Redis is an open source, in-memory, key-value data store that is typically used as a database or a cache. It uses an in-memory dataset, but data can be persisted through periodic writes or appends to disk. Due to its in-memory nature, Redis is very fast, and it can deliver high throughput at sub-millisecond latencies. It continues to rank highly in popularity among key value stores in the cloud, according to DB-engines.

As can be seen in figure 1, up to 25% performance improvement was observed with the AmpereOne® A192-32X processor compared to the AMD EPYC 9654. The AmpereOne® A192-32X demonstrated 2% higher throughput compared to the AMD EPYC 9754, all under an p99 Service Level Agreement (SLA) of 1 millisecond.

Figure 2 shows the energy efficiency of the three processors while running Redis. The AmpereOne® A192-32X demonstrated a 70% lead in performance/Watt compared to the AMD EPYC 9654 and a 30% advantage over the AMD EPYC 9754.

memtier_benchmark (developed by Redis Labs) was used as a load generator for benchmarking Redis. Each test was configured to run with multiple threads, multiple clients per thread, and with pipelining enabled.

Recent compilers have made significant progress towards generating optimized code for aarch64 and it is compiling Redis server with GCC (GNU Compiler Collection) 13.2.1 or newer is recommended.

Fedora38 Server Edition (kernel 6.4.13-200.fc38.aarch64) was used with Redis-server 7.2.0 compiled with gcc 13 for the tests. Similar client systems were used to generate requests in all tests.

Since it is realistic to measure throughput under a specified Service Level Agreement (SLA), a 99th percentile latency (p99) of 1 millisecond was used. This ensures that 99 percent of requests have a response time of 1 ms in the worst case.

The test was run for 3 minutes with a 1:10 get:set ratio, which is common for in-memory caches.An appropriate number of clients and threads/client was configured to load one instance of Redis, while ensuring the p.99 latency was at most 1ms. Next, the number of Redis instances was scaled up till one or more instances violated the p99 latency SLA. The aggregate throughput of all instances was then used as the primary performance metric. The test was run three times to ensure minimal run-to-run variation.

Fast in-memory caches are used in most cloud workloads today. Redis is a popular, high throughput in-memory key-value store that is applicable to low latency applications in a scale out configuration. For cloud application developers, choosing the AmpereOne® A192-32X Cloud Native Processor means compelling performance and energy efficiency, leading to SLAs being met while reducing your carbon footprint.

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System configurations, components, software versions, and testing environments that differ from those used in Ampere’s tests may result in different measurements than those obtained by Ampere.

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