We have a materialized view in our Postgres DB (11.12, managed by AWS RDS). We have a scheduled task that updates it every 5 minutes using REFRESH MATERIALIZED VIEW <view_name>. At some specific point last week, the time needed to refresh the view suddenly went from ~1s to ~20s. The view contains ~70k rows, with around 15 columns, all of them being integers, booleans or UUIDs.
Prior to this:
There were no changes in the server configuration.
There were no changes to the view itself. In fact, running EXPLAIN ANALYZE <expression used to create the view> returns that the query still gets executed in less than a second. If the query is ran with a client like Postico, it takes ~20s to fetch all the results (a bit consistent with the time needed to materialize it, although we assume this is due to the time needed for network transmission).
There were no changes in the schema or any significant record increase in the contents of the tables needed to compute the view.
RDS Performance Insights indicate that the query is mostly using CPU resources
I know this is probably not enough to get a solution, but:
Are there any server performance metrics or logs that could lead us to understand better this situation?
Is this just the normal time the server needs to persist the view to disk? If so, any idea of possible reasons why it started to take so long recently?
Here is a link to the execution plan.
EDIT: creating another materialized view with the same JOINS but selecting less columns performs as expected (~1s).
EDIT 2: setting enable_nestloop = false greatly speeds up the REFRESH operation (same performance as before). Would this indicate that refactoring the underlying query could solve the issue?
Try REFRESH materialized view concurrently.
When you refresh data for a materialized view, PostgreSQL locks the entire table therefore you cannot query data against it. To avoid this, you can use the CONCURRENTLY option.
REFRESH MATERIALIZED VIEW CONCURRENTLY view_name;
With CONCURRENTLY option, PostgreSQL creates a temporary updated version of the materialized view, compares two versions, and performs INSERT and UPDATE only the differences.
You can query against a materialized view while it is being updated. One requirement for using CONCURRENTLY option is that the materialized view must have a UNIQUE index.
Original poster here. This is more than a year old, but here's what happened and how we eventually fixed it.
TLDR:
-REFRESH MATERIALIZED VIEW <query> started to take much longer than executing the query used to construct the view (~1s vs ~20s).
After a couple of weeks this question was asked, the query itself started to behave similarly (taking ~20s to complete). At this point, the EXPLAIN ANALYZE started to show indications of performance issues with the query. So we ended up optimising the underlying query (the biggest performance gain being replacing some JOINS with a CTE).
After this, the performance of both the REFRESH MATERIALIZED VIEW <query> and the standalone query behaved correctly (execution time < 1s).
A still open question here is why the REFRESH MATERIALIZED VIEW <query> and the standalone query had different performance at some point in time? Was the DB query planner choosing different query plans depending on whether it was going to materialize the view or not? I guess if someone knows if such thing is possible, please comment.
Updates materialized view every time (or every 5 minutes) this is not a good way to refresh materialized. Then the meaning of using materialized view does not remain. Let me explain to you one of the ways I found with my own logic, based on my own experience, so you can find a more optimal way later. Assumed, we used two tables in our materialized view, and we need that is a changed data one of the two tables we will refresh materialized view. To do this during the update or delete table we must insert to the table (for example refresh_materialized table) one record (you can also use the trigger), through which will be performed refreshing materialized view
For example:
insert into refresh_materialized
(
refresh_status,
insert_date,
executed_date
)
values (
false,
now(),
null
)
And so in our schedule task, we can use this query:
select count(*) from refresh_materialized
where refresh_status = false
if the count(*) will be > 0 then we must refresh materialized view else do nothing. After the refreshing materialized view we must update this table:
update refresh_materialized
set
refresh_status = true,
executed_date = now()
where
refresh_status = false;
Related
I would like to do something like
results = select * from quick_table
if no results:
insert into quick_table select slow_query
results = select * from quick_table
return results
This is a pretty standard caching pattern. Is there any way I can do this in postgres that's more clever than just literally writing a function to do what I listed above?
The PostgreSQL feature that comes closest to what you want to do is a materialized view.
This creates a copy on disk of the results of your view, which you can then query as if it were a table. You can also add indexes to it in the usual way.
A caveat is that when you generate a materialized view, its data does not update automatically when the source tables’ data change. To reflect changes, you must issue a REFRESH MATERIALIZED VIEW command.
Typical approaches to refreshing are:
Run the refresh as a background task (e.g., in a cron job)
Add triggers to the source tables such that changing data in them causes the view to refresh.
Each approach has advantages and disadvantages, so the route you take will depend on your circumstance. It may also be useful to make sure you can add a unique index to your MV, as that will allow you to run concurrent refreshes; without that, a refresh places an exclusive lock on the view, so it won’t be readable until the refresh has finished.
Main question: I have several views depending on a PostgreSQL/PostGIS table and a final materialized view created by querying the other views. I need a fast and updatable final result (i.e. MV) to use in a QGIS project.
My aim is to update the starting table by overwriting it with new (lots of) values and hopefully have update views and materialized view. I use QGIS DB Manager to overwrite existing table but I get an error because of mv depending on it. If I delete mv, overwrite table and then recreate mv everything is ok but I'd like to avoid manual operations as much as possible.
Is there a better way to reach my goal?
Another question: If I set a trigger to refresh a mv when I update/insert/delete values in a table, would it work even in case of overwriting entire table with a new one?
Refreshing a materialized view runs the complete defining query, so that is a long running and heavy operation for a complicated query.
It is possible to launch REFRESH MATERIALIZED VIEW from a trigger (it had better be a FOR EACH STATEMENT trigger then), but that would make every data modification so slow that I don't think that is practically feasible.
One thing that might work is to implement something like a materialized view that refreshes immediately “by hand”:
create a regular table for the “materialized view” and fill it with data by running the query
on each of the underlying tables, define a row level trigger that modifies the materialized view in accordance with the changes that triggered it
This should work for views where the definition is simple enough, for complicated queries it will not be possible.
I am exploring materialized views to create de-normalized view to avoid joining multiple tables for read performance. APIs will read from the materialized views to provide data to clients.
I am using amazon aurora postgres (version 11).
I am using a unique index on the materialized view (MV) so that I can use the “refresh concurrently” option.
What I am noticing though is that when only a fraction of the rows get updated in one of the source tables and I try to refresh the view, it's pretty slow. In fact slower than populating the view for the first time. e.g.: to populate MV first time takes ~30 mins, refresh is taking more than an hour. less than 1% of rows have been updated. The main three tables involved in generating the MV have ~18 million, 27 million & 40 million rows.
The timeliness of the materialized view refresh is important so that data is not stale for too long.
I could go with custom tables to store the denormalized data instead of materialized views but would have to implement logic to refresh data. So planning to avoid that if possible.
Is there anything that can be done to speed up the refresh process of the materialized views?
Please let me know if you need more details.
thanks
Kiran
You can create a second materialised view and update it (not concurrently), and then swap the names.to the tables in a transaction.
I actually have no idea why postgres didn't implement CONCURRENTLY this way.
Refreshing a materialized view is slow even if little has changed, because every time the view is refreshed, the defining query is run.
Using CONCURRENTLY makes the operation even slower, because it is not a wholesale replacement of the materialized view contents, but modification of the existing data.
Perhaps you could create a denormalized table that is updated by a trigger whenever the underlying tables are modified.
Maybe I'm a bit late to the party, but if you would be on postgres 13 or later you could try this extension: https://github.com/sraoss/pg_ivm
They have some limitations but promise much faster rebuilt times.
Here's a bit more on it from pganalyze: https://pganalyze.com/blog/5mins-postgres-15-beta1-incremental-materialized-views-pg-ivm (in contrary to project authors, they want you to be on v14 or later)
We have a view that aggregates from a backing table. The idea is to reduce cpu load by using a pre-aggregated table, and to periodically refresh it with the following:
create new_backing_table (fill it)
begin
drop backingtable
rename new_backingtable to backingtable
commit
while in production. The latency caused by the refresh interval is acceptable. Incremental updates are possible but not desirable.
Anyone has a comment on this scheme ?
Check out materialized views. This may suit your use case. It can be used to store query results at creation then refreshed at a later time.
A materialized view is defined as a table which is actually physically stored on disk, but is really just a view of other database tables. In PostgreSQL, like many database systems, when data is retrieved from a traditional view it is really executing the underlying query or queries that build that view.
https://www.postgresql.org/docs/9.3/static/sql-creatematerializedview.html
I'm using postgresql 9.5.3. When I restoring the database, and it creating MATERIALIZED VIEW DATA, it takes me a long time, 3 hrs more. Is this really normal for postgresql?
pg_restore: creating MATERIALIZED VIEW DATA "public.mydata"
still on process. and my database.backup is 15gb sized
That depends on the view definition, the current table statistics and the data present.
You could examine the query plan for query defining the materialized view with EXPLAIN and see if there are any problems with the plan.
If it is a complicated query, maybe the problem is that autoanalyze didn't have time yet to calculate table statistics.
You can interrupt the statement with pg_cancel_backend() and recreate the materialized view later, perhaps after an ANALYZE, if it helps you to bring the rest of the database up quickly.