTL(&rcstate->ss.ps, &TTSOpsMinimalTuple);
+	rcstate->ss.ps.ps_ProjInfo = NULL;
+
+	/*
+	 * Initialize scan slot and type.
+	 */
+	ExecCreateScanSlotFromOuterPlan(estate, &rcstate->ss, &TTSOpsMinimalTuple);
+
+	/*
+	 * Set the state machine to lookup the cache.  We won't find anything
+	 * until we cache something, but this saves a special case to create the
+	 * first entry.
+	 */
+	rcstate->rc_status = RC_CACHE_LOOKUP;
+
+	rcstate->nkeys = nkeys = node->numKeys;
+	rcstate->hashkeydesc = ExecTypeFromExprList(node->param_exprs);
+	rcstate->tableslot = MakeSingleTupleTableSlot(rcstate->hashkeydesc,
+												  &TTSOpsMinimalTuple);
+	rcstate->probeslot = MakeSingleTupleTableSlot(rcstate->hashkeydesc,
+												  &TTSOpsVirtual);
+
+	rcstate->param_exprs = (ExprState **) palloc(nkeys * sizeof(ExprState *));
+	rcstate->collations = node->collations; /* Just point directly to the plan
+											 * data */
+	rcstate->hashfunctions = (FmgrInfo *) palloc(nkeys * sizeof(FmgrInfo));
+
+	eqfuncoids = palloc(nkeys * sizeof(Oid));
+
+	for (i = 0; i < nkeys; i++)
+	{
+		Oid			hashop = node->hashOperators[i];
+		Oid			left_hashfn;
+		Oid			right_hashfn;
+		Expr	   *param_expr = (Expr *) list_nth(node->param_exprs, i);
+
+		if (!get_op_hash_functions(hashop, &left_hashfn, &right_hashfn))
+			elog(ERROR, "could not find hash function for hash operator %u",
+				 hashop);
+
+		fmgr_info(left_hashfn, &rcstate->hashfunctions[i]);
+
+		rcstate->param_exprs[i] = ExecInitExpr(param_expr, (PlanState *) rcstate);
+		eqfuncoids[i] = get_opcode(hashop);
+	}
+
+	rcstate->cache_eq_expr = ExecBuildParamSetEqual(rcstate->hashkeydesc,
+													&TTSOpsMinimalTuple,
+													&TTSOpsVirtual,
+													eqfuncoids,
+													node->collations,
+													node->param_exprs,
+													(PlanState *) rcstate);
+
+	pfree(eqfuncoids);
+	rcstate->mem_used = 0;
+
+	/* Limit the total memory consumed by the cache to this */
+	rcstate->mem_limit = get_hash_mem() * 1024L;
+
+	/* A memory context dedicated for the cache */
+	rcstate->tableContext = AllocSetContextCreate(CurrentMemoryContext,
+												  "ResultCacheHashTable",
+												  ALLOCSET_DEFAULT_SIZES);
+
+	dlist_init(&rcstate->lru_list);
+	rcstate->last_tuple = NULL;
+	rcstate->entry = NULL;
+
+	/*
+	 * Mark if we can assume the cache entry is completed after we get the
+	 * first record for it.  Some callers might not call us again after
+	 * getting the first match. e.g. A join operator performing a unique join
+	 * is able to skip to the next outer tuple after getting the first
+	 * matching inner tuple.  In this case, the cache entry is complete after
+	 * getting the first tuple.  This allows us to mark it as so.
+	 */
+	rcstate->singlerow = node->singlerow;
+
+	/* Zero the statistics counters */
+	memset(&rcstate->stats, 0, sizeof(ResultCacheInstrumentation));
+
+	/* Allocate and set up the actual cache */
+	build_hash_table(rcstate, node->est_entries);
+
+	return rcstate;
+}
+
+void
+ExecEndResultCache(ResultCacheState *node)
+{
+	/*
+	 * When ending a parallel worker, copy the statistics gathered by the
+	 * worker back into shared memory so that it can be picked up by the main
+	 * process to report in EXPLAIN ANALYZE.
+	 */
+	if (node->shared_info != NULL && IsParallelWorker())
+	{
+		ResultCacheInstrumentation *si;
+
+		/* Make mem_peak available for EXPLAIN */
+		if (node->stats.mem_peak == 0)
+			node->stats.mem_peak = node->mem_used;
+
+		Assert(ParallelWorkerNumber <= node->shared_info->num_workers);
+		si = &node->shared_info->sinstrument[ParallelWorkerNumber];
+		memcpy(si, &node->stats, sizeof(ResultCacheInstrumentation));
+	}
+
+	/* Remove the cache context */
+	MemoryContextDelete(node->tableContext);
+
+	ExecClearTuple(node->ss.ss_ScanTupleSlot);
+	/* must drop pointer to cache result tuple */
+	ExecClearTuple(node->ss.ps.ps_ResultTupleSlot);
+
+	/*
+	 * free exprcontext
+	 */
+	ExecFreeExprContext(&node->ss.ps);
+
+	/*
+	 * shut down the subplan
+	 */
+	ExecEndNode(outerPlanState(node));
+}
+
+void
+ExecReScanResultCache(ResultCacheState *node)
+{
+	PlanState  *outerPlan = outerPlanState(node);
+
+	/* Mark that we must lookup the cache for a new set of parameters */
+	node->rc_status = RC_CACHE_LOOKUP;
+
+	/* nullify pointers used for the last scan */
+	node->entry = NULL;
+	node->last_tuple = NULL;
+
+	/*
+	 * if chgParam of subnode is not null then plan will be re-scanned by
+	 * first ExecProcNode.
+	 */
+	if (outerPlan->chgParam == NULL)
+		ExecReScan(outerPlan);
+
+}
+
+/*
+ * ExecEstimateCacheEntryOverheadBytes
+ *		For use in the query planner to help it estimate the amount of memory
+ *		required to store a single entry in the cache.
+ */
+double
+ExecEstimateCacheEntryOverheadBytes(double ntuples)
+{
+	return sizeof(ResultCacheEntry) + sizeof(ResultCacheKey) +
+		sizeof(ResultCacheTuple) * ntuples;
+}
+
+/* ----------------------------------------------------------------
+ *						Parallel Query Support
+ * ----------------------------------------------------------------
+ */
+
+ /* ----------------------------------------------------------------
+  *		ExecResultCacheEstimate
+  *
+  *		Estimate space required to propagate result cache statistics.
+  * ----------------------------------------------------------------
+  */
+void
+ExecResultCacheEstimate(ResultCacheState *node, ParallelContext *pcxt)
+{
+	Size		size;
+
+	/* don't need this if not instrumenting or no workers */
+	if (!node->ss.ps.instrument || pcxt->nworkers == 0)
+		return;
+
+	size = mul_size(pcxt->nworkers, sizeof(ResultCacheInstrumentation));
+	size = add_size(size, offsetof(SharedResultCacheInfo, sinstrument));
+	shm_toc_estimate_chunk(&pcxt->estimator, size);
+	shm_toc_estimate_keys(&pcxt->estimator, 1);
+}
+
+/* ----------------------------------------------------------------
+ *		ExecResultCacheInitializeDSM
+ *
+ *		Initialize DSM space for result cache statistics.
+ * ----------------------------------------------------------------
+ */
+void
+ExecResultCacheInitializeDSM(ResultCacheState *node, ParallelContext *pcxt)
+{
+	Size		size;
+
+	/* don't need this if not instrumenting or no workers */
+	if (!node->ss.ps.instrument || pcxt->nworkers == 0)
+		return;
+
+	size = offsetof(SharedResultCacheInfo, sinstrument)
+		+ pcxt->nworkers * sizeof(ResultCacheInstrumentation);
+	node->shared_info = shm_toc_allocate(pcxt->toc, size);
+	/* ensure any unfilled slots will contain zeroes */
+	memset(node->shared_info, 0, size);
+	node->shared_info->num_workers = pcxt->nworkers;
+	shm_toc_insert(pcxt->toc, node->ss.ps.plan->plan_node_id,
+				   node->shared_info);
+}
+
+/* ----------------------------------------------------------------
+ *		ExecResultCacheInitializeWorker
+ *
+ *		Attach worker to DSM space for result cache statistics.
+ * ----------------------------------------------------------------
+ */
+void
+ExecResultCacheInitializeWorker(ResultCacheState *node, ParallelWorkerContext *pwcxt)
+{
+	node->shared_info =
+		shm_toc_lookup(pwcxt->toc, node->ss.ps.plan->plan_node_id, true);
+}
+
+/* ----------------------------------------------------------------
+ *		ExecResultCacheRetrieveInstrumentation
+ *
+ *		Transfer result cache statistics from DSM to private memory.
+ * ----------------------------------------------------------------
+ */
+void
+ExecResultCacheRetrieveInstrumentation(ResultCacheState *node)
+{
+	Size		size;
+	SharedResultCacheInfo *si;
+
+	if (node->shared_info == NULL)
+		return;
+
+	size = offsetof(SharedResultCacheInfo, sinstrument)
+		+ node->shared_info->num_workers * sizeof(ResultCacheInstrumentation);
+	si = palloc(size);
+	memcpy(si, node->shared_info, size);
+	node->shared_info = si;
+}
--- a/src/backend/nodes/copyfuncs.c
+++ b/src/backend/nodes/copyfuncs.c
@@ -948,6 +948,33 @@ _copyMaterial(const Material *from)
 }


+/*
+ * _copyResultCache
+ */
+static ResultCache *
+_copyResultCache(const ResultCache *from)
+{
+	ResultCache *newnode = makeNode(ResultCache);
+
+	/*
+	 * copy node superclass fields
+	 */
+	CopyPlanFields((const Plan *) from, (Plan *) newnode);
+
+	/*
+	 * copy remainder of node
+	 */
+	COPY_SCALAR_FIELD(numKeys);
+	COPY_POINTER_FIELD(hashOperators, sizeof(Oid) * from->numKeys);
+	COPY_POINTER_FIELD(collations, sizeof(Oid) * from->numKeys);
+	COPY_NODE_FIELD(param_exprs);
+	COPY_SCALAR_FIELD(singlerow);
+	COPY_SCALAR_FIELD(est_entries);
+
+	return newnode;
+}
+
+
 /*
 * CopySortFields
 *
@@ -2340,6 +2367,7 @@ _copyRestrictInfo(const RestrictInfo *from)
 	COPY_SCALAR_FIELD(right_bucketsize);
 	COPY_SCALAR_FIELD(left_mcvfreq);
 	COPY_SCALAR_FIELD(right_mcvfreq);
+	COPY_SCALAR_FIELD(hasheqoperator);

 	return newnode;
 }
@@ -5024,6 +5052,9 @@ copyObjectImpl(const void *from)
 		case T_Material:
 			retval = _copyMaterial(from);
 			break;
+		case T_ResultCache:
+			retval = _copyResultCache(from);
+			break;
 		case T_Sort:
 			retval = _copySort(from);
 			break;

--- a/src/backend/nodes/outfuncs.c
+++ b/src/backend/nodes/outfuncs.c
@@ -846,6 +846,21 @@ _outMaterial(StringInfo str, const Material *node)
 	_outPlanInfo(str, (const Plan *) node);
 }

+static void
+_outResultCache(StringInfo str, const ResultCache *node)
+{
+	WRITE_NODE_TYPE("RESULTCACHE");
+
+	_outPlanInfo(str, (const Plan *) node);
+
+	WRITE_INT_FIELD(numKeys);
+	WRITE_OID_ARRAY(hashOperators, node->numKeys);
+	WRITE_OID_ARRAY(collations, node->numKeys);
+	WRITE_NODE_FIELD(param_exprs);
+	WRITE_BOOL_FIELD(singlerow);
+	WRITE_UINT_FIELD(est_entries);
+}
+
 static void
 _outSortInfo(StringInfo str, const Sort *node)
 {
@@ -1920,6 +1935,21 @@ _outMaterialPath(StringInfo str, const MaterialPath *node)
 	WRITE_NODE_FIELD(subpath);
 }

+static void
+_outResultCachePath(StringInfo str, const ResultCachePath *node)
+{
+	WRITE_NODE_TYPE("RESULTCACHEPATH");
+
+	_outPathInfo(str, (const Path *) node);
+
+	WRITE_NODE_FIELD(subpath);
+	WRITE_NODE_FIELD(hash_operators);
+	WRITE_NODE_FIELD(param_exprs);
+	WRITE_BOOL_FIELD(singlerow);
+	WRITE_FLOAT_FIELD(calls, "%.0f");
+	WRITE_UINT_FIELD(est_entries);
+}
+
 static void
 _outUniquePath(StringInfo str, const UniquePath *node)
 {
@@ -2521,6 +2551,7 @@ _outRestrictInfo(StringInfo str, const RestrictInfo *node)
 	WRITE_NODE_FIELD(right_em);
 	WRITE_BOOL_FIELD(outer_is_left);
 	WRITE_OID_FIELD(hashjoinoperator);
+	WRITE_OID_FIELD(hasheqoperator);
 }

 static void
@@ -3907,6 +3938,9 @@ outNode(StringInfo str, const void *obj)
 			case T_Material:
 				_outMaterial(str, obj);
 				break;
+			case T_ResultCache:
+				_outResultCache(str, obj);
+				break;
 			case T_Sort:
 				_outSort(str, obj);
 				break;
@@ -4141,6 +4175,9 @@ outNode(StringInfo str, const void *obj)
 			case T_MaterialPath:
 				_outMaterialPath(str, obj);
 				break;
+			case T_ResultCachePath:
+				_outResultCachePath(str, obj);
+				break;
 			case T_UniquePath:
 				_outUniquePath(str, obj);
 				break;

--- a/src/backend/nodes/readfuncs.c
+++ b/src/backend/nodes/readfuncs.c
@@ -2211,6 +2211,26 @@ _readMaterial(void)
 	READ_DONE();
 }

+/*
+ * _readResultCache
+ */
+static ResultCache *
+_readResultCache(void)
+{
+	READ_LOCALS(ResultCache);
+
+	ReadCommonPlan(&local_node->plan);
+
+	READ_INT_FIELD(numKeys);
+	READ_OID_ARRAY(hashOperators, local_node->numKeys);
+	READ_OID_ARRAY(collations, local_node->numKeys);
+	READ_NODE_FIELD(param_exprs);
+	READ_BOOL_FIELD(singlerow);
+	READ_UINT_FIELD(est_entries);
+
+	READ_DONE();
+}
+
 /*
 * ReadCommonSort
 *	Assign the basic stuff of all nodes that inherit from Sort
@@ -2899,6 +2919,8 @@ parseNodeString(void)
 		return_value = _readHashJoin();
 	else if (MATCH("MATERIAL", 8))
 		return_value = _readMaterial();
+	else if (MATCH("RESULTCACHE", 11))
+		return_value = _readResultCache();
 	else if (MATCH("SORT", 4))
 		return_value = _readSort();
 	else if (MATCH("INCREMENTALSORT", 15))

--- a/src/backend/optimizer/path/allpaths.c
+++ b/src/backend/optimizer/path/allpaths.c
@@ -4032,6 +4032,10 @@ print_path(PlannerInfo *root, Path *path, int indent)
 			ptype = "Material";
 			subpath = ((MaterialPath *) path)->subpath;
 			break;
+		case T_ResultCache:
+			ptype = "ResultCache";
+			subpath = ((ResultCachePath *) path)->subpath;
+			break;
 		case T_UniquePath:
 			ptype = "Unique";
 			subpath = ((UniquePath *) path)->subpath;

--- a/src/backend/optimizer/path/costsize.c
+++ b/src/backend/optimizer/path/costsize.c
@@ -79,6 +79,7 @@
 #include "executor/executor.h"
 #include "executor/nodeAgg.h"
 #include "executor/nodeHash.h"
+#include "executor/nodeResultCache.h"
 #include "miscadmin.h"
 #include "nodes/makefuncs.h"
 #include "nodes/nodeFuncs.h"
@@ -139,6 +140,7 @@ bool		enable_incremental_sort = true;
 bool		enable_hashagg = true;
 bool		enable_nestloop = true;
 bool		enable_material = true;
+bool		enable_resultcache = true;
 bool		enable_mergejoin = true;
 bool		enable_hashjoin = true;
 bool		enable_gathermerge = true;
@@ -2402,6 +2404,147 @@ cost_material(Path *path,
 	path->total_cost = startup_cost + run_cost;
 }

+/*
+ * cost_resultcache_rescan
+ *	  Determines the estimated cost of rescanning a ResultCache node.
+ *
+ * In order to estimate this, we must gain knowledge of how often we expect to
+ * be called and how many distinct sets of parameters we are likely to be
+ * called with. If we expect a good cache hit ratio, then we can set our
+ * costs to account for that hit ratio, plus a little bit of cost for the
+ * caching itself.  Caching will not work out well if we expect to be called
+ * with too many distinct parameter values.  The worst-case here is that we
+ * never see any parameter value twice, in which case we'd never get a cache
+ * hit and caching would be a complete waste of effort.
+ */
+static void
+cost_resultcache_rescan(PlannerInfo *root, ResultCachePath *rcpath,
+						Cost *rescan_startup_cost, Cost *rescan_total_cost)
+{
+	EstimationInfo estinfo;
+	Cost		input_startup_cost = rcpath->subpath->startup_cost;
+	Cost		input_total_cost = rcpath->subpath->total_cost;
+	double		tuples = rcpath->subpath->rows;
+	double		calls = rcpath->calls;
+	int			width = rcpath->subpath->pathtarget->width;
+
+	double		hash_mem_bytes;
+	double		est_entry_bytes;
+	double		est_cache_entries;
+	double		ndistinct;
+	double		evict_ratio;
+	double		hit_ratio;
+	Cost		startup_cost;
+	Cost		total_cost;
+
+	/* available cache space */
+	hash_mem_bytes = get_hash_mem() * 1024L;
+
+	/*
+	 * Set the number of bytes each cache entry should consume in the cache.
+	 * To provide us with better estimations on how many cache entries we can
+	 * store at once, we make a call to the executor here to ask it what
+	 * memory overheads there are for a single cache entry.
+	 *
+	 * XXX we also store the cache key, but that's not accounted for here.
+	 */
+	est_entry_bytes = relation_byte_size(tuples, width) +
+		ExecEstimateCacheEntryOverheadBytes(tuples);
+
+	/* estimate on the upper limit of cache entries we can hold at once */
+	est_cache_entries = floor(hash_mem_bytes / est_entry_bytes);
+
+	/* estimate on the distinct number of parameter values */
+	ndistinct = estimate_num_groups(root, rcpath->param_exprs, calls, NULL,
+									&estinfo);
+
+	/*
+	 * When the estimation fell back on using a default value, it's a bit too
+	 * risky to assume that it's ok to use a Result Cache.  The use of a
+	 * default could cause us to use a Result Cache when it's really
+	 * inappropriate to do so.  If we see that this has been done, then we'll
+	 * assume that every call will have unique parameters, which will almost
+	 * certainly mean a ResultCachePath will never survive add_path().
+	 */
+	if ((estinfo.flags & SELFLAG_USED_DEFAULT) != 0)
+		ndistinct = calls;
+
+	/*
+	 * Since we've already estimated the maximum number of entries we can
+	 * store at once and know the estimated number of distinct values we'll be
+	 * called with, we'll take this opportunity to set the path's est_entries.
+	 * This will ultimately determine the hash table size that the executor
+	 * will use.  If we leave this at zero, the executor will just choose the
+	 * size itself.  Really this is not the right place to do this, but it's
+	 * convenient since everything is already calculated.
+	 */
+	rcpath->est_entries = Min(Min(ndistinct, est_cache_entries),
+							  PG_UINT32_MAX);
+
+	/*
+	 * When the number of distinct parameter values is above the amount we can
+	 * store in the cache, then we'll have to evict some entries from the
+	 * cache.  This is not free. Here we estimate how often we'll incur the
+	 * cost of that eviction.
+	 */
+	evict_ratio = 1.0 - Min(est_cache_entries, ndistinct) / ndistinct;
+
+	/*
+	 * In order to estimate how costly a single scan will be, we need to
+	 * attempt to estimate what the cache hit ratio will be.  To do that we
+	 * must look at how many scans are estimated in total for this node and
+	 * how many of those scans we expect to get a cache hit.
+	 */
+	hit_ratio = 1.0 / ndistinct * Min(est_cache_entries, ndistinct) -
+		(ndistinct / calls);
+
+	/* Ensure we don't go negative */
+	hit_ratio = Max(hit_ratio, 0.0);
+
+	/*
+	 * Set the total_cost accounting for the expected cache hit ratio.  We
+	 * also add on a cpu_operator_cost to account for a cache lookup. This
+	 * will happen regardless of whether it's a cache hit or not.
+	 */
+	total_cost = input_total_cost * (1.0 - hit_ratio) + cpu_operator_cost;
+
+	/* Now adjust the total cost to account for cache evictions */
+
+	/* Charge a cpu_tuple_cost for evicting the actual cache entry */
+	total_cost += cpu_tuple_cost * evict_ratio;
+
+	/*
+	 * Charge a 10th of cpu_operator_cost to evict every tuple in that entry.
+	 * The per-tuple eviction is really just a pfree, so charging a whole
+	 * cpu_operator_cost seems a little excessive.
+	 */
+	total_cost += cpu_operator_cost / 10.0 * evict_ratio * tuples;
+
+	/*
+	 * Now adjust for storing things in the cache, since that's not free
+	 * either.  Everything must go in the cache.  We don't proportion this
+	 * over any ratio, just apply it once for the scan.  We charge a
+	 * cpu_tuple_cost for the creation of the cache entry and also a
+	 * cpu_operator_cost for each tuple we expect to cache.
+	 */
+	total_cost += cpu_tuple_cost + cpu_operator_cost * tuples;
+
+	/*
+	 * Getting the first row must be also be proportioned according to the
+	 * expected cache hit ratio.
+	 */
+	startup_cost = input_startup_cost * (1.0 - hit_ratio);
+
+	/*
+	 * Additionally we charge a cpu_tuple_cost to account for cache lookups,
+	 * which we'll do regardless of whether it was a cache hit or not.
+	 */
+	startup_cost += cpu_tuple_cost;
+
+	*rescan_startup_cost = startup_cost;
+	*rescan_total_cost = total_cost;
+}
+
 /*
 * cost_agg
 *		Determines and returns the cost of performing an Agg plan node,
@@ -4142,6 +4285,11 @@ cost_rescan(PlannerInfo *root, Path *path,
 				*rescan_total_cost = run_cost;
 			}
 			break;
+		case T_ResultCache:
+			/* All the hard work is done by cost_resultcache_rescan */
+			cost_resultcache_rescan(root, (ResultCachePath *) path,
+									rescan_startup_cost, rescan_total_cost);
+			break;
 		default:
 			*rescan_startup_cost = path->startup_cost;
 			*rescan_total_cost = path->total_cost;

--- a/src/backend/optimizer/path/joinpath.c
+++ b/src/backend/optimizer/path/joinpath.c
@@ -18,10 +18,13 @@

 #include "executor/executor.h"
 #include "foreign/fdwapi.h"
+#include "nodes/nodeFuncs.h"
 #include "optimizer/cost.h"
+#include "optimizer/optimizer.h"
 #include "optimizer/pathnode.h"
 #include "optimizer/paths.h"
 #include "optimizer/planmain.h"
+#include "utils/typcache.h"

 /* Hook for plugins to get control in add_paths_to_joinrel() */
 set_join_pathlist_hook_type set_join_pathlist_hook = NULL;
@@ -52,6 +55,9 @@ static void try_partial_mergejoin_path(PlannerInfo *root,
 static void sort_inner_and_outer(PlannerInfo *root, RelOptInfo *joinrel,
 								 RelOptInfo *outerrel, RelOptInfo *innerrel,
 								 JoinType jointype, JoinPathExtraData *extra);
+static inline bool clause_sides_match_join(RestrictInfo *rinfo,
+										   RelOptInfo *outerrel,
+										   RelOptInfo *innerrel);
 static void match_unsorted_outer(PlannerInfo *root, RelOptInfo *joinrel,
 								 RelOptInfo *outerrel, RelOptInfo *innerrel,
 								 JoinType jointype, JoinPathExtraData *extra);
@@ -163,6 +169,11 @@ add_paths_to_joinrel(PlannerInfo *root,
 	{
 		case JOIN_SEMI:
 		case JOIN_ANTI:
+
+			/*
+			 * XXX it may be worth proving this to allow a ResultCache to be
+			 * considered for Nested Loop Semi/Anti Joins.
+			 */
 			extra.inner_unique = false; /* well, unproven */
 			break;
 		case JOIN_UNIQUE_INNER:
@@ -354,6 +365,180 @@ allow_star_schema_join(PlannerInfo *root,
 			bms_nonempty_difference(inner_paramrels, outerrelids));
 }

+/*
+ * paraminfo_get_equal_hashops
+ *		Determine if param_info and innerrel's lateral_vars can be hashed.
+ *		Returns true the hashing is possible, otherwise return false.
+ *
+ * Additionally we also collect the outer exprs and the hash operators for
+ * each parameter to innerrel.  These set in 'param_exprs' and 'operators'
+ * when we return true.
+ */
+static bool
+paraminfo_get_equal_hashops(PlannerInfo *root, ParamPathInfo *param_info,
+							RelOptInfo *outerrel, RelOptInfo *innerrel,
+							List **param_exprs, List **operators)
+
+{
+	ListCell   *lc;
+
+	*param_exprs = NIL;
+	*operators = NIL;
+
+	if (param_info != NULL)
+	{
+		List	   *clauses = param_info->ppi_clauses;
+
+		foreach(lc, clauses)
+		{
+			RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);
+			OpExpr	   *opexpr;
+			Node	   *expr;
+
+			/* can't use result cache without a valid hash equals operator */
+			if (!OidIsValid(rinfo->hasheqoperator) ||
+				!clause_sides_match_join(rinfo, outerrel, innerrel))
+			{
+				list_free(*operators);
+				list_free(*param_exprs);
+				return false;
+			}
+
+			/*
+			 * We already checked that this is an OpExpr with 2 args when
+			 * setting hasheqoperator.
+			 */
+			opexpr = (OpExpr *) rinfo->clause;
+			if (rinfo->outer_is_left)
+				expr = (Node *) linitial(opexpr->args);
+			else
+				expr = (Node *) lsecond(opexpr->args);
+
+			*operators = lappend_oid(*operators, rinfo->hasheqoperator);
+			*param_exprs = lappend(*param_exprs, expr);
+		}
+	}
+
+	/* Now add any lateral vars to the cache key too */
+	foreach(lc, innerrel->lateral_vars)
+	{
+		Node	   *expr = (Node *) lfirst(lc);
+		TypeCacheEntry *typentry;
+
+		/* Reject if there are any volatile functions */
+		if (contain_volatile_functions(expr))
+		{
+			list_free(*operators);
+			list_free(*param_exprs);
+			return false;
+		}
+
+		typentry = lookup_type_cache(exprType(expr),
+									 TYPECACHE_HASH_PROC | TYPECACHE_EQ_OPR);
+
+		/* can't use result cache without a valid hash equals operator */
+		if (!OidIsValid(typentry->hash_proc) || !OidIsValid(typentry->eq_opr))
+		{
+			list_free(*operators);
+			list_free(*param_exprs);
+			return false;
+		}
+
+		*operators = lappend_oid(*operators, typentry->eq_opr);
+		*param_exprs = lappend(*param_exprs, expr);
+	}
+
+	/* We're okay to use result cache */
+	return true;
+}
+
+/*
+ * get_resultcache_path
+ *		If possible, make and return a Result Cache path atop of 'inner_path'.
+ *		Otherwise return NULL.
+ */
+static Path *
+get_resultcache_path(PlannerInfo *root, RelOptInfo *innerrel,
+					 RelOptInfo *outerrel, Path *inner_path,
+					 Path *outer_path, JoinType jointype,
+					 JoinPathExtraData *extra)
+{
+	List	   *param_exprs;
+	List	   *hash_operators;
+	ListCell   *lc;
+
+	/* Obviously not if it's disabled */
+	if (!enable_resultcache)
+		return NULL;
+
+	/*
+	 * We can safely not bother with all this unless we expect to perform more
+	 * than one inner scan.  The first scan is always going to be a cache
+	 * miss.  This would likely fail later anyway based on costs, so this is
+	 * really just to save some wasted effort.
+	 */
+	if (outer_path->parent->rows < 2)
+		return NULL;
+
+	/*
+	 * We can only have a result cache when there's some kind of cache key,
+	 * either parameterized path clauses or lateral Vars.  No cache key sounds
+	 * more like something a Materialize node might be more useful for.
+	 */
+	if ((inner_path->param_info == NULL ||
+		 inner_path->param_info->ppi_clauses == NIL) &&
+		innerrel->lateral_vars == NIL)
+		return NULL;
+
+	/*
+	 * Currently we don't do this for SEMI and ANTI joins unless they're
+	 * marked as inner_unique.  This is because nested loop SEMI/ANTI joins
+	 * don't scan the inner node to completion, which will mean result cache
+	 * cannot mark the cache entry as complete.
+	 *
+	 * XXX Currently we don't attempt to mark SEMI/ANTI joins as inner_unique
+	 * = true.  Should we?  See add_paths_to_joinrel()
+	 */
+	if (!extra->inner_unique && (jointype == JOIN_SEMI ||
+								 jointype == JOIN_ANTI))
+		return NULL;
+
+	/*
+	 * We can't use a result cache if there are volatile functions in the
+	 * inner rel's target list or restrict list.  A cache hit could reduce the
+	 * number of calls to these functions.
+	 */
+	if (contain_volatile_functions((Node *) innerrel->reltarget))
+		return NULL;
+
+	foreach(lc, innerrel->baserestrictinfo)
+	{
+		RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);
+
+		if (contain_volatile_functions((Node *) rinfo))
+			return NULL;
+	}
+
+	/* Check if we have hash ops for each parameter to the path */
+	if (paraminfo_get_equal_hashops(root,
+									inner_path->param_info,
+									outerrel,
+									innerrel,
+									&param_exprs,
+									&hash_operators))
+	{
+		return (Path *) create_resultcache_path(root,
+												innerrel,
+												inner_path,
+												param_exprs,
+												hash_operators,
+												extra->inner_unique,
+												outer_path->parent->rows);
+	}
+
+	return NULL;
+}
+
 /*
 * try_nestloop_path
 *	  Consider a nestloop join path; if it appears useful, push it into
@@ -1471,6 +1656,7 @@ match_unsorted_outer(PlannerInfo *root,
 			foreach(lc2, innerrel->cheapest_parameterized_paths)
 			{
 				Path	   *innerpath = (Path *) lfirst(lc2);
+				Path	   *rcpath;

 				try_nestloop_path(root,
 								  joinrel,
@@ -1479,6 +1665,22 @@ match_unsorted_outer(PlannerInfo *root,
 								  merge_pathkeys,
 								  jointype,
 								  extra);
+
+				/*
+				 * Try generating a result cache path and see if that makes the
+				 * nested loop any cheaper.
+				 */
+				rcpath = get_resultcache_path(root, innerrel, outerrel,
+											  innerpath, outerpath, jointype,
+											  extra);
+				if (rcpath != NULL)
+					try_nestloop_path(root,
+									  joinrel,
+									  outerpath,
+									  rcpath,
+									  merge_pathkeys,
+									  jointype,
+									  extra);
 			}

 			/* Also consider materialized form of the cheapest inner path */
@@ -1633,6 +1835,7 @@ consider_parallel_nestloop(PlannerInfo *root,
 		foreach(lc2, innerrel->cheapest_parameterized_paths)
 		{
 			Path	   *innerpath = (Path *) lfirst(lc2);
+			Path	   *rcpath;

 			/* Can't join to an inner path that is not parallel-safe */
 			if (!innerpath->parallel_safe)
@@ -1657,6 +1860,17 @@ consider_parallel_nestloop(PlannerInfo *root,

 			try_partial_nestloop_path(root, joinrel, outerpath, innerpath,
 									  pathkeys, jointype, extra);
+
+			/*
+			 * Try generating a result cache path and see if that makes the
+			 * nested loop any cheaper.
+			 */
+			rcpath = get_resultcache_path(root, innerrel, outerrel,
+										  innerpath, outerpath, jointype,
+										  extra);
+			if (rcpath != NULL)
+				try_partial_nestloop_path(root, joinrel, outerpath, rcpath,
+										  pathkeys, jointype, extra);
 		}
 	}
 }

--- a/src/backend/optimizer/plan/createplan.c
+++ b/src/backend/optimizer/plan/createplan.c
@@ -91,6 +91,9 @@ static Result *create_group_result_plan(PlannerInfo *root,
 static ProjectSet *create_project_set_plan(PlannerInfo *root, ProjectSetPath *best_path);
 static Material *create_material_plan(PlannerInfo *root, MaterialPath *best_path,
 									  int flags);
+static ResultCache *create_resultcache_plan(PlannerInfo *root,
+											ResultCachePath *best_path,
+											int flags);
 static Plan *create_unique_plan(PlannerInfo *root, UniquePath *best_path,
 								int flags);
 static Gather *create_gather_plan(PlannerInfo *root, GatherPath *best_path);
@@ -277,6 +280,11 @@ static Sort *make_sort_from_groupcols(List *groupcls,
 									  AttrNumber *grpColIdx,
 									  Plan *lefttree);
 static Material *make_material(Plan *lefttree);
+static ResultCache *make_resultcache(Plan *lefttree, Oid *hashoperators,
+									 Oid *collations,
+									 List *param_exprs,
+									 bool singlerow,
+									 uint32 est_entries);
 static WindowAgg *make_windowagg(List *tlist, Index winref,
 								 int partNumCols, AttrNumber *partColIdx, Oid *partOperators, Oid *partCollations,
 								 int ordNumCols, AttrNumber *ordColIdx, Oid *ordOperators, Oid *ordCollations,
@@ -453,6 +461,11 @@ create_plan_recurse(PlannerInfo *root, Path *best_path, int flags)
 												 (MaterialPath *) best_path,
 												 flags);
 			break;
+		case T_ResultCache:
+			plan = (Plan *) create_resultcache_plan(root,
+													(ResultCachePath *) best_path,
+													flags);
+			break;
 		case T_Unique:
 			if (IsA(best_path, UpperUniquePath))
 			{
@@ -1566,6 +1579,56 @@ create_material_plan(PlannerInfo *root, MaterialPath *best_path, int flags)
 	return plan;
 }

+/*
+ * create_resultcache_plan
+ *	  Create a ResultCache plan for 'best_path' and (recursively) plans
+ *	  for its subpaths.
+ *
+ *	  Returns a Plan node.
+ */
+static ResultCache *
+create_resultcache_plan(PlannerInfo *root, ResultCachePath *best_path, int flags)
+{
+	ResultCache *plan;
+	Plan	   *subplan;
+	Oid		   *operators;
+	Oid		   *collations;
+	List	   *param_exprs = NIL;
+	ListCell   *lc;
+	ListCell   *lc2;
+	int			nkeys;
+	int			i;
+
+	subplan = create_plan_recurse(root, best_path->subpath,
+								  flags | CP_SMALL_TLIST);
+
+	param_exprs = (List *) replace_nestloop_params(root, (Node *)
+												   best_path->param_exprs);
+
+	nkeys = list_length(param_exprs);
+	Assert(nkeys > 0);
+	operators = palloc(nkeys * sizeof(Oid));
+	collations = palloc(nkeys * sizeof(Oid));
+
+	i = 0;
+	forboth(lc, param_exprs, lc2, best_path->hash_operators)
+	{
+		Expr	   *param_expr = (Expr *) lfirst(lc);
+		Oid			opno = lfirst_oid(lc2);
+
+		operators[i] = opno;
+		collations[i] = exprCollation((Node *) param_expr);
+		i++;
+	}
+
+	plan = make_resultcache(subplan, operators, collations, param_exprs,
+							best_path->singlerow, best_path->est_entries);
+
+	copy_generic_path_info(&plan->plan, (Path *) best_path);
+
+	return plan;
+}
+
 /*
 * create_unique_plan
 *	  Create a Unique plan for 'best_path' and (recursively) plans
@@ -6452,6 +6515,28 @@ materialize_finished_plan(Plan *subplan)
 	return matplan;
 }

+static ResultCache *
+make_resultcache(Plan *lefttree, Oid *hashoperators, Oid *collations,
+				 List *param_exprs, bool singlerow, uint32 est_entries)
+{
+	ResultCache *node = makeNode(ResultCache);
+	Plan	   *plan = &node->plan;
+
+	plan->targetlist = lefttree->targetlist;
+	plan->qual = NIL;
+	plan->lefttree = lefttree;
+	plan->righttree = NULL;
+
+	node->numKeys = list_length(param_exprs);
+	node->hashOperators = hashoperators;
+	node->collations = collations;
+	node->param_exprs = param_exprs;
+	node->singlerow = singlerow;
+	node->est_entries = est_entries;
+
+	return node;
+}
+
 Agg *
 make_agg(List *tlist, List *qual,
 		 AggStrategy aggstrategy, AggSplit aggsplit,
@@ -7038,6 +7123,7 @@ is_projection_capable_path(Path *path)
 	{
 		case T_Hash:
 		case T_Material:
+		case T_ResultCache:
 		case T_Sort:
 		case T_IncrementalSort:
 		case T_Unique:
@@ -7083,6 +7169,7 @@ is_projection_capable_plan(Plan *plan)
 	{
 		case T_Hash:
 		case T_Material:
+		case T_ResultCache:
 		case T_Sort:
 		case T_Unique:
 		case T_SetOp:

--- a/src/backend/optimizer/plan/initsplan.c
+++ b/src/backend/optimizer/plan/initsplan.c
@@ -33,6 +33,7 @@
 #include "parser/analyze.h"
 #include "rewrite/rewriteManip.h"
 #include "utils/lsyscache.h"
+#include "utils/typcache.h"

 /* These parameters are set by GUC */
 int			from_collapse_limit;
@@ -77,6 +78,7 @@ static bool check_equivalence_delay(PlannerInfo *root,
 static bool check_redundant_nullability_qual(PlannerInfo *root, Node *clause);
 static void check_mergejoinable(RestrictInfo *restrictinfo);
 static void check_hashjoinable(RestrictInfo *restrictinfo);
+static void check_resultcacheable(RestrictInfo *restrictinfo);


 /*****************************************************************************
@@ -2208,6 +2210,13 @@ distribute_restrictinfo_to_rels(PlannerInfo *root,
 			 */
 			check_hashjoinable(restrictinfo);

+			/*
+			 * Likewise, check if the clause is suitable to be used with a
+			 * Result Cache node to cache inner tuples during a parameterized
+			 * nested loop.
+			 */
+			check_resultcacheable(restrictinfo);
+
 			/*
 			 * Add clause to the join lists of all the relevant relations.
 			 */
@@ -2450,6 +2459,7 @@ build_implied_join_equality(PlannerInfo *root,
 	/* Set mergejoinability/hashjoinability flags */
 	check_mergejoinable(restrictinfo);
 	check_hashjoinable(restrictinfo);
+	check_resultcacheable(restrictinfo);

 	return restrictinfo;
 }
@@ -2697,3 +2707,34 @@ check_hashjoinable(RestrictInfo *restrictinfo)
 		!contain_volatile_functions((Node *) restrictinfo))
 		restrictinfo->hashjoinoperator = opno;
 }
+
+/*
+ * check_resultcacheable
+ *	  If the restrictinfo's clause is suitable to be used for a Result Cache
+ *	  node, set the hasheqoperator to the hash equality operator that will be
+ *	  needed during caching.
+ */
+static void
+check_resultcacheable(RestrictInfo *restrictinfo)
+{
+	TypeCacheEntry *typentry;
+	Expr	   *clause = restrictinfo->clause;
+	Node	   *leftarg;
+
+	if (restrictinfo->pseudoconstant)
+		return;
+	if (!is_opclause(clause))
+		return;
+	if (list_length(((OpExpr *) clause)->args) != 2)
+		return;
+
+	leftarg = linitial(((OpExpr *) clause)->args);
+
+	typentry = lookup_type_cache(exprType(leftarg), TYPECACHE_HASH_PROC |
+													TYPECACHE_EQ_OPR);
+
+	if (!OidIsValid(typentry->hash_proc) || !OidIsValid(typentry->eq_opr))
+		return;
+
+	restrictinfo->hasheqoperator = typentry->eq_opr;
+}
--- a/src/backend/optimizer/plan/setrefs.c
+++ b/src/backend/optimizer/plan/setrefs.c
@@ -752,6 +752,15 @@ set_plan_refs(PlannerInfo *root, Plan *plan, int rtoffset)
 			set_hash_references(root, plan, rtoffset);
 			break;

+		case T_ResultCache:
+			{
+				ResultCache *rcplan = (ResultCache *) plan;
+				rcplan->param_exprs = fix_scan_list(root, rcplan->param_exprs,
+													rtoffset,
+													NUM_EXEC_TLIST(plan));
+				break;
+			}
+
 		case T_Material:
 		case T_Sort:
 		case T_IncrementalSort:

--- a/src/backend/optimizer/plan/subselect.c
+++ b/src/backend/optimizer/plan/subselect.c
@@ -2745,6 +2745,11 @@ finalize_plan(PlannerInfo *root, Plan *plan,
 			/* rescan_param does *not* get added to scan_params */
 			break;

+		case T_ResultCache:
+			finalize_primnode((Node *) ((ResultCache *) plan)->param_exprs,
+							  &context);
+			break;
+
 		case T_ProjectSet:
 		case T_Hash:
 		case T_Material:

--- a/src/backend/optimizer/util/pathnode.c
+++ b/src/backend/optimizer/util/pathnode.c
@@ -1576,6 +1576,56 @@ create_material_path(RelOptInfo *rel, Path *subpath)
 	return pathnode;
 }

+/*
+ * create_resultcache_path
+ *	  Creates a path corresponding to a ResultCache plan, returning the
+ *	  pathnode.
+ */
+ResultCachePath *
+create_resultcache_path(PlannerInfo *root, RelOptInfo *rel, Path *subpath,
+						List *param_exprs, List *hash_operators,
+						bool singlerow, double calls)
+{
+	ResultCachePath *pathnode = makeNode(ResultCachePath);
+
+	Assert(subpath->parent == rel);
+
+	pathnode->path.pathtype = T_ResultCache;
+	pathnode->path.parent = rel;
+	pathnode->path.pathtarget = rel->reltarget;
+	pathnode->path.param_info = subpath->param_info;
+	pathnode->path.parallel_aware = false;
+	pathnode->path.parallel_safe = rel->consider_parallel &&
+		subpath->parallel_safe;
+	pathnode->path.parallel_workers = subpath->parallel_workers;
+	pathnode->path.pathkeys = subpath->pathkeys;
+
+	pathnode->subpath = subpath;
+	pathnode->hash_operators = hash_operators;
+	pathnode->param_exprs = param_exprs;
+	pathnode->singlerow = singlerow;
+	pathnode->calls = calls;
+
+	/*
+	 * For now we set est_entries to 0.  cost_resultcache_rescan() does all
+	 * the hard work to determine how many cache entries there are likely to
+	 * be, so it seems best to leave it up to that function to fill this field
+	 * in.  If left at 0, the executor will make a guess at a good value.
+	 */
+	pathnode->est_entries = 0;
+
+	/*
+	 * Add a small additional charge for caching the first entry.  All the
+	 * harder calculations for rescans are performed in
+	 * cost_resultcache_rescan().
+	 */
+	pathnode->path.startup_cost = subpath->startup_cost + cpu_tuple_cost;
+	pathnode->path.total_cost = subpath->total_cost + cpu_tuple_cost;
+	pathnode->path.rows = subpath->rows;
+
+	return pathnode;
+}
+
 /*
 * create_unique_path
 *	  Creates a path representing elimination of distinct rows from the
@@ -3869,6 +3919,17 @@ reparameterize_path(PlannerInfo *root, Path *path,
 									   apath->path.parallel_aware,
 									   -1);
 			}
+		case T_ResultCache:
+			{
+				ResultCachePath *rcpath = (ResultCachePath *) path;
+
+				return (Path *) create_resultcache_path(root, rel,
+														rcpath->subpath,
+														rcpath->param_exprs,
+														rcpath->hash_operators,
+														rcpath->singlerow,
+														rcpath->calls);
+			}
 		default:
 			break;
 	}
@@ -4087,6 +4148,16 @@ do { \
 			}
 			break;

+		case T_ResultCachePath:
+			{
+				ResultCachePath *rcpath;
+
+				FLAT_COPY_PATH(rcpath, path, ResultCachePath);
+				REPARAMETERIZE_CHILD_PATH(rcpath->subpath);
+				new_path = (Path *) rcpath;
+			}
+			break;
+
 		case T_GatherPath:
 			{
 				GatherPath *gpath;

--- a/src/backend/optimizer/util/restrictinfo.c
+++ b/src/backend/optimizer/util/restrictinfo.c
@@ -217,6 +217,8 @@ make_restrictinfo_internal(PlannerInfo *root,
 	restrictinfo->left_mcvfreq = -1;
 	restrictinfo->right_mcvfreq = -1;

+	restrictinfo->hasheqoperator = InvalidOid;
+
 	return restrictinfo;
 }

@@ -366,6 +368,7 @@ commute_restrictinfo(RestrictInfo *rinfo, Oid comm_op)
 	result->right_bucketsize = rinfo->left_bucketsize;
 	result->left_mcvfreq = rinfo->right_mcvfreq;
 	result->right_mcvfreq = rinfo->left_mcvfreq;
+	result->hasheqoperator = InvalidOid;

 	return result;
 }

--- a/src/backend/utils/misc/guc.c
+++ b/src/backend/utils/misc/guc.c
@@ -1036,6 +1036,16 @@ static struct config_bool ConfigureNamesBool[] =
 		true,
 		NULL, NULL, NULL
 	},
+	{
+		{"enable_resultcache", PGC_USERSET, QUERY_TUNING_METHOD,
+			gettext_noop("Enables the planner's use of result caching."),
+			NULL,
+			GUC_EXPLAIN
+		},
+		&enable_resultcache,
+		true,
+		NULL, NULL, NULL
+	},
 	{
 		{"enable_nestloop", PGC_USERSET, QUERY_TUNING_METHOD,
 			gettext_noop("Enables the planner's use of nested-loop join plans."),

--- a/src/backend/utils/misc/postgresql.conf.sample
+++ b/src/backend/utils/misc/postgresql.conf.sample
@@ -366,6 +366,7 @@
 #enable_seqscan = on
 #enable_sort = on
 #enable_incremental_sort = on
+#enable_resultcache = on
 #enable_tidscan = on
 #enable_partitionwise_join = off
 #enable_partitionwise_aggregate = off

--- a/src/include/executor/executor.h
+++ b/src/include/executor/executor.h
@@ -275,6 +275,13 @@ extern ExprState *ExecBuildGroupingEqual(TupleDesc ldesc, TupleDesc rdesc,
 										 const Oid *eqfunctions,
 										 const Oid *collations,
 										 PlanState *parent);
+extern ExprState *ExecBuildParamSetEqual(TupleDesc desc,
+										 const TupleTableSlotOps *lops,
+										 const TupleTableSlotOps *rops,
+										 const Oid *eqfunctions,
+										 const Oid *collations,
+										 const List *param_exprs,
+										 PlanState *parent);
 extern ProjectionInfo *ExecBuildProjectionInfo(List *targetList,
 											   ExprContext *econtext,
 											   TupleTableSlot *slot,

--- a/src/include/executor/nodeResultCache.h
+++ b/src/include/executor/nodeResultCache.h
+/*-------------------------------------------------------------------------
+ *
+ * nodeResultCache.h
+ *
+ *
+ *
+ * Portions Copyright (c) 2021, PostgreSQL Global Development Group
+ * Portions Copyright (c) 1994, Regents of the University of California
+ *
+ * src/include/executor/nodeResultCache.h
+ *
+ *-------------------------------------------------------------------------
+ */
+#ifndef NODERESULTCACHE_H
+#define NODERESULTCACHE_H
+
+#include "nodes/execnodes.h"
+
+extern ResultCacheState *ExecInitResultCache(ResultCache *node, EState *estate, int eflags);
+extern void ExecEndResultCache(ResultCacheState *node);
+extern void ExecReScanResultCache(ResultCacheState *node);
+extern double ExecEstimateCacheEntryOverheadBytes(double ntuples);
+extern void ExecResultCacheEstimate(ResultCacheState *node,
+									ParallelContext *pcxt);
+extern void ExecResultCacheInitializeDSM(ResultCacheState *node,
+										 ParallelContext *pcxt);
+extern void ExecResultCacheInitializeWorker(ResultCacheState *node,
+											ParallelWorkerContext *pwcxt);
+extern void ExecResultCacheRetrieveInstrumentation(ResultCacheState *node);
+
+#endif							/* NODERESULTCACHE_H */
--- a/src/include/lib/ilist.h
+++ b/src/include/lib/ilist.h
@@ -394,6 +394,25 @@ dlist_move_head(dlist_head *head, dlist_node *node)
 	dlist_check(head);
 }

+/*
+ * Move element from its current position in the list to the tail position in
+ * the same list.
+ *
+ * Undefined behaviour if 'node' is not already part of the list.
+ */
+static inline void
+dlist_move_tail(dlist_head *head, dlist_node *node)
+{
+	/* fast path if it's already at the tail */
+	if (head->head.prev == node)
+		return;
+
+	dlist_delete(node);
+	dlist_push_tail(head, node);
+
+	dlist_check(head);
+}
+
 /*
 * Check whether 'node' has a following node.
 * Caution: unreliable if 'node' is not in the list.

--- a/src/include/nodes/execnodes.h
+++ b/src/include/nodes/execnodes.h
@@ -17,6 +17,7 @@
 #include "access/tupconvert.h"
 #include "executor/instrument.h"
 #include "fmgr.h"
+#include "lib/ilist.h"
 #include "lib/pairingheap.h"
 #include "nodes/params.h"
 #include "nodes/plannodes.h"
@@ -2037,6 +2038,71 @@ typedef struct MaterialState
 	Tuplestorestate *tuplestorestate;
 } MaterialState;

+struct ResultCacheEntry;
+struct ResultCacheTuple;
+struct ResultCacheKey;
+
+typedef struct ResultCacheInstrumentation
+{
+	uint64		cache_hits;		/* number of rescans where we've found the
+								 * scan parameter values to be cached */
+	uint64		cache_misses;	/* number of rescans where we've not found the
+								 * scan parameter values to be cached. */
+	uint64		cache_evictions;	/* number of cache entries removed due to
+									 * the need to free memory */
+	uint64		cache_overflows;	/* number of times we've had to bypass the
+									 * cache when filling it due to not being
+									 * able to free enough space to store the
+									 * current scan's tuples. */
+	uint64		mem_peak;		/* peak memory usage in bytes */
+} ResultCacheInstrumentation;
+
+/* ----------------
+ *	 Shared memory container for per-worker resultcache information
+ * ----------------
+ */
+typedef struct SharedResultCacheInfo
+{
+	int			num_workers;
+	ResultCacheInstrumentation sinstrument[FLEXIBLE_ARRAY_MEMBER];
+} SharedResultCacheInfo;
+
+/* ----------------
+ *	 ResultCacheState information
+ *
+ *		resultcache nodes are used to cache recent and commonly seen results
+ *		from a parameterized scan.
+ * ----------------
+ */
+typedef struct ResultCacheState
+{
+	ScanState	ss;				/* its first field is NodeTag */
+	int			rc_status;		/* value of ExecResultCache state machine */
+	int			nkeys;			/* number of cache keys */
+	struct resultcache_hash *hashtable; /* hash table for cache entries */
+	TupleDesc	hashkeydesc;	/* tuple descriptor for cache keys */
+	TupleTableSlot *tableslot;	/* min tuple slot for existing cache entries */
+	TupleTableSlot *probeslot;	/* virtual slot used for hash lookups */
+	ExprState  *cache_eq_expr;	/* Compare exec params to hash key */
+	ExprState **param_exprs;	/* exprs containing the parameters to this
+								 * node */
+	FmgrInfo   *hashfunctions;	/* lookup data for hash funcs nkeys in size */
+	Oid		   *collations;		/* collation for comparisons nkeys in size */
+	uint64		mem_used;		/* bytes of memory used by cache */
+	uint64		mem_limit;		/* memory limit in bytes for the cache */
+	MemoryContext tableContext; /* memory context to store cache data */
+	dlist_head	lru_list;		/* least recently used entry list */
+	struct ResultCacheTuple *last_tuple;	/* Used to point to the last tuple
+											 * returned during a cache hit and
+											 * the tuple we last stored when
+											 * populating the cache. */
+	struct ResultCacheEntry *entry; /* the entry that 'last_tuple' belongs to
+									 * or NULL if 'last_tuple' is NULL. */
+	bool		singlerow;		/* true if the cache entry is to be marked as
+								 * complete after caching the first tuple. */
+	ResultCacheInstrumentation stats;	/* execution statistics */
+	SharedResultCacheInfo *shared_info; /* statistics for parallel workers */
+} ResultCacheState;

 /* ----------------
 *	 When performing sorting by multiple keys, it's possible that the input

--- a/src/include/nodes/nodes.h
+++ b/src/include/nodes/nodes.h
@@ -74,6 +74,7 @@ typedef enum NodeTag
 	T_MergeJoin,
 	T_HashJoin,
 	T_Material,
+	T_ResultCache,
 	T_Sort,
 	T_IncrementalSort,
 	T_Group,
@@ -132,6 +133,7 @@ typedef enum NodeTag
 	T_MergeJoinState,
 	T_HashJoinState,
 	T_MaterialState,
+	T_ResultCacheState,
 	T_SortState,
 	T_IncrementalSortState,
 	T_GroupState,
@@ -242,6 +244,7 @@ typedef enum NodeTag
 	T_MergeAppendPath,
 	T_GroupResultPath,
 	T_MaterialPath,
+	T_ResultCachePath,
 	T_UniquePath,
 	T_GatherPath,
 	T_GatherMergePath,

--- a/src/include/nodes/pathnodes.h
+++ b/src/include/nodes/pathnodes.h
@@ -1494,6 +1494,25 @@ typedef struct MaterialPath
 	Path	   *subpath;
 } MaterialPath;

+/*
+ * ResultCachePath represents a ResultCache plan node, i.e., a cache that
+ * caches tuples from parameterized paths to save the underlying node from
+ * having to be rescanned for parameter values which are already cached.
+ */
+typedef struct ResultCachePath
+{
+	Path		path;
+	Path	   *subpath;		/* outerpath to cache tuples from */
+	List	   *hash_operators; /* hash operators for each key */
+	List	   *param_exprs;	/* cache keys */
+	bool		singlerow;		/* true if the cache entry is to be marked as
+								 * complete after caching the first record. */
+	double		calls;			/* expected number of rescans */
+	uint32		est_entries;	/* The maximum number of entries that the
+								 * planner expects will fit in the cache, or 0
+								 * if unknown */
+} ResultCachePath;
+
 /*
 * UniquePath represents elimination of distinct rows from the output of
 * its subpath.
@@ -2091,6 +2110,9 @@ typedef struct RestrictInfo
 	Selectivity right_bucketsize;	/* avg bucketsize of right side */
 	Selectivity left_mcvfreq;	/* left side's most common val's freq */
 	Selectivity right_mcvfreq;	/* right side's most common val's freq */
+
+	/* hash equality operator used for result cache, else InvalidOid */
+	Oid			hasheqoperator;
 } RestrictInfo;

 /*

--- a/src/include/nodes/plannodes.h
+++ b/src/include/nodes/plannodes.h
@@ -779,6 +779,27 @@ typedef struct Material
 	Plan		plan;
 } Material;

+/* ----------------
+ *		result cache node
+ * ----------------
+ */
+typedef struct ResultCache
+{
+	Plan		plan;
+
+	int			numKeys;		/* size of the two arrays below */
+
+	Oid		   *hashOperators;	/* hash operators for each key */
+	Oid		   *collations;		/* cache keys */
+	List	   *param_exprs;	/* exprs containing parameters */
+	bool		singlerow;		/* true if the cache entry should be marked as
+								 * complete after we store the first tuple in
+								 * it. */
+	uint32		est_entries;	/* The maximum number of entries that the
+								 * planner expects will fit in the cache, or 0
+								 * if unknown */
+} ResultCache;
+
 /* ----------------
 *		sort node
 * ----------------

--- a/src/include/optimizer/cost.h
+++ b/src/include/optimizer/cost.h
@@ -57,6 +57,7 @@ extern PGDLLIMPORT bool enable_incremental_sort;
 extern PGDLLIMPORT bool enable_hashagg;
 extern PGDLLIMPORT bool enable_nestloop;
 extern PGDLLIMPORT bool enable_material;
+extern PGDLLIMPORT bool enable_resultcache;
 extern PGDLLIMPORT bool enable_mergejoin;
 extern PGDLLIMPORT bool enable_hashjoin;
 extern PGDLLIMPORT bool enable_gathermerge;

--- a/src/include/optimizer/pathnode.h
+++ b/src/include/optimizer/pathnode.h
@@ -82,6 +82,13 @@ extern GroupResultPath *create_group_result_path(PlannerInfo *root,
 												 PathTarget *target,
 												 List *havingqual);
 extern MaterialPath *create_material_path(RelOptInfo *rel, Path *subpath);
+extern ResultCachePath *create_resultcache_path(PlannerInfo *root,
+												RelOptInfo *rel,
+												Path *subpath,
+												List *param_exprs,
+												List *hash_operators,
+												bool singlerow,
+												double calls);
 extern UniquePath *create_unique_path(PlannerInfo *root, RelOptInfo *rel,
 									  Path *subpath, SpecialJoinInfo *sjinfo);
 extern GatherPath *create_gather_path(PlannerInfo *root,

--- a/src/test/regress/expected/aggregates.out
+++ b/src/test/regress/expected/aggregates.out
@@ -2584,6 +2584,7 @@ select v||'a', case when v||'a' = 'aa' then 1 else 0 end, count(*)
 -- Make sure that generation of HashAggregate for uniqification purposes
 -- does not lead to array overflow due to unexpected duplicate hash keys
 -- see CAFeeJoKKu0u+A_A9R9316djW-YW3-+Gtgvy3ju655qRHR3jtdA@mail.gmail.com
+set enable_resultcache to off;
 explain (costs off)
  select 1 from tenk1
   where (hundred, thousand) in (select twothousand, twothousand from onek);
@@ -2599,6 +2600,7 @@ explain (costs off)
               ->  Seq Scan on onek
 (8 rows)

+reset enable_resultcache;
 --
 -- Hash Aggregation Spill tests
 --

--- 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
 --
@@ -3664,7 +3666,7 @@ select * from tenk1 t1 left join
  on t1.hundred = t2.hundred and t1.ten = t3.ten
 where t1.unique1 = 1;
                          QUERY PLAN                          
--------------------------------------------------------
+--------------------------------------------------------------
 Nested Loop Left Join
   ->  Index Scan using tenk1_unique1 on tenk1 t1
         Index Cond: (unique1 = 1)
@@ -3674,9 +3676,11 @@ where t1.unique1 = 1;
               Recheck Cond: (t1.hundred = hundred)
               ->  Bitmap Index Scan on tenk1_hundred
                     Index Cond: (hundred = t1.hundred)
+         ->  Result Cache
+               Cache Key: t2.thousand
               ->  Index Scan using tenk1_unique2 on tenk1 t3
                     Index Cond: (unique2 = t2.thousand)
-(11 rows)
+(13 rows)

 explain (costs off)
 select * from tenk1 t1 left join
@@ -3684,7 +3688,7 @@ select * from tenk1 t1 left join
  on t1.hundred = t2.hundred and t1.ten + t2.ten = t3.ten
 where t1.unique1 = 1;
                          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)
+         ->  Result Cache
+               Cache Key: t2.thousand
               ->  Index Scan using tenk1_unique2 on tenk1 t3
                     Index Cond: (unique2 = t2.thousand)
-(11 rows)
+(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)
+   ->  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
-(16 rows)
+(19 rows)

 select * from
  text_tbl t1
@@ -4247,12 +4256,12 @@ select * from
  lateral (select ss1.* from text_tbl t3 limit 1) as ss2
 where t1.f1 = ss2.f1;
                       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)
+               Output: (q1), (f1)
               ->  Seq Scan on public.text_tbl t3
-               Output: (i8.q1), t2.f1
-(22 rows)
+                     Output: q1, f1
+(28 rows)

 select * from
  text_tbl t1
@@ -4316,6 +4331,9 @@ where tt1.f1 = ss1.c0;
                     ->  Seq Scan on public.text_tbl tt4
                           Output: tt4.f1
                           Filter: (tt4.f1 = 'foo'::text)
+   ->  Result Cache
+         Output: ss1.c0
+         Cache Key: tt4.f1
         ->  Subquery Scan on ss1
               Output: ss1.c0
               Filter: (ss1.c0 = 'foo'::text)
@@ -4323,7 +4341,7 @@ where tt1.f1 = ss1.c0;
                     Output: (tt4.f1)
                     ->  Seq Scan on public.text_tbl tt5
                           Output: tt4.f1
-(29 rows)
+(32 rows)

 select 1 from
  text_tbl as tt1
@@ -4998,33 +5016,39 @@ 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                      
------------------------------------------------
+------------------------------------------------------
 Aggregate
   ->  Nested Loop
         ->  Seq Scan on tenk1 a
+         ->  Result Cache
+               Cache Key: a.two
               ->  Function Scan on generate_series g
-(4 rows)
+(6 rows)

 explain (costs off)
  select count(*) from tenk1 a cross join lateral generate_series(1,two) g;
                      QUERY PLAN                      
------------------------------------------------
+------------------------------------------------------
 Aggregate
   ->  Nested Loop
         ->  Seq Scan on tenk1 a
+         ->  Result Cache
+               Cache Key: a.two
               ->  Function Scan on generate_series g
-(4 rows)
+(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                      
------------------------------------------------
+------------------------------------------------------
 Aggregate
   ->  Nested Loop
         ->  Seq Scan on tenk1 a
+         ->  Result Cache
+               Cache Key: a.two
               ->  Function Scan on generate_series g
-(4 rows)
+(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;

--- a/src/test/regress/expected/partition_prune.out
+++ b/src/test/regress/expected/partition_prune.out
@@ -1958,6 +1958,9 @@ begin
        ln := regexp_replace(ln, 'Workers Launched: \d+', 'Workers Launched: N');
        ln := regexp_replace(ln, 'actual rows=\d+ loops=\d+', 'actual rows=N loops=N');
        ln := regexp_replace(ln, 'Rows Removed by Filter: \d+', 'Rows Removed by Filter: N');
+        ln := regexp_replace(ln, 'Hits: \d+', 'Hits: N');
+        ln := regexp_replace(ln, 'Misses: \d+', 'Misses: N');
+        ln := regexp_replace(ln, 'Memory Usage: \d+', 'Memory Usage: N');
        return next ln;
    end loop;
 end;
@@ -2087,7 +2090,7 @@ set enable_hashjoin = 0;
 set enable_mergejoin = 0;
 select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on ab.a = a.a where a.a in(0, 0, 1)');
                                           explain_parallel_append                                            
--------------------------------------------------------------------------------------------------------
+--------------------------------------------------------------------------------------------------------------
 Finalize Aggregate (actual rows=N loops=N)
   ->  Gather (actual rows=N loops=N)
         Workers Planned: 1
@@ -2096,6 +2099,10 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
               ->  Nested Loop (actual rows=N loops=N)
                     ->  Parallel Seq Scan on lprt_a a (actual rows=N loops=N)
                           Filter: (a = ANY ('{0,0,1}'::integer[]))
+                     ->  Result Cache (actual rows=N loops=N)
+                           Cache Key: a.a
+                           Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
+                           Worker 0:  Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
                           ->  Append (actual rows=N loops=N)
                                 ->  Index Scan using ab_a1_b1_a_idx on ab_a1_b1 ab_1 (actual rows=N loops=N)
                                       Index Cond: (a = a.a)
@@ -2115,13 +2122,13 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
                                       Index Cond: (a = a.a)
                                 ->  Index Scan using ab_a3_b3_a_idx on ab_a3_b3 ab_9 (never executed)
                                       Index Cond: (a = a.a)
-(27 rows)
+(31 rows)

 -- Ensure the same partitions are pruned when we make the nested loop
 -- parameter an Expr rather than a plain Param.
 select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on ab.a = a.a + 0 where a.a in(0, 0, 1)');
                                           explain_parallel_append                                            
--------------------------------------------------------------------------------------------------------
+--------------------------------------------------------------------------------------------------------------
 Finalize Aggregate (actual rows=N loops=N)
   ->  Gather (actual rows=N loops=N)
         Workers Planned: 1
@@ -2130,6 +2137,10 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
               ->  Nested Loop (actual rows=N loops=N)
                     ->  Parallel Seq Scan on lprt_a a (actual rows=N loops=N)
                           Filter: (a = ANY ('{0,0,1}'::integer[]))
+                     ->  Result Cache (actual rows=N loops=N)
+                           Cache Key: (a.a + 0)
+                           Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
+                           Worker 0:  Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
                           ->  Append (actual rows=N loops=N)
                                 ->  Index Scan using ab_a1_b1_a_idx on ab_a1_b1 ab_1 (actual rows=N loops=N)
                                       Index Cond: (a = (a.a + 0))
@@ -2149,12 +2160,12 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
                                       Index Cond: (a = (a.a + 0))
                                 ->  Index Scan using ab_a3_b3_a_idx on ab_a3_b3 ab_9 (never executed)
                                       Index Cond: (a = (a.a + 0))
-(27 rows)
+(31 rows)

 insert into lprt_a values(3),(3);
 select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on ab.a = a.a where a.a in(1, 0, 3)');
                                           explain_parallel_append                                            
--------------------------------------------------------------------------------------------------------
+--------------------------------------------------------------------------------------------------------------
 Finalize Aggregate (actual rows=N loops=N)
   ->  Gather (actual rows=N loops=N)
         Workers Planned: 1
@@ -2163,6 +2174,10 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
               ->  Nested Loop (actual rows=N loops=N)
                     ->  Parallel Seq Scan on lprt_a a (actual rows=N loops=N)
                           Filter: (a = ANY ('{1,0,3}'::integer[]))
+                     ->  Result Cache (actual rows=N loops=N)
+                           Cache Key: a.a
+                           Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
+                           Worker 0:  Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
                           ->  Append (actual rows=N loops=N)
                                 ->  Index Scan using ab_a1_b1_a_idx on ab_a1_b1 ab_1 (actual rows=N loops=N)
                                       Index Cond: (a = a.a)
@@ -2182,11 +2197,11 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
                                       Index Cond: (a = a.a)
                                 ->  Index Scan using ab_a3_b3_a_idx on ab_a3_b3 ab_9 (actual rows=N loops=N)
                                       Index Cond: (a = a.a)
-(27 rows)
+(31 rows)

 select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on ab.a = a.a where a.a in(1, 0, 0)');
                                           explain_parallel_append                                            
--------------------------------------------------------------------------------------------------------
+--------------------------------------------------------------------------------------------------------------
 Finalize Aggregate (actual rows=N loops=N)
   ->  Gather (actual rows=N loops=N)
         Workers Planned: 1
@@ -2196,6 +2211,10 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
                     ->  Parallel Seq Scan on lprt_a a (actual rows=N loops=N)
                           Filter: (a = ANY ('{1,0,0}'::integer[]))
                           Rows Removed by Filter: N
+                     ->  Result Cache (actual rows=N loops=N)
+                           Cache Key: a.a
+                           Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
+                           Worker 0:  Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
                           ->  Append (actual rows=N loops=N)
                                 ->  Index Scan using ab_a1_b1_a_idx on ab_a1_b1 ab_1 (actual rows=N loops=N)
                                       Index Cond: (a = a.a)
@@ -2215,12 +2234,12 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
                                       Index Cond: (a = a.a)
                                 ->  Index Scan using ab_a3_b3_a_idx on ab_a3_b3 ab_9 (never executed)
                                       Index Cond: (a = a.a)
-(28 rows)
+(32 rows)

 delete from lprt_a where a = 1;
 select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on ab.a = a.a where a.a in(1, 0, 0)');
                                        explain_parallel_append                                         
-------------------------------------------------------------------------------------------------
+--------------------------------------------------------------------------------------------------------
 Finalize Aggregate (actual rows=N loops=N)
   ->  Gather (actual rows=N loops=N)
         Workers Planned: 1
@@ -2230,6 +2249,10 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
                     ->  Parallel Seq Scan on lprt_a a (actual rows=N loops=N)
                           Filter: (a = ANY ('{1,0,0}'::integer[]))
                           Rows Removed by Filter: N
+                     ->  Result Cache (actual rows=N loops=N)
+                           Cache Key: a.a
+                           Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
+                           Worker 0:  Hits: N  Misses: N  Evictions: 0  Overflows: 0  Memory Usage: NkB
                           ->  Append (actual rows=N loops=N)
                                 ->  Index Scan using ab_a1_b1_a_idx on ab_a1_b1 ab_1 (never executed)
                                       Index Cond: (a = a.a)
@@ -2249,7 +2272,7 @@ select explain_parallel_append('select avg(ab.a) from ab inner join lprt_a a on
                                       Index Cond: (a = a.a)
                                 ->  Index Scan using ab_a3_b3_a_idx on ab_a3_b3 ab_9 (never executed)
                                       Index Cond: (a = a.a)
-(28 rows)
+(32 rows)

 reset enable_hashjoin;
 reset enable_mergejoin;

--- a/src/test/regress/expected/resultcache.out
+++ b/src/test/regress/expected/resultcache.out
+-- Perform tests on the Result Cache node.
+-- The cache hits/misses/evictions from the Result Cache node can vary between
+-- machines.  Let's just replace the number with an 'N'.  In order to allow us
+-- to perform validation when the measure was zero, we replace a zero value
+-- with "Zero".  All other numbers are replaced with 'N'.
+create function explain_resultcache(query text, hide_hitmiss bool) returns setof text
+language plpgsql as
+$$
+declare
+    ln text;
+begin
+    for ln in
+        execute format('explain (analyze, costs off, summary off, timing off) %s',
+            query)
+    loop
+        if hide_hitmiss = true then
+                ln := regexp_replace(ln, 'Hits: 0', 'Hits: Zero');
+                ln := regexp_replace(ln, 'Hits: \d+', 'Hits: N');
+                ln := regexp_replace(ln, 'Misses: 0', 'Misses: Zero');
+                ln := regexp_replace(ln, 'Misses: \d+', 'Misses: N');
+        end if;
+        ln := regexp_replace(ln, 'Evictions: 0', 'Evictions: Zero');
+        ln := regexp_replace(ln, 'Evictions: \d+', 'Evictions: N');
+        ln := regexp_replace(ln, 'Memory Usage: \d+', 'Memory Usage: N');
+        return next ln;
+    end loop;
+end;
+$$;
+-- Ensure we get a result cache on the inner side of the nested loop
+SET enable_hashjoin TO off;
+SELECT explain_resultcache('
+SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
+INNER JOIN tenk1 t2 ON t1.unique1 = t2.twenty
+WHERE t2.unique1 < 1000;', false);
+                                    explain_resultcache                                     
+--------------------------------------------------------------------------------------------
+ Aggregate (actual rows=1 loops=1)
+   ->  Nested Loop (actual rows=1000 loops=1)
+         ->  Bitmap Heap Scan on tenk1 t2 (actual rows=1000 loops=1)
+               Recheck Cond: (unique1 < 1000)
+               Heap Blocks: exact=333
+               ->  Bitmap Index Scan on tenk1_unique1 (actual rows=1000 loops=1)
+                     Index Cond: (unique1 < 1000)
+         ->  Result Cache (actual rows=1 loops=1000)
+               Cache Key: t2.twenty
+               Hits: 980  Misses: 20  Evictions: Zero  Overflows: 0  Memory Usage: NkB
+               ->  Index Only Scan using tenk1_unique1 on tenk1 t1 (actual rows=1 loops=20)
+                     Index Cond: (unique1 = t2.twenty)
+                     Heap Fetches: 0
+(13 rows)
+
+-- And check we get the expected results.
+SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
+INNER JOIN tenk1 t2 ON t1.unique1 = t2.twenty
+WHERE t2.unique1 < 1000;
+ count |        avg         
+-------+--------------------
+  1000 | 9.5000000000000000
+(1 row)
+
+-- Try with LATERAL joins
+SELECT explain_resultcache('
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;', false);
+                                    explain_resultcache                                     
+--------------------------------------------------------------------------------------------
+ Aggregate (actual rows=1 loops=1)
+   ->  Nested Loop (actual rows=1000 loops=1)
+         ->  Bitmap Heap Scan on tenk1 t1 (actual rows=1000 loops=1)
+               Recheck Cond: (unique1 < 1000)
+               Heap Blocks: exact=333
+               ->  Bitmap Index Scan on tenk1_unique1 (actual rows=1000 loops=1)
+                     Index Cond: (unique1 < 1000)
+         ->  Result Cache (actual rows=1 loops=1000)
+               Cache Key: t1.twenty
+               Hits: 980  Misses: 20  Evictions: Zero  Overflows: 0  Memory Usage: NkB
+               ->  Index Only Scan using tenk1_unique1 on tenk1 t2 (actual rows=1 loops=20)
+                     Index Cond: (unique1 = t1.twenty)
+                     Heap Fetches: 0
+(13 rows)
+
+-- And check we get the expected results.
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;
+ count |        avg         
+-------+--------------------
+  1000 | 9.5000000000000000
+(1 row)
+
+-- Reduce work_mem so that we see some cache evictions
+SET work_mem TO '64kB';
+SET enable_mergejoin TO off;
+-- Ensure we get some evictions.  We're unable to validate the hits and misses
+-- here as the number of entries that fit in the cache at once will vary
+-- between different machines.
+SELECT explain_resultcache('
+SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
+INNER JOIN tenk1 t2 ON t1.unique1 = t2.thousand
+WHERE t2.unique1 < 800;', true);
+                                     explain_resultcache                                     
+---------------------------------------------------------------------------------------------
+ Aggregate (actual rows=1 loops=1)
+   ->  Nested Loop (actual rows=800 loops=1)
+         ->  Bitmap Heap Scan on tenk1 t2 (actual rows=800 loops=1)
+               Recheck Cond: (unique1 < 800)
+               Heap Blocks: exact=318
+               ->  Bitmap Index Scan on tenk1_unique1 (actual rows=800 loops=1)
+                     Index Cond: (unique1 < 800)
+         ->  Result Cache (actual rows=1 loops=800)
+               Cache Key: t2.thousand
+               Hits: Zero  Misses: N  Evictions: N  Overflows: 0  Memory Usage: NkB
+               ->  Index Only Scan using tenk1_unique1 on tenk1 t1 (actual rows=1 loops=800)
+                     Index Cond: (unique1 = t2.thousand)
+                     Heap Fetches: 0
+(13 rows)
+
+RESET enable_mergejoin;
+RESET work_mem;
+RESET enable_hashjoin;
+-- Test parallel plans with Result Cache.
+SET min_parallel_table_scan_size TO 0;
+SET parallel_setup_cost TO 0;
+SET parallel_tuple_cost TO 0;
+-- Ensure we get a parallel plan.
+EXPLAIN (COSTS OFF)
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;
+                                  QUERY PLAN                                   
+-------------------------------------------------------------------------------
+ Finalize Aggregate
+   ->  Gather
+         Workers Planned: 2
+         ->  Partial Aggregate
+               ->  Nested Loop
+                     ->  Parallel Bitmap Heap Scan on tenk1 t1
+                           Recheck Cond: (unique1 < 1000)
+                           ->  Bitmap Index Scan on tenk1_unique1
+                                 Index Cond: (unique1 < 1000)
+                     ->  Result Cache
+                           Cache Key: t1.twenty
+                           ->  Index Only Scan using tenk1_unique1 on tenk1 t2
+                                 Index Cond: (unique1 = t1.twenty)
+(13 rows)
+
+-- And ensure the parallel plan gives us the correct results.
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;
+ count |        avg         
+-------+--------------------
+  1000 | 9.5000000000000000
+(1 row)
+
+RESET parallel_tuple_cost;
+RESET parallel_setup_cost;
+RESET min_parallel_table_scan_size;
--- a/src/test/regress/expected/subselect.out
+++ b/src/test/regress/expected/subselect.out
@@ -1092,18 +1092,20 @@ select sum(o.four), sum(ss.a) from
  ) ss
 where o.ten = 1;
                       QUERY PLAN                        
---------------------------------------------------
+---------------------------------------------------------
 Aggregate
   ->  Nested Loop
         ->  Seq Scan on onek o
               Filter: (ten = 1)
+         ->  Result Cache
+               Cache Key: o.four
               ->  CTE Scan on x
                     CTE x
                       ->  Recursive Union
                             ->  Result
                             ->  WorkTable Scan on x x_1
                                   Filter: (a < 10)
-(10 rows)
+(12 rows)

 select sum(o.four), sum(ss.a) from
  onek o cross join lateral (

--- a/src/test/regress/expected/sysviews.out
+++ b/src/test/regress/expected/sysviews.out
@@ -111,10 +111,11 @@ select name, setting from pg_settings where name like 'enable%';
 enable_partition_pruning       | on
 enable_partitionwise_aggregate | off
 enable_partitionwise_join      | off
+ enable_resultcache             | on
 enable_seqscan                 | on
 enable_sort                    | on
 enable_tidscan                 | on
-(19 rows)
+(20 rows)

 -- Test that the pg_timezone_names and pg_timezone_abbrevs views are
 -- more-or-less working.  We can't test their contents in any great detail

--- a/src/test/regress/parallel_schedule
+++ b/src/test/regress/parallel_schedule
@@ -119,7 +119,7 @@ test: plancache limit plpgsql copy2 temp domain rangefuncs prepare conversion tr
 # ----------
 # Another group of parallel tests
 # ----------
-test: partition_join partition_prune reloptions hash_part indexing partition_aggregate partition_info tuplesort explain compression
+test: partition_join partition_prune reloptions hash_part indexing partition_aggregate partition_info tuplesort explain compression resultcache

 # event triggers cannot run concurrently with any test that runs DDL
 # oidjoins is read-only, though, and should run late for best coverage

--- a/src/test/regress/serial_schedule
+++ b/src/test/regress/serial_schedule
@@ -203,6 +203,7 @@ test: partition_info
 test: tuplesort
 test: explain
 test: compression
+test: resultcache
 test: event_trigger
 test: oidjoins
 test: fast_default

--- a/src/test/regress/sql/aggregates.sql
+++ b/src/test/regress/sql/aggregates.sql
@@ -1098,9 +1098,11 @@ select v||'a', case when v||'a' = 'aa' then 1 else 0 end, count(*)
 -- Make sure that generation of HashAggregate for uniqification purposes
 -- does not lead to array overflow due to unexpected duplicate hash keys
 -- see CAFeeJoKKu0u+A_A9R9316djW-YW3-+Gtgvy3ju655qRHR3jtdA@mail.gmail.com
+set enable_resultcache to off;
 explain (costs off)
  select 1 from tenk1
   where (hundred, thousand) in (select twothousand, twothousand from onek);
+reset enable_resultcache;

 --
 -- Hash Aggregation Spill tests

--- a/src/test/regress/sql/join.sql
+++ b/src/test/regress/sql/join.sql
@@ -550,6 +550,7 @@ 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
@@ -559,6 +560,7 @@ 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

--- a/src/test/regress/sql/partition_prune.sql
+++ b/src/test/regress/sql/partition_prune.sql
@@ -464,6 +464,9 @@ begin
        ln := regexp_replace(ln, 'Workers Launched: \d+', 'Workers Launched: N');
        ln := regexp_replace(ln, 'actual rows=\d+ loops=\d+', 'actual rows=N loops=N');
        ln := regexp_replace(ln, 'Rows Removed by Filter: \d+', 'Rows Removed by Filter: N');
+        ln := regexp_replace(ln, 'Hits: \d+', 'Hits: N');
+        ln := regexp_replace(ln, 'Misses: \d+', 'Misses: N');
+        ln := regexp_replace(ln, 'Memory Usage: \d+', 'Memory Usage: N');
        return next ln;
    end loop;
 end;

--- a/src/test/regress/sql/resultcache.sql
+++ b/src/test/regress/sql/resultcache.sql
+-- Perform tests on the Result Cache node.
+
+-- The cache hits/misses/evictions from the Result Cache node can vary between
+-- machines.  Let's just replace the number with an 'N'.  In order to allow us
+-- to perform validation when the measure was zero, we replace a zero value
+-- with "Zero".  All other numbers are replaced with 'N'.
+create function explain_resultcache(query text, hide_hitmiss bool) returns setof text
+language plpgsql as
+$$
+declare
+    ln text;
+begin
+    for ln in
+        execute format('explain (analyze, costs off, summary off, timing off) %s',
+            query)
+    loop
+        if hide_hitmiss = true then
+                ln := regexp_replace(ln, 'Hits: 0', 'Hits: Zero');
+                ln := regexp_replace(ln, 'Hits: \d+', 'Hits: N');
+                ln := regexp_replace(ln, 'Misses: 0', 'Misses: Zero');
+                ln := regexp_replace(ln, 'Misses: \d+', 'Misses: N');
+        end if;
+        ln := regexp_replace(ln, 'Evictions: 0', 'Evictions: Zero');
+        ln := regexp_replace(ln, 'Evictions: \d+', 'Evictions: N');
+        ln := regexp_replace(ln, 'Memory Usage: \d+', 'Memory Usage: N');
+        return next ln;
+    end loop;
+end;
+$$;
+
+-- Ensure we get a result cache on the inner side of the nested loop
+SET enable_hashjoin TO off;
+SELECT explain_resultcache('
+SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
+INNER JOIN tenk1 t2 ON t1.unique1 = t2.twenty
+WHERE t2.unique1 < 1000;', false);
+
+-- And check we get the expected results.
+SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
+INNER JOIN tenk1 t2 ON t1.unique1 = t2.twenty
+WHERE t2.unique1 < 1000;
+
+-- Try with LATERAL joins
+SELECT explain_resultcache('
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;', false);
+
+-- And check we get the expected results.
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;
+
+-- Reduce work_mem so that we see some cache evictions
+SET work_mem TO '64kB';
+SET enable_mergejoin TO off;
+-- Ensure we get some evictions.  We're unable to validate the hits and misses
+-- here as the number of entries that fit in the cache at once will vary
+-- between different machines.
+SELECT explain_resultcache('
+SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
+INNER JOIN tenk1 t2 ON t1.unique1 = t2.thousand
+WHERE t2.unique1 < 800;', true);
+RESET enable_mergejoin;
+RESET work_mem;
+RESET enable_hashjoin;
+
+-- Test parallel plans with Result Cache.
+SET min_parallel_table_scan_size TO 0;
+SET parallel_setup_cost TO 0;
+SET parallel_tuple_cost TO 0;
+
+-- Ensure we get a parallel plan.
+EXPLAIN (COSTS OFF)
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;
+
+-- And ensure the parallel plan gives us the correct results.
+SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
+LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
+WHERE t1.unique1 < 1000;
+RESET parallel_tuple_cost;
+RESET parallel_setup_cost;
+RESET min_parallel_table_scan_size;