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From: Adam Brusselback <adambrusselback@gmail.com>
Date: Mon, 8 Dec 2025 15:58:27 -0500
Message-ID: 
 <CAMjNa7eFzTQ5=oZMQiB2bMkez5KP4A77JC7SRjeVEkOrh7cUHw@mail.gmail.com>
Subject: [Patch] Add WHERE clause support to REFRESH MATERIALIZED VIEW
To: PostgreSQL Hackers <pgsql-hackers@lists.postgresql.org>
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Attached is a patch implementing support for a WHERE clause in REFRESH
MATERIALIZED VIEW.

The syntax allows for targeted refreshes:
    REFRESH MATERIALIZED VIEW mv WHERE invoice_id = ANY('{1,2,3}');
    REFRESH MATERIALIZED VIEW CONCURRENTLY mv WHERE customer_id = 42;
    REFRESH MATERIALIZED VIEW mv WHERE order_date >= '2023-01-01';

I was inspired to implement this feature after watching the Hacking
Postgres discussion on the topic:
https://www.youtube.com/watch?v=6cZvHjDrmlQ

This allows the user to restrict the refresh operation to a subset of the
view. The qualification is applied to the view's output columns. The
optimizer can then push this condition down to the underlying base tables,
avoiding a full scan when only a known subset of data has changed.

Implementation notes:

1. The grammar accepts an optional WHERE clause. We forbid volatile
functions in the clause to ensure correctness.

2. Non-Concurrent Partial Refresh: When `CONCURRENTLY` is not specified,
the operation performs an in-place modification using a `ROW EXCLUSIVE`
lock.
    *   This mode requires a unique index to ensure constraint violations
are handled correctly (e.g., when a row's values change such that it
"drifts" into or out of the `WHERE` clause scope).
    *   It executes a Prune + Upsert strategy:
        * `DELETE` all rows in the materialized view that match the `WHERE`
clause.
        * `INSERT` the new data from the source query.
    *   It uses `ON CONFLICT DO UPDATE` during the insert phase to handle
concurrency edge cases, ensuring the refresh is robust against constraint
violations.

3. Concurrent Partial Refresh: When `CONCURRENTLY` is specified, it uses
the existing diff/merge infrastructure (`refresh_by_match_merge`), limiting
the scope of the diff (and the temporary table population) to the rows
matching the predicate. This requires an `EXCLUSIVE` lock and a unique
index, consistent with existing concurrent refresh behavior. It is much
slower than `Non-Concurrent Partial Refresh`

4. The execution logic uses SPI to inject the predicate into the source
queries during execution.

I have attached a benchmark suite to validate performance and correctness:

*   `setup.sql`: Creates a schema `mv_benchmark` modeling an invoicing
system (`invoices` and `invoice_lines`). It includes an aggregated
materialized view (`invoice_summary`) and a control table
(`invoice_summary_table`).
*   `workload_*.sql`: pgbench scripts simulating a high-churn environment
(45% inserts, 10% updates, 45% deletes) to maintain roughly stable dataset
sizes while generating significant refresh work.
*   `run_benchmark_comprehensive.sh`: Orchestrates the benchmark across
multiple scale factors and concurrency levels.

The benchmark compares strategies for keeping a summary up to date (vs
baseline):
*   Partial Refresh: Triggers on the base table collect modified IDs and
execute `REFRESH MATERIALIZED VIEW ... WHERE ...`.
*   Materialized Table (Control): A standard table maintained via complex
PL/pgSQL triggers (the traditional manual workaround).
*   Full Refresh (Legacy): Manually refresh the view after changes.

Results are below:
Concurrency: 1 client(s)
----------------------------------------------------------------------------------
Scale       Batch | Baseline TPS | Full (Rel)   Partial (Rel) Table (Rel)
---------- ------ | ------------ | ------------ ------------ ------------
20000           1 | 5309.05      | 0.002x        0.437x        0.470x
20000          50 | 1209.32      | 0.010x        0.600x        0.598x
20000        1000 | 56.05        | 0.164x        0.594x        0.576x
400000          1 | 5136.91      | 0    x        0.450x        0.487x
400000         50 | 1709.17      | 0    x        0.497x        0.482x
400000       1000 | 110.35       | 0.006x        0.507x        0.460x

Concurrency: 4 client(s)
----------------------------------------------------------------------------------
Scale       Batch | Baseline TPS | Full (Rel)   Partial (Rel) Table (Rel)
---------- ------ | ------------ | ------------ ------------ ------------
20000           1 | 19197.50     | 0x            0.412x        0.435x
20000          50 | 1016.14      | 0.007x        0.966x        1.036x
20000        1000 | 9.94         | 0.708x        1.401x        1.169x
400000          1 | 19637.36     | 0x            0.436x        0.483x
400000         50 | 4669.32      | 0x            0.574x        0.566x
400000       1000 | 23.26        | 0.029x        1.147x        0.715x

Concurrency: 8 client(s)
----------------------------------------------------------------------------------
Scale       Batch | Baseline TPS | Full (Rel)   Partial (Rel) Table (Rel)
---------- ------ | ------------ | ------------ ------------ ------------
20000           1 | 30358.32     | 0x            0.440x        0.457x
20000          50 | 262.75       | 0.026x        2.943x        2.740x
20000        1000 | 11.28        | 0.575x        0.840x        0.578x
400000          1 | 36007.15     | 0x            0.430x        0.464x
400000         50 | 6664.58      | 0x            0.563x        0.494x
400000       1000 | 11.61        | 0.058x        1.000x        1.277x


In these tests, the partial refresh behaves as O(delta) rather than
O(total), performing comparably to the manual PL/pgSQL approach but with
significantly lower code complexity for the user.

I recognize that adding a WHERE clause to REFRESH is an extension to the
SQL standard. I believe the syntax is intuitive, but I am open to
discussion regarding alternative implementation strategies or syntax if the
community feels a different approach is warranted.

New regression tests are included in the patch.

This is my first time submitting a patch to PostgreSQL, so please bear with
me if I've missed anything or made any procedural mistakes. I'm happy to
address any feedback.

Thanks,
Adam Brusselback

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<div dir=3D"ltr">Attached is a patch implementing support for a WHERE claus=
e in REFRESH MATERIALIZED VIEW.<br><br>The syntax allows for targeted refre=
shes:<br>=C2=A0 =C2=A0 REFRESH MATERIALIZED VIEW mv WHERE invoice_id =3D AN=
Y(&#39;{1,2,3}&#39;);<br>=C2=A0 =C2=A0 REFRESH MATERIALIZED VIEW CONCURRENT=
LY mv WHERE customer_id =3D 42;<br>=C2=A0 =C2=A0 REFRESH MATERIALIZED VIEW =
mv WHERE order_date &gt;=3D &#39;2023-01-01&#39;;<br><br>I was inspired to =
implement this feature after watching the Hacking Postgres discussion on th=
e topic: <a href=3D"https://www.youtube.com/watch?v=3D6cZvHjDrmlQ">https://=
www.youtube.com/watch?v=3D6cZvHjDrmlQ</a><br><br>This allows the user to re=
strict the refresh operation to a subset of the view. The qualification is =
applied to the view&#39;s output columns. The optimizer can then push this =
condition down to the underlying base tables, avoiding a full scan when onl=
y a known subset of data has changed.<br><br>Implementation notes:<br><br>1=
. The grammar accepts an optional WHERE clause. We forbid volatile function=
s in the clause to ensure correctness.<br><br>2. Non-Concurrent Partial Ref=
resh: When `CONCURRENTLY` is not specified, the operation performs an in-pl=
ace modification using a `ROW EXCLUSIVE` lock.<br>=C2=A0 =C2=A0 * =C2=A0 Th=
is mode requires a unique index to ensure constraint violations are handled=
 correctly (e.g., when a row&#39;s values change such that it &quot;drifts&=
quot; into or out of the `WHERE` clause scope).<br>=C2=A0 =C2=A0 * =C2=A0 I=
t executes a Prune + Upsert strategy:<br>=C2=A0 =C2=A0 =C2=A0 =C2=A0 * `DEL=
ETE` all rows in the materialized view that match the `WHERE` clause.<br>=
=C2=A0 =C2=A0 =C2=A0 =C2=A0 * `INSERT` the new data from the source query.<=
br>=C2=A0 =C2=A0 * =C2=A0 It uses `ON CONFLICT DO UPDATE` during the insert=
 phase to handle concurrency edge cases, ensuring the refresh is robust aga=
inst constraint violations.<br><br>3. Concurrent Partial Refresh: When `CON=
CURRENTLY` is specified, it uses the existing diff/merge infrastructure (`r=
efresh_by_match_merge`), limiting the scope of the diff (and the temporary =
table population) to the rows matching the predicate. This requires an `EXC=
LUSIVE` lock and a unique index, consistent with existing concurrent refres=
h behavior. It is much slower than `Non-Concurrent Partial Refresh` <br><br=
>4. The execution logic uses SPI to inject the predicate into the source qu=
eries during execution.<br><br>I have attached a benchmark suite to validat=
e performance and correctness:<br><br>* =C2=A0 `setup.sql`: Creates a schem=
a `mv_benchmark` modeling an invoicing system (`invoices` and `invoice_line=
s`). It includes an aggregated materialized view (`invoice_summary`) and a =
control table (`invoice_summary_table`).<br>* =C2=A0 `workload_*.sql`: pgbe=
nch scripts simulating a high-churn environment (45% inserts, 10% updates, =
45% deletes) to maintain roughly stable dataset sizes while generating sign=
ificant refresh work.<br>* =C2=A0 `run_benchmark_comprehensive.sh`: Orchest=
rates the benchmark across multiple scale factors and concurrency levels.<b=
r><br>The benchmark compares strategies for keeping a summary up to date (v=
s baseline):<br>* =C2=A0 Partial Refresh: Triggers on the base table collec=
t modified IDs and execute `REFRESH MATERIALIZED VIEW ... WHERE ...`.<br>* =
=C2=A0 Materialized Table (Control): A standard table maintained via comple=
x PL/pgSQL triggers (the traditional manual workaround).<br>* =C2=A0 Full R=
efresh (Legacy): Manually refresh the view after changes.<br><br>Results ar=
e below:<br>Concurrency: 1 client(s)<br>-----------------------------------=
-----------------------------------------------<br>Scale =C2=A0 =C2=A0 =C2=
=A0 Batch | Baseline TPS | Full (Rel) =C2=A0 Partial (Rel) Table (Rel) <br>=
---------- ------ | ------------ | ------------ ------------ ------------<b=
r>20000 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 1 | 5309.05 =C2=A0 =C2=A0 =C2=A0=
| 0.002x =C2=A0 =C2=A0 =C2=A0 =C2=A00.437x =C2=A0 =C2=A0 =C2=A0 =C2=A00.470=
x =C2=A0 =C2=A0 =C2=A0 <br>20000 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A050 | 120=
9.32 =C2=A0 =C2=A0 =C2=A0| 0.010x =C2=A0 =C2=A0 =C2=A0 =C2=A00.600x =C2=A0 =
=C2=A0 =C2=A0 =C2=A00.598x =C2=A0 =C2=A0 =C2=A0 <br>20000 =C2=A0 =C2=A0 =C2=
=A0 =C2=A01000 | 56.05 =C2=A0 =C2=A0 =C2=A0 =C2=A0| 0.164x =C2=A0 =C2=A0 =
=C2=A0 =C2=A00.594x =C2=A0 =C2=A0 =C2=A0 =C2=A00.576x =C2=A0 =C2=A0 =C2=A0 =
<br>400000 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A01 | 5136.91 =C2=A0 =C2=A0 =C2=
=A0| 0 =C2=A0 =C2=A0x =C2=A0 =C2=A0 =C2=A0 =C2=A00.450x =C2=A0 =C2=A0 =C2=
=A0 =C2=A00.487x =C2=A0 =C2=A0 =C2=A0 <br>400000 =C2=A0 =C2=A0 =C2=A0 =C2=
=A0 50 | 1709.17 =C2=A0 =C2=A0 =C2=A0| 0 =C2=A0 =C2=A0x =C2=A0 =C2=A0 =C2=
=A0 =C2=A00.497x =C2=A0 =C2=A0 =C2=A0 =C2=A00.482x =C2=A0 =C2=A0 =C2=A0 <br=
>400000 =C2=A0 =C2=A0 =C2=A0 1000 | 110.35 =C2=A0 =C2=A0 =C2=A0 | 0.006x =
=C2=A0 =C2=A0 =C2=A0 =C2=A00.507x =C2=A0 =C2=A0 =C2=A0 =C2=A00.460x =C2=A0 =
=C2=A0 =C2=A0 <br><br>Concurrency: 4 client(s)<br>-------------------------=
---------------------------------------------------------<br>Scale =C2=A0 =
=C2=A0 =C2=A0 Batch | Baseline TPS | Full (Rel) =C2=A0 Partial (Rel) Table =
(Rel) <br>---------- ------ | ------------ | ------------ ------------ ----=
--------<br>20000 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 1 | 19197.50 =C2=A0 =
=C2=A0 | 0x =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A00.412x =C2=A0 =C2=A0 =
=C2=A0 =C2=A00.435x =C2=A0 =C2=A0 =C2=A0 <br>20000 =C2=A0 =C2=A0 =C2=A0 =C2=
=A0 =C2=A050 | 1016.14 =C2=A0 =C2=A0 =C2=A0| 0.007x =C2=A0 =C2=A0 =C2=A0 =
=C2=A00.966x =C2=A0 =C2=A0 =C2=A0 =C2=A01.036x =C2=A0 =C2=A0 =C2=A0 <br>200=
00 =C2=A0 =C2=A0 =C2=A0 =C2=A01000 | 9.94 =C2=A0 =C2=A0 =C2=A0 =C2=A0 | 0.7=
08x =C2=A0 =C2=A0 =C2=A0 =C2=A01.401x =C2=A0 =C2=A0 =C2=A0 =C2=A01.169x =C2=
=A0 =C2=A0 =C2=A0 <br>400000 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A01 | 19637.36=
 =C2=A0 =C2=A0 | 0x =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A00.436x =C2=A0 =
=C2=A0 =C2=A0 =C2=A00.483x =C2=A0 =C2=A0 =C2=A0 <br>400000 =C2=A0 =C2=A0 =
=C2=A0 =C2=A0 50 | 4669.32 =C2=A0 =C2=A0 =C2=A0| 0x =C2=A0 =C2=A0 =C2=A0 =
=C2=A0 =C2=A0 =C2=A00.574x =C2=A0 =C2=A0 =C2=A0 =C2=A00.566x =C2=A0 =C2=A0 =
=C2=A0 <br>400000 =C2=A0 =C2=A0 =C2=A0 1000 | 23.26 =C2=A0 =C2=A0 =C2=A0 =
=C2=A0| 0.029x =C2=A0 =C2=A0 =C2=A0 =C2=A01.147x =C2=A0 =C2=A0 =C2=A0 =C2=
=A00.715x =C2=A0 =C2=A0 =C2=A0 <br><br>Concurrency: 8 client(s)<br>--------=
--------------------------------------------------------------------------<=
br>Scale =C2=A0 =C2=A0 =C2=A0 Batch | Baseline TPS | Full (Rel) =C2=A0 Part=
ial (Rel) Table (Rel) <br>---------- ------ | ------------ | ------------ -=
----------- ------------<br>20000 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 1 | 30=
358.32 =C2=A0 =C2=A0 | 0x =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A00.440x =
=C2=A0 =C2=A0 =C2=A0 =C2=A00.457x<br>20000 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=
=A050 | 262.75 =C2=A0 =C2=A0 =C2=A0 | 0.026x =C2=A0 =C2=A0 =C2=A0 =C2=A02.9=
43x =C2=A0 =C2=A0 =C2=A0 =C2=A02.740x<br>20000 =C2=A0 =C2=A0 =C2=A0 =C2=A01=
000 | 11.28 =C2=A0 =C2=A0 =C2=A0 =C2=A0| 0.575x =C2=A0 =C2=A0 =C2=A0 =C2=A0=
0.840x =C2=A0 =C2=A0 =C2=A0 =C2=A00.578x<br>400000 =C2=A0 =C2=A0 =C2=A0 =C2=
=A0 =C2=A01 | 36007.15 =C2=A0 =C2=A0 | 0x =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=
=A0 =C2=A00.430x =C2=A0 =C2=A0 =C2=A0 =C2=A00.464x<br>400000 =C2=A0 =C2=A0 =
=C2=A0 =C2=A0 50 | 6664.58 =C2=A0 =C2=A0 =C2=A0| 0x =C2=A0 =C2=A0 =C2=A0 =
=C2=A0 =C2=A0 =C2=A00.563x =C2=A0 =C2=A0 =C2=A0 =C2=A00.494x<br>400000 =C2=
=A0 =C2=A0 =C2=A0 1000 | 11.61 =C2=A0 =C2=A0 =C2=A0 =C2=A0| 0.058x =C2=A0 =
=C2=A0 =C2=A0 =C2=A01.000x =C2=A0 =C2=A0 =C2=A0 =C2=A01.277x<br><br><br><br=
>In these tests, the partial refresh behaves as O(delta) rather than O(tota=
l), performing comparably to the manual PL/pgSQL approach but with signific=
antly lower code complexity for the user.<br><br>I recognize that adding a =
WHERE clause to REFRESH is an extension to the SQL standard. I believe the =
syntax is intuitive, but I am open to discussion regarding alternative impl=
ementation strategies or syntax if the community feels a different approach=
 is warranted.<br><br>New regression tests are included in the patch.<br><b=
r>This is my first time submitting a patch to PostgreSQL, so please bear wi=
th me if I&#39;ve missed anything or made any procedural mistakes. I&#39;m =
happy to address any feedback.<br><br>Thanks,<br>Adam Brusselback</div>

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