What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
Yes—PostgreSQL can power high-traffic applications, including services used by millions of people, but there is no universal “maximum users” number. Capacity depends on query shape, working-set size, indexes, storage, CPU, connection concurrency, replication and durability settings. A successful design first controls connection concurrency and query cost, then adds replicas for read capacity and availability. Large-table partitioning, disciplined vacuuming and, only when simpler limits are understood, distributed PostgreSQL or sharding address the next constraints.
The difficult boundary is usually write capacity. Traditional primary/standby PostgreSQL sends writes to one primary, while replicas mainly serve reads. Scaling therefore starts with measuring the real workload and identifying whether the bottleneck is connections, queries, storage, reads, writes or maintenance.
What “scaling PostgreSQL” actually means
Scaling is not one operation. It can mean handling more requests per second, more simultaneous users, a larger dataset, longer retention, higher availability or stricter recovery targets. Those goals can conflict: synchronous replication improves durability but adds commit latency; many replicas improve read capacity but add network, storage and lag-management work; partitioning can improve locality but makes planning and memory use more complex.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →A useful first distinction is the workload mix:
- Read-heavy: product pages, feeds, searches and reporting often benefit from read replicas and caching while the primary continues to accept writes.
- Write-heavy: event ingestion, telemetry, status updates and high-frequency transactions concentrate pressure on the primary. Replicas do not turn a conventional topology into a multi-writer system.
- Mixed: most production systems need a primary for authoritative writes, carefully routed reads, pooling, query tuning and maintenance controls.
“Can PostgreSQL handle millions of users?” is therefore a workload question, not a database-brand question. Millions of registered accounts may generate modest concurrent database activity, while a much smaller user base can overwhelm a system with expensive queries or unbounded connections.
#1 Best Overall
- Powerful Turbo Fan:CAGIWIRU Electric Air Duster have reached speed up to 130000RPM,Can efficient remove dust and debris.With LED light and Three Adjustable Speed to meet with different cleaning tasks.
- With Attached USB C Fasting Charging cable.Charing 3 hours,Enjoy up to 240 minutes of use on the lowest setting, For third speed can use for 25mins.with four charging options to meet with your needs.
- Wide Application: attached 5 different nozzles,making it suitable for a variety of scenes, such as car,pet, pc, keyboards, and other electronic devices. It also serves for office and home clean duster.
- Handy Design & Portable:Cordless air duster electric nozzle is Light weight and small size, design and comfortable to hold and space saving, convenient to carry around. You can put it in your room or inside the car without taking up space.
- Economical &Reusable: Built durable materials with a long-lasting battery,our CAGIWIRU air duster it's a sustainable alternative to disposable air cans.
How PostgreSQL scales beyond one server
PostgreSQL supports standby servers through streaming replication. The primary writes changes to its write-ahead log (WAL), and standbys receive and replay those changes. A standby can provide failover and, when the application routes suitable queries to it, additional read capacity.
Asynchronous replication
Asynchronous replication is the default model described in PostgreSQL’s replication documentation. The primary can acknowledge a commit before a standby has replayed it, so a replica may be behind by some amount of replication lag. This is usually the best fit for read scaling when the application can tolerate slightly stale results.
Examples include a catalog page, an analytics view or a list that does not need to show a just-submitted change. The application should send consistency-sensitive reads—such as “show the order I just paid for”—to the primary or use an explicit mechanism that waits for the required replica state.
Free tools Windows power users keep installed
One-click scans. No signup required.
Synchronous replication
Synchronous replication waits for acknowledgement from a configured standby before confirming a commit. That can provide stronger durability if the primary fails, but every commit now depends on the network path and standby health. Poor network conditions or an unavailable synchronous target can increase latency or reduce write throughput. Choose it for a stated durability requirement, not simply because more replicas sound safer.
| Replication choice | What it provides | Main trade-off | Typical use |
|---|---|---|---|
| Asynchronous standby | Failover option and read capacity | Replica lag and potentially stale reads | Read-heavy applications that can route tolerant reads |
| Synchronous standby | Stronger commit durability | Higher commit latency and dependence on standby/network health | Workloads with explicit durability objectives |
| Primary only | Simplest write path | No replica read scale and a narrower failover plan | Moderate workloads where simplicity is the priority |
Connection pooling is the first high-traffic control
PostgreSQL uses a process-per-user connection model. Each active client connection consumes server resources, and idle sessions still occupy connection slots and memory. If every web request opens its own database session, a traffic spike can exhaust the database before CPU or storage is fully used.
Rank #2
- Screen Cleaner is Your Screen’s New Bestfriend! - EVEO is proud to present a screen cleaner that’s perfectly suited for your TV. Meet your new screen cleaner, computer screen cleaner, laptop cleaner, iPad cleaner, Macbook,tv cleaner or any other electronic device. Our screen cleaner spray and wipe along with the included plush microfiber cloth, easily removes dust, fingerprints & other smudges on the screen’s surface. Clean effectively for a streak-free screen
- Gentle on your Screens - Our screen cleaner spray and wipes is compatible to be used even for the most sensitive LCD, LED, CRT, and OLED screens in proper use. Be at ease knowing that this product is compatible with all the major brands: LG, SONY, Samsung, Sharp, iPad, iPhone, Android screens, Kindle, PC monitors - we’ve got you covered! Can be used as LCD screen cleaner, tv cleaner, smart tv screen cleaner, and more
- Premium Super Soft Plush Microfiber Cloth - Included in this efficient screen cleaning kit. This screen cleaner spray and wipe has our signature plush microfiber cloth specially designed to comply with screens and monitors and leave them streak-free. Has been shown to effectively absorbs stubborn stains, zero streaks, and no fiber shedding. Always use this together with our screen cleaner for the best results
- Easy-to-Use Screen Cleaner Kit + microfiber cleaning cloth - The EVEO Screen Cleaning Kit will easily be your new favorite tool when it comes to taking care of the things you love most, quick and easy solution for a streak-free screen. Simply screw on the sprayer gun on the screen cleaner bottle. Spray the cleaning liquid onto the microfiber cloth, then wipe the screen until desired cleanliness & shine is achieved.
- Quality Screen Care Kit Worth Purchasing - Enjoy a convenient Screen Cleaner anytime, anywhere you need it. Your satisfaction is our top priority. We stand behind the quality of our products and that’s why, you shouldn’t be worrying at all. We manufacture the Best Screen Cleaner. and can be used for laptop cleaning kit, laptop screen cleaner, tv screen cleaner for smart tv. MacBook screen cleaner, monitor cleaner or screen cleaner spray for electronic devices with microfiber cleaning cloth
A pooler such as PgBouncer, or a pool built into the application, keeps a bounded number of physical PostgreSQL connections and lets many requests share them. Excess requests wait in a controlled queue instead of creating unlimited backend processes. PostgREST’s documentation describes the same principle: a pool can serve many HTTP requests with fewer database connections.
A practical pooling design
- Set a database connection ceiling that leaves headroom for administration, replication and monitoring. Do not size it to the number of website users.
- Place a pool between the application and PostgreSQL. Configure the application to borrow a connection for a transaction and return it promptly.
- Keep transactions short. A connection held while waiting on an external API, file upload or user interaction is unavailable to other requests.
- Define queue limits and timeouts. Bounded waiting and admission control fail more predictably than allowing an unlimited backlog.
- Watch pool saturation and database activity together. A full pool can mean the database is slow, or that application code is holding connections unnecessarily.
Pooling reduces connection overhead; it does not make an expensive query cheap. Once connection concurrency is bounded, query plans usually become the next place to look.
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Tune queries and indexes before adding nodes
PostgreSQL’s planner chooses how to scan tables, join relations and apply filters. The quality of that plan is central to performance. Use EXPLAIN to inspect the chosen plan and EXPLAIN ANALYZE when you can safely execute the statement and measure actual row counts and timing.
EXPLAIN (ANALYZE, BUFFERS)
SELECT ...
FROM ...
WHERE ...;Compare estimated rows with actual rows, check whether a large table is being scanned unnecessarily, inspect join choices and verify that partition pruning occurs when expected. An index is useful only when it matches a real access pattern; adding indexes indiscriminately increases write cost, storage use and vacuum work.
Keep planner statistics current
The planner relies on table and column statistics to estimate cardinality. Stale statistics can produce a plan that is reasonable for yesterday’s data distribution but poor for today’s. Autovacuum and analyze activity, plus targeted maintenance on rapidly changing tables, help the planner make better choices.
Rank #3
- 【Large size】：Measuring 800 x 300 mm/ 31. 5×12 inches, which is wider and taller than others. Large mouse pad can perfectly fit your keyboard and mouse of any size, and there is also ample space for peripherals such as phones, tablets, etc.
- 【LED backlight mouse pad】：Blade Hawks RGB mouse pad provides 7 static modes and 7 dynamic modes will give you the options you are looking for. Power-off memory function remembers the last lighting mode you selected. The super glow fiber Chroma will give you a colorful gaming setup that you want.
- 【Anti-slip rubber base】：Made from Natural rubber, this gaming mousepad will firmly grip your desktop, allowing you to enjoy the ultimate precision in the games you are most passionate about. It also comes with a texture that enhances this ability even further.
- 【Smooth and waterproof surface】: Micro-textured cloth surface, for extremely precise mouse control and a smooth movement. When liquid splashes on the gaming mouse pad, it forms water droplets and slides down. Will not delay your work or gameplay.
- 【Plug and play】: This mouse and keyboard pad is powered by USB, plug and play, no driver required. One button control light modes. Press one time to change the lighting mode, press twice to change the brightness, press and hold about 3 seconds turn ON/OFF.
Reduce work in the transaction path
- Return only the columns and rows the request needs.
- Avoid repeated lookups that can be combined safely.
- Batch independent writes when transaction semantics allow it.
- Move non-critical work to a queue rather than making the user-facing request wait.
- Use rate limits to smooth bursts instead of allowing every request to compete for the same locks and connections.
These changes often increase useful throughput without adding a server. They also make later replication or sharding decisions easier because the workload is measured after obvious inefficiencies are removed.
Read replicas: when they help and when they do not
Read replicas are most effective when reads dominate and queries can be routed safely. A primary continues to process writes, while one or more standbys handle eligible read-only traffic. This can increase aggregate read capacity and provide a failover target.
Route by consistency requirement
Asynchronous standbys can lag. Route browsing, recommendations or delayed reports to them when a short-lived stale result is acceptable. Route account balances, permission checks, checkout confirmation and read-after-write flows to the primary unless your design explicitly guarantees the required visibility on a replica.
Monitor lag as a product constraint
Replication lag is not only an infrastructure metric. It determines how old a user’s result may be. Alert on lag, set a policy for when a replica is removed from the read pool, and make failover behavior explicit. If a replica is unhealthy, sending more traffic to it can turn a recoverable lag problem into an outage.
Adding replicas does not increase the primary’s write throughput. Every change still originates on the primary and must be written, retained and usually replayed by the standbys.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesRank #4
- 【Softer and More Comfortable】Vaydeer wrist rest has unique diamond pattern, which is the combination of softness and aesthetics. The materials of wrist rest are improved into higher quality memory foam and covered with silky smooth lycra. The computer wrist rest makes you as comfortable and cushiony as like rest your wrists on clouds.
- 【Ergonomic Wrist Saver】The wrist rests for keyboard and mouse comes with a 17.32×3.15×0.83 inch keyboard wrist pad and a 5.94×3.15×0.83 inch mouse wrist support. Based on ergonomic design, the unique concave shape is the perfect fit for your wrist joints. The wrist rest pad fits most computer keyboards and laptops, improve hand and wrist posture, release your wrist and arm stress.
- 【Non-Slip Rubber Bottom】Featuring an anti-skid silicone base on the bottom, this wrist keyboard support stays firmly in place on your desk, preventing the padding from sliding around, ensuring stable and consistent wrist support during extended computer sessions.
- 【Better Experience & Pain Relief】Our keyboard arm rest is beneficial to alleviate the soreness caused by direct contact and friction between your arm and a hard desk surface, reducing the risk of wrist fatigue or carpal tunnel. The soft texture of memory foam can evenly distribute the pressure around your wrists and provide good support with just enough give.
- 【Helpful in Multiple Scenarios】Whether you're working, studying, writing, typing, gaming, this keyboard and mouse rest combo is an essential accessory to add comfort and support to your hands and wrists. It’s also a great gift for men, women, family, friend, coworker, gamer, teacher, etc.
Why write-heavy workloads are harder
OpenAI’s 2026 engineering case study title refers to “800 million ChatGPT users,” and its team states: “Although PostgreSQL scales well for our read-heavy workloads, we still encounter challenges during periods of high write traffic.” The observation illustrates the general limit: conventional primary/standby PostgreSQL has one authoritative write path.
Before introducing a distributed design, reduce pressure on that path:
- Remove redundant writes. Do not persist state that can be derived reliably or is overwritten before anyone reads it.
- Batch safe operations. Group independent changes when this does not violate ordering, durability or transaction requirements.
- Smooth bursts. Queues, rate limits and back-pressure prevent short spikes from becoming connection and lock storms.
- Shorten inefficient transactions. Large transactions hold locks and create more cleanup work.
- Review indexes and constraints. Every write may update multiple indexes and check constraints; keep only those that serve a real requirement.
- Separate critical from non-critical writes. Keep the user-facing transaction small and process secondary effects asynchronously where correctness permits.
If the primary still reaches its CPU, storage, write-latency or capacity limits after these changes, the architecture may need partitioned ownership or a distributed PostgreSQL approach.
Partitioning very large tables
PostgreSQL supports range, list and hash partitioning. A partitioned table divides one logical table into smaller physical pieces. Partitioning helps when queries filter on the partition key and can therefore touch only a subset of partitions, a behavior known as partition pruning.
Choose a key that matches real access
- Range partitioning: useful for time windows or ordered numeric intervals.
- List partitioning: useful when a finite category, region or tenant grouping controls access.
- Hash partitioning: useful for distributing rows by a stable key when range or list boundaries are not natural.
Partitioning is also valuable for retention. Detaching or dropping an old partition can be much faster than deleting millions of rows individually, reducing long-running delete work and associated bloat.
Best Value
- 【No Drill Mounted】Cable management tray can be assembled in as little as 3 minutes or less-simply. Side clips easily clampe the edge of desk, pass through the holes on the sides, and secure excess cables with baskets. Desk cable management supports inward or outward installation to meet your needs in different scenarios, and provides you with great convenience when collecting and organizing wires.
- 【Rubber Pad Protects Desk】Wire organizer under desk built-in soft rubber pads on the clamp protect your desk from scratches, so you will not damage your furniture or office.
- 【Sturdy and Beautiful】Cord management under desk is made of high-quality carbon steel and holds about 15 lbs. The wire basket has 2 holes on each side of the tray to enable your cables to pass freely. Cable baskets are suitable for desk thickness use from 0.4" to 2.4".
- 【Space Saving & Keep Organized】Desk cable management tray holds all your power cords, outlet strips, USB hubs and computer transformers hidden from your feet, and the data cables will not enter the vacuum cleaner to keep your desk clean and tidy. Desk wire organizer is also suitable for use in the kitchen and outdoors.
- 【Reduce Safety Risks】Cable tray under desk keeps all plugs away from your kids, no more worry about tripping. Using 2 holes wire tray saves extra space, helps you easily cabling and wire protection. The mesh design is not easy to accumulate dust, but avoids safety accidents caused by messy wires.
Avoid excessive partition counts
More partitions are not automatically better. PostgreSQL’s partitioning guidance warns that excessive counts increase planning time and per-session memory use. A partition key should appear in common WHERE clauses, and the number and granularity of partitions should reflect retention windows, query locality and maintenance capacity.
Partitioning does not distribute writes across independent servers by itself. It organizes data within the PostgreSQL architecture; server-level write distribution requires a separate distributed design.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Vacuum, autovacuum and index health are scaling work
Updates and deletes leave dead tuples until VACUUM processes them. On frequently updated tables, accumulated dead tuples increase storage and can make scans and indexes less efficient. Regular vacuuming is therefore part of capacity planning, not an optional housekeeping task.
Recommended Free Tools
What to monitor
- Whether autovacuum is reaching hot tables frequently enough.
- Tables with growing dead-tuple counts or persistent bloat.
- Long-running transactions that prevent cleanup progress.
- Vacuum activity competing with user queries for I/O.
- Indexes whose cleanup is repeatedly skipped or delayed.
Global defaults are not always suitable. A table that receives far more updates than the rest of the database may need per-table autovacuum settings. Adjust carefully, observe the effect and preserve enough I/O and memory for foreground work.
When does PostgreSQL stop scaling?
There is no published universal users-per-server, queries-per-second or maximum-connection figure that applies to every PostgreSQL deployment. The practical limit is reached when one or more resources remain saturated after the workload is optimized:
- CPU is consumed by query execution, sorting, joins or encryption.
- Storage latency or throughput prevents reads and WAL writes from keeping up.
- Memory is insufficient for the working set, sorting or connection overhead.
- Connection queues grow despite pooling.
- Write latency, lock waits or transaction conflicts breach the application’s target.
- Replication lag exceeds the staleness the product can tolerate.
- Maintenance work cannot keep up with table churn and retention.
Measure these conditions with production-shaped traffic and representative data. A benchmark with a different query mix, cache state or durability configuration cannot establish a generic PostgreSQL limit.
Read replicas or sharding?
| Question | Prefer read replicas when… | Consider sharding or distributed PostgreSQL when… |
|---|---|---|
| Primary bottleneck | Reads consume capacity while writes remain manageable | Writes, storage or single-node resources remain limiting after optimization |
| Consistency | The application can tolerate lag on selected reads | Data ownership and cross-node consistency rules are explicitly designed |
| Data layout | Most queries can run against a full copy | Rows can be assigned by a stable key and queries are mostly local to a shard |
| Operations | You want a comparatively straightforward topology | The team can operate routing, placement, rebalancing and cross-shard behavior |
| Failure model | A standby improves failover readiness | The system needs capacity and fault domains beyond one primary’s limits |
The PostgreSQL wiki lists distributed approaches such as Citus that shard or replicate tables across servers and can parallelize queries. Those systems add routing, data-placement, cross-shard query, rebalancing and operational concerns. Treat them as a response to demonstrated limits, not as the first scaling step.
Windows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCrashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteQuick Recap
A step-by-step scaling playbook
- Measure the workload. Record read/write mix, peak concurrency, query latency, lock waits, storage behavior, dataset growth, retention and replica lag.
- Bound connections. Add or tune an application pool or PgBouncer, then set queue and timeout policies.
- Inspect plans. Use
EXPLAINand, where safe,EXPLAIN ANALYZE; refresh statistics and add or remove indexes based on observed access patterns. - Fix transaction shape. Remove redundant writes, shorten transactions, batch safe work and move secondary effects to queues.
- Strengthen maintenance. Verify autovacuum, analyze activity, dead tuples, index cleanup and long-running transactions.
- Add read capacity. Deploy asynchronous replicas for lag-tolerant reads; route consistency-sensitive reads to the primary. Use synchronous replication only for a defined durability requirement.
- Partition deliberately. Select a key used in common filters and retention operations; confirm partition pruning and keep partition counts manageable.
- Reassess the remaining limit. If one primary still cannot meet write, storage or latency targets, evaluate distributed PostgreSQL or sharding with explicit data-placement and cross-shard plans.
Decision guide for common application stages
| Situation | Next move | Reason |
|---|---|---|
| Moderate traffic and simple operations are the priority | Use one well-sized PostgreSQL primary with backups and monitoring | Avoids unnecessary topology and routing complexity |
| Connection setup or session count is the bottleneck | Introduce pooling and admission control | Shares fewer backend processes across more requests |
| Reads dominate and stale results are acceptable in some paths | Add asynchronous read replicas and route selectively | Increases read capacity while preserving one write authority |
| Large tables or retention deletes are difficult to maintain | Partition on a key used by filters and lifecycle operations | Enables pruning and faster partition-level retention work |
| Write, storage or single-node limits persist after tuning | Evaluate distributed PostgreSQL or sharding | Addresses limits replicas and indexes cannot remove, at higher complexity |
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
