I ran a few tests on some AWS machines (memtier on m5.8xlarge, redis/keydb on m5.4xlarge) so they can be replicated if needed. I used your configuration, however because the test completes so fast, I ran with more requests and over a series of runs with the following memtier command. Results are similar but more repeatable:
memtier_benchmark -s 172.31.49.176 --pipeline 1000 --hide-histogram --run-count=5 --requests=100000
I found similar relative results to what you were seeing, but much higher ops/sec which could be a result of network/server hardware. It can be noted that v6.0.16 is much faster than 5.3.3 as we have made a lot of perf improvements since then.
An important thing to understand is the pipeline configuration you are passing. Per memtier:
--pipeline=NUMBER à Number of concurrent pipelined requests (default: 1)`
So when you specify 1000 for the pipeline configuration, the assumption is that every call to the database has 1000 concurrent pipelined requests.
With KeyDB’s multithreading, there is not as much benefit if you are only pipelining in large amounts of requests. You will notice adding more threads to KeyDB will not make much of a difference in this scenario.
Most use cases may pipeline in smaller amounts, have only a percentage of calls pipelined, or maybe not at all. In these scenarios KeyDB is able to generate much higher throughput.
See the chart below where I benchmark on default configs (redis-server, keydb-server –server-threads 8 –server-thread-affinity true), where the memtier command is similar to that above but with different pipeline values passed:
As you can see, when pipelining smaller amounts of requests, the difference in throughput becomes quite noticeable. For most use cases not every request is a batch of 1000 pipelined requests, meaning for most cases KeyDB should be faster.
Take another example where we run two separate memtier instances concurrently, one with only one client pipelining, and the other with default load:
memtier_benchmark -s 172.31.49.176 --pipeline 1000 --hide-histogram --requests=10000000 --clients=1
memtier_benchmark -s 172.31.49.176 --hide-histogram --requests=100000 --threads=16
We see the following Total Ops/sec:
Hope this sheds a bit of insight