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| Similar benchmark to protostore, same client, but hacked to speak to Redis. | |
| 10M keys, randomly distributed between 1024 and 4096. Generated with mk_redis_data.py | |
| using Pythons random.randrange. I think the average size is higher than in previous benchmarks. | |
| I need to redo the benchmark with similar distribution. | |
| Server runs on r3.4xlarge. Redis uses 27G of RAM. With 50 clients it ends up using 100% CPU on one core, | |
| although it semes we are also limited by bandwidth at this point. 243MB/s is the max. With two redis | |
| server instances on the same machine, each with 50 clients, we no longer use all the CPU, | |
| but cannot go higher than 243MB/s. The test client is also running on r3.4xlarge, so I should redo the | |
| test with more client instances, maybe it's possible to squeeze more network throughput out of this machine. | |
| The avg rps is lower than in previous benchmarks, probably because the values are larger now. | |
| ============================ | |
| Concurrent clients: 50 | |
| Runtime: 30 s | |
| Total requests: 2937017 | |
| Avg rps: 97900.57 | |
| Bytes transferred: 7170.76 MB | |
| Bytes per second: 239.03 MB | |
| Roundtrip latencies: 50th: 497us 75th: 543us 90th: 582us 95th: 611us 99th: 778us 99.9: 1600us | |
| ============================ | |
| Concurrent clients: 25 | |
| Runtime: 30 s | |
| Total requests: 2252323 | |
| Avg rps: 75077.43 | |
| Bytes transferred: 5499.40 MB | |
| Bytes per second: 183.31 MB | |
| Roundtrip latencies: 50th: 321us 75th: 353us 90th: 393us 95th: 422us 99th: 548us 99.9: 1615us |
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