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To troubleshoot Redis proxy latency or connection failures, first measure the complete application request, then test DNS and reachability from both the client and a server-side vantage point. Next separate Redis command execution from network, proxy, TLS, authentication, pooling, and client-resource delays. Use application timing, SLOWLOG, latency monitoring, connection inspection, proxy telemetry, and packet-level checks together; none of those measurements alone describes the entire path.
The procedure below applies to applications using a generic network proxy, managed Redis access layer, or Redis Software’s built-in proxy. Redis Software-specific behavior and policy choices are labeled explicitly so they are not mistaken for universal Redis rules.
Start by defining what “latency” means
A request can become slow in several different places:
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- Application time: the elapsed time your service experiences from issuing a command until it receives and processes the response. This includes serialization, network round trips, proxy work, Redis execution, and client-library behavior.
- Redis command time: time spent executing a command on the Redis server. Redis Slow Log records commands exceeding its configured execution threshold, but it excludes client input and output.
- Proxy-defined time: Redis Software defines database latency from the first byte received by its proxy to the last byte sent in the command response. That interval excludes network round-trip time and application serialization. See Redis Software observability guidance.
Record the exact timestamp, endpoint, command or operation, client library, request duration, and complete error text for each incident. Note whether every command fails or only particular commands. Without that context, a “Redis timeout” can describe a firewall drop, an unavailable server, failed authentication, a slow command, an exhausted pool, or a client that is out of resources.
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Follow this diagnostic sequence
1. Capture an application-side baseline
Add timing around the Redis call at the application boundary, not only inside a Redis metric. Record connection-acquisition time separately from command round-trip time when the client library exposes both. Include pool wait time, serialization time, retries, and timeout values. Compare successful and failed requests during the same minute. Application timing is essential because server and proxy metrics omit portions of the end-to-end path.
2. Test DNS and basic reachability
Resolve the Redis endpoint name from the application host. A name that resolves on a developer workstation may fail inside a container, private subnet, or cluster node because of different DNS views. For Redis Software, the troubleshooting guide recommends checking endpoint DNS from the client and, when needed, from a cluster node: Redis Software troubleshooting pocket guide.
Use the same protocol and port as the application for a controlled test. For a non-TLS endpoint, try:
redis-cli -h redis.example.internal -p 6379 PINGFor a TLS endpoint, supply the documented CA and authentication options instead of bypassing certificate verification:
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redis-cli --tls -h redis.example.internal -p 6380 --cacert /path/ca.pem PINGA successful PONG proves that this vantage point can complete a basic request; it does not prove that the application’s credentials, pool, command workload, or proxy path is healthy. If the client test fails, repeat it from a cluster node or another host with an approved network path. Success from the node but failure from the application host points toward client-side DNS, routing, firewall, proxy rules, or TLS configuration.
3. Check routing, firewalls, and external proxy rules
Verify outbound rules on the client network, inbound rules on the Redis or proxy network, route tables, security groups, and any third-party firewall. Allow the cluster FQDN, the database endpoint IP where applicable, and the database port. Test from more than one network vantage point so that a single successful path does not hide an asymmetric route or restricted subnet. A connection that hangs until timeout often indicates dropped traffic; an immediate refusal more often indicates that the destination or an intermediate listener rejected the connection, but confirm with packet and service logs rather than relying on the wording alone.
4. Validate TLS and authentication
Confirm whether the proxy expects TLS and whether the client is using the matching scheme and port. Check the server certificate chain, hostname validation, protocol settings, username, password or ACL token, and—when mutual TLS is enabled—the client certificate and private key. Redis Software documents supplying its proxy/server certificate and, for mutual TLS, a client certificate and key in its TLS instructions: Redis Software TLS configuration.
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5. Separate command execution from network delay with Slow Log
Inspect the incident window with:
SLOWLOG GET 128Redis describes Slow Log as a system that logs queries exceeding a specified execution time. It measures server execution and excludes client I/O, so an empty or short Slow Log does not rule out proxy queueing, network delay, packet loss, connection acquisition, or application serialization. Read the command, execution duration, and timestamp alongside application timing. Documentation: Redis SLOWLOG.
6. Enable latency monitoring for server-side events
Redis Open Source latency monitoring is disabled by default. Set a threshold appropriate to your service objective, in milliseconds, then inspect the event data:
CONFIG SET latency-monitor-threshold 20
LATENCY LATEST
LATENCY HISTORY command
LATENCY GRAPH command
LATENCY DOCTORUse a threshold low enough to reveal meaningful events but high enough to avoid noise from normal scheduling variation. Do not treat a clean latency monitor as proof that the proxy or network is healthy: it observes server-side latency events, not the complete client round trip. See Redis latency monitoring.
7. Inspect connections, pools, and client health
Run CLIENT LIST with appropriate privileges and inspect connection age, idle time, client name, output-buffer memory, and other state. Redis client guidance is available at Redis client handling. Correlate this with the client process:
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- Measure CPU, memory, garbage-collection pauses, event-loop lag, and file-descriptor usage.
- Check whether the pool is exhausted or threads are waiting for a connection.
- Look for excessive connection creation and destruction, especially per-request connections.
- Verify connect, command, read, and pool-wait timeout settings separately.
- Give connections stable names where the client supports it, making
CLIENT LISTeasier to interpret.
A bounded, reused connection pool normally avoids handshake and authentication overhead. Redis Software guidance specifically recommends pooling instead of repeatedly opening and closing connections. Pool limits must still match concurrency and Redis capacity; an oversized pool can create its own connection and proxy pressure.
8. Correlate Redis and proxy resources
Align dashboards to the exact incident interval. Check Redis node CPU, memory, evictions, expirations, network traffic, shard distribution, active connections, and command rates. For Redis Software, also inspect proxy performance and connection counts in its observability views: Redis Software observability and monitoring.
Frequent connection cycling can consume proxy CPU, particularly with TLS handshakes. A single active proxy can become a bottleneck when connection count, bandwidth, or packet rate rises. These observations describe Redis Software deployments; a separate proxy may expose different counters and may bottleneck on different resources. Compare proxy CPU and queue indicators with application latency before changing topology.
9. Find expensive commands and oversized values
Inspect commands issued during slow requests and verify the data shape they process. Redis Software guidance calls out unbounded operations such as KEYS and broad HGETALL calls. Where semantics permit, use incremental scans:
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SCANfor keysSSCANfor setsHSCANfor hashesZSCANfor sorted sets
Scan cursors make work interruptible, but they can return duplicates and require the application to continue until the cursor returns to zero. They are not a blind replacement when the application requires an atomic snapshot. Investigate unusually large keys with the documented --bigkeys or --memkeys options, preferably against a replica or controlled maintenance window when workload impact matters. Do not change commands until you understand the required consistency and data volume.
10. Capture packets and compare vantage points when evidence conflicts
If application timing is high but Redis execution is low, capture network timing on the client and, where permitted, near the proxy or cluster node. Redis Software troubleshooting guidance mentions tools such as tcpdump and recommends comparing nodes, shards, proxy levels, and network timing: Troubleshooting Redis Software. Use captures to distinguish DNS delay, TCP retransmission, TLS handshake time, delayed responses, and connection resets. Protect credentials and payload data, and limit captures to the incident window.
Map the exact error to the failing layer
| Observed symptom | Likely areas to test first | Evidence that narrows it |
|---|---|---|
| Immediate “connection refused” | Wrong host or port, stopped listener, firewall rejection, proxy listener policy | TCP result from the same client host; listener and firewall logs |
| Connection timeout | Routing, dropped firewall traffic, unreachable endpoint, overloaded client or proxy | DNS result, route, packet capture, tests from a second vantage point |
| TLS handshake or certificate error | Wrong TLS mode or port, untrusted CA, hostname mismatch, expired certificate, missing mutual-TLS client key | Client TLS diagnostics and server certificate configuration |
| Authentication or authorization error | Wrong credentials, ACL, username, or database selection | Successful TCP/TLS handshake followed by an explicit Redis error |
| Intermittent pool timeout | Pool exhaustion, leaked connections, slow callers, too-small pool, client pauses | Pool wait histogram, active/idle counts, connection age, process health |
| Slow responses with low Redis execution time | Network round trip, proxy queueing, TLS churn, serialization, client scheduling | Application timing versus Slow Log and proxy interval |
| Only some commands are slow | Large values, expensive commands, hot keys, uneven shard workload | Command-level timing, key sizes, shard and CPU correlation |
Redis groups common connection-failure causes as network connectivity, unavailable server, authentication failure, timeout, and exhausted connection pool. The categories overlap at the application surface, so use the exact exception plus independent reachability tests rather than assigning a cause from the error label alone. See Redis error handling.
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Redis Software routes database access through proxy processes and reports a proxy-bounded latency interval. Compare that metric with application request duration, connection count, node CPU, network traffic, and command timing. A low proxy interval with high application latency suggests time outside the measured proxy interval, such as client serialization or network round trip. A rising proxy interval with stable command execution suggests proxy processing or queueing, but confirm with the available proxy and resource telemetry.
Redis Software’s proxy policy documentation describes a single-proxy default and an option to bind multiple proxies. The choice trades latency and network hops against proxy resource use and the number of application-to-proxy connections; it is not a universally best setting. Documentation: Configure proxy policy.
| Decision axis | Questions to answer before changing policy |
|---|---|
| Latency and hops | Does the proposed path remove a network hop, or add one for distribution or failover? |
| Proxy capacity | Are CPU, memory, bandwidth, or packet rate limiting the current proxy? |
| Connection count | Will applications open more proxy connections, and can clients pool them? |
| Traffic shape | Are large responses, high request rates, or bursty packets stressing one process? |
| Operations | Do you need multiple endpoints, failover behavior, or simpler topology? |
Change policy only after telemetry identifies topology or proxy capacity as the bottleneck. Otherwise, a policy change can add hops or connections without addressing the actual fault.
Common fixes after the evidence identifies the cause
When the client path is wrong
- Correct the endpoint or port supplied to the client and container environment.
- Fix private DNS visibility, route tables, security groups, or firewall allow-lists.
- Ensure the external proxy forwards the Redis protocol without idle or response limits shorter than the client’s expectations.
When TLS or credentials fail
- Use the correct CA bundle and hostname; renew or replace expired certificates.
- Provide the client certificate and key when mutual TLS is required.
- Verify ACL username, secret, and database permissions independently from transport testing.
When pooling or client health is the problem
- Reuse a bounded pool and release connections in every success and exception path.
- Set pool-wait timeouts so callers fail predictably instead of accumulating indefinitely.
- Remove per-request connect and disconnect behavior.
- Address client CPU, memory pressure, event-loop stalls, file-descriptor limits, and long garbage-collection pauses.
When commands or values are the problem
- Replace unbounded key or collection reads with bounded pagination or cursor-based scans where semantics allow.
- Reduce response size, split work, or redesign data structures that routinely produce oversized values.
- Investigate hot keys and shard imbalance before increasing timeouts; a longer timeout hides saturation rather than fixing it.
When the proxy is saturated
- Confirm proxy CPU, memory, connection, bandwidth, and packet-rate pressure during the same period as high latency.
- Reduce connection churn and reuse pooled TLS sessions where supported by the client.
- Evaluate Redis Software proxy policy or additional proxy capacity only when its telemetry shows that layer is limiting throughput.
Build a repeatable incident checklist
- Save the application request duration, exact exception, endpoint, command, client version, and incident timestamps.
- Measure connection acquisition, command round trip, serialization, retries, and timeout values separately.
- Resolve DNS and run an authenticated
PINGorINFOtest from the application host. - Repeat the test from a cluster node or second approved network vantage point.
- Verify route, firewall, external proxy, TLS, certificate, and authentication settings.
- Compare
SLOWLOG GETand latency-monitor output with application timings. - Inspect
CLIENT LIST, pool utilization, connection churn, client CPU, memory, and file descriptors. - Correlate Redis resources, shards, command rates, and proxy telemetry for the exact window.
- Check command cost and key size; capture packets if component timings still disagree.
- Apply the smallest evidence-based fix, then repeat the same measurements to verify that the failing layer changed.
This process prevents a common misdiagnosis: treating a Redis server metric as proof that the proxy and network are healthy. End-to-end timing, component measurements, and independent reachability tests must agree before the incident can be considered resolved.
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