The Tool Desk
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Do not start by deleting Linux caches or killing mongod. Those actions can hide the symptom, increase disk I/O, or interrupt writes without reducing the workload that requires memory.
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Why MongoDB appears to use all the RAM
MongoDB’s WiredTiger storage engine and the Linux kernel use different memory pools. WiredTiger allocates an internal cache for frequently accessed database pages. Linux also fills unused RAM with filesystem cache for recently read data. Both pools can make a VPS look “full” in tools such as free or a hosting dashboard.
MongoDB’s current v7.0 mongod documentation describes the default WiredTiger cache as the larger of 50% of (RAM − 1 GB) or 0.256 GB. That figure is only the internal WiredTiger cache limit; it is not a cap on the complete mongod process and not a promise that half of the machine’s RAM will remain free.
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The process also needs memory for connections, query execution, indexes, aggregation stages, journaling and other overhead. The kernel, SSH, monitoring, web servers, application workers and any second database instance need their own headroom. Linux may then use much of the remainder as filesystem cache. MongoDB explicitly notes that it can use free memory through this filesystem cache in addition to the WiredTiger cache.
“Used” is not the same as memory pressure
Look at the available column from free -h, not just used. Filesystem cache is generally reclaimable when another process needs it. A host with a large cache and healthy available memory can be operating normally, while a host with modest “used” memory but almost no available memory and active swap may be under real pressure.
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Measure before changing MongoDB
Take a baseline during representative workload. Record host memory, the resident size of mongod, WiredTiger cache usage, swap and disk activity. You need these values to choose a target and to tell whether a change helped.
Check host-level memory
free -h
swapon --show
top
ps -o pid,rss,vsz,cmd -C mongodfree -h reports total, used, free, shared, buffers/cache and available memory in human-readable units. Check whether swap is merely configured or is actively being used; a configured swap device is not proof that the system is paging. In top, sort by resident memory to identify other consumers. The ps command shows the resident set size (RSS) and virtual size for processes named mongod; RSS is the more useful first approximation of physical RAM occupied by the process.
Save the output with a timestamp, along with query latency and disk-I/O observations. A single quiet sample can understate the peak caused by reporting jobs, index builds or traffic bursts.
Inspect MongoDB’s resident memory and cache counters
Run serverStatus with an account that has permission to view server statistics:
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mongosh
use admin
db.serverStatus()In the returned document, record these fields when present:
mem.resident: approximately the RAM used by the database process, reported in mebibytes (MiB).wiredTiger.cache.maximum bytes configured: the configured WiredTiger cache ceiling in bytes.wiredTiger.cache.bytes currently in the cache: current bytes held in that cache.- WiredTiger dirty-byte fields and eviction counters: indicators of pending writes and pressure to evict pages.
MongoDB warns that available statistics can vary by release, so check the field names returned by your installed version rather than assuming every counter exists. A cache that is near its maximum, high eviction activity and increasing disk reads after a change indicate that the cache may have been reduced too far for the workload.
Check cgroup or container limits
If MongoDB runs in Docker, LXC, Kubernetes or another cgroup, the process may see less memory than the VPS itself. From mongosh, inspect:
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db.hostInfo()Compare hostInfo.system.memLimitMB with the VPS provider’s physical-RAM figure. MongoDB sizes its default cache against memory available to the process. Setting a cache based on the host’s RAM while the container has a smaller limit can leave no room for the process or its sidecars.
Understand what the cache setting does—and does not do
The WiredTiger setting controls one pool. In a YAML configuration it is commonly expressed as:
storage:
wiredTiger:
engineConfig:
cacheSizeGB: 0.5You can use cacheSizePct instead of cacheSizeGB, following the syntax documented for your installed MongoDB version. Neither option limits total mongod memory. Connections, operation working sets, index builds, internal structures, memory-mapped components where applicable, and the operating system remain outside that number.
MongoDB documents an effective lower bound beginning at 0.256 GB for configured cache values. Treat that as a documented minimum range, not as a recommendation for every deployment. A tiny cache can force frequent eviction and turn a memory problem into sustained disk I/O and higher latency.
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Calculate headroom instead of copying an example
First estimate the memory that must remain outside WiredTiger:
- the measured resident footprint of
mongodthat is not explained by the cache; - the operating system and filesystem-cache needs;
- your application, web server, workers, monitoring and SSH sessions;
- any second
mongodor other database; - a safety margin for traffic spikes, backups, index builds and maintenance.
Subtract those requirements from the memory limit visible to MongoDB. The remainder is the upper boundary for a trial cache value, and it should not be treated as a guaranteed free pool. Start with a modest reduction, observe the workload, and adjust only with evidence. The default assumes one mongod on a machine; multiple instances require their allocations to be planned together.
Reduce the WiredTiger cache safely
1. Confirm the active configuration
Find the configuration file used by the running service; on many Linux packages it is /etc/mongod.conf, but package and image layouts differ. Check the service definition or the process command line before editing. Make a dated backup of the file and note the current cache setting so rollback is immediate.
sudo cp /etc/mongod.conf /etc/mongod.conf.before-cache-changeDo not add a second conflicting storage.wiredTiger.engineConfig block. Edit the existing hierarchy and choose either cacheSizeGB or cacheSizePct, not both unless your version’s documentation explicitly permits that combination.
2. Choose a conservative trial value
Set a value that leaves room for the measured non-cache consumers. The following is only a syntax example; it is not a universal recommendation:
storage:
wiredTiger:
engineConfig:
cacheSizeGB: 0.5Do not blindly use 0.5 GB. On a larger VPS it may be unnecessarily restrictive; on a small container it may still be too large. Keep the value below the memory available to the container or cgroup, and account for the application and kernel before deciding.
3. Restart during a maintenance window
Changing the setting normally requires a mongod restart. Use the service manager appropriate to your installation, for example:
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sudo systemctl restart mongod
sudo systemctl status mongod --no-pagerSome container deployments restart through an orchestrator instead. Confirm that the service returns to a healthy state, accepts authenticated connections and has the expected replica-set or standalone role before sending production traffic to it. Keep the previous configuration available until validation is complete.
4. Verify after the restart
Repeat free -h, ps, db.serverStatus() and, where relevant, db.hostInfo(). Compare:
mem.residentand the RSS ofmongod;- the configured and current WiredTiger cache bytes;
- eviction work, dirty bytes and disk reads;
- available memory, swap activity and application latency.
A lower resident footprint with acceptable latency is the intended result. If eviction counters climb sharply, dirty bytes remain high, disk I/O saturates or queries slow, restore the prior value and investigate workload or capacity rather than repeatedly shrinking the cache.
Free memory used by competing services
Cache tuning cannot solve a VPS where several services collectively need more RAM than the machine provides. Use top or your process supervisor to identify non-MongoDB consumers, then remove or reconfigure only those you understand.
Stop unused or duplicate workloads
- Disable development servers, abandoned workers and scheduled build jobs that run on the database host.
- Reduce application-worker concurrency if it creates more simultaneous in-memory requests than the VPS can handle.
- Check for a second
mongod, test database or backup process using the same machine. - Schedule index builds, exports and reports outside peak periods; these operations can require temporary working memory and generate additional I/O.
MongoDB’s default cache assumption is effectively one database process per machine. If multiple instances are intentional, assign each a smaller cache and reserve memory for their combined overhead instead of allowing independent defaults to compete.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Should you clear Linux filesystem caches?
Usually, no. Linux may reclaim filesystem cache automatically when applications need memory. Manually dropping caches can make a dashboard’s “used” number fall for a short time, but it does not lower the WiredTiger limit or reduce the amount of data your workload needs. The kernel and MongoDB will refill the cache, often causing extra disk reads in the meantime.
Cache dropping can be a controlled diagnostic when investigating whether filesystem cache is masking a measurement, but it is not a production remedy. If you perform such a test, record the before-and-after latency and I/O, and do not interpret the temporary reduction as capacity that has been permanently recovered.
Use swap as an emergency cushion, not a sizing strategy
A small, fast swap area can provide time for an occasional memory spike and reduce the chance of an immediate out-of-memory kill. It does not create RAM. Sustained MongoDB paging usually produces high latency and indicates that the workload, cache allocation or VPS size is unsuitable.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAfter enabling or resizing swap, monitor swap-in and swap-out activity, query latency and disk utilization. If the system continuously pages, stop treating swap as a fix: reduce competing work, tune the cache based on measurements or move to a larger-RAM instance.
When a VPS resize is the correct fix
Choose a larger VPS or a separate database instance when the measured process, operating system, filesystem cache, application and safety margin still exceed available memory after reasonable cache tuning. MongoDB does not publish one universal RAM number because the correct size depends on dataset working set, connection count, indexes, query patterns, background jobs and co-located software.
A resize preserves more cache headroom and generally avoids the eviction and I/O penalty of an aggressively small cache. Before scheduling it, capture baseline values for resident memory, configured and current cache bytes, eviction activity, swap use, query latency and disk I/O. Repeat the same measurements afterward so the capacity decision is reviewable rather than based on a hosting-dashboard percentage.
Choose the remedy by trade-off
| Remedy | Immediate headroom | Performance effect | Persists after restart? | Operational risk | Cost |
|---|---|---|---|---|---|
| Reduce WiredTiger cache | Usually frees part of the internal cache allocation | More eviction and potentially more disk I/O | Yes, if saved in configuration | Requires restart and workload validation | No infrastructure cost |
| Stop or reconfigure competing services | Depends on the identified process | Can affect the stopped workload; may improve database stability | Only if service configuration is changed | Risk of interrupting an undiscovered dependency | No infrastructure cost |
| Drop filesystem caches | Temporary and often reclaimed quickly | Extra reads and possible latency spike | No | Diagnostic only; poor production remedy | No infrastructure cost |
| Add swap | Provides emergency virtual-memory cushion | Paging can be dramatically slower than RAM | Yes, when configured persistently | Can conceal an undersized VPS | Usually small storage cost |
| Resize VPS or separate MongoDB | Adds durable physical-memory capacity | Preserves cache and reduces eviction pressure | Yes | Migration, maintenance and configuration planning | Higher recurring infrastructure cost |
Troubleshooting common outcomes
The VPS still reports high memory after reducing the cache
Recheck mem.resident, RSS and wiredTiger.cache.bytes currently in the cache. The cache setting does not cap connections, indexes, operation memory or filesystem cache. Identify the largest remaining process with top and compare the cgroup limit if the database is containerized.
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The cache setting appears to have no effect
Confirm that you edited the configuration actually used by the running process, that the YAML indentation is valid and that the service restarted successfully. Query serverStatus after restart to see the effective maximum configured bytes. In a container, a mounted configuration file or orchestration manifest may override the host file.
Queries became slower after the change
Check eviction counters, dirty bytes and disk I/O against your baseline. A smaller cache may be evicting hot pages too frequently. Restore the previous value or choose an intermediate value, then reduce other consumers or resize the VPS instead of continuing to shrink WiredTiger.
MongoDB was killed by the operating system
Inspect kernel and service logs for out-of-memory events, then compare the event time with free -h, swap and application-worker activity. A cache reduction can help only if it leaves enough room for process overhead and the workload. If the combined requirement still exceeds the limit, add RAM or move the database.
A container behaves differently from the VPS host
Use db.hostInfo() and read hostInfo.system.memLimitMB. Size the cache against that limit, not the host’s advertised RAM. Also account for memory reserved by sidecars, agents and the container runtime.
Quick Recap
Operational checklist
- Record
free -h, swap status,toporpsoutput, MongoDBserverStatuscache fields and application latency. - Check
db.hostInfo()when a cgroup or container may impose a lower limit. - Estimate non-cache memory needs and leave explicit headroom for the kernel, filesystem cache, applications and maintenance.
- Back up the active configuration and make one conservative cache change.
- Restart through the correct service manager during a maintenance window.
- Repeat the measurements and watch eviction, dirty bytes, disk I/O, swap and latency under representative traffic.
- Roll back if performance degrades; remove competing consumers or resize the VPS if the workload still does not fit.
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