Subject: Limit GRAPH_TABLE path combinations to prevent memory exhaustion

From: SATYANARAYANA NARLAPURAM <[email-masked]>
Date: Wed, 29 Apr 2026 09:35:44 -0700

Hi hackers,

generate_queries_for_path_pattern_recurse() enumerates all path
combinations by recursing over the Cartesian product of matching elements
per pattern position.  Without IS label filters, each position matches
ALL tables of that kind, leading to N^K combinations (N tables, K
pattern positions).  Each combination allocates a Query node via palloc
causing unbounded memory growth.

A 8-table graph with a -element pattern reaches 81.3 GB RES in a few seconds
before I cancel the query. Tests in the patch (those were failed) can
reproduce the problem
without the fix included in the patch.

top - 15:04:19 up 43 days, 19:18,  5 users,  load average: 0.43, 0.19, 0.08
Tasks:   1 total,   1 running,   0 sleeping,   0 stopped,   0 zombie
%Cpu(s):  0.9 us,  0.8 sy,  0.0 ni, 98.3 id,  0.0 wa,  0.0 hi,  0.0 si,
 0.0 st
MiB Mem : 515766.2 total, 248412.7 free, 234847.7 used,  48014.7 buff/cache

MiB Swap:      0.0 total,      0.0 free,      0.0 used. 280918.6 avail Mem

    PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+
COMMAND
 649642 azureus+  20   0  212.2g  81.3g  33948 R 100.0  16.1   0:41.20
postgres

As a POC I added a pre-computation check that calculates the total number
of path combinations before entering the
generate_queries_for_path_pattern_recurse.
If the product exceeds MAX_GRAPH_TABLE_PATH_COMBINATIONS (set to 10,000),
the rewriter reports ERRCODE_PROGRAM_LIMIT_EXCEEDED with a hint suggesting
IS label filters to reduce the search space. The limit of 10,000 is
somewhat arbitrary
but conservative. It caps memory at roughly 5 MB of Query nodes.
Patterns that would exceed the limit without labels can always be made to
succeed
by adding IS expressions to pin specific positions to fewer tables.
Alternatively, we can consider adding a GUC to control the limit but appears
to be an overkill. Thoughts?

Thanks,
Satya

---

From: Ashutosh Bapat <[email-masked]>
Date: Thu, 30 Apr 2026 12:45:57 +0530

On Wed, Apr 29, 2026 at 10:05 PM SATYANARAYANA NARLAPURAM
<[email-masked]> wrote:
> before I cancel the query. Tests in the patch (those were failed) can reproduce the problem
> %Cpu(s):  0.9 us,  0.8 sy,  0.0 ni, 98.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st
>     PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND
>  649642 azureus+  20   0  212.2g  81.3g  33948 R 100.0  16.1   0:41.20 postgres
>

I tried to reproduce this problem on my laptop with queries included
in the patch. For me all the queries finished. First within 4 ms,
second within 133 ms and the third in 12ms. Did you try with more
edges or more rows in the tables?

> of path combinations before entering the generate_queries_for_path_pattern_recurse.
> IS label filters to reduce the search space. The limit of 10,000 is somewhat arbitrary
> Patterns that would exceed the limit without labels can always be made to succeed

I understand the problem and can understand the pain. Somebody who is
writing a query like ()-()-()-()-() ... should know that every pattern
that doesn't have a label will be matched against each vertex/edge.
Our documentation makes it explicit [1] "The above patterns would
match any vertex, or any two vertices connected by any edge ... ". But
if they are really interested in matching every vertex or edge they
don't have any other choice. Using restrictive label expressions would
lead to wrong or incomplete results. If they can use label expressions
they should do so anyway, otherwise they will end up with incorrect
results. To me it seems like somebody who is writing such a pattern
without providing enough resources is writing a bad query.

We have other places where queries can consume large amounts of memory
during planning or execution. Simply take the SQL query equivalent to
the above pattern. We do not have a way to prohibit such queries as
far as I know. I understand that SQL/PGQ makes it easy to write such
queries by hand. But that seems to be abusing a powerful tool.

Another example is joining partitioned tables with thousands of
partitions. We have a GUC which enables or disables partitionwise join
but there is no GUC to limit the number of tables or partitions being
joined.

I think we can document that such a pattern can result in large
queries which may consume memory.

Said that 81.3 GB looks unreasonably large for
generate_queries_for_path_pattern_recurse() alone. I guess a large
portion of it comes from planning and execution. How many rows did
those tables had? Which phase of query execution consumed that much
memory? Do you have a dump of memory contexts when it reaches that
limit?

[1] https://www.postgresql.org/docs/devel/queries-graph.html

-- 
Best Wishes,
Ashutosh Bapat

---

From: SATYANARAYANA NARLAPURAM <[email-masked]>
Date: Thu, 30 Apr 2026 12:01:04 -0700

Hi,

> memory? Do you have a dump of memory contexts when it reaches that

I am worried about a potential DOS by a low privileged user or an
accidental query
causing OOM.
Please find the attached patch that added traces to print the memory
context.
Patch also includes CFI to cancel the query which we didn't have earlier.

You can see traces like below once you run the repro:

2026-04-30 18:43:51.268 UTC [927121] LOG:  GRAPH_TABLE: before
generate_queries_for_path_pattern_recurse, current memory context
"MessageContext": 39392903168 bytes total
2026-04-30 18:43:51.268 UTC [927121] STATEMENT:  SELECT count(*) FROM
GRAPH_TABLE (g5 MATCH (a)-[e1]->(b)-[e2]->(c)-[e3]->(d)-[e4]->(e) COLUMNS (
a.id AS aid));
2026-04-30 18:43:51.268 UTC [927121] ERROR:  canceling statement due to
user request
2026-04-30 18:43:51.268 UTC [927121] STATEMENT:  SELECT count(*) FROM
GRAPH_TABLE (g5 MATCH (a)-[e1]->(b)-[e2]->(c)-[e3]->(d)-[e4]->(e) COLUMNS (
a.id AS aid));

Repro:
CREATE temp TABLE v1 (id int PRIMARY KEY, val int);
CREATE temp TABLE v2 (id int PRIMARY KEY, val int);
CREATE temp TABLE v3 (id int PRIMARY KEY, val int);
CREATE temp TABLE v4 (id int PRIMARY KEY, val int);
CREATE temp TABLE v5 (id int PRIMARY KEY, val int);
CREATE temp TABLE v6 (id int PRIMARY KEY, val int);
CREATE temp TABLE v7 (id int PRIMARY KEY, val int);
CREATE temp TABLE v8 (id int PRIMARY KEY, val int);
CREATE temp TABLE e1 (id int PRIMARY KEY, src int, dest int);
CREATE temp TABLE e2 (id int PRIMARY KEY, src int, dest int);
CREATE temp TABLE e3 (id int PRIMARY KEY, src int, dest int);
CREATE temp TABLE e4 (id int PRIMARY KEY, src int, dest int);
CREATE temp TABLE e5 (id int PRIMARY KEY, src int, dest int);
CREATE temp TABLE e6 (id int PRIMARY KEY, src int, dest int);
CREATE temp TABLE e7 (id int PRIMARY KEY, src int, dest int);
CREATE temp TABLE e8 (id int PRIMARY KEY, src int, dest int);
CREATE PROPERTY GRAPH g5
    VERTEX TABLES (
        v1 LABEL vl PROPERTIES (id, val) LABEL vl2 PROPERTIES (id, val),
        v2 LABEL vl PROPERTIES (id, val),
        v3 LABEL vl PROPERTIES (id, val),
        v4 LABEL vl PROPERTIES (id, val),
        v5 LABEL vl PROPERTIES (id, val),
        v6 LABEL vl PROPERTIES (id, val),
        v7 LABEL vl PROPERTIES (id, val),
        v8 LABEL vl PROPERTIES (id, val)
    )
    EDGE TABLES (
        e1 SOURCE KEY (src) REFERENCES v1 (id) DESTINATION KEY (dest)
REFERENCES v2 (id) LABEL el PROPERTIES (id),
        e2 SOURCE KEY (src) REFERENCES v2 (id) DESTINATION KEY (dest)
REFERENCES v3 (id) LABEL el PROPERTIES (id),
        e3 SOURCE KEY (src) REFERENCES v3 (id) DESTINATION KEY (dest)
REFERENCES v4 (id) LABEL el PROPERTIES (id),
        e4 SOURCE KEY (src) REFERENCES v4 (id) DESTINATION KEY (dest)
REFERENCES v5 (id) LABEL el PROPERTIES (id),
        e5 SOURCE KEY (src) REFERENCES v5 (id) DESTINATION KEY (dest)
REFERENCES v6 (id) LABEL el PROPERTIES (id),
        e6 SOURCE KEY (src) REFERENCES v6 (id) DESTINATION KEY (dest)
REFERENCES v7 (id) LABEL el PROPERTIES (id),
        e7 SOURCE KEY (src) REFERENCES v7 (id) DESTINATION KEY (dest)
REFERENCES v8 (id) LABEL el PROPERTIES (id),
        e8 SOURCE KEY (src) REFERENCES v8 (id) DESTINATION KEY (dest)
REFERENCES v1 (id) LABEL el PROPERTIES (id)
    );

SELECT count(*) FROM GRAPH_TABLE (g5 MATCH
(a)-[e1]->(b)-[e2]->(c)-[e3]->(d)-[e4]->(e) COLUMNS (a.id AS aid));