Redis is the backbone of many real-time systems. As dataset sizes grow beyond physical memory, choosing the right eviction strategy becomes critical for latency and throughput. This article explores the internals of Redis 7.x eviction algorithms, their interaction with the Linux kernel's page cache, and how to tune them on Votion Cloud's bare-metal instances.
Eviction Policy Taxonomy
Redis offers eight eviction policies: noeviction, allkeys-lru, volatile-lru, allkeys-lfu, volatile-lfu, allkeys-random, volatile-random, and volatile-ttl. Each policy trades off hit‑rate, CPU overhead, and memory fragmentation differently. We benchmark each policy under a Zipfian key access pattern with 10M keys and a 4 GB maxmemory limit.
Hardware Performance Benchmark Telemetry
4.9x HIGHER THROUGHPUT
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METRIC: Random Disk IOPS (k)TELEMETRY: REAL-TIME HARDWARE HARDENING AUDIT
Benchmark Results
The telemetry chart above shows 99th‑percentile latency (ms) vs. eviction policy. allkeys-lfu delivers the lowest tail latency under write‑heavy workloads, while volatile-lru excels when TTL‑based expiration is used. Notice the CPU spike for LFU due to frequency counter maintenance.
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Kernel & Hardware Tuning
On Votion Cloud's AMD EPYC 9654 nodes, we recommend disabling Transparent Huge Pages (THP) and setting vm.overcommit_memory=1. Pin Redis to a NUMA node with numactl --interleave=all to avoid cross‑socket memory latency. The CLI builder above generates a ready‑to‑deploy systemd unit with these parameters.
Conclusion
Choosing an eviction strategy is not a one‑size‑fits‑all decision. Combine workload profiling with the telemetry charts and cost estimator to right‑size your Redis fleet. Votion Cloud's integrated observability stack makes continuous tuning a first‑class operation.
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