Scale PostgreSQL by matching each technique to the bottleneck it addresses: use read replicas to serve eligible read-only queries, connection pooling to reduce the number of database connections applications hold open, and caching or materialized views to avoid repeating expensive work. These tools complement one another, but none automatically increases write capacity or makes an inefficient query efficient.
The right design depends on what is slow, how fresh each result must be, and what behavior your application expects from a database connection. Start by identifying the constraint; then add only the mechanism that addresses it.
As an Amazon Associate I earn from qualifying purchases.
What scaling PostgreSQL means
A PostgreSQL application can become constrained in several different ways. Queries may take too long because they scan or compute too much. Many application clients may consume more database connections than the server can handle comfortably. Read traffic may compete with writes on one primary. Or repeated requests may ask for data that changes infrequently but is costly to calculate.
Crashes, 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 minutePC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Those problems call for different responses. A replica can serve suitable reads, but it does not accept writes and may not yet contain the primary’s latest commit. A pooler can reuse database connections, but it does not reduce the work performed by a query. A cache can avoid repeated work, but introduces freshness and invalidation decisions. Measure the actual constraint before choosing among them.
#1 Best Overall
- [Ultra-Lightweight Ear Buds Designed for Small Ears] Each earbud weighs only 3.7g and features a compact, ergonomic in-ear design made especially for small ears. Secure, low-profile, and comfortable for workouts, all-day wear or overnight sleeping.The ultra-slim profile sits flush in your ear with no hard edges poking or rubbing, so you can even sleep on your side without discomfort.
- [Immersive Stereo Sound with TOZO OrigX Technology] TOZO OrigX tuning delivers clear vocals, balanced mids, and natural stereo sound for music, podcasts, and videos.
- [Long Battery Life for Daily Use] Get up to 7 hours of playtime on a single charge, with up to 32 hours total using the charging case—ideal for workdays, commuting, and extended listening sessions.
- [Bluetooth 5.3 & Stable Connection] Bluetooth 5.3 provides fast pairing, stable wireless performance, and reduced dropouts as you move around home or office.
- [Deep Bass with Clear Vocals] High-performance drivers produce punchy bass while keeping vocals clean and detailed for everyday listening.
- Read pressure: assess whether read-only requests can be routed to a standby without violating freshness requirements.
- Connection pressure: check how many database connections are open, how long they remain occupied, and whether clients are waiting for a connection.
- Repeated computation: identify expensive results that are requested repeatedly and determine how stale they may be.
- Write pressure: replicas and result caches do not make the primary accept more writes. Write throughput requires separate investigation of the write workload and its constraints.
There is no universal performance gain or ideal configuration in PostgreSQL’s manuals for these mechanisms. Their effect depends on the workload, network, deployment, and application behavior.
How PostgreSQL read replicas work
Streaming replication and freshness
In streaming replication, PostgreSQL sends write-ahead log (WAL) records from a primary server to standby servers as the records are generated. The standby replays those records to bring its data forward. PostgreSQL 18 documentation describes streaming replication as asynchronous by default: a transaction can commit on the primary before its changes are visible on a standby.
PostgreSQL’s documentation says the delay is typically under one second when the standby is powerful enough to keep up. That is qualified guidance, not a guarantee for every workload or network. A standby can lag further if it cannot receive or replay WAL quickly enough, or if replication is interrupted.
This creates an important application-level rule: a successful write followed immediately by a read from a lagging standby may return the old value. Keep read-after-write operations on the primary, or use an application consistency strategy that ensures the selected standby has caught up sufficiently. Send only requests whose freshness tolerance is known to the replica route.
What a hot standby can and cannot do
A standby running in hot standby mode can accept read-only queries while it replays WAL. It cannot take over as a second writable primary in ordinary replica operation. PostgreSQL 17’s Hot Standby documentation says these connections are strictly read-only; even temporary tables cannot be written.
Replica reads are most appropriate when the query is read-only, can tolerate the standby’s freshness boundary, and does not interfere unacceptably with WAL replay. They may help distribute eligible read load, but they do not distribute writes. Sending every SELECT to a replica is not automatically safe: some reads need the primary’s latest committed state, while others may be suitable for a standby.
Rank #2
- 【IP68 Waterproof Multifunctinal Smart Watch】Torjalph fitness activity tracker smart watch is IP68 waterproof technology ,you can wear it when swimming,handwashing,it's rain-proof which means that you perform multiple exercise modes outside without worrying about sweat or rain corroding your fitness smart watch,and this watch equipped with a 230 mAh large capacity battery magnetic charging ,and this sports activity tracker watch can be used 5-7 days If fully charged.
- 【Larger 1.83'' Full Screen Watch & Multi-dial Selection】1.83 inch full HD color touch screen smart watch for men / women provides excellent 240*284 resolution image quality ,This Bluetooth smart watch offers a better touch experience and remarkable immersion and ,more than 200 watch faces for you to choose from App"Da Fit ", you can change the dial style according to your mood to wear keeping the world on your wrist
- 【Accurate Activity Tracker Smart Watch 】sports watch activity tracker uses sweat and dust resistant breathable silicone strap, our Fitness Tracker Smart Watch for Android iOS Phones supports 12 sports modes,walking,running,bicycle,swimming,badminton,basketball,football,rope skipping. This bluetooth calls smart watch for men women is lightweight and stylish, bringing more convenient and relaxing sports experience while also providing more protection.
- 【Your 24 Hour Personal Daily Health Monitor】Fitness watch for women men full touch screen IP67 waterproof bluetooth smartwatch for Android iOS Phones with heart rate blood pressure monitor multifunction Andriod smart watch with steps counter sleep monitor sports fitness watch, monitoring blood oxygen saturation allows you to understand your physical condition in real time to track fitness progress ,to know your health status in time.
- 【More Functions Bluetooth Call Smart Watch】This fitness activity smart watch full screen for iPhone and Android phones includes a bunch of new features including:The bluetooth smart watch can hang up the phone, receive and display WhatsAPP /Text /Facebook /Twitter /Messenger reminder.Support sedentary reminder, alarm clock, stopwatch, timer,weather information,music control, remote camera control compatible with Android 5.0 and above or iOS 9.0 and above smartphones.
Long queries, replay conflicts, and recovery
WAL replay on a standby can conflict with a long-running read query—for example, when replay needs to apply changes that conflict with what the query is examining. Depending on the situation and standby configuration, the read may be canceled so replay can proceed, or replay may be delayed to give the query time to finish. These are operational trade-offs: allowing more delay can preserve a query but leave the standby further behind; canceling queries can disrupt reports or other long reads.
Recommended Free Tools
Test the actual query mix and observe query cancellations, replay delay, and replication lag. If long reports are important, consider whether they need a standby, whether they can be bounded or scheduled differently, and how much freshness delay is acceptable. A replica that appears healthy under short queries may behave differently during long-running reports.
WAL retention is part of replica reliability. If a standby falls behind after the WAL it needs has been recycled, it may have to recover from a WAL archive or be reinitialized. Plan for archiving, retention or replication slots, disk capacity, monitoring, and a tested recovery procedure. A replica is not a backup: it follows primary changes and does not replace independent backup and restore planning.
Asynchronous versus synchronous replication
Asynchronous replication lets the primary commit without waiting for a standby to confirm receipt or replay of the change. This reduces commit coupling to standby and network responsiveness, but allows the standby to lag. If the primary fails and a lagging standby is promoted, some acknowledged writes may not be present on that standby.
Synchronous replication can make a commit wait for confirmation from a standby. That can improve protection against losing acknowledged data in a failover scenario, depending on the chosen synchronous settings and failure scenario, but adds latency and makes commit availability more sensitive to the standby and network. PostgreSQL 16’s high-availability documentation frames these as trade-offs; it does not prescribe a universal setting or performance figure.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →| Choice | Commit behavior | Failover and availability trade-off | When to consider it |
|---|---|---|---|
| Asynchronous replication | The primary does not wait for standby confirmation before committing. | Can leave a lag window; promotion of a lagging standby can lose some acknowledged writes. Commit behavior is less coupled to standby or network availability. | When lower commit coupling is important and the service can tolerate the associated failover loss window. |
| Synchronous replication | A commit can wait for confirmation from a standby, according to the configured synchronous behavior. | Can improve acknowledged-write protection, while increasing latency and sensitivity to standby or network availability. | When the service’s durability and failover objectives justify the added commit dependency. |
The choice depends on service objectives, network conditions, standby placement, and workload. Decide what acknowledged-write loss is acceptable and what commit delay or loss of availability is tolerable; do not select a mode solely because it sounds safer or faster.
Rank #3
- [Low Noise High Fidelity] New bee guitar cord with 22AWG oxygen-free-copper center conductors provides enhanced signal transfer, the PVC insulation material and the high-density stranded copper double shield provide noise rejection for quiet operation
- [Ideal for Choice] New bee 1/4 straight and right angle metal connectors, ensures a secure connection in any instrument, pedal, or amp jack. L shape great fro on stage guitar stands
- [Wide Compatible] Suitable for mixer, power amplifier, microphone, effector, decoder, equalizer, electoronic organ, electric guitar, electric bass and other electronic equipments. Widely used in the installation of smart home, karaoke speaker, professional stage audio, etc
- [Easy Disassemble] Making incidental repairs quick and easy! Very nice, flexible cable
- [Dont Hesite to Order] 24 hours customer services and professional technology team are standing by
Route reads according to freshness needs
Replica use is a routing decision as much as a database decision. Separate the application’s read paths by consistency requirement rather than treating all reads as interchangeable.
- Identify read-after-write paths. Requests that must show a user the change they just made should read from the primary or use a consistency mechanism that proves the replica is caught up.
- Classify other reads. Determine which pages, reports, or background jobs can tolerate delayed visibility, and how much delay is acceptable.
- Direct only eligible traffic. Route eligible read-only queries to a standby through application logic, service discovery, or a routing layer. A pooler alone does not perform replica-aware query routing.
- Observe both sides. Monitor replica lag, primary and standby load, query duration, cancellations, and connection behavior. A replica route is useful only while it meets the workload’s freshness and reliability needs.
- Exercise failure and recovery. Test what happens when a standby falls behind, becomes unreachable, needs reinitialization, or is promoted. Ensure the application changes destinations safely and does not continue sending writes to a read-only standby.
PostgreSQL’s streaming replication documentation describes replication mechanics, not a deployment-specific load-balancing or failover solution. The application or another routing and high-availability component must provide the behavior needed by the service.
What connection pooling solves
Opening and maintaining a database connection for every application client can use database resources inefficiently, especially when the number of clients is much larger than the number of connections the database should serve concurrently. A connection pooler allows many client connections to share a managed set of PostgreSQL server connections over time.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Pooling is not a query accelerator by itself. It can reduce connection churn and limit the number of server connections in use, but it does not shorten a slow transaction or make the database perform less work for a query. If server connections remain occupied by long transactions, a pooler can still leave clients waiting. Observe waiting clients, active server connections, transaction duration, and database saturation before and after rollout.
Choose a PgBouncer pooling mode
PgBouncer offers session, transaction, and statement pooling. The critical difference is how long a client retains a particular PostgreSQL server connection. That changes which session-dependent features an application can safely use.
| Mode | When a server connection is reserved | Application compatibility and trade-off |
|---|---|---|
| Session pooling | For the duration of the client session. | Supports PostgreSQL features that rely on a stable session connection, but provides less opportunity to share each server connection across clients than transaction pooling. |
| Transaction pooling | For a transaction; the server connection is returned to the pool when that transaction ends. | Can reuse server connections efficiently across clients, but breaks some session-based features because the next transaction may use a different backend. |
| Statement pooling | For a query; the connection is returned after the statement. | Disallows multi-statement transactions, so applications must be compatible with that constraint. |
PgBouncer’s feature documentation warns that transaction pooling breaks some session-based PostgreSQL features. Its compatibility table marks items such as SET/RESET, LISTEN, session-level advisory locks, and PREPARE/DEALLOCATE as incompatible in transaction pooling. Prepared-protocol support has specific conditions, so check the current compatibility documentation against both your PgBouncer version and application driver.
Rank #4
- Digital Family Calendar: BSIMB digital calendar wall touch screen puts an end to your chaotic life. Designed for busy families, the digital calendar keeps you organised and in step with your family. Assign different colours and tasks to each family member to make family scheduling easier and more intuitive.
- Easy Setup and Auto-Sync: Plug in, connect to WLAN and auto-syn your electric calendar in minutes. Digital wall calendar compatible with Google calendar, iCloud calendar, Outlook calendar, Cozi calendar and Yahoo calendar. Add events directly on the device or through the ecalendar app. Electronic calendar stays current whether you're at work or traveling—no manual updates needed.
- All-In-One Home Assistant: Smart calendar builds habits and encourage independence with personalised chores. Display daily meal plan to reduce the daily ‘What's for dinner?’ stress. To-do lists keep track of everything you want. Turning chores into valuable achievements with stars and rewards, making life easier and more enjoyable.
- Manage Anytime Anywhere: BSIMB electronic calendar for family stay connected with the powerful ecalendar app. Access and manage your digital wall calendar anytime, anywhere with free features like stars & rewards, photo screensaver, chore charts, meals plans, to-do lists etc. Electronic planner paid features like Magic Import tailor advanced tools for users with special needs.
- Stay in Touch with Loved Ones: Share photos or videos from your phone to electronic wall calendar with app, instantly update your life moment, and keep in touch with your family, especially those who are far away; you can also surprise your smart calendar digital wall by sending him a wish card; it's also great for keeping tech-illiterate elders in touch with the youngsters.
Check the application before enabling transaction pooling
- Inventory connection-scoped state used by the application, including session settings, listeners, advisory locks, and prepared statements.
- Verify each feature against PgBouncer’s compatibility information for the version you plan to run; do not assume that behavior under direct PostgreSQL connections carries over.
- Confirm how the driver and application create, reuse, and close connections, and whether any state must persist across transactions.
- Test representative workflows under the intended pooling mode, including errors, retries, transactions, and concurrent requests.
- Roll out with monitoring for client waiting, server connection use, transaction length, and application errors; have a path to revert the mode if behavior is incompatible.
Session pooling is the more compatible choice when an application relies on a persistent database session. Transaction pooling can be a better fit when the application is designed to avoid backend-specific session state and needs server connections to be shared across clients. Statement pooling is appropriate only when its single-statement transaction constraint fits the application.
Free tools Windows power users keep installed
One-click scans. No signup required.
Size and operate the pool responsibly
Pool sizes are limits, not performance targets. The official PgBouncer configuration page reviewed for this article lists defaults of 20 for default_pool_size and 100 for max_client_conn. These are configuration defaults, not recommendations for a particular service; defaults may vary with configuration or version.
Set a server-connection budget across all application instances and pools, not just in one PgBouncer configuration. If each application instance can open its own pool, multiplying a per-instance limit by the number of instances can exceed what the database should handle. The cited configuration documentation does not establish a correct pool size for a specific workload.
- Waiting clients: rising wait times can indicate that the pool is exhausted, transactions are holding connections too long, or the database is saturated.
- Active server connections: compare actual use with the overall connection budget across all pools and services.
- Transaction duration: long transactions retain server connections in transaction pooling and can reduce how many clients the pool serves.
- Database saturation: increasing pool limits may move the queue into PostgreSQL rather than improve throughput.
- Deployment topology: account for all application replicas, background workers, administrative connections, and separate pools when calculating the total.
PgBouncer is a connection pooler, not a complete routing or failover layer. Its FAQ says it has no internal multi-host configuration or failover-host detection. If the service needs routing between replicas or automatic response to promotion, supply it through application logic, service discovery, or another routing/high-availability layer, and test how existing connections behave during a promotion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.PostgreSQL caching: pages versus query results
PostgreSQL benefits from more than one kind of cache. shared_buffers is memory PostgreSQL uses for shared database buffers, while the operating system also caches file data. These mechanisms help reuse database pages; they are different from caching a query’s result, which avoids repeating some or all of the query computation.
PostgreSQL 18’s resource-consumption documentation gives 25% of system memory as a reasonable starting point for shared_buffers on a dedicated database server with at least 1 GB of RAM. It also says allocating more than 40% of RAM is unlikely to work better in typical circumstances. These are official starting points, not guaranteed optima: memory available to the operating system and other processes, workload, and observed behavior still matter. Do not copy a ratio without checking the server’s role and measuring its behavior.
| Technique | What it reuses | Freshness and operational responsibility |
|---|---|---|
| Shared buffers and operating-system cache | Database pages or file data, reducing the need to fetch them from storage repeatedly when they remain cached. | PostgreSQL and the operating system manage page reuse; this does not by itself store a precomputed query result or remove query computation. |
| Materialized view | A persisted result of a defined query, read in table-like form instead of rerunning the underlying query each time. | The application or operator decides when to refresh it and what staleness is acceptable; refreshing regenerates the stored result. |
| Application-level result cache | A result retained outside the database for reuse by the application. | The application must define freshness, invalidation, behavior during cache failure, and protection against a burst of simultaneous cache misses. |
Use materialized views for refreshable results
A PostgreSQL materialized view stores the result of a query in table-like form. Readers can query the stored result rather than recomputing the underlying query for every request. PostgreSQL 17 documents REFRESH MATERIALIZED VIEW as the operation that regenerates the result.
This is useful when a costly result is read repeatedly and its data can be refreshed on a schedule that meets the application’s freshness requirement. It is not transparent, continuously updated caching: the result reflects the data available at the last refresh. Decide when refreshes run, what happens if one takes longer or fails, and whether users can accept the age of the stored result. The documentation does not establish a universal refresh interval or performance benefit.
- Identify a repeated query whose result is expensive to compute and whose source data changes at a rate compatible with a refreshable snapshot.
- Define the maximum acceptable age of the result before creating a refresh schedule.
- Create and test the materialized view using the PostgreSQL version and query behavior in your deployment.
- Schedule
REFRESH MATERIALIZED VIEWat a cadence that meets the freshness requirement, then monitor refresh completion and result age. - Ensure the application can handle stale or unavailable results in the way your service requires.
When an application cache is appropriate
An application cache can be useful when repeated requests need the same result and the application can make explicit choices about its lifetime and invalidation. It is not a substitute for a freshness policy. Before adding one, answer these questions:
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches- Freshness: how old may the result be for each read path?
- Invalidation: what event or time limit makes the cached result unusable after source data changes?
- Concurrent misses: if many requests miss the cache at once, how will the application avoid sending a burst of identical expensive queries to PostgreSQL?
- Cache outage: should requests fall back to the database, return an error, or use an older value, and can the database handle that fallback load?
- Consistency: how will a write and subsequent read behave if the cache still contains a previous result?
The appropriate external cache product or strategy depends on the application and is not specified by PostgreSQL’s database documentation. Treat cache failure and invalidation as part of the design rather than assuming the cache is always present and current.
A practical order for scaling changes
- Establish the bottleneck. Determine whether the service is limited by query work, read contention, connection use, writes, or repeated computation. Do not infer the cause from a rising user count alone.
- Fix the query or workload first when it is the cause. A replica or pooler does not make an inefficient query efficient. Avoid using caching to hide a query problem when the same work must still happen for each cache miss or refresh.
- Pool connections if connection management is the constraint. Choose a pooling mode only after checking application session behavior and calculating the total server-connection budget across instances.
- Add a replica for eligible read traffic. Define which reads may be stale, keep read-after-write paths consistent, and build monitoring and recovery around WAL replay and retention.
- Cache repeated results only with a freshness plan. Use a materialized view when scheduled recomputation meets the requirement, or an application cache when the application can manage invalidation and failure behavior.
- Re-evaluate after each change. Observe query latency, connection waits, database saturation, replication lag, cancellations, and result freshness. A change that shifts a bottleneck is not proof that the new layer solved the underlying capacity issue.
The version references here are deliberately scoped: streaming replication and resource-consumption guidance are from PostgreSQL 18 documentation; hot standby and materialized-view behavior are from PostgreSQL 17; the conceptual synchronous/asynchronous trade-offs are framed by PostgreSQL 16 high-availability guidance. PgBouncer feature and configuration documentation can evolve. Check the documentation for the exact PostgreSQL and PgBouncer versions deployed before relying on version-specific behavior.
Quick Recap
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.
