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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →MongoDB performance depends on how well your active data stays in memory and how quickly storage can serve the reads and writes that do not. You do not need enough RAM to hold the entire database: WiredTiger evicts less-used pages as its cache fills, while the operating system uses available memory for filesystem caching. If frequently used data is repeatedly evicted, storage latency and I/O become more important.
How MongoDB uses RAM and disk
WiredTiger uses an internal cache, and the operating system uses remaining available memory as a filesystem cache. When hot documents and indexes are cached, MongoDB can avoid physical reads. If a requested page is not cached, MongoDB may need to read it from storage; the resulting delay depends partly on the storage device and its workload.
MongoDB documents a default WiredTiger cache size of the larger of 50% of (RAM minus 1 GB) or 0.256 GB. This default assumes one mongod process on the machine. With multiple instances, containers, or other services, allocate less to WiredTiger as needed so the host has memory for filesystem caching and other work. MongoDB’s production notes describe the cache and filesystem-cache behavior.
Does the whole working set need to fit in memory?
No. The working set—the data and indexes used actively by the workload—does not have to fit entirely in RAM. WiredTiger evicts pages when it needs cache space. If the workload later needs an evicted page, it may have to be fetched from storage, increasing I/O and potentially latency. A larger effective cache can reduce that traffic for read-heavy workloads, but the benefit depends on which data is accessed and how often.
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MongoDB’s self-managed diagnostics FAQ explains that WiredTiger evicts pages to make room when its cache cannot load additional data. Eviction is normal; the concern is whether eviction and subsequent storage reads are affecting the workload.
When an SSD or faster storage helps
Storage performance matters when requested data is not already cached, and during write durability work. MongoDB’s production notes recommend SSD storage when available and economical, and say SATA SSD has delivered good results and price-performance. They identify RAID-10 as the preferred performance-oriented layout. These are general recommendations, not a guarantee that an SSD or a particular RAID layout will improve every workload.
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The same guidance notes that separate devices for data, journal, and logs can benefit some access patterns. Remote filesystems can be slower and may degrade performance. Consider storage capacity and endurance, durability needs, failure domains, and total cost alongside random-read latency, sustained writes, and IOPS under load.
How to tell whether RAM or storage is the bottleneck
Start with a baseline for normal operation, including the time of day and workload. Use serverStatus, including memory and wiredTiger.cache statistics, together with operating-system storage metrics. Look for trends rather than treating one counter in isolation.
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- Evidence pointing toward memory pressure: rising page faults, increasing cache eviction, growth in data changed but not yet written to disk, or signs that the frequently accessed working set is outgrowing the effective cache.
- Evidence pointing toward storage pressure: elevated read or write latency, persistent queue depth or IOPS saturation while cache behavior is acceptable, or journal and checkpoint activity limited by storage performance.
- Evidence to investigate beyond hardware: slowdown that does not track cache or storage pressure. Check CPU, concurrency, schema, indexes, and query plans before assuming more RAM or faster disks will solve it.
MongoDB’s monitoring documentation covers server statistics. Compare measurements before and after one change under a representative workload; MongoDB does not prescribe a universal RAM-to-IOPS ratio.
Should you add RAM or increase IOPS?
| Observed condition | More RAM is the stronger candidate | Faster storage or more IOPS is the stronger candidate |
|---|---|---|
| Frequent eviction or working-set pressure | Consider adding RAM when hot indexes and documents are repeatedly evicted or page faults are rising. | Not the first change if cache pressure is the main constraint. |
| Storage latency and saturation | Additional RAM may help if cache misses are driving the reads, but check cache evidence first. | Consider faster SSD or more provisioned IOPS when read/write latency, queue depth, or IOPS saturation remain high despite acceptable cache behavior. |
| Journal, checkpoint, or sustained-write constraint | More RAM is not automatically the fix. | Assess write latency and journal/checkpoint behavior; storage performance may be the limiting factor. |
| Slow queries without clear memory or storage pressure | Not established as the remedy by those symptoms alone. | Not established as the remedy by those symptoms alone; inspect query plans, indexes, CPU, and concurrency. |
MongoDB’s 2019 hardware best-practices article says additional RAM and disk IOPS commonly provide the highest performance benefit. That is a prioritization, not a universal upgrade rule or a promised performance gain. Benchmark your actual workload after each change.
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Linux readahead considerations
For WiredTiger on Linux, MongoDB recommends a readahead setting between 8 and 32. Database access is generally random, so higher readahead may provide little value and can hurt performance. Treat this as a storage-tuning consideration rather than a substitute for diagnosing cache pressure or saturated I/O. See the MongoDB production notes on readahead.
Quick Recap
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