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Graph/autovacuum-throughput-tuning

Pattern

Autovacuum Throughput Tuning

autovacuum-throughput-tuning

A recurring database-ops motif where autovacuum runs too slowly for massive tables, leading to MultiXactID pressure (wraparound risk and “members limit exceeded”), so teams partition tables and tune autovacuum memory/workers/cost to complete cleanup in time.

Node Details

Public graph metadata

Updated5/1/2026

Connections

30 linked nodes

Tension: When I run it on dev server again but with extended events capturing (or profiler trace) running, it runs slowly just like prod. Outcome: If I pause the trace, it's immediately fast again.

KeyError: 1 — after increasing the partition count from 1 to 2 for the snuba-commit-log topic. Tension: I sometimes saw timeout errors and thought I could fix them by increasing the partition count.

The idea is to update this model once hourly without blocking the API's ability to respond. Tension: update this model once hourly without blocking the API's ability to respond to CPU-bound tasks. Outcome: this is all good.

trying to perform data processing during the application's startup. Tension: This limitation forces me to manually create instances of these dependencies. Outcome: this approach doesn't work as expected.

Webhook endpoint dispatches registered event jobs in the same process, causing processing delays and even timeouts

The job is taking approximately 4 hours to complete — AWS Glue job that processes thousands of small JSON files from S3. Tension: which is unacceptable for our use case. Outcome: The job processes the files but is extremely slow.

I have a table named `payments`. This table consists of millions of records — at the end of every day I need to read the data. Tension: check for any payments that are past due date, if any payments are past due date, I need to mark the payment as "Overdue". Outcome: How can I achieve this in an optimized way using the AWS glue spark job?

Timeout waiting for connection from pool. Tension: Task 4217 in stage 4.0 failed 4 times. Outcome: Job aborted due to stage failure.

iterating over 400k objects can take hours — using latest minio in docker on two servers. Tension: Sometimes it works fine. Outcome: How to speed it up?

Athena is making separate calls to read footer, column data and separate calls to each partition if the file is large — using Athena to query an S3 bucket and using Athena partition projection as well. Tension: I use node, date and effective_time_start in query. Outcome: Apparently, this is how parquet works.

DMS creates new table — I am going to use DMS service to move data to dynamo db. Tension: i want data in the old table. Outcome: You should use AWS Glue for this.

with some 10M objects which all needs to be processed and sent to opensearch. Tension: the function will timeout by the time the for loop is exhausted. Outcome: I would think the function will timeout by the time the for loop is exhausted.

tx.Begin + tx.Commit + tx.Rollback + set_config + core query — on each query you must write something like `set_config('')` or `SET LOCAL` first. Tension: It significantly increases amount of code required on a backend. Outcome: just query.

ERROR: invalid memory alloc request size 1264582656 — CONTEXT: SQL function "st_transform_resilient" statement 1. Tension: No effect, no ERROR changes. Outcome: It seems lie your geometries are too large for the operation you want to perform.

will a transaction log only be created on difference, or even if it's the same value? — use the `MERGE INTO` statement in PostgreSQL 16 with the `WHEN MATCHED THEN UPDATE SET` option. Tension: I'm concerned about rapid disk space growth.

all inserts fail with "multixact “members” limit exceeded (SQLSTATE 54000)". Tension: autovacuum may run for days and not complete in time.

heavy parallel inserts (INSERT INTO ... SELECT ...) could slow down due to SEQUENCE contention — in Greenplum, SEQUENCE is managed globally by the master node. Tension: multiple segments request new IDs in parallel.

When I try do drop the column it times out after 20 mins. — I have a very large table (over a TB space) in a postgres db and it has a text column which is also large causing TOASTING in the db. Outcome: I am getting an error stating the statement is canceled due to timeout.

Repeating the name with each sample. Tension: an extremely inefficient use of space. Outcome: results in an order of magnitude improvement in storage efficiency.

I saw this value is empty in case of logical replication — There is xmin parameter in replication slot. Tension: it is clear that logical slots does not help in preventing vacuum process of dead tuples required by select queries on standby. Outcome: Logical replication slots have `catalog_xmin`.

The data will get removed from the index by some future vacuum. — Does PostgreSQL remove values from partial index if conditon is no longer met? Tension: freed up space is only reusable under one of two conditions. Outcome: you would need a REINDEX.

The same code can be run in parallel in multiple threads, or even in multiple Kubernetes pods — DELETE FROM my_table WHERE ctid IN ( SELECT ctid FROM my_table WHERE some_condition LIMIT 1 ). Tension: two threads start fighting for the same record.

"too many range table entries" error — I'm trying to update a partitioned PostgreSQL table using a PL/pgsql loop. Tension: it contains a self join on a large table. Outcome: If I hardcode the partition to update in the WHERE clauses, it works just fine.

Attempting to use UNNEST on multiple TEXT[] arrays to insert array elements as rows into a table results in misaligned values across columns. The inserted data ends up shifted (e.g., id/name/phone values come from different arrays).

Cannot limit a domain value to at most two occurrences in a table

An INSERT...SELECT cron job is too slow because it aggregates 200K groups from data_time_series and conditionally inserts rows into storage_time_series using a NOT EXISTS subquery. The conditional existence check becomes a performance bottleneck.

Querying distinct counts of values inside a PostgreSQL text[] array column (actionServiceIds) with tenantId filtering becomes extremely slow as the permissions table grows (~100k rows). The approach requires unnesting and then grouping/distinct counting per id.

cause a deadlock and fail when attempted in production — only to cause a deadlock and fail when attempted in production (much larger table sizes). Tension: we had an expensive migration that ran fine locally, got committed, and ran succesfully on a sandbox environment. Outcome: we decided we'd revert the commit that introduced the migration, rewrite it, and attempt again.

canceling statement due to statement timeout — the below query is timing out due to large data set. Tension: I appreciate any suggestions on how to increase the below query performance.

the resulting dump is not encrypted — when the task is executed. Tension: Oracle Vault should stop a DBA from reading the data, but how do I prevent a DBA to just to a expdp and then to import the data in clear form? Outcome: ORA-39327: Oracle Database Vault data is being stored unencrypted in dump file set.

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