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More instances are not inherently harmful. They help when application capacity is the constraint and downstream services can handle the added work. When those conditions do not hold, scaling out can multiply connections, cold-start work, retries, and duplicate requests.
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Why can more application servers make an outage worse?
Scaling out adds capacity to one tier, not automatically to the whole request path. A stateless web server can often handle more traffic by adding instances, but a database, cache, queue, or hot write path may have its own fixed limits and scaling requirements. Meta’s 2020 account of Shard Manager describes the difference: stateless requests can be routed across servers, while stateful data needs explicit placement and operational management across shards.
When new instances send more simultaneous requests to a constrained dependency, that dependency can queue work, exhaust connections, or slow down. The app tier may then spend more time waiting, even if it has spare CPU. If clients retry or reconnect during the slowdown, they can add still more requests to the same system.
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More instances can mean more dependency connections
Each application instance may open connections to a database, cache, or other service. That means an autoscaling event or deployment can increase the number of connections just as traffic rises. Patreon Engineering described this problem during live-event scaling: new app instances created new dependency connections, and excessive connections during deployments had already caused errors.
Whether connections are the limiting factor depends on the application and its dependencies. Check actual connection counts and connection wait time during scale-up rather than assuming that a healthy individual instance means the overall system is healthy.
Cold starts can create a burst of work
A newly started instance may have empty local caches or need to load configuration and other data before it can serve requests efficiently. If many instances start together, they can all ask a shared service for the same information. Similarly, when a popular cache entry expires or disappears, concurrent misses can send a burst of reads to the origin. Redis describes this class of synchronized demand as the thundering herd problem.
A popular item can also create a hot key: many requests target the same cached value or underlying record. If the workload includes writes, those requests may contend on the same row or key rather than spreading evenly across the system.
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Retries can turn a slowdown into repeated work
Queues absorb bursts only within their capacity and the time available to process the backlog. If work arrives faster than it can be completed, queue depth and waiting time rise. Increasing a queue limit can provide temporary headroom, but it does not remove the work causing the backlog.
Clients that retry too quickly can make the backlog worse. In its June 1, 2025 postmortem on the T3 Chat incident, Convex reported query rates rising from roughly 50 queries per second to more than 20,000 during spikes. It described clients reconnecting and sending the same queries that had contributed to the problem. Those figures describe that incident, not a general traffic threshold.
Adding workers is not always enough either. Meta’s 2020 account of its Async service says that adding workers did not fix a queueing design in which large use cases could dominate smaller ones. The service instead used per-use-case queues and techniques including deadlines, delay tolerance, time shifting, and batching.
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Trace a representative slow request across the app and its dependencies. Break its total time into work performed and time spent waiting; inspect a distribution such as P90 as well as averages, because a small but growing tail can signal trouble before every request is slow. Production traces helped Patreon identify unnecessary bootstrap requests and database queries during its live-event work.
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Use the wait location to decide what to inspect next:
| Where time accumulates | Signals to inspect | What the pattern may indicate |
|---|---|---|
| Application processing | Per-instance CPU, request concurrency, thread or event-loop saturation, and time spent in application code | The app tier may lack capacity, or may be doing avoidable work. |
| Database or cache connection acquisition | Open and waiting connections, connection errors, and whether counts jump when instances start | Connection pressure or startup behavior may be limiting requests. |
| Database query or write | Query latency, lock or contention waits, and whether work concentrates on a small set of records | A slow query, concentrated write path, or stateful data constraint may be involved. |
| Cache lookup followed by backend read | Cache hit and miss rates, expirations, cold starts, and concurrent reads for popular keys | Misses may be synchronized, or traffic may be concentrated on a hot key. |
| Queue or downstream service | Queue depth and limits, enqueue rate, service time, retry rate, and reconnect rate | Work may be arriving faster than it can be processed, or being repeated. |
These signals are clues, not proof by themselves. Compare them over the same time window as latency and traffic, and follow a slow request far enough to see whether it is doing expensive work or waiting for a dependency.
How should you diagnose the incident?
- Trace the request path. Identify where latency accumulates across the application and its dependencies. Determine whether the app tier is busy processing or blocked waiting.
- Compare dependency connections with instance count. During a scale-up or deployment, check whether connection counts, acquisition waits, or errors rise along with the number of app instances.
- Inspect queues and client behavior. Measure queue depth, limits, service time, retries, and reconnects. Look for clients repeating expensive work while the service is already behind.
- Check for synchronized cache misses and concentrated keys. Look at expiry patterns, cold starts, hit rates, and whether reads or writes cluster on a popular item or record.
- Change the measured constraint, then measure again. Pick the smallest intervention that addresses the observed wait, and compare latency and load after the change. A fix can move the bottleneck elsewhere.
Which fix fits the bottleneck?
Choose an intervention for the constraint you measured, not for the fact that traffic increased. Each option changes a different part of the system and has trade-offs.
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|---|---|---|
| Application CPU or per-process concurrency | Optimize expensive work or add app instances if dependencies have headroom. | More instances may increase connection and request pressure on shared dependencies. |
| Excessive or unnecessary reads and payload work | Remove irrelevant bootstrap data, avoid unnecessary client requests, or defer non-essential work. | Deferring work changes when users receive it; reducing payloads may require changing what the page loads. |
| Database capacity or state placement | Consider suitable replicas or partitioning when the workload and consistency needs permit. | Stateful scaling requires data placement and operational management; replicas and partitioning are not interchangeable solutions. |
| Queue backlog or uneven workload | Control concurrency, prioritize or isolate use cases, and batch or defer work that can tolerate delay. | Delay tolerance and deadlines differ by task; batching changes when work completes. |
| Cache stampede or hot key | Reduce synchronized misses or spread and control demand where the data model allows. | Caching can reduce repeated backend reads, but misses, expiry, or concentrated writes can still overload an origin. |
| Retry or reconnect storm | Use coordinated retry behavior and backoff, and limit repeated work during recovery. | Clients may recover more slowly, but uncoordinated retries can intensify an existing overload. |
Reduce the work before buying more capacity
More capacity can be appropriate, but it does not help if each request performs unnecessary work against a constrained service. Patreon Engineering’s live-event account describes removing irrelevant bootstrap work, skipping database queries, serializing a smaller payload, reducing unnecessary client requests, and delaying non-essential work. In that specific workload, Patreon reported a 57% reduction in chat-page P90 latency and almost 50% fewer requests at cold app launch. These are results from its live-event changes, not general performance guarantees.
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The principle is useful beyond that case: fewer avoidable operations can leave more capacity for necessary ones. As Patreon Engineering put it, “If scalability is about having capacity for necessary operations, and performance is about reducing the operations necessary, then it’s fair to say that a performant system will scale better.”
Scale stateful systems for the workload they actually have
If traces point to a database or a concentrated data path, adding web servers does not itself distribute that state. Meta’s Shard Manager article explains that managing shards involves placement, movement, balancing, replicas, and failure handling. Meta reported that its internal system managed tens of millions of shards on hundreds of thousands of servers across hundreds of applications in 2020; that describes Meta’s platform, not a sizing target for other systems.
Replicas, partitioning, and caching each change how data is served or placed, and their suitability depends on write patterns, consistency needs, and operational capacity. Diagnose whether the problem is read volume, write contention, connection pressure, or placement before choosing among them.
Control backlog and repeated requests
If a queue is growing, determine whether the cause is insufficient processing capacity, a sudden arrival burst, or work that is unfairly monopolizing workers. Increasing workers can help when processing capacity is genuinely short and downstream services can accept more concurrency. If the queue design lets one large workload block smaller or more urgent jobs, isolation, prioritization, deadlines, batching, or delay tolerance may be more effective.
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For retries, examine what clients do when a request fails or a connection drops. A reconnect that repeats the full workload can turn a brief failure into a second wave of demand. In the Convex postmortem, both retry behavior and restoring the deployment to its appropriate, more powerful hardware resources mattered to recovery; neither detail establishes a universal remedy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you know the change helped?
After each change, compare the same request paths and workload conditions: latency distribution, error rate, queue depth, dependency connection pressure, cache behavior, and retry volume. Verify that the targeted wait fell and that another dependency did not become the new limit. If a result improves only while traffic is lower, it is not evidence that the underlying constraint has been removed.
Engineering accounts and vendor documentation illustrate how these failure modes occur, but they do not establish how often adding servers worsens an outage across systems. Autoscaling is not inherently harmful, and databases are not always the bottleneck. The right conclusion comes from the observed request path and the work each added instance creates.
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