| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.
This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea
| git branch -m old_branch new_branch # Rename branch locally | |
| git push origin :old_branch # Delete the old branch | |
| git push --set-upstream origin new_branch # Push the new branch, set local branch to track the new remote |
Recursion is beautiful. As an example, let's consider this perfectly acceptable example of defining the functions even and odd in Scala, whose semantics you can guess:
def even(i: Int): Boolean = i match {
case 0 => true
case _ => odd(i - 1)
}
def odd(i: Int): Boolean = i match {
Fibers are an abstraction over sequential computation, similar to threads but at a higher level. There are two ways to think about this model: by example, and abstractly from first principles. We'll start with the example.
(credit here is very much due to Fabio Labella, who's incredible Scala World talk describes these ideas far better than I can)
Consider the following three functions
This is a collection of the things I believe about software development. I have worked for years building backend and data processing systems, so read the below within that context.
Agree? Disagree? Feel free to let me know at @JanStette. See also my blog at www.janvsmachine.net.
Keep it simple, stupid. You ain't gonna need it.
- Do you run a JVM inside a container on Kubernetes (or maybe OpenShift)?
- Do you struggle with REQUEST and LIMIT parameters?
- Do you know the impact of those parameters on your JVM?
- Have you met OOM Killer?
Hope you will find answers to these questions in this example-based article.