Building and debugging a high-throughput daemon in Rust

The async/await keywords in modern Rust make building high-throughput daemons pretty straightforward, but as I learned that doesn’t necessarily mean “easy.” Last month on the Scribd tech blog wrote about a daemon named hotdog which we deployed into production: Ingesting production logs with Rust. In this post, I would like to write about some of the technical challenges I encountered getting the performance tuned for this async-std based Rust application.

My first Bay Area job used a set of libraries with Python that made it similar to Stackless Python. Thanks to that early initiation, I now have a habit of approaching services with “tasks and channels” in mind. With that in mind, I picked up async-std and started work on hotdog:

Hotdog is a syslog-to-Kafka forwarder which aims to get log entries into Apache Kafka as quickly as possible.

It listens for syslog messages over plaintext or TLS-encrupted TCP connection and depending on the defined Rules it will route and even modify messages on their way into a configured Kafka broker.

What this means as far as application design is fairly simple:

  • Listener for syslog connections
  • When those connections are accepted, spawn a connection specific handler task
  • For each received log line which should be forwarded, pass that along to the task which will publish to Kafka.

Refer to the following amazing diagram:

                     syslog clients
                      |  |  |  |
                      v  v  v  v
                   +---------------+
                   | Listener Task | (spawning connection tasks
                   +------+--------+  with stream descriptor)
                          |
           +--------------------------------+ 
           |              |                 |
+----------v--------+  +--v---+  +----------v--------+
| Connection Task A |  |  ..  |  | Connection Task N |
+---------------+---+  +---+--+  +----+--------------+
                |          |          |
                +----+     |     +----+ (sending prepared
                     |     |     |       messages over channel)
                +----v-----v-----v-----+
                | Kafka Publisher Task |
                +----------------------+
                   |  |  |  |  |  |
                   v  v  v  v  v  v
                  Apache Kafka Brokers

This basic formula is what I followed with the initial versions (released as 0.2.x) and that was fast. I used perf to profile with real-world traffic from Scribd, all of which were JSON logs, and the vast majority of time spent by the daemon was in parsing and serialization overhead. Since Rust does not have garbage collection or a “runtime” per se, there is very little standing between the code and the work it has to do. When using an async runtime such as async-std, smol, or tokio, there is however some other code running around behind the scenes to be aware of. My profiling against async-std 1.5.0 did indicate some CPU time being burned while polling on Futures, but I considered that more than likely to be “my bug” rather than “async-std’s bug.”

I was shocked at how little there was to actually optimize!

Since I was curious and had time to kill, I rapidly adopted async-std 1.6.0 when it was published. The 1.6.0 release removed the entire async-std runtime in favor of smol a new (small) async runtime for Rust. Having followed the development of smol, I was giddy at the potential of seeing an even faster hotdog after the upgrade was complete. After my rudimentary local testing, I cut a release and deployed it to production. 🤞

Performance dropped like a rock.


I was stumped. Stuck, I moved on to other projects and kept an eye on smol’s issue tracker to see if anybody else might replicate the issue.

Sometimes it is however more important to be lucky than smart.

I managed to catch the author of smol on Discord, and between them and the author of async-std I was able to solicit some help in understanding how performance had cratered so dramatically. @stjepang shared this gist which allowed for wrapping futures in Inspect(), printing out debug information when futures take far too long to execute. With their help, we were able to identify a couple of tightly bound CPU-only loops within hotdog that through some fluke had not caused issues under async-std 1.5.0. The more efficient smol under the hood exposed the performance problems, those very same ones which I had observed before as seemingly excessive CPU time spent on polling Futures.

The “trick” to resolving the issues is seemingly as old as the cooperative multi-tasking world itself: throw some yields on it. In async-std this means task::yield_now().await;, which gives the async reactor a breather to let another task run.

Once the fix (0.3.3) had been deployed to production, I finally got the giddy excitement I had originally hoped for: the cluster scaled down to levels not previously seen.


In my opinion, Rust’s async runtimes make building high performance applications relatively straightforward. Unfortunately I don’t think they’re easily debuggable at this point. When everything goes great, they’re wonderful. When you find yourself inexplicably blocking somewhere it’s tricky to isolate. I’m sure there will be improvements in this area as time goes on, but the sharp edges can be a little sharper than somebody coming from Node or Java might expect.

hotdog is happily running along processing hundreds of thousands of log lines per minute.

That said, the number of times in my career where a simple yield addressed pesky performance issues is incalculable. 🙀