I am surprised and disappointed that the original paper and the blog post has zero reference to unikernel research, despite the fact that unikernel pretty much is the whole encompassing idea. I am wondering whether or not this is a missing or a different understanding the concept. Edit: Sorry I did not really get the difference between library OS and unikernels. It's still a lack of reference considering their connec…
The Demikernel is not a unikernel. It is a library OS compiled as a series of shared libraries. It is not compiled together with the application and doesn’t take into account what features the application uses. It is designed to work with kernel-bypass hardware, like DPDK.
High-level means not exposing hardware limitations to the application. The primary target applications are datacenter services, which spend much of their time processing network I/O. As network latencies lower to a few microseconds, datacenter applications like Redis will need kernel-bypass because the kernel will become too expensive for them. In our experiments with a 25Gb network, the Linux kernel and POSIX interf…
> High-level means not exposing hardware limitations to the application. This seems counter-intuitive. Hardware limitations mean different abstraction than OS-level APIs, as them to applications. Even POSIX does not expose hardware limitations. Rather, high-level in the paper is more like some suitable interface to a wide range of applications. I.e., high-level as it's targeted to be used directly by applications as…
I consider POSIX to be high-level. The RDMA and DPDK interface are not.
What does it even mean by high level? Once the API is high level enough it gets unusable by major users who are high end networking, GPU and graphics libraries and low latency sound. Nobody else truly needs to bypass the kernel. Even low latency can work with good real time task handling, making the users exactly two cases, who have special DMA handling in hardware already. If it means introducing special case kernel…
High-level means not exposing hardware limitations to the application. The primary target applications are datacenter services, which spend much of their time processing network I/O. As network latencies lower to a few microseconds, datacenter applications like Redis will need kernel-bypass because the kernel will become too expensive for them. In our experiments with a 25Gb network, the Linux kernel and POSIX interf…
Network I/O is a major bottleneck for Redis but they leave a lot on the table by being single threaded. I can speak from experience because I maintain a Multithreaded Fork: https://github.com/JohnSully/KeyDB
KeyDB can easily get 2-3x the QPS with half the latency.
High-level means not exposing hardware limitations to the application. The primary target applications are datacenter services, which spend much of their time processing network I/O. As network latencies lower to a few microseconds, datacenter applications like Redis will need kernel-bypass because the kernel will become too expensive for them. In our experiments with a 25Gb network, the Linux kernel and POSIX interf…
Network I/O is a major bottleneck for Redis but they leave a lot on the table by being single threaded. I can speak from experience because I maintain a Multithreaded Fork: https://github.com/JohnSully/KeyDB KeyDB can easily get 2-3x the QPS with half the latency.
This is IMHO a wrong analysis. Redis can be scaled by being single threaded by running multiple processes: then if you remove the overhead of the network stack, each process can deliver more QPS, not just better latency. By using threads (which Redis now in parts also does, but and gets 2X performance by making threaded just 0.01% of the code, that is, a single function) you continue to incur in the I/O penalty, just amortized in more threads, but it continues to be a waste. Also the latency you measure as reduced with threads is an illusion: it happens only during benchmarks because the instance is saturated more when running on a single thread. If you measure single-request latencies, they are dominated by the network stack latency.
Network I/O is a major bottleneck for Redis but they leave a lot on the table by being single threaded. I can speak from experience because I maintain a Multithreaded Fork: https://github.com/JohnSully/KeyDB KeyDB can easily get 2-3x the QPS with half the latency.
This is IMHO a wrong analysis. Redis can be scaled by being single threaded by running multiple processes: then if you remove the overhead of the network stack, each process can deliver more QPS, not just better latency. By using threads (which Redis now in parts also does, but and gets 2X performance by making threaded just 0.01% of the code, that is, a single function) you continue to incur in the I/O penalty, just…
Network I/O is a major bottleneck for Redis but they leave a lot on the table by being single threaded. I can speak from experience because I maintain a Multithreaded Fork: https://github.com/JohnSully/KeyDB KeyDB can easily get 2-3x the QPS with half the latency.
This is IMHO a wrong analysis. Redis can be scaled by being single threaded by running multiple processes: then if you remove the overhead of the network stack, each process can deliver more QPS, not just better latency. By using threads (which Redis now in parts also does, but and gets 2X performance by making threaded just 0.01% of the code, that is, a single function) you continue to incur in the I/O penalty, just…
The lower latency is not an illusion, it is indeed lower latency for servers with high load. If you don't have high load then I agree the need for threads is eliminated - but people using Redis for real work have traffic where this becomes an issue. Multiple processes require clustering or sharding each with its own set of overheads (both in CPU and human terms).
You and I disagree vehemently on this (hence the fork), but I really think your optimizing for your own simplicity not that of the user's. It should be the opposite since the developer has the most insight into the software.
This is IMHO a wrong analysis. Redis can be scaled by being single threaded by running multiple processes: then if you remove the overhead of the network stack, each process can deliver more QPS, not just better latency. By using threads (which Redis now in parts also does, but and gets 2X performance by making threaded just 0.01% of the code, that is, a single function) you continue to incur in the I/O penalty, just…
The lower latency is not an illusion, it is indeed lower latency for servers with high load. If you don't have high load then I agree the need for threads is eliminated - but people using Redis for real work have traffic where this becomes an issue. Multiple processes require clustering or sharding each with its own set of overheads (both in CPU and human terms). You and I disagree vehemently on this (hence the fork)…
I don't think you understood my comment. What I mean is that regardless of what you think of Redis and threads the fact that doing IO is so wasteful and adds latency and CPU time remains and is a constant.