Redis Observability

What to watch, what to run, and what to alert on. Covers the metrics every Redis deployment should monitor and the built-in commands for ad-hoc diagnosis.

When to apply

• Setting up monitoring or alerts for a Redis instance.
• Diagnosing a Redis performance regression (high latency, memory pressure, connection storms).
• Profiling a slow FT.SEARCH or pipeline.
• Wiring Redis metrics into Prometheus, Datadog, CloudWatch, or similar.

1. Monitor these metrics

These come from INFO and should be exported to your monitoring system.

info = redis.info()
hit_ratio = info["keyspace_hits"] / max(1, info["keyspace_hits"] + info["keyspace_misses"])
print(f"Memory:    {info['used_memory_human']}")
print(f"Clients:   {info['connected_clients']}")
print(f"Ops/sec:   {info['instantaneous_ops_per_sec']}")
print(f"Hit ratio: {hit_ratio:.1%}")

See [references/metrics.md](references/metrics.md).

2. Built-in commands for debugging

Reach for these when something looks off.

The two most useful for incident triage:

• SLOWLOG GET to find queries that exceeded the slowlog-log-slower-than threshold (10ms by default). The output shows the exact command and duration in microseconds.
• MEMORY DOCTOR for memory pressure — it returns a one-paragraph summary of what's unusual about memory usage right now.

for entry in redis.slowlog_get(10):
    print(f"{entry['duration']}μs  {entry['command']}")

See [references/commands.md](references/commands.md).

3. Redis Insight

For interactive use (running queries, browsing keys, profiling indexes), Redis Insight is the official GUI. It surfaces the same SLOWLOG / INFO / FT.PROFILE data visually and includes Redis Copilot for natural-language queries. Useful during development and incident response; not a replacement for exporting metrics to your monitoring system.

References

• Redis: Latency monitoring
• Redis Insight