Incident Post-Mortem: May 4, 2026 Database Upgrade

A note from our team

On Monday, May 4, 2026, Uscreen experienced a significant performance incident following a planned PostgreSQL database upgrade. Storefronts, the Admin Portal, and the APIs that power our mobile and TV apps were degraded or unavailable for portions of the day for many of our customers.

We know that when your storefront is slow, or your viewers can’t watch, it’s not just a technical issue — it affects your business, your subscribers, and your reputation. We’re deeply sorry for the disruption, and we owe you a clear, honest account of what happened, how we resolved it, and what we’re doing to make sure this doesn’t happen again.

This document is that account.

What happened, in plain language

We perform major database upgrades approximately once per year to keep the platform secure, fast, and on a supported version. The upgrade itself completed successfully and on schedule. The problem started after the upgrade, when production traffic returned to the freshly upgraded database.

After a major version upgrade, a database needs to “warm up” — its internal memory cache, which normally holds the most frequently accessed data ready for instant retrieval, starts empty. As traffic returned, our database needed to load enormous amounts of data into that cache simultaneously. We had not anticipated how severely this cold-start effect would compound with our current workload, and the database fell behind. Requests began to queue. Queries that normally took milliseconds took seconds. The platform slowed down dramatically for many customers.

It’s worth being direct about this: we tested this upgrade beforehand using a canary approach — running it in a non-production environment to validate it. And we performed a similar major upgrade last year without significant issues. What changed between then and now was the size of our data. Our platform has grown substantially over the past year, and the cold-start memory pressure that was manageable last year became severe at our current scale. Our pre-production environment did not faithfully replicate that scale, so it did not expose the problem before it hit production. This is the specific gap we are closing — see “What we’re changing” below.

Compounding the cold-start problem, a small number of high-volume application queries — ones that performed acceptably under normal conditions — became significantly slower under memory pressure. This created a feedback loop: slow queries held connections open, those connections couldn’t serve other requests, and the backlog grew faster than the system could clear it.

It took our team longer than we would have liked to correctly diagnose the structural cause. Our initial hypotheses — that statistics needed rebuilding, that specific code-level query fixes would resolve it — turned out to be only partial answers. The real fix required more decisive action: stopping the application entirely to let the database stabilize, expanding the database’s memory capacity, and then gradually bringing services back online.

Timeline (all times Eastern)

What was affected

Different services were affected at different times and to different degrees:

• Storefronts — Slow page loads and intermittent errors during the worst windows (approximately 4:00–11:00 AM ET, with severity peaking 4:00–6:00 AM and again briefly around midday)
• Admin Portal — Severe slowness and “Unexpected Server Error” pages for many creators
• Mobile and TV apps (API V1 and V2) — Slow or failing requests, recovering last
• Communities — The initial area where our investigation began, where high-volume queries were among the early contributors to the database load

Throughout the incident, no customer data was lost, no security boundaries were affected, and no payment or subscription data was compromised. The issue was performance, not integrity.

What we did to resolve it

Our response unfolded in two layers. The application-layer fixes (the “minor contributors”) helped, but the database-layer actions (the “major contributors”) actually ended the incident.

Major contributors to recovery

1. Stopped the application entirely. When new slow queries were arising faster than we could resolve them, we made the deliberate decision to halt all application traffic for a short window so the database could finish its work and return to a stable baseline. This was a planned, controlled outage of approximately 10–15 minutes — chosen because partial mitigation was no longer keeping pace with the problem.
2. Resized the database to add memory capacity. We moved to a larger database instance with significantly more memory available for the cache. This addressed the structural root cause: our previous configuration didn’t have sufficient memory headroom for the platform’s current workload during a cold start.
3. Brought services back online gradually. Rather than restoring all traffic at once and risking a repeat of the cold-cache problem, we restarted application servers in stages. This lets the database cache warm progressively, services come back in a controlled order, and confidence builds at each step. Storefronts came back first, followed by the Admin Portal, then the mobile and TV APIs.
4. Disabled a database optimization feature (JIT compilation) that, under our specific workload during the incident, was adding overhead rather than reducing it. This will be re-evaluated and tuned separately.

Minor contributors to recovery

These code-level fixes shipped during the incident genuinely helped — they reduced load on the database and improved query performance — but on their own, they couldn’t resolve the structural problem.

1. High-volume query optimization. A frequently-fired query that scanned a large amount of historical data on every call was bound to only look at a recent time window, dramatically reducing the work required per request.
2. Read traffic routing. We routed more of our API V2 read traffic to replica databases, taking pressure off the primary.
3. Targeted index improvement. An index was added to support a frequent storefront query.

We are continuing to ship these and similar improvements this week. They make the platform faster and more resilient regardless of upgrades.

Why did this take longer than it should have?

We want to be honest about this: the incident lasted longer than it needed to. Three things contributed:

1. Our initial diagnosis was incomplete. The first hypotheses we pursued — that database statistics needed rebuilding, that specific code-level query fixes would resolve it — were partial truths. The structural cause (insufficient memory for cold-cache recovery under our current workload) wasn’t identified until later in the incident. We chased the symptoms before fully understanding the system pressure.
2. We were slower to communicate publicly than we should have been. Our first status page update came hours after degradation began. Customers experiencing slowness deserved to hear from us sooner, even if all we could say was “we’re investigating.”
3. Our pre-production testing didn’t catch the scale-dependent failure mode. We did run this upgrade in a canary environment first, and a comparable upgrade last year went smoothly. But our test environment didn’t replicate the full size of our current production data, so the cold-start memory pressure that hit us at production scale never showed up in testing. Our growth over the past year crossed a threshold our procedures hadn’t been updated to account for.

We’re owning these honestly because they are exactly the things we can change.

What we’re changing so this doesn’t happen again

This is the most important section of this document. Several improvements are already in motion.

Database capacity and configuration

• The larger database instance is now permanent. We are not returning to the previous configuration. Our memory headroom is now sized appropriately for our current and projected workload.
• We are reviewing additional database configuration settings (memory tuning, JIT settings, connection limits) in light of the workload patterns we observed during the incident.
• Specific high-volume queries are being permanently optimized, including the ones that became hotspots during the incident — independent of any future upgrade.

Upgrade procedures

• A formal major-version upgrade runbook is being written with mandatory pre- and post-upgrade steps, including cache pre-warming, staged traffic restoration, and an extended observation period before declaring success.
• We are upgrading our pre-production canary environment to better mirror production scale. Our canary process worked correctly for last year’s upgrade, but production data growth has outpaced our test data. Going forward, the canary will be sized to faithfully reproduce production-scale memory pressure, query patterns, and connection load, so that scale-dependent failure modes show up before they reach customers.
• Pre-upgrade query performance baselines will be captured, so any regression after an upgrade is immediately visible by comparison.
• Capacity reviews are now part of every major upgrade plan. Before any future major upgrade, we will explicitly verify that the database's memory and connection capacity are sized for cold-start recovery, not just for steady-state operation.

Monitoring and alerting

• New alerts on response-time and database-pressure metrics so that degradation is detected automatically and the on-call team is paged within minutes of onset, not after customer reports accumulate.
• Database memory pressure and lock contention are being added to our standard dashboards as first-class signals.

Communication

• Our internal incident playbook now requires a public status page update within 15 minutes of any confirmed customer-facing degradation, regardless of whether we have a diagnosis yet.
• We will publish post-mortems for significant incidents as a standard practice, not just when asked.

Looking forward

We want to leave you with three things:

This kind of major maintenance happens approximately once per year. It’s not a routine event, and it’s not the new normal. We performed a similar upgrade last year that went smoothly, and most days, weeks, and months on Uscreen pass without anything like this incident. Today was a bad day, not a sign of bad days ahead.

Every change above is real. We’re not gesturing at improvement — we are funding it, scheduling it, and assigning it to specific people. The next major database upgrade will be a fundamentally different operation from this one.

We take this seriously because you trust us with your business. Your subscribers, your revenue, your reputation — they all depend on Uscreen being available and fast. We did not meet that standard yesterday. We are going to.

If you have questions or concerns, or were materially affected and want to discuss the impact on your business, please reach out to your account contact or to support@uscreen.tv. We are here, and we want to talk.

— The Uscreen Engineering and Operations team

Appendix: Technical detail (for technical customers)

For customers with engineering teams who want a deeper view:

• Database: Managed PostgreSQL, recently upgraded to the current major version
• Working set: Approximately 1 TB OLTP database
• Pre-incident memory cache configuration: Undersized for the cold-start scenario after a major upgrade; resized post-incident to provide significant additional headroom
• Peak symptoms during the incident: Database connections fully saturated, recent cache hit ratio dropped to ~36%, significant lock contention from concurrent backends competing for buffer access, dozens of concurrent long-running queries (>10s), application-server p95 latency at 5.25s, Apdex bottoming at 0.27
• Recovery state: Connections at normal idle levels, cache hit ratio 99.4–99.8%, p95 latency 0.29s, Apdex 0.99
• Initial diagnostic dead-ends ruled out during the incident: missing planner statistics (replica counters were misleading — actual statistics had replicated correctly), collation library drift (verified intact), and major configuration drift (verified carried over correctly from the prior version)
• Application-layer fixes shipped during the incident: bounded a high-volume time-series query to a recent time window, routed additional read traffic to replicas, and addressed related code paths
• Outstanding follow-up technical work includes additional indexes on hot lookup columns, improved indexing for a JSONB lookup pattern, partitioning of a large time-series table, and enabling automatic GET-request routing to replicas at the framework level