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Start at the application and work inward through the client library, network, proxy, and Redis server. Measure the complete request, then compare it with Redis command timing and proxy telemetry taken during the same incident window. This separates a slow command from network round-trip time, proxy queueing, TLS overhead, client-pool exhaustion, and simple reachability or authentication failures.
The procedure below applies to Redis deployments that use a separate network proxy, a managed service layer, or Redis Software’s built-in proxy. Metrics and policy names marked as Redis Software-specific do not automatically exist in other proxy architectures.
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First identify which latency you are measuring
“Redis latency” can describe several different intervals. An application timer normally includes serialization, connection-pool checkout, network round trips, proxy processing, Redis command execution, response transfer, and deserialization. Redis Slow Log measures only command execution after Redis receives a command. Redis Software’s documented proxy latency starts when the proxy receives the first response byte and ends when it sends the last response byte; it excludes network round-trip time and application-level serialization. Therefore, a healthy server-side metric cannot by itself prove that the end-to-end request is healthy.
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Record the exact start and end times in UTC, the endpoint, command or operation, client-library version, connection-pool state, error text, and whether every command or only particular operations are affected. Keep one sample of a successful request beside a failed or slow sample. This gives every subsequent test a common time window.
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Follow this diagnostic sequence
1. Capture the symptom at the application
Add timing around the complete Redis operation and, where the client exposes them, around pool checkout, connection establishment, command write, response read, and deserialization. Log the command category rather than sensitive key values. Include the configured connect timeout, command or socket timeout, retry count, and whether the request reused an existing connection.
Classify the event as a slow response, a connection timeout, a connection refusal, an authentication or TLS error, or a pool-acquisition timeout. Redis groups common connection failures into network connectivity, unavailable server, authentication failure, timeout, and exhausted connection-pool categories; the visible exception can overlap, so retain the exact underlying error and timestamp. See Redis error-handling guidance.
2. Test DNS and basic reachability from the client
Resolve the exact hostname used by the client, not a copied IP address. For a Redis Software endpoint, resolve the database FQDN from the application host and, when possible, from a cluster node. Differences can reveal split-horizon DNS, a missing private-zone association, stale records, or a route that exists only on one network.
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redis-cli -h <endpoint> -p <port> PING
redis-cli -h <endpoint> -p <port> INFO serverUse the TLS and authentication options required by the deployment; an unauthenticated test against an authenticated endpoint is not a valid reachability test. If the client host cannot connect, repeat the test from a cluster node or another approved server-side vantage point. Success from a node but failure from the client points toward client routing, firewall policy, DNS, proxy rules, or TLS trust rather than Redis command execution.
3. Check routes, firewalls, and external proxy rules
Verify outbound rules on the application network, inbound rules on the cluster or proxy network, security groups, network ACLs, and any corporate or cloud firewall between them. Allow the cluster FQDN, the database endpoint IP addresses, and the database port required by the deployment. If an external TCP or service-mesh proxy is in front of Redis Software’s proxy, inspect its listener, upstream target, health-check behavior, idle timeout, maximum connection count, and access-control list.
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Run the same DNS and protocol test from more than one network vantage point. A connection that works from a bastion but fails from an application subnet usually indicates a path or policy difference. A refusal means a host actively rejected the connection or no listener accepted it; a timeout generally means packets were dropped, routed incorrectly, or held by a stateful device. Treat those as clues, not proof, and confirm with firewall and proxy logs.
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Confirm whether the endpoint requires TLS, which server name must appear in certificate validation, and whether the client trusts the issuing CA. For Redis Software, the documented configuration can require the proxy or server certificate and, for mutual TLS, a client certificate and private key; see Redis Software TLS configuration.
Check certificate expiration, hostname matching, protocol and cipher compatibility, and the permissions on the client key. Then verify the Redis username, password or ACL token, and the database’s authentication mode. A controlled diagnostic can use the documented trust configuration to isolate certificate validation, but do not leave certificate checking disabled as a production fix. If TLS handshakes are slow, correlate handshake counts with connection creation: repeatedly opening connections, especially with TLS, can consume proxy CPU.
5. Separate command execution from the rest of the round trip
Inspect the Slow Log around the incident:
SLOWLOG GET 128The official definition is precise: “The Redis Slow Log is a system to log queries that exceeded a specified execution time.” It records commands whose execution exceeds the configured threshold, but it excludes client I/O. A clean log therefore does not rule out network delay, proxy queueing, response transfer time, connection-pool wait, or application serialization. Read the command’s duration and timestamp together with the application timer, rather than treating the Slow Log as an end-to-end benchmark. Details are in the SLOWLOG command documentation.
Check the configured maximum length and threshold before drawing conclusions. If the threshold is higher than the latency you are investigating, the command will not appear. Capture the log promptly because a busy instance can rotate older entries out of the list.
6. Enable and query the latency monitor for server events
Redis Open Source’s latency monitor is disabled by default. Enable it temporarily with a threshold aligned to your service objective:
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CONFIG SET latency-monitor-threshold <milliseconds>
LATENCY LATEST
LATENCY HISTORY <event-name>
LATENCY GRAPH <event-name>
LATENCY DOCTORLATENCY LATEST shows recent event samples; HISTORY provides a time series for a named event; GRAPH renders an ASCII history; and DOCTOR summarizes suspected latency causes. Set a threshold low enough to catch the problem but high enough to avoid noise, and restore the setting or make it persistent according to your change-management policy after the investigation. See Redis latency monitoring.
Latency-monitor events describe Redis-side pauses such as fork, command, or system delays. They do not measure the application’s network path. Compare their timestamps with Slow Log entries and application timings before assigning blame.
7. Inspect connections, pools, and client health
Run CLIENT LIST during a controlled observation period and examine connection age, idle time, client name, flags, selected database, and output-buffer memory. A rapidly increasing number of short-lived connections suggests connection churn; many idle connections may indicate an oversized pool; clients stuck in blocked states require command-specific investigation. Redis documents these fields in its client-handling reference.
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On the application host, check CPU saturation, memory pressure, garbage-collection pauses, file-descriptor limits, event-loop stalls, and thread-pool starvation. Verify that the pool has a finite maximum, that callers return connections in every success and error path, and that pool-acquisition timeout is distinguished from socket-connect timeout. Repeatedly opening and closing a connection for each request adds DNS, TCP, and possibly TLS handshakes; use a properly sized pool instead.
Check client-library socket and command timeouts against the proxy’s idle and request limits. A client timeout shorter than a valid long-running operation produces a false Redis failure; a timeout that is too long can retain pool slots and amplify an outage. Retry only idempotent operations where appropriate, and use bounded backoff so a proxy or server under pressure is not flooded by synchronized retries.
8. Correlate Redis resources with proxy telemetry
For Redis Software, inspect proxy performance and connection count over the exact application incident interval, alongside node CPU, memory, evictions, expirations, network traffic, shard distribution, and database-level load. Its observability guidance is at Redis Software observability and monitoring. A single active proxy can become a bottleneck when traffic, bandwidth, packet rate, or concurrent connections exceed its capacity. Frequent connection cycling, particularly with TLS, can raise proxy CPU even when Redis command execution remains short.
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For a generic proxy, look for equivalent measurements: active and queued connections, upstream-connect latency, bytes and packets per second, event-loop or worker utilization, retransmissions, resets, rejected connections, and queue depth. Do not assume that a metric called “backend latency” includes the client-to-proxy leg. Align proxy timestamps, Redis timestamps, and application timestamps to the same clock source or account for clock skew.
9. Find expensive commands and oversized values
Commands that scan or return large data sets can make a proxy appear slow because the proxy must receive and forward a large response. Redis guidance specifically calls out KEYS and HGETALL as operations to scrutinize. Where the data model permits, consider incremental alternatives such as SCAN, SSCAN, HSCAN, and ZSCAN; these still require correct cursor handling and do not make a full result set free.
Investigate data shape and key size before changing commands. The redis-cli options below can identify unusually large keys:
redis-cli -h <endpoint> -p <port> --bigkeys
redis-cli -h <endpoint> -p <port> --memkeysRun such scans in a maintenance window or against a replica when operational policy allows. They consume resources and can themselves affect latency. Compare payload size, serialization time, and response-transfer time; a short server execution time does not mean a multi-megabyte response will arrive quickly.
10. Change Redis Software proxy policy only after measuring a bottleneck
Redis Software supports proxy policies that trade latency and network hops against proxy resource use and the number of application-to-proxy connections. Its documentation describes a single-proxy default and an option to bind multiple proxies. Review Configure proxy policy before making a change.
Do not switch policy merely because a request is slow. First establish that proxy capacity or topology, rather than DNS, TLS, client pooling, a firewall, a hot shard, or a large response, is the limiting component. Record the current policy, expected traffic and packet rate, application-to-proxy connection count, and failover requirements. Change one variable, observe under representative load, and retain a rollback plan.
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| Observed symptom | Most useful first checks | What the result suggests |
|---|---|---|
| DNS resolution fails or returns an unexpected address | Resolve the endpoint from the application host and a server-side vantage point; compare private and public DNS views. | Investigate DNS zones, resolver reachability, service discovery, or stale records before Redis settings. |
| Connection refused immediately | Confirm the destination port, listener, proxy health, firewall reject logs, and whether the endpoint is in a failover transition. | An active reject or absent listener is more likely than command latency. |
| Connection attempt times out | Compare routes and firewall rules from multiple networks; inspect dropped packets and proxy listener limits. | Packets may be filtered, misrouted, or queued before Redis sees a connection. |
| TLS handshake or certificate error | Check TLS mode, SNI/hostname, CA trust, certificate dates, client key permissions, and mutual-TLS requirements. | The request has not reached command execution; fix identity or protocol negotiation first. |
| Authentication error after a successful handshake | Verify username, ACL permissions, secret rotation, and which endpoint or database the client actually reached. | Network reachability is working, but credentials or authorization are not. |
| Pool-acquisition timeout with few Redis errors | Inspect pool size, connection leaks, request concurrency, client CPU pauses, and socket-connect duration. | The application may be out of usable connections before Redis is contacted. |
| Slow application request but no Slow Log entry | Compare application timing with proxy metrics, network RTT, payload size, serialization, pool wait, and latency-monitor events. | The delay is likely outside Redis command execution or below the Slow Log threshold. |
| Only large reads or scans are slow | Measure response bytes, inspect large keys, review HGETALL/KEYS usage, and examine proxy bandwidth and packet rate. |
Payload transfer, data shape, or an expensive command may dominate the round trip. |
Redis Software-specific checks and policy trade-offs
Redis Software routes database access through proxy processes. Its proxy latency interval is useful for comparing proxy behavior over time, but it does not include the application’s network trip or serialization. During an incident, place proxy latency, proxy connection count, node CPU, network traffic, and application request duration on the same timeline. The Redis Software troubleshooting material also recommends checking cluster nodes, shards, proxy levels, and network timing with packet-capture tools such as tcpdump; see Troubleshooting Redis Software.
Policy selection is an engineering trade-off rather than a universal optimization:
| Decision axis | Single-proxy-oriented arrangement | Multiple-proxy-oriented arrangement |
|---|---|---|
| Network hops and expected latency | May keep routing simple, but can concentrate traffic through one process or location. | May place application connections closer to their traffic sources, but can add operational and routing complexity. |
| Proxy resource use | Lower process count can simplify management, while one proxy may hit CPU, bandwidth, or packet-rate limits. | Resources are distributed across processes, but each process needs capacity and monitoring. |
| Application-to-proxy connections | Applications may concentrate connections on one endpoint. | Connections can be spread across endpoints; pool sizing and endpoint selection become important. |
| Availability and operations | Fewer endpoints simplify configuration but may reduce placement choices. | Multiple endpoints can support placement or failover needs, with more configuration to validate. |
The table describes the documented trade-offs, not a promise that either arrangement is faster. Use measured latency, connection counts, traffic, and failure requirements to choose.
Build a repeatable incident record
For every event, retain a compact timeline containing:
- Application request duration broken into pool wait, connect, write, read, and deserialization where available.
- The exact exception text, command category, endpoint, client version, timeout values, and retry behavior.
- DNS answers, route and firewall-test results, and whether the same test worked from a cluster node.
- TLS and authentication outcome, including certificate or ACL errors.
SLOWLOG GET, latency-monitor output,CLIENT LISTobservations, and relevant Redis resource metrics.- Proxy connection, queue, CPU, bandwidth, packet-rate, reset, and upstream-latency measurements.
- Node CPU, memory, evictions, expirations, shard placement, and data-size findings.
This record prevents a common diagnostic mistake: declaring the proxy healthy because Redis command execution is short, or declaring Redis slow because the application timed out while waiting for a pool slot. A layer is exonerated only when its measurement covers the interval in question and the adjacent layers agree.
Prevent recurrence after the immediate fix
- Keep a bounded connection pool and alert on pool utilization, acquisition wait, connection churn, and handshake rate.
- Set connect, command, idle, and proxy timeouts deliberately; document which timeout produces each alert.
- Monitor application end-to-end latency beside Redis command latency and proxy-specific latency rather than substituting one for another.
- Alert on DNS failures, refused connections, authentication failures, TLS errors, proxy queue depth, and Redis resource saturation separately.
- Review commands that return unbounded collections, and measure key sizes and response bytes as data grows.
- Test from the same network paths used by production applications, including failover and private-network paths.
- When using Redis Software, document the proxy policy, endpoint layout, expected traffic profile, and rollback procedure for policy changes.
- Use packet captures only under an approved operational procedure and protect captured credentials or payload data.
The durable goal is not merely to increase a timeout. It is to identify whether time is spent before Redis, inside command execution, in the proxy, during response transfer, or in the client after the response arrives, then correct that specific layer.
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