In 2023-2024, after incidents that multiple customers of PostgresAI experienced, when production nodes were down because of LWLock:LockManager contention, we studied it in synthetic environments.

At that time, we managed to reproduce the issue only on large machines – ~100 or more vCPUs.

With PG18 release, this question started to bother me again: can we experience LWLock:LockManager on smaller machines?

Denis Morozov just published results of benchmarks that successfully reproduce LWLock:LockManager contention in PG18 on 16-vCPU VMs.

As before, we took standard pgbench, with -s100 (no partitioning), and started running the standard "--select-only" workload that SELECTs random rows in "pgbench_accounts". Originally, the table has only 1 index – so at planning time, Postgres locks 2 relations with AccessShareLock; then we add one more index at each step.

After 2-003, @ninjouz asked on X:

If fast-path locks are stored separately, how do other backends actually check for locks?

The answer reveals why fast-path locking is so effective - and why PG 18's improvements matter so much in practice. // See lmgr/README, the part called "Fast Path Locking".

Remember from 2-002: when you SELECT from a table, Postgres locks not just the table but ALL its indexes with AccessShareLock during planning. All of these locks go into shared memory, protected by LWLocks. On multi-core systems doing many simple queries (think PK lookups), backends constantly fight over the same LWLock partition. Classic bottleneck.

Instead of always going to shared memory, each backend gets its own private array to store a limited number of "weak" locks (AccessShareLock, RowShareLock, RowExclusiveLock).

As we discussed, Lock Manager manages heavyweight locks – various kinds of them (various modes, various levels of granularity). These locks are released only at the end of the transaction.

In the most trivial case, when you run a SELECT on a table, this table is locked with AccessShareLock. And not only the table, but all its indexes, which happens during planning time (always happens unless you use prepared statements). This is to protect against concurrent DROP. All of these are released only when the transaction ends.

Information about pending and successful heavyweight lock acquisition attempts is stored in shared memory and can be displayed in the "pg_locks" view. Operations on this table are also protected by locks – but this time, lightweight locks. They are short-lived, being quickly released.

Let's talk about relation-level locks and various confusions, surprises and what is worth to remember in practice.

The key page in Postgres docs describing relation-level locks is here: https://www.postgresql.org/docs/current/explicit-locking.html#LOCKING-TABLES

This page in the docs is called "13.3. Explicit Locking" and it might cause confusion because it talks about implicit locking (e.g., if you run a DML or DDL, locks are applied implicitly; while if you execute LOCK or SELECT .. FOR UPDATE, you explicitly request locks to be acquired). However, this might be just my own terminology bias.

This page has a useful "Table 13.2. Conflicting Lock Modes" that can help understand how a lock acquisition can be blocked by another, already acquired or pending (!) lock:

A possible confusion here is the word "row" used in some lock modes – we shouldn't think that those modes are row-level. They are still relation-level. There is a special concept of row-level locks, we'll dive into that separately. This confusion is covered in the docs though:

Remember that all of these lock modes are table-level locks, even if the name contains the word "row"; the names of the lock modes are historical.

Back to the table above, there is a very useful transformation of it in old blog post by Marco Slot, "PostgreSQL rocks, except when it blocks: Understanding locks" (2018, https://citusdata.com/blog/2018/02/15/when-postgresql-blocks/) -- it's incomplete, but speaks common operations and can be used as quick reference:

Next, another terminology confusion might come from reading the section, "13.3.1. Table-Level Locks". Let's not be confused, it talks not only about tables, but rather relations.

In Postgres, the word "relation" can be applied to many objects, we can see it looking at the docs for pg_class.relkind (https://postgresql.org/docs/current/catalog-pg-class.html):

r = ordinary table, i = index, S = sequence, t = TOAST table, v = view, m = materialized view, c = composite type, f = foreign table, p = partitioned table, I = partitioned index

To warm up, let's talk about lightweight and heavyweight locks (or "regular locks" or just "locks").

I'm using these materials:

• PG docs (first of all, https://www.postgresql.org/docs/current/explicit-locking.html)
• LockManager source code – src/backend/storage/lmgr (and first of all, README https://github.com/postgres/postgres/blob/master/src/backend/storage/lmgr/README)
• Egor Rogov's "PostgreSQL Internals", Part III "Locks"

I'm excited to announce that Postgres AI has started work on a new project – open-source Self-Driving Postgres (SDP).

In the AI era, Postgres is the natural choice for AI builders. With fast-growing database clusters, the highest level of automation is essential. AI-driven growth demands efficient, proactive, and intelligent database management. Our goal is to reduce manual interventions as much as possible to achieve the highest level of operational efficiency and reliability.

Preview environments are temporary deployment environments created for each pull request, offering major advantages over traditional shared staging environments. While platforms like Vercel (paid) and Coolify (open-source) solve application deployment, the database remains the bottleneck. Teams typically compromise: sharing one database (causing conflicts), deploying small test databases (lacking realistic data), or cloning large production databases (taking hours and costing heavily). DBLab 4.0's database branching solves this with O(1) economics, spinning up isolated production-scale Postgres clones in seconds for cost-effective full-stack previews.

Today, as part of our Launch Week (Day 4), we're excited to announce the release of our Postgres how-to guides collection – a comprehensive set of dozens of practical guides covering real-world PostgreSQL challenges.

This is Day 3 of Postgres AI launch week

It starts innocently enough.

You choose Postgres – solid, reliable, battle-tested. You pick a managed service like RDS or CloudSQL. They handle backups, high availability, disaster recovery. You can focus on building your product. Life is good.

Your startup grows. Users love what you've built. Data accumulates – gigabytes become terabytes. Traffic surges – hundreds of requests become thousands per second.

Then one day, everything changes.

Queries that ran in milliseconds now take seconds. Connection pools max out during peak hours. Replication lag appears out of nowhere. Your perfectly fine database suddenly isn't fine at all.

You reach out to your managed service support. The response? A generic checklist. "Have you tried increasing your instance size?" Days pass. Your users complain. Your team scrambles. The support ticket remains open, unhelpful.

This is when you realize: nobody cares about your database health as much as you do.

This is exactly when it's time to engage Postgres AI.

For 5+ years, the Postgres AI team has been rescuing companies from exactly this situation. Our clients include GitLab, Miro, Chewy, Midjourney, ClickUp, Photoroom, Gamma, Suno, Supabase – they faced a lot of database challenges during hypergrowth.

Our approach: comprehensive health checks using a methodology refined over hundreds of engagements. We call it Postgres AI Checkup – it identifies current issues and predicts future ones before they cripple your business.

Last year alone, we conducted 30+ deep checkups for 20+ companies. Today, we're launching this as a scalable service, enhanced with AI automation while keeping human experts at the core.

Start your first checkup: Console.Postgres.ai

Today we're launching the SQL query toolkit, a tool to analyze, explore, and share SQL queries with plans, that lives inside Postgres AI Console.