Q: A service depends on Kafka and Redis. Both clusters appear 100% healthy on monitoring dashboards, but service requests are intermittently failing. What is your debug path before escalating to application teams?
Forensic diagnostic path to isolate intermittent request failures in a high-throughput microservice dependent on healthy Kafka and Redis clusters.
Want to master this scenario in a live sandbox? The Linux Foundation's Prometheus Certified Associate (PCA) & Monitoring Labs covers this exact problem with hands-on terminal drills.
🛠️ Production Runbook & Step-by-Step Resolution
Check Socket States & Ephemeral Port / File Descriptor Limits
Log into the application pods or host nodes. Inspect socket connection allocation with `ss -s` and `lsof`. Look for thousands of sockets in `TIME_WAIT` or `CLOSE_WAIT` states. Check if the container process is approaching its max open files limit (`ulimit -n`).
# Inspect active socket connection states and file descriptors
ss -s
cat /proc/<pid>/limits | grep "Max open files"
ls -1 /proc/<pid>/fd | wc -lInspect Client-Side Connection Pool Starvation & Timeouts
Verify client configuration (e.g. Jedis/Lettuce for Redis, librdkafka/confluent-kafka for Kafka). If the client connection pool has `maxTotal = 50` connections and 500 concurrent worker threads burst, threads block waiting for an idle connection until the client timeout (e.g. 1000ms) triggers, throwing intermittent timeout exceptions.
Analyze Redis Single-Thread Latency & Slowlog
Run `redis-cli slowlog get 20`. Even if overall Redis CPU is low, Redis runs on a single core for command execution. A single O(N) command (like `KEYS *` or an unbounded `HGETALL`) blocks the event loop for 200ms, dropping incoming client TCP requests intermittently.
redis-cli -h redis.internal.tesla.com slowlog get 25Examine Kafka Producer Buffer Pools & Socket Send Buffers
Check if Kafka producers are hitting `buffer.memory` limits. When downstream network latency fluctuates, `max.block.ms` expires, causing the producer to drop messages before they reach Kafka brokers.
- Inspect socket states (ss -s) and file descriptors (ulimit -n) on client service pods.
- Verify client connection pool capacity (Jedis/Kafka producers) to detect pool exhaustion under burst traffic.
- Check redis slowlog for blocking O(N) commands choking the single-threaded event loop.
- Monitor Kafka client buffer.memory saturation and max.block.ms producer timeouts.