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Find Valkey latency by measuring three layers in order: the application and network path, Valkey’s own latency events and commands, and the host’s CPU, memory, storage, and scheduling behavior. At the same time, read used_memory, used_memory_rss, allocator fragmentation, peak usage, eviction counters, swap activity, and persistence state together. That sequence distinguishes slow command execution from round trips, fork pauses, swapping, allocator-retained pages, and genuine memory pressure before you change limits or workload behavior.
1. Define the latency objective before changing a setting
There is no universal Valkey latency threshold. A service that requires sub-millisecond reads needs a different alert boundary from one that accepts occasional delays of several seconds. Start with the application’s service objective and record the operations affected, the incident time window, and a latency distribution such as p50, p95, and p99.
- Identify which commands or endpoints are slow, not just that the server is slow.
- Record whether the issue is a sustained increase, a periodic spike, or a one-time pause.
- Capture the client location, Valkey node, persistence activity, memory state, and host load for the same time window.
Use that objective to choose the latency-monitor threshold later. A threshold is useful only when it represents blocking time your application actually considers a problem.
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2. Measure latency at the client, Valkey, and host layers
Compare application observations with a CLI round-trip test
Run a simple latency probe from a location representative of the affected clients:
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valkey-cli --latency -h <host> -p <port>The probe sends PING round trips. Its result includes client scheduling, network transit, host scheduling, and Valkey processing, so it is not a pure command-execution benchmark. Run it from more than one relevant network location when geography or service topology could be involved. A high application latency with a normal CLI result points you toward client behavior, command patterns, or a path-specific network problem; a high result from every location points more strongly toward the node or its host.
Measure intrinsic host latency on the Valkey server
If the node is a virtual machine or may be delayed by host scheduling, run the intrinsic-latency test on the Valkey host itself:
valkey-cli --intrinsic-latency 100The argument is a duration in seconds. This test is CPU intensive and can saturate a core, so schedule it carefully on production systems. It measures a host baseline rather than Valkey command latency. A large intrinsic-latency result indicates that CPU scheduling, virtualization, or the operating system can create pauses even when Valkey is not executing an expensive command.
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| Observation | Most useful next check |
|---|---|
| Application and CLI latency are both high, while intrinsic host latency is also high | Inspect CPU contention, virtual-machine scheduling, swapping, and operating-system pressure. |
| Application and CLI latency are high, but intrinsic host latency is normal | Inspect Valkey command events, persistence and fork events, network round trips, and client behavior. |
| Application latency is high while the CLI probe is normal | Compare client locations and workloads; investigate client-side queuing, connection use, command batching, and a path-specific network issue. |
3. Enable Valkey’s latency monitor and inspect event history
Set a threshold tied to the service objective
Latency monitoring is disabled by default because latency-monitor-threshold is zero. Set it in milliseconds to the blocking time that matters for your application. For example:
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CONFIG SET latency-monitor-threshold 100With a 100-millisecond threshold, only events exceeding 100 milliseconds are recorded. Replace that value with the objective you established; do not treat 100 milliseconds as a universal recommendation.
Use the event views in increasing detail
LATENCY LATEST
LATENCY HISTORY <event>
LATENCY GRAPH <event>
LATENCY DOCTORLATENCY LATESTshows the latest sample for each event type.LATENCY HISTORY <event>returns the time series for one event.LATENCY GRAPH <event>renders an ASCII graph that makes spikes easier to see.LATENCY DOCTORproduces a human-readable analysis and can suggest examining the slow log.
Event names distinguish different causes, including command, fast-command, fork, persistence activity, expiration, eviction, and active defragmentation. The current documentation describes a series with 160 elements; during continuous spikes from the same event it retains at least 160 seconds, and spikes in the same second are merged at the maximum. Treat those as implementation details of the documented version, not as a retention guarantee for every Valkey release.
Follow up on what the monitor identifies
If the doctor report points to a command, inspect command-level data with SLOWLOG and INFO commandstats. If it points to fork or persistence, correlate the timestamp with background saves or AOF rewrites and inspect latest_fork_usec. If it identifies expiration, eviction, or deletion, look for large values being removed in one operation. Deleting, expiring, or evicting a large object can block the server; where application semantics allow, split frequently removed large objects into smaller ones.
4. Check Valkey memory usage with the right fields
Collect all relevant INFO sections
INFO MEMORY
INFO STATS
INFO PERSISTENCE
INFO COMMANDSTATS
INFO LATENCYSTATSThe INFO command returns server information and statistics in a format that is easy for both computers and people to read. Save the output during the incident so you can compare it with a healthy period.
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| Field or section | What it tells you | How to use it |
|---|---|---|
used_memory |
Memory allocated by Valkey. | Use as the starting point for dataset and overhead calculations, not as a measure of resident pages. |
used_memory_dataset |
The dataset portion of Valkey’s memory. | Compare it with overhead to understand how much memory is consumed by data versus server structures. |
used_memory_overhead |
Valkey’s non-dataset overhead. | Include it when sizing limits; a dataset-only estimate leaves no room for administration structures and buffers. |
used_memory_rss |
Resident pages currently held in physical memory. | Compare with used_memory, peak usage, allocator fragmentation, swap, and host pressure. |
used_memory_peak |
The highest Valkey allocation observed. | Use it to judge whether today’s RSS reflects a previous workload peak. |
| Allocator fragmentation fields | How allocated chunks and resident pages differ inside the allocator. | Use with RSS and peak values to distinguish retained allocator pages from growth in live data. |
maxmemory context |
The configured memory boundary and related accounting. | Compare the limit with available host RAM and the eviction formula, not with used_memory alone. |
evicted_keys in INFO STATS |
The cumulative number of keys removed by the eviction policy. | Read with hit/miss behavior and policy before deciding that eviction is acceptable. |
INFO PERSISTENCE copy-on-write fields |
Memory effects associated with background persistence. | Correlate changes with RDB saves and AOF rewrites. |
INFO COMMANDSTATS and INFO LATENCYSTATS |
Per-command cost and latency distributions when latency tracking is available. | Find command types that consume disproportionate time or show high tail latency. Field availability depends on the deployed Valkey version and configuration. |
Understand why RSS can remain high
Deleting keys reduces live allocations, but an allocator may keep freed chunks for reuse instead of returning pages to the operating system. Consequently, RSS can remain high after a past peak without proving a memory leak. A large RSS-to-used_memory ratio is meaningful only alongside used_memory_peak, fragmentation fields, subsequent write behavior, and host pressure. If new writes reuse retained pages and live allocations remain stable, the high RSS may be allocator retention rather than a growing dataset.
The reverse relationship is also important: when used_memory is greater than RSS, some Valkey memory may have been swapped out. Swapped pages must be read from storage before use, and that I/O can create severe and irregular latency. Check host swap-in and swap-out activity, storage latency, and overall memory pressure at the same time.
5. Verify eviction accounting and policy
Apply the documented eviction formula
Valkey evaluates the boundary as:
used_memory - mem_not_counted_for_evict > maxmemorymem_not_counted_for_evict includes replication and AOF buffers. Therefore, used_memory by itself can make the server appear closer to the eviction limit than the amount actually counted. Valkey checks and applies the policy as commands add memory, and a large operation can temporarily exceed the configured boundary.
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During an incident, capture maxmemory, mem_not_counted_for_evict, evicted_keys, hit and miss behavior, and the command that was allocating data. An eviction counter that rises while hit rate collapses is a workload symptom, not merely a memory-setting symptom.
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Choose a policy from data semantics
For a cache with a strong popularity skew, the documentation identifies allkeys-lru as a reasonable general choice. A workload with uniform or cyclic access may fit another policy better. Changing policy changes which data survives; validate the effect on hit rate and misses before applying a live change. Do not use an eviction policy as a substitute for enough headroom when the data is not disposable.
6. Size memory for peaks, persistence, and overhead
Leave RAM below the Valkey limit
Set maxmemory below the host’s available RAM. The difference must cover Valkey overhead, allocator fragmentation, replication and AOF buffers, the operating system, and other processes. A limit equal to physical RAM leaves no space for those requirements and increases the chance of swapping or an operating-system out-of-memory response.
Account for copy-on-write peaks
During an RDB save or AOF rewrite, Valkey forks a child process. A write-heavy workload can use up to twice its normal memory while that child runs, because modified pages may be copied. The actual increase depends on how many pages are changed during the operation. Size for this peak, not just steady-state used_memory, and correlate the timing with INFO PERSISTENCE and latest_fork_usec.
Check paging and transparent huge pages
Swapping is a direct latency risk: a command that touches a swapped page can block while the operating system reads it from disk. Inspect swap and I/O before lowering or raising Valkey limits. Transparent huge pages can also contribute to latency and memory increases around fork. Check the operating-system configuration for the deployed environment and follow the guidance for the specific Valkey release and platform.
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7. Isolate command, network, persistence, and workload causes
Find expensive or blocking commands
Use SLOWLOG, INFO COMMANDSTATS, latency events, and command complexity together. A command can be slow because its algorithm scales with the size of a collection, because it returns a large payload, or because it removes a large object in one step. Inspect the data structure and cardinality before changing server limits.
Do not run KEYS against a large production keyspace. It can block the event loop while scanning the entire keyspace. When you need iteration, use incremental commands such as SCAN, SSCAN, HSCAN, or ZSCAN, and process each batch without assuming that one call returns a complete or stable snapshot.
Reduce avoidable network round trips
Each client-server round trip adds transit and scheduling time. A sequence of small requests can therefore produce high application latency even when each individual command is fast. Where command semantics permit, use pipelining or an aggregate operation to reduce round trips, then verify that response size and server-side work remain acceptable.
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Inspect LATENCY HISTORY fork and persistence-related events alongside latest_fork_usec and the timing of BGSAVE or an AOF rewrite. Durability settings trade latency and disk pressure against persistence behavior. Do not change fsync or durability settings during an incident without documenting the data-loss implications and confirming that the deployment can accept them.
Investigate expiration, eviction, and deletion spikes
Expiration, eviction, and deletion are normally distributed over time, but removing a very large value can create a synchronous pause. Use event names in the latency monitor to determine whether these operations coincide with spikes. If they do, redesign the value layout or removal pattern where application semantics allow, rather than assuming that more RAM alone will remove the pause.
8. Use a controlled incident sequence
- Freeze the time window. Record affected operations, client locations, p95 and p99 latency, error rate, and the start and end of the spike.
- Run layered probes. Compare application measurements,
valkey-cli --latencyfrom representative clients, and--intrinsic-latency 100on the Valkey host. - Turn on event capture if it is off. Set a threshold that matches the service objective, then collect
LATENCY LATEST, the relevantHISTORY,GRAPH, andDOCTORoutput. - Snapshot memory and counters. Save
INFO MEMORY,INFO STATS,INFO PERSISTENCE,INFO COMMANDSTATS, andINFO LATENCYSTATSoutput at the same timestamps. - Classify the memory state. Compare live allocations, RSS, peak usage, fragmentation,
mem_not_counted_for_evict, eviction counts, and host swap activity. - Match the event to a cause. Use slow-log and command statistics for command events; fork and persistence fields for background-save events; host metrics for intrinsic-latency or swap symptoms; and client/network traces for round-trip symptoms.
- Make one reversible change. Examples include reducing round trips, replacing a blocking iteration command, changing batch size, or moving a persistence operation. Record the expected effect before applying it.
- Re-measure after the change. Confirm application tail latency, event history, memory peak, eviction and hit/miss behavior, and host pressure. Keep the change only if the service objective improves without creating a durability or correctness problem.
9. Compare operating choices with the metrics that matter
When deciding whether to tune a self-managed node, change workload behavior, or evaluate a different operating arrangement, compare the same dimensions rather than relying on a single latency number.
| Decision dimension | Evidence to compare |
|---|---|
| Tail latency | Application-observed p95 and p99 for the affected operations, plus Valkey latency events. |
| Memory safety | Peak allocation, RSS, fragmentation, swap activity, and headroom below maxmemory. |
| Cache behavior | Eviction rate, hit and miss behavior, and whether the selected policy matches access patterns. |
| Durability versus latency | Persistence events, fork duration, disk pressure, and the data-loss consequence of any fsync change. |
| Host and network baseline | Intrinsic host latency, CLI round-trip latency from relevant locations, CPU contention, and scheduling behavior. |
| Operational control | Whether you can inspect host swap, transparent huge pages, persistence timing, and allocator behavior directly. |
10. Avoid common diagnostic mistakes
- Treating RSS as live data. Allocators can retain pages after deletions; check peak and fragmentation before calling it a leak.
- Comparing only
used_memorywithmaxmemory. Apply the eviction formula and include buffers excluded from eviction accounting. - Assuming a CLI PING is a server benchmark. It includes the network and client path.
- Running intrinsic-latency tests from the client. The test belongs on the Valkey host and can consume a CPU core.
- Using a generic latency threshold. Derive it from the application’s objective.
- Ignoring persistence peaks. Fork and copy-on-write can substantially increase memory during RDB saves and AOF rewrites.
- Blaming Valkey before checking swap and scheduling. Host paging and virtual-machine contention can dominate command time.
- Using
KEYSfor production iteration. Prefer incremental scan commands. - Changing durability or eviction policy without measuring consequences. Validate data-loss risk, hit rate, misses, and tail latency after every change.
Valkey’s latency troubleshooting guidance and field availability can change between releases, and the current troubleshooting page is marked as under review. Check the documentation shipped for the Valkey version you operate before relying on a default, field name, retention detail, or operating-system recommendation.
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