The same for the number of clients, from 50 to clients I got the same numbers. With only 10 clients it starts to get a bit slower. You can expect different results from different boxes. For example a low profile box like Intel core duo T clocked at 1. There are several third-party tools that can be used for benchmarking Redis.
Refer to each tool's documentation for more information about its goals and capabilities. The substitution changes every time a command is executed. Default tests use this to hit random keys in the specified range. Default 1 no pipeline. The test names are the same as the ones produced as output. Just open N idle connections and wait. A typical example would be: redis-benchmark -q -n Using this tool is quite easy, and you can also write your own benchmark, but as with any benchmarking activity, there are some pitfalls to avoid.
Redis is a server: all commands involve network or IPC round trips. Redis commands return an acknowledgment for all usual commands. Some other data stores do not. Comparing Redis to stores involving one-way queries is only mildly useful. Naively iterating on synchronous Redis commands does not benchmark Redis itself, but rather measure your network or IPC latency and the client library intrinsic latency.
Redis is an in-memory data store with some optional persistence options. Redis is, mostly, a single-threaded server from the POV of commands execution actually modern versions of Redis use threads for different things. It is not designed to benefit from multiple CPU cores. People are supposed to launch several Redis instances to scale out on several cores if needed. It is not really fair to compare one single Redis instance to a multi-threaded data store.
Network bandwidth and latency usually have a direct impact on the performance. It is a good practice to use the ping program to quickly check the latency between the client and server hosts is normal before launching the benchmark.
In many real world scenarios, Redis throughput is limited by the network well before being limited by the CPU. CPU is another very important factor. Being single-threaded, Redis favors fast CPUs with large caches and not many cores. At this game, Intel CPUs are currently the winners.
When client and server run on the same box, the CPU is the limiting factor with redis-benchmark. Speed of RAM and memory bandwidth seem less critical for global performance especially for small objects. Usually, it is not really cost-effective to buy expensive fast memory modules to optimize Redis. Redis runs slower on a VM compared to running without virtualization using the same hardware.
If you have the chance to run Redis on a physical machine this is preferred. However this does not mean that Redis is slow in virtualized environments, the delivered performances are still very good and most of the serious performance issues you may incur in virtualized environments are due to over-provisioning, non-local disks with high latency, or old hypervisor software that have slow fork syscall implementation.
When an ethernet network is used to access Redis, aggregating commands using pipelining is especially efficient when the size of the data is kept under the ethernet packet size about bytes.
Actually, processing 10 bytes, bytes, or bytes queries almost result in the same throughput. See the graph below. The most visible effect is that redis-benchmark results seem non-deterministic because client and server processes are distributed randomly on the cores.
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Create a free Team What is Teams? Learn more. Measure pipe throughput in the shell Ask Question. Asked 11 years, 2 months ago. Active 9 months ago. Viewed 20k times. Improve this question. Alex B Alex B 4, 5 5 gold badges 36 36 silver badges 41 41 bronze badges.
Add a comment. Active Oldest Votes. Improve this answer. Community Bot 1. Michael Mrozek Michael Mrozek 84k 31 31 gold badges silver badges bronze badges. I implemented a project in C with named pipes, thanks to standart file input-output based communication fopen, fprintf, fscanf It is very easy to use and dramatically faster then sockets.
In my experiences UDS are around 5x faster on modern systems in both latency and throughput. These benchmarks come from this simple benchmark code. Try for yourself. How are we doing? Please help us improve Stack Overflow. Take our short survey. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow.
Learn more. Asked 12 years, 3 months ago. Active 4 months ago. Viewed k times. Improve this question. Your mileage will vary. Then profile anonymous pipes, sockets of other domains and families, semaphores and shared memory or message queues SysV and POSIX , realtime signals with a word of data, or whatever. SysV message queues FTW!
I have no idea if they're fast, i just have a soft spot for them. What is "speed" in this case? Overall data transfer rate? Or latency how quickly the first byte gets to the receiver? If you want fast local data transfer, then it's hard to beat shared memory.
If latency is an issue, though, then the question gets more interesting Add a comment. Active Oldest Votes. Best results you'll get with Shared Memory solution.
Improve this answer. Thanks for the detailed benchmarking. Do you mean "multiprocessing.
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